Data & Analytics
Reaching further than dashboards and data lakes, the agentic oil field envisions artificial intelligence systems that reason, act, and optimize.
In the past year, publications on CO2, natural gas, and hydrogen storage have increasingly focused on the design, evaluation, and optimization of storage plans. These efforts encompass a broad spectrum of challenges and innovations, including the expansion of storage reservoirs from depleted gas fields and saline aquifers to stratified carbonate formations and heavy-o…
This paper presents a robust workflow to identify optimization opportunities in gas lift wells through real-time data analysis and a surveillance-by-exception methodology.
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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. -
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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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 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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This paper presents a novel application of artificial intelligence in computer vision for automating blowout-preventer pressure-chart-data extraction, demonstrating significant efficiency gains and a high return on investment.
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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 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.
