A Novel Method for Matching Reservoir Parameters Based on Particle Swarm Optimization and Support Vector Machine

When the reservoir physical properties are distributed very dispersedly, the matching precision of these reservoir parameters is not good. We propose a novel method for matching the reservoir physical properties based on particle swarm optimization (PSO) and support vector machine (SVM) algorithm. F...

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Bibliographic Details
Main Authors: Rongwang Yin, Qingyu Li, Peichao Li, Detang Lu
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2020/7542792
Description
Summary:When the reservoir physical properties are distributed very dispersedly, the matching precision of these reservoir parameters is not good. We propose a novel method for matching the reservoir physical properties based on particle swarm optimization (PSO) and support vector machine (SVM) algorithm. First, the data structure characteristics of the reservoir physical properties are analyzed. Then, the particle swarm differential perturbation evolution algorithm is used to cluster and characterize the reservoir physical properties. Finally, by using the SVM algorithm for feature reorganization and the least squares matching of the extracted reservoir physical properties, the feature quantity of the reservoir physical properties can be accurately mined and the pressure matching precision is improved. The experimental results show that employing the proposed method to analyze and sample the data characteristics of the physical properties of the reservoir is better. The extracted parameters can effectively reflect the physical characteristics of oil reservoirs. The proposed method has potential applications in guiding the exploration and development of oil reservoirs.
ISSN:1024-123X
1563-5147