Application of artificial intelligence techniques in the estimation of Youngâs modulus by conventional well logs

Abstract

Having information about Youngâs modulus is extremely essential for characterization of the hydrocarbon reservoirs. This property can be conventionally determined by core sample data analysis in laboratory that is time-consuming, critically expensive and discontinuous. Therefore, many researchers have always been looking for suitable methods to estimate Youngâs modulus with acceptable accuracy. The current research aims to create an advanced, precise model for estimating Youngâs modulus by utilizing back-propagation neural network (BPNN), support vector regression (SVR), and gene expression programming (GEP) methods based on the conventional well logs data. Thus, after determination of dynamic Youngâs modulus, some empirical correlations are proposed for estimation of static Youngâs modulus. The results demonstrate that the Jambunathan equation is more appropriate than other empirical models. Finally, artificial intelligence (AI) techniques were run, and their results indicated that all techniques (BPNN (R=0.999), SVR (R=0.997) and GEP (R=0.996)) deliver highly accurate values of static Youngâs modulus. Comparing these results shows that the BPNN technique is relatively more precise than other ones. Although, in this research, the GEP technique was not more accurate than BPNN and SVM techniques, it provides a new nonlinear equation that can be used for estimating Youngâs modulus in other similar fields. As a new finding, it was found that a simultaneous combination of the Jambunathan equation and BPNN technique delivers highly accurate results. Hence, it can be applied to slim down the cost of exploratory operations for determination of the Youngâs modulus of limestone rocks.