Data & Analytics
This article explores how AI is transforming oil and gas operations, including its real impact on methane reduction, predictive maintenance, energy efficiency, and whether it truly delivers measurable sustainability gains or just adds complexity.
AI is evolving into a practical tool that helps geoscientists and engineers work faster, evaluate more opportunities, and manage subsurface uncertainty.
Digital drilling technologies are enabling a shift toward more predictive, efficient, and sustainable operations.
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In the real world, where data is messy and workflows are rarely linear, automation often fails. That's where agentic AI comes in. -
The free, 1-day symposium will bring together graduate students, faculty, and industry leaders dedicated to advancing research, career growth, and energy innovation.
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Rajiv Nischal, head of ONGC’s Institute of Production Engineering and Ocean Technology (IPEOT), shares the company's latest developments in technology, sustainability, safety, and more.
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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.
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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.
