Unconfined compressive strength (UCS) prediction in real-time while drilling using artificial intelligence tools

Unconfined compressive strength (UCS) is a major mechanical parameter of the rock which has an essential role in developing geomechanical models. It can be estimated directly by lab testing of retrieved core samples or from well log data. These methods are very expensive and require huge efforts and time. Therefore, there is a need to develop a new technique for predicting UCS values in real-time. In this study, three artificial intelligence (AI) models were developed using artificial intelligence tools; artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) to predict UCS of the downhole formations while drilling based on real-time recording of the drilling mechanical parameters. These parameters include rate of penetration (ROP), mud pumping rate (GPM), stand-pipe pressure (SPP), rotary speed in revolution per minute (RPM), torque (T), and weight on bit (WOB). A dataset of 1771 points from a Middle Eastern field was used to build the developed models: for training and testing processes. A new UCS correlation was developed based on the optimized AI model. Another set of data (2175 data points unseen by the model) was used to validate the model and the developed UCS correlation. The developed ANN-model outperformed the ANFIS- and SVM-models with a correlation coefficient (R-value) of 0.99 and an average absolute percentage error (AAPE) of 3.48% between the predicted and actual UCS values. The new UCS correlation outperformed the available correlations for UCS prediction and it was able to predict the UCS with AAPE of 4.2% compared to the actual UCS values.