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Effectiveness of supercritical-CO2 and N2 huff-and-puff methods of enhanced oil recovery in shale fracture networks using microfluidic experiments

Applied energy, 2018-11, Vol.230 (C), p.160-174

2018

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Nanoscale simulation of shale transport properties using the lattice Boltzmann method: permeability and diffusivity

Scientific reports, 2015-01, Vol.5 (1), p.8089-8089, Article 8089

2015

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The shale gas revolution: Barriers, sustainability, and emerging opportunities

Applied energy, 2017-08, Vol.199 (C), p.88-95

2017

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Shale gas and non-aqueous fracturing fluids: Opportunities and challenges for supercritical CO2

Applied energy, 2015-06, Vol.147, p.500-509

2015

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Hydraulic fracturing fluid migration in the subsurface: A review and expanded modeling results

Water resources research, 2015-09, Vol.51 (9), p.7159-7188

2015

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Pore-scale study of dissolution-induced changes in permeability and porosity of porous media

Journal of hydrology (Amsterdam), 2014-09, Vol.517, p.1049-1055

2014

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Inertial Effects During the Process of Supercritical CO2 Displacing Brine in a Sandstone: Lattice Boltzmann Simulations Based on the Continuum‐Surface‐Force and Geometrical Wetting Models

Water resources research, 2019-12, Vol.55 (12), p.11144-11165

2019

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dfnWorks: A discrete fracture network framework for modeling subsurface flow and transport

Computers & geosciences, 2015-11, Vol.84 (C), p.10-19

2015

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The Effect of Wettability Heterogeneity on Relative Permeability of Two‐Phase Flow in Porous Media: A Lattice Boltzmann Study

Water resources research, 2018-02, Vol.54 (2), p.1295-1311

2018

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Quantum computing and preconditioners for hydrological linear systems

Scientific reports, 2022-12, Vol.12 (1), p.22285-22285, Article 22285

2022

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Permeability prediction of shale matrix reconstructed using the elementary building block model

Fuel (Guildford), 2015-11, Vol.160, p.346-356

2015

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Progressive transfer learning for advancing machine learning-based reduced-order modeling

Scientific reports, 2024-07, Vol.14 (1), p.15731-13, Article 15731

2024

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Enhancing high-fidelity nonlinear solver with reduced order model

Scientific reports, 2022-11, Vol.12 (1), p.20229-15, Article 20229

2022

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Model reduction for fractured porous media: a machine learning approach for identifying main flow pathways

Computational geosciences, 2019-06, Vol.23 (3), p.617-629

2019

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Computationally Efficient Multiscale Neural Networks Applied to Fluid Flow in Complex 3D Porous Media

Transport in porous media, 2021-10, Vol.140 (1), p.241-272

2021

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Effect of advective flow in fractures and matrix diffusion on natural gas production

Water resources research, 2015-10, Vol.51 (10), p.8646-8657

2015

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Deep Learning-Based quantifications of methane emissions with field applications

International journal of applied earth observation and geoinformation, 2024-08, Vol.132, p.104018, Article 104018

2024

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Evaluating the effect of internal aperture variability on transport in kilometer scale discrete fracture networks

Advances in water resources, 2016-08, Vol.94, p.486-497

2016

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Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management

Scientific reports, 2022-11, Vol.12 (1), p.18734-12, Article 18734

2022

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Molecular-Scale Considerations of Enhanced Oil Recovery in Shale

Energies (Basel), 2020-12, Vol.13 (24), p.6619

2020

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Pore-scale study of dissolution-induced changes in hydrologic properties of rocks with binary minerals

Water resources research, 2014-12, Vol.50 (12), p.9343-9365

2014

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A machine learning framework for rapid forecasting and history matching in unconventional reservoirs

Scientific reports, 2021-11, Vol.11 (1), p.21730-21730, Article 21730

2021

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A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

Journal of computational physics, 2021-10, Vol.443, p.110526, Article 110526

2021

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A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks

Nature Computational Science, 2021-12, Vol.1 (12), p.819-829

2021

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Legacy Well Leakage Risk Analysis at the Farnsworth Unit Site

Energies (Basel), 2023-09, Vol.16 (18), p.6437

2023

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Effectiveness of supercritical-CO2 and N2 huff-and-puff methods of enhanced oil recovery in shale fracture networks using microfluidic experiments

Nanoscale simulation of shale transport properties using the lattice Boltzmann method: permeability and diffusivity

The shale gas revolution: Barriers, sustainability, and emerging opportunities

Shale gas and non-aqueous fracturing fluids: Opportunities and challenges for supercritical CO2

Hydraulic fracturing fluid migration in the subsurface: A review and expanded modeling results

Pore-scale study of dissolution-induced changes in permeability and porosity of porous media

Inertial Effects During the Process of Supercritical CO2 Displacing Brine in a Sandstone: Lattice Boltzmann Simulations Based on the Continuum‐Surface‐Force and Geometrical Wetting Models

dfnWorks: A discrete fracture network framework for modeling subsurface flow and transport

The Effect of Wettability Heterogeneity on Relative Permeability of Two‐Phase Flow in Porous Media: A Lattice Boltzmann Study

Quantum computing and preconditioners for hydrological linear systems

Permeability prediction of shale matrix reconstructed using the elementary building block model

Progressive transfer learning for advancing machine learning-based reduced-order modeling

Enhancing high-fidelity nonlinear solver with reduced order model

Model reduction for fractured porous media: a machine learning approach for identifying main flow pathways

Computationally Efficient Multiscale Neural Networks Applied to Fluid Flow in Complex 3D Porous Media

Effect of advective flow in fractures and matrix diffusion on natural gas production

Deep Learning-Based quantifications of methane emissions with field applications

Evaluating the effect of internal aperture variability on transport in kilometer scale discrete fracture networks

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management

Molecular-Scale Considerations of Enhanced Oil Recovery in Shale

Pore-scale study of dissolution-induced changes in hydrologic properties of rocks with binary minerals

A machine learning framework for rapid forecasting and history matching in unconventional reservoirs

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks

Legacy Well Leakage Risk Analysis at the Farnsworth Unit Site

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