Reservoir rock minerals and their surface charge development have been the subject of several studies with a consensus reached on their contribution to the control of reservoir rock surface interactions. However, the question of what factors control the surface charge of minerals and to what extent do these factors affect the surface charge remains unanswered. Also, with several factors identified in our earlier studies, the question of the order of effect on the mineral surface charge was unclear. To quantify the mineral surface charge, zeta potential measurements and Deryaguin-Landau-Verwey-Overbeek (DLVO) theories, as well as surface complexation models, are used. However, these methods can only predict a single mineral surface charge and cannot approximate the reservoir rock surface. This is because the reservoir rock is composed of many minerals in varying proportions. To address these drawbacks, for the first time, we present the implementation of machine learning models to predict reservoir minerals' surface charge. Four different models namely the Adaptive Boosting Regressor, Random Forest Regressor, Support Vector Regressor, and the Gradient Boosting tree were implemented for this purpose with all the model predictions over 95% accuracy. Also, feature ranking of the factors that control the mineral surface charge was carried out with the most dominant factors being the mineral type, salt type, and pH of the environment. Findings reveal an opportunity for accurate prediction of reservoir rock surface charge given the enormous amount of data available.
© 2022 The Authors. Published by American Chemical Society.