Data & Analytics
Proxy models are transforming reservoir management by enabling engineers to rapidly evaluate and optimize thousands of operating scenarios, helping improve CO2-EOR, carbon storage, geothermal, and underground gas storage decisions while keeping high-fidelity reservoir simulation at the core of validation.
In this interview, Sushma Bhan, energy data, AI leader, and technical director of the SPE Data Science and Engineering Analytics Technical Section, shares lessons from 3 decades at Shell, discussing digital transformation, the future of AI in upstream operations, leadership development, and career advice for the next generation of energy professionals.
Texas A&M Researchers To Develop AI-Powered Drilling System To Accelerate Critical Mineral Discovery
Backed by a $3.5 million US Department of Energy grant, Texas A&M researchers are developing an AI-enabled drilling system that can identify critical minerals in real time, reducing exploration time and costs for rare earth element deposits.
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The 2021 Geothermal Experience Datathon focused on the application of analytics and data-science tools on oil and gas well-log data to assess geothermal potential in two North American basins. -
Earlier this year, 19 teams competed in a machine-learning contest held by the Data Analytics Study Group of SPE’s Gulf Coast Section. The was the first competition of its kind for SPE. Here, the organizers of the contest present some of the techniques used and lessons learned from the Machine Learning Challenge 2021.
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Smaller and independent upstream companies often have limited resources for data management. Nonetheless, their data are valuable and must be managed for that value to be realized. Geologists may just be in the perfect position to do the job, if they can get the training.
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The market may be different from what we have previously experienced, but the path to a successful digital transformation is durable and the core principles of success have not changed.
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The grant provides students with access to leading E&P software and mentoring opportunities to engage and prepare them for future careers.
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Start-and-stop data management initiatives and a mishmash of partial solutions are no longer viable for managing the digital oil field. Data management should be transformed from a cost center to a cash-flow-generating function.
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This column is intended to provide a starting point and a roadmap for professionals who want to learn data science and are struggling with the question, “Where do I start?”
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Where there are data generated and collected, there is analysis of the data. The ongoing digital transformation in the industry has opened many opportunities for professionals skilled in data analytics.
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A recent datathon and the team that took home the grand prize help paint a picture of both the industry’s’ digital transformation and how oil and gas engineers are embracing it to navigate uncertain times.
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As current sources of competitive advantage erode and new ones emerge, perhaps the only constant in a highly uncertain world is the criticality of good talent for success. Upskilling the workforce for analytics, therefore, needs to be a one of the top priorities as we sail unchartered waters.
