油气田开发

基于多变量时间序列及向量自回归机器学习模型的水驱油藏产量预测方法

  • 张瑞 ,
  • 贾虎
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  • “油气藏地质及开发工程”国家重点实验室 西南石油大学,成都 610500
张瑞(1994-),男,湖北武汉人,西南石油大学在读博士研究生,主要从事油藏工程与数值模拟方面的研究工作。地址:四川省成都市新都区西南石油大学石油与天然气工程学院,邮政编码:610500。E-mail:zhangruixiaoz@163.com

收稿日期: 2020-02-17

  修回日期: 2020-10-15

  网络出版日期: 2021-01-19

基金资助

霍英东教育基金会高等院校青年教师基金(171043); 四川省杰出青年科技人才项目(2019JDJQ0036)

Production performance forecasting method based on multivariate time series and vector autoregressive machine learning model for waterflooding reservoirs

  • ZHANG Rui ,
  • JIA Hu
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  • State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation of Southwest Petroleum University, Chengdu 610500, China

Received date: 2020-02-17

  Revised date: 2020-10-15

  Online published: 2021-01-19

摘要

提出了一种基于多变量时间序列(MTS)及向量自回归(VAR)机器学习模型的水驱油藏产量预测方法,并进行了实例应用。该方法在井网分析的基础上通过MTS分析对注采井组数据进行优选,并将井组内不同采出井产油量及注入井注水量作为彼此相关的时间序列,通过建立VAR模型从多个时间序列中提取出相互作用规律,挖掘注采井间流量的依赖关系从而进行产量预测。水驱油藏历史生产数据分析结果表明,与数值模拟历史拟合结果相比,机器学习模型产量预测结果具有更高精度,同时不确定性分析提升了预测结果的安全性。通过脉冲响应分析对注入井的采油贡献量进行评价,可为注水开发方案调整提供理论指导。图10表5参28

本文引用格式

张瑞 , 贾虎 . 基于多变量时间序列及向量自回归机器学习模型的水驱油藏产量预测方法[J]. 石油勘探与开发, 2021 , 48(1) : 175 -184 . DOI: 10.11698/PED.2021.01.16

Abstract

A forecasting method of oil well production based on multivariate time series (MTS) and vector autoregressive (VAR) machine learning model for waterflooding reservoir is proposed, and an example application is carried out. This method first uses MTS analysis to optimize injection and production data on the basis of well pattern analysis. The oil production of different production wells and water injection of injection wells in the well group are regarded as mutually related time series. Then a VAR model is established to mine the linear relationship from MTS data and forecast the oil well production by model fitting. The analysis of history production data of waterflooding reservoirs shows that, compared with history matching results of numerical reservoir simulation, the production forecasting results from the machine learning model are more accurate, and uncertainty analysis can improve the safety of forecasting results. Furthermore, impulse response analysis can evaluate the oil production contribution of the injection well, which can provide theoretical guidance for adjustment of waterflooding development plan.

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