Podcasts about arlas

Det har været noget af en rutsjebanetur på markederne den seneste uge. Det er gået op og ned. Og op og ned. Vi laver 'Sorte tals rutsjebane-indeks'. Hvilke sektorer har været ude på den vildeste rutsjebanetur - sådan en med loops og vilde fald? Og så vender vi også, om det vi har oplevet den seneste tid, måske er 'the new normal', som virksomheder skal finde ud af at navigere i. Og så vil Arla fusionere med deres tyske partner DMK. En fusion, der vil katapultere Arla op som Europas største mejeri. Vi ringer til Arlas direktør, der fortæller, hvordan det kom i stand - og om man kan blive for store, så man måske overhører andelsejerne. (Og så skal vi selvfølgelig også lige høre, hvilket produkt, der er direktørens yndlings). Til sidst vender vi den kommende flådeplan. Skal vi bygge krigsskibe herhjemme - og hvad vil det i så fald betyde for forsvarsindustrien? Og kan danske krigsskibe egentlig konkurrere på både pris og kvalitet med dem, som man kan købe uden for landets grænser? Vært: Ulrik Rosenkvist Schultz. Fast gæst: Sune Aagaard. Medvirkende: Tine Choi Danielsen, chefstrateg hos PFA Pension, Peder Tuborgh, adm. direktør for Arla og Hans Schneider, adm. direktør for Danske Flådeskibe.

Flere grønne organisationer klager til EU over den danske regerings nølen på vandmiljø-området. En langelandsk greve sælger nu ud af familiens ellers mangeårige besiddelser. Dyrenes Beskyttelse mener, at landmænd, der bryder dyrevelfærdsloven, nu slipper billigere. Og så vil 152 medarbejdere blive berørt af Arlas foreslåede sammenlægning af flere britiske mejerier. 

En ny stikprøveundersøgelse viser, at Bluetongue-smitten har haft godt fat i malkekvægsbesætningerne. Arlas regnskab glæder bestyrelsesformanden. Vandløbene har det værre end hidtil antaget og så har landmand fået bøder for at køre gylle ud i frostvejr.

Arlas topchef gennem 20 år, Peder Tuborgh, ifører sig hverken tætsiddende cykeltøj eller bestiger bjerge i fritiden. Hans liv er helliget to klare prioriteter: Jobbet som koncerndirektør i et af verdens største mejeriselskaber – og at være en nærværende ægtemand og far for sine fem børn. Vært: Anders Hvass Klipper og producer: Kathrine Wismann Musik: Christian Schødts-Sørensen Programmet er produceret af R*******k Productions for Lederstof.dk, som udgives af Lederne. Læs mere på www.lederstof.dk

Entrevista en Hoy por Hoy Zona Media con el alcalde de Peralta, Juan Carlos Castillo, y con la directora de la excavación de "Campo de Arlas", Txaro Mateo, sobre los avances en la investigación

De færreste forstod nok, hvad Aki-Matilda Høegh Dam sagde, da hun holdt sin tale ved Folketingets åbning. For hun insisterede på at holde den på grønlandsk og kun på grønlandsk. Til gengæld har hun opnået masser af omtale og i Søren Søndergaards øjne ”fuld plade”. Hun rider på en tidsånd, hvor mange vil skamme sig over ikke at forstå hende, siger Nanna Bernth. Men der er grænser for, hvor hårdt hun kan spænde buen. Vi diskuterer også, om der går for meget 'Landmand søger kærlighed' i Arlas nye kampagne. Eksperter: Nanna Bernth, Kulturens Analyseinstitut, og Søren Søndergaard, Danske Havne     Vært og redaktør: Marie Nyhus Tilrettelægger: Rose Marie Pontoppidan Thyssen Lyd og teknik: Rakkerpak Productions Klip og lydmix:  René Slott Musik: Christian Schødts-Sørensen

Entrevista en Hoy por Hoy Tafalla con el alcalde de Peralta, Juan Carlos Castillo, sobre el yacimiento arqueológico romano Campo de Arlas

Møde nr. 76 i salen 1) Besvarelse af oversendte spørgsmål til ministrene (spørgetid). SPØRGSMÅL: 2) 1. behandling af lovforslag nr. L 139: Forslag til lov om udbygning af Øresundsmotorvejen. Af transportministeren (Thomas Danielsen). (Fremsættelse 20.03.2024). 3) 1. behandling af beslutningsforslag nr. B 115: Forslag til folketingsbeslutning om billigere kollektiv transport for alle unge under 26 år. Af Sofie Lippert (SF) m.fl. (Fremsættelse 20.02.2024). 4) 1. behandling af beslutningsforslag nr. B 107: Forslag til folketingsbeslutning om at flytte ansvaret for handicapområdet væk fra kommunerne. Af Mette Thiesen (DF) m.fl. (Fremsættelse 30.01.2024). 1) Til udenrigsministeren af: Betina Kastbjerg Hvad mener ministeren om, at regeringen endnu ikke har udpeget en ambassadør til at sikre Danmarks engagement i arbejdet med at ændre konventionerne som lovet i regeringsgrundlaget? (Spm. nr. S 770, skr. begr. Medspørger: Kim Edberg Andersen (DD)). 2) Til udenrigsministeren af: Marianne Bigum Mener ministeren, at regeringen er forpligtet til at sikre EU-unionsborgerrettighederne for den 7-årige danske statsborger »Jonas«, herunder at »Jonas'« mor har afledt indrejseret? (Spm. nr. S 779, skr. begr.). 3) Til udenrigsministeren af: Trine Pertou Mach Hvad er ministerens opfattelse af Danmarks og det øvrige internationale samfunds indsats og ansvar for at forhindre yderligere dødsfald som følge af sult i Gaza? (Spm. nr. S 782). 4) Til udenrigsministeren af: Trine Pertou Mach Er det ministerens opfattelse, at den hollandske landsrets dom af 12. februar 2024 sætter spørgsmålstegn ved den danske praksis for eksport af dele til F-35-kampfly, som denne praksis så ud forud for dommen? (Spm. nr. S 783, skr. begr.). 5) Til justitsministeren af: Lisbeth Bech-Nielsen Hvad er ministerens holdning til, at Danmark snart som følge af det danske retsforbehold vil være det eneste land i EU inklusive Polen og Ungarn, som ikke har en klar regulering af politiets brug af ansigtsgenkendelse? (Spm. nr. S 781). 6) Til justitsministeren af: Lisbeth Bech-Nielsen Hvilke former for ansigtsgenkendelse, herunder ansigtsgenkendelse i realtid og/eller retrospektiv anvendelse af ansigtsgenkendelse, mener ministeren at politiet bør kunne anvende til bekæmpelse af kriminalitet? (Spm. nr. S 788). 7) Til justitsministeren af: Sascha Faxe Har den nuværende verdenssituation givet anledning til fornyede overvejelser om kriminalisering af internationale forbrydelser i dansk ret, og hvad er regeringens nuværende holdning til at kriminalisere internationale forbrydelser i dansk ret og tempoet i arbejdet med at gøre det? (Spm. nr. S 787). 8) Til justitsministeren af: Victoria Velasquez Kan det virkelig være ministerens holdning, at andre overgreb på børn end vold og seksuelle krænkelser forældes, og kunne regeringen ikke arbejde for, at overgreb på børn forstået i bred forstand ikke forældes? (Spm. nr. S 795 (omtrykt)). 9) Til miljøministeren af: Nick Zimmermann I lyset af ministerens tvetydige udtalelser i medierne er det så fortsat ministerens holdning, at Randers' borgere ikke må stå tilbage med regningen fra Nordic Waste, som ministeren lovede på besøget i Ølst? (Spm. nr. S 785, skr. begr.). 10) Til miljøministeren af: Marianne Bigum Mener ministeren, at det er rimeligt, hvis det er børnene og de gamle i Randers Kommune, der skal betale regningen for Nordic Waste-skandalen? (Spm. nr. S 791, skr. begr. Medspørger: Charlotte Broman Mølbæk (SF)). 11) Til social- og boligministeren af: Aki-Matilda Høegh-Dam Mener ministeren, at det er rimeligt, at initiativet vedrørende en oversættelse og tilpasning af psykologiske tests til grønlandske forældre, som var fastsat i finansloven 2023, nu er blevet forsinket på ubestemt tid, og hvordan vil ministeren forhindre yderligere forsinkelser af initiativet? (Spm. nr. S 565 (omtrykt)). 12) Til social- og boligministeren af: Victoria Velasquez Var undskyldningerne til godhavnsdrengene alene politiske ytringer og ikke en erkendelse af, at staten havde begået fejl, og hvorfor har man indgået forlig med godhavnsdrengene, men efterlader fejlanbragte børn i Åndssvageforsorgen med at skulle anlægge sager mod staten? (Spm. nr. S 793 (omtrykt)). 13) Til beskæftigelsesministeren af: Karsten Hønge Hvad mener ministeren om, at nogle lønmodtagere mister en del af efterlønspræmien, fordi de blev sendt i midlertidig arbejdsfordeling grundet coronakrisen, og at der ikke blev lavet undtagelser til opgørelsesmetoden af den skattefri præmie, når en lang række andre regler i dagpengesystemet var sat på pause under coronanedlukningerne? (Spm. nr. S 777). 14) Til beskæftigelsesministeren af: Peter Kofod Hvilke planer har ministeren for at hæve folkepensionen, således at pensionister kompenseres for det tab af indtægt, de har måttet lide som følge af inflationen og regeringens manglende regulering af folkepensionen i forbindelse med den ulovlige afvikling af store bededag? (Spm. nr. S 780). 15) Til børne- og undervisningsministeren af: Anne Hegelund Hvad er ministerens holdning til, at de økonomiske rammer i den nye folkeskoleaftale betyder, at man på den enkelte skole skal vælge mellem enten ikke at kompensere sfo'erne fuldt ud for den længere åbningstid, der følger med, når skoledagene bliver gjort kortere, eller at skære på den almindelige undervisning? (Spm. nr. S 771. Medspørger: Sigurd Agersnap (SF)). 16) Til skatteministeren af: Peter Kofod Hvad mener ministeren om at indføre et beskæftigelsesfradrag på 60.000 kr. pr. barn, der udløses ved det tredje barn, således at økonomien lettes for de børnerige familier og danske familier ad åre derved inspireres til at få flere børn? (Spm. nr. S 778 (omtrykt)). 17) Til skatteministeren af: Kim Edberg Andersen Mener ministeren, at man skylder Folketingets partier og danskerne at beregne tab af job i landbrugets følgeerhverv på baggrund af Svarerrapportens udregninger om konsekvenser ved en CO2-afgift? (Spm. nr. S 789). 18) Til ministeren for fødevarer, landbrug og fiskeri af: Susie Jessen Mener ministeren stadig, at en CO2-afgift på danske mælkebønder er relevant, efter at Arlas direktør Peder Tuborgh har meddelt, at Arlas landmænd på 2 år har reduceret drivhusgasudledningerne med over 1 mio. t, hvilket ifølge Arlatopchefen viser, at man kan løse landbrugets klimaproblemer uden at indføre en CO2-afgift? (Spm. nr. S 790, skr. begr.). 19) Til ministeren for fødevarer, landbrug og fiskeri af: Kristian Bøgsted Mener ministeren, at det er forsvarligt at indføre en CO2-afgift på landbruget, når kun omkring 40 pct. af danskere er enige i, at en afgift må gå ud over landbruget? (Spm. nr. S 792, skr. begr.). 20) Til ministeren for fødevarer, landbrug og fiskeri af: Søren Egge Rasmussen Ministeren sagde på samrådet i Miljø- og Fødevareudvalget om minkerstatninger den 3. april 2024 , at »minkavlerne skal have den erstatning, de er berettiget til, hverken mere eller mindre«, mener ministeren, at det princip er styrende for erstatningsniveauet? (Spm. nr. S 794). 21) Til transportministeren af: Susie Jessen Mener ministeren, at stat og kommune lever op til deres ansvar om at sikre en ordentlig infrastruktur i hele landet, når beboere på Askø i ren desperation nu vil se på muligheder for at overtage færgedriften fra kommunen? (Spm. nr. S 755, skr. begr. (omtrykt)). 22) Til transportministeren af: Theresa Scavenius Mener ministeren, at det er hensigtsmæssigt at give Transportministeriet og Vejdirektoratet myndighedskompetence over andre myndigheder som f.eks. Miljøstyrelsen, som der lægges op til med lovforslag nr. L 112, når de ikke har de faglige kompetencer, og er ministeren ikke bekymret for, hvilke konsekvenser det kan have for fagligheden af statens forvaltning i forhold til miljø, natur og klima? (Spm. nr. S 768). 23) Til transportministeren af: Theresa Scavenius Hvilke tiltag har ministeren til hensigt at tage i forbindelse med det anlægsprojekt, der gennemføres med lovforslag nr. L 112, for at tage højde for de eventuelle miljø- og sundhedsrisici, som projektet vil have for nærområdet og arbejdsmiljøet for anlægsarbejderne på projektet, nu, hvor der er en klar og tydelig dokumentation for asbest i linjeføringen? (Spm. nr. S 769, skr. begr.). 24) Til transportministeren af: Pelle Dragsted Mener ministeren, at det er fornuftigt i forhold til de danske klimamål at indføre nulmoms på indenrigsflyvninger og dermed svække mere klimavenlige alternativers konkurrenceevne? (Spm. nr. S 784). 25) Til transportministeren af: Pelle Dragsted Mener ministeren, at det er acceptabelt, at busruter skæres ned og nedlægges i hele landet – specielt uden for de store byer – så især ældre og unges frihed og mobilitet forringes, som Jyllands-Posten beskriver det i artiklen »Unge og ældre betaler prisen for nye nedskæringer i den kollektive bustrafik« fra den 2. april 2024, og hvad agter ministeren konkret at gøre her og nu for at stoppe afviklingen af livsvigtige busruter i Danmark? (Spm. nr. S 786. Medspørger: Jette Gottlieb (EL)).

Møde nr. 76 i salen 1) Besvarelse af oversendte spørgsmål til ministrene (spørgetid). SPØRGSMÅL: 2) 1. behandling af lovforslag nr. L 139: Forslag til lov om udbygning af Øresundsmotorvejen. Af transportministeren (Thomas Danielsen). (Fremsættelse 20.03.2024). 3) 1. behandling af beslutningsforslag nr. B 115: Forslag til folketingsbeslutning om billigere kollektiv transport for alle unge under 26 år. Af Sofie Lippert (SF) m.fl. (Fremsættelse 20.02.2024). 4) 1. behandling af beslutningsforslag nr. B 107: Forslag til folketingsbeslutning om at flytte ansvaret for handicapområdet væk fra kommunerne. Af Mette Thiesen (DF) m.fl. (Fremsættelse 30.01.2024). 1) Til udenrigsministeren af: Betina Kastbjerg Hvad mener ministeren om, at regeringen endnu ikke har udpeget en ambassadør til at sikre Danmarks engagement i arbejdet med at ændre konventionerne som lovet i regeringsgrundlaget? (Spm. nr. S 770, skr. begr. Medspørger: Kim Edberg Andersen (DD)). 2) Til udenrigsministeren af: Marianne Bigum Mener ministeren, at regeringen er forpligtet til at sikre EU-unionsborgerrettighederne for den 7-årige danske statsborger »Jonas«, herunder at »Jonas'« mor har afledt indrejseret? (Spm. nr. S 779, skr. begr.). 3) Til udenrigsministeren af: Trine Pertou Mach Hvad er ministerens opfattelse af Danmarks og det øvrige internationale samfunds indsats og ansvar for at forhindre yderligere dødsfald som følge af sult i Gaza? (Spm. nr. S 782). 4) Til udenrigsministeren af: Trine Pertou Mach Er det ministerens opfattelse, at den hollandske landsrets dom af 12. februar 2024 sætter spørgsmålstegn ved den danske praksis for eksport af dele til F-35-kampfly, som denne praksis så ud forud for dommen? (Spm. nr. S 783, skr. begr.). 5) Til justitsministeren af: Lisbeth Bech-Nielsen Hvad er ministerens holdning til, at Danmark snart som følge af det danske retsforbehold vil være det eneste land i EU inklusive Polen og Ungarn, som ikke har en klar regulering af politiets brug af ansigtsgenkendelse? (Spm. nr. S 781). 6) Til justitsministeren af: Lisbeth Bech-Nielsen Hvilke former for ansigtsgenkendelse, herunder ansigtsgenkendelse i realtid og/eller retrospektiv anvendelse af ansigtsgenkendelse, mener ministeren at politiet bør kunne anvende til bekæmpelse af kriminalitet? (Spm. nr. S 788). 7) Til justitsministeren af: Sascha Faxe Har den nuværende verdenssituation givet anledning til fornyede overvejelser om kriminalisering af internationale forbrydelser i dansk ret, og hvad er regeringens nuværende holdning til at kriminalisere internationale forbrydelser i dansk ret og tempoet i arbejdet med at gøre det? (Spm. nr. S 787). 8) Til justitsministeren af: Victoria Velasquez Kan det virkelig være ministerens holdning, at andre overgreb på børn end vold og seksuelle krænkelser forældes, og kunne regeringen ikke arbejde for, at overgreb på børn forstået i bred forstand ikke forældes? (Spm. nr. S 795 (omtrykt)). 9) Til miljøministeren af: Nick Zimmermann I lyset af ministerens tvetydige udtalelser i medierne er det så fortsat ministerens holdning, at Randers' borgere ikke må stå tilbage med regningen fra Nordic Waste, som ministeren lovede på besøget i Ølst? (Spm. nr. S 785, skr. begr.). 10) Til miljøministeren af: Marianne Bigum Mener ministeren, at det er rimeligt, hvis det er børnene og de gamle i Randers Kommune, der skal betale regningen for Nordic Waste-skandalen? (Spm. nr. S 791, skr. begr. Medspørger: Charlotte Broman Mølbæk (SF)). 11) Til social- og boligministeren af: Aki-Matilda Høegh-Dam Mener ministeren, at det er rimeligt, at initiativet vedrørende en oversættelse og tilpasning af psykologiske tests til grønlandske forældre, som var fastsat i finansloven 2023, nu er blevet forsinket på ubestemt tid, og hvordan vil ministeren forhindre yderligere forsinkelser af initiativet? (Spm. nr. S 565 (omtrykt)). 12) Til social- og boligministeren af: Victoria Velasquez Var undskyldningerne til godhavnsdrengene alene politiske ytringer og ikke en erkendelse af, at staten havde begået fejl, og hvorfor har man indgået forlig med godhavnsdrengene, men efterlader fejlanbragte børn i Åndssvageforsorgen med at skulle anlægge sager mod staten? (Spm. nr. S 793 (omtrykt)). 13) Til beskæftigelsesministeren af: Karsten Hønge Hvad mener ministeren om, at nogle lønmodtagere mister en del af efterlønspræmien, fordi de blev sendt i midlertidig arbejdsfordeling grundet coronakrisen, og at der ikke blev lavet undtagelser til opgørelsesmetoden af den skattefri præmie, når en lang række andre regler i dagpengesystemet var sat på pause under coronanedlukningerne? (Spm. nr. S 777). 14) Til beskæftigelsesministeren af: Peter Kofod Hvilke planer har ministeren for at hæve folkepensionen, således at pensionister kompenseres for det tab af indtægt, de har måttet lide som følge af inflationen og regeringens manglende regulering af folkepensionen i forbindelse med den ulovlige afvikling af store bededag? (Spm. nr. S 780). 15) Til børne- og undervisningsministeren af: Anne Hegelund Hvad er ministerens holdning til, at de økonomiske rammer i den nye folkeskoleaftale betyder, at man på den enkelte skole skal vælge mellem enten ikke at kompensere sfo'erne fuldt ud for den længere åbningstid, der følger med, når skoledagene bliver gjort kortere, eller at skære på den almindelige undervisning? (Spm. nr. S 771. Medspørger: Sigurd Agersnap (SF)). 16) Til skatteministeren af: Peter Kofod Hvad mener ministeren om at indføre et beskæftigelsesfradrag på 60.000 kr. pr. barn, der udløses ved det tredje barn, således at økonomien lettes for de børnerige familier og danske familier ad åre derved inspireres til at få flere børn? (Spm. nr. S 778 (omtrykt)). 17) Til skatteministeren af: Kim Edberg Andersen Mener ministeren, at man skylder Folketingets partier og danskerne at beregne tab af job i landbrugets følgeerhverv på baggrund af Svarerrapportens udregninger om konsekvenser ved en CO2-afgift? (Spm. nr. S 789). 18) Til ministeren for fødevarer, landbrug og fiskeri af: Susie Jessen Mener ministeren stadig, at en CO2-afgift på danske mælkebønder er relevant, efter at Arlas direktør Peder Tuborgh har meddelt, at Arlas landmænd på 2 år har reduceret drivhusgasudledningerne med over 1 mio. t, hvilket ifølge Arlatopchefen viser, at man kan løse landbrugets klimaproblemer uden at indføre en CO2-afgift? (Spm. nr. S 790, skr. begr.). 19) Til ministeren for fødevarer, landbrug og fiskeri af: Kristian Bøgsted Mener ministeren, at det er forsvarligt at indføre en CO2-afgift på landbruget, når kun omkring 40 pct. af danskere er enige i, at en afgift må gå ud over landbruget? (Spm. nr. S 792, skr. begr.). 20) Til ministeren for fødevarer, landbrug og fiskeri af: Søren Egge Rasmussen Ministeren sagde på samrådet i Miljø- og Fødevareudvalget om minkerstatninger den 3. april 2024 , at »minkavlerne skal have den erstatning, de er berettiget til, hverken mere eller mindre«, mener ministeren, at det princip er styrende for erstatningsniveauet? (Spm. nr. S 794). 21) Til transportministeren af: Susie Jessen Mener ministeren, at stat og kommune lever op til deres ansvar om at sikre en ordentlig infrastruktur i hele landet, når beboere på Askø i ren desperation nu vil se på muligheder for at overtage færgedriften fra kommunen? (Spm. nr. S 755, skr. begr. (omtrykt)). 22) Til transportministeren af: Theresa Scavenius Mener ministeren, at det er hensigtsmæssigt at give Transportministeriet og Vejdirektoratet myndighedskompetence over andre myndigheder som f.eks. Miljøstyrelsen, som der lægges op til med lovforslag nr. L 112, når de ikke har de faglige kompetencer, og er ministeren ikke bekymret for, hvilke konsekvenser det kan have for fagligheden af statens forvaltning i forhold til miljø, natur og klima? (Spm. nr. S 768). 23) Til transportministeren af: Theresa Scavenius Hvilke tiltag har ministeren til hensigt at tage i forbindelse med det anlægsprojekt, der gennemføres med lovforslag nr. L 112, for at tage højde for de eventuelle miljø- og sundhedsrisici, som projektet vil have for nærområdet og arbejdsmiljøet for anlægsarbejderne på projektet, nu, hvor der er en klar og tydelig dokumentation for asbest i linjeføringen? (Spm. nr. S 769, skr. begr.). 24) Til transportministeren af: Pelle Dragsted Mener ministeren, at det er fornuftigt i forhold til de danske klimamål at indføre nulmoms på indenrigsflyvninger og dermed svække mere klimavenlige alternativers konkurrenceevne? (Spm. nr. S 784). 25) Til transportministeren af: Pelle Dragsted Mener ministeren, at det er acceptabelt, at busruter skæres ned og nedlægges i hele landet – specielt uden for de store byer – så især ældre og unges frihed og mobilitet forringes, som Jyllands-Posten beskriver det i artiklen »Unge og ældre betaler prisen for nye nedskæringer i den kollektive bustrafik« fra den 2. april 2024, og hvad agter ministeren konkret at gøre her og nu for at stoppe afviklingen af livsvigtige busruter i Danmark? (Spm. nr. S 786. Medspørger: Jette Gottlieb (EL)).

Arlas 2023-omsætninger lander på samme niveau som 2022. Seks organisationer: Landbruget skal pålægges samme CO2-afgift som andre sektorer. Nye tal fra Københavns Universitet kan give fornyet usikkerhed om landbrugets klimaregnskab. Banker forbereder sig på CO2-afgift. Sandsynlighed for traktordemonstrationer i kølvandet på klimakrav.

Arlas økologiske andelshavere får nu målt deres biodiversitet, der er tilbagegang for lille dansk mejeri og så kalder folketingspolitiker nye højdekrav til grisetransporter for 'Helt på Månen'.

Estar presente en sus vidas sin ninguna manifestación de lástima. Nada lacera más que esa condescendencia que le hace sentir a las otras personas que ya no se les respeta ni se les considera capaz. See omnystudio.com/listener for privacy information.

Vand finder vej - og det samme gør data! Hør, hvordan Arla har ført Gartners Citizen Developer-tankegang ud i livet og skabt et 4-trins koncept, der dygtiggør medarbejderne, understøtter lokal innovation, favner AI & ChatGPT og demokratiserer data både i og uden for den globale organisation. Kunsten er således at være stor og effektiv uden at spænde ben for græsrødderne. Derfor ville ingen af Arlas initiativer kunne leve uden den stærke enterprisearkitektur, der skaber trygge rammer og sporbarhed, når Arla stiller rådata til rådighed for studerende og lader medarbejdernes gode idéer få liv. God fornøjelse i Dataklubben, der i dag har besøg af Søren Bækgaard Hansen, Director for Enterprise Architechture and Strategy i Arla.

Kuratorerne i konkursboet efter kødkoncernen Skare vil føre en sag om konkurskarantæne. SF ønsker en helt ny afgift på landbruget. Arlas hollandske konkurrent er i problemer, og så frygter de franske griseproducenter for fremtiden.

Arlas nye bæredygtighedsmodel vækker kritik i udlandet. Alternativet vil være et landbrugsparti for alle danskere. De våde marker volder problemer for landmændene. Danish Agro har lukket en foderfabrik, og så er finansloven for 2024 faldet på plads.

Hay un tema que a todas nos ha pasado y del cual hablamos poco: terminar una amistad. Y es raro porque en ese duelo nadie te cuida, apapacha y está al pendiente de que estés bien. Por eso quisimos hablar de esas amistades que soltamos, que perdimos, y también de el papel tan importante que tienen nuestras amigas en nuestras vidas. Síguenos en redes como @estas.morras y si te gusta este episodio, síguenos, compártelo, y danos 5 estrellitas. Hosted on Acast. See acast.com/privacy for more information.

Denne tirsdag starter vi med historien om, hvordan en CO2-afgift på landbruget og fødevarer bør indrettes, for det er eksperterne ikke helt enige om – og nu har også regeringen åbnet for muligheden for at lægge en afgift på oksekød og andre meget klimabelastende varer. Derudover skal vi lige omkring Arlas halvårsregnskab og en vild udvikling i Polens grisebestand.

Rumænien nu kommer Ukraine til undsætning ved at fordoble sin transit af ukrainsk korn via søvejene. Flere Arla-landmænd end ventet har indført klimatiltag, hvilket øger Arlas første udbetalinger for klimatiltag. Det konkursramte Mogens Nielsen Kreaturslagteri er blevet solgt. Og så vil landmænd øst for Storebælt godt kunne skrue ned for nervøsiteten, når det gælder kapløbet om at nå såning af efterafgrøder, vurderer VKST.  

Stor anerkendelse til Arlas klima-incitamentsmodel fra amerikansk professor. Hårdnakkede forlydender om imødekommelse af Rusland for at sikre forlængelse af kornaftale. Seges er klar med digitalt værktøj, der skal gøre alt med ESG-rapportering noget lettere. Vandresservoirer kan være løsning på tørkeudfordringer. Danskerne forventer at købe mere økologi, viser undersøgelse. Nordex Food får overskud på trods af pres fra cyberkriminelle. Souschef i Landbrug & Fødevarers public affairs-afdeling bliver ny rådgiver for Jakob Ellemann-Jensen. 

Meteorologerne varsler regn i dag, og der kommer rigtig meget af den. De lyngklædte heder er i fare for at forsvinde. En landmand efterlyser nu kompensation, efter hans køer er sat i karantæne på grund af PFAS. Dødeligheden blandt pattegrise stagnerer, og så er Arlas topchef genvalgt som formand for et særligt partnerskab.

Dagens Program:I dag har vi den store fornøjelse at dele vores sofasmørhul og Switch-controller med komiker, soldat og podcaster Masoud Vahedi. Masoud har i sin karriere brugt meget tid og energi på at understrege de paradoksale og hykleriske tendenser som en person fra Løgstør kan møde, hvis farven på din hud lige er DET tættere på et flot mahogniskab end Arlas øko skummetmælk.Dagens Gæst:Masoud Vahedihttps://www.instagram.com/masoudvahedi1983/Værter:Daniel Møgelhøj & Asgar BuggeProduceret af DNA LYD I/SGIVEAWAY:https://gleam.io/wdlRb/toptier-gamer-giveawayLinktree:https://linktr.ee/GameBoys24syv

Arlas nye klimaafregningsmodel vil samlet være en underskudsforretning. Vi bør betale mælkeproducenter med en lav produktionsværdi for at stoppe, lyder det fra Nykredits afgående landbrugsdirektør. Omkring 400 grise døde efter gårdbrand lørdag. Danhatch nåede en rekordhøj afsætning af daggamle kyllinger - men tabte stort på griseproduktion. FN hjælper Rusland med eksport af fødevarer og gødning. 

Der skal mere en frivillighed til at sikre drikkevandet ifølge sundhedsminister Magnus Heunicke, der præsenterer akutplan i dag. Svenske mælkeproducenter er utilfredse med Arlas nye klima-afregningsmodel. Arla er halvvejs i forhold til at nå sit mål om at reduceres indvejningen af økologisk mælk. EU-kommissionen forlænger aftale, der gør det muligt for Ukraines nabolande at blokere for indenlandsk salg af ukrainsk korn. Og så går det stærkere end nogensinde med jordfordelingsprojekter. 

Arlas lukrative datterselskab, Arla Food Ingredients, fortsætter vokseværket. Danmarks største økologiske griseproducent overvejer helt ny driftsgren. Allerede nu er massiv tro på en stærk global sojaproduktion. Det økologiske detailsalg fortsætter med at falde. Agri Invest præsterer igen rekordhøj indtjening. Concito opfordrer til hurtig 

I dag om et rekordoverskud hos Danmarks største mælkeproducent. Vi skal høre fra økologisk mælkeformand, der på ingen måder har opgivet fremtiden på grund af faldende salg af øko-mælk. Der er et nyt vandsamarbejde ved Øresund og så skal vi høre om kommune, der tager robotter i brug for at bekæmpe en støjende fugl.

Danish Agro har tirsdag offentliggjort et rekordregnskab. Arlas beslutning om at hjælpe økologiske landmænd med at gå tilbage til konventionel produktion vækker ikke udelt begejstring på Christiansborg, hos Coop dykkede salget af økologiske æg kun kortvarigt. Og så er der igen bøvl med IT hos Landbrugsstyrelsen.

Christel Schaldemose (S) vil presse på i Europa-Parlamentet for hurtigt forbud mod PFAS. Forbrugerrådet Tænk: Arlas regnskab kan tyde på, at priser på varer har været sat for højt. Konservative om 19-graders anbefaling: Tiden er ikke inde til at hæve temperaturen. Klaus Bondam efter Irmas død: Lav en Irma Allé i København. Værter: Anne Phillipsen & Jacob Grosen.See omnystudio.com/listener for privacy information.

I dag skal vi høre om konkursramte Skare, som kuratorerne lige nu forsøger at få solgt. Arlas formand har store håb til selskabets nye klimatillæg. Konservative langer ud efter kommuner i sag om beskyttelse af drikkevandsboringer og så giver bramgæs også syd for grænsen problemer for landmænd.

ALLT OM MATPRISINFLATIONEN med Ulf Mazur, grundare av Matpriskollen. Vi pratar om varför matpriserna har skjutit i höjden senaste året och de bakomliggande faktorerna. Hur fungerar dagligvaruhandeln och vilka saker ska man akta sig för? Är vi på en platå gällande matprisinflationen? Kan priserna gå ner under 2023? Varför lever kaffepriset sitt eget liv? Varför äger Arlas priser en bra indikation på framtiden? Finns det produkter som fallit i pris? Och vad krävs för att vi ska återgå till normal inflation? Alla dessa frågor och mycket mer med pristrendexperten, entreprenören, ekonomen och grundaren av Matpriskollen. En app som hjälper hundratusentals konsumenter att hitta marknadens bästa priser i dagligvaruhandeln.   Ulf ger också sina 10 bästa tips på hur du kan göra för att få ner dina matkostnader under 2023. Gillar du LoungePodden? Stötta gärna på Patreon: https://www.patreon.com/taimaz Swish: 0761 401 401 ❤️ Tack för ditt stöd! ❤️ Mer info på https://www.loungepodden.se Följ Taimaz Instagram: https://www.instagram.com/taimazghaffari/ Linkedin: https://se.linkedin.com/in/taimaz-ghaffari-22789b21 Youtube:  https://www.youtube.com/channel/UCLKiCeQSPOfRmhXA_1m9M2A

I dag skal vi omkring sidste års salg af traktorer. En udvidelse af biogasanlægget på Bornholm kan snart komme på tale. Så åbner grisegenetikselskab nu for et samspil med konkurrent, og endelig er Arlas indtjening under pres som følge af faldende efterspørgsel.  

Programledare: Jonathan Rollins Gäster: Eleni Tångstedt, Kirsty Armstrong, Pelle Helgesson, Jens Falk, Kerim Hrustanović, Amat Levin Lyssna på Amats podcast ”Svart Historia”: https://svarthistoria.com/blog/svart-historia-har-blivit-podd https://open.spotify.com/show/5Ak9pNQ23IRL4NT6jNzeS8?si=f5b03093ae0b4578 Relevanta länkar: …Julkalendern https://www.svtplay.se/julkalendern-kronprinsen-som-forsvann …Världaidsdagen https://sv.wikipedia.org/wiki/V%C3%A4rldsaidsdagen https://sv.wikipedia.org/wiki/V%C3%A4rldsaidsdagen#/media/Fil:AidsRusStamp1993.jpg …It's a sin https://www.hbomax.com/se/sv/series/urn:hbo:series:GYBNNbABUnb1QoQEAAABA?countryRedirect=1 …Arlas netto noll klimatavtryck https://news.cision.com/se/akta-vara/r/arlas--netto-noll-klimatavtryck-ar-arets-matbluff,c3492464 …diktaturkompensera https://crd.org/sv/diktaturkompensera/ …nya Ariel https://www.imdb.com/title/tt5971474/ …Will Smith-intervjun https://www.abc.net.au/news/2022-11-30/will-smith-addresses-oscar-slap-on-daily-show-with-trevor-noa... https://www.adlibris.com/se/bok/will-9781529124163?gclid=Cj0KCQiAm5ycBhCXARIsAPldzoW-8kRZHGKuiiixPYz... …Amats bok ”Svart Historia” https://www.nok.se/titlar/allmanlitteratur-sakprosa/svart-historia/ …The true size https://www.thetruesize.com/#?borders=1~!MTYxNTcxNDA.Mzc4MjE4Mg*MzU0ODkyMjg(MjAwMjkwMA~!CONTIGUOUS_U... Låtarna som spelades var: Contemplation - Toonorth BAYRAKTAR is Life - Taras Borovko feat. Monaco - Roboten He Got Game - Public Enemy, Stephen Stills Alla låtar finns i AMK Morgons spellista här: https://open.spotify.com/user/amk.morgon/playlist/6V9bgWnHJMh9c4iVHncF9j?si=so0WKn7sSpyufjg3olHYmg Stötta oss gärna på Swish, varje litet bidrag uppskattas enormt! 123 646 2006

Han er uddannet komponist, men har har snydt lidt og lavet alt muligt andet også. Du kender ham som ham den ene tykke bror der laver mad med smør. Eller som ham Lisbet Dahl skælder ud på i Cirkusrevyen. Han er restauratør, youtuber, og BMI benægter.. Og så elsker han Mette Blomsterberg.. Velkommen til Lisbet Dahl bedste ven.. James PriceI dag skal vi finde ud af hvor tit James har snydt lidt, vi skal snakke om hvordan Mette Blomsterberg kysser, og så skal vi selvfølgelig testsmage Arlas nye margarine-ost…Tsunami lærte: - Intet nyt om Niels Ellegaards ben- Kræsne børn skal slås - Han drikker for lidt- Han påstår, at han ikke har kysset med Mette Blomsterberg- Hans halvbror hedder David ________________________________________________Legenderne bag: Sebastian Peebles & Chano JørgensenMusik: Upright Music

Dagens erhvervsoverblik: Danske topchefer vil hellere have brede politiske samarbejder end regering henover midten, Arlas nye klimaplan sender 3,7 mia. årligt til klima-effektive landmænd, Det nationale kompromis, der sikrer 2 pct. af BNP i 2033 er nu til debat igen som en del af valgkampen. Vært: Sofie Rud (soru@borsen.dk)

I dag kommer vi forbi forsøg med sojadyrkning i Danmark, Inger Støjberg har været på besøgt hos Landbrug & Fødevarers formand, Søren Søndergaard, Arlas mejerier kan nu køre på olie og professor frygter, at farlig variant af fugleinfluenza er kommet til Europa for at blive.

Der er et bekymrende antal ulykker med børn involveret i landbruget. Arlas eksport til højt prioriterede vækstmarkeder som Kina og Nigeria står i stampe og Ærteproducenten Ardo vil lukke sin fabrik i Danmark.

Dyreaktivisten Dorthe Brauner Jensen er blevet sigtet for hærværk, fordi hun har givet en hest på Molslaboratoriet et stykke æble, og så har Arlas topchef Peder Tuborgh fået en ny rolle.

Aquí arranca piedra de toque, el momento de los viajes, la montaña y la aventura con todos los contenidos siempre accesibles en piedradetoque.es y en forma de reportaje también en el eldiario.es/Euskadi. Arracamos el año y lo hacemos con la furgoneta en forma de unidad móvil recorriendo el Pirineo francés en busca de sus estaciones de esquí más salvajes. Esta ruta nos llevará por La Piedra de Saint-Martin, Artouste y Gourette, siempre bajo la sombra de cimas tan emblemáticas como el Anie, Arlas y Pic de Midi de Ossau. Nos acompañaran las voces de las personas que nos hemos ido encontrando por el camino. Qué maravilla de escapada y qué buena manera de arrancar el año. Hoy en Piedra de Toque rumbo al pirineo francés en busca de su cara más salvaje

Vi starter den her nye uge ud med nyheden om, at Danish Crown er syv dage bagud med slagtningerne, og at det kan koste andelshaverne dyrt, historien om et biotek-firma, der tjener godt på rester fra Arlas osteproduktion og en imponerende nyetablering. 

Programledare: Elinor Svensson Gäster: Clara Kristiansen, Robin Berglund, Johannes Brenning Köp biljetter till Elinor Svenssons & Marcus Thappers föreställning ”ALLT” https://www.stauppklubben.com/allt Köp biljetter till Johannes Brennings föreställning ”Innan jag ångrar mig” https://billetto.se/e/johannes-brenning-innan-jag-angrar-mig-biljetter-566662 Bli Patron för att höra avsnittet i sin helhet, gå in på www.patreon.com/amkmorgon och signa upp för att få allt vi gör i en feed! Du får dessutom tillgång till arkivet, extramaterial, extrapoddar, extra allt. Förtur och rabatt på biljettsläpp och merchandise. Stötta AMK Morgon så vi kan ge er två timmar underhållning varje dag för alltid. Tack för att ni stöttar! Relevanta länkar: …Tulipan https://shop.cramersblommor.com/images/normal/tulipailedefrance.jpg …minknäring https://jordbruksverket.se/djur/ovriga-djur/djur-for-palsproduktion/ersattning-till-minkforetag-efte... …Pepe https://ca-times.brightspotcdn.com/dims4/default/22fc870/2147483647/strip/true/crop/960x539+0+0/resi... …mjölktwitter https://twitter.com/BenjaminDoverUN/status/847641037770117122?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetem... https://theconversation.com/how-the-alt-right-uses-milk-to-promote-white-supremacy-94854 …Arlas 50/50-lösning https://www.arla.se/om-arla/nyheter-press/2019/pressrelease/arla-lanserar-laktosfri-mjoelk-och-havre... …Idisslabloggarna http://idissla.blogspot.com/ https://idisslablog.wordpress.com/ …Nimis https://www.natursidan.se/wp-content/uploads/2018/01/1200px-Nimis4_kullaberg.jpg …Lars Vilks https://www.aftonbladet.se/nyheter/a/bG7w33/lars-vilks-och-poliser-doda-i-trafikolycka?fbclid=IwAR1u... Låtarna som spelades var: Belly Breathing - Chillhop Music Gimme Some Water - Eddiie Money Sailing - Rod Stewart You Sexy Thing - Hot Chocolate Alla låtar finns i AMK Morgons spellista här: https://open.spotify.com/user/amk.morgon/playlist/ 6V9bgWnHJMh9c4iVHncF9j?si=so0WKn7sSpyufjg3olHYmg Stötta oss gärna på Swish, varje litet bidrag uppskattas enormt! 123 646 2006 Bli patron på www.patreon.com/amkmorgon!
Extramaterial, extrapoddar, extra allt. Förtur och rabatt på biljettsläpp och merchandise. Och du stöttar AMK Morgon så vi kan ge er två timmar underhållning varje dag för alltid. Tack för att ni stöttar!

Der er kvægnørderi på programmet, når Anders og Mads, i selskab med fagspersoner fra branchen, fylder dine ører med sjov og spændende landbrugssnak. Det bliver underholdende, når både erfarende og mindre erfarende gæsteværter kommer forbi og sætter kløerne i køerne. I denne episode er Arne Dahl på besøg i studiet. Arne har en slægtsgård, som han nu har sat til salg. Det er der mange gode grunde til! Arne har lavet et opslag på Facebook, hvor han stiller spørgsmålstegn ved bl.a. Arlas ledelse - og deres behandling af andelshaverne. Han håber derfor, at vi kan sætte gang i debatten.

I avsnitt 37 av ”Talk to me”, hör ni Sarah Dawn Finer i en intervju med VD:n och fd internationella fotomodellen - Emma Wiklund. Sen många år nu så är Emma VD och grundare av egna hudvårdsmärket Emma S. Skincare. Men innan dess hann hon med att vara en av våra största modeller genom tiderna och dessutom även programledare och skådespelerska. Emma fick sitt genombrott som modell i Arlas kampanj för minimjölk och kom att göra omslag, visningar och kampanjer för modehus som Versace, Chanel, Jean-Paul Gaultier, och Dolce & Gabbana. Hennes stora internationella karriär som fotomodell tog henne från Huskvarna runt om till hela världen under 12 år, innan hon flyttade hem till Sverige och pensionerade sig själv som 32-åring. Väl hemma i Sverige började en helt ny karriär på TV och med bl.a. klädkedjan Lindex.2010 startade hon det svenska hudvårdsmärket Emma S. Skincare tillsammans med Nora Larssen. Hur fungerar egentligen modevärlden bakom kulisserna?Hur känns det att pensionera sig som 32åring?Vad var de stora förhoppningarna och tankarna när det var dags att starta ett eget hudvårdsmärke och varför är Ulf Lundell ”ansvarig” för att hennes karriär? Detta och mycket mer i en härlig intervju med Emma Wiklund i ”Talk to me”. See acast.com/privacy for privacy and opt-out information.

Lyckans ost är en podcast om och med ost, av och med Clara Kristiansen. Denna veckan pratar vi om vilka ostar du ska ha på midsommarfirandet, grillfesten, och alla sommarens picknickar. Ostarna vi äter är: Feta, Burrata, Cambozola, Manchego, Ubriaco al prosecco, Creme bonjours färskost med smak av BBQ och Arlas färskost med smak av Harissa. Jag finns på Patreon och blir SVINAGLAD för alla bidrag. https://www.patreon.com/user?u=21589169 Du kan även swisha en liten slant 0733705531 Gå med i Facebookgruppen Lyckans ostar https://www.facebook.com/groups/1160964234249778/ Följ mig på Instagram och Twitter, @clarulk Podden klipps av sommarens enmannaflickparad Dessi Hietala, @hietalaa.

Vi har firat Sveriges nationaldag och Kodjo Akolor skändar flaggan genom att dissa svenska jordgubbar. Youtubern Logan Paul har gått upp i ringen mot Floyd Mayweather. Arlas vanvårdade kor såldes vidare för att få det bättre men efter skandalen har de vanvårdats igen och nu ställs mejerijätten till svars. Komikern Christoffer Nyqvist har kollat på Tom Alandhs dokumentär "Partiledaren som klev ut ur kylan" om Håkan Juholt. Folk som släpper låt: Dag Tolstoy. OCH SVTs Johan Kücükaslan gästar oss och pratar om hur det var att hänga hemma hos Dejan Kulusevski, Alexander Isak, Albin Ekdal och de andra svenska fotbollsstjärnorna inför Fotbolls-EM.

Gäster: Ri Versteegh, Linnéa Björn, Jonas Frost, Carl Dackö, Adam Von Friesendorff Musik: BJOERN https://open.spotify.com/artist/7fNc8ClzpuYFYVhBOmD6aN?si=kha-y09lT86BK0NEQvLSkw ... Blir Patreon för att höra avsnittet i sin helhet, gå in på www.patreon.com/amkmorgon och signa upp för att få allt vi gör i en feed! Pengarna ska gå till anställandet av en redaktör ASAP. Du får dessutom extramaterial, extrapoddar, extra allt. Förtur och rabatt på biljettsläpp och merchandise. Stötta AMK Morgon så vi kan ge er två timmar underhållning varje dag för alltid. Tack för att ni stöttar! ... 
Vi pratar om: …Roundup https://www.bauhaus.se/ograsmedel-roundup-speed-1l?gclid=Cj0KCQjwi7yCBhDJARIsAMWFScOixamEkZaOMEikvt2... …Arlas semmeltävling https://www.stansbasta.se/kampanjer/stans-basta-semla-2021/ …The Cell https://www.imdb.com/title/tt0209958/ …Jonas Frosts business https://www.opennewdoors.se/ …Internet Historians video om omröstningar https://www.youtube.com/watch?v=HiTqIyx6tBU&t=0s …Nationalblomman https://www.mynewsdesk.com/se/svenskbotanik/pressreleases/sveriges-nationalblomma-aer-utsedd-3080793 …Floydfamiljens settlement https://edition.cnn.com/2021/03/12/us/george-floyd-minneapolis-settlement/index.html …Bulletin https://gofile.io/d/Qm6MA1 https://www.expressen.se/dinapengar/hor-nar-redaktionsmotet--pa-bulletin-urartar-i-brak/ https://bulletin.nu/ …träfracken https://www.aftonbladet.se/sportbladet/fotboll/a/jBmw6q/fotbollsikonen-inlagd-pa-sjukhus--efter-katt... …Matthew McConaughey som guvenör https://eu.usatoday.com/story/entertainment/celebrities/2021/03/12/matthew-mcconaughey-politics-runn... …Matthew McConaugheys bok https://www.adlibris.com/se/bok/greenlights-9781472280848?gclid=Cj0KCQjwi7yCBhDJARIsAMWFScMiWwMeb9qg... …dragspel https://upload.wikimedia.org/wikipedia/commons/3/36/Akordeon_guzikowy_Special_87_120_IV_11_5_Piccolo... Låtarna som spelades var: Sarah – Mauro Scocco Love The Way You Lie – Eminem, Rihanna Why - Bjoern Alla låtar finns i AMK Morgons spellista här: https://open.spotify.com/user/amk.morgon/playlist/ 6V9bgWnHJMh9c4iVHncF9j?si=so0WKn7sSpyufjg3olHYmg ... Stötta oss gärna på Swish, varje litet bidrag uppskattas enormt! 123 646 2006 … Bli patron på www.patreon.com/amkmorgon!
Extramaterial, extrapoddar, extra allt. Förtur och rabatt på biljettsläpp och merchandise. Och du stöttar AMK Morgon så vi kan ge er två timmar underhållning varje dag för alltid. Tack för att ni stöttar!

I årets första "riktiga" avsnitt pratar vi om Uppdrag Gransknings avsnitt om KRAV-gårdarna, KRAV-slaketerierna och LRFs förtroendevalda. Vi kan såklart inte heller låta bli att diskutera Arlas stora flopp då de stängde ner sin semmeltävling pga ett veganskt café höll på att vinna... Dagens lyssnarfråga: "Har ni något motto?"Skicka in din lyssnarfråga så tar vi med den i framtida avsnitt! Maila oss på: kvinnodjuren@gmail.comFölj oss på Instagram och Facebook: @kvinnodjuren See acast.com/privacy for privacy and opt-out information.

SHL-podden dumpar pucken i offensiv zon och återvänder till era öron för mer hockeysnack! Den här veckan:Cash-face!Svanen!Straffmissrekordet!Hellkvists ilska!Den gyllene medelvägen!Arlas lista!Bristedt!DIF i bottenstriden?Och mycket mer!SHL-podden klipps av Martin Gustafsson och produceras av Med Vän.SHL-podden i sociala medier:Twitter: SHL-poddenFacebook: SHL-bloggenSajt: shlbloggen.se See acast.com/privacy for privacy and opt-out information.

SHL-podden dribblar sig sedvanligt över blålinjen in i dina hörlurar med ett sprillans nytt avsnitt. Den här veckan om bland annat:Arla i vinnarhålet!Det bästa LHC kunde få in!Transatlantinvasionen!Konstiga Rahm-sparkningen!Vilken är den värsta regeln?Arlas lista!Och mycket mer!SHL-podden klipps av Martin Gustafsson och produceras av Med Vän.SHL-podden i sociala medier:Twitter: SHL-poddenFacebook: SHL-bloggenSajt: shlbloggen.se See acast.com/privacy for privacy and opt-out information.

Hvordan kan virtual reality-briller bruges til at vedligeholde maskiner på et mejeri? Og kan nedbrud i mejeriers produktionssystemer forudsiges med dataanalyser?Det og meget andet kan du høre om i 2. afsnit af SEGES' podcast ”Digitalisér eller Dø”, hvor værten Ivar Ravn har besøg af Arla Foods' it-direktør Torben Fabrin.I podcasten lukker Torben Fabrin dig ind i maskinrummet og løfter sløret for Arlas digitale rejse de seneste år. Han fortæller blandt andet om, hvordan virksomheden er gået all in på agil udvikling af nye it-løsninger, og hvordan coronakrisen har sat turbo på digitaliseringen i den andelsejede koncern.

SHL-podden är inte längre lika utspridda och det har resulterat i ett avsnitt som bland annat avhandlar:Starstruck av P-A Gullö!JVM-truppen!Fega domare!Arlas tombola-trauma!Helt obegripliga disciplinnämnden!Speltips!Och mycket mer!SHL-podden klipps av Martin Gustafsson och produceras av Med Vän.SHL-podden i sociala medier:Twitter: SHL-poddenFacebook: SHL-bloggenSajt: shlbloggen.se See acast.com/privacy for privacy and opt-out information.

Rickard Öste är kemiprofessorn och foodtech-forskaren som inte bara grundade Oatly utan även har uppfunnit havremjölken så som vi känner till den. I uppstartsfasen fick han Arla att flyga ner till Skåne för att smaka på deras havremjölk. De spottade ut smakprovet och sade: ”Nä, det här är helt ointressant, det kan ni aldrig sälja”. 2020 förväntas Oatly omsätta över två miljarder kronor världen över och är en av Arlas största konkurrenter i Sverige. Rickard Öste är professor, entreprenör, investerare och food tech-forskare. Han bor i Hong Kong men gjorde ett besök i loungen för att prata om hur de byggt upp detta miljardbolag på endast havre. En otrolig succé och resa som började redan på 80-talet och tog rejäl fart runt 2014. Då tog Oatly in VD:n Toni Petersson som tänkte annorlunda. Han ville att Oatly skulle bli ett lifestyle-varumärke istället för en mjölkleverantör. Missa inte detta grymma favorit i repris avsnitt med Rickard Öste! Glöm inte att ge oss fem stjärnor och en snäll kommentar i Podcaster-appen om ni uppskattar det vi gör  Och följ gärna @LoungePodden på Instagram och LinkedIn.

SHL-säsongen är äntligen igång och det ger vår kvartett massor att prata om i veckans avsnitt! Bland annat:Den indiska kapningen!Uppkäkade HV71!Mårthens Leksandsgnäll!Tambellini show!Lyft fram Christer Jonasson!"Slakten" i Luleå!Arlas giganter!Och mycket mer!SHL-podden klipps av Martin Gustafsson.SHL-podden i sociala medier:Twitter: SHL-poddenFacebook: SHL-bloggenSajt: shlbloggen.se See acast.com/privacy for privacy and opt-out information.

Lagavsnitten var en uppvärmning - nu är SHL-podden tillbaka på riktigt! Dagarna innan den efterlängtade premiären pratar vår kvartett bland annat om:Hatet mot ASMR!Långtidsspel på alla lag!Arlas blockade skott!Versteeg-utspelet!Virserums transatlantinvasion!Så slutar säsongen!Och mycket, mycket mer!SHL-podden klipps av Martin Gustafsson.SHL-podden i sociala medier:Twitter: SHL-poddenFacebook: SHL-bloggenSajt: shlbloggen.se See acast.com/privacy for privacy and opt-out information.

durée : 00:02:29 - Affaires classées par Thierry Sagardoytho - France Bleu - En l’an 1373, dans la vallée du Barétous, les paysans béarnais livrent une guerre fratricide à leurs voisins de Navarre. Ils se disputent l’eau d’une source de montagne qui coule au sommet du Pic d’Arlas. Deux années durant, le climat est très tendu.

Corona-krisen har lagt sin klamme hånd på næsten alt i vores liv. Hvordan vi opfører os, hvordan vi færdes og mødes - og hvor vi tager hen. Og så har den såmænd også ændret på vores indkøbs- og fødevarevaner. Noget tyder på, at vi kommer til at købe mere dansk og handle mere lokalt. Følg Pengene ser i denne uge på, hvilken indflydelse Corona har haft på det, vi vælger at kaste i indkøbskurven. Og det gør vi i selskab med Arlas topchef Peder Tuborgh og COOPs kommunikations- og analysechef Lars Aarup. Værter: Mette Simonsen og Jakob Ussing.

Ett fulltaligt SHL-podden kommer till er med ett avsnitt där högt och lågt blandas i rasande takt. Den här veckan avhandlas bland annat Rövgängshockey, Vispen!, har DIF verkligen slagit rekord?, Bulan vs Rönnberg-gate, Arlas hotade rekord, Fimpens jämna stats, spearing-gate, Stats-Mårthen och mycket mer!SHL-podden klipps av Martin Gustafsson.SHL-podden i sociala medier:Twitter: SHL-poddenFacebook: SHL-bloggenSajt: shlbloggen.se See acast.com/privacy for privacy and opt-out information.

Livepoddarna från Ostfestivalen får rulla på. Nu med Alex Racoveanu från Arla Unika. Som du kommer att höra är det Arlas premiumsortiment, hittills bara tillgängligt hos ett fåtal restuaranger i Sverige (bland annat Sjömagasinet, något som avhandlades i avsnittet med Ulf Wagner). Men, alldeles snart finns ostarna även över disk. Lyssna så får du veta … Fortsätt läsa "Alex från Arla Unika – premium och publikfavorit"

Spa-dag för Mårthen innebär att Stats-Jocke tar över rodret och guidar oss igenom bland annat Arlas tekniska haveri, när är spelare egentligen underskattade?, Roger Melins utbrott, vad borde vara slashing?, JVM-truppen, retrotröjor, Virserum och mycket annat!SHL-podden klipps av Martin Gustafsson.SHL-podden i sociala medier:Twitter: SHL-poddenFacebook: SHL-bloggenSajt: shlbloggen.se See acast.com/privacy for privacy and opt-out information.

Klimaforsker Sebastian Mernild om klimaets tilstand. Tre gange Greta, en lortehistorie og valg af bolig pga. energimærke. Klimapanelet diskuterer Arlas nye reklame "Skridt for skridt mod en bæredygtig fremtid" og lækket af regeringsforhandlingerne om en klimalov. Paneldeltagere: Flemming Møldrup, livsstilsekspert, Ane Høgsberg, komiker og Lasse Alfastsen, social iværksætter.

Klimaforsker Sebastian Mernild om klimaets tilstand. Tre gange Greta, en lortehistorie og valg af bolig pga. energimærke. Klimapanelet diskuterer Arlas nye reklame "Skridt for skridt mod en bæredygtig fremtid" og lækket af regeringsforhandlingerne om en klimalov. Paneldeltagere: Flemming Møldrup, livsstilsekspert, Ane Høgsberg, komiker og Lasse Alfastsen, social iværksætter.

Klimaforsker Sebastian Mernild om klimaets tilstand. Tre gange Greta, en lortehistorie og valg af bolig pga. energimærke. Klimapanelet diskuterer Arlas nye reklame "Skridt for skridt mod en bæredygtig fremtid" og lækket af regeringsforhandlingerne om en klimalov. Paneldeltagere: Flemming Møldrup, livsstilsekspert, Ane Høgsberg, komiker og Lasse Alfastsen, social iværksætter.

Klimaforsker Sebastian Mernild om klimaets tilstand. Tre gange Greta, en lortehistorie og valg af bolig pga. energimærke. Klimapanelet diskuterer Arlas nye reklame "Skridt for skridt mod en bæredygtig fremtid" og lækket af regeringsforhandlingerne om en klimalov. Paneldeltagere: Flemming Møldrup, livsstilsekspert, Ane Høgsberg, komiker og Lasse Alfastsen, social iværksætter.

Rickard Öste är kemiprofessorn och foodtech-forskaren som inte bara grundade Oatly utan även har uppfunnit havremjölken så som vi känner till den. I uppstartsfasen fick han Arla att flyga ner till Skåne för att smaka på deras havremjölk. De spottade ut smakprovet och sade: ”Nä, det här är helt ointressant, det kan ni aldrig sälja”. 2020 förväntas Oatly omsätta över två miljarder kronor världen över och är en av Arlas största konkurrenter i Sverige. Rickard Öste är professor, entreprenör, investerare och food tech-forskare. Han bor i Hong Kong men gjorde ett besök i loungen för att prata om hur de byggt upp detta miljardbolag på endast havre. En otrolig succé och resa som började redan på 80-talet och tog rejäl fart runt 2014. Då tog Oatly in VD:n Toni Petersson som tänkte annorlunda. Han ville att Oatly skulle bli ett lifestyle-varumärke istället för en mjölkleverantör. Missa inte denna grymma historia med fantastiska Rickard Öste i LoungePodden! Glöm inte att ge oss fem stjärnor och en snäll kommentar i Podcaster-appen om ni uppskattar det vi gör  Och följ gärna @LoungePodden på Instagram och LinkedIn.

In this episode of Beneath the Subsurface we introduce our Geoscience and Data & Analytics intern teams for our summer internship program. Erica kicks off the episode with Jason and Sri talking about how the programs have come about and changed overtime here at TGS, how they select and recruit for the program, and the scope of the projects that the internships tackle this summer. Erica then spends time with both teams of interns discussing the experience in the program, what they’ve learned, and everything they’ll be taking away and applying back to their studies and upcoming careers. TABLE OF CONTENTS00:00 - Intro00:50 - Team Leader Segment with Jason and Sri01:09 - The Geoscience Internship Program04:42 - The Data & Analytics Internship Program07:29 - Advice for Program Applicants11:54 - Data & Analytics Intern Team Introductions13:32 - The D&A Summer Projects15:18 - Lessons Learned Pt. 117:20 - The TGS Internship Experience Pt. 120:24 - Future Careers21:41 - Advice for Future Interns & Reasons to Apply Pt. 124:34 - Valuable Take Aways Pt. 126:01 - Geoscience Intern Team Introductions28:36 - The Geoscience Summer Projects31:33 - Lessons Learned Pt. 233:14 - The TGS Internship Experience Pt. 234:12 - Advice for Future Interns & Reasons to Apply Pt. 239:28 - Valuable Take Aways Pt. 2EXPLORE MORE FROM THE EPISODEARLASSALT NET TGS DATA LIBRARYEPISODE TRANSCRIPTErica Conedera:00:12Hello and welcome to Beneath the Subsurface a podcast that explores the intersection of geoscience and technology. From the Software Development Department here at TGS, I'm your host, Erica Conedera. This time around, we'll be chatting with our newest batch of intrepid students in TGS' dynamic and immersive internship program. As you will hear, they are a diverse group of future innovators from around the world. They bring with them a wide range of skills and interests and work together to collaborate on exciting real world projects. We'll start our conversation today with a quick introduction from the leaders of our internship program. I'm here with Sri Kainkarayam, the data science lead and Jason Kegel with the geoscience team who heads up the geoscience intern program. And we're going to talk a little bit about the internship programs. Jason, how has this program changed in the last five years?Jason Kegel:01:09When we first started the program, I want to say 2013, 2014, it was out of the Calgary office in Canada. The interns there were mainly from some of our Calgary schools nearby. And then it started to grow 2014, 2015 to include some of our Texas schools, UT, Baylor, University of Houston. As it's grown, we've decided to add more projects and more sort of interesting work to the projects. We've also been able to bring on some of our original interns into roles within the company. So over the last five years, I'd say the biggest thing that's grown is the, the number of interns. So in Calgary, when this first started we had one intern and then that same intern came back a second year and we brought another one on. And then we got one in Houston. And then as that grew, we had a couple in Houston and a couple in Calgary.Jason: 02:09And then the past couple of years we've had four each year. So we had four last year and four this year. So we've really been able to sort of guide new projects around that to where we can really include their schoolwork and what they're doing in their university work with what we're doing here at TGS and hopefully build a sort of cohesive project for them to work on. And that's sort of the struggle with a lot of internship projects that we've done over the past years is to incorporate what they want to do as students and as interns and as their career grows, with what we'd like to see them do and encourage them to do within TGS.Erica:02:49Does that go into the consideration of which interns you end up picking, what their specialties are or what they're looking to do with what you need?Jason:02:58No, not necessarily, a lot of the times the interns, so for example, last year we were working very closely with a couple of schools that we wanted to bring data into. So some of our production data our Longbow group into with the University of Lafayette. So we were working really closely with a few professors out of that school and a few professors with UH. So we had recommendations from the professors themselves with students that they thought might work nicely with us with - in terms of their knowledge of data already and their knowledge of well log use and seismic, so they can kind of jump in running without having to learn too much in the beginning, without too much of a learning curve. So in aspects of that, and that's, that's more that we look for. So the, the professors we're working with, along with how long it will take them to, to get up and running with things.Jason:03:51Our current group of students is sort of a more advanced set of students who are working on their PhDs or in their later years of their master's degrees. So they've already seen a lot of these areas and worked with a lot of the data. So we do look for sort of more advanced students now, whereas when we first started the program, we were, we were happy to get anybody, some people that were not sure if they were going to be geoscientists, but you know, we're in the geoscience program with their bachelor's and that was okay too. I think we still got a lot out of having them here, working with us. but as we've grown, we've been putting them on more and more advanced projects and they've really been able to help out.Erica:04:29Cool, sounds like they've added a lot of value.Jason:04:30They definitely do. And it's nice to have sort of fresh faces around in the summertime and, and it really, really fills in for everybody that goes on vacation in the summer.Erica:04:39(Laughter) Right? Awesome.Jason:04:39The office doesn't seem so empty.Erica:04:42Awesome. So for the data analytics team, the internship program is new. I think this is your first batch of interns, correct Sri?Sri Kainkaryam:04:57Yes. So the data science team started sometime around November, 2017 so this is, although this has been our second summer, this is our first batch of interns that are projects, both, trying to test out novel algorithms, novel approaches, also try and apply ideas from high performance computing to building workflows, and also try and build sort of, user interfaces or ability to, deploy these for various users. So, there are broadly three buckets in which these projects fall into. And, it's an, it's, it was an interesting time looking for an intern because data science as, as a domain is, sits at the intersection of sort of three, broadly non intersecting sets, right? So geoscience, computing as well as machine learning or deep learning and folks having adequate background in all three of them, they sort of fit the -the mold of a good intern.Sri:06:02So it was in some sense was a little hard initially to try and find an intern. So I think we have a talented group of interns working on two of the broad offerings that we have right now. One of them is Salt Net, that is trying to interpret salt bodies from seismic images, and one is called ARLAS that is curve completion and aspects of petrophysics that can be done on, on wells that are available in an entire basin. So, it's, it's been four weeks into the internship program and the interns, the interns are pretty smart. They're motivated and it's been a fun experience so far.Erica:06:43Is it a 12 week program in total?Sri:06:46It's around a 12 week program. Some of them I think are here for a little longer than that. So, one of them is, trying to build a tensorflow port of our salt network flow because tensorflow community comes with a bunch of advantages such as, like, ability to deploy, it also comes with a JavaScript library called tensorflow JS that that makes it easy to do machine learning in the browser. So we want to make use of that infrastructure and the community built infrastructure. And that's one of the reasons why, one of the interns is spending time trying to build, trying to put our workflow in onto tensorflow.Erica:07:29So if you guys had some advice to give to people looking to get into the internship program, would you have anything you'd want to let them know?Sri:07:37So from the perspective of data science internships, given that how fast the field is moving, especially for students looking for data science internships in, in the space of oil and gas, the first and foremost thing is having an ability to understand various aspects, various various sources of data or aspects of data in the upstream domain. Because, just to give you an example, somebody who's worked on deep learning of natural images throughout, the moment you try and apply similar algorithms onto seismic images, it's a completely different domain. So, what are the, what are some of the assumptions that you can make? And that's where having a strong domain background really helps.Sri:08:30And I think the second thing that is, that's becoming very important in the marketplace right now is, is with, with platforms like GitHub or, you know, various open source projects. You can actually showcase your code. So pick a problem, learn a few, learn some approaches or try out some novel approaches, and put out the code out there. Put that on your resume because that adds a lot of weight, in your, in your ability to make a case for an internship rather than somebody who hasn't, who says, oh, I have, I have a strong programming background, but there's no way for somebody who's evaluating the person to see the code. So that these days has become a really strong advantage for, for a lot of students. So a couple of the students that are working with us this summer, they actually have active GitHub profiles where they've posted code, they've contributed code, various projects and so on. And as a consequence, like we looked at their profiles and backgrounds and like, oh, this is an obvious fit to our group and this person also has a background. A couple of them were like Ph.D students in geophysics, so it's an obvious fit for our team. So it was, it was all, it was a no-brainer for us to get them to come work with us this summer,Erica:09:53Jason?Jason:09:53On the geoscience side, it's, it's quite a bit different really. A lot of the students that are in university going for, for geoscience and wanting to go into the oil and gas industry have mainly just academic experience. So we really just want somebody that can sort of get up to speed quickly with sort of what an explorationist in an oil and gas company would do is look at essentially what we're bringing them in to do is what a sort of a mini, really quick exploration studies on basins where they don't have to go full on to drill a well, but they still need to have the ideas behind it where they can use the data, they have to evaluate an area and come up to speed quickly with, with getting those presentations out. So having really good presentation skills and having just a background enough to be able to learn on their own and pick up concepts quickly really helps. We see that a lot with, since we do get a lot of our interns through their advisors at different universities, that that really helps. But it also doesn't hinder it. We've also had lots of students that have applied, that have came from different universities where we don't know the advisors and it's just a matter of them going through the interview process and showcasing that they're, they're able to get to speed quickly. So, anybody can really go, go and do this type of work if they have the, the ability to learn.Erica:11:14Awesome.Sri:11:14I think that's an interesting point that Jason brought up. The ability to learn things fast and, sort of the ability to, appreciate various data sets and trying to understand and bring them together. I think that's a huge advantage for, for students. And based on my interaction with students in our group as well as Jason's group, I think TGS this summer has a fabulous group of interns.Erica:11:43Okay. Well thank you guys for talking to us about the internship program and we're very happy to talk to your respective groups and see what they have to say. Thank you.Sri:11:52Thank very much.Jason:11:53Thank you.Erica:11:56I'm sitting here with our first group of interns from the data and analytics group. To my left, we have Michael Turek from Florida State University. His major is computer science. He has a B.S. In computer science as an Undergrad. What are your career goals? What are you working towards?Michael Turek:12:15Yes. So part of me taking an internship here at TGS was to help figure that out. And so, well, you know, my interests rely mostly in machine learning and things like this. So something pretty, along those lines.Erica:12:31Awesome. Well we hope you, we'll help you figure that out. While you're here. Going around the table, we have Lingxiao Jia from the University of Wyoming. Your major is geophysics and you're working towards your PhD studying seismic imaging, migration and inversion. What kind of career are you working towards?Lingxiao Jia:12:50I plan to work as a Geoscientist in the oil and gas industry.Erica:12:56Awesome.Lingxiao:12:56Yeah, I like to do programming, so mostly on that.Erica:13:06Cool. All right. And then to my right, we had Deepthi Sen, from Texas A&M, majoring in petroleum engineering, working towards your PhD, studying reservoir engineering. What's your career goal, Ms. Deepthi?Deepthi Sen:13:21I'd like to, get a full time employment in the oil industry, preferably working on something related to machine learning in reservoir engineering. So yeah, that's why one of the reasons why I'm here too.Erica:13:33Awesome. Yeah. Oh, we're glad all of you are here. So can you guys describe for us, the projects you're working on? I'm not sure if you guys are all working on the same project or if you're working on different projects.Deepthi:13:45We are working on different projects. So right now I'm working on something which, involves clustering well logs, into good and bad, sections.Deepthi:13:57I use machine learning and a few algorithms that I use for my graduate research too.Erica:14:04Very cool. What's a bad section?Deepthi:14:07A bad section as in, there are certain depths at which, certain well logs behave erratically so we want, do not want to use that data, so we have to cluster it out. So, in order to do that manually for, you know, thousands of wells, it's impossible. So that's where machine learning comes into play.Erica:14:27Very cool. Very useful too. Lingxiao?Lingxiao:14:32I'll be working on using machine learning to do the recognition of geoscience features. For example, there could be faults, it could be picking horizons, could be recognizing salt domes, something like that.Erica:14:48Wow. Very complex and over my head. (Laughter) I'm sure it's very important though. And you, sir?Michael:14:57Yeah, so I'm working on translating the models that TGS' data analytics team uses to predict salt patches in the earth. So they use, they use models written in a module called Pi Torch and I'm converting that to tensorflow 2.0Erica:15:17Cool. Very cool. So what have you guys learned along the way so far? I know this is kind of the beginning for you, but-Michael:15:28Yeah, so it's, it's somewhat difficult to- so much, is kind of the answer to that question. But a lot of what I've learned boils down to more of the theory side of machine learning. Coming into the internship I didn't know a whole lot about the backend of machine learning, mostly just applying it. So learning how all these models work and why they work and things like that in terms of, the actual actually applying machine learning. That's what I've learned. I've also learned though, perhaps more importantly, working with a team and collaborating and things like that, which has been-Erica:16:10So hands on, real-world experience. What do you guys say to that? Ladies, I should say (Laughter) to my right.Deepthi:16:17So as I said, the research that I do is again, on machine learning. So I get to use similar algorithms to another, I would say facet of oil and gas. So I worked in reservoir engineering back in Grad school. Here I'm working on, petrophysics, so I kind of see how the same algorithms and same concepts can be applied in two different, areas, which is quite eye opening. Yeah. And apart from that I'm learning new algorithms and learning new math, which, I would think that's very important for, for my Grad school too, so, one good thing about TGS is that, they are quite, you know, they don't mind, publishing. So as a PhD student, that's very important to me. So that's one thing I look forward to too.Erica:17:08Yeah. Awesome.Lingxiao:17:10For me, it has helped me get a deeper understanding of how much, how machine learning works and how it could be applied to the field of Geo Sciences.Erica:17:20Cool. So talking about TGS more broadly, like as a culture, how would you say it's like working here, if someone were to ask you from school, what's it like working at TGS? What's that company like? What would you say?Deephti:17:36It's a very friendly atmosphere and, it is different from Grad School, in the sense that, I think Grad School, hours are more flexible than in an industry environment. But then, the focus is different and this is more, you know, I would think this more social than Grad school and, you know, being here, this is my first internship in the US, the environment is very friendly and you know, people look out for each other it's great.Erica:18:15Cool.Lingxiao:18:15Yeah. People here are so helpful and the, I have had a great time. I really enjoy this internship by far. Yeah.Erica:18:26Awesome.Michael:18:26It's wonderful. You're working in small teams and so you get to know everyone pretty well. It's very tight knit and those people are smart and very helpful kind people. It's, it's, it's wonderful.Erica:18:37Cool. Any surprises along the way? Anything you weren't expecting?Michael:18:44So, no, I wouldn't say there's anything that surprised me. I mean apart from the environment I had a much more perhaps rigid definition of, you know, you go to work and do your job and that's kind of that, but it's much more relaxed and that was, I guess, somewhat surprising.Erica:19:01Okay. I like that. Yeah. How bad the drive was maybe?Deepthi:19:06Yeah, I stay close by.Erica:19:09That's good. That's the way to do it. (Laughter) Yeah. What are you guys looking forward to for the remainder of your internships?Michael:19:17Yeah, so I'm looking forward since I'm rewriting these, these models and an interface for them, it'll be exciting to see them, how they perform and also to actually see the data and analytics team using them and hopefully finding them useful.Erica:19:31Yeah to see value for what you're working on. Absolutely.Deepthi:19:34So I'm about to finish the first part of my project, so I would like to wrap it up, you know, produce some good results and maybe get a publication out of it. And after that, yeah, I have a plan for what is to be done next, regarding the same, using the same similar approach but in a different setting. Yeah. So I'm looking forward to that.Erica:19:59Can you tell us what the different setting is or is that classified?Deepthi:20:03I'm not sure. (Laughter)Erica:20:05Right. We'll leave that one alone.Lingxiao:20:08So doing an internship here at TGS is an amazing adventure. I learn and discover new things everyday and I feel time passes very quickly, and everything is moving at a timely manner. So it's pretty good.Erica:20:24Nice. So I think we kind of touched upon how you guys are going to apply what you've learned here, at your careers as you go forward. Is there any particular job title that you guys think you're going to go towards?Deepthi:20:44Yeah. I probably will be going for a data scientist role, or I can say because of my background in reservoir engineering, I can go both on the data and science roles or the reservoir engineering roles. But yeah, from my experience here, I would, I think I would prefer to go to the data and data science roles because, there are like lots of opportunities out there and, the experience that I've gained here, I, I think it's going to be very helpful finding a full time position later on. Yeah.Lingxiao:21:18I could consider becoming a Geoscientist in the oil and gas or becoming a structural engineer because I have a programming background.Michael:21:32Yeah. I wouldn't say I have any career title I'm, I'm seeking out, but perhaps data scientist, but I'm not sure.Erica:21:41So what advice would you give to the interns who are going to be coming behind you?Michael:21:46Yeah. So probably to just build strong relationships with the team that you're in. Learn as much as you can, as deeply as you can.Deepthi:21:58Yeah. I would suggest that before coming in, you can go through, or if they have a set plan for you. In my case they did. So I had read up and you know, known what I'm going to work on so you can, you know, straight away start working on the project you have a rather than, you know, spend a lot of time, reading up those things that can happen before you start the internship. And yes, once you're here, it's, very important to like keep in touch, you know, meet the mentors every day or you know, update them so you have a clear path that you need to, yeah.Erica:22:44Lingxiao?Lingxiao:22:44I would suggest to go talk with people and you see what everyone is working on.Erica:22:51So learn, learn what other people are doing as well.Lingxiao:22:55Yeah.Erica:22:55That, yeah, that makes good sense. So why did you guys apply for the internships here?Michael:23:05So I applied, cause I was just looking for an internship and I had heard that, well I had heard that, (Laughter)Erica:23:14Honest.Michael:23:14(Laughter) I had heard good reviews from people who I respect and and I knew that they had a new data and analytics team doing machine learning, doing things with machine learning. That piqued my interest. And so I told them I was interested.Erica:23:28So kind of diverge off of that. So what programs are you guys using? Like actual hands on programs?Michael:23:36Yeah. So, programs for me are pretty, pretty simple. I use, a coding ID, visual Studio Code, and an Internet browser.Erica:23:43Whoa, okay.Michael:23:46I do that to do my work.Erica:23:47Google and a calculator, alright.Michael:23:49Yeah, pretty much.Erica:23:52Deepthi?Deepthi:23:52Uh, what was the question again?Erica:23:56What programs do you guys use?Deepthi:23:59Again, I guess we are in the process of making a program, so what I use is just Jupyter, it's very basic.Erica:23:59It's built on Python correct?Deepthi:23:59Yes, it is Python, I use Jupyter ID, and I'm in the process of making something useful from scratch.Erica:24:22So lastly, would you guys recommend a TGS internship to your fellow students?All:24:27Yes, definitely. Yes. Yes, yes. Yeah. Awesome. Yes.Erica:24:34Okay. So open question to the table. What are you going to take back to your program that you learned from your internship here? Starting with Michael to the left?Michael:24:42Yeah, so I'm learning a lot about machine learning and so in computer science that's obviously going to be a direct parallel. I can take that back. But I really think that what I'm learning most here that I'll take back is just how to collaborate with people, how to talk with people in a team and work in that way. I think that'll -Erica:25:05Life skills.Michael:25:11Yes.Erica:25:11Lingxiao?Lingxiao:25:11So, since machine learning in such a hot topic. Now, the work that I did here could be really extended into a project in my PhD research. So, yeah I'm currently working on that.Erica:25:28Awesome. Deepthi?Deepthi:25:29So right now we're working on a clustering of time series data. So my, one of the projects that I'm working, at my Grad school is also on time series data, and I think I might be able to, you know, use the insights that I gained from, from TGS, directly to my, research. So that's something that I'm looking forward to.Erica:25:52Awesome. Okay, well thank you guys for talking with us today and I guess we'll let you get back to work now.Michael:25:59Thank you for having us.Deepthi:26:00Thank you.Lingxiao:26:01Thank you.Erica:26:01And now our last group for this episode, the geoscience interns.Erica:26:08Going around the table clockwise, we have Sean Romito. You're from the University of Houston, majoring in geology. You are working towards your PhD and you are studying magnetic basement structure of the Caribbean plate, tectonostratigraphy of South Gabon and Camamu-Almada conjugate basins. I totally know what all of that means. What career are you working towards?Sean Romito:26:35Oh, hello. Thank you for having me. Definitely exploration Geoscientist, this is kind of where I've been propelling my career, ever since I started with a bachelor's and I've just kinda been stepping towards that goal.Erica:26:51Awesome. All right. Now we have Geoff Jackson from the University of Louisiana at Lafayette Majoring in petroleum geology. Your program is a master's degree and you graduated last spring. Congratulations!Geoff Jackson:27:07Thank you!Erica:27:07You studied a prospect lead off of a salt dome in southern Louisiana, and you cannot give us any more details than that.Geoff:27:14Unfortunately yes.Erica:27:14Very mysterious. So what, what are your career goals?Geoff:27:19Uh, similar to Sean's I was going to say, I can probably speak for the group here, but we're all just trying to be geologists and getting on with an operator, going to say probably best case scenario.Erica:27:28Awesome. Next we have Hualing Zhang, from the University of Houston, majoring in geology, working towards a PhD. And you're studying structural analysis and gravity modeling in the Permian Basin in West Texas. And you are originally from Urumqi, Northwest China and you got interested in geology about traveling around. That is so cool. So is your career goal the same?Hualing:27:53Yeah, basically similar, I'm working towards a career goal in the oil industry. Yeah. Since, like, my dad is also a geologist. Yeah. He works in PetroChina. So yeah, that's also my career goal.Erica:28:08Awesome. Yeah. Awesome. All right. And lastly, Cahill Kelleghan from Colorado School of Mines, majoring in geology. You're working towards a Masters of science and geology, and you're studying sedimentology and basin analysis / modeling with your thesis being in the Delaware Basin. So career goals?Cahill:28:28I'm pretty similar. I like to be in exploration geology and I really like sedimentology. So yeah, just applied geo science.Erica:28:36Awesome. Cool. So can you describe for us the projects that you guys are working on this summer? Same project or different project?Sean:28:46TGS has kind of tasked us with, I'm putting together some potential prospects or ideas of places we can look and most of that's going to be happening, well, we think it'd be North America and North American basins. And so we've kind of gotten access to some of their pretty amazing software, access to a lot of different databases and kind of putting that all together for a big picture of something useful that they can hopefully use from our projects. So I don't know if you guys want to add anything.Geoff:29:15Yeah, I mean, for one thing with these projects that's been very helpful to leverage the software that TGS has, specifically Longbow and access to their wealth of onshore well data that they have there. So we've been kind of bringing all of that together to generate these areas where we think that we should move further into as a company.Hualing:29:40Yeah. Also the first two weeks we're like working separately. We each have a study area and it's just a information gathering and doing researches and moving forward. Right now we are working in pairs. So, me and Geoff, we are working on similar location and to do like a research in a more detailed way. Yeah.Erica:30:05So you guys mentioned the software programs you're using. So aside from Longbow, what other programs do you use?Cahill:30:14Um, a lot, a lot of work in Kingdom. But Longbow yeah. Longbow and Kingdom. I'd say probably the big two. Yeah. yeah.Sean:30:25Any, I mean, any time you talk about geology, Arc Gis is going to come up. So we've definitely been using that a lot as well.Erica:30:32Okay. And is that different than what you were familiar with, from school or is this the same training that you had?Sean:30:39Well, Longbow is completely different. You know, even looking at production data is not something that I, you know, geoscientists when we ever, we go through academia, we even get exposed to. We use Kingdom. But I think it's, it's more of on a limited basis. I've, I've really been able to work a lot with, the, the well interpretation suites here at TGS that I hadn't worked with before.Erica:31:03Cool. How do you, do you find that challenging or kind of a natural extension of what you are already working with?Sean:31:11I mean, I, yeah, challenging, interesting, different. The team here, the geoscience team here has been very helpful, with the different, features. I'd say there are bugs. Some people might say they're features with the Kingdom software. (Laughter) but I'd say challenging. Yeah, but, but in a good way, not, not as a, you know, wringing out your hands kind of way.Erica:31:33So what else have you guys learned besides Longbow?Geoff:31:37I think for me is just kind of seeing just like what a day-in and day-out sort of process is like. So like having worked in the field, I never walked, I've never worked in a corporate environment before, but just kind of seeing how teams integrate and work together, it's going to say I've never seen that portion before. And so for me it's been fun, you know, going from classroom and then getting the actual hands on application of what we learned in the classroom. That's what's been fun for me so far.Erica:32:01Anyone else agree? Agree, disagree?Sean:32:03I agree. Yeah. No, I mean another thing that I feel a lot of us, especially me and with my Phd projects, they're very wide scale. I'm not talking about basins, I'm talking about plates. And so it's been very rewarding to kind of zoom in. Even if we are still basin scale, that's a lot smaller than I'm used to. So I'm able to kind of get lost in the details more than I would in a very large scale study.Hualing:32:28I think also a good thing is we learn from each other. Like where were you working together? Yeah, we're getting familiar with the software and if any of us found something and others will get around and see what we found. And I think that's very important for us to learn.Erica:32:48Yeah, absolutely.Cahill:32:50Yeah, I think kind of going off that as well and we obviously us for come from different backgrounds in Geo Science and what we've worked in and we kinda bring those backgrounds and each of our own projects and we kind of can come together and help each other out in different areas that we might not be more experienced with, like certain, well log interpretations or mapping things, stuff like that. So, so yeah, it's, it is helpful to have a team.Geoff:33:14Good overlap.Erica:33:14What's it like working at TGS, culture wise? The people, the food?Sean:33:22(Laughter) well they treat us well hereGeoff:33:24I was gonna say no complaints there. Yeah, I mean getting started in know there's always a learning curve, but I mean I guess as much of a learning curve as there could be, you know, everyone around here has been as helpful as possibly could be, you know, to help make that climb that much less steep, if that's a good way of wording it. But that's kind of what I would think.Cahill: 33:43The food is definitely good. Healthy. I like it.Sean:33:45Can't complain about free lunches.Cahill:33:47Yeah. But, but I mean I think the culture here is really, everyone's been extremely nice and even just within the geoscience team, a lot of nice guys; Cian and Alex, they've been so helpful with any questions we have, whether it be geology related or software related, and we've had company outings already. Going on Top Golf is super fun. Everyone's very open to meeting different branches and whatnot. So that was really fun.Erica:34:12Why did you apply? Did it, for TGS' internship program in particular?Sean:34:17Well. Yeah. So, our professor, me and Hualing, we have the same, advisor at the University of Houston. Dr. Paul Mann. And he was actually the one that reached out to us because, James, the head of the Geoscience Department here, had reached out to him looking for good candidates. and he had asked us if we wanted to, to join up. We, we kind of, you know, we researched it. We, I was, I talked to James on the phone and it just seemed like something, so different from what I was doing at the moment that I felt like it was a great opportunity to jump back. And it, I have absolutely no regrets.Erica:34:54Awesome.Geoff:34:54Yeah, my story is pretty much the same thing. My thesis advisor was, was good friends with James K and so he reached out to me and saying, pretty much the same deal as him. Looked into you guys, obviously cause say Jason, I met you before. So that, and also, the interns from last year, I was going to say I was good friends with them too. So I knew what they did. And so, here I am.Erica:35:17Any surprises along the way? Anything that you weren't expecting that you've encountered during your time here?Cahill:35:25I guess one thing is, it shouldn't be surprising, but I'd always is that I'm working with really big data sets. There's always lots of errors you have to put up with. And even with the amazing technology we have, there's always, there's always a human aspect to it, that's always interesting, that we've dealt with in our data at least so far.Hualing:35:44I think for me it's the flexible working time and my, yeah, he didn't request a specific time to be here or like a specific time to leave. So that's like really helpful for my schedule that I can make adjustment along and try to see by what time range works best for me. Yeah.Geoff:36:08Yeah, that's definitely been nice. I feel, like you said having to commute from Spring. I was going to say, getting to come in maybe later or earlier as need be. It's always definitely nice to dodge that traffic.Erica:36:22What are you guys looking forward to working on for the remainder of your internship here?Geoff:36:27Well, I'm really excited to see the end product of what we're doing, especially because, we're going to be presenting it to upper management, and presenting it to our, our geoscience team as well. I think that's really going to help bringing it all together. Cause right now we know we're all working on our separate areas as well. I mean, we're still two teams in a certain area, but it's still very much our own work. And so that, that finish line I think is going to be where it all comes together and I see more bigger, I see a bigger picture than maybe I'm seeing right now.Geoff:36:57Yeah. I think one aspect that I like about is, it's not just busy work. You know, we're actually adding value to the company with an end result. Kind of like what Sean said.Erica: 37:06No making coffee?All:37:08(Laughter) Danggit. For ourselves, we make coffee for ourselves.Erica:37:14Um, what advice would you give to other students wanting to intern here?Cahill:37:20Say like, don't be afraid to get into anything that you're not experienced with. Whether it's geology or software related. Since coming here, I feel like you can learn a lot from a lot of different people and there's a lot of different backgrounds here and people are all open to helping you or talking about their passion and their little branch of geology or geoscience. And so I would say don't be afraid to ask questions and go up to random people and say, hey, what do you do here? And what are you into? Because chances are they're happy or passionate about their job and you can probably learn something from it.Geoff:37:54Yeah. Maybe to add onto those, don't feel like you have to know everything beforehand coming in. Cause I mean you're not, no one's gonna know everything. Kind of like what Cahill said, there's plenty of resources around. You don't feel afraid to ask. No. Everyone out here is more than willing to give their time to help you out for what you might have a problem with. And we've had that reiterated to us time and time again. So, I mean, it's been nice to know.Sean:38:17Hmm. And, I don't know if before we talked about how we got the internship, and I feel personal connections are the biggest, you know, it's not about going on a website and clicking apply. It's about going to the conferences and meeting people from TGS and they're extremely friendly. We've all seen that firsthand. So I'd definitely recommend, and I, I would recommend it as well that you would get an internship with TGS, but just go up and see them during conferences, talk to them, ask them about opportunities, say, Hey, what are you guys doing? Be interested. and even if you don't get something out of it, that's fine. You're still gonna make connection, connections and learn about where the industry's heading.Hualing:38:53Yeah, I definitely agree with Sean, cause I met Alex on with, the person, our geoscience group, we met during the AAPG meeting at San Antonio and I talked to him and, he talked to me about his project and what I may be expecting for my interns. I think that definitely helped. And yeah, when I first day, when I came here, I saw him as, hey, yeah, that's, yeah. I feel like familiar and yeah, I'm more easy to get along. Yeah.Erica:39:28What have you gained during your time here at TGS that you're gonna take with you as you continue your studies and your career?Sean:39:36Everything we just talked about. Yeah, no, I mean that, that's a good sum up question. So the, the connections we've made with all the people here, not just in the Geo science team, every, every other team that there has that there is at this company. All the skills that we're learning with these different programs, the different perspectives we're getting because we're looking at, again, not just geological data, we're looking at, these problems more holistically. All that and above, I think is what we're going to take with us.Cahill:40:02Yeah. I think, you pretty much nailed it on the head. It's seeing the, the geoscience in an actual industry application in its own way. It's a lot of different moving parts coming together for an end product that's ultimately valuable and generates business. And then seeing how that works, you know, if on a fundamental level that's, that's pretty interesting and being able to be a part of, it's pretty cool. So.Erica:40:27Well, awesome. Well, thank you guys for being here. Thank you for talking with us today, and we'll let you get back to work.

In the second episode of Beneath the Subsurface we pick back up with a deep dive into onshore seismic technology in unconventional plays. Wayne Millice, Mike Perz, and Jason Kegel dig through seismic technologies, pre-stack seismic attributes, acquisition developments, and our predictions for the future of seismic and the unconventional realm. Erica Conedera, your host, new to the onshore seismic world, explores the challenges and sometimes over-hyped solutions with onshore acquisition and processing with our guests. TABLE OF CONTENTS0:00 - Intro1:51 - Onshore TGS History2:35 - Acquiring Onshore Data5:00 - The Migrated Stack7:28 - Resolution: The Bug Bear of Processing8:38 - Pre-stack Migration9:55 - Pre-stack Attributes; The Good and the Bad12:05 - Pre-stack: The Secret Sauce13:48 - Noise, Noise, Noise15:38 - The Future of Unconventionals; ARLAS, AI, and ML18:35 - Joint Study with FracGeo: Pre-stack Depth Migration20:39 - Analytic Ready LAS (ARLAS) and velocity Models24:33 - Acquisition Technology; Surface and Subsurface27:10 - Azimuthal Sampling - AVO and Velocity Inversion28:22 - The Q Problem (Anelastic Attenuation)30:08 - Frequency Problems35:21 - Interaction with Acquisition and Processing37:42 - The Future of Seismic in Unconventionals41:24 - ConclusionEXPLORE MORE FROM THE EPISODE:Advances with Land Seismic for Characterizing Reservoirs Workshop with Christof Stork, Mike Perz, Bruce Hootman, Rodney JohnstonARLAS and tgs.ai Subsurface Intelligence A Candid Look at the Value of Pre-Stack Depth Migration for Unconventionals with Mariana Roche Davies at Geophysical Society of Houston TGS Data LibraryEPISODE TRANSCRIPTErica Conedera: 00:12 Hello and welcome to Beneath the Subsurface a podcast that investigates the intersection of geoscience and technology. In our second episode, we'll deep dive into seismic technologies, pre-stack seismic attributes, acquisition developments, and our predictions for the future of seismic and the unconventional realm. From the software development department here at TGS. I'm Erica Conedera, your host and complete newcomer to the world of onshore seismic. I hope you'll find our discussion today as informative and enjoyable as I did.Erica:00:45Um, so let's start with introductions to my left.Jason Kegel00:49Yeah. My name is Jason Kegel. I've been with TGS for six years. I'm a geologist. I've worked on almost every one of the onshore US seismic programs that we have.Erica:00:59Awesome.Wayne Millice:01:00I'm Wayne Millice. I'm the gray beard of the group. I've been with TGS only about 11 years, but are, sorry, eight years. But I've been in the business about 35 years I'm the VP of onshore multiclient. And I'm here to hopefully teach some people about the value of seismic in our business.Mike Perz:01:19I'm Mike Perz. I am the director of technology and the onshore group. So I'm responsible for looking after all matters technical in support that group. And I'm not quite as gray bearded as the gentleman sitting to my right, but I have been in the industry for about 25 years. So I'm kind of blondish with whisps of gray, I guess you'd say. (Laughter) No spring chicken.Erica:01:42Awesome. So let's kick off the discussion for today. If you will Wayne by giving us a brief description of TGS' involvement in onshore.Wayne:01:51Sure. TGS was primarily an onshore-offshore company. Up until about 2011 and 2011, we started the onshore business, January I believe, if I remember correctly. And that's how long I've been here, since January, 2011. In 2012, we acquired a company called Arcis in Canada that gave us an instant library of about 15,000 square kilometers in the western Canadian sedimentary basin. And in 2012 we started our first project in the US. And, we have you a since grown the library from the initial 15,000 square kilometers or so until about a 34,000 square kilometer based our database based in the US and Canada. So it's been a, it's been a fun run and it's going well.Erica:02:35Awesome. So Mike, can you take it over for seismic technology? What do we do with the data once we get it?Mike:02:44Sure. So the first thing that happens is that data has to be processed and I always like to call a seismic processing the Rodney Dangerfield of the E&P chain. And the reason I say that is as you might predict, it gets very little respect, certainly in terms of the almighty buck and the price, the price point'sWayne:03:04Very little budget.Mike:03:05Yeah, very, very little budget. And it's kind of ironic because as Wayne and I have discussed a lot, it's the seismic processing step where we have maximal client engagement usually during the course of a multi client project and reputations are won and lost on the processing. But again, very little dollar value flows with it. I don't fully understand why the valuation isn't higher, but it's a problem that I certainly can't fix. So we kind of, in a way, we try to almost leverage that fact that it's a fairly, fairly cheap technology and we take it very seriously at TGS. So with that preamble about why it isn't the most highly valued element of the, of the chain, let's talk about some of the key outputs from processing. So the thing called the migrated stack is probably the single most important processed attribute in an unconventional play in say, offshore environments like the Gulf of Mexico seismic technology is no one buys CEO's of a big oil companies as an important de-risking tool for say sub salt plays the, in the case of unconventionals, I would not say that seismic has that same kind of universal traction whereby everybody in the c suites on down know about seismic. Nevertheless, it is gaining a lot of momentum.Erica:04:34And when you say unconventionals, can you elaborate on that?Mike:04:38Yeah, I'm talking actually we're all going to be restricting the scope of this discussion to the shale plays onshore shale plays. In a, well North America primarily, primarilyWayne:04:50Our primary focus on probably the Permian and the scoop and stack too. But there are several, several basins in the, in the US market that you could consider unconventional.Erica:04:58Okay.Mike:05:00Right? Yeah. So back to this business of the migrated stack, it is well accepted that it's a very useful thing in unconventional, development. And the primary reason for that is it helps in a delineating landing zones for the lateral wells and also geosteering and hazard avoidance. And I don't know, Jason, if you wanted to expand on a geological perspective of why those things are so important in the, in the depth domain. With seismic, you can start really understanding how to land your wells and doing geosteering in the unconventional world. That's one of the most important things that people are doing right now with their seismic.Jason:05:41Geosteering in particular and finding these landing zones has been important because these reservoirs are, we're looking for is the conventional reservoirs can be anywhere from 10 to 50 feet, which is a lot of times right around the [Clears throat]. The area of seismic resolution, what we found to be more difficult is sort of calibrating everything together. So when we have the data, so calibrating the well logs, the tops, some of the understanding the differences in the different tool parameters your measured while drilling tool parameters versus your after drilling parameters and how that relates back to a depth calibration has been very important in the seismic industry. bringing all those things together to geosteer real-time to actually find these landing zones has been something that a lot of different softwares have attempted to do. And bring this into a multi-client aspect where the operator can instantly get a depth to calibrate and volume that they can geosteer on or look at their regional area of interest onshore has been very different than offshore seismic, which has traditionally had that depth migrated volume to begin with.Wayne:06:53I can expand on one thing that Jason said too when we're talking about regional views on the petroleum systems. So our TGS has a strategy to date has been to get assets that are contiguous within these, with these within these basins so you can understand the regional view of it or of an oil producing basin or hydrocarbon producing basin. So it's important in our opinion that we get a large regional view. That's why you'll see you somewhere databases online. When you look at our, when you look at our projects, they're very contiguous and very focused on one area.Mike:07:28Yeah. Jason gave a nice description of of why we might want to use migrant stacks, for geosteering. And he touched on something important. You brought up resolution and you talked about thin beds on the order of 10 feet to 50 feet. And one of the real bug bears are an unfortunate reality in the seismic processing world is the fact that we really cannot dive down to smaller resolutions than, than those beds. In fact, we're probably operating in, in the order of like, wavelengths of hundreds of feet. So resolving those beds is pretty tricky. We can detect them sometimes but not resolve them and we're always being pushed on the processing side to do a better job. And it's disappointing because all, sometimes all the acquisition equipment in the world isn't gonna help you through that. Mother Nature is cruel in a way and she chews up the high frequencies and there really hasn't been a breakthrough in seismic processing technology to allow us to bash through that, that limitation. So resolution is an ongoing issue and we're always squeezed by it in the unconventional context in the, especially for this geosteering. So that's worth noting. And one other quick thing, Jason mentioned pre-stack depth migration and that's an important new technology in unconventionals. Technology has been around forever for 20-25 years in the Gulf of Mexico, but it's really gaining ground in unconventionals and in in fact, TGS, shameless plug for a talk. TGS is going to be hosting a talk in early June, June 6th. Mariana Roche Davies is going to talk about pre stack depth migration and why it's valuable in unconventional plays.Wayne:09:07We should be plugging a lots of things here, shouldn't we all sorts of-all sorts of shamelessMike:09:11shamelessly plug. (Laughter)Mike:09:13So, so if, if I could move away from the migrated stack, I just want to talk about the second big thing that seismic data is used for on the and the processing side. And that's the, the pre-stack data are used for generating attributes and we sometimes call this AVO analysis or Pre-stack and conversion. And the interesting thing here is that while the migrated stack has quite a lot of acceptance as a, as a really good de-risking tool for the reasons we mentioned, there is less universal acceptance o- the, these pre stack derived seismic attributes.Mike:09:55Some I can think of one really technically astute interpreter from a Permian player who's very successful and they don't touch the pre-stack attributes because there are too contaminated by noise. On the other hand, you go to the SEG or URTeC and all that, there's tons of talks on using these pre-stack attributes. So it depends on who you talk to. Some people use them, some people don't. My hope is that they're going to be used more and more down the road. We're kind of pinning a lot of our own technical direction on that, on that premise.Jason:10:22No pre-stack attributes have always sort of been the holy grail for, for people to find their, find their sweet spots. Right. I mean, looking at AVO in context, I mean that's the, the number one thing, right? And people are always absolutely to define their bright spot, right? And there's been tons of wells drilled just on that. But then to bring in rock mechanics and what they're doing with, with more pre-stack attributes in rock Brittleness and actually trying to look at Poisson's ratio and Young's modulus. When we start to look at those, we start to actually correlate the actual rock properties to what we're getting from our are sound frequencies. The more we can, we can do that and the better we can actually accomplish that is in the academic world has always been the, the, the driver. Right? And you can't talk to hardly any anybody that's teaching geophysics or rock mechanics or geology nowadays that doesn't want to talk about how to correlate your, your wells to your seismic. And it all comes down to understanding densities and shear wave and you're, you're compressed wave wireline tools and bringing that back to the, to the seismic world. unfortunately Mike is correct in saying that a lot of operators in these unconventional zones don't necessarily don't necessarily use it. They'll use it on their, on their own. They'll use a proprietorially, they'll use their own individual softwares to do that. But in a multi client aspect, it hasn't really caught as much traction as is, I think it will. And I think one of the big things that might push that is, regional is that, that's something you guys think the idea to have more regional studies of pre-stack attributes in pre stack, volumes.Mike:12:05Yeah, I think, I think that's a good idea. I mean what one of the nice things with our huge well database at TGS as we can, we can leverage that massive information source into these regional studies. And one thing I forgot to mention was that this pre-stack conversion or attribute business, it does very well to have a lot of well control and we've got lots of that here. So that would, that would certainly help garner interest. One of the big problems, I think that that detracts from acceptance is just that there are not kind of generic workflows for what to do with the pre-stack attributes. Once you, once you have them, it's quite easy to, stare down a migrated stack and figure out, I steer here, I land here.Mike:12:49That's it. You know that that protocol is easy to understand. What do you do with all these attributes? And different companies have their own secret sauce for that and sometimes they're quite tightly guarded about what they, what they do. So I think that may change in the future. We hope it does.Erica:13:02Why do you think it might change?Mike:13:04I just, I just think it will behoove everybody to leverage the seismic more everybody would win from, from thatErica:13:12To be more transparent with their methodologies or?Mike:13:16Possibly, I mean I think as as technologies emerge that-Wayne:13:19Or we push or we push the methodology, for instance, we have the data points internally that we need to start pushing those to those new solutions so to speak or so push them out and then our customers will create their own secret sauce from hopefully some of our solutions that we're aware of or a team.Mike:13:34And even as they push their secret sauce as the years tick away, typically people give up, they cough up their secret sauce to make a bad extended, a lousy metaphor. But they tend to divulge it and public domain and we all benefit from it.Wayne:13:46It's another paper at URTeC.Mike:13:48Exactly. So yeah, I guess this seismic technology thing is my bailiwick. That's why I'm doing a lot of the talking here that I was going to move on now to future future looking at data processing first of all and take a stab at what what I think are important technologies of the future. One is an old thing, it's noise, noise, noise, getting rid of noise, especially in places like the Permian. The Permian is so nasty as regards seismic soundings. You've got these horrible near surface layers, of anhydrites and salts interspersed and then you get these, these fills zones where the salt collapses and it, it kind of bedevil's all your seismic tools in many ways. And so that's why that one operator I was telling you about is reluctant to look at at their pre stack data for fear of the noise, screwing up their analysis. So we've got to do a better job at noise. We've got to do a better job at eliminating multiple energy. Full wave form inversion is a fairly well established technology offshore. We need to leverage that knowledge and get it going. Working better onshore for us, gets a nice velocity models among other things. Those are good for feeding this pre-stack depth migration technology.Erica:15:02What are the challenges of leveraging that?Mike:15:04Good question. The data are noisier on land typically. And so that isn't totally compatible with the full waveform inversion model toErica:15:13So you have to adapt the model.Mike:15:14Adapted, got adapted to handle topography, things like that. And there are people are, people are doing that. We were certainly very active in that, in that space at TGS. Some of our competitors are as well. But again, I don't think there was this sort of routine commercial use at this point. I mean I know there's not just yet, but we're getting there. So yeah, those, those are kind of the big, the big things.Mike:15:36Now the last thing I was going to ramble on about a bit was taking a future look at interpretation. So where would interpretation be going for for unconventionals? Cause I mean, Jason, check me if I'm wrong, it's really a different beast than conventional plays where interpreters have, there there special ways to stare down data and pick sweet spots and bright spots. This is not that, that same thing. I and I could be off base here. I'm just prognosticating. I think that, one important thing in the future we'll be using machine learning and at TGS we could leverage our data and analytics group for this stuff and basically use machine learning to tease out complicated relationships between seismic attributes and production and completion data points with the view towards being able to predict from the attributes alone where the next landing zone should be the next well.Erica:16:32It's shameless plug. Our first episode was all about machine learning and AI. So please check it out if you haven't already.Wayne:16:38on there. So there're interesting conversations that our AI summit to sort of speak about who would be picking the next location. Would it be AI being confirmed by a human or human confirming AI. So there was a, that was pretty interesting discussion of that, that ti's a good point to bring up.Mike:16:57Yeah, for sure.Jason:16:57And when it comes to interpretation in particular with seismic and how machine learning can help having all of that data readily available in the cloud is, or the first step, right? So when it comes to machine learning, it's just a matter of the more data you have the in the, in the machine, the better you're going to have it coming out. But that's everything that TGS does have, right? The well data start including tops, production completion techniques, different attributes for seismic. Then you actually get the machines starting to actually tell you where your reservoirs are going to have sort of different permeabilities, right? If you could start understanding where these different permeabilities come in and these shales, very slight variations can lead to huge benefits in production. So that's a, that's a very big thing that we would love to be able to do, but it's not quite there yet.Mike:17:48Yeah, I mean I think you've raised a good point. We feel like we have all the ducks in a row here at TGS and it's, it's interesting because there- others before us have played around with multivariate analysis too to try to fit these attributes to things like production. They don't have the breadth of data that we have at TGS and they don't have as ready access to a lot of these things. So we're, we're poised to do some, some pretty cool stuff. So watch this space as they say. The only other thing I was going to say on on future looking interpretation wise, and I again I - disclaimers cause I could be wrong, but I believe that that combining seismic with geomechanical modeling software, may be an important thing to that end. And again, what is this our third shameless plug?Wayne:18:32Well we keep doing it because that's what we're here for. (Laughter)Mike:18:35So we're undertaking a joint study with FracGeo, a Geo mechanical modeling software and Services Company in the Permian Basin on our west Kermit Dataset in the Delaware. And we're going to be reporting back on that soon. But basically we're just, we're taking our seismic data and post-stack attributes like curvature to predict fault locations and that becomes feedstock for their Geo mechanical modeling stuff. And also the stuff you brought up, Jason Poisson's ratio and all the things we glean from inversions, those will go into their geomechanical modeling process as well. So that you know, hopefully that's a new sector in which seismic can be used.Erica:19:11We realized that we missed something, We need to circle back around to the topic related to pre-stack depth migration gentlemen.Mike:19:20Yeah. Pre-Stack definite migration in unconventionals. We kind of give it short shrift. I just wanted to add a few more more things. I had mentioned that it's a very established technology pre-stacked depth migration in offshore plays, Gulf of Mexico and such, and it's only been over the last couple of years that operators are using pre-stack depth migration a lot for unconventionals.Mike:19:40It's interesting to note you don't get the jaw dropping improvements on the migrated stacks that you do in the Gulf of Mexico because the data are not nearly as structured. Right, Jason?Jason:19:50Right, in most areas when people say railroad tracks, they're not kidding.Mike:19:54Yeah, yeah. So, so you don't get these amazing glossy brochure image improvements on the stocks, but the, the benefits come in subtler but still important ways. For example, you get natural output and in depth is one, one really important thing and another thing you get better fault definition after pre-stack depth migration. Sometimes I think the real prize can be the actual velocity model itself. One really important difference in velocity model building for pre-stack depth migration in the unconventional onshore case compared to offshore is that in the former case, in the onshore case, we've got so much more well data to constrain or lock down our velocity models, especially at TGS with our massive well database.Mike:20:39And so that's, that's a really, really good thing. So that's why I feel quite confident at the end of the day the velocity models are so responsibly constructed that you really can trust those depths and you get this natural depth conversion after depth migration that's as good or better than what an interpreter would do using his favorite or her favorite method for for depth converting time process data and on that well topic are TGS so-called ARLAS synthetic, well construction using machine learning. That's really gonna help our depth model building. We've yet to exploit it, but we're going to basically be able to get way more sonic wells through this ARLAS process to constrain interval velocitiesJason:21:24And that's, that's a big benefit in the shallow, we start looking at the, the shallower area for drilling hazards and drilling risk. we also start looking at that for water, for water. So in the Delaware, it's a big issue, not only just produced water and injected water and saltwater disposal, but making sure that the, the drinking water in the aquifer water that's usually in the shallower intervals is safe. So it's an environmental concern that we look into having that velocity model better structured in the upper sections that we normally don't look too much into and we're looking at exploration per se onshore, helps quite a bit with that, both environmental and with, with hazard mitigation.Mike:22:05And the ARLAS construction will help that process, right?Jason:22:10Oh, absolutely. The ARLAS dataset- any type of velocity model that can improve on the, the prior velocity model is of big concern. So you can get back to geosteering. Anything that helps that velocity model. A lot of times when they are geosteering, they'll have realtime velocity model building as the mud loggers are providing new information. They cross different faults, they notice different things that can instantly update the velocity model they're using to help steer that well. So it just goes back to the fact that having the best velocity model up front is going to help the, the final piece of the puzzle, which is landing that well on the, the zone where you can get the most oil or gas out of it.Jason:22:53And that's been shown there. There's been a bunch of studies that have shown this, but there was one in the Balkan a few years ago that showed that using 3D depth seismic helped reduce their costs with 75% just by having their geosteerers use seismic. So that's you know, it's a known value for, for the, the seismic industry and the oil and gas industry to, to geosteer with depth migrated volumes. And it's nice to see that and the multiclient aspect that starting to really catch hold.Mike:23:26Absolutely. And let's just push it onto those pre-stack attributes.Jason:23:29No, I know, we just need it in the attributes.Erica:23:33Okay.Jason:23:34Particularly with faults. All right, so you're talking about some of the coherence studies with the post-stack, but when we can take some of that pre-stack ideas about Brittleness and Poisson's and Young's modelists and looking at those pre-stacks, bring it to the post-stack to where we can start identifying the fault structures and how those faults work. If you're interpreting those faults on your seismic before you go into your completion plan, then you have a much better idea of how you can track that well horizontally. So these wells nowadays, are a mile two miles long, some cases, I mean there, there they go for quite a ways going over some of these faults that have 20 feet to 50 feet to throw can greatly throw off where you're steering that well. So any type of better velocity model, will help you guide that. And a lot of times these faults, they're under seismic resolution. Again, so any type of fault or any kind of deviation that you can see in the seismic or with that velocity model is going to help you with your, your drilling plan and your completion plan.Erica:24:33Okay, so to pivot a little bit; acquisition technology?Wayne:24:36Well, I can chat a little bit about that. So I was in the contractor community for many, many years and back in the day we are pretty happy with, if you take it up from a spatial sampling standpoint, we were pretty happy at the end of the day when we were getting 100,000, 200,000 traces per square mile.Mike:24:56How long ago was it? How long have you been? 55Wayne:24:58Long time, yeahMike:24:59when did you enter the industry? 65 years?Wayne:24:59At least 65 years. Yeah, (Laughter)Wayne:25:04I was still microfilming, right? (Laughter)Erica:25:04Sick burnWayne:25:04I've been getting- yeah, I get that usually from him, so that's okay. But now, the contractor community has made significant investments in equipment and we're actually acquiring datasets that are, millions have millions of traces per square mile, not just 1 million, but millions of traces per square mile. Now they've been doing this quite a bit in the, Middle Eastern markets because of the terrain. The train's fairly simplistic over there. So the ability to put several thousand source points in one square mile or one square kilometer or whichever you choose to measure by Canadian or US, has- is quite simple. Whereas in the US, or the North American market per se, there is a lot more, what do we call, obstructions and they come from several people from several things. Mostly people I didn't slip there. That was a purposeful-Mike:25:58Freudian slip.Wayne:25:58Freudian slip yeah, But, so now that technology that high trace density wide azithmuth fully azimuthly sampled, that technology or that product is now available in the North American market. So, and it's getting more prevalent. We're starting to see a new acquisition techniques mostly with surface source because you're still limited in what you can do. Subsurface source, for instance, a dynamite, right. But with a vibroseis or any or other surface sources, you're able to acquire data probably for about the same amount of money. It was, like I said, I was getting 250,000 per square mile in 1996 and I'm getting millions for the same number today. Right. So it's a, they've seen significantly increased their their traits count, unfortunately haven't increased their profitability so that that's still a problem in the industry for the most part. But they're working on that. Hopefully at some point we can hopefully at some point we can, (Laughter) we can, get to a 10 million traces per squad or mildly because, go ahead.Mike:27:10I was going to say, you brought up the azimuthal sampling and that, that reminds me, I, I've been conspicuous by my silence on azimuthal AVO and velocity inversion techniques and these techniques are, are in use today using surface seismic to help characterize horizontal stress anisotropy and the presence of fractures and I kind of on purpose didn't get into it too much. I'm bringing it up now because I know that some, some of the, some of the listeners are probably wondering why we're not talking about it, that these things can be, can be useful and unconventional plays. But I'm avoiding too much mentioned because there's somewhat controversial and they have a, in my opinion, limited realm of applicability when they work, they work very well, but they have been oversold in over-hyped. So like I could, I felt I had to, I had to go there cause you brought up azimuthal. I'm going to turn you back to your, to your, your comments though.Wayne:28:01So as Mike, as Mike mentioned earlier, denser is better, but, as we've seen and we've tested and we've done all kinds of things in the field that mother nature has different ideas no matter how dense, we shoot these things. Once we drive that sound signal into of the ground, we don't know what's going to happen to it at the end of the day. So,Mike:28:22Yeah, for, for example, Q, I like to say Q can rear its ugly head Q mean is my proxy for anelastic attenuation. And I don't care how, how many sources and receivers you deploy, you can deploy them every, every fraction of an inch and you're not, you're not gonna change the fact that you lose your high temporal frequencies. And so that you know that that's a real problem. And then certain brands of noise are really well suited to being crushed or eradicated through dense spatial sampling. So that's wonderful. But some things like random noise, sorry, like, like really, really tricky linear noise. that's heavily aliased. If it's complicated enough, then you might need really, really fine sampling to deal with it. And that's still kind of a research topic. Random noises, easier, random noise. The denser, the denser it is, the more you'll, you'll beat down the random noise. No quibbles about it..Erica:29:12Maybe this is overly simplistic, but what causes Q, where does that come from?Mike:29:18Oh no, that's, that's a good question. It basically, every time the earth vibrates because a seismic wave is passing through it, the vibration has some loss to heat. And so it's not a pure elastic phenomenon. There's an energy bleed off and that, that basically that, that, that effect winds up, it's been, it's fairly, fairly straight forward and demonstrate that that kills the high frequencies of your seismic waves.Erica:29:45Okay.*Mike:29:46So yeahErica:29:48If it's straight forward, then what-Wayne:29:50It's straight forward for Mike (Laughter)Mike:29:53It's straight forward from the viewpoint of the textbooks. I not going to derive that in real time, are you kidding me? No. My mind is mush over the years as I become more managerial and sales focused. So, but it's, it's well appreciated. It's well established in the community.Jason:30:08So how can new acquisition technologies help to mitigate some of those issues? Like are there other things on the horizon that there we're doing or you think that might, that might be out there to increase the frequency spectrum both low and high?Mike:30:20I, well maybe, let me return to the, the noise thing that first before I forget to reiterate, some of the spatial sampling might help to, to kill coherent noise that's alias. If you get a sample, fine enough to remove the alias. So that's, that's a good thing. But back back now to acquisition and the spectrum, the temporal frequency spectrum. Well on the high end with this Q effect or anelastic attenuation, honestly I don't think all the acquisition in the world is going to help you. If we need, we need to break through in other ways. Then there are some ideas about sparse spike deconvolution that had been around for a while. Maybe those will, those will improve over the years. On the low frequency side we are doing tangible things in the field. I don't know, Wayne, if you wanted to speak to them on the source and receiver side or,Wayne:31:11Sure. We're starting to do some, some experimenting, I think it's actually become more than experiment. We're actually acquiring projects with what we call either low frequency or low dwell sweeps, so we're starting in a real low frequencies and moving, moving slowly through the lower frequencies and then ramping up through the high frequency. So we're driving that spectrum a little bit wider so to speak. Right. So there's a lot of analytics going on on whether that works or not right now. Like you can comment from the processing side, but-Mike:31:40well it's interesting. Yeah.Wayne:31:42The equipment's there to do it as always. There's always been the equipment to do all this neat stuff, but stuff we create the data. Three C's a good example. We create three component data, but a lot of times we only use the p wave and not the transverse and the inver- and the, the, the, the three. So we don't use the three, we just use two and we create these volumes, but we got other stuff that sits on the shelf. But now we're starting to utilize some of these, low frequency start points, so to speak with a vibrators.Mike:32:09Yeah. Right. And same ditto on the receiver side, right?Wayne:32:11Yep. Yeah. Oh yeah. We're trying to, trying to go with the five hertz damp and phones instead of 10 hertz. We're trying all these things, but have we gotten there and put it into production mode yet? I think we're on the cusp.Mike:32:23Well, it's, it's, it's, it's interesting because a lot of clients are very interested in these technologies and there's definitely theoretical promise and we've demonstrated on synthetics that, you know, you can get good results by, by caring a lot about the low end. And we ran it a fascinating test that hopefully we're going to publish at an upcoming SEG workshop. Shameless plug number five, right?Wayne:32:42Four or five?Mike:32:44Five, six, I can't remember. So, so I'm a co organizer. Christof Stork is, is the chief organizer and along with Bruce Hootman and Rodney Johnston and myself work organizing this SEG workshop on land processing and acquisition. And we're gonna, we're gonna dive into some of these, some of these, some of these topics. And one of the things we're talking about is, are we actually really enjoying the benefits of this low frequency attention that we're, you know, that we're foisting on the soundings in the field. Are those low frequencies coming out at the end of the day after all our inversion products? And Are we really reaping the benefits? It's not clear. We ran an interesting internal tests where we, we acquired data with the low low hertz or low frequency phones and I think we had low dwell sweeps. We certainly had have lots of energy on the source side, on the low end and after preliminary processing the result, cause we had a control experiment where we didn't do all this low frequency attention and the preliminary processing showed that that when you were really attentive in the field to these low frequencies, you got a better answer. But guess what? After we got to final processing and we're able to use a second pass of something called deconvolution to really widen the spectrum, we found very little difference between the conventional acquisition mode and the and the the low frequency effort. This is at odds with some of the, some of the literature, and I'm not disputing other people's findings, but there might be a subtle effect with an area dependency to it. We'll see.Wayne:34:13But is a subtle effect enough to justify asking one of our contractors to go spend x number of dollars on equipment to upgrade their crews, right? Or it's,Mike:34:24I know it's a, it's a tough, it's a tough question. Tough question. You know, I guess if price points on the cruise side drop enough, sure it's Gravy, why not? But if not it might not be worth it. You might spend your money on other other things. I'm not sure.Jason:34:35Was it not the low frequencies that help you differentiate liquids in, in some of the inversions that you do further down the road? Is that the, that's the the biggest benefit, right?Mike:34:47That's I believe, I believe it's very helpful. The low frequencies certainly helped to, to lock down the low frequency model for the inversion they give you support. Where are you, at low frequencies, where you don't typically have such support with conventional surface seismic and, and I'm not an expert in inversion, but my understanding is some of the fluid effects do tend to show themselves better when you've got the right answer for the low frequency model. And that's facilitated by having some of these low frequency acquisition techniques in play.Jason:35:21You had mentioned earlier how the seismic technology and processing is the sort of the, the biggest area where we get interaction with our clients. Right. And it seems to be undervalued in that sense with acquisition. Is that a way we can of push that to, to fill that gap so we have that interaction and on both sides?Mike:35:44So interaction on the acquisition side?Jason:35:46Yeah.Mike:35:46Well it's a good question. I mean, my understanding is there's typically not a ton of engagement at the field acquisition stage yet. There's obviously some,Wayne:35:54Actually I would say yeah, there certainly is our one, our pre funders, write a check, they want to have some, implement some, some say so to speak what's going on. But mostly once we've made an agreement, on parameters, all that stuff is pretty much on us to deliver what we said we'd deliver. So, but we do where we really interact with our customers, we help them, we take problems off their plate so to speak, by taking on the acquisition piece, the acquisition piece is the most labor intensive, right. And, but where we really start to get in with our customers and when we, after we get the data, we've done the field acquisition, we interact with our customers from the processing side a lot. So it's important to us that like we said processing's a small piece of our AFE, but it's the most important because that's what we deliver, and that's what they see. Right. So, the, the nobody, no, I always say this to my guys to say nobody remembers the farmer that shot at you. Nobody remembers the vibrator they got stuck in the field, but they always remember if you're AVO volume was crap when they delivered it. Right. So they always remember that. But none of us other than other stuff that went on the field ever matters when they're looking at and looking at data on that workstation. Right? Yeah.Mike:37:07So this, the poor sister in the E&P chain is the processing somehow is, it seems to continually be this, this critical, critical engagement point for, for the client. I mean, I guess the client, they don't, they don't like having to deal with permitting and stuff.Wayne & Mike:37:29No, they only pay - like you guys - take the load off.Wayne:37:31We're taking that load off them. That's a big load. Trust me.Erica:37:34So jumping ahead, what do you predict for the future of seismic in the unconventional space?Mike:37:42Well, I think I state this without proof of course, but I believe that there's going to be an increased use of seismic, including outside-Wayne:37:51Well, the, the data that there's a lot of, there's a lot of data that's been acquired in the US and Canada for that matter. But a lot of it's getting dated, right? So when we're talking about, just like denser is better. We mentioned that earlier, right? Denser is better. So we're finding that a lot of these processing techniques that, Mike has been mentioning earlier, don't apply very well to older data data sets that don't have high resolution and aren't sampled very well. So we're finding, probably a lot of these older servers, you're going to get over it or getting acquired again, right? So that's, that's one marketplace. But as the unconventional space goes on, I think you're going to find, find it. A lot of these, like I said, a lot of these older datasets and a lot of the, are you going to make some discoveries within these data as the processing techniques get better and as we use the attributes better and all those things.Mike:38:42Yeah, 100% yeah. And I was going to say, I believe from my conviction that there'll be an increased use of seismic for that to reach for that to actually come into play. I think that we need to, as an industry use these pre-stack attributes that Wayne just mentioned more and more. And we also, I believe need to start using 3C converted wave data more. We didn't get into converted wave data at all on this Chit Chat.Wayne:39:06That's another, maybe that's another podcast.Mike:39:08It's - it could, in of its own, but you know, there, there's some great promise with that technology, like so many technologies, it's been oversold and over hyped to some degree. But there's some really interesting case studies in western Canada that show that it's got great potential. We had awesome converted wave soundings.Wayne:39:24Yeah.Mike:39:24On the loyal survey. Yeah. And that's so, so that might help to propel the increased use of seismic as well as increased use of these attributes. So that's, that's what I think is going to, it's going to happen.Jason:39:35One other thing, I really think that seismic is going to help in completion engineering. I'm going, I think that's sort of where it's going to now and where it's sort of, we've seen that happen with some of the pre-stack attributes and just to use seismic first off and understanding exactly where to perf and exactly where to make your completion intervals and where you're going to get the best production, on top of all the regional work you do to, to start out.Wayne:39:58And that'll impact the funding cost per barrel for our customers. So that's going to, we hope that that's the, again, the value of seismic, right? So how's that going to drive our business? How it's going to drive our customer's business at the end of the day.Mike:40:11Yeah, absolutely. And I mean one fundamental thing I forgot to mention, and Jason, you check me if I'm wrong, but I think what's happening in the unconventional spaces that there's a a slowly growing recognition that's actually probably accelerating right now. That to the tune that hey, we can't just go factory production style with completing all of our acreages there's enough geological heterogeneity that the production in this set of laterals here from this pad is kind of different than over here or even among the laterals in a pad. Why is this one so different? Parent Child Interactions, let's understand them better and all these burning questions, they're demanding some sort of better gaze into the subsurface and that is seismic.Jason:40:53That is seismic and that's where I think that's where you're absolutely right. That's where the future is driving it. If you can understand the parent child relationships between your multi well pads and pads next to you and how you're going to complete the entire basin on a stacked play basis, using seismic is going to be your, one of your only real tools to help out. And the better you have the air velocity models hammered down, the better you have your pre-stack attributes that can be involved in that study, the better off we are and I think we're well on our way.Erica:41:24Awesome. Well, thank you gentlemen for being here for our second episode. This was a really, educational discussion for me as someone who is not from a seismic background. And I'm sure I've heard listeners as well.Mike:41:37Been our pleasure, Erica. Yeah, yeah, yeah.Jason:41:39Thanks Erica.Wayne:41:40Yup. Good for-Thanks for dragging us all in here.

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

Jenny och Jessica får besök av Arla Foods marknadsdirektör Cecilia Kocken. Hör om Arlas arbete med hållbarhet och det nya arbetssättet Netto Noll Klimatavtryck. Hur ska mjölken hålla sig modern och konkurrenskraftig, visst håller mjölken alltid längre än "bäst före"-datumet och hur ser Cecilias planer ut för Arla de kommande åren?

Havredryck istället för champagne!? Ja, man kan förstås skåla i vad som helst, och denna gång är det fyra sorters havredryck som fyller Edwards och Mats’ bägare. Det är ju fortfarande fastetider, det vill säga läge för späkning. Vilket inte hindrar de båda gastronomerna att ta sig an provningen med initierade proffsgommar (och de kan heller inte låta bli att spekulera om vilka drinkar som skulle lämpa sig att komponera med havredryck).Förutom fasta är det vår, vilket får Edward att tala om jakten på den perfekta gräsmattan, rosbeskärning och mycket annat som får Mats att undra om detta håller på att bli en trädgårdspodd, och Edward att sakna P1:s ”Trädgårdsdags”. Vilket får Mats att i sin tur sakna P2-programmet ”Alltid på en söndag” med Vassilis Bolonassos, och ännu ett önsketema för en framtida poddepisod har fötts: om radioprogram vi saknar och får fantomsmärtor av.En lyssnare tar över en stor del av programmet medelst en inmailad liten uppläxning om uttal och uttryck, vilket får Edward att lära åtminstone poddens redaktör ett för henne nytt ord: ”anakolut” (en osammanhängande och ofta ogrammatisk konstruktion, vanlig i så kallat oreflekterat talspråk såsom poddprat). En annan lyssnare efterlyser små tips för att ett hem ska kännas generöst och välkomnande, vilket får igång både Edward och Mats ordentligt med tips på gästtofflor, vattenflaskor, fruktskålar och mycket annat. Edward berättar också om hur det gick till när han äntligen blev kvitt sitt beroende av att rota i andras badrumsskåp efter öronpinnar, sedan en väninna gillrat en fälla med … Lecakulor – och så deklamerar han Bo Bergmans ”Brev på elden”.P S Dessa drycker inmundigades:• Arlas mjölk- och havredryck• Oatly havredryck mellan• ICA:s havredryck, ekologisk• Garant havredryck, ekologisk See acast.com/privacy for privacy and opt-out information.

Mårthen och "Fimpen" gästas av den tidigare SHL- och NHL-forwarden Michael Holmqvist, idag tränare i Djurgårdens J20-lag. Fokus ligger på juniorer och det hakar även "Stats-Jocke" på!När Holmqvists offensiv sågades av Mike Babcock!Talangerna i Djurgården!Faran att stressa fram som junior!Föräldrahets!Det svenska JVM-fiaskot!"Arlas" speltips!"Stats-Jocke gräver i juniorpoäng!Virserum!SHL-podden på sociala medier:Twitter: SHL-poddenFacebook: SHL-bloggenSajt: shlbloggen.se See acast.com/privacy for privacy and opt-out information.

Hanne Søndergaard er kvinden bag Arla Foods’ globale marketing-indsats. I denne udgave af CMO Talk forklarer hun, hvordan hun får enderne til at mødes og får skabt resultater på vidt forskellige markeder – og hvilke KPI’er hendes team arbejder efter.

Mårthen och nyligen CMore-etablerade ”Arla” gästas av Pro Hockeys chefredaktör tillika Eliteprospects-bossen Peter Sibner via Skype. Stats-Jocke fjärdelinar i studion.Några russin ur kakan:• Tysta småländska R• NHLPA 93• Örebros tränarbyte• Huvudtacklingar - vad man man göra åt dem?• SHL-podden tar rygg på Virserums SGF:s drömmar om division 2• Stats-Jocke om nordamerikaner i Elitserien/SHL genom tiderna• Arlas speltips och quizSHL-podden på sociala medier:Twitter: @shlpoddenFacebook: SHL-bloggenSajt: www.shlbloggen.se See acast.com/privacy for privacy and opt-out information.

Mårthen och ”Fimpen” gästas av Brendheta Andrée Brendheden via Skype. Stats-Jocke fjärdelinar i studion.Några russin ur kakan:• Andreés komplexa relation till Malmö• Foppas lekstuga på halv halvfart• Johan Lindbom sparken - rätt eller fel?• HV71 har fina siffror men dåliga resultat - en seger för hockeyn?• Debatt om Olas förslag om upp- och nedflyttning• Stats-Jocke om utvisningar• Arlas speltips och quizSHL-podden på sociala medier:Twitter: @shlpoddenFacebook: SHL-bloggenSajt: www.shlbloggen.se See acast.com/privacy for privacy and opt-out information.

Mårthen och Fimpen har med Arla på Skype och Stats-Jocke befordras tillfälligt upp i förstalinan.Några russin ur kakan:• Karjala Cup - ska de bästa spelarna vara med?• Vad innebär det för klubbarna att ha spelare borta på landslagsäventyr?• Fimpens inlineslandskamper• Mårthens missuppfattade tv-krönika om Fedor Fedorov• Nyförvärvsgenomgång• Åldrade finska viner (spelare)• Brendheta listan, Arlas speltips och quizSHL-podden på sociala medier:Twitter: @shlpoddenFacebook: SHL-bloggenSajt: www.shlbloggen.se See acast.com/privacy for privacy and opt-out information.

Mårthen och ”Fimpen” gästas av före detta superbacken Thomas Johansson som var sportchef i Djurgården i tre säsonger fram till i våras.Stats-Jocke fjärdelinar.Några russin ur kakan:• Thomas känslor för Japanresande Sergei Bautin• Hajarna, Räv Blå och AC Camelen• Varför lämnade Thomas Djurgården?• Sportchefsnörderi• Hockeylabbet och statistikens betydelse för klubbarna• Stats-Jocke: Målglada backar & Kent Anderssons makalösa målfyrverkeri• Brendheta listan, Arlas speltips & QuizSHL-podden på sociala medier:Twitter: @shlpoddenFacebook: SHL-bloggenSajt: www.shlbloggen.se See acast.com/privacy for privacy and opt-out information.

21.-26. oktober 2018 Ugens Udvalgte: Ryanairs håndtering af en racistisk passager, Alternativets Carolina Magdalene Maier skal rejse mindre, og voksne mennesker skal stoppe med at gå i flyverdragt! Valle fra osteproduktionen er Arlas nye guld, og vi tager en omgang mere med Carolina Magdalene Maier. Må tyske socialdemokrater gå med Rolex? Nyt fødevareforlig - Jacob Elleman is coming for you, og Danmarks lystfiskere får ny digital platform. Svindler-Britta vil ikke betale afgift, og så er der digt. Værter: Lasse Rimmer, Oliver Routledge, Majbritt Maria Nielsen.

Mårthen och ”Arla” gästas av super-PT:n Andreas Öhgren, som jobbar med de flesta stora svenska NHL-proffsen.Stats-Jocke fjärdelinar.Några russin ur kakan:• Allt om hur de bästa tränar• Lee Gorens händer• NHL96 på Sega• Varför fixade inte Andreas Foppas fot?• Vem har varit tyngst och längst genom SHL-historien?• "Arlas" svingar• Mustapha Lemieux• Brendheta listan, Arlas speltips & QuizSHL-podden på sociala medier:Twitter: @shlpoddenFacebook: SHL-bloggenSajt: www.shlbloggen.se See acast.com/privacy for privacy and opt-out information.

Anne-Marie Eklund Löwinder, säkerhetschef på Internetstiftelsen, berättar i det här avsnittet av IVA-podden om hur du skapar ett säkert lösenord, vilket lösenord som är viktigast att hålla reda på och varför många ändå inte gör som hon säger. Det hon kallar lösenordsparadoxen. Hon varnar för att säkerhetsriskerna kommer att öka i takt med att allt fler prylar kopplas upp till nätet. Du får också veta varför hon regelbundet åker till Washington och övervakar en nyckelceremoni som gör att det går att lita på att en domän inte är kapad. Och så berättar Anne-Marie Eklund Löwinder varför hon just nu syns på Arlas mjölkpaket.  

Anne-Marie Eklund Löwinder, säkerhetschef på Internetstiftelsen, berättar i det här avsnittet av IVA-podden om hur du skapar ett säkert lösenord, vilket lösenord som är viktigast att hålla reda på och varför många ändå inte gör som hon säger. Det hon kallar lösenordsparadoxen. Hon varnar för att säkerhetsriskerna kommer att öka i takt med att allt fler prylar kopplas upp till nätet. Du får också veta varför hon regelbundet åker till Washington och övervakar en nyckelceremoni som gör att det går att lita på att en domän inte är kapad. Och så berättar Anne-Marie Eklund Löwinder varför hon just nu syns på Arlas mjölkpaket.  

Mårthen och ”Fimpen” gästas av Sportbladets journalistveteran Tomas Ros.Stats-Jocke fjärdelinar.Några russin ur kakan:• Grande Locos matcher mot Costa Nostra• Smeknamnet “Vältaren”• Jörgen Lindgren out• Jeremy Roenick• Sommarstugan vid namn SHL• Olow Sundström• Stats om poängsprutande målvakter• Brendheta listan, Arlas speltips & QuizSHL-podden på sociala medier:Twitter: @shlpoddenFacebook: SHL-bloggenSajt: www.shlbloggen.se See acast.com/privacy for privacy and opt-out information.

Mårthen och ”Arla” gästas av före detta SHL- och NHL-spelaren och numera superscouten Josef Boumedienne.Stats-Jocke fjärdelinar.Några russin ur kakan:• Josefs radiotrollande i Albany• Talangscouting - hur funkar det?• Ulf Elfvings omedvetna höga forecheck• Columbus William Karlsson-ångest• Rasmus Dahlins galna U16-matcher• Jonathan Davidssons ljusa framtid• Talangstats• Brendheta listan, Arlas speltips & QuizSHL-podden på sociala medier:Twitter: @shlpoddenFacebook: SHL-bloggenSajt: www.shlbloggen.se See acast.com/privacy for privacy and opt-out information.

NordicBets SHL-podden är här igen. I veckans program snackar Mårthen, Andrée och Arla som vanligt om kämpande Rögle som slogs på träningen. Det tycker dock Arla är bra då han avslöjar att han aldrig varit med om så många interna slagsmål som i guld-HV71 förra säsongen. Det blir även snack om strafförberedelser, Djurgårdens framfart och så följer vi äntligen upp snacket om specialiserade målskyttar. Och så Andrées och Arlas betting-tävling såklart.Åsikter? Skicka ett mail till shlpodd@gmail.com eller via twitter @shlpodden och #shlpodden. See acast.com/privacy for privacy and opt-out information.

Det här är poddversionen av ATL TV. I det här avsnittet hör vi bland annat om tobaksodling i Sverige, Arlas väg in i Ryssland och skogens veteraner.

Arlas nytillträdda vd Patrik Hansson gästar i det här avsnittet av Varumärkespodden. Lynxeyes Johan Ekelin intervjuar Patrik om den resa han vill göra med varumärket Arla, relationen till ägarna, mjölkbönderna, och hur Arla ska stärka banden till de svenska konsumenterna. Varumärkespodden produceras av strategikonsulten Lynxeye i samarbete med Dagens Media.

Lofsan är på tyngdlyftningskurs och Jessica ska till Paris. I veckans avsnitt blir det fokus på träning vid graviditet, mens och kramp. Dessutom gästar Arlas hälsocoacher och pratar om mat, ekonomi och hushåll. See acast.com/privacy for privacy and opt-out information.

Jessica berättar om sina erfarenheter på resande fot med mycket packning, och Lofsan pratar om det personliga varumärket ur ett hälsoperspektiv. Det blir snack om hungriga barn och att träna när man har ont om tid. Dessutom handlar "Arlas hälsocoacher" om mat, prestation och träning tillsammans. See acast.com/privacy for privacy and opt-out information.

Jessica provocerar på Instagram genom att dricka juicer, medan Lofsan gör det samma genom att äta cupcakes. Hur ska man bete sig på sociala medier för att bli accepterad? Det blir mycket snack om vinter- och vårträning och värdet av att ha en PT. I veckans inslag av Arlas hälsocoacher diskuteras hur man kan dra ner på sina matkostnader och ändå äta bra.  See acast.com/privacy for privacy and opt-out information.

I årets första avsnitt av Träningspodden hoppar träningsprofilen Clara Fröberg in och vikarierar för Jessica Almenäs. Tillsammans med Lofsan tittar hon tillbaka på 2015 och blickar framåt mot träningsåret som just har börjat. Dessutom gästar Arlas hälsocoacher och pratar om återhämtning.  See acast.com/privacy for privacy and opt-out information.

Lofsan har vunnit pris som årets PT och Jessica har börjat med tyngre styrketräning. Det blir snack om hur man ska träna under graviditeten och om hur man klär sig för vinterlöpning, samt ett avsnitt av Arlas hälsocoacher om viktnedgång.  See acast.com/privacy for privacy and opt-out information.

Initierade marknadsanalyser, politiska utmaningar, världsmjölkskrisens påverkan på svenska mjölkbönder, utblick hos en polsk mjölkproducent och möt köttdjursuppfödaren som satsar. Lantbrukspodden blickar in i 2015 genom att i detta avsnitt göra nedslag i olika produktionsgrenar. Dessutom: Exklusiv intervju med Arlas svenska VD Henri de Sauvage och skogsårets viktigaste händelser.

Matpoden besöker Restaurangakademin och vinnarna i Arlas tävling Kock i skolan. Vinnarna från hela landet får en heldagars utbildning med Andreas Hedlund. Ola & Andreas snackar med trevliga och nöjda deltager, arrangör och såklart kursledaren själv. See acast.com/privacy for privacy and opt-out information.

Kyckling är bra och prisvärd vardagsmat. Det tycker Richard Waje som är veckans kock. Med erfarenhet av att arbeta i både Australien och England med varierande kök och stilar hämtar han inspiration från alla världens hörn, men för Waje är det främst smaken som är viktig. Det ska smaka kraftfullt och inte mesigt. Det ska vara yta och fräs på råvarorna så att "den goda brynta smaken" famträder, som Waje säger. Denna vecka lagar han fyra varianter av kyckling - helkokt kyckling i currysås, het kycklinggryta med ingefära och chili, fransk ragu med vitt vin och makaroner och örtpanerad kyckling med auberginröra. Mums! Vi får också förlja med vår reporter Aziza Dhaouadi till Runstensskolan som har tagit hem priset Arlas guldko för att de har Sveriges bästa skolmat! Kocken Johan Beer förbereder dagens färska lax. Denna vecka testade panelen fryst falafel från Simply green, Findus. Hör vad de tyckte om den: 

När Ulrika Nandra lagar mat ska det dofta och knäppa. Varmt och smakrikt karaktäriserar hennes kokkonst och hon influeras förstås av sitt indiska påbrå, pappa är indier och mamma svenska. Ulrika är journalist och småbarnsmamma och med lust och nyfikenhet låter hon sig inspireras av nya platser, dofter och smaker. Fredagstacos byts ut mot kryddig lamm keema och till dessert bjuds len mangoglass. Vi tackar och bockar! Det är inte alls svårt att göra sin egen kryddblandning - Ulrika berättar hur: Varm kryddig mat passar bra när mörkret faller och regnet piskar på rutan - prova! Ibland är det roligt att testa något helt nytt, byt ut flingorna eller frukostmackan mot en mättande frukostchapati: Här är goda förslag på Garam Masala- och Curryblandningar som du kan göra själv: ...och så här gör du ett luftigt smakrikt ris: Bra att veta: Dessutom har Menys reporter Bosse Sjöqvist åkt till Vänersborg för att prova Koppargrillens legendariska bearnaisesås. Lyssna till Bosses hyllning av en svunnen tid! Den här veckan testade panelen olika turkiska yoghurtsorter, Lindahls i spann, Milko och Arlas närmare bestämt. Hör deras omdömen här: Fler ljudklipp från programmet: