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Pre-order Michele's book! deployempathy.com/order/Michele Hansen  00:00Welcome back to Software Social. This episode is sponsored by the website monitoring tool, Oh Dear. Oh Dear does everything they can to help you avoid downtime like scheduled task monitoring, SSL certificate expiration notifications and more. But downtime happens. When it does, it's how you communicate in times of crisis that make the difference. Oh Dear makes it easy to keep your customers up to date during critical times. You can sign up for a 10 day free trial with no credit card required at OhDear.app. Colleen Schnettler  00:35So Michele, do you have a,  Michele Hansen  00:38Hey,  Colleen Schnettler  00:38Good morning. Do you have a numbers update for us on your book? Michele Hansen  00:43I do. So my presale went live about a week and a half ago, when our episode with Sean went live. That was my deadline. And, I've sold 43 copies right now. Yeah, it's kind of exciting. Um, it's not all people I know, which is exciting. Colleen Schnettler  01:06That's very exciting. Michele Hansen  01:08I love how supportive people have been. And it also, it makes me, it's just reassuring that people I don't know are buying it. But yeah, so that puts it right now, just, and this is just the raw, you know, number of times $29, which is $1,247. Colleen Schnettler  01:30That's amazing. Congratulations. Michele Hansen  01:33Yeah. Thank you. And I got my first payout yesterday, which after, like, taxes, and everything else, was $912. Colleen Schnettler  01:41Wow. Michele Hansen  01:42Which was kind of exciting, and gives me a little bit of budget to work with, with, like, you know, hiring a proofreader, and using some, like, layout tools, but, you know, so I was pulling these numbers, and because, you know, everybody loves numbers and whatnot. And I was thinking about it. So, so I got this, this message from someone yesterday, who had started reading the book, and it was actually someone I don't know. And if I can just kind of read what they, what they said. Colleen Schnettler  02:25Yes, please.Michele Hansen  02:26And so I had a personal aha moment reading distinction between sympathetic, empathetic and solution based responses. My sympathetic conclusion based responses are leaving no space for empathetic, something I need to address. I'm an engineer and an architect by trade, and I'm looking to do a better job interviewing the humans attached to our work. But I'm also thinking about your book from the sense that a better balance of empathy will help me be a better teammate as well. And, like, getting that was so moving for me because it made me think about how, you know, I'm not writing this book for the money. Like, yes, the book needs to make money, because I've been working on it for four months now and have, you know, there's a lot of time I haven't spent working on Geocodio. Oh, like, I've been a pretty bad Geocodio employee the past couple of months, like, full honesty, right? So like, I have to, like, it has to have been, you know, worth my time. But like, I am not, I'm not motivated by that, like, I am motivated by this, by like, you know, like, I have this like, secret dream goal. Well, I mean, it's not a secret cuz I've, like, tweeted about it, but like, whatever. You know, Mathias sometimes says to me, he's like, I know you were thinking about something because you tweeted about it. And I'm like, oh, I forgot to, like, verbalize that. Anyway, um, I have this dream that through the process of learning this for interviewing, and, like, product development and marketing reasons, people will understand how to be more empathetic and use that in their daily lives. Like, everyone has a capacity for empathy. Everybody can learn it, not everybody is taught it or shown it so they don't really learn it. But everyone has a capacity for it. And, but also, like, very few people, you know, put like, be more empathetic, like, learn how to learn how to use empathy, like on their to do list every day. But they put write a landing page, get more customers, build a feature, like, reply to all of those customers and intercom like, those are the things that end up on a to do list. And so I have this like, kind of, I don't know, like, naive dream that like people will read this and apply these skills to the things they're already doing, but in doing so, learn how to be more empathetic in their daily life or you know, as a as a team member or whatnot. And just getting this message really, it was so motivating, but also so soul-nourishing because it really made me feel like, like the book has done what I wanted it to do. Like, this is what I set out to achieve and, like, this message makes me feel like the book is a success, regardless of how many copies it sells. Like, so it was just like, it was kind of a, it was kind of a, like a moment, like it was, it also sort of like if you're having this effect, like you can, like, stop rearranging it, like, you know, I feel like I've done a rewrite every week for, like, the past eight weeks. Yeah, time to time to ship the gosh darn thing. Colleen Schnettler  05:57That is wonderful. So what I just heard you say is, this book is secretly teaching us how to be better humans, wrapped up in a book about customer interviews. Michele Hansen  06:09Yes, wrapped up in a book about which features you should prioritize, and how to, you know, pick a pricing model based on what people's usage patterns are, and, like, how to understand what people want and write better landing pages. All that stuff they're already trying to do. But then yeah, there's, there's this kind of bigger message. Like, I feel like so much of good UX practice is good human being practice. Colleen Schnettler  06:35Yeah. Michele Hansen  06:36Um, and, I mean, I, I really learned about empathy by doing interviews myself. So this, I mean, it's, it's, it's very personal for me in a way that, like, the book is, I don't know, it is very, very personal for me. And it's not just about showing empathy to other people. It's also about showing empathy to yourself, too, which is just as important. Colleen Schnettler  07:06So I have not read the book yet, unfortunately. Can you tell me briefly, what the difference is between empathy and sympathy that that writer wrote into you? Because we talk about it a lot, but we've never defined it, really. Michele Hansen  07:22Yeah, that's true. So empathy is when you, basically when you, when you try to understand the other person's context without judgment, and it doesn't mean that you agree with what they're saying. You're just trying to find the context behind what they're saying or what they're doing. Because, sort of, most of us, basically, we assume that our, there's this assumption that our actions make sense from our perspective. That is to say you wouldn't go out and do something if it didn't make sense to you, like, maybe very few people might, but like, for the most part, we have this underlying assumption that, that the things that we do make sense to us.  And so you're basically trying to find that internal context for why somebody does something, and then you reflect it back for them. So for example, if you came to me and started telling me about how, like, I don't, I don't know something you were struggling with, like, let's say, you felt like you were banging your head up against the keyboard all week on some, like, coding problem and it was really frustrating for you. An empathetic response to that would be man, that sounds really hard and like you were working really hard on it and it was super frustrating for you. A sympathetic response would be, oh, I'm sorry you went through that. So a sympathetic response creates distance between the person who is speaking and the person who has aired something, and that might not be a complaint or a frustration. It could be like something positive, but it creates distance. And sometimes it's called fake empathy. Like, I feel like this is what you see in a lot of, like, really bad public figures, celebrity apologies. It's like, I'm sorry, that offended you. It's like, no, that's wrong. Like, like, that's not, that's not actually apologizing. And then there's also kind of this other element that I feel like is this sort of, like, solution-based responses, which comes from a place of caring, and I think us as product builders, I know me, like, we really fall into this, is someone, like, if you came to me with some, some problem. If I just said, oh, well, have you tried this? Which, I'm trying to solve your problem, I'm showing care, right? Like, I wouldn't propose a solution to your problem if I didn't care about you and making that solution better. The problem is, is that it doesn't validate your experience and it doesn't acknowledge your experience. So, while it comes from a good place, it's not empathetic because it doesn't say, wow, like, that was really hard for you. Like it doesn't, it doesn't fake make you feel seen or heard. And it could end up being, through the course of a conversation, you end up explicitly asking me like, do you have any advice for how I could do this? Like, what should I try? I feel like I've tried all these other things. But an empathetic response starts with acknowledging what the other person has gone through.  Colleen Schnettler  10:25Okay. Okay  Michele Hansen  10:26And then also checking in with them, like, do you, do you want me to listen to you about this? Or do you want me to help you brainstorm ideas? Colleen Schnettler  10:33Okay. Michele Hansen  10:33Like, so but I think that's, that's like one of those that really, like, it took me a while to wrap my head around that because the other thing about a solution response, especially in the context of a customer interview, or whatnot, is that you need all the context behind, behind why someone does something and why they went through something in order to really build something that solves the problem for them in a way that they understand and they're capable of grokking. Right? Because we need all of the context behind it, not just the functional context, but also sort of the emotional and social context of things in order to build a product that someone feels like is speaking to their experience and the problem they have. Does that make sense? Colleen Schnettler  11:18Yeah, it, it does. It's, it feels like a subtle difference, though. Like, when I try to understand your problem in your context, in your context, the sympathy for versus the empathy, like, it feels very subtle to me. Michele Hansen  11:34It is subtle, but like, um, I mean, it's, it's subtle. You know, it's the difference between, I'm sorry, that was hard for you and that was hard for you. Like, those are a subtle difference between them, but there is a huge difference between that and what someone would receive. Colleen Schnettler  11:53Yeah, I can see that. Michele Hansen  11:55And because when you say, I'm sorry, that happened to you, it emphasizes that it didn't happen to me. Colleen Schnettler  12:01Right, okay. Michele Hansen  12:01It actually, like, Brené Brown talks about this a lot. I'm sorry, that happened to you. It, it makes the other person feel more alone because it emphasizes that they are the only one who experienced that, and it makes them feel isolated.  Colleen Schnettler  12:18Okay.  Michele Hansen  12:19And she has a great way of responding, I'm sorry, of phrasing this, and I don't know if I'm doing it justice. But basically it creates that distance, and feeling alone and feeling like you're the only person who went through something is a really, really hard feeling, especially when you have just gone through something frustrating, and it doesn't have to be a big thing. It could just be, you know, the fact that I spent my week fighting with Grammarly, like, like that could be the problem we're discussing. And, but if you said oh, I'm sorry, you went through that, like, it reminds me that you didn't go through that.  Colleen Schnettler  12:55Hmm. Okay.  Michele Hansen  12:57And it was like, oh, yeah, that was like, maybe it was just me, like, maybe I was doing something wrong, like, am I using it wrong? Like is like, like, you know, it creates all of that doubt and feeling of sort of loneliness in it. Colleen Schnettler  13:11And so tell me the empathetic response again. Michele Hansen  13:14That sounds really hard. Colleen Schnettler  13:15That sounds really hard. Okay, right. So you're not, you're trying not to create that distance where they're an individual isolated, Michele Hansen  13:23Right. Colleen Schnettler  13:24And you're over here. Michele Hansen  13:25And it doesn't start out with I, right? Like, the sympathetic response to start with, you know, like, I'm sorry, that offended you.  Colleen Schnettler  13:33Okay. Michele Hansen  13:34Versus the difference between like, that offended you. Because when you say it that way, you're sort of asking for elaboration. Colleen Schnettler  13:41Right. Right. Michele Hansen  13:42Versus I'm sorry, I offended you just shuts it off.  Colleen Schnettler  13:46Wow, I say that all the time. I'm sorry, XYZ happened to you. Michele Hansen  13:50I said it all the time, too, then I started learning about this stuff. And I was like, I'm accidentally like, a jerk, and I didn't even realize it. But so many of us speak this way. And we learn the way we speak from the people around us. And if the people around you, when you were learning to speak, didn't speak empathetically, even if they're otherwise nice people. like, then it would make sense why you think this way and don't realize it. Colleen Schnettler  14:15Interesting. Michele Hansen  14:16Like, it's totally normal to not realize that what you have been saying is actually not empathetic. Like, like, it is a, it is a learned skill for many people. I mean, the people who have it built in are the people whose, you know, parents really made it a focus when they, when they had their kid. Like, but for most of us, it's kind of oh, I guess I should stop saying that. Like, I remember how at one point, like, when I was in my early 20s, I was at a job and somebody was like, you know, you really shouldn't say well, actually. Like, I don't know if you realize how you are coming across. Like, I know you don't mean anything by it, but like, it's, it's kind of like, and I was like, oh, crap, I do that all the time. Okay, like, mental note, like, mental dictionary update: stop. Like, so it doesn't, you know, it doesn't mean that you're not a nice person or that you're not an empathetic person or that you're not, you don't have a capability for empathy, it simply means that you haven't learned it and all of the various implications of it and we can call learn. Colleen Schnettler  15:15Okay. Yeah. Well, thank you for, for telling me about that. Like, that's really interesting. I didn't know that. I find that like, this whole thing, empathy and psychology, as I'm trying to, as I'm talking to people and trying to sell my product, I have found that it really, and I already knew this, but like, now I'm seeing it, it really makes a difference. Can I just tell you about this one issue, which I find so interesting? Michele Hansen  15:42Yes.  Colleen Schnettler  15:43Okay. So the way my product works is you upload files to the cloud, and then I provide you a dashboard where you can see all of those files. I have gotten several requests now from people to allow them to tag the files. Michele Hansen  16:02Oh, yeah, like Drew asked for that. Right? Colleen Schnettler  16:04Yeah. So I've been trying to figure out why people want to tag the files. He's not the only one who asked for it. Some other people have asked for it. The reason these people want to tag the files is because they want to be able to mass delete all of the files they've uploaded in a development environment. Why did they want to do that? From what I'm understanding, they want to do that so those files, like, because those aren't production files, they're not, like, cluttering up their dashboard. So when those people have asked me about this, I said, well, look, if you exceed your storage, because I don't have a mass delete function right now, and I don't have that, I'll just give you more storage. But nobody likes that answer. It's like, and so I think it's like a mental psychological thing where they want, like, a nice, clean dashboard. I don't know, I just find this really interesting, because I'm like, storage is cheap. I'll give you more storage until I implement this. But, but it's like, it's, like, as human beings, they really want, like, to segment stuff. I don't know, it's like mental. That's kind of the way I've been, I've been thinking about it. Like, as human beings, they don't want files that they don't need on their dashboard, even if they don't have to pay for them. But I'm like, I don't know. So, so that's just kind of been an interesting one for me. I'm like, but you literally like, I'm not gonna make you pay for those files. It's fine. They can just be there in outer space. But no one, yeah, that's an interesting one that keeps coming up. Michele Hansen  17:25Yeah, it sounds like they, like, that clutter is creating a certain like,  Colleen Schnettler  17:33Mental clutter or something psychological clutter. Michele Hansen  17:36Nervousness, or something. And then there's also this element of wanting to, like, mentally, like to mentally separate things like, I'm sort of, I'm reminded of one of my favorite economics papers called Mental Accounting by Richard Thaler, which is basically on how people like, they create different jobs for different bank accounts and investment accounts, and like, you know, for example, people might have one brokerage account that's just for, like, they have like fun money versus they have their serious 401k. Or like, some people have many different bank accounts for, you know, for different purposes. And it, there's, there's probably a broader term for this, but since I come from an econ background, that's, but like, people wanting to create these different mental categories, and basically, like, it's almost like they want to go, sort of, it's like mentally going to IKEA and buying one of those room divider shelves with all the different boxes you can slide boxes in and, like, being able to look at it and see that everything is in all of its little different categories and is in its place. And they know like, you know which things are in which box, and it looks all nice and organized from the outside. Colleen Schnettler  18:51Yeah, I am going to do it because I have found I use my own product for my clients, and I have found I desire the same thing. But I think you're absolutely right. Like, from a purely practical perspective, it doesn't matter. But from, like, a human organizational mental box perspective, like, it seems to make people happy. Michele Hansen  19:11Yeah, like, there's that functional perspective of it. But then there's the emotional perspective of feeling like everything is organized. And then I also wonder if there's a social element where like, maybe they're afraid one of their coworkers will use a file that was only for development, or because there's so many files and they're all in one list, someone will use the wrong file or, like, I wonder if there's any, any sort of elements around that going on? Colleen Schnettler  19:41Yeah. Could be. I didn't ask that. That's, Michele Hansen  19:47So when someone asks you for that, what did you say back to them, exactly? Colleen Schnettler  19:52Well, the first time someone asked me, I said, that's a great idea. I'm totally gonna do that.  Michele Hansen  19:58Okay. That's an understandable response.  Colleen Schnettler  19:59I know you're over there thinking, like, have I taught you nothing, Colleen? You have taught me. That was before we were doing a podcast. Michele Hansen  20:06No, that was a starting point, and that's a perfectly understandable reaction to that. What did you start saying after that? Colleen Schnettler  20:15So the second request I got was via email. So I didn't really have the back and forth that I would have had when I'm talking to someone on the phone or on Slack. And, so this person, I asked them kind of what their use case was, and I also told them in the email that they, you know, I wasn't going to charge them for development files. So if storage became a problem, we could work something out until I had the, you know, a bulk delete API set up. And this person was looking to segment files so they could do a mass delete of the development files. And they also brought up they thought it would be great to be able to segment files, like via model. So you could have, here's all my avatar files over here, here's all my resumes over here, which would be really cool. I mean, that I can totally see the value because and then you're then in your admin, yeah, then in your admin dashboard, you could easily filter based on, you know, what your tag was. And it's really not hard to do, I just haven't done it. But I do like, I do like that idea. And that, to me, makes a lot of sense because I think people really like, like we just talked about, like, you like to have your stuff in the appropriate boxes. Michele Hansen  21:34I think it's hard sometimes when somebody proposes an idea that we get the value of because we would use it ourselves. It can be really hard to say, can you walk me through how you would use that?  Colleen Schnettler  21:46Yeah it is. Michele Hansen  21:47Like, because their reasons may be different. And we really, we need all of those reasons because the reasons I would do something might be different than the reasons why somebody else would do something. But when we understand something, it feels very unnatural to ask for clarification, even when we don't need it. But it's so reasonable.  Colleen Schnettler  22:08That's exactly what it is. It feels so weird, because I'm like, yeah, totally. That's a great freaking idea. Yeah, it is odd. Michele Hansen  22:16I sometimes feel like it's, I wonder if this comes from, like, conditioning in school where, like, I feel like the kid who asks a lot of questions is, you know, sort of branded as annoying. I was definitely that kid in math class. Like, I just always seemed to understand it two weeks after the test. And I wonder if it's like that fear that like, oh, God, like, am I going to be the person who asks questions. And then we have this like, sense that being the person who asks questions, even one that might be sort of a quote, unquote, like dumb question that's clarifying something. Get you like, like, I wonder if there's kind of this built in social conditioning around that, that makes us not want to ask those clarification questions. And we're like, okay, I think I can guess what they want, so I'm just not gonna ask further about that. But, but when we're building a product, you need to be able to, like, look in all the different nooks and crannies of how they're thinking. Colleen Schnettler  23:08Yeah, definitely. That definitely is valuable. To your point, you might use it one way, and they might want it for something totally different. So I really do think, like, throughout the course of this podcast, and since we've been spending a lot of time talking about customer interviews over the past several months, that I've gotten way better at it, because it's, it's my instinct, just to say, yeah, I totally agree, because I do totally agree. So why, I think for me, it's not like, I'm not I don't I'm not scared of asking clarifying questions. I think it's more like, I don't want to waste any more time. Like, I'm like, okay, cool. Let's not waste anyone's time, and let's just go do it. So I have, I do really think I've grown a lot in that, in that kind of sphere of pausing, slow down Colleen, because not really good at slowing down. And, you know, kind of dive into what they want and why they want it. So I think that's been good. Michele Hansen  24:02It can be kind of tough as like, I feel like we're both pretty enthusiastic and kind of like, like, have you ever been called bubbly? Colleen Schnettler  24:11Yeah, of course. Michele Hansen  24:11Yeah, I have been called bubbly, too. Yeah. So like, I like feel like enthusiastic people want to be like, yeah, that sounds awesome. Like, it's so, it's so counter,to like how I would interact with someone socially. Colleen Schnettler  24:25Yeah, I agree. So, so anyway, that was something, I was thinking about that when you were talking all about, you know, empathy and sympathy and psychology, is how much these kinds of factors play into product building.  Michele Hansen  24:41Yeah and building an intuitive product that, that makes sense to people. Like it's, it's really hard to build something that's intuitive because it requires understanding the user's mental model of how something works, and you can't understand their mental model unless you have, you know, really, you know, poked through every nook and cranny of how they think about it. And also seeing what are the similarities at scale across many different customers. You can't just build it for one particular person, right? Like this, I think this is like, do we want to do we want to do more definitions? Because now I'm excited to get into definitions between Human Centered Design versus activities under design. But if we are, we are feeling good on definition today, then, Colleen Schnettler  25:29I don't know what those are. Yeah, go ahead. Michele Hansen  25:32So like, you probably hear people talk about human-centered design, right? Colleen Schnettler  25:37I mean, no, but okay, I believe you, so not me.  Michele Hansen  25:40So like humans, I feel like this kind of came really into it, like, especially in, in tech in the past, like, I don't know, 10,10-15 years, like, you like, think about the human behind it. And like, this is where a lot of like, agile stories come from, is like, as an administrator, I would like to be able to update the billing page, whenever we get a new credit card, like, like, those kinds of stories that if you've worked in the corporate world, you have seen the ads of so and so like, those kind of stories. And like, creating personas, and maybe there's like a picture of a person, and there's their age, and there's like, you know, like, all of those kinds of things that's very, like human-centered designs, and you're designing for people and understanding what those people need. Then there's activity-centered design, which is designing for things that people might be trying to accomplish, but not for specific people, if that makes sense. So it's like, so if you're thinking, I just used an example of like, a billing administrator. The human-centered design approach with a persona might be you know, this is Susan, and she lives in Iowa, she has been working in insurance for 20 years, she has a dog named Charlie, like she prefers to use her iPad on the weekends, but during the week, she uses Windows like, it's like that kind of stuff. Activity-centered design would be like, when billing administrators are going through this process, they want to be able to, you know, these are the different kinds of things they're thinking about, these are the different functions that they need to be able to do. Here are the different things they might be feeling. Like, do they want to be updating a credit card? Like, how does that make them feel, like, is that, is that enjoyable for them? Is that frustrating? Like, are there other people they're working with on this? Do they need to go get a p-card from someone else? Like, what is this entire process they're going through that is independent of them as a specific person and independent of the product? And then how does the product help them get through that entire activity, either easier, faster, or cheaper.  I feel like I just dropped like,  Colleen Schnettler  27:54There's a lot.  Michele Hansen  27:54A lot.  Colleen Schnettler  27:55I'm gonna have to re-listen to that one.  Michele Hansen  27:56But basically,  Colleen Schnettler  27:57So what's the, Michele Hansen  27:58Activity-centered is kind of the approach that I take. And that's the, the approach in the book is designing a process that exists regardless of the person and regardless of the process.  Colleen Schnettler  28:10Okay. Michele Hansen  28:10The product, I think I messed that up. Colleen Schnettler  28:13Okay, so which one is better? Do you have all the answers, Michele? Tell us. Michele Hansen  28:18I am not going to throw bombs in the design world here. I mean, you know, there's, there's value in designing for specific people, right, and, and specific types of people, especially when you're talking about accessibility and whatnot. But fundamentally, you know, like, activity center design is okay, what it, what is the thing that someone's trying to accomplish? For example, 500 years ago, you may have solved, you know, entertain me at home, when I'm alone on a Saturday night with cards or dice, right. And now you might solve it with Netflix. But that fundamental process that you're going through to not be bored when you're in your house on the weekend, like, that process and that desire is relatively constant, which is the thing about activity-centered design approaches is that you're looking at a process that is consistent over time, because you're speaking to sort of broader, underlying goals. And this types of products, someone might use the different functional and social and emotional things that might be important to them are different, but the overall process is the same. And so this is what I think about a lot when we're like thinking about the process that someone is going through and designing something that's intuitive for them and building that mental model is understanding, okay, why do they need to be able to tag things and why do they need to be able to mass delete these things, and what is this overall thing they're trying to do? And it sounds like it's sort of, to feel like all of their files are organized and they can find things when they want to, and that desire to be organized is a relatively consistent desire. Colleen Schnettler  30:03Yeah, I think one of the things, one of the phrases we use at work is to surprise and delight the user. And I feel like this falls into the surprise and delight category. Like it's not necessary, but it's delightful.  Michele Hansen  30:19You just used the phrase ‘at work'. Does that mean when you are working? Or? Colleen Schnettler  30:26Oh, just when I'm, just this company that I've been contracting for for a while likes to use that phrase. Michele Hansen  30:31Okay, gotcha. Colleen Schnettler  30:32So this to me feels, Michele Hansen  30:34I didn't know if you'd suddenly gone off and gotten a full time job without telling me. Colleen Schnettler  30:39Well, I'll tell you if I do that. I may be considering that. That's like a whole ‘nother podcast episode. I feel like we don't have enough time to dive into that. Michele Hansen  30:50We'll do that in a future episode. Colleen Schnettler  30:52Colleen's life decisions. But yeah, so, this feature, I feel like, is delightful. And when we talk about like design, you know, in the context, you were just saying, I think it does fit into the, the latter category. Michele Hansen  31:10Yeah. And I can, I can understand how someone, or you might even, or probably, I feel like if we had talked about this, like, six months or a year ago, the reaction kind of would be like, this feels like we're really splitting hairs over something that's super obvious, and why don't I just go build it? Colleen Schnettler  31:29Well, yeah, Michele Hansen  31:30Which, I think it's a very understandable reaction. Colleen Schnettler  31:34Yeah, I mean, I think the problem I'm having, and I know everyone in my position has this problem. It's just, there's just not enough time to do all these things. Like, one part of me wants to take like six months and just do all the things, right? And then the other part of me wants to balance my life with building this business, and is trying to be patient with, with my constraints as a human. So I know, you know, everyone has those, that struggle, everyone who's working and trying to do this. But yeah, I'd love to add all these things. Like, I want to do all the things of course I do. Michele Hansen  32:10Speaking of which, building the business, we started this episode with my numbers update. Do you want to give us a little numbers update before we go? Colleen Schnettler  32:31So I do want to tell a little story about this. Storytime. So, someone who's kind of a prominent bootstrapper had a tweet the other day about how for his SaaS, he just implemented file uploading using some JavaScript library, and it took him like, I don't know, like a day. So not an insignificant amount of time, but not a huge amount of time. It's a long time if you're a developer to take all day. But I saw, so, like, I saw his tweet, and I was like, oh, like, why didn't he use Simple File Upload? Like, clearly my product is crap. Okay, so this happened at like 9am. So then, like, later in the day, this just happened a couple days ago, I went to see if I had any new signups. And as you know, like, I've been pretty flat for like two or three weeks now, signups have been pretty flat. So, in one day, I got $325 boost in my MRR. One day. Michele Hansen  33:19What? Colleen Schnettler  33:20That has never happened in the history of my product, like ever. I was like, whoa. Michele Hansen  33:25So did someone Tweet it, like, add it to that thread, or, like what happened? Colleen Schnettler  33:29No, no one added it to the thread. And I didn't add it to the thread because he was clearly looking for a non-paid solution. So it seems like it wasn't that he hated my product or it was bad, he just wasn't looking for this kind of solution I was offering. I don't really know what happened. But a whole bunch of people signed up. Michele Hansen  33:50These two things happened on the same day, and you don't have any conclusively linking them, but it feels suspicious that they wouldn't be linked. Colleen Schnettler  34:00It's super weird, right?  Michele Hansen  34:01Yeah.  Colleen Schnettler  34:02Um, so I am trying to like, I'm just really starting to try and get into, like, Google Analytics and understand that. Anyway, so that was, my point of that story is like, you know, this is, we're never bored. I'm never bored, right? Like one day, I'm like, this thing is miserable. The next day, I'm like, I'm the most brilliant person in the world. Like, it's never, it's never boring. I guess my point of that story was it's all over the place. I'm all over the place with, with this product. And some days I feel like it's just not, not as good as it should be. Some days I feel like I'm charging too much. And then other days I have, like I realized I have, there's all this power in this thing I built that no one is utilizing. So that's something I really want to spend some time getting some content going out there and spend some time, like, showing people why it's more powerful than, than, you know, other solutions they've been using. Michele Hansen  34:58You seem really fired up.  Colleen Schnettler  35:00I am. I, I've just had like, a, it's been, like, a really good week. I mean, from a work perspective. And although I didn't get to spend the time, you know, I got, okay. I don't have a lot of time to spend on the product the next month or so, so I'm just taking it in little bits, right. And so this week, it's a tiny thing, but someone pointed out to me, and I think this also plays into psychology. Okay, so my marketing site is built in Tailwind UI. My application site is built off of Bootstrap. Bootstrap and Tailwind are not friends. I can't just throw Tailwind into my Bootstrap site. Michele Hansen  35:37If it makes you feel better, the Geocodio dashboard was on Bootstrap, and the Geocodio marketing website was on Railwind for, like, a really long time, like, like, you, like, we were on the like, 2013 version of Bootstrap for, like, a very long time. And it wasn't until like maybe six months or a year ago that we actually got them both on Tailwind. So you're not the only one. Okay, so back to yours. Colleen Schnettler  36:06So this. Okay, so if you are on my marketing site, and you click through to sign up to get the free trial, here's the thing that happens. The nav bars are different. Michele Hansen  36:17Mmm.  Colleen Schnettler  36:18Yeah, it's not good, and someone pointed it out to me. They were like, oh, I had to click back and forth a few times to make sure it was still the same application. And I was like, oh, my goodness. And so I can't, but it was like, it was, so it's just this visual thing. But this he pointed out, he was like, you know, that's, that made me think I was at the wrong place, it might make me close the window. Michele Hansen  36:40Yeah it might make them think something was wrong, or, like, they accidentally got led off to another site that wasn't the right one. And like, maybe it's, like, phishing or something, like. Colleen Schnettler  36:50Exactly, that's exactly what this guy said. And I was like, oh, my gosh. And so, so my, my Simple File Upload technical accomplishment this week, was basically like, and because I can't, my application is pretty complicated. I can't just pull out Bootstrap and drop in Tailwind. That's gonna take me forever. So I actually, like, just stole, stole is the wrong word. I grabbed some of the Tailwind styles and just over, you know, and overrode my Bootstrap styles just for the navbar. So anyway, the point is, now the nav bars look the same. And it's like, it sounds like a small thing. But like, I think the mental block for, if you sign up and I drop you to a totally different site, you're like, wait, what?  Michele Hansen  37:29Like, yeah, it's like, something is, like, the brain is a little bit like, danger, something is different. Colleen Schnettler  37:34Yeah, exactly. So, so another, so it was another big CSS week for me, which is not my forte, but I got it.  Michele Hansen  37:41I wrote JavaScript this week, which is not my forte. Colleen Schnettler  37:46Oh, jack of all trades.  Michele Hansen  37:48Well, we wrote stuff that, that's not our forte, and you're going back and forth between feeling like it's amazing and you've built something super powerful. And then, also feeling like it's, really has a long way to go, and is it ever going to get there, which, honestly, is how I feel, like, I feel the exact same way about my book. Like, every day, it's like, oh, my God, this is a hot mess. And then I'm like, actually, this is amazing and I should just publish it now. Like, I think that's, I think that's just like part of building something, whether it's a book or you know, software. I mean, yeah. Colleen Schnettler  38:31And honestly, I think it's part of the fun. Like, I honestly do, like I, it makes it interesting. Like, I've worked jobs that are really boring, and they're really boring. Like, this is way more exciting.Michele Hansen  38:52I think that's the thing I love about being an entrepreneur is that it's always different. And sometimes it's different in ways that are super boring and require a lot of paperwork. And sometimes it's different in ways that are like, super awesome, and exciting. But the fact that it is so different all the time is, is what makes it fun and makes me feel like I get to, like, feel lucky that I get to do this as my job.  On that note, perhaps we should sign off for this week. Thank you so much for listening. If you enjoyed this episode, please leave us a review on iTunes or tweet at us. We love hearing what you think about it. Have a good one.

00:00You are now listening to the project Kuwait project Kuwait, project Kuwait, where we stop at nothing to bring you the right backs on health, fitness and psychology, featuring some of the world's most experienced professionals. So you can learn and play with your hosts make them dirty, and messy. 00:21Learning how to communicate, when you can't speak the same language, how to still get your point across is so helpful. When I came back to the states and started coaching again, just that level of confidence of like, hey, if I can coach someone that doesn't speak my language, how to move better, how to have a right attitude in a workout, how to live a healthy lifestyle, it brought a huge amount of confidence back over and I feel like I grown. Yeah, you know, 10 x times making communication harder, almost made communicating easier. 00:53They are very communal, and they live so closely with one another of like, what they provide, like the trading and the skills and how they can help each other out. It's just so well connected. That was like an enlightening experience. I'm sure lockup It was also that ability to connect with other people is always like such a great measure of health. And I think that community aspect is so important, like looking at how happy they were having like so little. 01:19All this and more in today's episode. 01:22Welcome back to The expat series. I am joined today by Brad Clark. And before I get into like who you are and why brought you here, I want you to fill in the blank with this question. Strangers would describe me as blank, but only I know that I am blank. 01:42What's your answer? strangers would describe me as mysterious. Although I know or Yeah, only I know that I am only I know that. I have a plan and I know exactly what I want. However, I it does not seem like it but I am on a I am on a path and I know exactly where I'm going. But I only let a few people like into that part of me. You know, that's awesome. Try to keep it a little mysterious for sure. A little unknown. 02:15The name that this app for that we're recording on gave him the name of inventive Moonwalker, we should change it to elusive Moonwalker, maybe 02:24I'll take that. But I do like the invented moonwalk in my that could be a new handle somewhere. 02:30Love it. 02:32So for those that don't know, Brad, Brad and I grew up in our fitness journey at CrossFit Omaha. We went through the level one cert together did our internship right around the same time learning from some of the best fitness coaches in the CrossFit industry. At that time. I didn't jump into coaching classes as quickly as Brad did. I worked more on the operation side of the gym, social media retail, which is where I got the connections to come to Kuwait working for circuit plus. and recruiting for Kuwait isn't easy. A lot of people know this, but I tapped into my network. And, Brad, do you remember the timeline of you coming out? Because I just remember being like, yeah, you should come travel the world. Yalla. And then you were there 03:14was a wallet. Let's go. Yes. So I was actually just talking tSupport the show (https://www.instagram.com/p/Bl8NPB2H4Mf/?igshid=1m9w8d28oarlu&utm_source=fb_www_attr)

In this episode, friend of the show Cluadia Brdar explains how to choose a design style for your home or investment propertyClaudia takes us through:The main types of home design styles, including contemporary, traditional and HamptonsThe features of the different design styles such as colour palettes and texturesHow to decide which style to use in your homeThis episode is part of a very special DIY renovation series.⠀Claudia from @therenovateavenue and Founder of the DIY Renovation Academy will join us once a month on the show to take you through EVERYTHING you need to know to DIY your reno.⠀⠀This is like a little sneak peek into her super popular DIY Renovation Academy course.⠀⠀This series is exclusively for Dream Home Movement listeners.Follow the Claudia and The Renovate AvenueFacebookInstagramWebsiteFollow the Dream Home MovementFacebookInstagramWebFollow Carl and Jo VioletaFacebookInstagramWebGuest bioClaudia and her husband Pete have renovated small properties right through to large scale projects. Several years ago they bought a run-down 1960's weatherboard house (purchased for $460K) and ended up completing a major renovation ($75K), creating a beautiful sought after home ... on a tight budget! They set the suburb record for a property price of $1.035 million (2017)!They're currently renovating a 1960's brick house to sell.Both Pete and Claudia have a real hands-on DIY approach when it comes to renovating, focusing on achieving that ‘WOW’ factor ... thinking bigger, outside-the-box, and never afraid to take a risk.Claudia created The Renovate Avenue to share her knowledge, and inspire others to climb the property market via the renovation avenue, building a financially stable future to retire earlier (and do what you love).Transcript*This transcript is automated, so may not be 100% accurate*00:00:00 - 00:00:05you want to know what I think is really exciting, it's that there are so many00:00:05 - 00:00:10different design stars that you can choose from for your house or your property00:00:10 - 00:00:15. You want to know what I think is really overwhelming and confusing that00:00:15 - 00:00:19there are so many different designs, tiles that you can choose from for your00:00:19 - 00:00:25house and property. So to help me out and I can't be alone with this problem,00:00:25 - 00:00:30I'm sure to help me out. I invited Claudia Baradar from the Renovate Avenue00:00:30 - 00:00:38on the show to talk to us about how to choose a design style for your house or00:00:38 - 00:00:43property. Eso. When I keep saying or property, it could be your investment00:00:43 - 00:00:48property that you're going to have tenanted or your house that you are going to00:00:48 - 00:00:53live in. So in this episode, Claudie will take us through the different types of00:00:53 - 00:01:01design styles, how to choose a design style based on your personal style, thie00:01:01 - 00:01:10actual property, the environment that the pop property is located in, and also00:01:10 - 00:01:16what you're actually going to use the property. For now, if Claudia's name00:01:16 - 00:01:22sounds familiar, then I'm guessing you're a regular listener to the show Claudia00:01:22 - 00:01:29came on last season and did a bit of a miniseries for us on how to plan a00:01:29 - 00:01:34renovation project. And to now we're kind of taking it to the next step. And00:01:34 - 00:01:40she is joining us again this season, Season five, to do another little miniseries00:01:40 - 00:01:46on How to style your property. So this week we're looking at in this episode, I00:01:46 - 00:01:50should say we're looking at choosing your design style when she comes back00:01:50 - 00:01:55again in about a month's time. We're going tio, explore how to make your00:01:55 - 00:02:00home Starling work when you've chosen your design style. And then she's00:02:00 - 00:02:06going to come back for the final episode off her little exclusive miniseries for00:02:06 - 00:02:12the Dream Home Movement and talk about how to create a style board or some00:02:12 - 00:02:18people call it a mood board. So I'm so high up. Tio, have Claudia coming00:02:18 - 00:02:23back on the show again for another exclusive miniseries. We are so lucky.00:02:23 - 00:02:29And before we get stuck into my interview with Claudia, I would like to00:02:29 - 00:02:34introduce myself in case we haven't already met. My name's Joe via letter E.00:02:34 - 00:02:39I'm the host of this show, and I also own a business called Violet of finance.00:02:39 - 00:02:46With my husband, Carl, we help people secure home loans, property00:02:46 - 00:02:52investment loans, refinance their mortgages to save money on their payments00:02:52 - 00:02:59and or to finance their home renovations So you can find us at Violet of finance00:02:59 - 00:03:07. It's just got one T v i o L. A finance on the Internet. We've got a website on00:03:07 - 00:03:13instagram and also on Facebook. So thank you so much for tuning in. Lovely00:03:13 - 00:03:17to meet you if I haven't done so already. And now over to my chat with00:03:17 - 00:03:22Claudia, Welcome to the Dream Home movement. This's your weekly dose of00:03:22 - 00:03:28home and property inspiration bring you clever tips and advice from the very00:03:28 - 00:03:40best experts and really like Renno storeys with your host Joe Violeta. Claudia,00:03:40 - 00:03:43thank you so much for joining me on the dream home movement today. It's00:03:43 - 00:03:48lovely to chat with you again. Love being here. I love having a chat with you.00:03:48 - 00:03:54Now what we're talking about today is how to choose your design style. And00:03:54 - 00:03:58this is something that I'm particularly interested in because we're buying a new00:03:58 - 00:04:07home soon. So we're going to take that opportunity, Teo, just reet restyle like00:04:07 - 00:04:11from the beginning, the entire place. We've had all our furniture for ages. So00:04:11 - 00:04:17we're looking at starting again, and I'm just so overwhelmed and confused00:04:17 - 00:04:24about what style to choose. There seems to be so many choices out there. So00:04:24 - 00:04:29what are some of the different design styles we can choose from? Let's start at00:04:29 - 00:04:32that point, and then we'll figure out how we actually choose. What is there to00:04:32 - 00:04:39choose from? Okay, look, there's a fair, few different styles, Joe. It can be00:04:39 - 00:04:44overwhelming. I know there's so many different designs out there. Look, let's00:04:44 - 00:04:50start with contemporary style because I think it's the most popular one, and it's00:04:50 - 00:04:55usually what's in style right now. So it's the current trends at the moment,00:04:55 - 00:05:01contemporary style. So if you're not sure what contemporary style looks like,00:05:01 - 00:05:05then you can try going to the mainstream retail stores such as freedom and00:05:05 - 00:05:11Design, and you'll soon get an idea of what the current trends are and where00:05:11 - 00:05:15they're at. That's usually your contemporary style, just to make it easier just to00:05:15 - 00:05:23give you a tip. Then you've got your traditional style, which is usually feature00:05:23 - 00:05:28pieces made from dark woods. Their ornate Lee, detailed as well and00:05:28 - 00:05:34traditional design draws its inspiration from 18th and 19th century England and00:05:34 - 00:05:40France. So this explains why it's common to find EXP offensive textiles like00:05:40 - 00:05:47silk, velvet and linen used in upholstery to curtains as well. And the fabrics00:05:47 - 00:05:52feature a variety of different patterns, such as florals, stripes and plaids as00:05:52 - 00:06:00well. So that's just sort of traditional style And what it looks like a traditional00:06:00 - 00:06:05eso contemporary that, Yeah, that would change often then, wouldn't it? So00:06:05 - 00:06:10what? Okay, so what's contemporary style now would be different to what00:06:10 - 00:06:14was contemporary style a couple of years. That's right. That's right. Yeah,00:06:14 - 00:06:18people get contemporary and modern sort of mixed up. The contemporary is00:06:18 - 00:06:24quite what's in trend now what styles Aaron fashion in trend now. So, like I00:06:24 - 00:06:28said, Teo, if you go into shops like freedom, it will give you an idea of what00:06:28 - 00:06:34in style at the moment. Okay, got it and then traditional. So I like watching00:06:34 - 00:06:37Do you watch the crown? I like watching that show the crown about the royal00:06:37 - 00:06:44family. It's quite good. So when you just described traditional that I'm Justin00:06:44 - 00:06:50visit, envisioning scenes from the crown. It would. I guess it's that kind of00:06:50 - 00:06:59royal traditional style, European style, almost that's your more traditional00:06:59 - 00:07:05style. Then we've got something about the Hampton style, which is quite00:07:05 - 00:07:10seaside coastal inspired, so you'll see a lot of the houses around the coast00:07:10 - 00:07:15Hamptons style inspired, and they've got your plantation shutters, your00:07:15 - 00:07:22nautical style decor, timber floor boards to the moulding, a long hallways and00:07:22 - 00:07:28staircases. Then it's also blue and white striped patterns for pillows, just to00:07:28 - 00:07:35give you a visual idea as well. White plush sofas. The painter Whitewood s.00:07:35 - 00:07:40O. Yet the intention is to create a relaxed, style, comfortable environment that00:07:40 - 00:07:46is inspired by the beach in the ocean. So that's your Hamptons style that's very00:07:46 - 00:07:55popular here on the Peninsula Hampton style. I've noticed E. S E. Touched on00:07:55 - 00:07:59. Modern Modern is a broad designed term, I think, and like I said to you00:07:59 - 00:08:05before modern and contemporary style often get mixed up. Modern refers to a00:08:05 - 00:08:11home with clean, crisp, sharp lines. It's a simple colour palette. Thie use of00:08:11 - 00:08:18materials is usually metal, glass and steel, and modern design also employs a00:08:18 - 00:08:26sense of simplicity and in every element including the furniture. So its sleek00:08:26 - 00:08:35there's not much clutter, sharp lines, and that's more of a modern style. Yeah,00:08:35 - 00:08:41that does so. The modern is very different. Teo temporary. I thought they00:08:41 - 00:08:45were the same thing, but they're very different. Got a lot of people. A lot of00:08:45 - 00:08:51people think modern contemporary are the same style, but there is a difference00:08:51 - 00:08:56there. That's probably a good way off describing contemporary and what I'm00:08:56 - 00:09:01just a difference in them. Eso There's also mid century modern. This's another00:09:01 - 00:09:07type of modern style just to confuse everyone. So mid Century Modern is a00:09:07 - 00:09:12streamline retro look that was popular in the 19 fifties and sixties, and it's a00:09:12 - 00:09:18classic decorating style that has really never gone out of fashion. It focuses on00:09:18 - 00:09:22functionality, too, so it's more the retro style really big in the fifties and sixties00:09:22 - 00:09:30. I quite like that, and again that the mid century modern is quite popular here00:09:30 - 00:09:34around the morning to peninsula as well, because you've got those houses from00:09:34 - 00:09:42that error and people are trying Teo freshen them up, but still keep the essence00:09:42 - 00:09:47of the original high. Yes, yes, exactly. And it's I think it's a great idea doing00:09:47 - 00:09:50that as well. So it, like you say, keeping the essence of the home and the style00:09:50 - 00:09:56that it was built in. I think if you've got an old with aboard house by the sea, I00:09:56 - 00:10:01think putting something in that theon visit of what that style leaves and what if00:10:01 - 00:10:05the body is going against it is probably not a great idea. So it's really00:10:05 - 00:10:10embracing when it was built in embracing that, So that's that's what that's my00:10:10 - 00:10:16opinion. But then we've got industrial style, so that's an urban look with an00:10:16 - 00:10:23edge. Industrial is characterised by raw textures, exposed materials such as00:10:23 - 00:10:28bricks and metal fixtures and finishes. I think of a really cool warehouse in00:10:28 - 00:10:35New York, my ideal place to live with stripped timber for awards, concrete00:10:35 - 00:10:41flaws, exposed beings, metal pipes and really low lighting. I quite like that00:10:41 - 00:10:47style, but it's not for everyone as well. So that's your industrial style. I love00:10:47 - 00:10:54that style it is, and I do picture it being like a loft in New York, with some00:10:54 - 00:10:59really gorgeous artwork from an up and coming artists on the walls and that00:10:59 - 00:11:05sort of stuff very very. I don't know if I'm quite cool enough for that. I would00:11:05 - 00:11:10like to pay e Love that style as well. What else? Have weak up? Another style00:11:10 - 00:11:17. Eclectic, So it collected his quite quirky fun. It's very mismatched. There's00:11:17 - 00:11:21no matching zour formatted patterns. It's just pretty much anything goes. But00:11:21 - 00:11:27it's done in a really fun on quirky style, so mixture of pieces as well could be00:11:27 - 00:11:33new and old pieces to S O. That's why eclectic is quite fun and that embodies00:11:33 - 00:11:38the person that lives there. I think is, well, that Yeah, I see. I see Cem00:11:38 - 00:11:43eclectic rooms on instagram. But I get would that be a hard look to pull off?00:11:43 - 00:11:47Do you think If you're not quite sure what you're doing and get it wrong, you00:11:47 - 00:11:51could definitely get it wrong. So it's really having an eye for colour and pattern00:11:51 - 00:11:56yet and having a bit of fun with it. A cz well, but it can. It can look quite zany00:11:56 - 00:12:03. Maybe if if there's too much going just like embrace the zaniness Gracia in a00:12:03 - 00:12:11freak s o. And there's also Bohemian, which is quite popular. A swell and00:12:11 - 00:12:14bohemian Khun mix up with contemporary because we're behaving. It is quite00:12:14 - 00:12:19on trend at the moment, and it embodies a sort of free form flowing. Ah, lot00:12:19 - 00:12:23of plants. You know, a lot of the chroma at the moment, which is very00:12:23 - 00:12:30popular and a lot of wicker A cz Well, so Bahamian is quite quite on trended00:12:30 - 00:12:35moment as well. It is. It's all about the weaker right now. Yes, love a bit of00:12:35 - 00:12:39makeup. So this is I mean, look that some of our styles that we've got00:12:39 - 00:12:45happening at the moment there's there's Mohr a CZ Well, that that's probably00:12:45 - 00:12:52the main ones that I've covered very briefly. Okay, good. So once we're00:12:52 - 00:12:58aware of what all the different design styles are, how do we decide which style00:12:58 - 00:13:04Teo use in our home? Yeah, OK. Eso Joe, this really depends on what00:13:04 - 00:13:11outcome you want for your home or property first. So So start at the end and00:13:11 - 00:13:15look, think about why you're keen and getting your style right. Are you00:13:15 - 00:13:20looking to sell your home or you wanting to stay There s o really have a think00:13:20 - 00:13:24about what the outcome is for you. What? The right outcome is, and that way00:13:24 - 00:13:27we'll give you a good road. Teo travel and know which way you're heading.00:13:27 - 00:13:32So are you planning on staying in your home for the next 20 years? Or are you00:13:32 - 00:13:36wanting to get the best price for your potential buyers? So knowing exactly00:13:36 - 00:13:42what it is that you want to achieve and then implementing the right style based00:13:42 - 00:13:48on that, it will differ for each of the outcomes. Joe. So if you are planning to00:13:48 - 00:13:53sell than design your home for your best potential buyers, make your property00:13:53 - 00:14:00is beautiful as possible. Choose a contemporary design, so a current style on00:14:00 - 00:14:04DH. Incorporate current styles, making the space look and feel desirable and00:14:04 - 00:14:08inviting a cz well for your potential buyers. If you have a home by the coast in00:14:08 - 00:14:13the peninsula, then consider a Hamptons or coastal resort style. That's the best00:14:13 - 00:14:18way to go. Maybe stay away from industrial industrial or eclectic style. And00:14:18 - 00:14:25don't put your Gothic doll collection on this way, wanting to sell at the best00:14:25 - 00:14:31possible price, then really keep your style. Have a think about the potential00:14:31 - 00:14:35buyers that are coming looking at your property and really design and style for00:14:35 - 00:14:42them, Not not for you. So if you desire, if it decided to stay in your home and00:14:42 - 00:14:48and you want to really make it authentic, then I truly believe that home should00:14:48 - 00:14:53be a reflection of who we are. And if you're not sure where to start, and I've00:14:53 - 00:14:59got some tips right for any of you that want to get started. So answer yourself,00:14:59 - 00:15:05Joe. So first things first to really get a picture of what your style is and what00:15:05 - 00:15:10will really work for you and your family looking your closet and noticed the00:15:10 - 00:15:15styles that you wear. Notice what you love what pieces draw you in and00:15:15 - 00:15:20inspire and have a good look and see what colour is. The patterns are your00:15:20 - 00:15:25favourite. Firstly, just have a really good think about what draws your eye,00:15:25 - 00:15:31Teo. Certain pieces in your closet. That's number one thing I can suggest. E I00:15:31 - 00:15:39never would have thought to do that in your closet. Also just backtracking a00:15:39 - 00:15:46little bit. Thanks for the tip on putting my gossip doll collection away. I will00:15:46 - 00:15:54put that into storage if ever I sell s o you looking a closet. Have a look at what00:15:54 - 00:15:58pieces You absolutely love these air address that you love the Excuse me, the00:15:58 - 00:16:02patterns The Colo Urs, what really draws you in? Even if you go out shopping00:16:02 - 00:16:06, what has always drawn you in what pieces? What colours again? Like I said00:16:06 - 00:16:12, what is it that you love? Next thing is look at your favourite most love.00:16:12 - 00:16:15Peace is in your house and bring them all together and notice what you love00:16:15 - 00:16:20about these objects again. The lines the Colo Urs, What does it provide to you00:16:20 - 00:16:27? What emotion does it a vote so really Get to know what you love. That's how00:16:27 - 00:16:34you start next. I would suggest if you're not already on instagram or interest,00:16:34 - 00:16:40create an account and start searching and saving images off various interior00:16:40 - 00:16:44styles and spaces that you absolutely love that resonate with you that evoke00:16:44 - 00:16:51really good emotion. Start saving those images, screenshot ing them, and and00:16:51 - 00:16:55that you'll start noticing. A common theme is well within the images that you00:16:55 - 00:17:02saved. You will start taking notice of all the torus and the tones and the styles00:17:02 - 00:17:08and these sort of images. Will there'll be a on almost flow and a common00:17:08 - 00:17:13theme that will that will come to you. So it will really indicate what sort of00:17:13 - 00:17:23style you like. Right? So that's a really good three tips to start with, just to get00:17:23 - 00:17:29an idea off. What you like what you love, right? Okay, So think off the00:17:29 - 00:17:36purpose for the styling. Is it to sell toe livin and benefits to sell its a little bit of00:17:36 - 00:17:41a different approach? We're going Mohr, like modern or contemporary. Sorry00:17:41 - 00:17:45. No, no modern modernism, modern or contemporary. We're going00:17:45 - 00:17:52contemporary, continue current current. But if it's for us to live in, then we00:17:52 - 00:17:57really wanted to reflect us. And so you're almost going on like a little jenny of00:17:57 - 00:18:04self discovery. You like, you want to find the authentic you you want to make00:18:04 - 00:18:08your space something that you will love. And when you walk into you, it will00:18:08 - 00:18:13evoke positive emotion. Yes, for sure. So you want to create something that00:18:13 - 00:18:17you will enjoy and that you will find inviting for you and the people that leave00:18:17 - 00:18:23your family. Okay. Now we touched a bit on the difference between styling00:18:23 - 00:18:29for yourself and styling for sale. Is there any difference between the way we00:18:29 - 00:18:34should style our property if we're wanting to get tenants into it apart? A00:18:34 - 00:18:40supposed to selling it? Is that different, or is it pretty much the same? Yeah,00:18:40 - 00:18:45pretty much the same for your tenants. I mean, it's your investment property.00:18:45 - 00:18:50You want to get top dollar for it as well. You also want to create an00:18:50 - 00:18:53environment for attendance of they're gonna love because they'll look after00:18:53 - 00:18:59your look after the home as well. So I would invest some money into that so00:18:59 - 00:19:04you can also get the most out of it. And again, it's getting the most out of your00:19:04 - 00:19:08rental income as your investment property and also for your tenants. So I00:19:08 - 00:19:15would also create a beautiful environment for tenants to. So obviously, you're00:19:15 - 00:19:22not putting us much money. A cz you are a mature investment property. But00:19:22 - 00:19:25again, just be smart about how much you're putting into your investment00:19:25 - 00:19:31compared to your forever home and does very yeah, okay, but still do put a bit00:19:31 - 00:19:40of love into it to try to attack, not attack, attract thanked the right tenants00:19:40 - 00:19:45exactly exactly what air some practical tips. We've already covered a few of00:19:45 - 00:19:50them, but let's just give everyone a really practical action plan. So tips that00:19:50 - 00:19:58listeners can do straightaway to really get their style choice sorted. Sure, All00:19:58 - 00:20:04right. So number one Why are you wanting to design and style your property?00:20:04 - 00:20:08What's the outcome you want to achieve? That's your number one with a start.00:20:08 - 00:20:13Number two. How do you ascertain what style to incorporate, So once you00:20:13 - 00:20:18know your outcome, then work out what style it is you're going to create, the00:20:18 - 00:20:20one that will work best and in line with your outcome. So the one that will00:20:20 - 00:20:26bring the most value to you Number three is start researching. So start00:20:26 - 00:20:30researching now. That's where you can start. Well, if you're selling than00:20:30 - 00:20:34research properties in the area and take notes on what other properties have00:20:34 - 00:20:38created and what they're sold for, So go to the properties. If they're on sale in00:20:38 - 00:20:43the area and have a look, they would see what others have done and also see00:20:43 - 00:20:49what properties have reached in sale time as well. So if you're staying put like00:20:49 - 00:20:53I said, if you want to stay in your forever home. Then work out your best. I'll00:20:53 - 00:20:57know your authentic self and know what you'll love tourists around yourself00:20:57 - 00:21:00with the most what will work for you. What? We will make you the most00:21:00 - 00:21:06happiest. Yes, those those three to start with now. Perfect. I love it. Well, I00:21:06 - 00:21:10personally am going to start to pull everything out my wardrobe and have a00:21:10 - 00:21:15look. And I got I got a pretty good idea of what I like a ce faras clothing goes00:21:15 - 00:21:24very plain and lots of blue and white stripes go Hampton style. I think so. I00:21:24 - 00:21:32think so. The great the great flag. Well, e I just need to move to grace I e00:21:32 - 00:21:39quite well, e that's what it is. A Hampton style. Joe, if you're saying blues00:21:39 - 00:21:46and whites possibly maybe a coastal thing a resort style, that sounds good to00:21:46 - 00:21:50me. Well, thank you so much, Claudia. That was so informative for everyone00:21:50 - 00:21:57listening. I would love to see, I'd love to hear from you and hear what your00:21:57 - 00:22:00style is. So I'd love to hear the outcomes from you pulling everything out of00:22:00 - 00:22:04your wardrobe. Looking at the stuff in your around your house And how you00:22:04 - 00:22:10decide what your style is. Let me know. Message me on Facebook or00:22:10 - 00:22:15instagram a dream home movement and let me know what your personal00:22:15 - 00:22:21design style is And let Claudia No. A cz. Well, Claudia, how can people find00:22:21 - 00:22:26you follow you contact you or that sort of stuff? Yeah. Instagram Facebook.00:22:26 - 00:22:31I'm on there. The renovate Avenue s O. Please get in touch. Say hello. And if00:22:31 - 00:22:35you want to dive deeper into choosing your design style, I highly recommend00:22:35 - 00:22:39that you cheque out Claudia's course as well. Claudia, can you tell us a little00:22:39 - 00:22:44bit about your course? Yes, The D I Y renovation academy. It's an online00:22:44 - 00:22:49course, and also you get a one on one assistance with myself. And there's also00:22:49 - 00:22:53a Facebook group, eh? So we get to support each other through our renovation00:22:53 - 00:22:58journey, so you'll find all that information on my website. And also social00:22:58 - 00:23:02media, Facebook and Instagram. Brilliant. Thank you so much, Claudia, I'm00:23:02 - 00:23:08looking forward to chatting of you again in about four weeks time. Make sure00:23:08 - 00:23:14you tune in for that episode. We are going to talk about how to make your00:23:14 - 00:23:19home styling work. So once you've chosen your design style that your mission00:23:19 - 00:23:23for the next month, you then need to figure out Well, how do I actually make00:23:23 - 00:23:27this work? And that's what we will be covering next time I chat with Claudia00:23:27 - 00:23:33in about four weeks time. Until then, Do tune in again next week and make00:23:33 - 00:23:37sure that you subscribe to the podcast. So you never miss an episode. Thank00:23:37 - 00:23:46you so much for tuning in by listeners by Claudia. Thank you. Thanks for00:23:46 - 00:23:50joining us on the Dream home movement. Be sure to come over and say hi on00:23:50 - 00:23:55Facebook and Instagram. I hope that your dream home projects are going well00:23:55 - 00:23:58and I look forward to chatting with you again next week.

Jesus said to the crowds: “To what shall I compare the people of this generation? What are they like? They are like children who sit in the marketplace and call to one another, ‘We played the flute for you, but you did not dance. We sang a dirge, but you did not weep.’”  Luke 7:31-32So what does this story tell us?  First of all, the story means that children are ignoring the “songs” of each other.  Some children sing a song of sorrow and that song is rejected by others.  Some sang joyful songs for dancing, and others did not enter into the dance.  In other words, the appropriate response was not given to the offer of their music.This is a clear reference to the fact that so many of the prophets who came before Jesus “sang songs” (meaning preached) inviting people to have sorrow for sin as well as to rejoice in the truth.  But despite the fact that the prophets poured out their hearts, so many people ignored them.Jesus gives a strong condemnation of the people of that time for their refusal to listen to the words of the prophets.  He goes on to point out that many called John the Baptist one who was “possessed” and they called Jesus a “glutton and drunkard.”  The condemnation of the people by Jesus especially focuses upon one particular sin: Obstinacy.  This stubborn refusal to listen to the voice of God and change is a grave sin.  In fact, it is traditionally referred to as one of the sins against the Holy Spirit.  Do not let yourself become guilty of this sin.  Do not be obstinate and refuse to listen to the voice of God.The positive message of this Gospel is that when God speaks to us we must listen!  Do you?  Do you listen attentively and respond wholeheartedly?  You should read it as an invitation to turn your full attention to God and listen to the beautiful “music” He sends forth.Reflect, today, upon your willingness to listen.  Jesus strongly condemned those who did not listen and refused to hear Him.  Do not be counted among their number.Lord, may I listen, hear, understand and respond to Your sacred voice.  May it be the refreshment and nourishment of my soul.  Jesus, I trust in You.Source of content: catholic-daily-reflections.comCopyright © 2020 My Catholic Life! Inc. All rights reserved. Used with permission via RSS feed.

 A Cyberside Chat Live - Making Financial Decisions For Today...SUMMARY KEYWORDSbusinesses, virus, participants, withdrawal, robert, loan, plan, money, remote, parts, year, business owner, 401k, employee, bit, furloughs, pay, matching, act, marketSPEAKERSRobert Young, Jess Coburn Jess Coburn  00:47Morning I'm Jess Coburn CEO replied innovations and this is another episode of fireside chats. Today I have with me Robert Young Robert, could you talk a little bit about Robert Young  00:58money thanks guys for having me on. I'm Robert Young. I'm the principal investment manager and actually compliance officer for ra called young financial services. We're essentially a hedge fund, we have two parts of our practice. We manage money privately for individuals, but we also have a corporate side that we manage corporate pension plans. And that's, I think, what has more bearing on today's conversation than the private money management side. There's some exciting things happening that could help business owners and that I'm sharing with you.  Jess Coburn  01:32So you've been doing this for a while, Robert, Robert Young  01:34over 25 years, had the old practice before then, like most advisors came up through a training program or training system, licensed through 766 and then eventually reached a point in the amount of money that we managed. We go directly and be registered with the SEC and through FINRA, so that's it. Jess Coburn  01:55For a business that has a 401k What is it exactly that you do? Robert Young  02:00So on a 401k side, we manage the investments in the available for participants. We analyze those and make sure that they're operating against their peers in the lowest cost or best performance, then we also act as fiduciaries. we oversee a number of back office parts of the plan. And then we educate participants and we use some of the information for private money management side, and just give that to participants to help them and advise them on the best strategy to reach their goals. So that's really it. And that brings us some things happening today that you might be aware. Jess Coburn  02:38Yes, I'm curious. I know there's been a ton of changes. I'm curious how that impacts, let's say the business owner, let's start there. Robert Young  02:44Alright, so actually, we'll start with the business owner and then business owner slash participants. Perfect. So the first thing is, with course the virus is shutting down and throwing businesses, although we're reopening. I think there's a lot of trepidation on moving forward there, simply furloughs still going on, not every business is rolling out. and business owners are finding we're finding that some are having some challenges on revenue and some concerns about cash flow. So the first and most important thing that was now available to a business owner is if you have a 401k and you are using a match a safe harbor metric 3% match or better, you can suspend that or you can lower that matching rate. Normally, you couldn't do that in a year that could really helping a cash flow or expenses that businesses is being impacted by definitely. So Jess Coburn  03:47probably gonna say and then if you needed to do that, it's fairly straightforward and easy to do it well. Robert Young  03:53Yes, it is easy now normally wouldn't be that their plan amendments have to be done and With your every platform out there from paychecks and ADP to john Hancock empowerment and power and Transamerica, all of them are making it user friendly for a trustee to be able to make those planned amendments, and then be able to engage or initiate a reduced matching program. And again, if you're somebody who's used to paying out 10 1520 $100,000 a month in matching, suddenly you get to eliminate that. There is a caveat though, of course, it's no good deed goes unpunished, right. So, at the end of the year, if you choose to suspend that your plan would now go under testing or top heavy testing and there's a possibility that people who are used to putting in the maximum amount may have had some of that redistributed back to them because the plan doesn't meet certain IRS RQL or labor criteria. So But you need cash flow today, you willing to maybe have some people get some money back in the future? It's an option. Jess Coburn  05:08Yeah, definitely an option. Definitely something to look at. Now, as a participant, what's my options there? Robert Young  05:14Alright, so I'm glad you went. Thank you. So we now have two things for participants. Typically, when a participant, there's a caveat to that, and I'll get to in a second, when a participant wants to take a loan now, their limits have always been 50% of the current value up to $50,000. So rounding numbers, to get the 50, you have to at least have an account value of $100,000. And then when you take that loan out, there's some there's some restrictions on that. That has been eliminated, you can now take 100% of the account value up to 100,000. So basically, everything becomes double. So if you have an account value of $100,000, you can now take that out as long as you pay yourself with the course next five years. So that's one of the most significant parts of loans. And then there's a withdrawal change has been made, it's probably a little bit more exciting. If I touch on that, please. So on the withdrawal side, if somebody has to take money out, the virus is now considered a catastrophic event. Typically, you couldn't take a withdrawal out, unless you're in Florida hurricane came through or you're about to lose your home for economic reasons. You have now mounting, excuse me, get mountain that's mounting as a result of medical expenses. The virus is now considered one of those tragic events. So someone could take him to draw out, draw them out, they can now take up to $100,000 out. But here's the exciting part. The mandatory 20% tax withholding for the federal government has been waiting to not have to withhold 20% of that hundred thousand I use that as a round number also So if you're under 59 and a half, you're no longer currently 10% of the drop, empty. And then finally, what's best is to not incur all the taxes on that withdrawal. And that current tax year, you can spread that out over three years. So using the example of 100,000, you could put 33,000 this year on your tax report next year 33. And then the final year 34,000. It's also important is, let's say take the withdrawal out, and somewhere in the next three years, you come back into 100,000, you can pay yourself back that hundred and walk away without any penalty at all. No taxes paid. Jess Coburn  07:40So you have three years that you could you could withdraw from your fun and pay it back over here.  Robert Young  07:46Withdraw this year, you have three years to pay back the taxes. So let's say you said Okay, you know what, I'm going to take the hundred thousand out this year. But I know by the third year, I'm willing to gamble, I'll have all hundred thousand back I could put it back in so I'm not going to pay taxes for the next two years, I'll wait to the third year 2023 to count that hundred thousand. But in the next 24 to 36 months, you put that hundred back. You're free and clear.  Jess Coburn  08:13That's I mean, that's fantastic. Right, that's a excellent opportunity to get access to funds that you normally wouldn't have access to. And then with the loan, so you could actually do a loan to yourself as well from your funds. Robert Young  08:27That's right, you've always been able to, but the limit is now been increased from 50 to 100,000. Jess Coburn  08:33And is that taxable as well, Robert Young  08:35it's not you're paying yourself back the loan rate since the Fed fed Fed funds rate is around zero. Your loan rate could be anywhere from point four and a half 2% just depends on what your plan platform dictates. And you'd look at those individually. Jess Coburn  08:53Now, is this the right time to be taking money out of your 401k though, Robert Young  08:57if you're furloughed, out of work and you need funds for, I guess that's really up to the individual. Exactly in their MBA tax plan. Now, Jess Coburn  09:10Robert, let's kind of switch gears a little bit. So a lot of people are focused on the stock market right now. Right? It's starting to come back. Is this the time to buy? Is this the time to sell what's going on in the market? Robert Young  09:23So my opinion, not to be confused with facts. Jess Coburn  09:26Fair enough. Fair enough. We'll even give you the the ticker banner there. Thank you.  Robert Young  09:31So we believe in value investing. And we also believe that the idea that the market has priced in the worst case scenario of economic news that you hear in the, I guess, financial stages, we don't believe that that's natural. We think that there may be a possibility of another dip. Definitely the Markets be choppy. So if I were going to enter back into the market if I went to cash a long time ago, do it slowly and pick and choose where you want to go. And I'll give you some free advice. So yeah, stick with the basics. Good, good, solid stuff and check the technology, right stick with healthcare, keeping utilities, secret, the things that make common sense. Maybe, even though some areas are very sexy right now, like financials or energy or biotech, I think I would stick with some of the leaders not only in the recovery, but also the future. Okay, fair enough. Jess Coburn  10:39Let's let's talk a little bit about going remote. I imagine that young financial services you've all gotten remote. Robert Young  10:47Not really, you know, unfortunately, you see behind me, files, Manila files and folders. We're actually exploring now because of this like most businesses, and that is Again, just gives credit to what we're talking about with technology. You know, I think every business is going to be looking to see if they do they need as much office space. Can things I know, for example, Florida Power and Light is rethinking how their HR department is being utilized. Because now they've been into remote for last 30 days. Now they're wondering if they need to bring them back in to the HR department, can they still maintain remote status? So we're not, but that's something we have to explore because our future clients, the ones that don't know that they're gonna be clients yet. We need to be able to access them and they may not be in a position to to meet us in a traditional way. Jess Coburn  11:49You know, I had an interesting conversation the other day with a gentleman that specializes in commercial real estate and he pointed out that a lot of them Businesses now some of them are going to want to go remote. And initially I thought while we're going to see this huge exodus of commercial office space as more and more employees go remote, and businesses look to reduce costs, and he pointed out that they're going to need actually more space for their employees, because in a call center, they're going to need six feet between each employee in order to maintain, you know, safe distances, they're going to need to expand hallways and make hallways wider. And that there's been this consistent decrease in the amount of space per employee that was being leased. And now they're going to expand that out, because each employee is going to need more space. So while there, while there may be fewer employees in the office, they made that occupying the same or even more space, I thought that was really, really interesting. Robert Young  12:50Right, right. You know, that's why like we do, I bring together experts in their field, because you get this this information as input You would not normally consider, I think what you're doing is very important, because it does educate people on what the future could look like. Jess Coburn  13:07Well, I'm not the only one that does it, you do a fair bit of video and commentary yourself. Robert Young  13:12Mind usually related to having to explain why something went down so severely or in those rare occasions, we've actually picked up that goes up very, you know, very well. Jess Coburn  13:23But that's important, though, right? In your business, you're constantly advising your customers on what they should or shouldn't do, or maybe not necessarily telling them what they should or shouldn't do. But giving them more information so that they can make that decision as an informed decision. Robert Young  13:40Right. So, you know, we speak to everybody when we send out a newsletter each month. We speak to everybody each quarter, we see somebody minimum twice a year, but during this virus, our corporate clients, especially, were spent the entire week just from nine o'clock til Oh, dark hundred. Just calling everybody client to make sure that they're aware of which one of the cares act and discussing their options. So absolutely, we want to make sure. I think that's what an advisor does. Jess Coburn  14:09Yeah. Fantastic. Robert. Robert, is there anything you wanted to add or that we didn't touch on? There's thanks important. Robert Young  14:15Just I can't think other than that. Just warn everybody about this part of it. The new levels with the Coronavirus, the limits are some very exciting, but of course, everything comes at a cost. So there is a prerequisite, you know, a participant does have to show that they are being directly affected by the virus or spouse or they have to have homecare for a child that's directly affected as a result of the virus. So there's some prerequisites for that. And if anybody has any questions, I'm more than happy to field any questions. Certainly. I think we're all here to support each other during this time, and any other time and you cannot act as a just an information resource. That's it. Jess Coburn  15:00I appreciate it. I appreciate you coming on. If you don't mind if you could share your contact information for those that are listening certainly Robert Young  15:06suggests you'd like to reach me at 561-427-6767. Again, that's 561-427-6767 that's my phone number does actually go to my cell phone the evenings or weekends, you can always reach me anytime every client notice that or just my website, young fs.com. Jess Coburn  15:26Thank you for Robert, I appreciate you taking the time to meet with me and have our cyber side chat. I know we had scheduled to go live a little bit later and we jumped the gun and went early. But hey, it was a fantastic chat. Thank you so much. I appreciate it. I'm gonna throw up your contact information here for anyone that might want it And with that, I'll say thank you again.

In the final episode of Beneath the Subsurface Season One, we're focusing on Well Data Products and the full gamut of subsurface intelligence that can be gleaned from leveraging Well Data with Seismic. Caroline Brignac sits down with Jason Kegel, Ted Mirenda and Katie Fearn for a deep dive into the evolution of well data and how it’s used in today’s workflows.EXPLORE MORE FROM THE EPISODEProduction ForecastingCompletion DataWell Data ProductsInterpretation ProductsWell Production DataGlobal Well DataUS BasinsTABLE OF CONTENTS00:00 - Intro01:42 - Evolution of Well Data Products at TGS03:25 - Production Data & it's Uses07:38 - Production Data and Thesis Work09:09 - Longbow: A Well Performance Visualization Tool with Analytics12:08 - What is Well Performance Data Used For?15:04 - Validated Well Headers & Interpretation20:26 - Well Logs and Production Data for Students, Interns & Early Career22:30 - Historical Production and Well Data24:43 - The Marriage of Seismic and Well Data: Interpretation26:48 - Historical Data and Microfiche?!29:44 - What About Offshore Well Data Products?34:34 - How Much Gulf Of Mexico Data Does TGS Have?39:00 - Seismic or Well Data... Why Not Both?40:20 - Analytics Ready LAS Data (ARLAS)43:49 - Eye Opening Data for Early Career48:48 - TGS Projects & Careers51:37 - Conclusion  EPISODE TRANSCRIPTCaroline:00:12Hello and welcome to Beneath the Subsurface a podcast that explores the intersection of geoscience and technology. This is Caroline Brignac from the well data products group at TGS. In This episode we'll explore our well data products and how they prove to be critical datasets for any exploration and development program. So go ahead and we'll get started with introductions for today's podcast. We've got Jason Kegel with us. Jason why don't you to tell us a little bit about yourself? Jason:00:39Sure. My name's Jason. I work with the geology group here at TGS. I'm a geologist I've been here for six years. I work pretty closely with our well data products and our seismic products. Caroline:00:50Awesome. Thanks Jason. We also have Ted Miranda with us. Ted, why don't you tell us a little bit about yourself? Ted:00:55Sure. Thank you. Caroline. Ted Mirenda. So I'm with TGS. Well, data products group. I've been here for 10 years now. A primary task was to bring production data to TGS and commercialize that product. It's been a lot of work and exciting. Caroline:01:12That's awesome. I'm really excited about having Katie with us. She's a production geologists for a super major. Katie, welcome. Why don't you tell us a little bit about yourself and your experience with TGS. Katie:01:21Thank you, Caroline. I am a recent graduate school graduate and I loved my time at TGS where I got to use Longbow and R360 and then I carried those things that I had learned and into my schoolwork in grad school and it's been awesome. Caroline:01:39So Katie, you started with us as an intern, correct? Katie:01:42Correct. Caroline:01:42That's awesome. Well, we're really excited to have you here and talk a little bit about what your experience with TGS, our products and how you use it in the industry. So one thing that we know a lot about TGS is that it's known as a seismic company. However, TGS offers a wide range of other products such as products in well data. Ted, would you mind telling us a little bit about the well data products division and how it's evolved over time? Ted:02:07Sure. I guess we can step back to 2002 when TGS officially acquired a little company called A2D that gave A2D's the resources to further go out and I believe in acquire Riley's electric log inventory. So that led to the largest commercial well log library. Other resources that TGS provided or enabled was the ability to digitize hard copies and raster logs to LAS. And that library has grown over time where I came into play now 10 years ago after growing the LAS library TGS made the the decision to what's next with well data, well, let's bring in production data. That's when I came into mix. We started building our production data library up. It's been a long challenging project, but it's really paid off. One of the things that critical decision we decided to do was not acquire any production data assets, but build that data from the ground floor up. That meant more work. But in the long run, it's a more valuable product. Caroline:03:25So when you talk about production data, what exactly are you talking about and what does that look like? Ted:03:30Well, we're talking about the full historical production record of every well in the United States. So when you think about different pieces of information that our clients use and need what the well has produced, the reservoir fluids captured from each wellbore is about as important a piece of information as you can have going forward. So we capture that information, really important to tie it to the proper wellbore and a really detailed well header record. There's a lots, a lot of other processes that we do with that as well to then provide the data to our clients. Caroline:04:16So we know that we have, Jason has some experience as well as Katie with this dataset. Would you mind telling us about how you guys use it in your role in the industry? Jason:04:25Sure. So I know at TGS we use the production data quite a bit, looking at our different mapping projects we have. So when we look across the entire, especially United States and look for new areas to shoot on shore seismic, we like to have a really good background information on what companies are actually producing, how much they produced in the past. Can a lot of times tell you where the, where the new plays are and it's always been said that where you found oil before you'll find oil again. And that's been proven over and over again. When we look at the Permian basin, which has been producing since, you know, the 1910, 1920s and today it's one of the biggest basins in the world and we're still finding oil there. So it's nice to really see those historical records of production and where people have gone. On top of that, the Longbow database gives you completion information so you can start really seeing where exactly within the geology has been drilled and how they have done it. So you can get some engineering insight into that as well. Over the years at TGS we've brought all that together to really start looking at new areas where clients want to go and where we can start bringing them seismic. Caroline:05:34So Katie, we knew that you started off as an intern here at TGS a few years ago and we know that you worked with Jason on his team to help sort of guide where we'd go next with our products. What was your experience with the production dataset and Longbow? Katie:05:48So I used the production and information during my project, both at school and at during internship to help me understand the reservoir better so that I could clear up any uncertainties that I was curious about. So for example, I use production data during my time at school to help me understand if there was any reserves left that were not taken out. Ted:06:19Yeah, I know a lot of our clients then use that data to look for bypass opportunities. Another one of the many capabilities of leveraging production data. Jason talked about moving into the completion data side of what we call completion data. Kind of led that evolution. You know, horizontal drilling, unconventional tight reservoirs, fracking, I mean that led to a whole new need for different attributes captured about a well record. So we identify those pretty early on. I had been collecting those and now provide that kind of information to our clients. Not just perf intervals. What is the, what is the producing interval subsurface depth, but the length of the lateral that's being completed and produced correlating production rates, any U R S 2000 foot laterals, another way to really do better well economics and evaluation of assets. So it's, it never ends, you know, the data needs are constantly evolving and changing as industry changes and we follow that path. Jason:07:38So Katie, you said that you use some of our production data with your thesis work, correct. And that was in the, in Louisiana, the Tuscaloosa Marine shale, right? Katie:07:47Yes. Jason:07:47So the Wells and the data that you used there, were they mostly conventional Wells or where we also tried to look at some of the unconventional Wells there too, to define that play that you are looking at. Katie:08:01Right. So I would say the majority, I also focused on the lower Tuscaloosa, which was mostly conventional Wells. Jason:08:09So those Wells, they helped you define that play area and then you had to go deeper and deeper into the log data. Correct. Trying to see exactly what the formation was made up. And you did a sort of a real exploration study of that lower Tuscaloosa Marine shale Longbow helped you kind of understand exactly where the production had become historically and where it might go now and where, where people are drilling currently in the Tuscaloosa Marine shale. Katie:08:39Right. And we also did that with the Austin chalk too. That was another one of our big projects. Jason:08:44Right. And then when you, in the group that was here all from the university of Lafayette worked with us, we also looked up into the Haynesville and looked at some of the smack over units using Longbow quite a bit, looking for trends in conventional plays historically and then seeing where those went unconventionally and if Longbow is the, the main generator of the majority of that data. Caroline:09:09So for those of you listening in who may not be familiar with Longbow is that is our our visualization tool that sits on top of our well performance database. Ted, would you like to add to that? Ted:09:19Yeah, that's right. So you know, production data is a fairly complex data model, right? So you need a tool to search and search your way through that data library, identify Wells that are appropriate to your project assignments so Longbow started out as really as that initial search engine. Hey, you're connecting to almost 5 million Wells, right? In a cloud based database and you're typically going in your assignment, you're going to identify subsets of Wells based on location, geology, formation, operator assets. Hey, examine these assets that are for sale and tell me if it's worth it, right? So Longbow provides that search engine. However, over the, the years and the time, we've incorporated quite a bit of analytics into the search engine. So we're really proud of that. It's if you can think of having a search engine connected to a live database of every well and include analytics, make a bubble in contour map on six month cumulative by zone, you know, all that in one. It saves time. So it's been exciting. We've had great feedback from clients and we are really focused on, Hey, what do our clients want? That's what we put in Jason:10:46When you go. When you talk about analytics Ted, what has been the biggest benefit of forecasting for Longbow? Ted:10:54Well, okay, so that is another good point. Production data being the historical production for the wellbore. Again, the reservoir fluid produced once me and my team, I felt we were comfortable and really good at acquiring that data. I always wanted to move into the forecasting realm as well. So we have added to the, to the product feature every single month. Now every, well all active wells get forecasted to their economic limit, giving our clients quick access to EURs. So from that perspective, I can look at historical data for an example like Katie gave about looking for bypass opportunities. Where did prior operators leave hydrocarbon in the ground with forecasting, I can look at, okay, what's the total proposed value of an asset? How much is that asset going to produce? How much remains that's already there in the, in the analytic tool. So, and again, the different analytic tools include besides mapping, probability graphs, scatter plotting and charts. It's the full gamut. Jason:12:08So we have, Katie who has worked with this data as an intern. I work with this data internally with project development and sales. And then I know that I've gone out with you before and we, we sell this data, we try to give our clients opportunities to use this data. Are our clients, strictly exploration type geologists or engineers or do we have other sort of venues where this data's important in the oil and gas industry? Ted:12:36You know, that's a good point. I mean, our clients cover all those gamuts. You know, one thing, again, with production data, it's a valuable piece of information across an integrated oil company. Enterprise exploration, geologists exploration is of course petroleum engineering department, reservoir engineers that have to forecast production. It's really become a big tool also in the A&D world investment banking A&D world at oil companies, business development. And that's what I like about production data. Everybody finds a use and value out of it, Jason:13:23Right? And it seems everybody wants to know how long that well is going to last and where the next well is next to it. It's going to produce as much that really hard to find that information from anything other than production data. Ted:13:33And what's, you know, what's, what's recently happened and I was looking at right, or like writing a paper on this topic. But you know, right now, most of the think tank forecast for supply, they're all like redoing those and lowering them, you know, the Unconventionals. And we, when we started doing our forecast models, we realized that the horizontal Wells had to be looked at differently. And the decline rates on those, those Wells now are, what would I say, exceeding what we thought they would be. Ted:14:08We had this, you know, unconventional production had made perhaps a real the world with the real comfortable setting of endless oil supply and and you see the think tanks now readjusting those forecasts. So our model changed as well. We're looking at studies and how long Unconventionals are really going to produce and readjusting the EURs. And does that also have quite a bit to do with parent child relationships and how they're stacking Wells within the reservoir? It does, and right now that's what everybody's trying to figure out. That is really challenging looking at spacing, refracking spacing, how does another child affect the, the, the parent well and etc. What is the proper spacing that we try and provide the data to our clients to help them do that? Jason:15:04Right. And in some of those cases you said before with our header products that we have, that really has led to Delineating some of the production data with the validated well header. Can you explain a little bit more about how the validated well header helps understand different laterals and how that traces back to production? Ted:15:25Yeah. Yeah. And that's that's another key point, I think what was attractive to building production data here at TGS? You know, you go out and collect production data and for the most part, I mean, when you're getting public production data, the reality is that data is really coming in at a surface level. I mean, what does the state regulator care about? They just want to know how much did operator produce. So your severance, you're paying severance tax below the surface, they're not so much concerned about which zone is that coming from in which borehole? So here at TGS we have, we can leverage our validated well header dataset, which is our proprietary header where we've gone in, looked at the subsurface and identified missing boreholes. So we are in the process of tying our production data now to that validated header. So really moving production data down to the, to the, what we call the 12 digit API level. And that's really making a difference to our clients. Jason:16:39I know it's helped internally where we've gone used the perforation information. Ted:16:43That's right. Yup. Jason:16:45And actually track the perforations. And I'm not sure if you, you might've done this with this, some in your internship, Katie, where we looked at the perforated intervals on the Wells and then when we are doing our cross sections, we would actually see exactly where the perforations were and see where that oil was coming from. And that helps in a lot of situations in basins where you, you don't know a lot about the basin or you're going somewhere new and you're mapping and we'd see, you know, you'd see the Austin chalk and the Buddha and the Eagleford and you try to wonder, well, where exactly in those formations are they getting the oil from? Without those perforations that we'd got from Longbow, we couldn't truly track that back. We've been doing that more and more with the help from interns when you were here a few years ago and also with our newer interns to, to really try to understand that and then provide that on another level through R360 to start understanding where these Wells are actually producing from, which in some states they don't, they don't provide that information. Ted:17:42That's right. And that that really is a really neat project. I know for me and my team at the, and Ted talking about the production data, leveraging Jason and the geoscientists and the interpretation type work you do on your workstations where we can take our production, our perfs, you guys load it in, match it up with the LAS, correlate that production to the actual producing zone. It takes a while to do that, but we're doing that in projects going across different basins and it's really exciting. Jason:18:15No, it's been, it's been very valuable for us that in some of the test information that Longbow has also has in some states like Oklahoma and Texas, let's say, they don't have produced water for a lot of the production. So the only things that you can look back are some of the actual, that the test data that you have where you can find that water. And then a lot of these areas where you're running analytics on some of these Wells to see when they watered out or how much water they have per volume of oil. That's the only place you can get it. And then when you max that match that back to the perforated interval, you can really start understanding some more about those horizons and how much oil or how much oil you have left, but also how much water you're getting out, which is a huge issue right now with a lot of the unconventionals is water not only how much water you're putting in to stimulate if that's what you're doing, but how much formation water you're actually taking out and that could be a, that could be that the factor in having a well that's a good well or not good at all. Caroline:19:19So I know we've touched on production data and the well performance database that TGS offers, but TGS also offers other data like well logs, various types of well logs our validated well header that Jason just mentioned. Katie, I'm curious about your experience as a student getting data from TGS. Can you tell us a little bit of what that was like and how you use other well data with production data to help solve some of the, the issues you guys were running into? Katie:19:48I'm sure. Well, TGS was really helpful because like Jason said, if Jason and Ted said to the state, you don't have to provide good data to the public. So TGS' well logs, their production data was far superior to anything that I saw. So it definitely helped not just at school cause I use this product at UL but I also got to use it in our projects. So it made the uncertainties that were, we were curious about less uncertain. Right. Cause the subsurface is always uncertain. Caroline:20:26So to follow to build on that, Jason, how do you, how do you work more with well logs and production data together, especially when you're working with a group of young interns like Katie and her, her fellow interns Jason:20:39Well one of the things that we do in our group quite a bit is either look for for new areas or sort of redefine basins that have already had had exploration. So the main thing we do when we do that as we get as many well logs as we possibly can. So that's the, the LAS that we have for those areas. Working for TGS is nice because we have access to quite a bit of data. So we pull all those together and we start just doing cross-sections and fence diagrams and make picking our formation tops so that we have a real good general understanding of the basin. As we're doing that, we're also looking at the production data. So each one of those Wells is either a producer or not a producer or maybe it was just a stratigraphic exploration well. But the reason those Wells exist are to make somebody money. So hopefully they're all producers. Jason:21:32So we learned as much from a dry hole as we do from a hole that's not dry. That's where the production data comes in really handy cause we can see exactly how much oil they got out of that well when it was drilled, when it was plugged and abandoned. Some of the issues that might've gone on with it. So we can understand from looking at just some of the well logs themselves than the caliper per se, to see where you had the whole breakup and see where you might've had engineering issues with that well, where they might have crossed faults that might've caused to loss of production in certain areas. And we can tie that back using production to see exactly how these reservoirs work. And we can track that around better to see where explorationists, might need help delineating new fields or new areas. And that's where the seismic comes in with TGS to where we can try to get the seismic out to help limit some of these problems that were we might be seeing in some of the Wells. Caroline:22:30Out of curiosity I know that we offer a long range of historic production data. Recently we just acquired a company called Lasser that goes back far beyond the 1970s. As a geologist, would you say that having a larger dataset going back further in time is more beneficial for you to help solve problems? Jason:22:54Absolutely. So the one thing we've always ran into is not enough data, right? We always want more data. We want to see the complete picture of the entire basin. So having that data that goes farther back in time, that historic production data really helps because we have a lot of those well logs that are sort of historic historics our well logs and our Las don't stop at 1970 or earlier. The production data depending on state isn't necessarily at a strict cutoff of 1970 but that historic data really helps with that production to really start understanding how those wells were drilled. And like I said before exactly what was it producer and what wasn't producer and if it was producing, how long did it produce for? There's been lots of of technology advances that have really increased how much oil you can get out of the ground or gas you can get of the ground. Jason:23:45That's on a purely engineering basis and you can start to see that in the production data, but you can really start seeing that in some of the LAS data when you start looking at the curves and understanding some of the petrophysics behind the Wells. And not only that, you start understanding the basin. So when you look at some of these really old wells, a lot of them are really shallow just to sort of understand that's as far as they could drill to. That's where the technological limit was. But depending on the basin, some, some people in the forties and 50s had drilled all the way to basement. You really want those type of data points when you're understanding the entire basin. The deeper you understand the basin, the more history you can put into it. The more basin modeling you can do. If you can understand the basin from initial infill to present day and the erosion intervals that have been between there. We see that quite a bit in our base in temperature models, which is one of the products that we do that builds off of our LAS data. Caroline:24:43What other tools, interpretation tools do you use internally that TGS helps provide or provides to our clients? Jason:24:49Well firstly I mentioned the basin temperature models. That's one that we, we helped build and we provide to clients and that's a product where we look at the entire basin. We pick the tops in it from 2000 to 3000 Wells from the LAS. And then we do basin temperature modeling on that entire basin with grids and horizons, start understanding the the basin from completed from basement all the way up to the top and understanding the infill. We also provide other products, sort of worldwide called our facies map browser. And this is mainly offshore, but this is looking at sequence stratigraphy within offshore basins. Jason:25:29This one we also use well data and seismic data where we can and integrate the two of them to, to have a real good understanding and picture of the basin. So the geologists that use this data can jump right in to the basin and have a real good working knowledge of what's going on there. One thing in the industry, I've been in this industry for eight years now and I've seen lots of mergers and you know, lots of layoffs unfortunately with people, but groups shrink and grow all the time. And when they grow, people need to jump into new basins they've never been. So one thing that we provide with some of our well data products like the facies map browser and the basin temperature models easily help people easily get acclimated with basins they may have never worked. It's a, it's a real quick and easy way to understand the stratigraphy and understand some, some components of the basin you might not have thought about before. Jason:26:25Then we've been moving on with the basin temperature model is that the background into TOC models. So actually looking at total organic carbon within the same basin using the background of our basin temperature model and then working with core labs to really understand some of our vitrinite reflectance and core data points. So that's the new thing we're doing particularly in the Permian basin. Ted:26:48And I want to add another point on Lasser that Lasser acquisition, which was a, again, exciting for our team. Jason talked about the need for historical data. Sure. acquiring that data set. Now, the only way you could really replicate that public data is if you went to physically went to the individual railroad commission, district offices and loaded up a bunch of microfiche. So that data's digital. We've got it now. What's really neat is we're running it through our modern QA and QC processes. So adding data production volumes in Texas all the way back to the 30s, and then taking further, taking the lease level production data and allocating it to a well level. Nobody in industry is doing that right now from nobody from a vendor perspective. So that project that's ongoing and will be completed before the end of the year. Having historical production back to the 30s allocated to the well level, excited about that and proud of our team to get that done. Caroline:27:55Not to ask a silly question, but what is microfiche is that what you said? Ted:28:01I said microfiche, yeah. Jason:28:01You don't remember Microfiche? (Laughter) Caroline:28:02You're talking to a millennial. Jason:28:04I feel so old. Ted:28:06The point there is the data is not digital, it's manual, it's on microfilm. Microfiche it's lots and lots of hours of labor to recapture that data in database format. And now that we've got it, it's going to be real exciting. Jason:28:27My experience with microfiche was always in elementary school going to the library. So at the library they always had stacks of microfiche that had historical newspapers from the past and you can still find them and they're really, they're almost like little slides like you remember, do you remember what slides looked like? (Laughter) No, it's done. That's true. It's already 2020. [inaudible] There was a special microfiche reader to see them. And you flip through each one of them. But that's how they always documented historical papers. So we'd go back and have to do research projects and you'd have to go find your little microfiche from the library. And when you looked it up, you would slide through and it was like a little projector screen that read the fiche from like the little, little tiny film and scrolled through the little film. So it is almost like a negative Ted:29:17It's a picture of a document. So I'm not the only millennial in the room. So Katie, I'm gonna make a safe assumption that you did not know what that was? Katie:29:23Nope, no, I had no idea what that was, but I have seen it in movies. So thank you for that visual like connected the two for sure. Ted:29:31That's right. But that, that tells you how you know how- Caroline:29:37How hard to find it, how hard to find that data is. Ted:29:39That's right. There weren't computerized records back then, but we still need the data Caroline:29:44Absolutely. Katie:29:44So you've talked a lot about onshore, so do you offer the same kinds of products offshore as well or what do you, how does it go from onshore to offshore? Jason:29:58That's a good question, actually, because with TGS and with the amount of data that we have onshore as really dense area of log data per se, so we can do areas like the Permian, the Eagleford or the DJ basin and fill them in with 5,000 Wells and pick tops and all 5,000 of those Wells. And they all have temperature points. So we can do our base in temperature models there. Offshore, it gets a little bit more difficult because there are, the data's not so close together and offshore particularly say in the Gulf of Mexico, the geology gets a little more tricky, particularly with basin temperature models because you start dealing with more salt. You start dealing with just having the water to sediment differences that you'll- we understand pretty well, but the more well data you have, the more we can make those interpretive products. Jason:30:55So we have, sort of, different products offshore and like I mentioned before, we have the facies map browser is almost exclusively offshore because we can do that along mainly 2D lines, so long 2D lines that go over large areas and are- usually have a few wells connected to them in exploration areas. So the newest one of those is what we're trying to start now in Mexico and the Mexican side of the Gulf of Mexico where a few years ago we shot a really large 185,000 kilometer 2D survey called Gigante. So we interpreted that whole survey and we shot gravity and magnetics over it. So we actually have a gravity and magnetics model that we've built on that area that helps a lot in exploration, but we've also interpreted all the seismic to pick certain horizons. We would like to go a few steps further and actually understand your stratigraphic facies and your sequence stratigraphy that's in there. Jason:31:56And that's what we're, we're trying to do now with the Mexican side of the Gulf of Mexico. And it's a little bit easier there because there's less wells there and a lot of the operators that are moving in there since they opened up Mexico aren't there. So they don't have as big a knowledge base as they do in the U S Gulf of Mexico. And that big large knowledge base in the U S Gulf of Mexico from the operators that have been there for 40 or 50 years has really limited multi-client type interpretation studies. Because say the Exxons or the Shells or the Chevrons have been in these basins for so long, especially the Gulf of Mexico that they have the working knowledge of those basins and they train their employees on that pretty easily. So they don't necessarily need an outside company like TGS to sort of give them the boost or the the heads up or the, the first step to get into a basin. Jason:32:53Whereas in other basins around the world where we have facies, map browsers, we've had them for a while, we have new companies coming in and going more often. So they sort of like having that extra layer of knowledge that we can offer on shore. In the Gulf of Mexico though we did do a post-well analysis, which is just looking at specific wells and I think we have a little over a hundred now and they're either dry holes or or discoveries and they sort of show the stratigraphy they show why it was a dry hole or why was it a discovery. We match that up with seismic and certain areas so you can see the structures that were being drilled at the time. So we do have that. And then in the Mexico side of Mexico and the Gulf of Mexico, we have production data on both sides now. Jason:33:41So we actually have the contract with the Mexican government to provide not only the seismic but the well log data in Mexico, but also the production data in Mexico. On the U.S. Gulf, we have the contract to deliver log data. So companies that drill in the U.S. Gulf of Mexico, they actually send their log data to TGS. We hold it for the 26 month timeframe. And then we clean that data up. We provide our LAS plus package. We provide that back to the BOEM or BSCE, the government entity that sort of controls the Gulf of Mexico. And then we also provide that to any other company that would like to purchase it. So we're the - TGS is actually, we've had that contract for a little over 10 years now and we've just renewed it this year. Katie:34:34So like how much coverage do you have in the Gulf of Mexico? Data-Wise. Jason:34:38Data-wise? So all of it really. So with the, with the recent acquisition of spectrum, we now have 2D coverage that extends all the way from Florida to the Rio Grande Valley really. So we have 2D coverage that covers, there are, TGS is a seismic company. Our core seismic area has always sort of been 3D seismic anyway, has always sort of been the Mississippi Canyon, DeSoto Canyon, Atwater Valley area. We have lots of 3D seismic. We're currently shooting seismic there. We'll just finished up some new nodal surveys there and doing reprocessing. But we have 2D and 3D coverage across the whole area and well data we have all of it. We have every well that's ever been drilled in the Gulf of Mexico. Ted:35:27On the production data song for Gulf of Mexico. The data's really, really nice from that perspective. I mean every well is reported oil, gas and water, monthly production. Well tests are extensive in the Gulf of Mexico. Perhaps the federal government does a better job of reporting well test data, making sure operators are testing those Wells annually and semiannually and getting that data out to public. So you also get access to certain pressure data in there, you know, flowing tubing pressure, bottomhole pressure, et cetera. So that data sets we like working with that. And now on the Mexico side, you know, we've got full coverage of Mexico petroleum industry. There's about 21,000 Wells with production in Mexico. About 1100 of those are offshore and we have captured and calculated monthly production for all of those Wells. So that was a fun project. Learning to translate certain wellheader attributes from Spanish to English that was fun to do. Converting units of measurement down there from a, you know, average daily rates to total monthly production. Bottom line is that data's now standardized in our library monthly oil and barrels in Mexico, monthly gas and MCF water in barrels. And,looking at the data, there are world-class wells in Mexico, so I think the continued release of data from Mexico. Hopefully we'll stay on track there with the, the government releasing data. Like I said, there's there's been some really gigantic flow rates down there, particularly offshore and no reason to think there's not great opportunity there. Seismic Katie:37:36Where's your seismic that you just shot in Mexico. Where does the location lies? Jason:37:40So the, the 2D seismic that's there, the original Gigante is all offshore and covers the entire Mexican Gulf of Mexico 2D. So it covers everything and it even goes sort of around the horn of the Yucatan near Belize. So it covers everything sort of almost into the Caribbean. We've also been doing looking at reprocessing efforts to extend some of our, to extend the seismic onshore to offshore and the Sureste and Tampico areas. And then we're also looking at 3D programs as well. Katie:38:15Very nice. Jason:38:16So there's quite a bit there. And that's not the only place that we have seismic or well log data. So TGS is actually always, I always try to remind me, we have well log data worldwide. So we have data. Do you know Russia and Africa and Australia and Malaysia all over Europe. And all over South America as well. And seismic too. I sort of focus on Western hemisphere so I know a little bit more about that part, but that's still quite a quite a large area sometime. And we're we're, we're looking at wells and seismic all across, both North and South American. Ted:38:53Don't forget Canada. Jason:38:55And Canada too, we have quite a bit of seismic in Canada as well. Caroline:39:00Nice. So one question I have for the table, we know that as TGS is predominantly a seismic company, but we also offer well data. How does that, how does that really help our clients when we offer two very different and unique datasets together? Jason:39:19I think the biggest part of that is making a complete geologic picture for explorationists. So you need the seismic to really sort of understand areas where we don't have well data and that well data really helps the seismic become better. One of the good examples of that is in some of our reprocessing efforts we're doing offshore, we're incorporating as much well data as we can, particularly Sonic data so that we can really understand the velocity models. And really make sure that we can tie those velocity models when they come out and with our seismic comes out in depth that our wells tie perfectly with them. The more well data we have, the better our seismic is going to be at the end of the day. We've always tied a few Wells that we can here and there, but since TGS has so much well data, it's a real benefit to our clients to be able to use that in the seismic processing and in reprocessing as more wells come out. Caroline:40:20So I'm just curious, you know, we are now offering a new product in the well data group. That's our analytics ready LAS that basically allows us to offer even more data. How do you feel about the machine learning algorithms that we're using in forecasting or with well logs? How do you feel about using that as geologists, Katie and Jason? Jason:40:42So one of the things that we've noticed quite a bit with this is you get a really nice big picture and particularly with analytics ready, we like to call it just ARLAS AR-LAS is that that big picture of that first presentation you can get, particularly when it comes to velocity models in Sonic where you don't have seismic. So one of the great images, and I don't know if I can explain this well through through radio, but one of the great images that you can have is with regular well data you have lots of lots of holes. So we didn't drill every place we could and then every place we drilled through time, we didn't do every log we could do. So a lot of the well logs that we have, particularly on onshore might have one or two curves. They might have a resistivity and a gamma ray or some of the older ones just might have an SP curve. Jason:41:32What can start doing with AR or the analytic ready Las is incorporate sort of Sonics into all of those logs and start understanding where we have those deviations in Sonic across the whole area where it hasn't been drilled. So from a big picture, it really helps you understand how that would tie together where you might want to drill next or what might, what interesting features you wouldn't see where a well isn't drilled without having seismic. And if you have seismic then you can tie them both together as well to kind of have a better understanding of of your depth processing. Ted:42:13And I might add onto that AI question back on the production forecasting a challenge. So we're offering both methodologies now of course we have our, you know, our traditional hyperbolic curve fit type forecasting algorithms that work well and offering the physics based you know, probabilistic spread forecasting new. Your question is how do we think about that? It's like, how does the industry think about that? I know everybody's talking about it. Everyone's trying to figure it out. To me, getting a million forecast in a couple of seconds is impressive. Right? And getting that full spread on each, well a P 10 through a P 99 forecast right at your fingertips. It's powerful stuff. Caroline:43:07Yeah. I'd be really curious to see where machine learning and artificial intelligence takes TGS in the future with other types of derivative products that we end up discovering and producing and really making sure that we're getting these to the industry to reduce cycle time. So I think that's pretty cool. Jason:43:22Yeah, absolutely. Yeah, I think we're, we're already moving in that direction with filling in log curves and in the seismic side trying to understand different seismic bodies. So using machine learning and AI to serve as a tool to understand where salt is in a quicker, more timely fashion or to even start understanding easier ways to define horizons or define some amplitude attributes as well. Jason:43:49[To Katie] So you've seen our data and played with our data and hopefully in the future is you're, you know, experiencing your geology career, you'll get to use it much more. Ted:44:01I think she's just scratched the surface with our data, right. I know all that data. Jason:44:06You had the unique opportunity to use it to come into our -come into the company and see what it was like to have that much data at your fingertips. Can you tell us a little bit about how, what that was like and how, how that's different from then to school to now that you're, you're in the industry. Katie:44:24So I came into TGS knowing nothing, well, not knowing nothing, but you know, minimal. You think you, every time you start somewhere you like think you know something, but you really don't, which I've learned again third time. Ted:44:37Right? Katie:44:37So at TGS, I wouldn't say it was just, I learned how to work with all this data, which was overwhelming at first. It was like I learned how to, I don't know, act, not just like socially in an office, right? But I also learned like what's important, what's not important. It's easy to get bogged down in the details when you go from zero to 100 real quick. Caroline:45:03So you've really had a unique perspective. Especially compared to a lot of us at TGS, you started off in an internship with us getting into the data and learning the data, applying the data. Right. And then I believe maybe you've even used it in your thesis. Katie:45:20Right. Caroline:45:20And now that you're in the industry, what has that looked like for you? Ted:45:26How about, how about how do you access data being an industry now? Katie:45:31When I've looked at data, it tells me, it makes me feel comfortable. It clears up uncertainties.. It's not telling me what's going on, but at least I'd like have more of a general idea. So when I look at these large amounts of data that I get for a project, let's say like I did in grad school, it's okay, I have this data. What does the data tell me? Does it tell me if it's pinching out? Does it tell me if it's, you know, this big chunk or maybe the depositional environment. That's what I looked at a lot in well logs the petrophysics. Jason:46:08No, it's understandable. You get thrown a lot of data in these situations and it's how you put that together, how you can efficiently use it. And that's something that we're always trying to make easier for people. It helps in a lot of situations, particularly in, in super major type of companies or in a lot of different companies, even smaller companies that they have geo techs that efficiently use our data before they give it to you. Right? So a lot of times you never, you'll never get to see the first part of, you know, where did this data come from because it all just ends up on your desktop. Right? Katie:46:42Right. So like I, what I liked about my experience I guess at TGS is I saw the beginnings, right? What a geotech would put it in. So I like got to see that visual fresh or put my own spin on it when we were using Longbow. So making those bubble plots or looking at URs and decline curves. I don't have, I don't, I haven't gotten that experience yet, but I'm a Guppy. Caroline:47:10So it was like you were getting access to data sets such as the, you know, the EURs and the forecasting database that you probably didn't necessarily have access to while you were working on your masters. Katie:47:21Right. And didn't know about until it came to TGS. Ted:47:26And the ability to build that project from scratch. I imagine a lot of times now in industry, you walk in and sit down and there are gigantic projects already existing and workflows established as opposed to like starting at the beginning. Katie:47:46Right. Which is overwhelming. Like I remember Jason was like, Hey, y'all are going to map from Mississippi, Louisiana and Texas. That was very overwhelming. Now I just, you know, you get a project and it, someone's already, most of the time, I don't know picked through it. So you don't, it's not very fresh. Jason:48:09But now you're not afraid of the deep end of the pool. Katie:48:10I don't know about that... Jason:48:10Right. We threw you right in the deep end and I, you can swim. You're ready to go. Katie:48:18Oh no. I'm still learning. Jason:48:18Well that's good. Never wanna stop learning. Ted:48:22We're all still learning. Katie:48:22Right. But I'm really still learning. As a new worker bee. Jason:48:30So Katie, is there anything we haven't seen you in a little while? I know that you're, you're in Louisiana now. Is there anything that you want to ask us that you're interested in from a, from your perspective after you've graduated and are now moving onto bigger and better things that might help you in the future? Katie:48:48Maybe not something that would- maybe wouldn't help me in the future, but also help other people that are looking for jobs. Is, are y'all looking for employment? Like looking to employ anyone or what does that look like? It sounds like you're doing a lot of work. So do you have people to fill these positions or are you, how does that go for y'all? Do you even know? Jason:49:10Well, that's one of those great HR questions where, you know, we're always, we're always just busy enough to need new people. (Laughter) Caroline:49:20And I think with, you know, new departments that were growing especially new datasets like Ted is talking about Mexico and Canada, I feel like it really helps to position us to grow, you know, as a company as a whole. So opportunities are always always coming up. Yeah. Jason:49:36I know particularly with our internship program, we're always looking for, you know, young, exciting new talent that can, that can come in and help us out. But also like you did learn about data from sort of the bottom up and take that knowledge base to other companies. So we don't only like training people to come and stay with us or we're perfectly happy bringing in interns and having them go out in the world and and learn something from us that they can bring somewhere else. Katie:50:06Oh sorry. I would say that that's why I like had not, I think that working at TGS was nice for others to see cause they knew that I had experience I guess with production data, which is a cool talking point I think. Caroline:50:22And just to build off of that, Ted has done a really great job building this new initiative, which is getting our well performance data in the universities to work with people like you, Katie, while you were getting your masters to make sure that we're able to provide data to other other programs and get geologists or young geologists access to data sets that they wouldn't have or wouldn't be familiar with whenever they're entering the workforce. Ted:50:48That's right. So, you know, we're happy to donate donate our products, donate production data and Longbow to the universities. As you know, at ULL they brought it into the geoscience and engineering groups. And now we're sitting on the, what the 20 workstations in the lab and part of the curriculum. So it's exciting at the same time, giving the students access to these data products learning actual, you know, working product tools. When they do get hired and hit the, hit the workforce, they're ahead of the game and ready to go. Now, from my selfish perspective, it helps to get feedback and make the products better. So it's a win win for both. Caroline:51:37Well, thanks everyone for coming out today and having this conversation, you know, hanging out, covering a lot of really awesome topics, kind of, you know, exploring where TGS is headed next, where we've been, where we're going. Katie, you know, especially thanks to you for coming all the way from New Orleans to sit with us and kind of give us your insight and your opinions and let us know how it's, how the journey has been for you. So thanks, Jason. Thanks Ted looking forward to the next, the next episode. Katie:52:01Thank you for having me. Jason:52:03Yeah, thanks Katie, it's been great Ted:52:04Thank you.

In the inaugural episode of Beneath the Subsurface, we delve into the exciting realm of AI and Machine Learning as a blossoming new part of the energy industry. Arvind Sharma and Robert Gibson discuss and debate the impacts of disruptive technology, the importance of robust data libraries when building AI solutions, and the future of our industry with AI and ML solutions. With your host for the episode, Erica Conedera, we explore the factors that pushed our slow moving industry to this tipping point in technology and where it could be leading us.  TABLE OF CONTENTS:0:00 - Intro1:03 - Factors that brought AI to O&G5:32 - Job creation with AI12:05 - Career paths and team compositions in the industry15:30 - Industry pain point solutions with AI and ML21:32 - Clouds, open source and democratization24:24 - Kaggle and crowdsourcing Salt Net30:51 - Kaggle challenges with Well Data33:58 - Catching up with silicon valley36:49 - Approaching solutions with AI44:18 - Disciplining data and metadata to get to the "good stuff"EPISODE TRANSCRIPTErica Conedera:00:00Hello and welcome to Beneath the Subsurface a podcast that investigates the intersection of geoscience and technology. And in our first episode, we'll be diving into the dynamic field of AI and machine learning as it relates to the oil and gas industry. We'll be discussing the impact of disruptive technology, the importance of robust data libraries when building AI solutions, and exciting possibilities for the future oil and gas. From the TGS software development team. My name is Erica Conedera. And with me today are Arvind Sharma, our VP of data and analytics, and Rob Gibson, our director of strategy, sales, data and analytics. Thank you gentlemen for being with us today for our first episode.Rob Gibson:00:48Glad to be here.Arvind Sharma:00:49Thank you Erica.Erica:00:51So let's start our discussion today by talking about the factors that brought the industry to AI and machine learning. Why now? Why not sooner? Why not later?Rob:01:03Well I'll start. Um, so thank you for the introduction, my name's Rob Gibson. I've been with TGS for almost 20 years now. And in that time, the thing that I have kind of seen over the 20 years in this company, , and probably another eight or nine in the industry, is that we've always been a little slow to adopt technology. And I come from the IT side of the world, software engineering, database design - so from my perspective, it's always been a little bit slow to bring in new technology.Rob:01:34And the things where I've seen the biggest change has been fundamental shifts in the industry, whether it's a crash in oil price, or, or some other kind of big disruptor in the industry as a whole, like the economy, not just our industry but the entire economy. But in middle of 2014 with the current downturn, that's really where I finally started to see the big shift toward AI, toward machine learning, towards IOT in particular.Rob:02:00But it seems like it took a big, big change in the industry where we lost hundreds of thousands of people across the industry and we really still needed a lot of work to get done. So technology has been able to kind of fill in the void. So, even as the downturn happened, we kind of started to level off at the bottom of the downturn and that's when companies started to see that we really needed to inject some more technology to get those decisions made. So generally speaking, I would say that this industry has been a little slow to move to adopt technology even though the industry has got a lot of money to invest in those kinds of things.Arvind:02:34Um, so thank you Erica for that question. And, I'm going to slightly disagree, more broadly, I agree with rob that um, oil and gas industry is historically a little slow in adopting technology, but, the reason I think is a slightly different, I think a oil and gas work in very difficult area where we need to have very robust proven up technologies to work. And in general, we wait a little bit for the technology to prove itself before adopting into, um, more difficult areas. So if we look at a little bit historical view, um, we have been on the leading edge of technology for a very long time. Um, some of the early semiconductors were built by your physical, um, companies. Um, then, as we moved to, PC revolution, we started actually PC, um, we started to actually pick up PCs into office very quickly, not as good as the silicon graphics people, but, soon afterwards, and then when the technology evolution started happening more in the silicon valley, then we started to regress a little bit. We continued on the part of what we were doing, whereas there was a divergence somewhere between mid nineties where silicon valley started to actually develop a little bit faster and we started to lag behind. And I think as Rob said, that, 2014 was a good time because at that time there was a need for us to adopt technology to increase our efficiency and, fill the gap that was created due to capital constraint. And as well as fleeing of, some of the knowledge base, employees - from our sector.Rob:04:39That's a good point on the technology side because you said that we kind of diverged away from where silicon valley really took off in the mid nineties. I entered into the industry in '94. So for me, my entire career has been that diverging process and just now it feels really good. Like we're finally catching up, not only catching up, but we've got customers, we've got employees who are sitting inside of the top tech companies in the world sitting at Google's facilities, even though they're an oil and gas company, sitting and working with Amazon, with Oracle, with IBM, with all these top names. And yet they're doing it in collaboration with the industry. Where in the past, it was almost like the two things were somewhat separated and now they are on a converging path. They've got the technology, we've got the data, at least in our space. And those two things coming together is kind of the critical mass we need to see some success.Erica:05:32So on that note, what kind of jobs do you think are going to be created in the future as the industries continue to convergence?Rob:05:40You know, that's a, that's a great prognostication. I mean, it's kind of interesting when you look back at like airbnb and Uber and those kinds of things. Nobody saw those coming and nobody knew what that was going to look like five years into their business, not to mention 10 or 15. I think that's what we're looking at in the oil and gas industry as well. We still have to find oil and gas. We still have to explore. We still have to be technologists, whether it's IT technology or G&G technology, we still have to operate in those spaces. But the roles may be very different. I'm hoping that a lot more of the busy legwork is a lot easier for us to work with and it has been historically, but we're still going to have to do those core G&G jobs. I just don't know what they're going to look like five years from now.Arvind:06:29I mean the way I see it is that it will be high-gradation to, like it will be more fulfilling jobs. The future jobs hopefully will be more fulfilling. So because a good portion of the grunt work, the work that everyone hated to do, but they had to do it to get to the final work, like final interesting work. Hopefully all those things will this machine learning and AI and broader digitization will help alleviate that part. And even whether you are technologist, whether you are a geologist, whether you're a geophysicist or whether you're a decision-maker. Like in all of those, um, you will start moving from the low value work to high value work. The technologist who was looking into log curve, they will actually start evaluating the log curve rather than just digitizing it. And that's, in my view, it's a more fulfilling job job compared to just doing the mundane work. And I, so that's the part first part is that what kind of job it, my hope is that it will be more fulfilling.Arvind:07:43Now the second is how many and what type of job, um, as Rob said that, the speed at which this is moving, we, it will be very difficult for us to do the prediction. Is that like if we sit here and say that they are, these are the type of job that will be created in five years, we'll be doing a disservice. We can actually make some guided prediction in which there will be need for geologist or geophysicist or petrophysicist and other people to do in what form will they be a pure geophysicist or a geophysicist who is a has a lot more broader expertise, a computer science and geophysicist working together. Those are the kinds of roles that will be needed in future because for a very long time we have operated in silos because it's not just technology is changing is the way we work is also changing is that we have operated in silos that we develop something, throw it over the fence. They, they catch it most of the time and then actually move into the next silo, and so on and so forth. Is that what-Rob:08:58You hope they do anyway.Arvind:08:59Yeah. I hope that they do anyway, but so that's the sequential process now. Some of them will be done by machines. Some of them will be done by human. And then you have to actually create a workflow which is like fulfilling as well as efficient for the capital investor.Erica:09:19Perhaps less siloed off?Arvind:09:21Less siloed off. So there will be team of teams and the team will actually move very frequently. So it will be almost like a self organization is that these are the four people needed to solve this problem. Let's take those four people and work on that problem. And then when that problem is solved or productionized, then they actually go solve the different problems.Arvind:09:43And so it will rather than back in the days or even today, hi- fully hierarchy of system, it will still be there, will be CEO (Laughter) and but there will be more, um, team of different group and different expertise, um, very quickly building and dismantling and those, that's the agile methodology that will be needed to take this technology and use it for, like basically doing things better.Erica:10:18So to kind of hone in on where you're saying, your background is in both geophysics and um, software engineering, correct?Arvind:10:26Okay. So sorry, I didn't actually talk about myself. (Laughter) So, um, I joined the TGS a little more than a year back, um, started as a chief geophysicist and then moved into this role. But before that, most of my career has been with BP and before that for a software company. So I have worked as a software engineer for some time and then got my PhD in geophysics and then worked for a little more than 10 years in BP all the way from writing imaging.Arvind:11:01So basically fundamental imaging, algorithm writing to drilling wells. So, in my short career I have seen a lot of things and what I do see is that, there has, there is a lot of silos in BP as well as in TGS. And BP is also working on it - breaking. I have a lot of friends there who are saying is that there is a significant effort in technology and modernization is happening in changing the culture rather than- it's not just about changing PC from going from a laptop to iPad. That's a- that's a tool. But the fundamental change will happen in the thought process. And if we want to actually use machine learning and these kinds of digital technology then it needs to be very integrated and the silo mentality is not going to work. You have to look at the problem as a holistic to solve it.Erica:12:02Yeah.Arvind:12:02So, so that's the background. So that's my background.Erica:12:05Yeah. So I asked because I wondered if you think that your career path is going to be the future of the industry, do you think that there are going to be more people with a dual background in both computer science and geophysics?Arvind:12:19So that's a very polite way to say that. My, I am actually looking at that my career is the right career. So, no and yes and no both. I do think that people will become more generalist and they will have deep expertise. And it's counter intuitive - is that generalist and deep expertise is not the same. Like we are used to someone who has a very deep expertise and that are not generalists about other topicsErica:12:57Narrow and deep.Arvind:12:57So very narrow expertise. But very deep and they have shallow expertise, very broad. Those are back in the days I think we are moving towards a deep expertise in several different narrow fields. So you need like, so to truly get good collaboration and innovation, you have to have deep expertise in several different fields to integrate them together.Erica:13:27So Rob, it looks like you're chomping at the bit here. (laughter)Arvind:13:30Deep and broad. So like what we need is deep and broad.Rob:13:34Yeah. When, when Arvind was talking about, kind of the career and, and some of the other topics, two things came to mind on the technology side of things. If you look back at AT&T, they had a choice and they did investigation and some pretty deep research on whether or not they needed to move into mobile cell phone technology. And they made the choice. They did a big expensive study and spent hundreds of millions of dollars or tens of millions of dollars to identify that they needed to be prepared for an industry of say, a million cell phone users by a certain year. And that number was, I don't know, 150 times wrong. It was way, way higher than that. And you could use the same thing with Kodak. They invented the digital camera and then lost the digital camera battle. And struggled in the industry. We want to make sure that we're looking broad enough to understand what's coming down the pipe and can adapt and change to that. Not just from the individual roles in the company, but the company direction as a whole.Arvind:14:34To give a concrete example is that , I have a background in geology or physics and computer science or Rob has background in geoscience and computer science and the data analytics team. It likes our TGS data analytics team. They have, we have people who have the um, physics backgrounds. They have PhD in physics and then they have worked in geophysics and then working on well logs. Then, the other one, Sathiya - he is a geophysicist who now is working on more of a deep learning problem. And a Sribarath is the team leader. He is a geophysicist. Who is it more of a computer scientist who is working on these two problems. So, our team composition itself, the TGS data analytics team composition itself is built in a multidisciplinary fashion.Erica15:30Yeah. So I'm glad that you brought up are our current team here cause I kind of wanted to pivot to the problems that we're using AI to solve for right now. You know, like what, what are the pain points in the industry and how are we using AI for that?Arvind:15:46So, so the pain point in the industry, are I'll talk about one, is it one which is very close to my heart. I was a, so in BP I did a lot of salt interpretation. So anything which requires a lot of human intervention is a big choke point because our data set is getting bigger, larger and larger with a lot more volumes to it are a lot more information to it and we have limited human resources and we want to actually take those human resources and mobilize them to do more high value work rather than doing a lot more um, grunt work. Salt model building is an example. And where we, we actually, our data analytics team started working there. So I'll, I'll work, I'll talk about that later. But that's an example where a lot of judgment call is made early, which don't require a lot of human judgment call early interpretation. Is the true place where automation and digital transformation can actually help.Erica:17:04Rob, what's your take on this?Rob:17:06Well, the Nice thing about what we're doing with salt picking is we're really helping us and our clients reduce the time it takes to get to the indecision. On my side of,of the house, my background with TGS has largely on the well data side of things. So it's not so much about reducing the amount of time of processing the data as it is getting a higher value data set in the hands of our clients. So historically, especially in the onshore U.S., there's a significant lack of data that's reported to the regulatory agencies. So we source that data as do a lot of other people. We source data from our, our, our customers, our partners operators. We process that data, but the most important thing that we can do with that is take that huge volume of data, the largest commercially available in the industry and add more to it so that the operators are able to get to that decision making process. So like Arvind said, if we can avoid the grunt work and get them to the point where they're actually making business decisions, that's what we're doing with our analytics ready LAS Dataset. We're in-filling the gaps in the curves because they either weren't run or weren't reported. We're predicting what the missing curves would look like, based on an immense volume of data. So it's not so much about getting the product created faster, although that is another goal that we've got. Of course, we're a commercial company. We're trying to get products to our customers and make money like anybody does. But the ultimate goal with our current analytics ready LAS product is to get the most complete dataset available so that the operators can make better decisions in the subsurface; drill less wells, drill more productive wells, drill wells faster. All of those things go into why we chose to go down that that path.Arvind:18:50So, looking at a higher level. The question that you asked was like what are the choke points and how we had actually using digital transformation in machine learning and AI to help that. Um, I think we published something like our CEO talked about that in the um, few months, a month back, Norwegian Energy day. There was a nice plot that, shows that most of the time we are acquiring data for a purpose. Like we are acquiring data to solve a geologic problem so that we can actually make a decision whether to drill somewhere, or not drill somewhere whether to buy acreage or not buy acreage by our clients. So when you take that data, you have to convert that into information, that information need to convert it into knowledge. And that knowledge is what enables our clients to make better, faster and cheaper decisions.Arvind:19:51And that cycle converting from data to knowledge to decision and enabling their decision is actually is the big choke point. If you want me to say one, this is that your point is that how to actually take data and convert to knowledge fastest way and cheapest way. And that's where most of our effort is. So salt, model building is an example where we right now it takes us somewhere between the nine months to a few years when we acquire data to provide the clients with the final image that they can do interpretation and make decision. This is too long of a time. In this day and age it needs to be compressed and a good portion of that compression can happen, by better compute. But some of them cannot happen without doing a deep learning where humans are involved in like for example, salt models building where like you can actually throw as much computer it as possible. But since the cycle time requires human to drill that model, it will be the limiting cases that, so there we want to actually enable the interpreters to take our salt net, which is our algorithm and accelerate the early part of it so that they have more time to do high quality work and build and build that model faster, reduce that cycle time so that our clients can make better, faster and cheaper decisions.Rob:21:32It's been interesting to watch the transition too with our industry and the technology at the same time we've moved to the cloud, right? All of our data's now sitting at a cloud provider and if you would have looked at the oil industry five years ago, there's a very security minded mindset around the industry that says, I need to keep that data because it's a very, very critical and I want to make sure the only, I've got access to it. So there was a lot of fear about putting data in the cloud several years ago. Now you look at the cloud providers and they're spending literally billions of dollars on things like security and bandwidth and access, things that didn't exist five, 10 years ago. So that transition to be able to go to the cloud, where all, where, all of our data sits today. More and more of our clients are going there as well. And the nice thing about that is you can ramp up your needs, on compute capacity, on disk capacity, on combining data sets across partners, vendors, other operators, and collaborate and work on that data set together to come up with solutions that you couldn't possibly have done before. So it's, it's fun actually to watch that transition happen.Arvind:22:43It is going a little tangent to the question that you asked her, but, because there's a very important point about the cloud services the the biggest cloud platform is Kubernetes by Google and that's actually open source. So Google developed that and made it open source available for anyone who wants to build a cloud infrastructure. They can have it. That's the, the most to use open source, platform that, available today. So that's changing the way people work. Like red hat or Linux, Unix, Sun, Sun, microsystem or Microsoft or apple. They are very, like, even in technology sector, they are very controlling of what they are providing to their consumers. They control that environment. Whereas now things are changing in which the open source systems like, which is publicly available is becoming one of the most dominant form of a software platform. Um, if you look at android for machine learning, it's tensorflow, Pi Torch. Those are open systems software that is a democratizing the technology so that anyone and everyone can, is able to take that next step and the solve complex problem because the base is available for them. They don't have to build the base. They can actually focus on solving the high value complex problem.Erica:24:24Speaking of both Google and open source and democratizing, problem solving. So TGS recently had a Kaggle challenge, correct, can you speak a little bit about that?Arvind:24:35So, yeah, that actually, so when I joined TGS, I had, one data scientist that we, we were working with, like we were still building the data science team and we started working on the salt net problem. We had an early, um, success. We were able to do some of those things and then we realized that there is like ocean of data scientists who are across the world. We don't have actually access to that Google actually open source and they have, they're working on their problem, they're working on Apple's problem, they're working on very interesting problems. So why they're not working on it at two different reason. One is that they don't have access to it in a second, the problem is not interesting enough for them. So Kaggle was our effort to make it accessible to everyone and make it interesting so that people will work on it.Arvind:25:30So just for the, um, description of Kaggle, Kaggle is the world's largest, data science crowdsourcing platforms. So crowdsourcing is a, um, where you put the problem and it's a platform or website where the, um, the problem description is given and data science scientists to work on their like on their spare time, nights and weekend or that's their hobby or that's their job. And they solved that problem. They submit to submit on that platform and they get instantaneous result that, how a good their solution was. So that's the Kaggle is the one of the largest world's largest platform for that recently acquired by Google. So we actually approached Kaggle that- can we actually put the one of the complex problem that we have on this website or this platform and they worked with us. And so we partnered together to host the oil and gas first serious problem for the automatically building salt model. And we actually, so to Rob's point, um, the hardest problem was getting the data rights that are convincing our management that it's okay to release a certain portion of data. We had to work really hard to create an interesting problem and that once we released that data, um, this competition was very successful in the sense that if they were around 80 plus thousand different solutions, just think of the scope of itRob:27:06From almost 3000 different teamsArvind:27:093,800. So close to 4,000 people. Oh yeah. 3000 team and comprise of almost 4,000 data scientists across the world work on this problem for three months and gave us more than 80,000 different solutions. We would have never got anything like this working day and night with whole TGS working on this problem.Rob:27:32I, I found it interesting because I like did a search on Google for our, TGS salt net.Arvind:27:39Yeah.Rob:27:40And if you look at the results just on Youtube, you'll find probably 20 different videos of PhD students, data scientists getting their master's degree who are using that problem that we posted out there as part of their thesis or as part of their Grad student work to show that, that the data science process that they went through as part of their education. And now that's out there for everybody to use.Erica:28:02So this is a major disruptor isn't it, to the industry because we have basically non geologists, non geophysicists solving problems for-Rob:28:12Yeah it's, it's definitely, we, there was a lot of teams, right? So there was some that had geoscience backgrounds, some that didn't, but most of them, they just come from a data science background, right? So they could have stats or math or computer science or anything. And when they applied this, it was interesting to see the collaboration on the Kaggle user interface where the teams were out there saying, hey, I tried this. What did you guys try? And the whole idea of crowdsourcing and, and the idea that we're kind of in somewhat of a unique position where we can do that. We can, we own the data. We don't license it from somebody else. Um, it's the data that we own that we can put out there. So we've got a huge volume that we can leverage and put it into a community like that where we can actually see some of those results come in.Erica:28:57So to kind of put you on the spot-Arvind:28:59Can I- one thing to say after that to is not just about data owning the data because there are several different companies who own data, even oil and gas company, they have their own data library. I honestly think that, it says volume about TGS, that TGS was willing to take a bet on this kind of futuristic idea and like go on a limb. But, and this is, I'm just giving credit to the senior management here, that they were, they're allowed us to actually go with this. That was one of the bigger hurdle than just to owning data, that management buy-inRob:29:39Second only to data preparation for the challenge itself.Arvind:29:42Second only to the data preparation, it took us a lot of time to build-Rob:29:45YeahArvind:29:45an interesting problem. It's not just about like you have to create an interesting problem to-Erica:29:51to attract the right talent.Arvind:29:52So the winner was a group from a Belarus and the Japan. They have never met. They have never seen each other other than the Facebook.Erica:30:02Wow.Arvind:30:03And did they actually met on this Kaggle platform? They were working on this problem. They found out that there they are approaching with the two different ways and they actually teamed up so that they can combine this to create a better solution. Combining both of their effort and that that's actually happens to be the winning combination. But a traditional method won't allow us to tap into this kind of resources or brain power. That to someone from Belarus and Japan working together whom we don't know solving our problem and that is going to be a disruptor and we have to be ready to capitalize on it rather than be afraid of it.Erica:30:51Right. And that's why I wanted to go to rob, not to put you on the spot here, but as someone coming from the well data side, do you see any potential future Kaggle challenges using well data?Rob:31:05Yeah, the, that could absolutely be in our future. I think at this point we're really trying to frame the problems that we're trying to solve for our customers. And if we decide that one of those problems deserves, some time in the public, like on Kaggle, then we can absolutely go that direction. Not a problem whatsoever. At the moment though, our real focus is trying to figure out where can we provide the most value to the clients and we're kind of letting them steer us in a, you know, a way we have got our own geology department internally so we know what we need to do with our internal well data in order to high grade it to the next level product. However, we're really taking direction from our clients to make sure that we're moving in that direction. So yeah, I could see us having a problem like that, especially if it's starting to get into a Dataset that, , needs to be merged with another data set that maybe, we need support from, somewhere else in the industry. We're in a different industry.Arvind:31:59Just a few minutes on that is,the next problem I think that Kaggle need from oil and gas is a more on the solution side. So the knowledge to- like information to knowledge site in which you are all taking very different type of data set. For example, success failure database for the basin. And building a, prospect level decision that requires a, as Rob said, that collaboration, that the TGS collaborating with one of the E&P company or someone else, like those two or three companies and now bringing their data together because at the end of the day, this integration is what everyone is looking for. Can we actually create an interesting integration problem and put it on the Kaggle competition. So, any listener, if they're in, they have a good problem, they can actually contact Rob, or me. That, because we are always looking for good partners to solve complex problems. We can't solve all the problem by ourselves, neither other people. It does require teams to build the right kind of Dataset, interesting problems in to, to get into the board.Erica:33:22Okay. So we've talked about how we got here to this point in the industry with AI machine learning and we've talked about what we're doing today with the, um, let's move on to the future where we think AI will take, um, the industry. So to follow up on something that Arvind had said earlier, so you had said that we sort of fell behind silicon valley at some point. How, how far behind do you think we are right now in terms of years if you can make that estimation?Arvind:33:58Oh, that's a tough question but I'll try to answer it in a roundabout way. Is it that when I say that we lag behind, we lag behind in the compute side of it, like the AI side of it and some of the visualization and web-based technology when it comes to high performance computing, we were still leading up to very- probably in some of the spaces we are still leading. So storage and high performance compute which is both, oil and gas defense and Silicon Valley. All three are working. Um, we are not that far behind actually we might be at the cutting edge of it. And that was one of the reason that we didn't actually focus on the AI side because we were solving the problem in more high compute way and we are using bigger and bigger machine solving, more complex problems more physics based complex physics based solutions.Arvind:35:04So when it comes to solving physics based solution, we are still, at the front of the pack. But when it comes to solving a heuristic auto machine learning or AI based solution, we are behind, we are behind in robotics and things like that and we are catching up. So when you think of a mid midstream and downstream where there's a lot of the internet of things, IOT instruments, so things are getting is like instrumentized and there are a lot of instruments which are connected to each other and real time monitoring, predictive maintenance. Those are happening and happening at a very rapid rate. And that will actually, we'll, we'll catch up in a few years in, in midstream and downstream side or mostly instrumentation side where we are truly lagging is subsurface because it's not the problem that Ian, and like, silicon valley was trying to solve.Arvind:36:05A subsurface problem are complex. They are very different type of problem; that someplace you have very dense data, someplace We have very sparse data. How to actually integrate that and humans are very good at integrating different scale of information in a cohesive way, whereas that problem is not the problem that silicon like, technology sector was trying to solve. And so we are trying to actually take the solutions that they are building to solve different problem and integrating it or adapting that to solve our problem. So that's where like I see like, so I think it's a non answer but that's what the best I have. (Laughter)Erica:36:49It was a very good answer. So how does this change the way that we're building our products then our approach to getting our products out there?Rob:36:58Well, one of the, one of the things I'll start with is we're actually seeing our clients adopt analytics teams, analytics approaches, machine learning. there's a lot of, there's a lot of growth in that part of the industry. and they've gotten past the point where they don't believe that a predictive solution is the right solution. You know, with our ARLAS product, we're creating an analytics ready LAS dataset where we're predicting what the curves would look like, where there's currently gaps in the curve coverage. The initial problem the customers had was, do they believe that the data's accurate? We're starting to get past those kinds of problems. We're starting to get to the point where they believe in the solutions and now they're trying to make sure that they've got the right solutions to fit within their workflows in their organization. So I think the fact that they've actually invested in building up their own analytics teams where they've injected software engineering, geology and geophysics, a data science and kind of group them all together and carved them off, or they can focus on maybe solving 20% of the problems that they actually, attempt. That's kind of where the industry has gotten to, which means we now have an opportunity to help them get to those levels.Arvind:38:10You see that a change in conferences, and, meetings and symposiums that, like for example SEG Society of exploration geophysicists and, that, conference three years back there was one session about machine learning and last year, machine learning has the largest number of sessions in that conference. So you're looking at a rapid adaptation of a machine learning as a core technology in oil and gas and at least in subsurface, but most of them is at the very early phases, people are trying to solve the easier problem, the problem they can solve rather than the problem that need to be solved. So that's where there's a differentiation happening that everyone wants to work on machine learning and most of the people are actually taking solution to your problem rather than taking problem finding solution for a problem which is relevant. So,Rob:39:21I think that's pretty fair because,you've got to get some sort of belief internally and if you can prove that you've got kind of a before and after, here's what I did to make this decision or the wells that are drilled in the production I've got and here's what I predicted was going to happen. And you can start to see those two things align. Then you start to get belief in something. If you just use something that's predictive only and you've got nothing to compare it to, it may be the right solution. But do you have the belief that your company is going to run with it? So that's why I think we're starting to see them solve problems that we know can be solved initially rather than the big problem of say, if I shoot seismic here, I can predict how much oil I'm going to produce. That's a big problem and it's at different resolutions and scales than we believe we can solve and, and be definitive about it today. but I think that, I think I agree with you that they're, they're really focused on, on proving that this technology, that analytics that AI/ML is going to work for the problems that they know about.Arvind:40:24Agreed only up to a point is that, the reason and why I think it ML/AI solutions are different is because, in physics, one of our basic assumption is that, if we solve a toy problem, you can scale the same way is the same solution will apply on a bigger problem. That's not the case for machine learning solutions. The solution that is applicable for a toy problem is not going to scale. You need to actually retrain the data and the solution becomes different as the scale of the problem increases. So although it's, interesting to see that a lot of a small problem are very easy problem people are taking to- people are solving a lot of easy problem using machine learning. To show that machine learning works, that's good. But to truly take advantage of machine learning, you have to actually solve, try to solve one of the complex problem because you already have a solution for those easy problems.Arvind:41:40Why do we need machine learning? So for example, ARLAS is a good example. Our analytic ready LAS in which we are predicting well logs from the available, well logs. Now if I have only one well, or a few wells then I actually want my petrophysicist to go through the physics based modeling and solve that problem. I don't need AI to solve that problem. I have actually solutions which works there. If the solution that I need is that how to solve this problem on a scale of Permian basin or a scale of U.S. So like what we have done for ARLAS that the first basin we started was Permian is where we took all the data that we have as a training data or actually a good portion of that data as a training data set. We build that model, which is actually based in scale model that can actually ingest all the like 320,000 wells we have. So we used thousands and thousands of well as a training build a very robust model to actually solve that problem and now that solution is available for the whole basin. That's the kind of solutions that are problem that AI is good at solving and has actually best potential not for solving few wells. Learning about AI by solving a few wells is great, but as a product or as a true application of AI, we need to actually look at tackling the big problems.Rob:43:11Yeah, I agree. There's been a lot of, shall we say analytics companies that come out with a claim of being able to perform some sort of machine learning basis and they've got a great interface and everything looks really good. And the story behind it is that it's been taught on five wells or 10 wells in our learning set was in the tens of thousands of wells, which is why I believe in the data set that we've built.Arvind:43:40At a very high level, machine learning is like teaching a kid, like someone has come out of graduate school and they want to actually learn something and you are showing them this is how we actually do. The more things they see, the better they will get, the more experience they will have and the better their capability or work will be. So it requires the, the whole concept of machine learning or AI is that you want to actually train with massive amount of very high quality data set and that actually solves more complex problems.Erica:44:18How do you discipline data?Arvind:44:22So you are saying that did- have you talked to our lead data scientist and he calls him to himself a data janitor, that most of the time he spent is cleaning of the data and organizing the data so that he can actually do the high quality like the machine learning AI work. So if he spends his time like out of a hundred hours, 60 or 70 hours- so he's actually organizing, categorizing data set so that he can do the fun stuff in the last 30 40 hours. I mean that's actually, that's better than a good, most of the places where people spend 90 hours doing the curation and 10 hours doing the fun stuff. And that was one of the reasons why we had to build the data lake because one of the thing is that we need all the data to be readily available in a kind of semi usable format that I don't need to spend time learning about the 2003 data is different than 2015 data versus 2018 data.Arvind:45:34I need to actually consume it as one big dataset. So last whole year we spend actually considerable, considerable amount of time and effort in building our data lake in which we actually took all of our commercial legacy, data set and moved it on cloud. The two things that we did is one we standardized the data set so that lead data scientists don't have to spend on doing janitorial of data janitorial work and a second is creating metadata. So what Metadata is that aggregate information.Arvind:46:06For example, Arvind Sharma what is the Meta data about Arvind Sharma um, that he is five feet 10, I don't have a lot of hair. (Laughter) He drives some car and he, he has gone to- he has a PhD like so some aggregate information like out of her, like rather than cell by cell information about Arvind, what is the minimum, set of aggregate information that you can use to define Arvind. So that's the metadata about any data set. So what we did when we are moving this a massive amount of data set into our data lake for each of these data set, we extracted this aggregate information that where it was recorded, when it was recorded, what are the basic things done to this data set? What is the maximum amplitude in this volume? What is the minimum amplitude in this volume? What does the average amplitude in this? So those things we actually use it because a lot of analytics is that some of the higher level analytics will be about integrating the information about data set, like Facebook uses information about people to make some of the decision. We are not that creepy as that Facebook, but (laughter) it's, it's like taking the information about the data set and actually learning creating knowledge about the basin.Rob:47:37It's interesting when you were talking about the data janitorial work and how we've kind to standardize our data set on the, on the cloud because it kind of brings it full circle back to something you said early on. And that was that we want our customers to be able to get to that decision making point sooner without having to do all that data, janitorial work. I've been going to data management conferences for 25 years and I hear the same thing every year for 25 years. I spend "fill in the blank" percentage of my time, 60 70, 80% of my time looking for data and the remainder are actually working with it. That's what an analytics ready data set it's going to allow us and our customers to be able to do is not have to do all that janitorial work, but actually get to the point where I can actually start interpreting what that data means to me to make decisions.Erica:48:30So looking towards the future of the industry, do you think we're going to continue to ramp up in terms of speed and getting to the good stuff, the fun part? Do you think that's going to continue to logarithmically increase?Rob:48:44Probably faster than we can ever imagine. I think the, I think the change that we saw with companies moving to the cloud companies going toward, service based solutions, companies moving toward high volume, normalized consistent datasets, all of these things have been moving at light-light speed compared to what they were, the, the past 25 years. Up until today, every day about probably about every three weeks. We basically, have got some new technology that's been released that we can start adopting and putting into our workflows that wasn't there three weeks, three weeks prior, open source. It comes back to that topic as well. More and more of these tech firms are putting the data out as open source means we could leverage it and get to solutions faster. So to answer the question, absolutely faster than we can possibly imagine.Erica:49:28Well, awesome. I cannot wait to get to this future, with both of you.Erica:49:41Well, thank you so much for talking with us today. Being part of our first episode of Beneath the Subsurface, it was an absolute pleasure. If our listeners want to learn more about what TGS is doing with AI, you can visit TGS.com You can visit our new TGS.ai platform and, we'll have some additional show notes on our website, to go along with this episode.Arvind:50:06Thank you Erica.Rob:50:07Yeah, thanks a lot. I appreciate it.Conclusions and plugs:Check out the newly launched tgs.ai to dig deeper in to the data with subsurface intelligence. Gain detailed subsurface knowledge through robust analytics with our integrated data and machine learning solutions at tgs.ai Discover Geoscience AI solutions, Cloud Computing, Data Management, and our Data Library. Learn more about TGS at tgs.com

Welcome back to Tokusatsu Girlfriend, the podcast where Laur and I sit down to chronicle her journey into the scary and expansive world of Tokusatsu! This episode follows the Kamen Rider Fourze episodes 13 - 20, which include the fan favorite Kamen Rider Meteor, as well as the shocking twist of Scorpio!Upgrade today over on https://www.patreon.com/squallcharlson for the uncut full episode made for PATREON ONLY! Expect some shoutouts, cut out goofs or gags, and other things others won't normally be able to listen to! This is for you! There will also be an after show going up shortly following this release just for Patreon too!The next episode of the podcast will follow episodes 21 - 32So switch on your favorite headphones, and let's go!Support the show (https://www.patreon.com/squallcharlson)

Mid-winter blues got you down? We've got the remedy for that. Whether its getting outside in the darkest time of year for a European-style light festival, hearing music from one of Portland's premiere Americana stars, or meeting Portland's brand new city commissioner, Chloe Eudaly, this week's show is guaranteed to warm your soul.Chloe Eudaly on Her Big Step from Indie Bookstore Owner to City Commissioner - 1:23Chloe Eudaly started work as Portland’s newest commissioner amid a winter weather event that shut down city offices. In a town wracked by tumultuous gentrification, Eudaly is a single mom, a renter, and an eastsider who beat a much better-funded incumbent. In her first act as Commissioner, Eudaly has put up a proposal requiring landlords to pay relocation costs when tenants are evicted without cause that will go before council on Feb. 2. The Portland Winter Light Festival Shines Away the S.A.D. - 11:08You can stay in and be cozy at this time of year; no one would blame you. But the Portland Winter Lights Festival is giving you a reason to head outside on Feb. 1–4. Now in its second year, the fest fills Portland's waterfront between OMSI and the Zidell Yards with flashing drones, epics projections, other crazy light art and free events to celebrate the spirit of winter and the warmth of community. What It Takes to Design an Iconic Album Cover - 17:32So much goes into the making of a great record, but sometimes it’s the album art that bumps a great record into the realm of the iconic. Think about the collection of famous faces on the front of the Beatles “Sgt. Pepper's Lonely Hearts Club Band” or that baby bobbing in the pool on the cover of Nirvana’s "Nevermind," or that very trippy prism on Pink Floyd’s "Dark Side of the Moon." Portland is home to many artists, and opbmusic welcomed two innovative designers, Orion Landau of the indie metal record label Relapse and Aaron Draplin of Draplin Design Company, into the studio to chat about their careers and which album covers inspire them. PDX Jazz Brings Music and Art to the Classroom - 24:54How do you get kids to understand a musical form that has no rules, no walls? Art can help. Volunteers with PDX Jazz have spent the winter fanning out in metro-area schools, playing music for kids and talking to them about jazz history. Teachers like Katie Robinson at Boise-Eliot/Humboldt then work with the kids on designing jazz album covers. All the students’ work will be part of a Feb. 7 art show at the Ace Hotel's event space, The Cleaners, in Portland. Douglas County Voters Grapple with their Vote to Close the Libraries - 28:39As a kid, do you remember trips to the library? Story time? Or maybe you stop by as an adult to get books and movies or use the computers. Douglas County readers will no longer have that option. In the November election, they failed to pass a measure that would create a special taxing district to fund the libraries, and now all of the branches of the Douglas County Library System, centered around Roseburg, will close by the end of May. Live Music from Americana Chameleon Tony Furtado - 34:46Songwriter and multi-instrumentalist Tony Furtado is an Americana chameleon. He effortlessly shifts between bluegrass, folk, blues, old time, and rock sounds. It’s a fascinating career arc that was beautifully captured in his most recent release, a live album called “Cider House Sessions” recorded at Portland’s Reverend Nat’s Hard Cider. Writer Sallie Tisdale on Mining the Deeply Personal - 40:31The Portland writer Sallie Tisdale is one of Oregon’s true literary treasures. She’s the author of eight books, including “Talk Dirty to Me” and “Stepping Westward,” but she is first and foremost an essayist — someone who can make art out of her process of trying to make sense of the world. Her latest book, "Violation," is a collection of essays that she wrote over the past three decades.

Pastor Zenzile Legend February 16, 2014 Judges 61The Israelites did evil in the eyes of the Lord, and for seven years he gave them into the hands of the Midianites. 2Because the power of Midian was so oppressive, the Israelites prepared shelters for themselves in mountain clefts, caves and strongholds. 3Whenever the Israelites planted their crops, the Midianites, Amalekites and other eastern peoples invaded the country. 4They camped on the land and ruined the crops all the way to Gaza and did not spare a living thing for Israel, neither sheep nor cattle nor donkeys. 5They came up with their livestock and their tents like swarms of locusts. It was impossible to count them or their camels; they invaded the land to ravage it. 6Midian so impoverished the Israelites that they cried out to the Lord for help. 7When the Israelites cried out to the Lord because of Midian, 8he sent them a prophet, who said, “This is what the Lord, the God of Israel, says: I brought you up out of Egypt, out of the land of slavery. 9I rescued you from the hand of the Egyptians. And I delivered you from the hand of all your oppressors; I drove them out before you and gave you their land. 10I said to you, ‘I am the Lord your God; do not worship the gods of the Amorites, in whose land you live.’ But you have not listened to me.” 11The angel of the Lord came and sat down under the oak in Ophrah that belonged to Joash the Abiezrite, where his son Gideon was threshing wheat in a winepress to keep it from the Midianites. 12When the angel of the Lord appeared to Gideon, he said, “The Lord is with you, mighty warrior.” 13“Pardon me, my lord,” Gideon replied, “but if the Lord is with us, why has all this happened to us? Where are all his wonders that our ancestors told us about when they said, ‘Did not the Lord bring us up out of Egypt?’ But now the Lord has abandoned us and given us into the hand of Midian.” 14The Lord turned to him and said, “Go in the strength you have and save Israel out of Midian’s hand. Am I not sending you?” 15“Pardon me, my lord,” Gideon replied, “but how can I save Israel? My clan is the weakest in Manasseh, and I am the least in my family.” 16The Lord answered, “I will be with you, and you will strike down all the Midianites, leaving none alive.” 17Gideon replied, “If now I have found favor in your eyes, give me a sign that it is really you talking to me. 18Please do not go away until I come back and bring my offering and set it before you.” And the Lord said, “I will wait until you return.” 19Gideon went inside, prepared a young goat, and from an ephah of flour he made bread without yeast. Putting the meat in a basket and its broth in a pot, he brought them out and offered them to him under the oak. 20The angel of God said to him, “Take the meat and the unleavened bread, place them on this rock, and pour out the broth.” And Gideon did so. 21Then the angel of the Lord touched the meat and the unleavened bread with the tip of the staff that was in his hand. Fire flared from the rock, consuming the meat and the bread. And the angel of the Lord disappeared. 22When Gideon realized that it was the angel of the Lord, he exclaimed, “Alas, Sovereign Lord! I have seen the angel of the Lord face to face!” 23But the Lord said to him, “Peace! Do not be afraid. You are not going to die.” 24So Gideon built an altar to the Lord there and called it The Lord Is Peace. To this day it stands in Ophrah of the Abiezrites. 25That same night the Lord said to him, “Take the second bull from your father’s herd, the one seven years old. Tear down your father’s altar to Baal and cut down the Asherah pole beside it. 26Then build a proper kind of altar to the Lord your God on the top of this height. Using the wood of the Asherah pole that you cut down, offer the second bull as a burnt offering.” 27So Gideon took ten of his servants and did as the Lord told him. But because he was afraid of his family and the townspeople, he did it at night rather than in the daytime. 28In the morning when the people of the town got up, there was Baal’s altar, demolished, with the Asherah pole beside it cut down and the second bull sacrificed on the newly built altar! 29They asked each other, “Who did this?” When they carefully investigated, they were told, “Gideon son of Joash did it.” 30The people of the town demanded of Joash, “Bring out your son. He must die, because he has broken down Baal’s altar and cut down the Asherah pole beside it.” 31But Joash replied to the hostile crowd around him, “Are you going to plead Baal’s cause? Are you trying to save him? Whoever fights for him shall be put to death by morning! If Baal really is a god, he can defend himself when someone breaks down his altar.” 32So because Gideon broke down Baal’s altar, they gave him the name Jerub-Baal that day, saying, “Let Baal contend with him.” 33Now all the Midianites, Amalekites and other eastern peoples joined forces and crossed over the Jordan and camped in the Valley of Jezreel. 34Then the Spirit of the Lord came on Gideon, and he blew a trumpet, summoning the Abiezrites to follow him. 35He sent messengers throughout Manasseh, calling them to arms, and also into Asher, Zebulun and Naphtali, so that they too went up to meet them. 36Gideon said to God, “If you will save Israel by my hand as you have promised— 37look, I will place a wool fleece on the threshing floor. If there is dew only on the fleece and all the ground is dry, then I will know that you will save Israel by my hand, as you said.” 38And that is what happened. Gideon rose early the next day; he squeezed the fleece and wrung out the dew—a bowlful of water. 39Then Gideon said to God, “Do not be angry with me. Let me make just one more request. Allow me one more test with the fleece, but this time make the fleece dry and let the ground be covered with dew.” 40That night God did so. Only the fleece was dry; all the ground was covered with dew. Judges 7 1Early in the morning, Jerub-Baal (that is, Gideon) and all his men camped at the spring of Harod. The camp of Midian was north of them in the valley near the hill of Moreh. 2The Lord said to Gideon, “You have too many men. I cannot deliver Midian into their hands, or Israel would boast against me, ‘My own strength has saved me.’ 3Now announce to the army, ‘Anyone who trembles with fear may turn back and leave Mount Gilead.’ ” So twenty-two thousand men left, while ten thousand remained. 4But the Lord said to Gideon, “There are still too many men. Take them down to the water, and I will thin them out for you there. If I say, ‘This one shall go with you,’ he shall go; but if I say, ‘This one shall not go with you,’ he shall not go.” 5So Gideon took the men down to the water. There the Lord told him, “Separate those who lap the water with their tongues as a dog laps from those who kneel down to drink.” 6Three hundred of them drank from cupped hands, lapping like dogs. All the rest got down on their knees to drink. 7The Lord said to Gideon, “With the three hundred men that lapped I will save you and give the Midianites into your hands. Let all the others go home.” 8So Gideon sent the rest of the Israelites home but kept the three hundred, who took over the provisions and trumpets of the others. Now the camp of Midian lay below him in the valley. 9During that night the Lord said to Gideon, “Get up, go down against the camp, because I am going to give it into your hands. 10If you are afraid to attack, go down to the camp with your servant Purah 11and listen to what they are saying. Afterward, you will be encouraged to attack the camp.” So he and Purah his servant went down to the outposts of the camp. 12The Midianites, the Amalekites and all the other eastern peoples had settled in the valley, thick as locusts. Their camels could no more be counted than the sand on the seashore. 13Gideon arrived just as a man was telling a friend his dream. “I had a dream,” he was saying. “A round loaf of barley bread came tumbling into the Midianite camp. It struck the tent with such force that the tent overturned and collapsed.” 14His friend responded, “This can be nothing other than the sword of Gideon son of Joash, the Israelite. God has given the Midianites and the whole camp into his hands.” 15When Gideon heard the dream and its interpretation, he bowed down and worshiped. He returned to the camp of Israel and called out, “Get up! The Lord has given the Midianite camp into your hands.” 16Dividing the three hundred men into three companies, he placed trumpets and empty jars in the hands of all of them, with torches inside. 17“Watch me,” he told them. “Follow my lead. When I get to the edge of the camp, do exactly as I do. 18When I and all who are with me blow our trumpets, then from all around the camp blow yours and shout, ‘For the Lord and for Gideon.’ ” 19Gideon and the hundred men with him reached the edge of the camp at the beginning of the middle watch, just after they had changed the guard. They blew their trumpets and broke the jars that were in their hands. 20The three companies blew the trumpets and smashed the jars. Grasping the torches in their left hands and holding in their right hands the trumpets they were to blow, they shouted, “A sword for the Lord and for Gideon!” 21While each man held his position around the camp, all the Midianites ran, crying out as they fled. 22When the three hundred trumpets sounded, the Lord caused the men throughout the camp to turn on each other with their swords. The army fled to Beth Shittah toward Zererah as far as the border of Abel Meholah near Tabbath. 23Israelites from Naphtali, Asher and all Manasseh were called out, and they pursued the Midianites. 24Gideon sent messengers throughout the hill country of Ephraim, saying, “Come down against the Midianites and seize the waters of the Jordan ahead of them as far as Beth Barah.” So all the men of Ephraim were called out and they seized the waters of the Jordan as far as Beth Barah. 25They also captured two of the Midianite leaders, Oreb and Zeeb. They killed Oreb at the rock of Oreb, and Zeeb at the winepress of Zeeb. They pursued the Midianites and brought the heads of Oreb and Zeeb to Gideon, who was by the Jordan.

Ken Harding 24/07/11 Key Verses: Hebrews 6:4-12 2 Thessalonians 2:1-4 Matthew 12:30-32So for us today there are things we can do to strengthen our faith and prevent backsliding.1. Let God's Word dwell in you richly - do not neglect it.2. Draw near to the Lord daily in earnest prayer, surrendering our wills to Him.3. Reject temptation to sin and when we are aware of Sin and failure confess and forsake it.4. Keep warm by fellowship with believers and keep yourselves in the love of God.