Mitigating the Uncertainties of Geomechanical Models by Estimating the Shear Wave Slowness Using Highly Accurate Deep Neural Network Models | ||
| Journal of Mining and Environment | ||
| مقاله 10، دوره 16، شماره 3، مرداد و شهریور 2025، صفحه 963-996 اصل مقاله (9.15 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2025.15294.2932 | ||
| نویسندگان | ||
| Mahdi Bajolvand* 1؛ Ahmad Ramezanzadeh2؛ Amin Hekmatnejad1؛ Mohammad Mehrad2؛ Shadfar Davoodi3؛ Mohammad Teimuri4 | ||
| 1Departamento de Ingeniería de Minería, Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile | ||
| 2Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Iran | ||
| 3School of Earth Sciences & Engineering, Tomsk Polytechnic University, Lenin Avenue, Tomsk, Russia | ||
| 4Iranian Central Oil Fields Company (ICOFC), Tehran, Iran | ||
| چکیده | ||
| Shear Wave Slowness Log (DTSM) is one of the most important petrophysical logs applicable for studying reservoirs, especially geomechanical studying of the oil and gas fields. However, lack of this parameter in wellbore logging can import great sources of uncertainty into geomechanical studies. This study aims to provide solutions for decreasing the uncertainty of geomechanical models with estimation of the DTSM log using the high accurate deep machine learning models. The main idea is using data from offset fields for extending the range of training data and improving the estimation ability and generalizability of machine learning models. For this purpose, petrophysical data from 8 wells of 4 Iranian oil fields were collected. In the first stage, data preprocessing was performed for reducing the effects of wrong data, missing value, noises, and outliers. Then, machine learning (regression learning-based and deep neural network-based) and analytical models implemented for estimating DTSM. The results indicated that the Gated Recurrent Unit (GRU) model with the values of 1.9 and 2.14 for RMSE and 0.99 for R-square had the most exact answers, for training and test data, respectively. Meanwhile, evaluation of the accuracy of the models on the validation well data indicated that GRU model with the values of 2.43 and 0.93 had been the most accurate model for RMSE and R-square, respectively. Accordingly, using a multi-field comprehensive data bank and applying machine learning methods are strongly recommended to estimate the DTSM, for the cases where limited offset data is available. | ||
| کلیدواژهها | ||
| Shear wave slowness؛ Machine learning؛ Deep learning؛ Petrophysical logs؛ Analytical models | ||
| مراجع | ||
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