基于智能算法的油气田地应力三维预测

doi:10.11885/j.issn.1674-5086.2019.03.27.03

西南石油大学学报(自然科学版) ›› 2021, Vol. 43 ›› Issue (2): 84-92.DOI: 10.11885/j.issn.1674-5086.2019.03.27.03

基于智能算法的油气田地应力三维预测

袁多^1,2, 吴超^1,2, 卢运虎³, 张东清^1,2

1. 页岩油气富集机理与有效开发国家重点实验室, 北京昌平 102206;
2. 中国石化石油工程技术研究院, 北京昌平 102206;
3. 中国石油大学(北京)石油工程学院, 北京昌平 102249

收稿日期:2019-03-27 出版日期:2021-04-10 发布日期:2021-04-23
通讯作者: 袁多,E-mail:yuanduo.2007@163.com
作者简介:袁多,1988年生,男,汉族,天津大港人,副研究员,博士,主要从事石油工程岩石力学、应用地球物理等领域进行研究。E-mail:yuanduo.2007@163.com
吴超,1976年生,男,汉族,安徽明光人,高级工程师,博士,主要从事石油工程岩石力学、钻井工艺优化等领域进行研究。E-mail:wuchao9138@163.com
卢运虎,1984年生,男,汉族,湖北宜昌人,高级工程师,博士,主要从事石油工程岩石力学、储层改造技术等领域进行研究。E-mail:luynhu840517@163.com
张东清,1974年生,男,汉族,河北衡水人,高级工程师,硕士,主要从事钻井设计方法、钻井工艺优化等领域进行研究。E-mail:zhangdq@sinopec.com
基金资助:
国家科技重大专项（2016ZX05021-003-002）

Three-dimensional Prediction of In-situ Stress in Oil and Gas Field Based on Intelligence Algorithms

YUAN Duo^1,2, WU Chao^1,2, LU Yunhu³, ZHANG Dongqing^1,2

1. State Key Laboratory of Enrichment Mechanism and Effective Exploitation of Shale Oil and Gas, Changping, Beijing 102206, China;
2. SINOPEC Research Institute of Petroleum Engineering, Changping, Beijing 102206, China;
3. School of Petroleum Engineering, China University of Petroleum, Changping, Beijing 102249, China

Received:2019-03-27 Online:2021-04-10 Published:2021-04-23

摘要/Abstract

摘要： 为了解决复杂沉积构造环境导致未钻区域的地应力定量预测难度大的问题，根据层速度、地应力、叠后地震信息之间的定量关系，运用BP神经网络、模拟退火等智能算法提出了用于不同工况条件的两种油气田地应力三维预测方法。在完钻井数量较多、实测信息较丰富的工区使用BP神经网络算法，利用地震数据空间速度信息与岩石力学方法建立地应力三维数据体；在实测数据较少的工区，运用模拟退火算法直接搜寻合成与实际地震记录达最优匹配下的地应力解向量。该技术在东部某油田的主要工区进行了现场应用，得到了具有较高精度和分辨率的地应力预测结果，验证了基于智能算法的油气田地应力三维预测方法的可行性。

关键词: 油气田地应力, 三维预测, 智能算法, BP神经网络, 模拟退火

Abstract: Complex sedimentary and tectonic environment results in the difficulty of predicting in-situ stress quantitatively. Two new three-dimensional in-situ stress prediction methods for different working conditions based on intelligence algorithm are proposed using the relationships between interval velocity, in-situ stress and post stack seismic information. With abundant well logging and test data, in-situ stress is predicted by the rock physics and mechanics method based on spatial velocity information obtained using BP neural network algorithm. With sparse well points and a small number of data, simulated annealing algorithm is utilized to directly search for the stress solution vector with the best match of synthetic and actual seismic records. These methods have been applied in an oilfield in eastern China and achieves high accuracy and resolution prediction results, which proves the feasibility of these methods.

Key words: in-situ stress in oil and gas field, three-dimensional prediction, intelligence algorithm, BP neural network, simulated annealing

中图分类号:

TE21

袁多, 吴超, 卢运虎, 张东清. 基于智能算法的油气田地应力三维预测[J]. 西南石油大学学报(自然科学版), 2021, 43(2): 84-92.

YUAN Duo, WU Chao, LU Yunhu, ZHANG Dongqing. Three-dimensional Prediction of In-situ Stress in Oil and Gas Field Based on Intelligence Algorithms[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2021, 43(2): 84-92.

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基于智能算法的油气田地应力三维预测

Three-dimensional Prediction of In-situ Stress in Oil and Gas Field Based on Intelligence Algorithms

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