Determination of the total organic carbon (TOC) based on conventional well logs using artificial neural network

Total organic carbon (TOC) is the measure of the amount of carbon available in an organic compound and is often used as an essential factor for unconventional shale resources evaluation. Previous models for TOC determination based on density log data considered the presence of organic matter is proportional to the formation bulk density. Also model those based on: resistivity log, sonic, or density logs as well as the formation level of maturity (LOM) were used to determine the TOC. These models assumed linear relation between resistivity and porosity logs. Previous correlations showed very low coefficient of determination of the estimated TOC compared to the actual laboratory data. The objective of this paper is to develop an empirical correlation to determine the TOC for Barnett and Devonian shale formations based on conventional logs using artificial neural network (ANN). Core TOC data (442 data points) and well logs (resistivity, gamma ray, sonic transit time, and bulk density) from Barnett shale were used to develop the ANN model. For the first time an empirical correlation for TOC was developed based on the weights and biases of the ANN model. The developed correlation was then applied to estimate TOC for Devonian shale. The developed ANN model predicted the TOC based on conventional well-log data with high accuracy. The average absolute deviation (AAD) and coefficient of determination (R-2) for the predicted TOC compared with the measured TOC for Barnett shale are 0.91 wt% and 0.93, respectively. The developed model outperformed the previous available models in estimating TOC for Devonian shale as well with AAD of 0.99 wt% and R-2 of 0.89 compared to AAD of 1.16 wt% or more and R-2 of 0.65 or less for the available correlations. The developed empirical correlation was used to estimate the TOC with high accuracy for Barnett shale and Devonian shale formations. The developed correlation will help the geological and reservoir engineers predict the TOC using well logs without the need to measure TOC in the laboratory.