Data mining/analysis
A self-updating and customizable data-driven strategy for real-time monitoring and management of screenout, integrated with proppant filling index and safest fracturing pump rate, is proposed to improve operational safety and efficiency at field scale.
This paper addresses the challenges of integrating huge amounts of data and developing model frameworks and systematic workflows to identify opportunities for production enhancement by choosing the best candidate wells.
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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This paper describes how a surveillance, analysis, and optimization plan was used to resolve subsurface uncertainties and optimize a reservoir development plan and provides lessons learned and best practices. -
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.
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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.
