Data & Analytics
Technology and partnerships remain important, while phased approaches may supplant lengthy appraisal programs, experts said during CERAWeek.
CERAWeek panelists see AI as a way to leverage data and people in interpreting data for exploration, but a cultural shift at companies may still be needed.
This paper describes an approach to creating a digital, interconnected workspace that aligns sensor data with operational context to place the completions engineer back into a central role.
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This guest editorial explores the rise of agentic AI and its potential effect on oil and gas professionals. -
This paper proposes a novel approach toward drilling maximum-reservoir-contact wells by integrating automated drilling and geosteering software to control the downhole bottomhole assembly, thereby minimizing the need for human intervention.
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This paper offers an exploration into the field applications of multiphase flowmeters (MPFMs) across global contexts and the lessons learned from implementation in a smart oil field that uses several types of MPFM.
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For more than a century, LSU has shaped petroleum engineering education, but few assets showcase its impact like the PERTT Lab. With six deep test wells and rare reservoir-depth gas-injection capabilities, the facility is helping drive breakthroughs in well control, carbon-dioxide injection, and next-generation energy technologies.
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The Norwegian major said it is using artificial intelligence for predictive maintenance throughout its facilities and for interpretation of seismic data from the Norwegian continental shelf.
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This paper describes a data-driven well-management strategy that optimizes condensate recovery while preserving well productivity.
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Even as output hits record highs, a growing recognition of the Permian’s maturity is opening the door for new technologies to improve performance.
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This work presents the development of fast predictive models and optimization methodologies to evaluate the potential of carbon-dioxide EOR and storage operations quickly in mature oil fields.
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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.
