融合地应力特征的致密储层压裂缝长机器学习预测

陆雪皎; 李洪畅; 李宇征; 王思仪; 王晶; 杨焕英; 平义

doi:10.13809/j.cnki.cn32-1825/te.2025253

油气藏评价与开发 >

2025 2025253

DOI: https://doi.org/10.13809/j.cnki.cn32-1825/te.2025253

融合地应力特征的致密储层压裂缝长机器学习预测

陆雪皎 ,
李洪畅 ,
李宇征 ,
王思仪 ,
王晶 ,
杨焕英 ,
平义

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1.中国石油长庆油田分公司勘探开发研究院,陕西西安 710018;
2.低渗透油气田勘探开发国家工程实验室,陕西西安 710018

陆雪皎（1988—）,女,硕士,高级工程师,主要从事油藏渗流力学、油田开发与提高采收率工作。地址：陕西省西安市未央区凤城四路长庆科技楼,邮政编码710018。E-mail： lxj2016_cq@petrochina.com.cn。

收稿日期: 2025-05-28

网络出版日期: 2025-12-29

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Machine learning prediction of fracture length in tight reservoirs by integrating geostress characteristics

LU Xuejiao ,
LI Hongchang ,
LI Yuzheng ,
WANG Siyi ,
WANG Jing ,
YANG Huanying ,
PING Yi

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1. Exploration and Development Research Institute of China Petroleum Changqing Oilfield Branch, Xi'an, Shaanxi 710018, China;
2. National Engineering Laboratory for Exploration and Development of Low Permeability Oil and Gas Fields, Xi'an, Shaanxi 710018, China

Received date: 2025-05-28

Online published: 2025-12-29

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摘要

为解决鄂尔多斯盆地华庆油田致密油藏开发中面临的天然裂缝预测精度低、人工压裂设计缺乏理论支撑等关键问题,采用多学科融合的裂缝网络表征与压裂计算方法,基于452口井的成像测井、岩心资料和岩石力学实验数据,构建了“测井响应-地应力场-裂缝参数”的多元耦合模型,建立了考虑电阻率降幅大于30%、声波时差增幅大于10%等定量指标的天然裂缝识别标准。通过序贯高斯模拟与Oda（离散裂缝网络随机建模）算法,实现了研究区三维裂缝网络重构（北东60°~90°走向占比66.7%,长度5~95 m,渗透率0~18×10^-3μm²）,模型验证吻合度达近90%。在地应力场表征方面,采用修正Eaton（伊顿）法反演得到水平主应力差4~8 MPa（最大水平主应力为北东75°）。基于天然裂缝及地应力场的研究,研发了融合PKN/P3D（珀金斯-克恩-诺德伦德/拟三维）模型与XGBoost（极端梯度提升）算法的压裂缝长预测系统,关键创新点为结合经典压裂模型PKN/P3D模型,同时考虑PKN/P3D模型中部分参数现场获取困难,将研究区部分井利用成熟商业软件Kinetix（一款动态压裂模拟软件）计算的压裂缝作为机器学习样本,从而得到该研究区所有井及邻近井区的压裂缝计算。现场应用表明：预测结果较微地震监测数据平均误差仅7.2%,利用研究成果指导B195-100X井重复压裂后产量提升42%。该研究形成了1套适用于无地震资料区的致密油藏“裂缝识别-地应力表征-缝长预测-压裂优化”的一体化技术路径,为鄂尔多斯盆地及类似地质条件的致密油藏高效开发提供了可复制的理论方法和技术范式。

关键词： 致密油藏; 大斜度; 天然裂缝展布; 压裂缝; 机器学习; 地应力

本文引用格式

陆雪皎 , 李洪畅 , 李宇征 , 王思仪 , 王晶 , 杨焕英 , 平义 . 融合地应力特征的致密储层压裂缝长机器学习预测[J]. 油气藏评价与开发, 2025 : 2025253 . DOI: 10.13809/j.cnki.cn32-1825/te.2025253

Abstract

In order to solve the key problems of low prediction accuracy of natural fractures and lack of theoretical support for artificial fracturing design in the development of tight oil reservoirs in Huaqing Oilfield, Ordos Basin, a multidisciplinary fusion of fracture network characterization and fracturing calculation method was adopted. Based on imaging logging data, core data, and rock mechanics experimental data of 452 wells, a multivariate coupling model of "logging response geostress field fracture parameters" was constructed, and a natural fracture identification standard considering quantitative indicators such as resistivity reduction>30% and acoustic time difference increase>10% was established. By using sequential Gaussian simulation and Oda algorithm, the three-dimensional fracture network reconstruction in the study area was achieved (with a NE60°~90° orientation accounting for 66.7%, a length of 5 m to 95 m, and a permeability of 0~18×10^-3 μm²), and the model validation agreement reached nearly 90%. In terms of characterizing the geostress field, the modified Eaton method was used to invert the horizontal principal stress difference of 4~8 MPa (σ H_=NE75 °). Based on the study of natural fractures and geostress fields, a fracturing fracture length prediction system was developed by integrating the PKN/P3D model and XGBoost algorithm. The key innovations include: combining the classic fracturing model PKN/P3D model, while considering the difficulty of obtaining some parameters in the PKN/P3D model on site, the fracturing fractures calculated using the mature commercial software Kinetix for some wells in this study area were used as machine learning samples to obtain fracturing calculations for all wells and adjacent well areas in the study area. On site applications have shown that the prediction results have an average error of only 7.2% compared to microseismic monitoring data. Using research results to guide the B195-100X well to increase production by 42% after repeated fracturing. This study has developed an integrated technical pathway for "fracture identification geostress characterization fracture length prediction fracturing optimization" in tight oil reservoirs suitable for areas without seismic data, providing replicable theoretical methods and technical paradigms for efficient development of tight oil reservoirs in the Ordos Basin and similar geological conditions.

Key words： tight oil reservoir; high inclination; natural fracture distribution; pressure fracture; machine learning; geostress

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