Digital Transformation
The energy sector is rapidly transforming toward a data-driven, decentralized future where combining human expertise with AI and machine learning unlocks new efficiencies, solves complex challenges, and creates a decisive competitive advantage.
AI is transforming oil and gas, but the real change will come from young professionals (YPs) who bridge technology and field expertise. By leading pilots, building networks, and challenging old assumptions, YPs can drive the industry’s digital transformation from within.
By integrating AI into every layer of the energy ecosystem, from renewable forecasting to dynamic pricing, the path toward secure, sustainable, and affordable energy becomes not just possible but achievable.
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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. -
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.
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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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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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With slopes, pies, points, and lines in your illustration arsenal, how do you choose the chart that best tells your data story?
