Total organic carbon (TOC) estimation using ensemble and artificial neural network methods; a case study from Kazhdumi formation, NW Persian gulf

Total Organic Carbon (TOC) is one of the most important geochemical parameters in source rock evaluation, utilized to characterize the hydrocarbon generation potential. Artificial intelligence (AI) methods have significant features, including decreased time and high cost-effectiveness, providing a sufficient database, reducing risk, and enabling better decision-making. AI methods based on these significant features have become efficient tools in various approaches to geological studies such as prediction and classification. This study uses an artificial neural network (ANN), and ensemble algorithms including random forest, bagging, and least-square boosting to evaluate the organic richness of the Kazhdumi Formation in the Persian Gulf. The Kazhdumi Formation with Cretaceous age is well-known as one of the most important source rocks in the Zagros and NW of the Persian Gulf. The obtained results of Rock-Eval pyrolysis and conventional well logs e.g., sonic, neutron, density, spectral gamma-ray, resistivity, and acoustic impedance are utilized for TOC estimation. The performance of AI algorithms was analyzed using root mean squared error (RMSE) and mean absolute error (MAE) equations and the obtained results from the error evaluation analysis show that the least square boosting (LSB) algorithm has the lowest error than the other proposed algorithms in this investigation. The cross-validation analysis between the determined TOC values of blind samples of Rock-Eval pyrolysis and the AI algorithms e.g., the ANN and random forest, bagging, and least-square boosting are evaluated 0.89, 0.90, 0.94, and 0.95, respectively. The geochemical study of the Kazhdumi Formation as a potential source of rock shows that this formation has a good potential for hydrocarbon generation. According to the geochemical evaluation based on the cross-plot analysis, the Kazhdumi Formation contains type II, and type II-III kerogen, which is affected by the sedimentation conditions of this formation. This investigation has confirmed the ability of ensemble methods for TOC value estimation, and it can further be applied to investigate other geological properties.