ECD Sensitivity Analyses and Prediction Based on Interpretable Machine Learning

MA Lei; ZHOU Bo; ZHANG Ningjun; YANG Heng; CAI Xinshu; LIU Zheng; XU Tongtai

doi:10.12358/j.issn.1001-5620.2023.05.003

Volume 40 Issue 5

Dec. 2023

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MA Lei, ZHOU Bo, ZHANG Ningjun, et al.Ecd sensitivity analyses and prediction based on interpretable machine learning[J]. Drilling Fluid & Completion Fluid，2023, 40（5）：563-570 doi: 10.12358/j.issn.1001-5620.2023.05.003

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MA Lei, ZHOU Bo, ZHANG Ningjun, et al.Ecd sensitivity analyses and prediction based on interpretable machine learning[J]. Drilling Fluid & Completion Fluid，2023, 40（5）：563-570 doi: 10.12358/j.issn.1001-5620.2023.05.003

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ECD Sensitivity Analyses and Prediction Based on Interpretable Machine Learning

doi: 10.12358/j.issn.1001-5620.2023.05.003

1.
PetroChina Tarim Oilfield Company, Korla, Xinjiang 841000
2.
Kunlun Digital Technology Co., Ltd., Beijing 102206
3.
Beijing Shida Huyang Petroleum Technology Development Co., Ltd., Beijing 102206

Received Date: 2023-03-27
Rev Recd Date: 2023-04-25
Publish Date: 2023-12-25

Abstract

Abstract

The calculation of equivalent circulation density (ECD) of drilling fluids is very cumbersome and time-consuming, and the pattern of sensitivity of the ECD has not been quite understood yet. To solve this problem, 1928 data from the Block Keshen were acquired and analyzed using the ProHydraulic software to calculate the theoretical ECD. Based on the calculation some characteristic parameters were determined. Key factors affecting the sensitivity of the ECDs of a drilling fluid, such as mud properties, drilling parameters and sizes of the annular spaces are analyzed using interpretable machine learning technology. Using linear regression, an empirical equation for calculating the ECDs of drilling fluids in the Block Keshen, which covers 12 main characteristic parameters, is constructed. ECD calculation with the model shows that the model gives results that fit the practical values with excellence. The coefficient of determination of the testing set is 0.963, and the average absolute error is only 0.04, indicating that this empirical equation is simple and efficient in ECD calculation in practical engineering application.
- ECD,
- Sensitivity analysis,
- Interpretable machine learning,
- Linear regression model

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References(18)

References

[1]	BOURGOYNE A T, MILLHEIM K K, CHENEVERT M E, et al. Applied drilling engineering: 2[M]. Richardson, TX: Society of Petroleum Engineers Richardson, 1986.
[2]	陈浩. 钻井过程中基于ECD的工程风险评价方法研究[D]. 青岛: 中国石油大学(华东), 2018. CHEN Hao. Research on drilling risk assessment during drilling process based on ECD[D]. Qingdao: China University of Petroleum(East China), 2018.
[3]	张更,李军,柳贡慧,等. 海上高温高压井环空ECD精细预测模型[J]. 钻井液与完井液,2021,38(6):698-704. ZHANG Geng, LI Jun, LIU Gonghui, et al. A precise model for prediction of annular ECD in offshore HTHP wells[J]. Drilling Fluid & Completion Fluid, 2021, 38(6):698-704.
[4]	马贤明, 管志川, 罗鸣, 等. 水平井钻井过程中ECD的动态预测方法研究[C]//2019第四届能源, 环境与自然资源国际会议论文集, 2019: 53-59. MA Xianming, GUAN Zhichuan, LUO Ming, et al. Study on the dynamic prediction method of ECD during the drilling process of horizontal wells[C]//Proceedings of the 4th International Conference on Energy, Environment and Natural Resources, 2019: 53-59.
[5]	翟羽佳,汪志明,郭晓乐. 深水钻井水力参数计算及优选方法[J]. 中国海上油气,2013,25(1):59-63,68. ZHAI Yujia, WANG Zhiming, GUO Xiaole. The method for calculation and optimization of deep water drilling hydraulic parameters[J]. China Offshore Oil and Gas, 2013, 25(1):59-63,68.
[6]	吕华东. 高温高压条件下的井低波动压力预测模型研究[D]. 大庆: 东北石油大学, 2014. LYU Huadong. Prediction model of surge pressure at high temperature and high pressure[D]. Daqing: Northeast Petroleum University, 2014.
[7]	VAJARGAH A K, SULLIVAN G, OORT E V. Automated fluid rheology and ECD management[C]//SPE Deepwater Drilling and Completions Conference. Galveston, Texas, USA, 2016: SPE-180331-MS.
[8]	ASHENA R, BAHREINI H, GHALAMBOR A, et al. Investigation of parameters controlling equivalent circulating density ECD in managed pressure drilling MPD[C]//SPE International Conference and Exhibition on Formation Damage Control. Lafayette, Louisiana, USA, 2022: SPE-208869-MS.
[9]	PETRIE S W, DOLL R. Benefits of using continuous circulation systems in ERD wells to manage ECD, Bottom hole pressure and hole cleaning[C]//SPE/IADC Middle East Drilling Technology Conference and Exhibition. Abu Dhabi, UAE, 2021: SPE-202140-MS.
[10]	王凯,和鹏飞,陈波,等. 基于Wellplan的ECD敏感性分析与精确预测技术[J]. 石油化工应用,2021,40(2):16-19,33. WANG Kai, HE Pengfei, CHEN Bo, et al. Sensitivity analysis and accurate prediction of ECD based on Wellplan[J]. Petrochemical Industry Application, 2021, 40(2):16-19,33.
[11]	高永德,董洪铎,胡益涛,等. 深水高温高压井钻井液当量循环密度预测模型及应用[J]. 特种油气藏,2022,29(3):138-143. GAO Yongde, DONG Hongduo, HU Yitao, et al. Prediction model and application of drilling fluid equivalent circulating density in deepwater High-Temperature and High-Pressure wells[J]. Special Oil & Gas Reservoirs, 2022, 29(3):138-143.
[12]	ABDELGAWAD K Z, ELZENARY M, ELKATATNY S, et al. New approach to evaluate the equivalent circulating density (ECD) using artificial intelligence techniques[J]. Journal of Petroleum Exploration and Production Technology, 2019, 9(2):1569-1578. doi: 10.1007/s13202-018-0572-y
[13]	AHMED R, ENFIS M, MIFTAH-EL-KHEIR H, et al. The effect of drillstring rotation on equivalent circulation density: modeling and analysis of field measurements[C]//SPE Annual Technical Conference and Exhibition. Florence, Italy, 2010: SPE-135587-MS.
[14]	ROBINSON T, GOMES D, MEOR HASHIM M M H, et al. Real-Time estimation of downhole equivalent circulating density ECD using machine learning and applications[C]//IADC/SPE International Drilling Conference and Exhibition. Galveston, Texas, USA, 2022: SPE-208675-MS.
[15]	GAMAL H, ABDELAAL A, ELKATATNY S. Machine learning models for equivalent circulating density prediction from drilling data[J]. ACS Omega, 2021, 6(41):27430-27442. doi: 10.1021/acsomega.1c04363
[16]	ALSAIHATI A, ELKATATNY S, GAMAL H, et al. A statistical machine learning model to predict equivalent circulation density ECD while drilling, based on principal components analysis PCA[C]//SPE/IADC Middle East Drilling Technology Conference and Exhibition. Abu Dhabi, UAE, 2021: SPE-202101-MS.
[17]	LUNDBERG S M, ERION G G, LEE S I. Consistent individualized feature attribution for tree ensembles[EB/OL]. (2019-03-07)[2023-05-05]. http://export.arxiv.org/abs/1802.03888v3.
[18]	WEISBERG S. Applied linear regression[M]. Hoboken, NJ: John Wiley & Sons, 2005.

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ECD Sensitivity Analyses and Prediction Based on Interpretable Machine Learning

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