Application of machine learning in evaluation of the static young’s modulus for sandstone formations

Prediction of the mechanical characteristics of the reservoir formations, such as static Young’s modulus (E_static), is very important for the evaluation of the wellbore stability and development of the earth geomechanical model. E_static considerably varies with the change in the lithology. Therefore, a robust model for E_static prediction is needed. In this study, the predictability of E_static for sandstone formation using four machine learning models was evaluated. The design parameters of the machine learning models were optimized to improve their predictability. The machine learning models were trained to estimate E_static based on bulk formation density, compressional transit time, and shear transit time. The machine learning models were trained and tested using 592 well log data points and their corresponding core-derived E_static values collected from one sandstone formation in well-A and then validated on 38 data points collected from a sandstone formation in well-B. Among the machine learning models developed in this work, Mamdani fuzzy interference system was the highly accurate model to predict E_static for the validation data with an average absolute percentage error of only 1.56% and R of 0.999. The developed static Young’s modulus prediction models could help the new generation to characterize the formation rock with less cost and safe operation.