Machine-Learning Predictive Model for Semiautomated Monitoring of Solid Content in Water-Based Drilling Fluids

Abstract

Accurate and frequent monitoring of the solid content (SC) of drilling fluids is necessary to avoid the issues associated with improper solid particle concentrations. Conventional methods for determining SC, such as retort analysis, lack immediacy and are labor-intensive. This study applies machine learning (ML) techniques to develop SC predictive models using readily available data—Marsh funnel viscosity and fluid density. A dataset of 1290 data records was collected from 17 wells drilled in two oil fields located in southwest Iran. Four ML models—least squares support vector machine (LSSVM), multilayered perceptron neural network, extreme learning machine, and generalized regression neural network—were developed to predict SC from the compiled dataset. Multiple assessment techniques were applied to attentively evaluate the models’ prediction performances and select the best-performing, SC prediction model. The LSSVM model generated the least errors, exhibiting the lowest root-mean-square error values for the training (1.80%) and testing (1.84%) subsets. The narrowest confidence interval, 0.18, achieved by the LSSVM model confirmed its reliability for SC prediction. Leverage analysis revealed minimal influence of outlier data on the LSSVM model's SC prediction performance. The trained LSSVM model was further validated on unseen data from another well drilled in one of the studied oil fields, demonstrating the model’s generalizability for providing credible close-to-real-time SC predictions in the studied fields.