AI/machine learning
The deals, with a potential value exceeding $3.7 billion, are aimed at strengthening Saudi Aramco’s supply chains and expanding its use of industrial artificial intelligence.
This work applies fuzzy logic, a well-established artificial-intelligence technique, to quantitatively assess connectivity between injectors and producers in a giant presalt field in the Santos Basin of Brazil.
The DOE-backed EGS-Twin project aims to simulate geothermal production systems, helping operators better predict performance and maximize output.
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The authors of this paper propose an artificial-intelligence-assisted work flow that uses machine-learning techniques to identify sweet spots in carbonate reservoirs. -
This paper presents an approach for automatic daily-drilling-report classification that incorporates new techniques of artificial intelligence.
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The authors of this paper present the results of implementing a rig-automation solution applied to 20 wells in Ecuador in 2022.
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This paper describes a method with multitiered analysis to leverage machine-learning techniques to process passive seismic monitoring data, pumping and injection pressure, and rate for fracture and fault analysis.
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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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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 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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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.
