Recently, I had the fortune to make a trip to Houston sponsored by Enovate Upstream to attend the URTeC 2021 Conference, despite only having had joined the company as a Research Data Scientist in April 2021. I was happy to attend and provide some technical insight into the machine learning and artificial intelligence that Enovate Upstream uses in their product offerings at the company booth. However, my attendance at URTeC 2021 led to much more for me individually.

So much happened on Monday, the first day of the conference. First it was really great to reconnect with the Enovate Upstream team. Early that morning, I walked with Ty Summers, John Estrada, and Laura Santos to view some of the booths that other companies had put up. I laughed as Ty asked John to explain what some of the companies were on about. 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 light hearted. It was really nice to almost get a kind of personal tour from John guided by Ty’s questions, because I’m still quite new to this particular domain. While my background in chemistry allows me to understand what’s happening in petroleum engineering on a surface level, I still don’t yet have the ability to pick out the differences between drilling, production, and service companies.

After a short bout, Laura and I broke off to listen to a presentation given by Emerson. The presentation was really interesting, on the topic of what’s known as the “sweet spot”, and touched on an algorithm I’m familiar with as a data scientist. Laura’s background is in petroleum engineering, but she also codes in python and contributed significantly to one of Enovate’s current product offerings. 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. I wish I had the presenter’s information because she was fantastic. I ended up asking her a question specific to the machine learning process after the crowd started to disperse. 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 is seen a miracle cure all solution and the process by which answers are provided by models and algorithms is not part of common knowledge. This interaction really set the tone of the conference for me. It also allowed me to develop a strategy for engagement with those coming from that petroleum engineering stand point while assisting Enovate to raise brand awareness to support our start up endeavors.

While browsing the other booths on my own after lunch, I chatted with Volker Hirsinger from Petrosys. Our conversation reminded me of another time, it took me back to many of the conversations that I had with my professor and mentor, Kiley Miller, while I was a student and as I have been developing my career. Volker’s excitement for the developing technology was tangible and we ended up discussing how his own skillset has adapted over time to met 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 forage 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. This conversation allowed me to be more open. It communicated that in oil and gas, solutions speak louder than tested processes. This allowed me to refine further what my stance should be in talking with people at the conference.

Later that afternoon, I was able to attend a panel session, “Data Issues: Management, Integrity, Legacy” moderated by Isaac Aviles. I honestly have enough notes from the dynamic discussion contributed to by Eduardo Zavala, Kim Padeletti, Jaime Cruise, Phillip Jong, Phil Neri, and Dr. Junxian Fan for an entire blog post on its own. However, I was able to attend with my coworker Huy Bui, and our discussion enriched my experience there. Largely the panel was a call to action for those in oil and gas with decision making power to allow data to have the things that it needs to become powerful. There was some discussion surrounding the differences between datasets and datalakes, with the implicit argument that having more access to more data yields more powerful results. As a data scientist, this resonated with me. It’s really easy to discard data that isn’t relevant, but I can’t create data where there is none. Something that wasn’t discussed during the panel session was data literacy. The vast majority 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 honestly constitutes the vast 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 both Huy and I, as data scientists, to understand why our work might meet resistance and what misconceptions we might need to dispel in the future. That experience again shaped the many conversations that followed at URTeC 2021.

It was so interesting to me to witness first hand the stepwise innovation that is common to the field of oil and gas. There was still resistance to things that were too new or unfamiliar at a conference for sharing new technologies. Or at least that’s what I thought at first. I soon learned that tentativeness was also abundant. People were willing to consider new options so long as there was evidence of a positive outcome. People, like myself, did ask questions and were fascinated by the new methods used, but slow to consider using them in their own space. I found out that many, like Volker, had touched on some sort of automation using data, and found that it didn’t really 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.