Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques

Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Abdulwahab Z. Ali, Mohamed Abouelresh and Abdulazeez Abdulraheem
Additional contact information
Ahmed Abdulhamid Mahmoud: College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Salaheldin Elkatatny: College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Abdulwahab Z. Ali: Center of Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Mohamed Abouelresh: Center of Environment and Water, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Abdulazeez Abdulraheem: College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

Sustainability, 2019, vol. 11, issue 20, 1-15

Abstract: Total organic carbon (TOC) is an essential parameter used in unconventional shale resources evaluation. Current methods that are used for TOC estimation are based, either on conducting time-consuming laboratory experiments, or on using empirical correlations developed for specific formations. In this study, four artificial intelligence (AI) models were developed to estimate the TOC using conventional well logs of deep resistivity, gamma-ray, sonic transit time, and bulk density. These models were developed based on the Takagi-Sugeno-Kang fuzzy interference system (TSK-FIS), Mamdani fuzzy interference system (M-FIS), functional neural network (FNN), and support vector machine (SVM). Over 800 data points of the conventional well logs and core data collected from Barnett shale were used to train and test the AI models. The optimized AI models were validated using unseen data from Devonian shale. The developed AI models showed accurate predictability of TOC in both Barnett and Devonian shale. FNN model overperformed others in estimating TOC for the validation data with average absolute percentage error (AAPE) and correlation coefficient (R) of 12.02%, and 0.879, respectively, followed by M-FIS and SVM, while TSK-FIS model showed the lowest predictability of TOC, with AAPE of 15.62% and R of 0.832. All AI models overperformed Wang models, which have recently developed to evaluate the TOC for Devonian formation.

Keywords: total organic carbon; artificial intelligence; barnett shale; devonian shale (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/20/5643/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/20/5643/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:20:p:5643-:d:276021

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().