AI/machine learning
As carbon capture scales up worldwide, the real challenge lies deep underground—where smart reservoir management determines whether CO₂ stays put for good.
This article is the third in a Q&A series from the SPE Research and Development Technical Section focusing on emerging energy technologies. In this piece, Zikri Bayraktar, a senior machine learning engineer with SLB’s Software Technology and Innovation Center, discusses the expanding use of artificial intelligence in the upstream sector.
This article presents a results-driven case study from an ongoing collaboration between a midstream oil and gas company and Neuralix Inc.
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Declining costs to launch monitoring satellites, as well as artificial intelligence, which makes parsing terabytes of emissions data feasible, have given the oil and gas industry an emerging tool for environmental stewardship. -
The software that the duo is working on aims to optimize and automate the moving of drilling rigs.
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The authors of this paper describe a procedure that enables fast reconstruction of the entire production data set with multiple missing sections in different variables.
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This paper presents an approach to optimize the location of wellhead towers using an algorithm based on multiple parameters related to well cost.
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This paper presents a physics-assisted deep-learning model to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models.
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We must admit that the oil field is still in the early days of its digital journey. It’s time to give serious thought to the expectation/reality gap, the cultural differences between the way we’ve always done things and the way that digital is changing us, and the pain points that may trip us up unless we’re careful.
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Machine learning has been shown to have a promising role in oil and gas explorations in recent years. Among the applications, determining a proper location for injection and production wells along with their optimal operating conditions is a complex problem.
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This article explains what deep learning is and how it works and presents an example use case from the energy industry.
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The agreement will put SLB’s Delfi software to work in Ineos’ oil and gas operations.
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The authors of this paper describe a technology built on a causation-based artificial intelligence framework designed to forewarn complex, hard-to-detect state changes in chemical, biological, and geological systems.
