Formation evaluation
The authors of this paper propose a hybrid approach that combines physics with data-driven approaches for efficient and accurate forecasting of the performance of unconventional wells under codevelopment.
This paper develops a deep-learning work flow that can predict the changes in carbon dioxide mineralization over time and space in saline aquifers, offering a more-efficient approach compared with traditional physics-based simulations.
The authors of this paper propose an artificial-intelligence-assisted work flow that uses machine-learning techniques to identify sweet spots in carbonate reservoirs.
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The authors of this paper aim to identify the effect of various geologic controls on hydrocarbon maturation in the Delaware Basin by restoring regional cross sections and performing simulations of organic-matter maturity. -
The goal of this paper is to improve the understanding of uncertainties affecting well performance and reservoir connectivity in an offshore Malaysian field.
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A critical challenge for those designing carbon dioxide storage sites is predicting where the injected gas will go. One of the only sure bets is to assume that any model of a gas plume that looks symmetrical is likely wrong.
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This paper provides a work flow based on research in two methodologies to estimate formation-water salinity, enhancing the quality of saturation evaluation for quick decision-making during logging operations.
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This paper presents a mathematical analysis of how incorrect estimates of initial reservoir pressure may affect rate-transient analysis in ultralow-permeability reservoirs.
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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 describe a logging-while-drilling nuclear-magnetic-resonance method applied in Bohai Bay, China, as an alternative to radioactive source-porosity measurements.
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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.
