Research Status of and Trends in 3D Geological Property Modeling Methods: A Review

Liu, Yuyang; Zhang, Xiaowei; Guo, Wei; Kang, Lixia; Gao, Jinliang; Yu, Rong; Sun, Yang; Mao, Pan

doi:10.3390/app12115648

Cited by 10 publications

(2 citation statements)

References 68 publications

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“…Deep learning approaches have been applied for optimization-based MPS, for example, neural networks calculate realizations (also called fake images or generated images in deep learning) from a trained model to output local conditional probabilities [22,23]. Additionally, a recursive convolutional neural network calculates MPS realizations assuming stationarity by predicting the multiple-point conditional distributions of each node in the feature maps [15].…”

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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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

View full text Add to dashboard Cite

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“…[1][2][3][4][5][6][7][8][9][10][11]. However, there are still some problems, such as the small sample size, which cannot meet the requirements of deep learning, the difficulty of algorithm exploration, and application [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Research on the Construction Method of a Training Image Library Based on cDCGAN

Yao,

Liu,

Pan

2023

Applied Sciences

Self Cite

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There is a close relationship between the size and property of a reservoir and the production and capacity. Therefore, in the process of oil and gas field exploration and development, it is of great importance to study the macro distribution of oil–gas reservoirs, the inner structure, the distribution of reservoir parameters, and the dynamic variation of reservoir characteristics. A reservoir model is an important bridge between first-hand geologic data and other results such as ground stress models and fracture models, and the quality of the model can influence the evaluation of the sweet spots, the deployment of a horizontal well, and the optimization of the well network. Reservoir facies modeling and physical parameter modeling are the key points in reservoir characterization and modeling. Deep learning, as an artificial intelligence method, has been shown to be a powerful tool in many fields, such as data fusion, feature extraction, pattern recognition, and nonlinear fitting. Thus, deep learning can be used to characterize the reservoir features in 3D space. In recent years, there have been increasing attempts to apply deep learning in the oil and gas industry, and many scholars have made attempts in logging interpretation, seismic processing and interpretation, geological modeling, and petroleum engineering. Traditional training image construction methods have drawbacks such as low construction efficiency and limited types of sedimentary facies. For this purpose, some of the problems of the current reservoir facies modeling are solved in this paper. This study constructs a method that can quickly generate multiple types of sedimentary facies training images based on deep learning. Based on the features and merits of all kinds of deep learning methods, this paper makes some improvements and optimizations to the conventional reservoir facies modeling. The main outcomes of this thesis are as follows: (a) the construction of a training image library for reservoir facies modeling is realized. (b) the concept model of the typical sedimentary facies domain is used as a key constraint in the training image library. In order to construct a conditional convolutional adversarial network model, One-Hot and Distributed Representation is used to label the dataset. (c) The method is verified and tested with typical sedimentary facies types such as fluvial and delta. The results show that this method can generate six kinds of non-homogeneous and homogeneous training images that are almost identical to the target sedimentary facies in terms of generation quality. In terms of generating result formats, compared to the cDCGAN training image generation method, traditional methods took 31.5 and 9 times longer. In terms of generating result formats, cDCGAN can generate more formats than traditional methods. Furthermore, the method can store and rapidly generate the training image library of the typical sedimentary facies model of various types and styles in terms of generation efficiency.

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Applying 3D geological modeling to predict favorable areas for coalbed methane accumulation: a case study in the Qinshui Basin

Yang,

Tang,

Zhang

et al. 2024

Front. Earth Sci.

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Research Status of and Trends in 3D Geological Property Modeling Methods: A Review

Cited by 10 publications

References 68 publications

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

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

Research on the Construction Method of a Training Image Library Based on cDCGAN

Applying 3D geological modeling to predict favorable areas for coalbed methane accumulation: a case study in the Qinshui Basin

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