نشریه علمی-پژوهشی مکانیک سنگ

نشریه علمی-پژوهشی مکانیک سنگ

پیش‌بینی پنجره ایمن وزن گل حفاری با استفاده از مدل‌های ترکیبی یادگیری ماشین مبتنی بر پرسپترون چندلایه و الگوریتم‌های بهینه‌سازی

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشگاه صنعتی بیرجند
2 دانش آموخته مهندسی نفت، باشگاه پژوهشگران جوان و نخبگان، دانشگاه آزاد اسلامی، اهواز، ایران
3 عضو هیات علمی گروه مهندسی عمران دانشگاه صنعتی بیرجند
10.22034/irsrm.2026.583681.1082
چکیده
تعیین دقیق پنجره ایمن وزن گل حفاری یکی از مسائل کلیدی در مهندسی حفاری است که نقش مهمی در جلوگیری از ناپایداری چاه، هرزروی گل و افزایش هزینه‌های عملیاتی دارد. در این پژوهش، جهت پیش‌بینی حداقل وزن گل زیر فشار شکست و حداکثر وزن گل بالاتر از فشار شکست، از مدل‌های ترکیبی یادگیری ماشین استفاده شده است. در این راستا، شبکه عصبی پرسپترون چندلایه با چهار الگوریتم بهینه‌سازی شامل الگوریتم کرم شب‌تاب، جستجوی گرانشی، کلونی زنبورعسل و الگوریتم رقابت استعماری ترکیب شد. داده‌های مورد استفاده شامل اطلاعات سه چاه از یک میدان گازی در جنوب ایران بوده که به سه بخش آموزش (70٪)، اعتبارسنجی (15٪) و آزمون (15٪) تقسیم شدند. نتایج آماری نشان داد که، «الگوریتم پرسپترون چندلایه- الگوریتم جستجوی گرانشی» از دیگر مدل‌ها، دقیق‌تر است. چنان-که زیرمجموعه آزمایشی الگوریتم مذکور، برای حداقل وزن گل زیر فشار شکست با مقدار ریشه میانگین مربعات خطای 12.283پوند بر اینچ مربع و ضریب تعیین0.90و حداکثر وزن گل بالای فشار شکست با ریشه میانگین مربعات خطا 12.536 پوند بر اینچ مربع و ضریب تعیین 0.9417 کمترین خطا را نشان داده‌اند. بر این اساس، استفاده از مدل‌های ترکیبی یادگیری ماشین می‌تواند دقت پیش‌بینی پنجره ایمن وزن گل را به‌طور قابل توجهی افزایش دهد و به‌عنوان ابزاری کارآمد در تصمیم‌گیری‌های عملیاتی حفاری مورد استفاده قرار گیرد.
کلیدواژه‌ها

[1] Chen, C. Ji, G. Wang, H. Huang, H. Baud, P. Wu, Q. (2022). Geology-engineering integration to improve drilling speed and safety in ultra-deep clastic reservoirs of the Qiulitage structural belt. Advances in Geo-Energy Research, (6), 347-356.
[2] Asadi, A. (2017). Application of artificial neural networks in prediction of uniaxial compressive strength of rocks using well logs and drilling data. Procedia Engineering, (191), 279-286.
[3] Perchikolaee, RS. Shadizadeh, SR. Shahryar, K. Kazemzadeh, E. (2010). Building a Precise Mechanical Earth Model and its Application in Drilling Operation Optimization: A Case Study of Asmari Formation in Mansuri Oil Field. SPE, 132204.
[4] Zevgolis, IE. Deliveris, AV. Koukouzas, NC. (2018). Probabilistic design optimization and simplified geotechnical risk analysis for large open pit excavations. Computers and Geotechnics, (103), 153-164.
[5] Jiang, G. Tengfei, D. Kaixiao, CUI. Yinbo, HE. Xiaohu, Q. Lili, Y. (2022). Research status and development directions of intelligent drilling fluid technologies. Petroleum Exploration and Development, (49), 660-670.
[6] Khakzad, N. Khan, F. Amyotte, P. (2013). Quantitative risk analysis of offshore drilling operations: A Bayesian approach. Safety science, (57), 108-117.
[7] Al-Nutaifi, AM. (2014). Wellbore Instability Analysis in a Highly Fractured Carbonate Gas Reservoirs. International Petroleum Technology Conference, Kuala Lumpur, Malaysia, 1-10.
[8] Maleki, S. Gholami, R. Rasouli, V. Moradzadeh, A. Riabi, RG. Sadaghzadeh, F. (2014). Comparison of different failure criteria in prediction of safe mud weigh window in drilling practice. Earth-Science Reviews, (136), 36-58.
[9] Akbarpour, M. Abdideh, M. (2020). Wellbore stability analysis based on geomechanical modeling using finite element method. Modeling Earth Systems and Environment, (6), 617-626.
[10] Epelle, EI. Gerogiorgis, DI. (2020). A review of technological advances and open challenges for oil and gas drilling systems engineering. AIChE Journal, (66), 16842.
[11] Jung, H. Jeon, J. Choi, D. Park, J-Y. (2021). Application of machine learning techniques in injection molding quality prediction: Implications on sustainable manufacturing industry. Sustainability, (13), 4120.
[12] Zhang, W. Li, H. Li, Y. Liu, H. Chen, Y. Ding, X. (2021). Application of deep learning algorithms in geotechnical engineering: a short critical review. Artificial Intelligence Review, (54), 1-41.
[13] Abidin, MH. (2014). Pore pressure estimation using artificial neural network. A project dissertation submitted to the bachelor of Petroleum Engineering, PETRONAS University of Technology.
[14] Kiss, A. Fruhwirth, RK. Pongratz, R. Maier, R. Hofstätter, H. (2018). Formation breakdown pressure prediction with artificial neural networks. SPE, D023S09R02.
[15] Ahmed, A. Elkatatny, S. Ali, A. Abughaban, M. Abdulraheem, A. (2020). Application of artificial intelligence techniques in predicting the lost circulation zones using drilling sensors. Journal of Sensors, (2020), 1-18.
[16] Zahiri, J. Abdideh, M. Ghaleh Golab, E. (2019). Determination of safe mud weight window based on well logging data using artificial intelligence. Geosystem Engineering, (22), 193-205.
[17] Matinkia, M. Amraeiniya, A. Behboud, MM. Mehrad, M. Bajolvand, M. Gandomgoun, MH. (2022). A novel approach to pore pressure modeling based on conventional well logs using convolutional neural network. Journal of Petroleum Science and Engineering, (211), 110-156.
[18] Phan, DT. Liu, C. AlTammar, MJ. Han, Y. Abousleiman, YN. (2022). Application of artificial intelligence to predict time-dependent mud-weight windows in real time. SPE Journal, (27), 39-59.
[19] Beheshtian,S., Rajabi,M., Davoodi,S., Wood,D.A, Ghorbani,H., Mohamadian,N., Ahmadi Alvar,M., Band,S. (2022),  Robust computational approach to determine the safe mud weight window using well-log data from a large gas reservoir, Marine and Petroleum Geology (142) ,105772.
[20] Zhang, W., Li, H., Liu, Y., & Chen, X. (2025). Machine learning approach for prediction of safe mud weight window based on geochemical drilling log data. Frontiers in Earth Science, 13, 1529320. https://doi.org/10.3389/feart.2025.1529320
[21] Chen, C., Wang, G., Huang, H., Wu, Q., & Baud, P. (2025). Prediction of mud weight window based on geological sequence matching and a physics-driven machine learning model. Processes, 13(7), 2255. https://doi.org/10.3390/pr13072255
 [22] Gao, G. Hazbeh, O. Davoodi, S. Tabasi, M. Rajabi, M. Ghorbani, H. et al. (2023). Prediction of fracture density in a gas reservoir using robust computational approaches. Frontiers in Earth Science, (10), 1023578.
[23] Sánchez-Gutiérrez, ME. González-Pérez, PP. (2022). Multi-Class Classification of Medical Data Based on Neural Network Pruning and Information-Entropy Measures. Entropy, (24), 196.
[24] Ross, A. Leroux, N. De Riz, A. Marković, D. Sanz-Hernández, D. Trastoy, J. et al. (2023). Multilayer spintronic neural networks with radiofrequency connections. Nature Nanotechnology, (1), 1-8.
[25] Guo, H. Guo, C. Xu, B. Xia, Y. Sun, F. (2021). MLP neural network-based regional logistics demand prediction. Neural Computing and Applications, (33), 3939-3952.
[26] Desai, M. Shah, M. (2021). An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical eHealth, (4), 1-11.
[27] Da Silva, IN. Hernane Spatti, D. Andrade Flauzino, R. Liboni, LHB. dos Reis Alves, SF. et al. (2017). Artificial neural network architectures and training processes. Switzerland: Springer, 62 .
[28] Gautam, GD. Mishra, DR. (2019). Firefly algorithm based optimization of kerf quality characteristics in pulsed Nd: YAG laser cutting of basalt fiber reinforced composite. Composites, (176), 107340.
[29] Ghorbani, H. Moghadasi, J. Wood, DA. (2017). Prediction of gas flow rates from gas condensate reservoirs through wellhead chokes using a firefly optimization algorithm. Journal of Natural Gas Science and Engineering, (45), 256-271.
[30] Çelik, Y. Kutucu H. (2018). Solving the Tension/Compression Spring Design Problem by an Improved Firefly Algorithm, IDDM, (1), 1-7.
[31] Naserbegi, A. Aghaie, M. Minuchehr, A. Alahyarizadeh, G. (2018). A novel exergy optimization of Bushehr nuclear power plant by gravitational search algorithm (GSA). Energy, (148), 373-385.
[32] Rashedi, E. Rashedi, E. Nezamabadi-Pour, H. (2018). A comprehensive survey on gravitational search algorithm. Swarm and evolutionary computation, (41), 141-158.
[33] Duman, S. Sönmez, Y. Güvenç, U. Yörükeren, N. (2012). Optimal reactive power dispatch using a gravitational search algorithm. IET generation, transmission & distribution, (6), 563-576.
[34] Hashemi, A. Dowlatshahi, MB. Nezamabadi-Pour, H. (2021). Gravitational Search Algorithm: Theory, Literature Review, and Applications. Handbook of AI-based Metaheuristics, Boca Raton: CRC Press, 119-150.
[35] Sharma, A. Choudhary, S. Pachauri, RK. Shrivastava, A. Kumar, D. (2020). A review on artificial bee colony and it’s engineering applications. Journal of Critical Reviews, (7), 4097-4107.
[36] Aslan, S. Karaboga, D. (2022). A genetic Artificial Bee Colony algorithm for signal reconstruction based big data optimization. Applied Soft Computing, (88), 106053.
[37] Rahnema, N. Gharehchopogh, FS. (2020). An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering. Multimedia Tools and Applications, (79), 32169-32194.
[38] Talatahari, S. Azar, BF. Sheikholeslami, R. Gandomi, AH. (2012). Imperialist competitive algorithm combined with chaos for global optimization. Communications in Nonlinear Science and Numerical Simulation, (17), 1312-1319.
[39] Mesleh, AM. (2017). Lung Cancer Detection Using Multi-Layer Neural Networks with Independent Component Analysis: A Comparative Study of Training Algorithms. Jordan Journal of Biological Sciences, (10), 239-249.
[40] Shawli, A. (2011). Scoring the SF-36 health survey in scleroderma using independent component analysis and principle component analysis. Master of Science thesis. McGill University, Department of Mathematics and Statistics.
[41] Rutledge, DN. Bouveresse, DJ-R. (2013). Independent components analysis with the JADE algorithm. TrAC Trends in Analytical Chemistry, (50), 22-32.
[42] Ghasemi, M. Ghavidel, S. Ghanbarian, MM. Massrur, HR. Gharibzadeh, M. (2014). Application of imperialist competitive algorithm with its modified techniques for multi-objective optimal power flow problem: a comparative study. Information Sciences, (281), 225-247.
[43] Li, B. Tang, ZB. (2022). Double-assimilation of prosperity and destruction oriented improved imperialist competitive algorithm with computational thinking. IEEE, (1), 1-8.
[44] Bizon, K. Continillo, G. Lombardi, S. Sementa, P. Vaglieco, BM. (2016). Independent component analysis of cycle resolved combustion images from a spark ignition optical engine. Combustion and Flame, (163), 258-269.

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