Machine learning assisted estimation of total solids content of drilling fluids

Abstract

Characterization and optimization of physical and chemical properties of drilling fluids are critical for the efficiency and success of drilling operations. In particular, maintaining the optimal levels of solids content is essential for achieving the most effective fluid performance. Proper management of solids content also reduces the risk of tool failures. Traditional solids content analysis methods, such as retort analysis, require substantial human intervention and time, which can lead to inaccuracies, time-management issues, and increased operational risks. In contrast to human-intensive methods, machine learning may offer a viable alternative for solids content estimation due to its pattern-recognition capability. In this study, a large set of laboratory reports of drilling-fluid analyses from 130 oil wells around the world were compiled to construct a comprehensive data set. The relationships among various rheological parameters were analyzed using statistical methods and machine learning algorithms. Several machine learning algorithms of diverse classes, namely linear (linear regression, ridge regression, and ElasticNet regression), kernel-based (support vector machine) and ensemble tree-based (gradient boosting, XGBoost, and random forests) algorithms, were trained and tuned to estimate solids content from other readily available drilling fluid properties. Input variables were kept consistent across all models for interpretation and comparison purposes. In the final stage, different evaluation metrics were employed to evaluate and compare the performance of different classes of machine learning models. Among all algorithms tested, random forests algorithm was found to be the best predictive model resulting in consistently high accuracy. Further optimization of the random forests model resulted in a mean absolute percentage error (MAPE) of 3.9% and 9.6% and R${}^2$ of 0.99 and 0.93 for the training and testing sets, respectively. Analysis of residuals, their histograms and Q-Q normality plots showed Gaussian distributions with residuals that are scattered around a mean of zero within error ranges of $\pm$1% and $\pm$4%, for training and testing, respectively. The selected model was further validated by applying the rheological measurements from mud samples taken from an offshore well from the Gulf of Mexico. The model was able to estimate total solids content in those four mud samples with an average absolute error of 1.08% of total solids content. The model was then used to develop a web-based graphical-user-interface (GUI) application, which can be practically used at the rig site by engineers to optimize drilling fluid programs. The proposed model can complement automation workflows that are designed to measure fundamental rheological properties in real time during drilling operations. While a standard retort test can take approximately 2 h at the rig site, such kind of real-time estimations can help the rig personnel to timely optimize drilling fluids, with a potential of saving 2920 man-hours in a given year for a single drilling rig.