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\begin{document}

\title{A Multilayer Perceptron Neural Network to Estimate Total Organic Carbon
(TOC) Content Using Well Logs in Raniganj Sub-Basin, India}



\author[1]{Mannat Khanna}%
\author[1]{Shaik Nasif Ahmed}%
\author[1]{Nimisha Vedanti}%
\affil[1]{Affiliation not available}%


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  \date{June 02, 2024}


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\emph{Mannat Khanna*\textsuperscript{1}, Shaik Nasif
Ahmed\textsuperscript{2,3}, and Nimisha Vedanti~\textsuperscript{2,3}}

\emph{\textsuperscript{1}Department of Geophysics, Kurukshetra
University, Kurukshetra, India}

\emph{\textsuperscript{2}CSIR-National Geophysical Research Institute,
Hyderabad-500007, India}

\emph{\textsuperscript{3}Academy of Scientific and Innovative Research
(AcSIR), Ghaziabad-201002, India}

\emph{*Presenting author: mannat1230@gmail.com}

\textbf{Abstract}

The Total Organic Carbon (TOC) content of source rock is arguably the
most essential parameter to assess the development potential of an
unconventional reservoir. Traditionally, TOC is estimated using
laboratory methods such as Rock-Eval pyrolysis, elemental analysis, etc.
However, since the presence of organic carbon has an effect on the
response of well logging tools, TOC may be estimated using wireline
logs, which provides a continuous depth profile of TOC.

In this study, the aim is to evaluate the TOC content depth profile for
shale in the Barren Measure Formation of the Raniganj sub-basin. The
existing methods of TOC estimation from wells logs have limited
application and accuracy in predicting TOC content, especially for the
Barren Measure shale which has a high variation of TOC with depth in the
broad range of 2 - 8 wt. \% as shown in Fig 1. \selectlanguage{greek}Δ\selectlanguage{english}logR Method is a
popularly used technique that estimates TOC using resistivity and
porosity logs, but it has limiting assumptions and also requires prior
information on the thermal maturity of the source rock. A popular
Artificial Neural Network (ANN) based model to determine TOC in Barnett
and Devonian shale formations was given by Mahmoud in 2017. However, the
ANN architecture proposed by Mahmoud, as well as the \selectlanguage{greek}Δ\selectlanguage{english}logR Method are
not adequate in predicting TOC with satisfactory accuracy for the Barren
Measure Shale.

Hence, we developed a new model using Multilayer Perceptron (MLP)-ANN
for predicting TOC content from the caliper, gamma, resistivity, and
bulk density logs. The MLP-ANN has 4 layers and has been optimized using
the Adam optimizer. The loss function used is mean squared error. The
model is created using TOC values from Rock-Eval pyrolysis of 111 core
samples from 3 wells in the Raniganj sub-basin, of which 10 data points
are reserved for testing the model. It is compiled using the Keras API
with a TensorFlow backend. The hyper-parameters of the algorithm are
fine-tuned to obtain the best model for improved prediction of TOC.

Based on Pearson correlation coefficient `r', root mean square error
(RMSE), and mean absolute error (MAE) (as tabulated in Table 1.), the
developed ANN model outperformed the existing methods in predicting the
TOC content for Barren Measure Shale. The estimated TOC-depth curves may
further be used in the model-based inversion scheme of 3D seismic data
to obtain a TOC model for the Raniganj sub-basin.\selectlanguage{english}
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