Reservoir characterization
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.
The authors of this paper use a pattern-review technique in a complex brownfield as a tool to understand reservoir connectivity and dynamic fluid movements across the field.
The authors of this paper describe a fiber-optic diagnostic method based on Rayleigh frequency shift that provides new insights about the characteristics of the near-wellbore region during production.
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The COVID-19 pandemic naturally has affected SPE meetings, causing many to be rescheduled or postponed indefinitely, but SPE papers continue to be a crucial source of technical knowledge. The selected papers explore simple and complex innovative approaches toward reservoir characterization to work around the absence of certain data. -
The authors describe an integrated multiscale data methodology involving machine-leaning tools applied to the Late Jurassic Upper Jubaila formation outcrop data.
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The authors develop a collocated finite-volume method to study induced seismicity as a result of pore-pressure fluctuations.
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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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This paper describes an integrated work flow developed for 3D seismic reservoir characterization of deep and thin layers without sufficient well data in a South China Sea formation.
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The author writes that careful calibration of a common, simple rock physics model can provide valuable insights into reservoir and seal elastic properties.
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Two well-test logging operations have been carried out for the first time in a conventional carbonate reservoir in safe operating conditions and with repeatable results.
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The past couple of years has been a rollercoaster for energy professionals, but it did not stop the incredible achievements in machine-learning (ML) techniques, particularly neural networks to improve seismic imaging.
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SponsoredImprove decision making and reduce the uncertainty of CCS projects with accurate, detailed subsurface insights that help you estimate storage capacity, run play chance mapping, improve your injection strategy, and simulate carbon plumes over time.
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The paper demonstrates the ability of deep-learning generative models to enable new shale-characterization methods.
