基于MLR-ANN算法的地应力场反演与裂缝预测

doi:10.11885/j.issn.1674-5086.2022.08.20.01

西南石油大学学报(自然科学版) ›› 2024, Vol. 46 ›› Issue (3): 1-12.DOI: 10.11885/j.issn.1674-5086.2022.08.20.01

• 地质勘探 • 下一篇

基于MLR-ANN算法的地应力场反演与裂缝预测

张伯虎^1,2, 胡尧², 王燕², 陈伟², 罗超^3,4

1. 油气藏地质及开发工程全国重点实验室·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. 页岩气评价与开采四川省重点实验室, 四川成都 610056;
4. 中国石油西南油气田公司页岩气研究院, 四川成都 610056

收稿日期:2022-08-20 发布日期:2024-06-26
通讯作者: 胡尧，E-mail:2323167988@qq.com
作者简介:张伯虎，1978年生，男，汉族，四川南充人，教授，博士，主要从事深部岩石力学及地质力学方面的教学与研究工作。E-mail:zbh_cd@126.com;胡尧，1999年生，男，汉族，四川广安人，硕士研究生，主要从事地应力反演与裂缝分析预测方面的研究工作。E-mail:2323167988@qq.com;王燕，1996年生，女，汉族，四川泸州人，硕士，主要从事地应力反演和工程管理方面的研究工作。E-mail:wangyan-1966@qq.com;陈伟，1963年生，男，汉族，重庆北碚人，教授，博士，主要从事构造地质学教学与构造几何变形分析方面的研究工作。E-mail:584610691@qq.com;罗超，1982年生，男，汉族，四川隆昌人，高级工程师，博士，主要从事页岩气地质等方面的研究。E-mail:luochao2001@petrochina.com.cn
基金资助:
中国石油-西南石油大学创新联合体科技合作项目（2020CX020100）

Ground Stress Field Inversion and Fracture Prediction Based on MLR-ANN Algorithm

ZHANG Bohu^1,2, HU Yao², WANG Yan², CHEN Wei², LUO Chao^3,4

1. National Key Laboratory of Oil and Gas Reservoir Geology and Exploration, Southwest Petroleum University, Chengdu, Sichuan 610500, China;
2. School of Geoscience and Technology, Southwest Petroleum University, Chengdu, Sichuan 610500, China;
3. Provincial Key Laboratory of Shale Gas Evaluation and Exploitation of Sichuan, Chengdu, Sichuan 610056, China;
4. Shale Gas Research Institute, Southwest Oil & Gas Field Company, PetroChina, Chengdu, Sichuan 610056, China

Received:2022-08-20 Published:2024-06-26

摘要/Abstract

摘要： 中国页岩气储层埋藏深，受构造运动影响，地应力分布规律复杂，传统方法很难准确反演区域地应力大小和方向°提出多元线性回归和人工神经网络的耦合算法，对川南长宁—建武区块的页岩气储层及周边地应力场进行反演，并采用综合破裂系数法，对储层裂缝进行预测，划分裂缝发育区域。研究表明，基于多元回归和神经网络的耦合算法能准确反演区域的地应力场分布规律。研究区的地应力以挤压应力为主，方向在 NE115°左右。受构造运动产生的断层周边应力较为集中，易发育剪切裂缝，裂缝以发育和较发育程度为主。研究区在邻近龙马溪组底部的五峰组上段和构造大断层部位裂缝发育程度较高。研究成果对该区块完善页岩气开采的井网布置、压裂优化设计和套管损坏防治等有一定的参考价值。

关键词: 多元线性回归, 神经网络算法, 页岩气储层, 地应力场反演, 裂缝预测

Abstract: Shale gas reservoirs are deeply buried in China, and the distribution law of ground stress is complex due to tectonic movement. It is difficult for traditional methods to reflect the magnitude and direction distribution of regional in-situ stress accurately. A coupling algorithm of multiple linear regression and artificial neural network is proposed to invert the shale gas reservoir and surrounding ground stress in Changning-Jianwu Block, southern Sichuan. Using the comprehensive fracture coefficient method, the reservoir fractures are predicted and the fracture development areas are divided. The in-situ stress in the study area is mainly compressive stress, and the direction is about NE115°. The stress around the fault caused by tectonic movement is relatively concentrated, and shear cracks are easy to develop. The cracks are mainly developed and medium developed. The study area has a high degree of fracture development in the upper part of the Wufeng Formation and the structural fault near the bottom of the Longmaxi Formation. The research results have important reference value for well pattern arrangement, fracturing optimization design and casing damage prevention of shale gas extraction.

Key words: multiple linear regression, artificial neural network, shale gas reservoir, ground stress field inversion, coupled algorithm, fracture prediction

中图分类号:

TE121

张伯虎, 胡尧, 王燕, 陈伟, 罗超. 基于MLR-ANN算法的地应力场反演与裂缝预测[J]. 西南石油大学学报(自然科学版), 2024, 46(3): 1-12.

ZHANG Bohu, HU Yao, WANG Yan, CHEN Wei, LUO Chao. Ground Stress Field Inversion and Fracture Prediction Based on MLR-ANN Algorithm[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2024, 46(3): 1-12.

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基于MLR-ANN算法的地应力场反演与裂缝预测

Ground Stress Field Inversion and Fracture Prediction Based on MLR-ANN Algorithm

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