Generating the Rock Strength Profile While Drilling Complex Lithologies in real-time by Employing Artificial Neural Networks

The rock unconfined compressive strength (UCS) is considered one of the most important rock geomechanical properties as it is practically used in designing drilling programs and reservoir fracture jobs. The practical way for determining the UCS by lab measurements is costly and time-consuming, in addition, the empirical correlations require logs data for UCS estimation. The main objective of this study is to employ machine learning techniques for predicting the rock UCS from only the surface drilling parameters for complex lithology. The study provides UCS predictive model by using an artificial neural network (ANN). The data used was collected during drilling different formations with a complex lithology. A cleaned data set (2,926 measurements) was used for building the ANN model. The model was trained, tested, and optimized to provide high accuracy prediction for UCS. The results showed an overall strong UCS prediction with a correlation coefficient (R) greater than 0.99 and less than 5.52% an average absolute percentage error (AAPE). Furthermore, the model was validated with unseen data set and proved the high accuracy performance level (R of 0.99 and AAPE of 6.9%) that enhance the model application for UCS prediction in the practical drilling operations that will save extra cost and time.