Reservoir
The company credited its theory of shale oil enrichment for the significant increase in the quantity of proven reserves at the field.
In this study, a deep-neural-network-based workflow with enhanced efficiency and scalability is developed for solving complex history-matching problems.
This work investigates the root cause of strong oil/water emulsion and if sludge formation is occurring within the reservoir using a robust integrated approach.
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This study presents a production-optimization method that uses a deep-learning-based proxy model for the prediction of state variables and well outputs to solve nonlinearly constrained optimization with geological uncertainty. -
In this work, a perturbed-chain statistical associating fluid theory equation of state has been developed to characterize heavy-oil-associated systems containing polar components and nonpolar components with respect to phase behavior and physical properties.
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The paper describes a parameter inversion of reservoirs based on featured points, using a semi-iterative well-test-curve-matching approach that addresses problems of imbalanced inversion accuracy and efficiency.
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This paper aims to present thoroughly the application of subsurface safety injection valves in extremely high-temperature environments.
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These three papers leverage machine learning and hybrid methods to tackle challenges in forecasting, optimization, and reservoir characterization.
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Advanced tracer technology was deployed in Oklahoma to analyze production across lateral well sections.
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The era of refracs as mere experiments is long gone; today, they’re a strategic necessity fueling portfolio growth.
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After 5 years of in-depth diagnostic research, the Oklahoma City-based operator shares more insights on fracture behavior.
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The first phase of the Norwegian project is expected to receive its first carbon dioxide this year, with the second phase slated to start operations in late 2028.
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This paper introduces a novel optimization framework to address CO2 injection strategies under geomechanical risks using a Fourier neural operator-based deep-learning model.
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