Application of deep learning and random forest algorithms in a machine learning-based well log analysis for a small data set of a sand zone

Porosity, permeability and water saturation of the reservoir zone were predicted by the RF analysis, compared with those obtained by the DL analysis and validated with the core measurements. It was found that there is a significant improvement in the analysis running time and the accuracy of the RF-predicted well log answers compared to those results by DL analysis. It is therefore recommended that more applications of RF-based well log analysis be done for clastic reservoirs in Vietnam in the future.