AI/machine learning
The USGS has said up to 19 million tons of lithium reserves are contained in the briny waters of the Smackover formation in Arkansas.
Subject-matter experts from industry and academia advanced distributed fiber-optic sensing technologies and their implementation in flow measurement during a special session.
This paper investigates the use of machine-learning techniques to forecast drilling-fluid gel strength.
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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. -
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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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.
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The company has announced that it will be expanding the use of generative AI to assist its employees.
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The oil and gas industry is embracing digital technology not just as a differentiator but as an enabler of innovation. The simple reality is that, if one doesn’t, they risk being out of the game.
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AIQ, ADNOC, and SLB announced a new software suite that integrates artificial intelligence into reservoir analysis and field development projects.
