Total organic carbon (TOC) content is one of the crucial
parameters
that determine the value of the source rock. The TOC content gives
important indications about the source rocks and hydrocarbon volume.
Various techniques have been utilized for TOC quantification, either
by geochemical analysis of source rocks in laboratories or using well
logs to develop mathematical correlations and advanced machine learning
models. Laboratory methods require intense sampling intervals to have
an accurate understanding of the reservoir, and depending on the thickness
of the interested formation, it can be time-consuming and costly.
Empirical correlations based on well logs (e.g., density, sonic, gamma
ray, and resistivity) showed fast predictions and very reasonable
accuracies. However, other important parameters such as thermal neutron
logs have not been studied yet as a potential input for providing
reliable TOC predictions. Also, different studies estimate the TOC
based on the well-logging data for various formations; however, limited
studies were reported to predict the TOC for the Horn River Formation.
Therefore, the objective of this study is to estimate the TOC variations
based on the thermal neutron logs using one of the largest source
rocks in Canada: The Horn River Formation. More than 150 data sets
were collected and used in this work. The parameters of the artificial
neural network (ANN) model were fine-tuned in order to improve the
model’s prediction performance. Furthermore, an empirical correlation
was developed utilizing the optimized ANN model to allow fast and
direct application for the developed model. The developed correlation
can predict the TOC with an average absolute error of 0.52 wt %. The
proposed TOC model was able to outperform the previous models, and
the coefficient of determination was increased from 0.28 to 0.73.
Overall, the proposed TOC model can provide high accuracy for TOC
ranges from 0.3 to 6.44 wt %. The developed model can provide a real-time
quantification for the organic matter maturity, helping to allocate
the zones of mature organic matter within the drilled formations.