AI/machine learning
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 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.
This work uses a novel pseudosteady-state-based simulation to reduce training-data-generation cost while maintaining high-performance predictions of data-driven proxy models for carbon-sequestration projects.
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In this third work in a series, the authors conduct transfer-learning validation with a robust real-field data set for hydraulic fracturing design. -
This research aims to develop a fluid-advisory system that provides recommendations for optimal amounts of chemical additives needed to maintain desired fluid properties in various drilling-fluid systems.
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Industry experts dissected the challenges in deploying artificial intelligence across the energy sector during a special session at SPE’s Annual Technical Conference and Exhibition.
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AI is transforming the field of cybersecurity, offering new possibilities and challenges for both defenders and attackers, but AI also can introduce new vulnerabilities and risks and raise new ethical, legal, and social issues for cybersecurity.
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This paper presents a case study highlighting the demonstration, refinement, and implementation of a machine-learning algorithm to optimize multiple electrical-submersible-pump wells in the Permian Basin.
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This paper presents a closed-loop iterative well-by-well gas lift optimization workflow deployed to more than 1,300 operator wells in the Permian Basin.
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This paper explores the use of machine learning in predicting pump statuses, offering probabilistic assessments for each dynacard, automating real-time analysis, and facilitating early detection of pump damage.
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This paper focuses on developing a model that can be used in an automated, end-to-end flare-smoke detection, alert, and distribution-control solution that leverages existing flare closed-circuit television cameras at manufacturing facilities.
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This paper describes an experimentation trial deploying and operating a computer-vision system on a deepwater rig to measure drilled cuttings in real time using a remotely monitored camera system.
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Real-time wellhead monitoring aims to help Romania meet new EU methane emission regulations.
