Mitigating the Uncertainties of Geomechanical Models by Estimating the Shear Wave Slowness Using Highly Accurate Deep Neural Network Models

Bajolvand, Mahdi; Ramezanzadeh, Ahmad; Hekmatnejad, Amin; Mehrad, Mohammad; Davoodi, Shadfar; Teimuri, Mohammad

doi:10.22044/jme.2025.15294.2932

Document Type : Original Research Paper

Authors

¹ Departamento de Ingeniería de Minería, Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile

² Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Iran

³ School of Earth Sciences & Engineering, Tomsk Polytechnic University, Lenin Avenue, Tomsk, Russia

⁴ Iranian Central Oil Fields Company (ICOFC), Tehran, Iran

https://doi.org/10.22044/jme.2025.15294.2932

Abstract

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.

Keywords

Main Subjects

Well Logging & Petrophysics

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Journal of Mining and Environment

Mitigating the Uncertainties of Geomechanical Models by Estimating the Shear Wave Slowness Using Highly Accurate Deep Neural Network Models

References

References

Volume 16, Issue 3
May and June 2025
Pages 963-996

Mitigating the Uncertainties of Geomechanical Models by Estimating the Shear Wave Slowness Using Highly Accurate Deep Neural Network Models

References

References

Volume 16, Issue 3May and June 2025Pages 963-996

Volume 16, Issue 3
May and June 2025
Pages 963-996