Reservoir
This work describes a study in which distributed data parallel training, paired with a node-local caching pipeline, enabled efficient multigraphics-processing-unit scaling for a CO₂-storage graph-neural-network surrogate while maintaining generalization.
This paper presents a study that confirms glass-reinforced-epoxy-lined tubing as a reliable, cost-effective solution for long-term water-injection service in moderate-salinity offshore environments.
This paper presents a novel reservoir engineering/reservoir simulation approach—a data-driven interwell-connectivity model augmented as a digital twin—to predict reservoir dynamics and optimize operations in the Changqing oil field of China.
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Suriname's wait to become a significant oil producer may be nearing an end as the French supermajor begins early development studies.
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Diamondback Energy has agreed to spin off its water operations. Now, who’s next?
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On a pro forma basis, the mineral and royalty arm of the Midland-based oil company owns interests covering more than 32,000 net acres in the Permian Basin.
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SponsoredThe data that comes with mapping flow behavior at the stage level of unconventional wells was once accessible only through the installation of costly and intrusive diagnostic methodologies like fiber optic or running production logging. New-generation FloTrac ultrahigh-resolution nanoparticle tracer technology with subatomic spectroscopic measurement techniques now de…
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This paper sheds light on newer frontiers of tracer applications with unconventional uses to gain flow insights from an oil and gas reservoir.
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This paper presents a comprehensive technical review of applications of distributed acoustic sensing.
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The authors of this paper analyze a robust, well-distributed parent/child well data set using a combination of available empirical data and numerical simulation outputs to develop a predictive machine-learning model.
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In this paper, example machine-learning models were trained using geologic, completion, and spacing parameters to predict production across the primary developed formations within the Midland Basin.
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The authors of this paper describe a technology built on a causation-based artificial intelligence framework designed to forewarn complex, hard-to-detect state changes in chemical, biological, and geological systems.
