Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network.

Reservoir facies modeling is an important way to express the sedimentary characteristics of the target area. Conventional deterministic modeling, target-based stochastic simulation, and two-point geostatistical stochastic modeling methods are difficult to characterize the complex sedimentary microfa...

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Autores principales:	Jianpeng Yao, Wenling Liu, Qingbin Liu, Yuyang Liu, Xiaodong Chen, Mao Pan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9ec93d9dff5e4ee192b3d66fc3ce013d
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spelling	oai:doaj.org-article:9ec93d9dff5e4ee192b3d66fc3ce013d2021-12-02T20:10:17ZOptimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network.1932-620310.1371/journal.pone.0253174https://doaj.org/article/9ec93d9dff5e4ee192b3d66fc3ce013d2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253174https://doaj.org/toc/1932-6203Reservoir facies modeling is an important way to express the sedimentary characteristics of the target area. Conventional deterministic modeling, target-based stochastic simulation, and two-point geostatistical stochastic modeling methods are difficult to characterize the complex sedimentary microfacies structure. Multi-point geostatistics (MPG) method can learn a priori geological model and can realize multi-point correlation simulation in space, while deep neural network can express nonlinear relationship well. This article comprehensively utilizes the advantages of the two to try to optimize the multi-point geostatistical reservoir facies modeling algorithm based on the Deep Forward Neural Network (DFNN). Through the optimization design of the multi-grid training data organization form and repeated simulation of grid nodes, the simulation results of diverse modeling algorithm parameters, data conditions and deposition types of sedimentary microfacies models were compared. The results show that by optimizing the organization of multi-grid training data and repeated simulation of nodes, it is easier to obtain a random simulation close to the real target, and the simulation of sedimentary microfacies of different scales and different sedimentary types can be performed.Jianpeng YaoWenling LiuQingbin LiuYuyang LiuXiaodong ChenMao PanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253174 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jianpeng Yao Wenling Liu Qingbin Liu Yuyang Liu Xiaodong Chen Mao Pan Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network.
description	Reservoir facies modeling is an important way to express the sedimentary characteristics of the target area. Conventional deterministic modeling, target-based stochastic simulation, and two-point geostatistical stochastic modeling methods are difficult to characterize the complex sedimentary microfacies structure. Multi-point geostatistics (MPG) method can learn a priori geological model and can realize multi-point correlation simulation in space, while deep neural network can express nonlinear relationship well. This article comprehensively utilizes the advantages of the two to try to optimize the multi-point geostatistical reservoir facies modeling algorithm based on the Deep Forward Neural Network (DFNN). Through the optimization design of the multi-grid training data organization form and repeated simulation of grid nodes, the simulation results of diverse modeling algorithm parameters, data conditions and deposition types of sedimentary microfacies models were compared. The results show that by optimizing the organization of multi-grid training data and repeated simulation of nodes, it is easier to obtain a random simulation close to the real target, and the simulation of sedimentary microfacies of different scales and different sedimentary types can be performed.
format	article
author	Jianpeng Yao Wenling Liu Qingbin Liu Yuyang Liu Xiaodong Chen Mao Pan
author_facet	Jianpeng Yao Wenling Liu Qingbin Liu Yuyang Liu Xiaodong Chen Mao Pan
author_sort	Jianpeng Yao
title	Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network.
title_short	Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network.
title_full	Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network.
title_fullStr	Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network.
title_full_unstemmed	Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network.
title_sort	optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9ec93d9dff5e4ee192b3d66fc3ce013d
work_keys_str_mv	AT jianpengyao optimizedalgorithmformultipointgeostatisticalfaciesmodelingbasedonadeepfeedforwardneuralnetwork AT wenlingliu optimizedalgorithmformultipointgeostatisticalfaciesmodelingbasedonadeepfeedforwardneuralnetwork AT qingbinliu optimizedalgorithmformultipointgeostatisticalfaciesmodelingbasedonadeepfeedforwardneuralnetwork AT yuyangliu optimizedalgorithmformultipointgeostatisticalfaciesmodelingbasedonadeepfeedforwardneuralnetwork AT xiaodongchen optimizedalgorithmformultipointgeostatisticalfaciesmodelingbasedonadeepfeedforwardneuralnetwork AT maopan optimizedalgorithmformultipointgeostatisticalfaciesmodelingbasedonadeepfeedforwardneuralnetwork
_version_	1718375015724875776

Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network.

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