Predicting water-based drilling fluid filtrate volume in close to real time from routine fluid property measurements

Drilling operations depend on precisely controlling drilling fluid filtration volume (FV), which affects formation integrity, costs, and borehole stability. Maintaining optimal FV is essential to prevent well control issues, yet forecasting it is challenging due to process complexity and measurement limitations. This study adapts machine and deep learning (ML/DL) models to predict FV in almost real-time based on more easily measured fluid properties. Radial-basis-function neural network (RBFNN), generalized regression neural network (GRNN), multilayer perceptron (MLP), convolutional neural network (CNN), and Gaussian process regression (GPR) ML models are applied to 1186 records of density, viscosity, and solids content in water-based drilling fluids deployed in fourteen wellbores. CNN outperformed other models with the lowest root mean square error (RMSE) of 0.5381 mL and demonstrated resilience to overfitting and noisy data, unlike RBFNN and GRNN. The proposed method provides reliable near-real-time FV predictions, which could be beneficial in optimizing drilling operations by helping prevent potential drilling-fluid-related issues. Fast and accurate FV forecasting from routine fluid properties represents a crucial advancement for drilling operations, highlighting the need for future dataset expansion to encompass a wider range of conditions and fluid types.