Application of Random Forest method in oil and water layer identification of logging data: a case study of the Liaohe depression

Abstract

Accurate identification of oil and water layers is the basis of qualitative evaluation of reservoir fluid properties or industrial value and selection of testing layers of the well. The traditional oil and water layer identification is mainly based on the extensive use of the well’s logging and logging data, which is inefficient and easy to leak interpretation or misinterpretation for those reservoirs with complex geological conditions. In this paper, the random forest method of machine learning is used to select the lithology, porosity, permeability, movable fluid, oil saturation, S0, S1, S2, Tmax of rock as characteristics; smote oversampling is used to expand the sample, and the packet estimation is used to establish the oil and water layer identification model. This method is simple and easy to use, not prone to severe overfitting, and can find the potential rules in the data. The classification performance is excellent, and the accuracy rate can reach more than 89.9%, which solves the problem of low accuracy in oil-water layer identification in the past.