A machine learning approach to filtrate loss determination and test automation for drilling and completion fluids

Drilling fluid property characterization currently involves several manually executed analytical tests, conducted in accordance with American Petroleum Institute (API) recommended practices 13B-1 and 13B-2. Standard (API) and high-pressure, high-temperature (HPHT) filter press units are used for filtrate loss (FL) measurements. However, these test methods have certain important disadvantages. FL tests are conducted at standardized conditions that generally do not reflect downhole oil and gas well conditions in terms of downhole pressure, temperature and filter medium encountered, present safety concerns due to their elevated pressure and temperature, and are performed only infrequently by a human mud engineer. The human measurement also introduces concerns around inaccuracies due to inconsistent practices and interpretation bias of the results. In addition, FL measurements are hard to automate given their manual, human-centric operating tasks. In this paper, we investigate if it is really necessary to automate FL measurements, or if the (strictly qualitative) information they provide can be obtained in a smarter, more advanced way. For this purpose, the relationship between fluid properties was investigated in detail using machine learning and deep learning techniques. Random forest (RF), XGBoost (XGB), support vector machine (SVM), multilayer perceptron (MLP) and multi-linear regression models were trained and tested to predict API and HPHT FL of water-based muds (WBM) based on fluid property readings of rheology, density, and temperature. A similar approach was also used for HPHT filtrate loss prediction of oil-based muds (OBM), taking into account their electrical stability and water content. A key advantage of this approach is that these WBM and OBM fluid properties can be obtained in real-time with measurements that are relatively simple and easy to automate (e.g. obtaining fluid density automatically and continuously from a Coriolis mass-flow meter measurement). Thus, real-time assessment of API and HPHT FL becomes possible without ever having to actually carry out any filter press measurements, thereby also eliminating the need to directly automate these measurements. The models were verified by dedicated laboratory experiments. The developed models estimated API and HPHT FL of WBM, and HPHT FL of OBM with mean absolute errors (MAE) of 0.56 ml/30min, 1.15 ml/30min and 0.79 ml/30min respectively, well within the measurement accuracy of the observations by a human mud engineer.