Physically-Data Driven Approach for Predicting Formation Leakage Pressure: A Dual-Drive Method
-
Published:2023-09-08
Issue:18
Volume:13
Page:10147
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
Li Huayang12ORCID, Tan Qiang12, Li Bojia12, Feng Yongcun12, Dong Baohong12, Yan Ke12, Ding Jianqi12, Zhang Shuiliang3, Guo Jinlong4, Deng Jingen12, Chen Jiaao5
Affiliation:
1. School of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102200, China 2. State Key Laboratory of Petroleum Resource & Prospecting, China University of Petroleum (Beijing), Beijing 102249, China 3. CNOOC Tianjin Branch, Tianjin 300459, China 4. Shanghai Quartermaster and Energy Quality Supervision Station, Quartermaster and Energy Quality Supervision Station, Joint Logistics Support Force, Shanghai 200137, China 5. School of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao 266580, China
Abstract
Formation leak-off pressure, which sets the upper limit of the safe drilling fluid density window, is crucial for preventing wellbore accidents and ensuring safe and efficient drilling operations. The paper thoroughly examines models of drilling physics alongside artificial intelligence techniques. The study introduces a dual-driven method for predicting reservoir pore pressure by integrating long short-term memory (LSTM) and backpropagation (BP) neural networks, where the core component is the LSTM-BP neural network model. The input data for the LSTM-BP model include wellbore diameter, formation density, sonic time, natural gamma, mud content, and pore pressure. The study demonstrates the practical application of the method using two vertical wells in Block M, employing the M-1 well for training and the M-2 well for validation. Two distinct input layer configurations are devised for the LSTM-BP model to evaluate the influence of formation density on prediction accuracy. Notably, Scheme 2 omits formation density as a variable in contrast to Scheme 1. The study’s results indicate that, for input layer configurations corresponding to Scenario 1 and Scenario 2, the LSTM-BP model exhibits relative error ranges of (−2.467%, 2.510%) and (−6.141%, 5.201%) on the test set, respectively. In Scenario 1, the model achieves mean squared error (MSE), mean absolute error (MAE), and R-squared (R2) values of 0.000229935, 0.011198329, and 0.92178272, respectively, on the test set. Conversely, for Scenario 2, the model demonstrates a substantial escalation of 992.393% and 240.674% in MSE and MAE, respectively, compared to Scenario 1; however, R2 diminishes by 66.920%. Utilizing the trained LSTM-BP model, predictions for formation lost pressure in Well M-2 reveal linear correlation coefficients of 0.8173 and 0.6451 corresponding to Scenario 1 and Scenario 2, respectively. These findings imply that the predictions from the Scenario 1 model demonstrate stronger alignment with results derived from formulaic calculations. These observations remain consistent for both the BP neural network algorithm and the random forest algorithm. The aforementioned research results not only highlight the elevated predictive precision of the LSTM-BP model for intelligent prediction of formation lost pressure, a product of this study, thereby furnishing valuable data points to enhance the security of drilling operations in Block M, but also underscore the necessity of deliberating both physical relevance and data correlation during the selection of input layer variables.
Funder
National Natural Science Foundation of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference33 articles.
1. Arshad, U., Jain, B., Ramzan, M., Alward, W., Diaz, L., Hasan, I., Aliyev, A., and Riji, C. (2015, January 6). Engineered solution to reduce the impact of lost circulation during drilling and cementing in Rumaila Field. Proceedings of the International Petroleum Technology Conference, Doha, Qatar. 2. Geomechanics of Lost-Circulation Events and Wellbore-Strengthening Operations;Mehrabian;SPE J.,2015 3. Research progress and prospect of plugging technologies for fractured formation with severe lost circulation;Sun;Pet. Explor. Dev.,2021 4. Temporary sealing technology to control formation damage induced by drill-in fluid loss in fractured tight gas reservoir;Kang;J. Nat. Gas Sci. Eng.,2014 5. Prediction and control model of shale induced fracture leakage pressure;Zhai;J. Pet. Sci. Eng.,2021
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|