Summary.

Artificial intelligence (AI) and machine learning (ML) have the potential to reshape the oil and gas exploration and production landscape. Once viewed as a promising novelty, AI and ML are not far away from becoming mainstream for all exploration and production companies. Earlier many researchers have worked on using intelligent analyses such as Artificial Neural Network (ANN), deep learning (DL), Fuzzy, Genetic Algorithm (GA) in well log interpretation, which are supposed to be effective for large data sets. Random forest (RF) algorithm so far has not been much applied for well log analysis. In this research, a code in Python language was developed for DL and RF analyses for well log interpretation. To highlight the advantages of the RF-based well log analysis we applied the new code for a small data set over a 50 m depth zone consisting of clay and sand zones.

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 easurements. 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.

Key words: Machine learning (ML), Python, random forest (RF), well log analysis, sand reservoir. 

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