Drilling Speed Enhancement Method for Extended Reach Wells Based on Machine Learning and Bayesian Optimization

ZHANG Weiguo; JIANG Kun; SONG Yu; DENG Chenghui; HUANG Yiqiang; MA Yi

doi:10.11911/syztjs.2025027

Volume 53 Issue 2

Apr. 2025

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Petroleum Drilling Techniques > 2025 > 53(2): 38-45. > DOI: 10.11911/syztjs.2025027

ZHANG Weiguo, JIANG Kun, SONG Yu, et al. Drilling speed enhancement method for extended reach wells based on machine learning and Bayesian optimization [J]. Petroleum Drilling Techniques, 2025, 53(2)：38−45. DOI: 10.11911/syztjs.2025027

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Drilling Speed Enhancement Method for Extended Reach Wells Based on Machine Learning and Bayesian Optimization

1.
Engineering Technology Department, CNOOC Limited., Beijing, 100053, China
2.
College of Artificial Intelligence, China University of Petroleum (Beijing), Beijing, 102249, China
3.
Shenzhen Branch of CNOOC Limited, Shenzhen, Guangdong, 518000, China

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Received Date: January 09, 2025
Revised Date: March 25, 2025
Available Online: March 31, 2025

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Abstract

Abstract

The complex wellbore trajectories and significant horizontal displacements of offshore extended reach wells lead to increased downhole friction, severely affecting drilling efficiency. By leveraging drilling and logging data, this study proposes a novel method for rate of penetration (ROP) enhancement in extended reach wells with ROP prediction and drilling parameter optimization based on machine learning. Firstly, the original field data were pre-processed by filtering and normalization, followed by correlation analysis, revealing that ROP has strong correlation with drilling parameters such as weight on bit (WOB) and rotary speed, as well as wellbore trajectory parameters such as inclination angle and horizontal displacement. Based on these findings, ROP prediction models were developed using BP neural networks, random forests, and support vector machines. The results show that the BP neural network model outperforms the others, providing relatively accurate ROP predictions for offshore extended reach wells. Finally, the Bayesian optimization algorithm was employed to optimize parameters such as WOB, rotary speed, and displacement for ROP enhancement. The optimization results show that the ROP increases by 18.86% on average after the optimization of drilling parameters. The research results reveal the influence of drilling parameters and wellbore trajectory parameters on the ROP of extended reach wells and provide a theoretical basis for increasing the ROP of extended reach wells.
- drilling parameters,
- rate of penetration,
- machine learning,
- Bayesian optimization,
- extended reach well

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[1]	高德利,黄文君,李鑫. 大位移井钻井延伸极限研究与工程设计方法[J]. 石油钻探技术,2019,47(3):1–8. GAO Deli, HUANG Wenjun, LI Xin. Research on extension limits and engineering design methods for extended reach drilling[J]. Petroleum Drilling Techniques, 2019, 47(3): 1–8.
[2]	张剑,肖禹涵,周忠易,等. 基于TDCSO优化CNN-Bi-LSTM网络的井底钻压预测方法[J]. 石油钻探技术,2024,52(5):82–90. ZHANG Jian, XIAO Yuhan, ZHOU Zhongyi, et al. Downhole WOB prediction method based on CNN-Bi-LSTM network optimized by TDCSO[J]. Petroleum Drilling Techniques, 2024, 52(5): 82–90.
[3]	李乾,王磊,王喜杰,等. 东海大位移水平井降摩减阻技术研究与实践[J]. 中国海上油气,2022,34(6):149–156. LI Qian, WANG Lei, WANG Xijie, et al. Research and practice of friction and drag reduction technology for extended reach horizontal wells in the East China Sea[J]. China Offshore Oil and Gas, 2022, 34(6): 149–156.
[4]	纪国栋,陈畅畅,郭建华,等. 万米深井钻柱减振增能提速方法研究[J]. 石油钻探技术,2024,52(2):100–107. doi: 10.11911/syztjs.2024038 JI Guodong, CHEN Changchang, GUO Jianhua, et al. Research on vibration reduction, energy enhancement, and acceleration methods for drilling strings of 10 000-meter deep wells[J]. Petroleum Drilling Techniques, 2024, 52(2): 100–107. doi: 10.11911/syztjs.2024038
[5]	呼怀刚,黄洪春,汪海阁,等. 国内外PDC钻头新进展与发展趋势展望[J]. 石油机械,2024,52(2):1–10. HU Huaigang, HUANG Hongchun, WANG Haige, et al. New progress and development trends of PDC bits in China and Abroad[J]. China Petroleum Machinery, 2024, 52(2): 1–10.
[6]	佘朝毅. 四川盆地超深层钻完井技术进展及其对万米特深井的启示[J]. 天然气工业,2024,44(1):40–48. doi: 10.3787/j.issn.1000-0976.2024.01.004 SHE Zhaoyi. Progress in ultra-deep drilling and completion technology in the Sichuan Basin and its implications for extra-deep wells of more than ten thousand meters in depth[J]. Natural Gas Industry, 2024, 44(1): 40–48. doi: 10.3787/j.issn.1000-0976.2024.01.004
[7]	李中. 渤海深层探井钻井关键技术现状及展望[J]. 钻采工艺,2024,47(2):35–41. doi: 10.3969/J.ISSN.1006-768X.2024.02.05 LI Zhong. Challenges and technology trends prediction of deep exploration well drilling in Bohai Sea[J]. Drilling & Production Technology, 2024, 47(2): 35–41. doi: 10.3969/J.ISSN.1006-768X.2024.02.05
[8]	HEGDE C, GRAY K. Evaluation of coupled machine learning models for drilling optimization[J]. Journal of Natural Gas Science and Engineering, 2018, 56: 397–407. doi: 10.1016/j.jngse.2018.06.006
[9]	郑双进,江厚顺,熊梦园,等. 基于数据驱动和机理模型的机械钻速预测[J]. 钻采工艺,2025,48(1):78–87. doi: 10.3969/J.ISSN.1006-768X.2025.01.10 ZHENG Shuangjin, JIANG Houshun, XIONG Mengyuan, et al. Data driven and mechanistic model based prediction of rate of penetration[J]. Drilling & Production Technology, 2025, 48(1): 78–87. doi: 10.3969/J.ISSN.1006-768X.2025.01.10
[10]	伊鹏,刘衍聪,郭欣,等. 基于改进自适应遗传算法的钻井参数优化设计[J]. 石油机械,2010,38(2):30–33. YI Peng, LIU Yancong, GUO Xin, et al. Optimized design of drilling darameters based on enhanced adaptive genetic algorithm[J]. China Petroleum Machinery, 2010, 38(2): 30–33.
[11]	刘光星,李巧花. 基于改进蚁群算法的钻进参数优化[J]. 西安石油大学学报(自然科学版),2019,34(4):31–36. doi: 10.3969/j.issn.1673-064X.2019.04.006 LIU Guangxing, LI Qiaohua. Optimization of drilling parameters based on improved ant colony algorithm[J]. Journal of Xi’an Shiyou University(Natural Science Edition), 2019, 34(4): 31–36. doi: 10.3969/j.issn.1673-064X.2019.04.006
[12]	刘兆年,赵颖,孙挺. 渤海区域基于数据驱动的钻井提速[J]. 西南石油大学学报(自然科学版),2020,42(6):35–41. LIU Zhaonian, ZHAO Ying, SUN Ting. Data-driven drilling acceleration in Bohai XX Block[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 35–41.
[13]	GAN Chao, CAO Weihua, WU Min, et al. Prediction of drilling rate of penetration (ROP) using hybrid support vector regression: a case study on the Shennongjia Area, Central China[J]. Journal of Petroleum Science and Engineering, 2019, 181: 106200.
[14]	SOARES C, GRAY K. Real-time predictive capabilities of analytical and machine learning rate of penetration (ROP) models[J]. Journal of Petroleum Science and Engineering, 2019, 172: 934–959. doi: 10.1016/j.petrol.2018.08.083
[15]	NAJJARPOUR M, JALALIFAR H, NOROUZI-APOURVARI S. Half a century experience in rate of penetration management: application of machine learning methods and optimization algorithms: a review[J]. Journal of Petroleum Science and Engineering, 2022, 208(Part D): 109575.
[16]	FENG Hao, ZHOU Yadong, ZENG Weili, et al. A physics-based PSO-BPNN model for civil aircraft noise assessment[J]. Applied Acoustics, 2024, 221: 109992. doi: 10.1016/j.apacoust.2024.109992
[17]	苏兴华,孙俊明,高翔,等. 基于GBDT算法的钻井机械钻速预测方法研究[J]. 计算机应用与软件,2019,36(12):87–92. doi: 10.3969/j.issn.1000-386x.2019.12.014 SU Xinghua, SUN Junming, GAO Xiang, et al. Prediction method of drilling rate of penetration based on GBDT algorithm[J]. Computer Applications and Software, 2019, 36(12): 87–92. doi: 10.3969/j.issn.1000-386x.2019.12.014
[18]	张宏韬,唐芳,吴坤,等. 基于粒子群优化BP神经网络的激光扫描投影系统畸变预测方法[J]. 光子学报,2024,53(6):0611001. doi: 10.3788/gzxb20245306.0611001 ZHANG Hongtao, TANG Fang, WU Kun, et al. Distortion prediction method of laser scanning projection system based on PSO-BP neural network[J]. Acta Photonica Sinica, 2024, 53(6): 0611001. doi: 10.3788/gzxb20245306.0611001
[19]	陈亮,郝祎纯,李巧茹,等. 改进SSA优化的BP神经网络交通量预测模型[J]. 哈尔滨工业大学学报,2024,56(7):94–101. CHEN Liang, HAO Yichun, LI Qiaoru, et al. Traffic volume forecast model based on BP neural network optimized by improved sparrow search algorithm[J]. Journal of Harbin Institute of Technology, 2024, 56(7): 94–101.
[20]	邹红梅,朱成涛. 基于LSTM和BP神经网络的水库入库径流中长期预测比较研究[J]. 水文,2024,44(4):27–31. ZOU Hongmei, ZHU Chengtao. Comparative study on mid-long term prediction of reservoir inflow based on LSTM and BP neural network[J]. Journal of China Hydrology, 2024, 44(4): 27–31.
[21]	秦长坤,赵武胜,贾海宾,等. 基于模态分解和深度学习的煤矿微震时序预测方法[J]. 煤炭学报,2024,49(9):3781–3797. QIN Changkun, ZHAO Wusheng, JIA Haibin, et al. A method for predicting the time series of microseismic events in coal mines based on modal decomposition and deep learning[J]. Journal of China Coal Society, 2024, 49(9): 3781–3797.
[22]	盖建. 基于自动机器学习的采油井压裂效果预测方法[J]. 油气地质与采收率,2023,30(1):161–170. GE Jian. Prediction method for hydraulic fracturing effect of oil production well based on automatic machine learning technology[J]. Petroleum Geology and Recovery Efficiency, 2023, 30(1): 161–170.
[23]	郝杨杨,邹宇. 基于BP神经网络的上海生鲜农产品物流需求预测[J]. 上海海事大学学报,2024,45(1):39–45. HAO Yangyang, ZOU Yu. Logistics demand forecast of fresh agricultural products in Shanghai based on BP neural network[J]. Journal of Shanghai Maritime University, 2024, 45(1): 39–45.
[24]	雍锐. 智能钻井多目标协同优化系统研究与应用[J]. 钻采工艺,2024,47(3):9–14. YONG Rui. Research and application of intelligent drilling advisory system[J]. Drilling & Production Technology, 2024, 47(3): 9–14.
[25]	葛亮,滕怡,肖国清,等. 基于井下环空参数的溢流智能预警技术研究[J]. 西南石油大学学报(自然科学版),2023,45(2):126–134. GE Liang, TENG Yi, XIAO Guoqing, et al. Research on overflow intelligent warning technology based on downhole annulus parameters[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2023, 45(2): 126–134.
[26]	高云伟,罗利民,薛凤龙,等. 基于Stacking集成学习的机械钻速预测方法[J]. 石油机械,2024,52(5):17–24. GAO Yunwei, LUO Limin, XUE Fenglong, et al. ROP prediction method based on stacking ensemble learning[J]. China Petroleum Machinery, 2024, 52(5): 17–24.
[27]	姜宝胜,白玉湖,徐兵祥,等. 基于集成学习的致密气藏产能预测新方法[J]. 中国海上油气,2024,36(5):120–127. JIANG Baosheng, BAI Yuhu, XU Bingxiang, et al. A novel approach for predicting production capacity of tight gas reservoirs based on ensemble learning[J]. China Offshore Oil and Gas, 2024, 36(5): 120–127.
[28]	CHEN Xuyue, WENG Chengkai, DU Xu, et al. Prediction of the rate of penetration in offshore large-scale cluster extended reach wells drilling based on machine learning and big-data techniques[J]. Ocean Engineering, 2023, 285(part 2): 115404.
[29]	汤明,王汉昌,何世明,等. 基于PCA-BP算法的机械钻速预测研究[J]. 石油机械,2023,51(10):23–31. TANG Ming, WANG Hanchang, HE Shiming, et al. Prediction for rate of penetration based on PCA-BP algorithm[J]. China Petroleum Machinery, 2023, 51(10): 23–31.
[30]	黄哲. 探管式智能钻头参数测量装置研制与现场试验[J]. 石油钻探技术,2024,52(4):34–43. doi: 10.11911/syztjs.2024004 HUANG Zhe. Development and field test of probe-type intelligent bit parameter measurement device[J]. Petroleum Drilling Techniques, 2024, 52(4): 34–43. doi: 10.11911/syztjs.2024004

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Drilling Speed Enhancement Method for Extended Reach Wells Based on Machine Learning and Bayesian Optimization

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