Multi-objective optimization for oil-gas production process based on compensation model of comprehensive energy consumption using improved evolutionary algorithm

Tan Liu,Yonggang Wang,Lina Wang,Nannan Zhang,Qinyun Yuan

doi:10.1177/0144598720976632

Abstract

This paper establishes an error compensation multi-objective optimization model of oil-gas production process for optimizing these production indices, including overall oil production, overall water production and comprehensive energy consumption per ton of oil. In order to reduce the error between the model output and the actual value of comprehensive energy consumption per ton of oil, combining the mechanism model with least squares support vector machine (LS-SVM) error model optimized by Bayesian optimization algorithm (BOA), a hybrid model is established to predict the comprehensive energy consumption, in which the mechanism model is used to describe the overall characteristics of oil-gas production process, and LS-SVM error model is established to compensate the mechanism model error. Then, in order to improve the performance of Pareto non-dominated solutions, an improved non-dominated sorting genetic algorithm-II with multi-strategy improvement (IMS-NSGA-II) is proposed to solve the error compensation multi-objective optimization model. Finally, the effectiveness and superiority of the the proposed optimization method are verified by the experiment results on some stand test problems and the optimization problem for the oil-gas production process in a block of an oil production operation area.

Highlights

Technological progress has increased the diversity of energy alternatives, fossil energy is still the main energy in the modern society
The mechanism model is used to describe the overall characteristics of oil-gas production process, and a least squares support vector machine (LS-SVM) error model based on Bayesian optimization algorithm is established to compensate the model error which can’t be described by the mechanism model, the output of hybrid model is as follows: yc 1⁄4 y þ e^
Through the analysis of oil-gas production process, the input vector X is composed of the stroke and the stroke times of each oil well, oil heating temperature and pump head constitute, and the comprehensive energy consumption per ton of oil is taken as the output of the model

Summary

Introduction

Technological progress has increased the diversity of energy alternatives, fossil energy is still the main energy in the modern society. In this hybrid model, the mechanism model is used to describe the overall characteristics of oil-gas production process, and a LS-SVM error model based on Bayesian optimization algorithm is established to compensate the model error which can’t be described by the mechanism model, the output of hybrid model is as follows: yc 1⁄4 y þ e^. Through the analysis of oil-gas production process, the input vector X is composed of the stroke and the stroke times of each oil well, oil heating temperature and pump head constitute, and the comprehensive energy consumption per ton of oil is taken as the output of the model. Where A 1⁄4 kr2E1 þ r2E2, Eb1est and Eb2est are the values of E1 and E2 at wbest, respectively. r is the vector differential operator, and the optimal value kbest is obtained by maximizing lnpðkjD; HÞ

Third level of inference

Conclusions