基于时序动态分析的油井产量预测研究

doi:10.11885/j.issn.1674-5086.2020.05.22.04

西南石油大学学报(自然科学版) ›› 2020, Vol. 42 ›› Issue (6): 82-88.DOI: 10.11885/j.issn.1674-5086.2020.05.22.04

• 油气田人工智能技术与应用专刊 • 上一篇下一篇

基于时序动态分析的油井产量预测研究

杨洋^1,2, 程悦菲², 谯英³, 刘炯⁴

1. 油气藏地质及开发工程国家重点实验室·西南石油大学, 四川成都 610500;
2. 西南石油大学地球科学与技术学院, 四川成都 610500;
3. 西南石油大学计算机科学学院, 四川成都 610500;
4. 中国石油川庆钻探工程有限公司安全环保质量监督检测研究院, 四川广汉 618300

收稿日期:2020-05-22 发布日期:2020-12-21
通讯作者: 谯英,E-mail:teachqiao@163.com
作者简介:杨洋,1981年生,男,汉族,四川绵阳人,副教授,博士,主要从事机器学习理论下的GIS时空大数据挖掘与应用软件研发方面的研究。E-mail:843850989@qq.com;程悦菲,1995年生,女,汉族,四川南充人,硕士研究生,主要从事机器学习、深度学习及大数据挖掘等方面的研究工作。E-mail:583687278@qq.com;谯英,1972年生,女,汉族,四川射洪人,副教授,硕士,主要从事油田数据融合和智慧管道巡检数据处理方面的研究。E-mail:teachqiao@163.com;刘炯,1979年生,男,汉族,四川泸州人,高级工程师,硕士,主要从事油气田QHSE工程技术信息化及数据应用研究工作。E-mail:liuj_kt@cnpc.com.cn
基金资助:
南充市科技项目（18SXHZ0025）

A Study on Oil Well Production Prediction Based on Time Series Dynamic Analysis

YANG Yang^1,2, CHENG Yuefei², QIAO Ying³, LIU Jiong⁴

1. State Key Laboratory of Oil Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, Sichuan 610500, China;
2. School of Earth Science and Technology, Southwest Petroleum University, Chengdu, Sichuan 610500, China;
3. School of Computer Science, Southwest Petroleum University, Chengdu, Sichuan 610500, China;
4. Safety Environment Quality Supervision & Testing Research Institute, CNPC Chuanqing Drilling Engineering Co. Ltd., Guanghan, Sichuan 618300, China

Received:2020-05-22 Published:2020-12-21

摘要/Abstract

摘要： 针对目前常用的油井产量预测方法效果并不理想的问题，开展时间序列分析来进行油井产量动态预测研究。采用时间序列分析结合残差修正方法，建立具有时序动态分析能力的产量差分自动回归移动平均模型（Autoregressive Integrated Moving Average，ARIMA），得出预测初始值与真实油井产量的残差；通过构建支持向量机（Support Vector Machine，SVM）时序预测模型进行残差修正，获得油井产量组合预测值；并将长短期记忆网络（Long Short-Term Memory，LSTM）模型与上述方法进行对比。实验表明，组合预测模型、LSTM模型的预测结果平均相对误差率分别为9.81%和32.44%。说明组合模型预测更精准，为油井产量的动态预测提供了一种有效方法，可作为油井在生产计划时的快速实时辅助依据，具有实用价值。

关键词: 油井产量预测, ARIMA模型, 残差, SVM, LSTM

Abstract: The effect of oil well production prediction method currently used is not ideal. This study aims at dynamic prediction of oil production, using time series analysis combined with residual error correction. We build an ARIMA (Autoregressive integrated moving average) model with the ability of time series dynamic analysis, to predict the initial value and the real residual oil well production; the residual error was corrected by constructing the (SVM) Support Vector Machine time series prediction model to obtain the predicted value of oil well production combination. The LSTM (Long Short-Term Memory) model is compared with the above methods. The experimental results show that the average relative error rates of the combined prediction model and the LSTM model are 9.81% and 32.44% respectively. The conclusion is that the combined model prediction is more accurate, and provides an effective method for the dynamic prediction of oil well production, which can be used as a fast and real-time auxiliary basis for oil well production planning and has practical value.

Key words: oil well production prediction, ARIMA model, residual error, SVM, LSTM

中图分类号:

TE341

杨洋, 程悦菲, 谯英, 刘炯. 基于时序动态分析的油井产量预测研究[J]. 西南石油大学学报(自然科学版), 2020, 42(6): 82-88.

YANG Yang, CHENG Yuefei, QIAO Ying, LIU Jiong. A Study on Oil Well Production Prediction Based on Time Series Dynamic Analysis[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 82-88.

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基于时序动态分析的油井产量预测研究

A Study on Oil Well Production Prediction Based on Time Series Dynamic Analysis

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