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Processes
Volume 14
Issue 11
10.3390/pr14111687
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Method for Predicting Three Formation Pressures in Ultra-Deep Wells in the Southern Margin Based on Well–Seismic Data Fusion and Machine Learning

by
Jiangang Shi
1,
Wenhui Dang
1,
Wei Zhang
1,
Hong Huang
2,
Yuyuan Hu
3,* and
Yuqiang Xu
3
1
Oil Production Technology Research Institute, Xinjiang Oilfield Company, Qingdao 266580, China
2
Exploration Department, Xinjiang Oilfield Company, Karamay 834000, China
3
College of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
*
Author to whom correspondence should be addressed.
Processes 2026, 14(11), 1687; https://doi.org/10.3390/pr14111687
Submission received: 8 April 2026 / Revised: 10 May 2026 / Accepted: 20 May 2026 / Published: 22 May 2026
(This article belongs to the Special Issue Application of Artificial Intelligence in Oil and Gas Engineering)
Versions Notes

Abstract

Aiming at the challenges faced by ultra-deep wells in the southern margin of the Junggar Basin, such as strong uncertainty in formation information, difficulties in accurate prediction of three formation pressures, narrow safe density windows, and prominent downhole risks, this paper establishes a method for predicting three formation pressures with confidence levels and quantitatively evaluating drilling risks based on well–seismic fused data. Firstly, rock mechanics and in situ stress calculation models are optimized to characterize the strong uncertainty of deep formations. Then, high-precision well–seismic fused interval velocity is generated through multi-source data preprocessing, well–seismic data fusion, and construction of the MLP-TFT-MCMC hybrid model. On this basis, three formation pressures are calculated, and a pressure profile with confidence levels is constructed using an improved t-distribution. Verified by a field well case, the predicted results are consistent with actual data, providing technical support for the safe and efficient drilling of subsequent ultra-deep wells.
Keywords: southern margin ultra-deep wells; well–seismic fusion; formation pressure; quantitative evaluation southern margin ultra-deep wells; well–seismic fusion; formation pressure; quantitative evaluation

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MDPI and ACS Style

Shi, J.; Dang, W.; Zhang, W.; Huang, H.; Hu, Y.; Xu, Y. A Method for Predicting Three Formation Pressures in Ultra-Deep Wells in the Southern Margin Based on Well–Seismic Data Fusion and Machine Learning. Processes 2026, 14, 1687. https://doi.org/10.3390/pr14111687

AMA Style

Shi J, Dang W, Zhang W, Huang H, Hu Y, Xu Y. A Method for Predicting Three Formation Pressures in Ultra-Deep Wells in the Southern Margin Based on Well–Seismic Data Fusion and Machine Learning. Processes. 2026; 14(11):1687. https://doi.org/10.3390/pr14111687

Chicago/Turabian Style

Shi, Jiangang, Wenhui Dang, Wei Zhang, Hong Huang, Yuyuan Hu, and Yuqiang Xu. 2026. "A Method for Predicting Three Formation Pressures in Ultra-Deep Wells in the Southern Margin Based on Well–Seismic Data Fusion and Machine Learning" Processes 14, no. 11: 1687. https://doi.org/10.3390/pr14111687

APA Style

Shi, J., Dang, W., Zhang, W., Huang, H., Hu, Y., & Xu, Y. (2026). A Method for Predicting Three Formation Pressures in Ultra-Deep Wells in the Southern Margin Based on Well–Seismic Data Fusion and Machine Learning. Processes, 14(11), 1687. https://doi.org/10.3390/pr14111687

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