An Artificial Neural Network Approach for Predicting TOC and Comprehensive Pyrolysis Parameters from Well Logs and Applications to Source Rock Evaluation

Abstract

Understanding source rocks' organic content and thermal maturity is crucial in assessing their hydrocarbon potential. To address this, our study focused on developing an accurate artificial neural network (ANN) model for estimating total organic carbon (TOC) content and a complete set of pyrolysis parameters from conventional well logs. The accuracy of the ANN-based technique in estimating TOC content was found to be significantly higher (correlation coefficient of 0.95) compared to the results obtained using Passey's method (correlation coefficient of 0.44). Additionally, the ANN model provided highly accurate predictions for the pyrolysis parameters S1, S2, S3, and Tmax, with correlation coefficients of 0.85, 0.90, 0.86, and 0.93, respectively. The study focused on the Abu Gabra Formation in the Hamra field, and the ANN data analysis revealed that the source rock in this area is of fair to good quality. The assessment of kerogen type indicated a mixed kerogen type II and type III, suggesting the potentiality for oil and gas generation. The predicted parameters further confirmed that the Abu Gabra source rock is thermally mature and capable of generating indigenous hydrocarbons. The results of the ANN-based modeling were consistent with laboratory measurements, demonstrating the reliability of the predictions for comprehensive source rock evaluation using well logs.