Recently, I attended the URTeC 2021 Conference, sponsored by Enovate Upstream. My role included providing technical insight into the machine learning and artificial intelligence that Enovate Upstream uses in its product offerings.
Reconnecting with the team was great. I walked with Ty Summers, John Estrada, and Laura Santos to view all the booths around URTeC. With John’s strong background in petroleum engineering and Ty’s quick eye for design and the expression of content, their conversation was lively and lighthearted.
From all the in-person sessions at URTeC, Laura and I had the chance to attend an interesting one by Emerson. Laura’s background is in petroleum engineering, but she also codes in python and contributed significantly to the current product offering. We had a brief discussion about the intersection of the domain knowledge of lithology and how this algorithm could reliably be applied to tease out formation information by using geomechanical parameters. The speaker was fantastic, and I asked her a question specific to the machine learning process. She was surprised at the specificity of the question given the relative “newness” of the application of machine learning to oil and gas problems. It hit on a limitation of the technique she had spent ten minutes developing buy in for.
In general, machine learning and artificial intelligence are seen as a miracle cure-all solution. The process by which answers are provided by models and algorithms is not part of common knowledge.
This interaction set the tone of URTeC for me. It also allowed me to develop a strategy for engagement with those coming from that petroleum engineering standpoint while raising our own brand awareness and support our start up endeavors.
I had the chance to talk to Volker Hirsinger from Petrosys. Volker’s excitement for the developing technology was tangible and we ended up discussing how his own skillset has adapted over time to meet the changing needs of the oil and gas industry. I was surprised to learn that he himself had developed a python application or two. We also discussed using atypical information sources to forge a path towards a solution; and meeting resistance along the way. I could tell in that moment that I was speaking to a pioneer. It was truly a pleasure.
Later that afternoon, I attended a panel session, “Data Issues: Management, Integrity, Legacy” moderated by Isaac Aviles, with my coworker Huy Bui. Our discussion enriched my experience. Largely the panel was a call- to-action for oil and gas decision makers regarding data. There was discussion surrounding the differences between datasets and datalakes, with the implicit argument that having more access to more data yields more powerful results. This resonated with me. It’s easy to discard data that isn’t relevant, but I can’t create data where there is none.
The panel didn’t discuss data literacy. Most of the time for data to become useful it must be processed. Even with machine learning at our disposal, data still must be manually labeled and processed. This takes time and constitutes the majority of my workflow. The argument during the panel was to devote both time and resources to this potentially very powerful tool and to give those that are informed a voice. It was a good experience for Huy and I to understand why our work might meet resistance and the misconceptions we might need to dispel in the future.
It was so interesting to me to witness firsthand the stepwise innovation that is common in oil and gas. My initial view: a resistance to things too new or unfamiliar at a conference for sharing new technologies. I soon learned that tentativeness was also abundant. People were willing to consider new options with evidence of positive outcomes. People, like myself, did ask questions and were fascinated by the new methods used, but slow to consider using them.
I discovered many, like Volker, had touched on some sort of automation using data, and found that it didn’t work for them or that the results were unexpected. I think this is probably why I felt like there was an underlying urgency in the low attendance panel on “Data Issues.” What I do know is that I still have a lot to learn in applying my skill set in this particular field.