Machine-learning predictions of high-pressure high-temperature filtrate loss in oil-based drilling fluids from well-site measurements

Abstract

Precise and reliable predictions of filtrate loss (FL) from drilling fluids/muds under high-pressure-high-temperature (HPHT) conditions are required to prevent formation damage, maintain wellbore stability, and optimize drilling efficiency in complex reservoirs. Traditional laboratory-based measurements are too time consuming to provide HPHT FL values that are useable for decision-making while drilling. This study, therefore, develops machine learning (ML) models that enable frequent and prompt predictions of HPHT FL with high accuracy and reliable precision. To this end, a large dataset was compiled containing five input parameters, namely mud temperature (MT), mud weight (MW), funnel viscosity (FV), mud alkalinity (MA), and electrical stability (ES), and a single target variable HPHT FL. Following meticulous data preprocessing, four predictive models were developed using established ML algorithms: multilayer perceptron neural network (MLPNN), support vector regression (SVR), extreme gradient boosting (XGBoost), and extreme learning machine (ELM). For each ML algorithm, five separate model instances were developed and evaluated, with XGBoost providing the best performance in predicting the target parameter (root mean square error (RMSE) = 0.096 cc/30 min for the testing subset). The superior performance of the XGBoost model was further confirmed by residual, uncertainty, overfitting, and robustness evaluations, indicating its excellent generalization capability. Shapley additive explanations (SHAP) analysis identified ES as the most influential input parameter and FV as the least influential for XGBoost’s HPHT FL predictions. The XGBoost model as developed offers a substantial improvement compared to laboratory FL analysis, enabling more efficient and quicker monitoring of FL in challenging HPHT downhole environments.