Data & Analytics
Breakthroughs in energy, similar to those seen in AI, require coordinated progress across multiple fields and the resolution of structural bottlenecks. As a result, a successful energy transition depends on integrated advances in infrastructure, policy, technology, and investment rather than isolated efforts.
The Genesis Mission is a US Department of Energy initiative that integrates AI, national labs, and cross-sector collaboration to accelerate scientific discovery, strengthen energy innovation, and enhance national security.
This commentary by the chair of the SPE Data Science and Engineering Analytics Technical Section examines how AI is reshaping petroleum engineering careers, highlighting growing risks to entry‑level training, judgment development, and the future pipeline of subject-matter experts in high‑consequence industries.
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With a record-breaking number of participants, this year's datathon proved that collaboration is the catalyst, data is the tool, and innovation is the outcome. -
David Nnamdi, SPE, speaks about his work as a data scientist and engineer, his development of Sequestrix, an open-source CO2 transport network optimization tool, and where he sees data science and AI’s role in the future of sustainable energy.
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The fifth edition of the SPE Europe Energy GeoHackathon, beginning on 1 October, focuses on how data science can advance geothermal energy and drive the energy transition.
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AI is beginning to transform well management by helping engineers predict electrical submersible pump failures before they happen, optimize drawdown more efficiently, and generate reliable forecasts even when data is scarce or noisy.
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The oil and gas industry's shift to smart fields—driven by automation, AI, and real-time data—requires petroleum engineers to master digital technologies alongside traditional skills.
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Part 1 of this series focuses on the disciplines of geology and geophysics, petrophysics, and reservoir engineering using real-world field examples from Malaysia and the author's experiences in training undergraduate students in Malaysian universities.
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This article explores the integration of hydrogen into existing natural gas infrastructure and introduces practical solutions, including the application of machine learning models, to support decision-making and infrastructure adaptation in the energy transition.
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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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This research developed a clear framework for assessing and selecting fit-for-purpose software. The study focuses on the role of a data-driven approach in the decision process, with application to operational software systems in the oil and gas industry.
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Digital transformation in the oil and gas industry is likened to a major home renovation—requiring a clear vision, skilled collaboration, patience, and investment in lasting solutions. Though the process is challenging, the end goal is an improved, future-ready operation.
