I. M. Mohamed

TL;DR: The obtained results showed that ANN is the best AI technique for estimating the static Young’s modulus with high accuracy as compared with ANFIS and SVM and an empirical correlation based on the weights and biases of the optimized ANN model was developed.

TL;DR: In this paper, the authors developed accurate and simple empirical models using wireline log data (bulk density, gamma ray, and neutron porosity) to predict the sonic travel times (P-wave and S-wave) using three robust artificial intelligence techniques, namely: support vector machine (SVM), artificial neural network (ANN), and adaptive neurofuzzy interference systems (ANFIS), were employed and compared based on their prediction performance.

TL;DR: In this paper, an accurate and robust correlation for static Young's modulus to be estimated directly from log data without the need for core measurements was developed, which was tested for different cases with different lithology such as calcite, dolomite and sandstone.

TL;DR: In this paper, the effects of temperature, injection flow rate, and injection scheme on the permeability alteration due to CO2 injection into heterogeneous dolomite rock is addressed experimentally.