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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Rockwell Automation’s Luis Gamboa explains his company’s new solution designed to allow operators to collect, sort, and reconcile the quality and quantity of data from multiple sources to optimize field data. -
As E&P companies slowly ramp up drilling and production activities while adjusting to a new period of “lower for longer” prices, the time is right for widespread adoption of the IoT.
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Digitization in oil and gas is entering a new era thanks to the increased capabilities and lower cost of huge computing power
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With the availability of more-complex smart-well instrumentation, immediate evaluation of the well response is possible as changes in the reservoir or well occur.
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The complete paper evaluates optimization techniques to develop, or support, business cases for intelligent or smart wells.
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The most-competitive emerging systems blend artificial intelligence to bring better efficiency to the human work that results in good business decisions. As a result, we waste less time and fewer resources finding and manipulating data and focus more on complex engineering judgment.
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An investigator from the US National Energy Technology Laboratory examines the role remotely operated vehicles played in flow rate estimation from the Macondo well.
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A real-time production-surveillance and -optimization system has been developed to integrate available surveillance data with the objective of driving routine production optimization.
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This paper describes how the use of production-modeling tools, coupled with field pressure/temperature-data-acquisition systems and programming software, served as a means to improve production allocation and surveillance on a real-time basis in the Greater Angostura Field.
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The application of high-precision downhole temperature sensors has resulted in pressure-transient analysis (PTA) being complemented or replaced by temperature-transient analysis (TTA).
