Reservoir
The company credited its theory of shale oil enrichment for the significant increase in the quantity of proven reserves at the field.
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.
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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This paper aims to present thoroughly the application of subsurface safety injection valves in extremely high-temperature environments. -
In this study, a deep-neural-network-based workflow with enhanced efficiency and scalability is developed for solving complex history-matching problems.
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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.
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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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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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