JOURNAL OF ROCK MECHANICS

JOURNAL OF ROCK MECHANICS

Prediction of Elastic Parameters of Carbonate Reservoir from Well Logging Data Using Machine Learning and Multivariate Regression Methods

Document Type : Original Article

Authors
Mine Exploitation Engineering Department, Faculty of Mining and Metallurgical Engineering, Yazd University, Yazd, Iran
Abstract
Geomechanical parameters, such as Young's modulus and Poisson's ratio, play a crucial role in drilling and production operations of oil and gas wells. Determining these parameters can greatly assist in understanding the well's condition and issues. Since laboratory testing to determine these parameters is very expensive and time-consuming, this study attempts to predict linear geomechanical parameters based on multivariate regression and machine learning methods using well logging data (including sonic transit time, density, and porosity) from a gas well in southern Iran. After constructing the models, the results obtained from each method were compared, and the results showed that the machine learning method had a better performance in predicting elastic parameters with lower mean absolute percentage error, root mean square error, and higher coefficient of determination. Additionally, empirical relationships based on information related to weights and biases of the multilayer perceptron (MLP) network method were developed to predict elastic parameters. These relationships were validated using well A data from one of Iran's fields, and the results showed that the obtained equations had a suitable validity with coefficient of determination values of 0.97 and 0.96 for Young's modulus and Poisson's ratio, respectively. Therefore, these new equations can be used for new wells based on well logging data without the need for any machine learning software.
Keywords
Subjects

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