AI/machine learning
Supervised learning was used to develop an ensemble of models that account for historical production data, geolocation parameters, and completion parameters to forecast production behavior of oil and gas wells.
The combined effort aims to reduce the time necessary for and increase the detail and accuracy of seismic interpretation, including for carbon sequestration studies.
The national oil company credits lean operating practices and AI for making the three-well, 45,000 B/D project economically viable.
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This paper presents an approach for automatic daily-drilling-report classification that incorporates new techniques of artificial intelligence. -
Edge computing is propelling computer vision into a new era, catalyzing the development of smart devices, intelligent systems, and immersive experiences.
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By incorporating AI-powered solutions, companies can tailor wellness plans to cater to the diverse needs of their workforce, fostering a more inclusive and supportive environment.
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The authors of this paper describe a suite of technologies that enables enhanced well robustness and performance modeling and monitoring of carbon storage facilities.
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The authors of this paper describe a project aimed at automating the task of cuttings descriptions with machine-learning and artificial-intelligence techniques, in terms of both lithology identification and quantitative estimation of lithology abundances.
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This paper presents the proof of concept of artificial-intelligence-based well-integrity monitoring for gas lift, natural flow, and water-injector wells.
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The authors of this paper present a machine-learning-based solution that predicts pertinent gas-injection studies from known fluid properties such as fluid composition and black-oil properties.
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Finding and producing crude and natural gas is far, far removed from the days of acting on a geologist’s hunch or a wildcatter’s gut feeling.
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It is imperative for energy companies to assess potential legal ramifications of integrating artificial intelligence into their operations.
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Few oil and gas companies give data science projects the better part of a decade to prove out, but that’s just what this one did.
