A new prediction model of bottomhole weight on bit in offshore extended-reach drilling based on the integration of intelligent algorithms and the physical models of rate of penetration

A new prediction model of bottomhole weight on bit in offshore extended-reach drilling based on the integration of intelligent algorithms and the physical models of rate of penetration

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Shuohang Xue^a^,^b, Yanan Geng^a, Xuyue Chen^b^,^*, Yi Wu^a, Changsuo Zhou^a, Bingxuan Li^a, Pengbo Li^b

Petroleum Research | 2026, 11(3) : 912 - 926

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Petroleum Research | 2026, 11(3): 912-926

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A new prediction model of bottomhole weight on bit in offshore extended-reach drilling based on the integration of intelligent algorithms and the physical models of rate of penetration

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Shuohang Xue^a^,^b, Yanan Geng^a, Xuyue Chen^b^,^*, Yi Wu^a, Changsuo Zhou^a, Bingxuan Li^a, Pengbo Li^b

Affiliations

^aCNOOC Research Institute Ltd, Beijing, 100028, China

^bMOE Key Laboratory of Petroleum Engineering, China University of Petroleum, Beijing, 102249, China

Published: 2026-09-10 doi: 10.1016/j.ptlrs.2026.01.005

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Abstract

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Offshore extended-reach drilling is widely employed for the efficient development of deepwater hydrocarbon fields. However, the complex subsea formation environment and downhole frictional losses lead to significant inaccuracies and limitations in bottomhole weight on bit (WOB) calculations, severely impacting drilling operations. Therefore, this study proposes an improved bottomhole WOB prediction model that combines CNN-BiLSTM networks with Transformer encoders. By introducing a loss function (named DRE) based on the rate of penetration equation into the intelligent algorithm, four models were constructed: CNN-BiLSTM, CNN-Encoder-BiLSTM, CNN-BiLSTM (DRE), and CNN-Encoder-BiLSTM (DRE). The eight models were compared and evaluated, including both the established models and conventional models. Experimental results show that the newly developed CNN-Encoder-BiLSTM (DRE) model parameters' influence degree basically conforms to the DRE with high generalization capability. On the test well, it achieved MAE of 0.859, MRE of 0.1, MAPE of 10%, MSE of 1.201, and R² of 0.867, demonstrating good performance. The research results prove that the new model is suitable for bottomhole WOB prediction in offshore extended-reach wells, which is expected to improve drilling efficiency and enable subsequent complex situation analysis, providing new ideas for intelligent drilling research in the marine sector.

Key words

Bottomhole weight on bit / Intelligent algorithms / Extended-reach well / Offshore drilling / Deepwater drilling / Offshore oil and gas

Cite this Article

Shuohang Xue, Yanan Geng, Xuyue Chen, Yi Wu, Changsuo Zhou, Bingxuan Li, Pengbo Li. A new prediction model of bottomhole weight on bit in offshore extended-reach drilling based on the integration of intelligent algorithms and the physical models of rate of penetration[J]. Petroleum Research, 2026 , 11 (3) : 912 -926 . DOI: 10.1016/j.ptlrs.2026.01.005

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National Natural Science Foundation of China(U24B2031; 52174012; 52394250; 52394255; 52234002; U22B20126; 51804322)
"14th Five-Year Plan" Major Science and Technology Project of CNOOC: "Research on Intelligent Drilling Downhole Data Monitoring and Transmission Technology"(KJGG-2023-15-0201)

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Year 2026 volume 11 Issue 3

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Article Info

doi: 10.1016/j.ptlrs.2026.01.005

Receive Date：2025-06-27
Online Date：2026-10-14
Published：2026-09-10

Article Data

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History

Received：2025-06-27
Revised：2025-12-25
Accepted：2026-01-08

Funding

National Natural Science Foundation of China(U24B2031; 52174012; 52394250; 52394255; 52234002; U22B20126; 51804322)

"14th Five-Year Plan" Major Science and Technology Project of CNOOC: "Research on Intelligent Drilling Downhole Data Monitoring and Transmission Technology"(KJGG-2023-15-0201)

Affiliations

^aCNOOC Research Institute Ltd, Beijing, 100028, China

^bMOE Key Laboratory of Petroleum Engineering, China University of Petroleum, Beijing, 102249, China

Corresponding:

^* E-mail address: chenxuyue2011@163.com (X. Chen).

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表12种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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