Multiple-point geostatistical simulation based on conditional conduction probability

Cui, Zhesi; Chen, Qiyu; Liu, Gang; Ma, Xiaogang; Que, Xiang

doi:10.1007/s00477-020-01944-4

Cited by 19 publications

(4 citation statements)

References 42 publications

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“…END FOR 12. PS is used to train the CNN model in the way of transfer learning, and obtain the trained neural network model CNN PS 13. Test C with CNN PS to find the best matching training image 5 Lithosphere…”

Section: Deep Convolution Neural Network and Transfermentioning

confidence: 99%

“…Since the first MPS method, extended normal equation simulation (ENESIM) [1], was proposed, MPS has rapidly gained attention in the field of reservoir modeling and has been widely applied in a variety of areas such as modeling fluvial [2][3][4] and deltaic [5] reservoirs, microscopic pore modeling [6,7], and other petroleum-related topics. Until today, several MPS algorithms have been introduced: for example, the probabilistic modeling algorithm via SNESIM Program [8], the pattern similarity matching modeling algorithm represented by SIMPAT [9], the direct sampling modeling algorithm by DS [10], the image quilting modeling algorithm represented by CIQ [11], and other algorithms [12][13][14], as well as the optimization and improvement methods for prediction accuracy, efficiency, memory, and nonstationary problems [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Training Image Optimization Method Based on Convolutional Neural Network and Its Application in Discrete Fracture Network Model Selection

2021

Lithosphere

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Training image (TI) is important for multipoint statistics simulation method (MPS), since it captures the spatial geological pattern of target reservoir to be modeled. Generally, one optimal TI is selected before applying MPS by evaluating the similarities between many TIs and the well interpretations of target reservoir. In this paper, we propose a new training image optimization approach based on the convolutional neural network (CNN). First, candidate TIs were randomly sampled several times to obtain the sample dataset. Then, the CNN was used to conduct transfer learning for all samples, and finally, the optimal TI of the conditioning well data is selected through the trained CNN model. By taking advantage of the strong learning ability of CNN in image feature recognition, the proposed method can automatically identify differences in spatial features between the conditioning well data and the samples of the training image. Hence, it effectively resolves the difficulty of spatial matching between discrete datapoints and grid structures. We demonstrated the applicability of our model via 2D and 3D training image selection examples. The proposed methods effectively selected the appropriate TI, and then the pretreatment techniques for improving the accuracy of continuous TI selection were achieved. Moreover, the proposed method was successfully applied to training image selection of a discrete fracture network model. Finally, sensitivity analysis was carried out to show that sufficient conditioning data volume can reduce the uncertainty of the optimization results. By comparing with the improved MDevD method, the advantages of the new method are verified in terms of efficiency and reliability.

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Section: Deep Convolution Neural Network and Transfermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Training Image Optimization Method Based on Convolutional Neural Network and Its Application in Discrete Fracture Network Model Selection

2021

Lithosphere

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“…In addition to the above‐mentioned methods, multiple‐point geostatistics (MPS) was proposed for modeling heterogeneous structures (Arpat & Caers, 2007; Q. Chen et al., 2020). Over the past few decades, there have been proliferation of researches on both MPS‐based methods and applications (Q. Chen et al., 2018, 2019; Cui, Chen, Liu, Ma, & Que, 2021; G. Liu et al., 2022; Mariethoz et al., 2010). MPS‐based methods can represent high‐order characteristics extracted directly from training images (Mariethoz & Lefebvre, 2014).…”

Section: Introductionmentioning

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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“…However, variograms‐based geostatistical methods are difficult to characterize the spatial heterogeneity of truncated multi‐Gaussian models and object‐based models (Hu & Chugunova, 2008). Several attempts in the context of MPS have been made that can capture and reproduce the high‐order characteristics (Arpat & Caers, 2007; Chen et al., 2019; Cui, Chen, Liu, Ma, & Que, 2021; Mariethoz et al., 2010). Such approaches extract heterogeneous features based on statistical learning from training images to reproduce the distribution of spatial attributes.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Subsurface Hydrogeological Structures With Convolutional Conditional Neural Processes on Limited Training Data

Cui

Chen

2022

Water Resources Research

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The exploration of geological resources and the protection of the environment are driving geoscience research toward the large-scale complex surface and subsurface survey (Bergen et al., 2019). Hydrogeological modeling is one of the most important methods to obtain a heterogeneous and continuous understanding of subsurface hydrological phenomena (Tahmasebi, 2018). There are many cases that confirm the effectiveness of hydrogeological modeling in characterizing subsurface structures in earth sciences (Harken et al., 2019).

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Multiple-point geostatistical simulation based on conditional conduction probability

Cited by 19 publications

References 42 publications

Training Image Optimization Method Based on Convolutional Neural Network and Its Application in Discrete Fracture Network Model Selection

Training Image Optimization Method Based on Convolutional Neural Network and Its Application in Discrete Fracture Network Model Selection

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

Characterization of Subsurface Hydrogeological Structures With Convolutional Conditional Neural Processes on Limited Training Data

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