A Support Vector Machine Model with Hyperparameters Optimised by Mind Evolutionary Algorithm for Assessing Permeability of Rock

Wenjin Zhu,Zhiming Chao,Guotao Ma

doi:10.1155/2020/4718493

Abstract

In this paper, a database developed from the existing literature about permeability of rock was established. Based on the constructed database, a Support Vector Machine (SVM) model with hyperparameters optimised by Mind Evolutionary Algorithm (MEA) was proposed to predict the permeability of rock. Meanwhile, the Genetic Algorithm- (GA-) and Particle Swarm Algorithm- (PSO-) SVM models were constructed to compare the improving effects of MEA on the foretelling accuracy of machine learning models with those of GA and PSO, respectively. The following conclusions were drawn. MEA can increase the predictive accuracy of the constructed machine learning models remarkably in a few iteration times, which has better optimisation performance than that of GA and PSO. MEA-SVM has the best forecasting performance, followed by PSO-SVM, while the estimating precision of GA-SVM is lower than them. The proposed MEA-SVM model can accurately predict the permeability of rock indicating the model having a satisfactory generalization and extrapolation capacity.

Highlights

Natural geological formations that mainly consist of rock are optimal sites for many engineering projects, such as hydropower stations, oil and gas reservoirs, and coal mines [1,2,3]
Rock in underground oil and gas reservoirs is often subjected to artificial water injection to improve the tightness of underground oil and gas reservoirs, and for the host rock for hydropower stations, water is forced into the pores and cracks of rock under a hydrostatic load [11, 12]
Many works have been carried out by scholars, among of which is Mind Evolutionary Algorithm (MEA) proposed by Chengyi et al [39] to overcome the aforementioned defects of Genetic Algorithm (GA) and Particle Swarm Optimisation Algorithm (PSO) to some extent and improve the optimisation effects [40, 41]. e better performance of MEA than that of GA and PSO on increasing the estimating accuracy of machine learning models has been proved by researchers in the engineering field [37, 42, 43]

Summary

Introduction

Natural geological formations that mainly consist of rock are optimal sites for many engineering projects, such as hydropower stations, oil and gas reservoirs, and coal mines [1,2,3]. E better performance of MEA than that of GA and PSO on increasing the estimating accuracy of machine learning models has been proved by researchers in the engineering field [37, 42, 43]. To the best knowledge of the authors, currently, the application of MEA in improving the performance of machine learning models for predicting permeability of rock has not been reported. SVM model combined with optimisation algorithm of Mind Evolutionary Algorithm (MEA) was established to predict permeability of rock. To improve the forecasting accuracy and operational efficiency of the machine learning models, the input and output parameters were normalized to the range of 0-1 using the following equation. Where xNormalized represents the normalized value, x represents the original value, xmin represents the minimum value, and xmax represents the maximum value

Machine-Learning Algorithm and Optimisation Algorithms

Hyperparameters Optimisation

Quality Assessment

Results and Analysis

Engineering Application

Conclusions