Digital Transformation
Tiger Skid, a custom-built cyber-physical training and testing platform, simulates real-world energy systems and industrial processes vulnerable to cyber-physical attacks.
As the energy sector rapidly evolves to address climate change, tools such as the En-ROADS Climate Solutions Simulator are essential for young professionals seeking to understand the complexities of the transition and make informed, impactful decisions.
Artificial intelligence is transforming—not replacing—petroleum engineering. As AI-driven, data-centric methods replace traditional deterministic models, engineers must adapt by acquiring skills in data science, algorithmic thinking, and software tools. The industry’s evolution raises a critical question: Will petroleum engineers evolve with these changes or risk beco…
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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. -
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?
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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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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.
