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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Vydáno v:	PloS one Ročník 16; číslo 6; s. e0253174
Hlavní autoři:	Yao, Jianpeng, Liu, Wenling, Liu, Qingbin, Liu, Yuyang, Chen, Xiaodong, Pan, Mao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	San Francisco Public Library of Science 22.06.2021 Public Library of Science (PLoS)
Témata:	Accuracy Algorithms Analysis Artificial intelligence Artificial neural networks Belts Biology and Life Sciences Computer and Information Sciences Deep learning Earth crust Earth Sciences Ecology and Environmental Sciences Editing Electronic mail Evaluation Evolution Funding Geological mapping Geology Geostatistics Image processing Image reconstruction Laboratories Lithology Modelling Neural networks Oil and gas exploration Oil and gas fields Oil exploration Oil field equipment Oil fields Orogeny Petroleum Physical Sciences Research and Analysis Methods Reservoirs Reviews Seismological data Simulation Statistical methods China Beijing China
ISSN:	1932-6203, 1932-6203
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have read the journal’s policy and have the following competing interests: WL, YL, and XC are employees of PetroChina. QL is an employee of Sinopec. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0253174