Physics-Based Machine Learning Models for Real-Time Prediction of Rheological and Filtration Properties for KCl Polymer Mud

Abstract

Abstract The rheology of drilling fluid is a crucial component during drilling operations to achieve optimum performance and avoid non-productive time resulting from drilling problems. In field operations, measuring the rheological and filtration properties of drilling fluids necessitates a significant amount of preparation time and experimental work. Often, such data analysis is not conducted on a footage basis. However, some surface parameters, such as mud weight using the mud balance, marsh funnel viscosity using the Marsh funnel, and mud flow line temperature, can be measured in real-time. This study introduces physics-based machine learning models for real-time prediction of both rheological and filtration properties. The machine learning algorithms used include support vector machines (SVM), extreme gradient boosting (XGB), random forests (RF), and multilayer perceptron networks (MLP). These models predict the rheological and filtration properties based on collected field experimental data. The model inputs are mud weight, marsh funnel viscosity, and flow line temperature, while the targets are apparent viscosity (AV), yield point (YP), plastic viscosity (PV), shear stress based on Fann readings at 600, 300, 200, 100, 6, and 3 shear rates in terms of revolutions per minute (RPM), and filtration loss volume. The developed machine learning models underwent hyperparameter tuning based on cross-validation to select the optimum model for each algorithm using the coefficient of determination (R2) and absolute average relative error (AAPRE). Furthermore, all machine learning model predictions were tested and validated using various datasets. The SVM models' performance ranged from 0.85 to 0.97 in terms of R2, with most AAPRE values less than 2%, reaching a maximum of 6%. The developed RF models ranged from 0.94 to 0.97 in terms of R^2, with AAPRE values between 2 and 3%. XGB models ranged between 0.95 and 0.98 in terms of R^2, with AAPRE values from 1 to 2%. The MLP models ranged from 0.991 to 0.99 in terms of R^2, with AAPRE values less than 6%. The developed machine learning models exhibited high accuracy in predicting the mud's rheological properties in terms of PV, YP, and n, with AAPRE values less than 1.94%, 1.77%, and 4%, respectively.