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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Registration is open for the SPE Europe Energy GeoHackathon, which will be held in October and November. It will be preceded by 4-week online bootcamp sessions on data science and geothermal energy, which will begin on 2 October. -
This article presents the application of a reinforcement learning control framework based on the Deep Deterministic Policy Gradient. The crack propagation process is simulated in Abaqus, which is integrated with a reinforcement learning environment to control crack propagation in brittle material. The real-world deployment of the proposed control framework is also dis…
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SPE and Project Innerspace are organizing the first Geothermal AInnovation Competition. Teams from around the world are invited to participate in this virtual competition aimed at showcasing the potential of AI-assisted work flows in the geothermal life cycle.
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This paper describes a work flow that integrates data analysis, machine learning, and artificial intelligence to unlock the potential of large relative permeability databases.
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The authors of this paper describe a solution using machine-learning techniques to predict sandstone distribution and, to some extent, automate the process of optimizing well placement.
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Artificial intelligence (AI) tools have been used in geological survey methods for many years. Gaining insight into the scale and trends of this implementation could assist surveyors in making informed decisions about buying or developing new technologies.
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Oil and gas operators such as Shell and Oxy are now employing AI together with a vast network of sensors and other machine-learning software to stamp out problems before they happen.
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The artificial intelligence technology is expected to increase understanding of subsurface structures.
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While the industry is adopting the technology, one expert highlights areas where the oil and gas sector could speed up the adoption of artificial intelligence.
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SponsoredThis Q&A highlights the benefits of AI and ML to automate work flows and analyze data at a much faster rate—within minutes. These capabilities deliver a fit-for-basin approach designed specifically for US-centric work flows.
