Real-time evaluation of the dynamic Young’s modulus for composite formations based on the drilling parameters using different machine learning algorithms

The dynamic Young’s modulus (E dyn ) is a parameter needed for optimizing different aspects related to oil well designing. Currently, E determined from the knowledge of formation bulk density, in addition shear and compressional velocities, which are not always available. This study introduces three machine learning (ML) models, namely, random forest (RF), adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC), support vector regression (SVR), estimation only real-time available drilling parameters. ML models were learned on 2054 datasets collected Well-A then tested validated 871 2912 Well-B Well-C, respectively. results showed that optimized accurately predicted wells considered this study. SVR model outperformed both RF ANFIS-SC evaluating all wells. For validation data, was assessed low average absolute percentage errors 3.64%, 6.74%, 1.03% using RF, ANFIS-SC,