Rheology Predictive Model Based on an Artificial Neural Network for Micromax Oil-Based Mud

The current research introduces a new success case toward monitoring the drilling fluid rheology with an automation system by implementing the machine learning application. For the first time, four developed models were developed to predict the fluid rheology of Micromax oil-based mud type by implementing the artificial neural network to overcome the conventional field lab tests for the mud rheology monitoring that take more time and have human errors. The developed models predict the mud rheological properties as the plastic and apparent viscosities, yield point, and flow behavior index to be in real-time for evaluating the mud functionality. Mud weight and Marsh funnel were utilized as the only inputs for the rheology prediction models as these inputs have easy high-frequency measurements during the drilling operation. Real field data set of 244 complete measurements are used to train the models and another unseen set of 138 points for testing the models' accuracy. The correlation coefficient (R) and average absolute percentage error were calculated between the predicted values and the actual measurements to determine the models performance. Through the models’ development, the models perform a high accuracy level that has a coefficient of correlation higher than 0.96 for all models except the yield point model which shows 0.92, besides, an accepted average absolute percentage error that ranges from 4 to 9.3% for the training and testing phases. Furthermore, the study proposes model-based correlations for predicting the drilling fluid rheology for easy application in the rig site without the need for running the codes.