Reservoir simulation
In this study, a deep-neural-network-based workflow with enhanced efficiency and scalability is developed for solving complex history-matching problems.
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. -
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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The authors introduce a novel framework combining dynamic mode decomposition, a data-driven model-reduction technique, with direct data assimilation to streamline the calibration of carbon-dioxide plume evolution models.
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This paper discusses the concept, applications, and continual evolution of a new 3D temperature and spectral-acoustics modeling and logging approach.
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A detailed comparison of two leading software platforms demonstrates the effective application of these platforms in modeling complex reservoir dynamics and biochemical reactions in geological formations for risk assessment in underground hydrogen storage.
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The main goal of this research work was to determine subseismic faults and fracture corridors and their characteristics, including density and orientation, for a Paleocene fractured carbonate reservoir.
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In this paper, an energy-based 3D fracture-reconstruction method is proposed to derive the complex fracture network from microseismic data in a shale gas reservoir.
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A numerical simulation study based on experimental data of 2D and 3D models is presented to examine immiscible fingering during field-scale polymer-enhanced oil recovery.
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The authors present an open-source framework for the development and evaluation of machine-learning-assisted data-driven models of CO₂ enhanced oil recovery processes to predict oil production and CO₂ retention.
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The authors of this paper propose hybrid models, combining machine learning and a physics-based approach, for rapid production forecasting and reservoir-connectivity characterization using routine injection or production and pressure data.
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