吉林大学学报(信息科学版)2024,Vol.42Issue(5) :799-807.

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

张强 薛陈斌 彭骨 卢青
吉林大学学报(信息科学版)2024,Vol.42Issue(5) :799-807.

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

张强 1薛陈斌 1彭骨 1卢青2
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作者信息

  • 1. 东北石油大学计算机与信息技术学院,黑龙江大庆 163318
  • 2. 东北石油大学现代教育技术中心,黑龙江大庆 163318
  • 折叠

摘要

为解决油田开发指标的预测问题,提出了一种基于物质平衡方程和Informer的融合模型.首先,通过物质平衡方程领域知识建立油田开发产量递减前后的机理模型;其次,将所建机理模型作为约束与Informer模型损失函数进行融合建立符合油田开发物理规律的指标预测模型;最后,采用油田实际生产数据进行实验分析,结果表明相比于纯数据驱动的几种循环结构预测模型,本融合模型在相同数据条件下的预测效果更优.该模型的机理约束部分能引导模型的训练过程,使其收敛速度更快,且波峰波谷处的预测更准确.该融合模型具有更好的预测能力和泛化能力和比较合理的物理可解释性.

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

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基金项目

国家自然科学基金资助项目(42002138)

黑龙江省自然科学基金资助项目(LH2022F008)

黑龙江省博士后专项基金资助项目(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)

出版年

2024
吉林大学学报(信息科学版)
吉林大学

吉林大学学报(信息科学版)

CSTPCD
影响因子:0.607
ISSN:1671-5896
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