An Approach for Estimation of Uniaxial Compressive Strength and Internal Friction Angle using Well Log Data and Deep Learning Algorithms | ||
| Journal of Mining and Environment | ||
| مقاله 15، دوره 16، شماره 5، مهر و آبان 2025، صفحه 1759-1780 اصل مقاله (3.14 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2024.15006.2859 | ||
| نویسندگان | ||
| Farhad Mollaei؛ Ali Moradzadeh* ؛ Reza Mohebian | ||
| School of Mining, College of Engineering, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| The important aspects of this study are to estimate the mechanical parameters of reservoir rock including Uniaxial Compressive Strength (UCS) and friction (FR) angle using well log data. The aim of this research is to estimate the UCS and FR angle (φ) using new deep learning (DL) methods including Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and CNN + LSTM (CL) by well log and core test data of one Iranian hydrocarbon field. As only 12 UCS and 6 FR core tests of single well in this field were available, they were firstly calculated, and then generalized to other depths using two newly derived equations and relevant logs. Next, the effective input logs' data for predicting these parameters have been selected by an auto-encoder DL method, and finally, the values of UCS and φ angle were predicted by the MLP, LSTM, CNN, and CL networks. The efficiency of these four prediction models was then evaluated using a blind dataset, and a range of statistical measures applied to training, testing, and blind datasets. Results show that all four models achieve satisfactory prediction accuracy. However, the CL model outperformed the others, yielding the lowest RMSE of 1.0052 and the highest R² of 0.9983 for UCS prediction, along with an RMSE of 0.0201 and R² of 0.9917 for φ angle prediction on the blind dataset. These findings highlight the high accuracy of deep learning algorithms, particularly the CL algorithm, which demonstrates superior precision compared to the MLP method. | ||
| کلیدواژهها | ||
| Mechanical parameters؛ Log data؛ DL models؛ Core data؛ Feature selection | ||
| مراجع | ||
|
[1] Cargill, J. S., & Shakoor, A. (1990, December). Evaluation of empirical methods for measuring the uniaxial compressive strength of rock. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts (Vol. 27, No. 6, pp. 495-503). Pergamon.
[2] Tuğrul, A., & Zarif, I. H. (1999). Correlation of mineralogical and textural characteristics with engineering properties of selected granitic rocks from Turkey. Engineering geology, 51(4), 303-317.
[3] Karakus, M., & Tutmez, B. (2006). Fuzzy and multiple regression modelling for evaluation of intact rock strength based on point load, Schmidt hammer and sonic velocity. Rock mechanics and rock engineering, 39, 45-57.
[4] Yagiz, S. (2011). P-wave velocity test for assessment of geotechnical properties of some rock materials. Bulletin of Materials Science, 34, 947-953.
[5] Singh, R., Vishal, V., Singh, T. N., & Ranjith, P. G. (2013). A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Computing and Applications, 23, 499-506.
[6] Aladejare, A. E. (2020). Evaluation of empirical estimation of uniaxial compressive strength of rock using measurements from index and physical tests. Journal of Rock Mechanics and Geotechnical Engineering, 12(2), 256-268.
[7] Hazbeh, O., Rajabi, M., Tabasi, S., Lajmorak, S., Ghorbani, H., Radwan, A. E., & Molaei, O. (2024). Determination and investigation of shear wave velocity based on one deep/machine learning technique. Alexandria Engineering Journal, 92, 358-369.
[8] Mollaei, F., Moradzadeh, A., & Mohebian, R. (2024). Estimation brittleness index in carbonate environments using log and lithology data and deep learning techniques. Italian journal of engineering geology and environment, (1), 49-66.
[9] Mollaei, F., Moradzadeh, A., & Mohebian, R. (2024). Novel approaches in geomechanical parameter estimation using machine learning methods and conventional well logs. Geosystem Engineering, 27(5), 252-277.
[10] Gokceoglu, C., & Zorlu, K. (2004). A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Engineering Applications of Artificial Intelligence, 17(1), 61-72.
[11] Yilmaz, I., & Yuksek, A. G. (2008). An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mechanics and Rock Engineering, 41(5), 781-795.
[12] Tiryaki, B. (2008). Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees. Engineering Geology, 99(1-2), 51-60.
[13] Sarkar, K., Tiwary, A., & Singh, T. N. (2010). Estimation of strength parameters of rock using artificial neural networks. Bulletin of engineering geology and the environment, 69, 599-606.
[14] Dehghan, S., Sattari, G. H., Chelgani, S. C., & Aliabadi, M. A. (2010). Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Mining Science and Technology (China), 20(1), 41-46.
[15] Manouchehrian, A., Sharifzadeh, M., & Moghadam, R. H. (2012). Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics. International Journal of Mining Science and Technology, 22(2), 229-236.
[16] Rabbani, E., Sharif, F., Koolivand Salooki, M., & Moradzadeh, A. (2012). Application of neural network technique for prediction of uniaxial compressive strength using reservoir formation properties. International journal of rock mechanics and mining sciences, 56, 100-111.
[17] Singh, T. N., & Verma, A. K. (2012). Comparative analysis of intelligent algorithms to correlate strength and petrographic properties of some schistose rocks. Engineering with Computers, 28, 1-12.
[18] Yesiloglu-Gultekin, N., Gokceoglu, C., & Sezer, E. A. (2013). Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances. International Journal of Rock Mechanics and Mining Sciences, 62, 113-122.
[19] Majdi, A., & Rezaei, M. (2013). Prediction of unconfined compressive strength of rock surrounding a roadway using artificial neural network. Neural Computing and Applications, 23, 381-389.
[20] Mishra, D. A., & Basu, A. (2013). Estimation of uniaxial compressive strength of rock materials by index tests using regression analysis and fuzzy inference system. Engineering Geology, 160, 54-68.
[21] Rezaei, M., Majdi, A., & Monjezi, M. (2014). An intelligent approach to predict unconfined compressive strength of rock surrounding access tunnels in longwall coal mining. Neural Computing and Applications, 24, 233-241.
[22] Mohamad, E. T., Jahed Armaghani, D., Momeni, E., & Alavi Nezhad Khalil Abad, S. V. (2015). Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bulletin of Engineering Geology and the Environment, 74, 745-757.
[23] Armaghani, D. J., Safari, V., Fahimifar, A., Mohd Amin, M. F., Monjezi, M., & Mohammadi, M. A. (2018). Uniaxial compressive strength prediction through a new technique based on gene expression programming. Neural Computing and Applications, 30, 3523-3532.
[24] Saedi, B., Mohammadi, S. D., & Shahbazi, H. (2018). Prediction of uniaxial compressive strength and elastic modulus of migmatites using various modeling techniques. Arabian Journal of Geosciences, 11, 1-14.
[25] Rezaei, M., & Asadizadeh, M. (2020). Predicting unconfined compressive strength of intact rock using new hybrid intelligent models. Journal of Mining and Environment, 11(1), 231-246.
[26] Wang, M., & Wan, W. (2019). A new empirical formula for evaluating uniaxial compressive strength using the Schmidt hammer test. International Journal of Rock Mechanics and Mining Sciences, 123, 104094.
[27] Fattahi, H. (2020). A new method for forecasting uniaxial compressive strength of weak rocks. Journal of Mining and Environment, 11(2), 505-515.
[28] Hassan, M. Y., & Arman, H. (2022). Several machine learning techniques comparison for the prediction of the uniaxial compressive strength of carbonate rocks. Scientific reports, 12(1), 20969.
[29] Dadhich, S., Sharma, J. K., & Madhira, M. (2022). Prediction of uniaxial compressive strength of rock using machine learning. Journal of The Institution of Engineers (India): Series A, 103(4), 1209-1224.
[30] Afolagboye, L.O., Ajayi, D.E. and Afolabi, I.O. (2023). Machine learning models for predicting unconfined compressive strength: A case study for Precambrian basement complex rocks from Ado-Ekiti, Southwestern Nigeria, Scientific African.
[30] Afolagboye, L. O., Ajayi, D. E., & Afolabi, I. O. (2023). Machine learning models for predicting unconfined compressive strength: A case study for Precambrian basement complex rocks from Ado-Ekiti, Southwestern Nigeria. Scientific African, 20, e01715.
[31] Ibrahim, A. F., Hiba, M., Elkatatny, S., & Ali, A. (2024). Estimation of tensile and uniaxial compressive strength of carbonate rocks from well-logging data: artificial intelligence approach. Journal of Petroleum Exploration and Production Technology, 14(1), 317-329.
[32] Alloush, R.M., Elkatatny, S.M., Mahmoud, M.A., Moussa, T.M., Ali, A.Z. and Abdulraheem, A. (2017). Estimation of Geomechanical Failure parameter from well logs using artificial intelligence techniques, SPE.
[32] Alloush, R. M., Elkatatny, S. M., Mahmoud, M. A., Moussa, T. M., Ali, A. Z., & Abdulraheem, A. (2017). Estimation of geomechanical failure parameters from well logs using artificial intelligence techniques. In SPE Kuwait Oil and Gas Show and Conference (p. D031S010R002). SPE.
[33] Pham, T. A., Tran, V. Q., & Vu, H. L. (2021). Evolution of deep neural network architecture using particle swarm optimization to improve the performance in determining the friction angle of soil. Mathematical Problems in Engineering, 2021(1), 5570945.
[34] Hiba, M., Ibrahim, A. F., Elkatatny, S., & Ali, A. (2022). Prediction of cohesion and friction angle from well-logging data using decision tree and random forest. Arabian Journal of Geosciences, 15(1), 26.
[35] Faraj, A. K., Abdul Hussein, H. A. H., & Abed Al-Hasnawi, A. N. (2022). Estimation of Internal Friction Angle for The Third Section in Zubair Oil Field: A Comparison Study. Iraqi Journal of Oil and Gas Research (IJOGR), 2(2), 102-111.
[36] Shahani, N. M., Ullah, B., Shah, K. S., Hassan, F. U., Ali, R., Elkotb, M. A., ... & Tag-Eldin, E. M. (2022). Predicting angle of internal friction and cohesion of rocks based on machine learning algorithms. Mathematics, 10(20), 3875.
[37] Nguyen, T., Shiau, J., & Ly, D. K. (2024). Enhanced earth pressure determination with negative wall-soil friction using soft computing. Computers and Geotechnics, 167, 106086.
[38] Alavi, A.H., Gandomi, A.H., Mollahassani, A., Akbar Heshmati, A., & Rashed, A. (2010). Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks. Journal of Plant Nutrition and Soil Science, 173(3), 368-379.
[39] Phyo, P. P., & Byun, Y. C. (2021). Hybrid ensemble deep learning-based approach for time series energy prediction. Symmetry, 13(10), 1942.
[40] Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Comput. 9 (8), 1735–1780.
[41] Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2016). LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10), 2222-2232.
[42] Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27-48.
[43] Lecun, Y., Bottou, L., Bengio, L. & Haffner, P. (1998). Gradient -based learning applied to document recognition", Proceedings of the IEEE, 86,(11), 2278 –2324.
[44] Anemangely, M., Ramezanzadeh, A., Amiri, H., & Hoseinpour, S. A. (2019). Machine learning technique for the prediction of shear wave velocity using petrophysical logs. Journal of Petroleum Science and Engineering, 174, 306-327.
[45] Kingm, D.P., & Ba, J. (2014). Adam: A Method for Stochastic Optimization. https://arxiv. org/abs/1412.6980.
[46] Duchi, J., Hazan, E., & Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization. Journal of machine learning research, 12(7), 2121–2159.
[47] Tieleman, T. & Hinton, G. (2012). Lecture 6.5‐rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning, 4(2), 26-30.
[48] Christaras, B. (1997). Landslides in iliolitic and marly formations. Examples from north-westem Greece. Engineering Geology, 47(1-2), 57-69.
[49] Plumb, R. A. (1994). Influence of composition and texture on the failure properties of clastic rocks. In SPE/ISRM Rock Mechanics in Petroleum Engineering (pp. SPE-28022). SPE.
[50] Xu, B., Tan, Y., Sun, W., Ma, T., Liu, H., & Wang, D. (2023). Study on the prediction of the uniaxial compressive strength of rock based on the SSA-XGBoost model. Sustainability 15, 5201.
[51] Kochukrishnan, S., Krishnamurthy, P., Yuvarajan, D., & Kaliappan, N. (2024). Comprehensive study on the Python-based regression machine learning models for prediction of uniaxial compressive strength using multiple parameters in Charnockite rocks. Scientific Reports.
[52] Zhao, J., Li, D., Jiang, J., & Luo, P. (2024). Uniaxial Compressive Strength Prediction for Rock Material in Deep Mine Using Boosting-Based Machine Learning Methods and Optimization Algorithms. CMES-Computer Modeling in Engineering & Sciences, 140(1).
[53] Daniel, C., Yin, X., Huang, X., Busari, J. A., Daniel, A. I., Yu, H., & Pan, Y. (2024). Bayesian optimization-enhanced ensemble learning for the uniaxial compressive strength prediction of natural rock and its application. Geohazard Mechanics, 2(3), 197-215.
[54] Niu, L., Cui, Q., Luo, J., Huang, H., & Zhang, J. (2024). Unconfined compressive strength prediction of rock materials based on machine learning. Journal of Engineering and Applied Science, 71(1), 137.
[55] Kalabarige, L. R., Sridhar, J., Subbaram, S., Prasath, P., & Gobinath, R. (2024). Machine Learning Modeling Integrating Experimental Analysis for Predicting Compressive Strength of Concrete Containing Different Industrial Byproducts. Advances in Civil Engineering, 2024(1), 7844854.
[56] Yasar, E., & Erdogan, Y. (2004). Correlating sound velocity with the density, compressive strength and Young's modulus of carbonate rocks. International Journal of Rock Mechanics and Mining Sciences, 41(5), 871-875.
[57] Tercan, A. E., & Ozcelik, Y. I. L. M. A. Z. (2006). Canonical ridge correlation of mechanical and engineering index properties. International Journal of Rock Mechanics and Mining Sciences, 43(1), 58-65.
[58] Khanlari, G. R., Heidari, M., Momeni, A. A., & Abdilor, Y. (2012). Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods. Engineering Geology, 131, 11-18.
[59] Iyeke, S. D., Eze, E. O., Ehiorobo, J. O., & Osuji, S. O. (2016). Estimation of shear strength parameters of lateritic soils using artificial neural network. Nigerian Journal of Technology, 35(2), 260-269.
[60] Mohammadi, M., Fatemi Aghda, S. M., Talkhablou, M., & Cheshomi, A. (2022). Prediction of the shear strength parameters from easily-available soil properties by means of multivariate regression and artificial neural network methods. Geomechanics and Geoengineering, 17(2), 442-454. | ||
|
آمار تعداد مشاهده مقاله: 470 تعداد دریافت فایل اصل مقاله: 143 |
||