Digital Oil Field
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 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.
This paper introduces in-pipe inspection technologies enabling high-resolution digital measurements of tubular internal diameter and wall thickness for critical downhole applications.
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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. -
The paper presents the design and successful field deployment of the first closed-loop hydraulic fracturing program.
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The authors describe a study on key technologies for intelligent risk monitoring of workover operations.
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The authors write that by replacing outdated, labor-intensive processes with an integrated, cloud-based platform, companies can streamline planning, improve accuracy, and foster better coordination across teams and vendors.
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The authors write that deployment of artificial-intelligence-based high-gas/oil ratio well-control technology enabled stabilization of well performance and maintenance of optimal production conditions.
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This paper presents the first global application of autonomous drilling in deepwater and the journey to reach optimal drilling parameters, integrating proprietary tools from the project’s business partners.
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The paper describes the revalidation of a deepwater prospect that resulted in a no-drill decision.
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The oil and gas industry is undergoing a significant shift with the advent of intelligent operations. This transformation is enabling upstream operations to move away from a reactive and manual mode of operation toward a more efficient, safe, and optimal state of operation.
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In this paper, a case study is described in which a software solution enabled prescriptive optimization of well delivery using a physics-informed machine-learning approach for predictive identification and characterization of well-construction risks.
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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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