Digital Transformation
Digital transformation in oil and gas depends less on adopting advanced technologies and more on maturing data so people and processes can reliably convert raw information into aligned, asset-level value.
Agentic AI can enhance subsurface workflows when its autonomy is deliberately designed around physics, data integrity, and accountable decision-making through architectures that separate reasoning, computation, interpretation, and validation.
Mark your calendars for the first SPE Live featuring the 2025 TWA Energy Influencers.
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Data validation is not a direct out-of-the-box process and requires planning and even budgeting, but high-quality data can save your time, money, and effort. -
"Digital gives you superpower...volunteering makes you a superstar," says Josh Etkind while sharing his insights on digital transformation and his vision as SPE Gaia chair.
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The enthusiasm for AI practice is growing rapidly across all industries. This article gives a brief overview of AI's key elements.
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A key part of the energy ecosystem is the trading of commodities and the freight that transports them. Throughout this process, optionality allows traders to find opportunities and create value for their respective businesses.
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The SPE Research Portal uses artificial intelligence technology, fortified by industry knowledge, to address the long-term challenges of finding and analyzing information in unstructured data.
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In the ongoing digital transformation in the industry, it's not enough if we only adapt and improve data-processing capabilities; we should also empower human interaction, study, engagement, and collaboration through the use of that data.
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A workforce with the right know-how to maximize the tools being used or investigated in the industry is a critical feature to support a clear route forward.
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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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To develop improved predictive models of complex real-world problems, one needs to pursue a balanced perspective. Ultimately, the physics we know needs to rely on data to unmask the physics that we do not yet know.
