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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The complete paper describes some of the technical challenges faced in deepwater operations and the methodology adopted for implementing an integrated production surveillance and optimization (IPSO) system to mitigate the risks. -
Statoil’s integrated operations center on the Norwegian continental shelf is one of several initiatives operators and service companies have set in motion to improve condition monitoring and maximize production on their assets.
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There are two ongoing trends in the industry. The first is an increasing wave of digitization. Second, piggybacking on the increased access to sensor and equipment data, analytics and artificial intelligence are on an exponential rise in the industry.
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The papers selected for this issue cover advances and opportunities in well testing. They also apply reservoir fundamentals as well as sound engineering judgment, using quantity but also quality data sets from conventional and unconventional assets.
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The adoption of emerging technologies is essential for all businesses, and even more important for well-established industries because of the perpetual threat of disruption from competitors.
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When you think about the Internet of Things, do you think about the extensive global subsea communications network?
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The digitalization of oil and gas facilities is becoming a new technical arena. Effective solutions can be used to convert data into information and knowledge, which can then be used to improve maintenance operations.
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With only about 3% of data from industrial assets used for decision making or meaningful purpose, vessel, rig, and fleet owners are looking at ways to connect existing networks and assets to achieve optimized operational performance.
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Supervisory control and data acquisition systems no longer simply monitor operations and produce large volumes of data in static displays, but now collect production data from all operation data sources and contextualize and present them to workers in real time as meaningful, actionable information.
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Sensor systems for pipeline inspections from Ingu and Rheidiant are among the initial selections to receive funding under Chevron’s CTV Catalyst Program.
