A Self-Adaptive Artificial Neural Network Technique to Estimate Static Young's Modulus Based on Well Logs

• DOI: 10.2118/200139-ms
• Publication Date: 2022-03-21T00:04:04Z
• Authors: Ahmed Abdulhamid ...; Salaheldin Elkatatny; Abdulwahab Ali; Tamer Moussa
• Keywords: Data set; Data point; Shear modulus

Abstract Static Young's modulus (Estatic) is an essential parameter needed to develop the earth geomechanical model, (E) considerably varies with change in lithology. Recently, artificial intelligence (AI) techniques were used estimate Estatic for carbonate formation. In this study, neural network (ANN) was sandstone ANN design parameters optimized using self-adaptive differential evolution (SaDE) optimization algorithm. The model trained predict from conventional well log data such as bulk density, compressional time, and shear time. 409 points Well-A train which then tested 183 unseen same validated on 11 a different (Well-B). developed SaDE-ANN estimated training set very low average absolute percentage error (AAPE) of 0.46%, high correlation coefficient (R) 0.999 determination (R2) 0.9978. And values testing AAPE, R, R2 1.46%, 0.998, 0.9951, respectively. These results confirmed accuracy model.