TY - EJOUR
AU - Maleki, Shahoo
AU - Ramazi, Hamid Reza
AU - Shahrabi, Mohammadjavad Ameri
PY - 2024
DA - April
TI - Application of artificial intelligence techniques in the estimation of Young’s modulus by conventional well logs
T2 - Iranian Journal of Earth Sciences
VL - 16
L1 - https://oiccpress.com/Iranian-Journal-of-Earth-Sciences/article/72/
DO - 10.57647/j.ijes.2024.1601.04
N2 - Having information about Young’s 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 Young’s modulus with acceptable accuracy. The current research aims to create an advanced, precise model for estimating Young’s 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 Young’s modulus, some empirical correlations are proposed for estimation of static Young’s 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 Young’s 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 Young’s 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 Young’s modulus of limestone rocks.
IS - 1
PB - OICC Press
KW - Well logs data, Back-propagation neural network, Support vector machine, Core sample data, Gene expression programming, Young’s modulus 
EN -