Multi-condition controlled sedimentary facies modeling based on generative adversarial network

Hu, Fei; Wu, Chen; Shang, Jiangwei; Yan, Yiming; Wang, Leiquan; Zhang, Huan

doi:10.1016/j.cageo.2022.105290

Cited by 9 publications

(3 citation statements)

References 28 publications

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“…We plan to integrate other conditioning data such as probability maps, geophysical interpretations, etc. (Hu et al., 2022; Song et al., 2021; Song, Mukerji, & Hou, 2022; Song, Mukerji, Hou, Zhang, & Lyu, 2022). It can improve the usability of SA‐RelayGANs and achieve better applications in real‐world scenarios.…”

Section: Discussionmentioning

confidence: 99%

SA‐RelayGANs: A Novel Framework for the Characterization of Complex Hydrological Structures Based on GANs and Self‐Attention Mechanism

Cui,

Chen,

Liu

et al. 2024

Water Resources Research

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Increased availability and quality of surface and subsurface observations provide the chance to improve the perception of hydrological phenomena. An important factor that hinders our understanding of hydrological structures is the characterization of heterogeneous patterns with continuous attributes inside the structures (e.g., porosity, permeability, fluid saturation, etc.). Unlike categorical attributes, continuous attributes convey more realistic characteristics but require more computational resources to characterize such complex earth systems. In this work, we propose a novel deep learning approach for the characterization of complex hydrological realism with continuous attributes based on generative adversarial networks (GANs) and self‐attention mechanism, named SA‐RelayGANs. To address the complexity of heterogeneous hydrological structures, we divide the modeling process into two stages: facies construction stage and property reconstruction stage. In the first stage, we employ an improved GAN with self‐attention mechanism to construct the heterogeneous structures while adhering to hard conditioning constraints. In the second stage, we utilize another GAN with an attribute enhancement term to reconstruct realizations based on the constructed structures and observations. SA‐RelayGANs can successfully predict the statistical distributions of heterogeneous structures with continuous attributes by using limited observations. This study highlights the effectiveness of using GANs to characterize the heterogeneous patterns of hydrological realism and the application over large geoscience fields.

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Section: Discussionmentioning

confidence: 99%

SA‐RelayGANs: A Novel Framework for the Characterization of Complex Hydrological Structures Based on GANs and Self‐Attention Mechanism

Cui,

Chen,

Liu

et al. 2024

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…They capture the spatial and geological patterns using 2D sections from the 3D volume as conditioning data and check the quality of their results using facies histograms and semivariograms. Similarly, [27] train a conditional GAN with realizations conditioned to hard data, conditioned with numerical codes representing geological properties, that impose geological characteristics of a river, like orientation and type (e.g., meandering and braided). [37] propose shift-invariant convolutional layers in the PatchGAN model to avoid inconsistency and artifacts in generated subsurface geological models.…”

Section: Gan For Facies Modelingmentioning

confidence: 99%

“…Unlike the neural networks for the optimization-based MPS, the generative adversarial networks (GAN) are based on adversarial training of the generator and the discriminator, in other words the objective function based on the discriminator is learned [24]. GANs have been widely explored for subsurface applications, e.g., the stochastic reconstruction of porous media [25], seismic wave discrimination for earthquake warning [26], and reservoir modeling [27][28][29][30] Prior to their implementation for supporting subsurface development decisionmaking, the evaluation and checking of subsurface models is crucial. The works mentioned above lean towards application-oriented objectives and lack an enhanced assessment of the reproduction of nonstationary structures.…”

Section: Introductionmentioning

confidence: 99%

Minimum Acceptance Criteria for Subsurface Scenario-based Uncertainty Models from Single Image Generative Adversarial Networks (SinGAN)

Liu,

Salazar,

et al. 2024

Preprint

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Evaluating and checking subsurface models is essential before their use to support optimum subsurface development decision making. Conventional geostatistical modeling workflows (e.g., two-point variogram-based geostatistics and multiple-point statistics) may fail to reproduce complex realistic geological patterns (e.g., channels), or be constrained by the limited training images and computational cost. Deep learning, specifically generative adversarial network (GAN), has been applied for subsurface modeling due to its ability to reproduce spatial and geological patterns, but may fail to reproduce commonly observed nonstationary subsurface patterns and often rely on many training images with the inability to explore realizations around specific geological scenarios. We propose an enhanced model checking workflow demonstrated by evaluating the performance of single image GAN (SinGAN)-based 2D image realizations for the case of channelized subsurface reservoirs to support robust uncertainty around geological scenarios. The SinGAN is able to generate nonstationary realizations from a single training image. Our minimum acceptance criteria expand on the work of Leuangthong, Boisvert, and others tailored to the nonstationary, single training image approach of SinGAN by evaluating the facies proportion, spatial continuity, and multiple-point statistics through histogram, semivariogram, and n-point histogram, along with evaluating the nonstationarity reproduction through multiple distribution checks ranging from local scale pixel distribution to multiscale local distribution. Additionally, our workflow incorporates reduced-dimensionality analysis through self-attention, providing a flexible approach for deep learning-based enhanced model realization to single training image comparison. With our proposed workflows, the robust application of SinGAN is possible to explore uncertainty around geological scenarios.

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Deep multimodal fusion for 3D mineral prospectivity modeling: Integration of geological models and simulation data via canonical-correlated joint fusion networks

Zheng,

Deng,

et al. 2024

Computers & Geosciences

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Multi-condition controlled sedimentary facies modeling based on generative adversarial network

Cited by 9 publications

References 28 publications

SA‐RelayGANs: A Novel Framework for the Characterization of Complex Hydrological Structures Based on GANs and Self‐Attention Mechanism

SA‐RelayGANs: A Novel Framework for the Characterization of Complex Hydrological Structures Based on GANs and Self‐Attention Mechanism

Minimum Acceptance Criteria for Subsurface Scenario-based Uncertainty Models from Single Image Generative Adversarial Networks (SinGAN)

Deep multimodal fusion for 3D mineral prospectivity modeling: Integration of geological models and simulation data via canonical-correlated joint fusion networks

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