Data mining/analysis
This paper presents a robust workflow to identify optimization opportunities in gas lift wells through real-time data analysis and a surveillance-by-exception methodology.
This paper describes a data-driven well-management strategy that optimizes condensate recovery while preserving well productivity.
This paper explores the evolving role of the digital petroleum engineer, examines the core technologies they use, assesses the challenges they face, and projects future industry trends.
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The complete paper describes a combination of best practices and innovative techniques that help to provide rig-based and rigless opportunities by estimating potential and risk in a naturally fractured reservoir. -
The authors of this paper describe the development and implementation of a data-streaming solution that allows for real-time processing and interpretation of fiber-optics data.
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The authors of this paper present a system, developed to achieve data transmission using a time-division approach, that includes sealed chambers for microchip storage, a power-release device, and a circuit.
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This paper presents agile technologies that integrate data management, data-quality assessment, and predictive machine learning to maximize asset value using underused legacy core data.
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The authors write that child-well performance increases with spacing and decreases with infill timing and that the parent cumulative production at child-well completion is an effective indicator of child-well performance.
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Digital systems are helping ensure US independent Diversified Energy continues to grow and optimize production from its mature assets.
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This paper evaluates learnings from the past 30 years of methods that aim to quantify the uncertainty in the subsurface using multiple realizations, describing major challenges and outlining potential ways to overcome them.
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Cryptocurrency is not the only game in town when it comes to using natural gas at the wellhead to reduce flaring. There are self-driving cars, the coming “metaverse,” language processing, chat bots, and more, all of which require advanced computing and a lot of energy. The demand is driving an expansion of services for Crusoe Energy.
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This paper presents a screenout-classification system based on Gaussian hidden Markov models that predicts screenouts and provides early warning.
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The two companies have teamed up in an attempt to cut downhole costs with a project that aims to extract more information from reduced data-acquisition programs.
