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.