Total organic carbon (TOC) prediction using machine learning methods based on well logs data

Abstract

Evaluation of a source rock can use several parameters, one of which is the determination of Total Organic Carbon (TOC). The determination of TOC is a method that relies on expensive laboratory testing and is limited by the availability of rock samples. TOC prediction using well log data can be performed on most oil and gas wells, which can provide information regarding organic content and continuous data recording. So, the prediction method using well log data is an ideal method to determine TOC in source rock units. The purpose of this study is to predict the TOC value using a well log by applying the machine learning method with the Multi-Layer Perceptron Artificial Neural Network (ANN) technique. Eighteen data samples from the Talang Akar Formation were used for training and testing the MLP-ANN model. The well log data used to predict TOC are density log (RHOB), transit time (DT), deep resistivity (ILD), gamma-rays (GR), and neutron porosity (NPHI), and produce a high correlation (R2 0.87 and the mean absolute percentage error (AAPE) 10%) against the resulting MLP-ANN model. The TOC prediction technique carried out will help a geophysicist (geophysicist and reservoir geology) to evaluate the source rock in an oil and gas field without the need to have a large number of source rock sample data.