Estimation of shear wave velocity in an Iranian oil reservoir using machine learning methods

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{EBRAHIMI:2022:JPSE,

• author = "Arash Ebrahimi and Amin Izadpanahi and Parirokh Ebrahimi and Ali Ranjbar",
• title = "Estimation of shear wave velocity in an Iranian oil reservoir using machine learning methods",
• journal = "Journal of Petroleum Science and Engineering",
• volume = "209",
• pages = "109841",
• year = "2022",
• ISSN = "0920-4105",
• DOI = "doi:10.1016/j.petrol.2021.109841",
• URL = "https://www.sciencedirect.com/science/article/pii/S0920410521014601",
• keywords = "genetic algorithms, genetic programming, Shear wave velocity, Machine learning, Dipole sonic imager (DSI), Multi-layer perceptron, Artificial neural network, Multi-gene genetic programming, Wireline logs",
• abstract = "Shear wave velocity is considered as one of the most important rock physical parameters which can be measured by dipole sonic imager (DSI) tool. This parameter is applied to evaluate porosity and permeability, rock mechanical parameters, lithology, fracture assessment, etc. On the other hand, this data is not available in all wells and hence, an accurate and reliable estimation of this parameter with the least uncertainty is of great importance in reservoir characterization. In this study, regression, multi-layer perceptron artificial neural network (MLP-ANN), adaptive neuro-fuzzy inference system (ANFIS) and multi-gene genetic programming (MGGP) methods are used to estimate the shear wave velocity using well log data. Also, the reported empirical correlations in the literature are also investigated in the studied field. The input data include depth, effective porosity, Vp, gamma ray logs (natural and spectral), neutron log, density log and caliper log from the Bangestan Group Formation in one of the fields in southwestern Iran. In this study, all the expressed methods are compared based on the best coefficient of determination (R2), root mean square error (RMSE), mean squared error (MSE), average absolute relative error (AARE), and average relative error (ARE). Among the used methods, MGGP was developed for using the useful features of this method including sensitivity analysis and correlation. Sensitivity analysis is performed on the input data using the MLP-ANN and MGGP method. Also, a correlation is suggested based on the MGGP method which is able to predict the shear wave velocity using the mentioned input parameters. The results show that the MLP-ANN method is more accurate, reliable and efficient compared to other methods studied in this paper. R2 for the train, validation, and test phase are 0.9973, 0.9901 and 0.9898, respectively. The results of sensitivity analysis imply that compressional wave velocity has the highest impact on the shear wave velocity. Finally, Young Dynamic Modulus and Poisson Dynamic Ratio are computed using both real and predicted shear wave velocities. The results indicate that these two parameters can be calculated with high accuracy using predicted shear wave velocity",

}

Genetic Programming entries for Arash Ebrahimi Amin Izadpanahi Parirokh Ebrahimi Ali Ranjbar

Citations