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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Researchers borrowed equations from calculus to redesign the core machinery of deep learning so it can model continuous processes like changes in health. -
An increasingly buzzy term tossed around at industry events, “digital twin” is leveraging data analytics, machine learning, and artificial intelligence to improve efficiencies from design to decommissioning.
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As shale plays are becoming economically viable, operators have fast-adopted best practices to optimize drilling and completion processes to drive down the lifting costs. Adoption of data-driven analytics to improve completion design, drive efficiency, and yield economic gains has been less swift.
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Digitalization in the oil and gas industry has been the focus of much discussion, but little has been written on the slow rate of adoption. This paper outlines some of the barriers the industry faces as it assimilates into Industry 4.0—automation and data integration in manufacturing.
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BHGE is developing an analytics and machine-learning approach that offers descriptive and predictive insights on frac hits, with the aim of eventually offering a real-time monitoring capability to be deployed during frac jobs.
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The international major has been playing with intelligent programs for years, but this new deal shows that it is now ready to scale those efforts up to cover hundreds of thousands of pieces of equipment.
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This paper demonstrates the viability of a production-data-classification approach adapted from real-time face detection for identifying restimulation candidates.
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BP has invested more than $100 million into nine different startup companies in the past 2 years—but only one of them wants to turn your brain into a piece of its software.
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A new detection and alerting methodology, validated on more than 100 North America onshore wells, blends well information and real-time data to determine a probabilistic belief system. An operator used the system to detect, predict, and alert rig crews to washouts and pump failures.
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Unstructured data, such as process logs, safety reports, and public records, make up the bulk of data produced from the oil field. Emerging NLP technology has been designed to help make sense of this data, enabling better insights into near-accidents.
