Causes of Multi-Mechanism Abnormal Formation Pressure in Offshore Oil and Gas Wells

被引:0
作者
Xu, Yang [1 ,2 ]
Yang, Jin [1 ]
Hu, Zhiqiang [3 ]
Zhao, Quanmin [2 ]
Li, Lei [1 ]
Yin, Qishuai [1 ]
机构
[1] China Univ Petr, Coll Safety & Ocean Engn, Beijing 102249, Peoples R China
[2] SINOPEC Int Petr Explorat & Prod Corp, Beijing 100029, Peoples R China
[3] Sinopec Res Inst Petr Engn Co Ltd, Beijing 102206, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 22期
关键词
offshore oil and gas wells; abnormal formation pressure; Rio del Rey Basin; undercompaction mechanism; high-temperature fluid expansion; machine learning classification; Bayesian optimization; PORE PRESSURE;
D O I
10.3390/app142210149
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
This study thoroughly investigates the complex origins of abnormal formation pressure in offshore oil and gas wells, taking the Rio del Rey Basin in Cameroon as a case study. Renowned for its abundant oil and gas resources, the area faces unique challenges in predicting overpressure due to its high-temperature and high-pressure reservoir characteristics. By quantitatively analyzing the main mechanisms such as undercompaction, high-temperature fluid expansion, and mud diapirism, the study addresses the complexities of overpressure prediction. This paper introduces an innovative analytical framework that combines hierarchical clustering algorithms with the LightGBM model. Further refined by the application of Bayesian optimization, the model intelligently adjusts hyperparameters to enhance predictive accuracy. Utilizing well logging data and applying machine learning techniques, the paper classifies and identifies different mechanisms causing abnormal pressures, achieving a model prediction accuracy of 0.942. The research findings highlight the predominant role of the undercompaction mechanism, accounting for approximately 70% of the abnormal high-pressure events in the study area. Fluid expansion and shale diapirism contribute smaller but significant proportions of 10% and 20%, respectively. These quantitative insights into the pressure mechanisms are vital for optimizing drilling operations and reducing risks in oil and gas exploration. The study's hybrid approach, integrating geophysical analysis with advanced computational techniques, sets a precedent for future research. It provides new avenues for applying machine learning to understand complex geological phenomena in similar geological environments and makes a significant contribution to the strategic planning of hydrocarbon exploration and production activities.
引用
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页数:20
相关论文
共 28 条
[21]   Acoustic Prediction and Risk Evaluation of Shallow Gas in Deep-Water Areas [J].
Yang Jin ;
Wu Shiguo ;
Tong Gang ;
Wang Huanhuan ;
Guo Yongbin ;
Zhang Weiguo ;
Zhao Shaowei ;
Song Yu ;
Yin Qishuai ;
Xu Fei .
JOURNAL OF OCEAN UNIVERSITY OF CHINA, 2022, 21 (05) :1147-1153
[22]  
Yin Q., 2019, P 29 INT OC POL ENG
[23]   Machine Learning for Deepwater Drilling: Gas-Kick-Alarm Classification Using Pilot-Scale Rig Data with Combined Surface-Riser-Downhole Monitoring [J].
Yin, Qishuai ;
Yang, Jin ;
Tyagi, Mayank ;
Zhou, Xu ;
Hou, Xinxin ;
Wang, Ning ;
Tong, Gang ;
Cao, Bohan .
SPE JOURNAL, 2021, 26 (04) :1773-1799
[24]   Downhole quantitative evaluation of gas kick during deepwater drilling with deep learning using pilot-scale rig data [J].
Yin, Qishuai ;
Yang, Jin ;
Tyagi, Mayank ;
Zhou, Xu ;
Wang, Ning ;
Tong, Gang ;
Xie, Renjun ;
Liu, Hexing ;
Cao, Bohan .
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2022, 208
[25]   Horizontal tectonic stress as a cause of overpressure in the southern margin of the Junggar Basin, northwest China [J].
Zhang, Fengqi ;
Lu, Xuesong ;
Botterill, Scott ;
Gingras, Murray ;
Zhuo, Qingong ;
Zhong, Hongli .
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2021, 205
[26]  
Zhang Y., 2021, J. Nat. Gas Sci. Eng, V92, P103899
[27]  
Zhang Y., 2016, J. Nat. Gas Sci. Eng, V36, P1184
[28]   Estimating Formation Pore Pressure in Tectonic Compression Zones [J].
Zhi, Y. ;
Honghai, F. ;
Gang, L. ;
Yunlong, W. .
PETROLEUM SCIENCE AND TECHNOLOGY, 2012, 30 (08) :766-774