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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Bibliographic Details
Published in:PloS one Vol. 16; no. 6; p. e0253174
Main Authors: Yao, Jianpeng, Liu, Wenling, Liu, Qingbin, Liu, Yuyang, Chen, Xiaodong, Pan, Mao
Format: Journal Article
Language:English
Published: San Francisco Public Library of Science 22.06.2021
Public Library of Science (PLoS)
Subjects:
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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Description
Summary: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.
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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