AI/machine learning
The DOE-backed EGS-Twin project aims to simulate geothermal production systems, helping operators better predict performance and maximize output.
This paper explores how artificial intelligence (AI), cognitive models, and integrated digital tools can transform readiness from periodic drills into a continuous, measurable capability.
This paper presents an autonomous, data-driven solution designed specifically for intermittent well optimization.
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The national oil company credits lean operating practices and AI for making the three-well, 45,000 B/D project economically viable. -
From the first supercomputer to generative AI, JPT has followed the advancement of digital technology in the petroleum industry. As the steady march of innovation continues, four experts give their views on the state and future of data science in the industry.
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The act aims to create safeguards around general purpose artificial intelligence, limit the use of biometric identification systems by law enforcement, and ban social scoring the untargeted scraping of facial images from CCTV footage to create facial recognition databases.
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More than two dozen artificial intelligence systems are being celebrated for delivering massive value to the national oil company.
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The company says it has used more than 30 AI tools to unlocked significant value across its full value chain.
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This paper presents an approach using artificial neural networks to predict the discharge pressure of electrical submersible pumps.
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SEG and SPE join forces to offer access to a robust research portal that harnesses the power of AI and ML.
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This article explores the effect of quantum computing on data science and AI, looking at the fundamental concepts of quantum computing and the key terms used in the field. It also covers the challenges that lie ahead for quantum computing and how they can be overcome.
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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.
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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.
