计算机与数字工程2023，Vol.51Issue(11)：2597-2601,5.DOI:10.3969/j.issn.1672-9722.2023.11.023
基于PSO算法优化GRU神经网络的孔隙度预测
Optimization of Porosity Prediction by GRU Neural Network Based on PSO Algorithm
摘要
Abstract
Porosity parameter is one of the important parameters to characterize the oil storage capacity of rocks and is also an important physical parameter for reservoir evaluation.The traditional method of porosity calculation is based on linear equation,and the prediction accuracy is not high and time consuming.To solve the problem,a porosity parameter prediction model based on parti-cle swarm optimization(PSO)algorithm to optimize the gated recycling unit(GRU)neural network is proposed.This model can well reflect the nonlinear relationship between porosity parameters and logging curves.Firstly,the prediction model of GRU neural network is constructed,and then the super parameters of the prediction model of GRU neural network are optimized by the particle swarm optimization algorithm with global optimization ability,easier convergence and better robustness,which can effectively im-prove the prediction accuracy of the model and reduce the time of cross validation.Correlation analysis is carried out on the acreage of the actual logging data,sort out the logging data,and the porosity parameters so as to set then the PSO-porosity GRU helps neu-ral network training and parameter prediction model test,and compared with the traditional GRU helped neural network forecasting model and back propagation(BP)back propagation,the results of the neural network prediction model for comparative analysis,the results show that PSO-GRU helped on porosity prediction model has better accuracy.关键词
孔隙度/门控循环神经网络/粒子群算法/测井数据/相关性分析Key words
porosity/gated recurrent unit neural network/particle swarm optimization/well logging data/correlation analysis分类
天文与地球科学引用本文复制引用
文必龙,李小东..基于PSO算法优化GRU神经网络的孔隙度预测[J].计算机与数字工程,2023,51(11):2597-2601,5.基金项目
黑龙江省教育科学规划重点课题(编号:GJB1421103) （编号:GJB1421103）
黑龙江省高等教育教学改革项目(编号:SJGY20200125)资助. （编号:SJGY20200125）
