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In this special issue of Oil IT Journal we report from the excellent 4th EAGE Digitalization Conference and Exhibition that took place in Paris earlier this year. Two overarching themes emerge from the show. A belief in the future importance of artificial intelligence and a belief in the future of OSDU, previously, and still for most, the ‘open subsurface data universe’. A related issue that came across loud and clear is the tendency, for some major oil companies, to repatriate software development in-house.
Both AI and OSDU are potential disruptors. Many see AI as having the potential to disrupt ‘regular’ science and scientists. OSDU is similarly presented as changing companies’ relationships with software vendors as more stuff moves in-house, including, notionally, in-house developed, OSDU-compliant versions of ‘commercial’ applications. Both disruptors pose an interesting challenge to the vendor community: go along with the majors and provide ‘compliant’ software and services or push back with helpful warnings on how hard it is to develop industry-strength, scalable software.
Currently the AI folks appear to be working on point solutions to specific problems where data can be accessed from available sources. To go further with AI, more comprehensive sets of clean, well organize data is needed. OSDU is sometimes cited in this context although as we show in this issue, populating OSDU is proving hard and requires funding. While OSDU development is collaborative, companies are on their own when it comes to the huge task of loading their legacy data. Our meta-analysis of presentations made at the EAGE event suggests that for now, AI activity revolves around fairly low-value activities (scan, OCR, pattern recognition). The exception to this is the huge interest shown for large language models (LLMs) with some interesting geoscience applications – notably GeoRAG* from CGG (now Viridien), AskEarth (AIQ) and ENI’s work with Avvale). On the AI downside, there is resistance from geoscientists and others to the roll-out of the data scientists ‘minimum viable products’. On the OSDU downside there is … well where do we start?
In fact we start here in this issue with Part 1 of our EAGE report dealing with some of OSDU’s trials and tribulations. All the stuff that you will not hear on LinkedIn in fact. Part 2 (in our next issue) will cover the AI/GenAI side of the show. Speaking in the Innovation Leadership session, Eric Ewig (PGS) opined that we spend to much time talking about ‘the solution’ and encouraged us to ‘fall in love with the problem not the solution*’. He was referring generally to the AI side of the business where there are a multitude of problems that are worth ‘falling in love with’. But how do you apply this very good advice to OSDU? What problem is OSDU addressing that anyone could ‘fall in love with’? Ideas on a postcard please.
* Uri Levine wrote a book about this.
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