吉林大学学报（信息科学版）2024，Vol.42Issue(5)：799-807,9.
基于Informer融合模型的油田开发指标预测方法
Method for Predicting Oilfield Development Indicators Based on Informer Fusion Model
摘要
Abstract
A fusion model based on material balance equation and Informer is proposed to solve the prediction problem of oilfield development indicators.Firstly,the mechanism model before and after the decline of oil field development production is established through the knowledge of the material balance equation field.Secondly,the established mechanism model is fused with the loss function of the Informer model as a constraint to establish an indicator prediction model that conforms to the physical laws of oil field development.Finally,the actual production data of the oil field is used for experimental analysis.The results indicate that compared to several purely data-driven cyclic structure prediction models,this fusion model has better prediction performance under the same data conditions.The mechanism constraints of this model can guide the training process of the model,so that its rate of convergence is faster,and the prediction at the peak and trough is more accurate.This fusion model has better predictive and generalization abilities,and has a certain degree of physical interpretability.关键词
Informer模型/机理模型/深度融合模型/预测Key words
Informer model/mechanism model/deep fusion model/prediction分类
信息技术与安全科学引用本文复制引用
张强,薛陈斌,彭骨,卢青..基于Informer融合模型的油田开发指标预测方法[J].吉林大学学报（信息科学版）,2024,42(5):799-807,9.基金项目
国家自然科学基金资助项目(42002138) （42002138）
黑龙江省自然科学基金资助项目(LH2022F008) （LH2022F008）
黑龙江省博士后专项基金资助项目(LBH-Q20077) （LBH-Q20077）
黑龙江省优秀青年教师基础研究支持计划基金资助项目(YQJH2023073) （YQJH2023073）
