Data mining/analysis
A roundtable discussion during CERAWeek pointed to the necessity of a mindset shift for the oil and gas industry to tap into AI’s true potential.
Technology and partnerships play a pivotal role in how the oil industry finds and produces energy from frontier regions and brownfields, both now and in the future.
The authors make the case that data science captures value in well construction when data-analysis methods, such as machine learning, are underpinned by first principles derived from physics and engineering and supported by deep domain expertise.
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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. -
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.
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The authors write that the application of electrical parameters to diagnose working conditions and meter production of pumping wells is the key to the low-cost Internet-of-Things construction.
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This paper describes the application of a synthetic seismic-catalog-generation method followed by application of a neural network on a seismic data set for an oil-producing field in the North Sea.
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A supervised machine-learning algorithm is developed to classify drilling parameters that increase rate of penetration and bit endurance for use in unconventional fields in Australia.
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This paper describes how severe rig limitations were overcome through an optimization plan in which an optimal bottomhole assembly was designed and drilling practices were customized.
