Asset Management
Utah, Colorado, Arizona, and New Mexico have formed the Mountain West Geothermal Consortium to turn geothermal energy into gigawatts of baseload power.
Xu outlines the thinking and theories that led his team to six world-class oil discoveries that total more than 100 million tons of proven geological reserves since 2021.
The deal adds physics-based reservoir modeling and real-time decision workflows to SLB’s digital portfolio.
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Only about one-third of Africa’s discovered hydrocarbon resources have reached commercialization. -
This article is the sixth and final Q&A in a series from the SPE Research and Development Technical Section focusing on emerging energy technologies. In this final edition, Matthew T. Balhoff, SPE, of The University of Texas at Austin shares his views on the future of upstream education.
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This paper introduces a system that leverages sophisticated algorithms and user-friendly interfaces to tackle the challenge of developing complex, compartmentalized reservoirs effectively.
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The authors of this paper apply a deep-learning model for multivariate forecasting of oil production and carbon-dioxide-sequestration efficiency across a range of water-alternating-gas scenarios using field data from six legacy carbon-dioxide enhanced-oil-recovery projects.
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This paper assesses the technical feasibility of geological carbon storage in the operator’s Brazilian brownfields, focusing on mature oil fields and associated saline aquifers.
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Updates about global exploration and production activities and developments.
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Experience in subsurface production and lift design is shaping a new generation of geothermal operations built for reliability and scalability.
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This paper explores the evolving role of the digital petroleum engineer, examines the core technologies they use, assesses the challenges they face, and projects future industry trends.
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This study illustrates the new capabilities, tailored for carbon-dioxide storage applications, of a modeling framework that provides a quantitative, risk-based assessment of the long-term integrity of legacy plugged and abandoned wells.
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This paper describes an auto-adaptive workflow that leverages a complex interplay between machine learning, physics of fluid flow, and a gradient-free algorithm to enhance the solution of well-placement problems.
