Artificial intelligence approach to depositional facies characterization based on electrical log data

Abstract

This research aims to determine the depositional facies from electrical log data using the gradient boosting classifier method, which comprises a powerful algorithm. The electrical logs used are gamma-ray (GR), resistivity (ILD), neutron porosity (NPHI), and density (RHOB), while the output is in the form of images. The training data consists of 4 wells in Jambi sub-Basin, South Sumatera Basin, while the testing data comprises 5 wells with gamma-ray, resistivity, NPHI, and RHOB as input. Several scenarios are used to predict the facies model, namely training and validation dataset by using and isolating facies in well combination input, and with or without feature augmentation. Furthermore, the values collected were validated using F1 score. The result showed that 85.5% and 84.7% of F1 scores were allocated to training and validation to increase accuracy in scenarios without facies isolation and with feature augmentation. Therefore, the gradient boosting classifier method is reliable enough to characterize depositional facies in the associated area of interest.