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 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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Adoption of digital technologies will continue to improve the offshore sector, including improved well efficiency, real-time directional drilling, lower maintenance costs, and safer operations. -
The authors of this paper propose a novel work flow for the problem of building intelligent data analytics in heavy-oil fields.
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This paper presents an analytics solution for identifying rod-pump failure capable of automated dynacard recognition at the wellhead that uses an ensemble of ML models.
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As you read the examples in this section, you will see that a change is already under way in that the methods that are being used are increasingly not oil-and-gas-specific but instead follow patterns that are being used in other industries.
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This digital deal is helping to make augmented reality a new reality for oil and gas operations.
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The operator piloted a new well-completion design combining inflow-control valves (ICVs) in the shallow reservoir and inflow-control devices (ICDs) in the deeper reservoir, both deployed in a water-injector well for the first time in the company’s experience.
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The two technology startups aim to bring scale to the visual side of oilfield automation with a new deal that will cover 90% of US energy assets.
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The company plans an innovative application of oceanographic instrumentation to maximize recovery at its Johan Sverdrup oil field in the North Sea.
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The need to understand the future trends of the oil industry has never been greater than it is today.
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In this study, the authors investigated a fully data-driven approach using artificial neural networks (ANNs) for real-time virtual flowmetering and back-allocation in production wells.
