Reservoir characterization
The opening ceremony highlighted maximizing production sustainably to meet global demand, integration of simulation and optimization in a single platform with automation, and energy security.
Large geological models are needed for modeling the subsurface processes in geothermal, carbon-storage, and hydrocarbon reservoirs. The size of these models contributes to the computational cost of history matching, engineering optimization, and forecasting. To reduce this cost, low-dimensional representations need to be extracted. Deep-learning tools, such as autoenc…
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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This work evaluates and compares the performance of rate normalization and pressure deconvolution for both synthetic and tight-oil examples. -
The authors describe an approach to achieve reliable estimation of field gas initially in place.
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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.
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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.
