Total organic carbon (TOC) is the amount of carbon present in an organic compound and is often used as an essential factor for unconventional shale resources evaluation. The previous models for TOC determination were either based on density log data only and considered the presence of organic matter is proportional to the bulk density, or based on resistivity log, sonic or density logs as well as the formation level of maturity (LOM), where these models assumed a linear relation between resistivity and porosity logs. The average absolute deviation (ADD) of the previous model was not less than 1.20wt% of TOC with a coefficient of determination (R2) of less than 0.85.
The objective of this research is to develop new empirical correlation to determine the TOC based on well logs using artificial neural network for Barnett shale formation. Core TOC data (442 data point) and well logs (resistivity, gamma ray, sonic transit time, and bulk density) were used to develop the ANN model. For the first time, the ANN model will change to a white box by extracting the weights and biases of the model to form the empirical equation.
The results obtained showed that TOC is strong function of bulk density, and moderate function of gamma ray, compressional sonic time, and week function of deep resistivity. The developed ANN model is able to predict the TOC based on conventional log data with high accuracy (the ADD is 0.91wt% of TOC and R2 between estimated and actual TOC is 0.93). The developed empirical equation for TOC determination from the ANN model outperformed the previous available models, which had an ADD of 1.20 wt% or more and R2 of less than 0.85. The developed TOC model and equation can be applied using simple computer without the need for a specific software.
The novelty of this new research is the simplicity and high accuracy of the developed model for estimating the total organic carbon based on conventional log data. The developed empirical equation will help the geologists and reservoir engineers to predict the TOC without the need for hard lab work or complicated softwares.
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