Well interference evaluation and prediction of shale gas wells based on machine learning

ZHANG Qing; HE Feng; HE Youwei

doi:10.13809/j.cnki.cn32-1825/te.2022.03.011

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

Well interference evaluation and prediction of shale gas wells based on machine learning

更新时间：2025-12-05

- Well interference evaluation and prediction of shale gas wells based on machine learning
- Petroleum Reservoir Evaluation and Development Issue 3, Pages: 487-495(2022)
- 作者机构：
  
  1. 中国石油集团川庆钻探工程有限公司页岩气勘探开发项目经理部,四川成都 610051 西南石油大学油气藏地质及开发工程国家重点实验室,四川,成都,610500
  2. 西南石油大学油气藏地质及开发工程国家重点实验室,四川,成都,610500
- 作者简介：
- 基金信息：
- DOI：10.13809/j.cnki.cn32-1825/te.2022.03.011
  CLC： P181;TE37
- Published：2022
- 稿件说明：
移动端阅览
ZHANG Qing, HE Feng, HE Youwei. Well interference evaluation and prediction of shale gas wells based on machine learning[J]. Petroleum Reservoir Evaluation and Development, 2022, (3): 487-495.
DOI：

ZHANG Qing, HE Feng, HE Youwei. Well interference evaluation and prediction of shale gas wells based on machine learning[J]. Petroleum Reservoir Evaluation and Development, 2022, (3): 487-495. DOI： 10.13809/j.cnki.cn32-1825/te.2022.03.011.

摘要

页岩气藏井间干扰严重制约气井生产，井间干扰程度评价与预测对页岩气高效开发具有重要意义。现有研究主要聚焦页岩气井间干扰现象、生产动态特征以及数值模拟参数优化等方面，但页岩气井间干扰程度定量评价及预测方面的研究较少，且参数体系不全，难以客观评价页岩气井间干扰程度。因此，采用机器学习方法综合考虑地质参数、压裂参数及生产参数，对A页岩气藏井间干扰程度进行评价及预测。先对初始数据进行数据处理，提高数据质量，然后基于处理后的数据，应用聚类分析及随机森林算法评价及预测Y页岩气井间干扰程度。结果表明：A页岩气藏中井间干扰程度低、中、高的井数占比分别为25.93%、37.03%、37.04%，其中压裂因素对A页岩气藏井间干扰程度评价结果影响最大。调参后的页岩气井间干扰程度预测结果达到92.07%，表明所建立的预测模型可应用于实际页岩气井间干扰程度预测，且模型精确度较高，为页岩气井井间干扰量化评价及预测提供了一种有效手段。

Abstract

Inter-well interference seriously affects the production of shale gas wells. The evaluation and prediction of well interference degree is of great significance to the efficient development of shale gas. But the existing research mainly focuses on the interference phenomenon between shale gas wells

production performance

and parameter optimization through numerical simulation. There are few studies on the quantitative evaluation and prediction of the interference degree between shale gas wells

and the selected parameters is incomplete

which makes it difficult to objectively evaluate the well interference between shale gas wells. Therefore

the machine learning method is used to comprehensively consider the geological parameters and fracturing parameters to evaluate and predict the degree of interference between wells in the shale gas reservoir. Firstly

the initial data are processed to improve the data quality. Then

based on the processed data

cluster analysis and random forest algorithm are used to evaluate and predict the interference degree of shale gas wells. The results show that the proportions of the wells with low

medium and high well interference in the shale gas reservoirs are 25.93 %

37.03 % and 37.04 %

respectively. The fracturing factors show significant influence on the well interference degree in the shale gas reservoirs. After parameters optimization

the prediction results of well interference degree reaches 92.07 %

indicating that the developed prediction model can be applied to forecast the well interference degree in shale gas reservoirs.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Classification of shale gas “sweet spot” based on Random Forest machine learning

Research progress on machine learning in CO₂ enhanced oil and gas recovery and geological storage

A machine learning-based method for recovery rate prediction in fractured water-driven gas reservoirs

Development characteristics and intelligent identification method of natural fractures: A case study of the Upper Triassic Xujiahe Formation in the western Sichuan Depression, Sichuan Basin

Research progress on shale gas productivity evaluation: concepts, methods and future directions

Related Author

NIE Yunli

GAO Guozhong

YE HONGYING

CAO CHENG

ZHAO YULONG

ZHANG LIEHUI

ZHU HAONAN

LI QINGPING

Related Institution

College of Geophysics and Petroleum Resources, Yangtze University

Cooperative Innovation Center of Unconventional Oil and Gas, Yangtze University（Ministry of Education & Hubei Province）

PetroChina Southwest Oil & Gasfield Company

Research Institute of Petroleum Exploration and Development

PetroChina Changqing Oilfield Company

AI问答

⁰