Prediction of Total Organic Carbon in Organic-Rich Shale Rocks Using Thermal Neutron Parameters

Amjed Hassan; Emad Mohammed; Ali Oshaish; Dhafer Badhafere; Korhan Ayranci; Tian Dong; Umair Bin Waheed; Ammar El-Husseiny; Mohamed Mahmoud

doi:10.1021/acsomega.2c06918

Prediction of Total Organic Carbon in Organic-Rich Shale Rocks Using Thermal Neutron Parameters

ACS Omega. 2023 Jan 24;8(5):4790-4801. doi: 10.1021/acsomega.2c06918. eCollection 2023 Feb 7.

Authors

Amjed Hassan¹, Emad Mohammed¹, Ali Oshaish¹, Dhafer Badhafere¹, Korhan Ayranci¹, Tian Dong², Umair Bin Waheed¹, Ammar El-Husseiny¹, Mohamed Mahmoud¹

Affiliations

¹ College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran31261, Saudi Arabia.
² Key Laboratory of Tectonics and Petroleum Resources of Ministry of Education, China University of Geosciences, Wuhan430074, China.

Abstract

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.