Main Control Factors of Fluid Loss in Drilling and Completion in Shunbei Oilfield by Unmanned Intervention Algorithm
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32870
Main Control Factors of Fluid Loss in Drilling and Completion in Shunbei Oilfield by Unmanned Intervention Algorithm

Authors: Peng Zhang, Lihui Zheng, Xiangchun Wang, Xiaopan Kou


Quantitative research on the main control factors of lost circulation has few considerations and single data source. Using Unmanned Intervention Algorithm to find the main control factors of lost circulation adopts all measurable parameters. The degree of lost circulation is characterized by the loss rate as the objective function. Geological, engineering and fluid data are used as layers, and 27 factors such as wellhead coordinates and Weight on Bit (WOB) used as dimensions. Data classification is implemented to determine function independent variables. The mathematical equation of loss rate and 27 influencing factors is established by multiple regression method, and the undetermined coefficient method is used to solve the undetermined coefficient of the equation. Only three factors in t-test are greater than the test value 40, and the F-test value is 96.557%, indicating that the correlation of the model is good. The funnel viscosity, final shear force and drilling time were selected as the main control factors by elimination method, contribution rate method and functional method. The calculated values of the two wells used for verification differ from the actual values by -3.036 m3/h and -2.374 m3/h, with errors of 7.21% and 6.35%. The influence of engineering factors on the loss rate is greater than that of funnel viscosity and final shear force, and the influence of the three factors is less than that of geological factors. The best combination of funnel viscosity, final shear force and drilling time is obtained through quantitative calculation. The minimum loss rate of lost circulation wells in Shunbei area is 10 m3/h. It can be seen that man-made main control factors can only slow down the leakage, but cannot fundamentally eliminate it. This is more in line with the characteristics of karst caves and fractures in Shunbei fault solution oil and gas reservoir.

Keywords: Drilling fluid, loss rate, main controlling factors, Unmanned Intervention Algorithm.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 331


[1] Feng Ying, Wang Xiaonan. Analysis of lost circulation mechanism and lost circulation prevention and plugging technology in Hangjinqi work area (J). Western exploration engineering, 2017,29 (10): 82-85+87.
[2] Li Zhanjun. Mechanism and Countermeasures of leak prevention and plugging in southern Dagang Oilfield (J). PetroChina, 2017 (10): 113-114.
[3] Li Ning, li long, Wang Tao, Liu Xiao, Liu Yushuang, Zhou Zhishi, Guo Bin. Leakage mechanism analysis and leakage control measures of salt gypsum layer and target layer in front of Kuqa mountain (J). Guangzhou chemical industry, 2020,48 (11): 101-103.
[4] Wang haibiao. Research on intelligent identification and processing decision of lost circulation (D). Southwest Petroleum University, 2017.
[5] Liu Biao, Li Chenxiao, Li Shuanggui, et al. Lost circulation prediction based on support vector regression (J). Drilling and production technology, 2019,42 (06): 17-20 + 1-2.
[6] Abbas A K, Hamed H M, Al-Bazzaz W, et al. Predicting the Amount of Lost Circulation While Drilling Using Artificial Neural Networks: An Example of Southern Iraq Oil Fields(C)// SPE Gas & Oil Technology Showcase and Conference. 2019.
[7] Alkinani H H, Al-Hameedi A, Dunn-Norman S, et al. Prediction of Lost Circulation Prior to Drilling for Induced Fractures Formations Using Artificial Neural Networks(C)// SPE Oklahoma City Oil and Gas Symposium. 2019.
[8] He Pengfei, Liu Xiaobin, Chen Zhen, Shi Min, Chen Yushan. Study on lost circulation prediction based on depth neural network model (J). Tianjin Science and technology, 2019,46 (S1): 21-23.
[9] Hou X, Yang J, Yin Q, et al. Lost Circulation Prediction in South China Sea using Machine Learning and Big Data Technology(C)// Offshore Technology Conference. 2020.
[10] Sabah M, Talebkeikhah M, Agin F, et al. Application of decision tree, artificial neural networks, and adaptive neuro-fuzzy inference system on predicting lost circulation: A case study from Marun oil field(J). Journal of Petroleum Science & Engineering, 2019.
[11] Li Jian, Fu Xiaobin, Wu Yuanyuan. Lost circulation classification algorithm based on optimized ID3 (J). Computer Engineering, 2019,45 (02): 290-295.
[12] Li Z, Chen M, Jin Y, et al. Study on Intelligent Prediction for Risk Level of Lost Circulation While Drilling Based on Machine Learning(C)// 52nd U.S. Rock Mechanics/Geomechanics Symposium. 2018.
[13] Al-Hameedi, AT, Alkinani, et al. Insights into Mud Losses Mitigation in the Rumaila Field, Iraq(J). Journal of Petroleum & Environmental Biotechnology, 2018, 9(1):1-10.
[14] Zheng Lihui, Yan Jienian, Chen Mian, Zhang Guangqing. Optimization model of working fluid cost control in oil and gas wells (J). Acta petrologica Sinica, 2005 (04): 102-105.
[15] Zheng Li-hui, Wang Jin-feng, Li Xiao-peng, Zhang Yan, Li Du. Optimization of rheological parameter for micro-bubble drilling fluids by multiple regression experimental design (J). J. Cent. South Univ. Technol. (2008) 15(s1): 424−428.
[16] Zheng Lihui, Liu Hao, Zeng Hao, Wu Tong, Zhang Wenchang, Wang Chao. Evaluation of damage degree of working fluid in fractured reservoir by flow replacement permeability (J). Natural gas industry, 2019,39 (12): 74-80.
[17] Zheng Lihui, Li Xiuyun, Su Guandong, Zhao Wei, Gong Xuguang, Tao Xiujuan. Study on suitability of damage evaluation method for coalbed methane working fluid reservoir (J). Natural gas industry, 2018,38 (09): 28-39.
[18] Tao Shan, Yu Xing, song Hai, Liao Yamin, Chang Qifan, fan Jingjing. Using big data method to find the main controlling factors of wellbore collapse in the production process of Shunbei carbonate reservoir (J). Petroleum drilling and production technology, 2020,42 (05): 627-631.
[19] Ren Yiwei, Nie Shuaishuai, Duan Baojiang, Liu Ting, Wang Wensheng, Lou Xuanqing. A Novel Method for Quantitative Analysis of Engineering Factors Influencing CBM Production(J). International Journal of Engineering and Technical Research (IJETR)ISSN: 2321-0869 (O) 2454-4698 (P), Volume-1, Issue-1, June 2016.
[20] Jinfeng Wang, Lihui Zheng, Bowen Li, Jingen Deng. A novel method applied to optimize oil and gas well working fluids (J). International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869 (O) 2454-4698 (P), Volume-5, Issue-1, May 2016.