Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks

Abstract This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models. It can reproduce a wide range of conceptual geological models while possessing the flexibility necessary to honor constraints such as well data. Compared with...

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Autores principales:	Tuan-Feng Zhang, Peter Tilke, Emilien Dupont, Ling-Chen Zhu, Lin Liang, William Bailey
Formato:	article
Lenguaje:	EN
Publicado:	KeAi Communications Co., Ltd. 2019
Materias:	Geological facies Geomodeling Data conditioning Generative adversarial networks Science Q Petrology QE420-499
Acceso en línea:	https://doaj.org/article/2d915f0f0add477db016aee0965eb3c5
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spelling	oai:doaj.org-article:2d915f0f0add477db016aee0965eb3c52021-12-02T09:55:27ZGenerating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks10.1007/s12182-019-0328-41672-51071995-8226https://doaj.org/article/2d915f0f0add477db016aee0965eb3c52019-06-01T00:00:00Zhttp://link.springer.com/article/10.1007/s12182-019-0328-4https://doaj.org/toc/1672-5107https://doaj.org/toc/1995-8226Abstract This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models. It can reproduce a wide range of conceptual geological models while possessing the flexibility necessary to honor constraints such as well data. Compared with existing geostatistics-based modeling methods, our approach produces realistic subsurface facies architecture in 3D using a state-of-the-art deep learning method called generative adversarial networks (GANs). GANs couple a generator with a discriminator, and each uses a deep convolutional neural network. The networks are trained in an adversarial manner until the generator can create “fake” images that the discriminator cannot distinguish from “real” images. We extend the original GAN approach to 3D geological modeling at the reservoir scale. The GANs are trained using a library of 3D facies models. Once the GANs have been trained, they can generate a variety of geologically realistic facies models constrained by well data interpretations. This geomodelling approach using GANs has been tested on models of both complex fluvial depositional systems and carbonate reservoirs that exhibit progradational and aggradational trends. The results demonstrate that this deep learning-driven modeling approach can capture more realistic facies architectures and associations than existing geostatistical modeling methods, which often fail to reproduce heterogeneous nonstationary sedimentary facies with apparent depositional trend.Tuan-Feng ZhangPeter TilkeEmilien DupontLing-Chen ZhuLin LiangWilliam BaileyKeAi Communications Co., Ltd.articleGeological faciesGeomodelingData conditioningGenerative adversarial networksScienceQPetrologyQE420-499ENPetroleum Science, Vol 16, Iss 3, Pp 541-549 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Geological facies Geomodeling Data conditioning Generative adversarial networks Science Q Petrology QE420-499
spellingShingle	Geological facies Geomodeling Data conditioning Generative adversarial networks Science Q Petrology QE420-499 Tuan-Feng Zhang Peter Tilke Emilien Dupont Ling-Chen Zhu Lin Liang William Bailey Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks
description	Abstract This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models. It can reproduce a wide range of conceptual geological models while possessing the flexibility necessary to honor constraints such as well data. Compared with existing geostatistics-based modeling methods, our approach produces realistic subsurface facies architecture in 3D using a state-of-the-art deep learning method called generative adversarial networks (GANs). GANs couple a generator with a discriminator, and each uses a deep convolutional neural network. The networks are trained in an adversarial manner until the generator can create “fake” images that the discriminator cannot distinguish from “real” images. We extend the original GAN approach to 3D geological modeling at the reservoir scale. The GANs are trained using a library of 3D facies models. Once the GANs have been trained, they can generate a variety of geologically realistic facies models constrained by well data interpretations. This geomodelling approach using GANs has been tested on models of both complex fluvial depositional systems and carbonate reservoirs that exhibit progradational and aggradational trends. The results demonstrate that this deep learning-driven modeling approach can capture more realistic facies architectures and associations than existing geostatistical modeling methods, which often fail to reproduce heterogeneous nonstationary sedimentary facies with apparent depositional trend.
format	article
author	Tuan-Feng Zhang Peter Tilke Emilien Dupont Ling-Chen Zhu Lin Liang William Bailey
author_facet	Tuan-Feng Zhang Peter Tilke Emilien Dupont Ling-Chen Zhu Lin Liang William Bailey
author_sort	Tuan-Feng Zhang
title	Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks
title_short	Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks
title_full	Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks
title_fullStr	Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks
title_full_unstemmed	Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks
title_sort	generating geologically realistic 3d reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks
publisher	KeAi Communications Co., Ltd.
publishDate	2019
url	https://doaj.org/article/2d915f0f0add477db016aee0965eb3c5
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_version_	1718397874304188416

Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks

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