Subsurface sedimentary structure identification using deep learning: A review
The reliable identification of subsurface sedimentary structures (ie, geologic heterogeneity)
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …
Data‐worth analysis for heterogeneous subsurface structure identification with a stochastic deep learning framework
Reliable characterization of subsurface structures is essential for earth sciences and related
applications. Data assimilation‐based identification frameworks can reasonably estimate …
applications. Data assimilation‐based identification frameworks can reasonably estimate …
[HTML][HTML] Advancements and perspectives in embedded discrete fracture models (EDFM)
The Embedded Discrete Fracture Model (EDFM) has emerged as a prominent piece of
technology used for embedding the hydraulic behavior of rock joints in reservoir numerical …
technology used for embedding the hydraulic behavior of rock joints in reservoir numerical …
An integrated inversion framework for heterogeneous aquifer structure identification with single-sample generative adversarial network
Generating reasonable heterogeneous aquifer structures is essential for understanding the
physicochemical processes controlling groundwater flow and solute transport better. The …
physicochemical processes controlling groundwater flow and solute transport better. The …
3D CNN-PCA: A deep-learning-based parameterization for complex geomodels
Y Liu, LJ Durlofsky - Computers & Geosciences, 2021 - Elsevier
Geological parameterization enables the representation of geomodels in terms of a
relatively small set of variables. Parameterization is therefore very useful in the context of …
relatively small set of variables. Parameterization is therefore very useful in the context of …
Variational autoencoder or generative adversarial networks? a comparison of two deep learning methods for flow and transport data assimilation
Groundwater modeling is an important tool for water resources management and aquifer
remediation. However, the inherent strong heterogeneity of the subsurface and scarcity of …
remediation. However, the inherent strong heterogeneity of the subsurface and scarcity of …
Predicting thermal performance of an enhanced geothermal system from tracer tests in a data assimilation framework
Predicting the thermal performance of an enhanced geothermal system (EGS) requires a
comprehensive characterization of the underlying fracture flow patterns from practically …
comprehensive characterization of the underlying fracture flow patterns from practically …
Machine learning assisted history matching for a deepwater lobe system
High exploration costs resulting in sparse datasets and complicated geological structures in
deepwater depositional systems make the reservoir characterization extremely difficult. To …
deepwater depositional systems make the reservoir characterization extremely difficult. To …
Hydrogeophysical characterization of nonstationary DNAPL source zones by integrating a convolutional variational autoencoder and ensemble smoother
Detailed characterization of dense nonaqueous phase liquid (DNAPL) source zone
architecture (SZA) is essential for designing efficient remediation strategies. However, it is …
architecture (SZA) is essential for designing efficient remediation strategies. However, it is …
Improved history matching of channelized reservoirs using a novel deep learning-based parametrization method
R Yousefzadeh, M Ahmadi - Geoenergy Science and Engineering, 2023 - Elsevier
Most of the geological parametrization techniques used in history matching of sub-surface
formations including the deep learning-based methods could not capture the non-linear and …
formations including the deep learning-based methods could not capture the non-linear and …