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

Author

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  • 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)

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.

Suggested Citation

  • Ahmed Abdulhamid Mahmoud & Salaheldin Elkatatny & Abdulwahab Z. Ali & Mohamed Abouelresh & Abdulazeez Abdulraheem, 2019. "Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques," Sustainability, MDPI, vol. 11(20), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:20:p:5643-:d:276021
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    References listed on IDEAS

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    1. Ahmed Abdulhamid Mahmoud & Salaheldin Elkatatny & Weiqing Chen & Abdulazeez Abdulraheem, 2019. "Estimation of Oil Recovery Factor for Water Drive Sandy Reservoirs through Applications of Artificial Intelligence," Energies, MDPI, vol. 12(19), pages 1-13, September.
    2. Ahmed Abdulhamid Mahmoud & Salaheldin Elkatatny & Abdulwahab Ali & Tamer Moussa, 2019. "Estimation of Static Young’s Modulus for Sandstone Formation Using Artificial Neural Networks," Energies, MDPI, vol. 12(11), pages 1-15, June.
    3. Dhafer A. Al-Shehri, 2019. "Oil and Gas Wells: Enhanced Wellbore Casing Integrity Management through Corrosion Rate Prediction Using an Augmented Intelligent Approach," Sustainability, MDPI, vol. 11(3), pages 1-17, February.
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    Cited by:

    1. Tadeusz Kwilosz & Bogdan Filar & Mariusz Miziołek, 2022. "Use of Cluster Analysis to Group Organic Shale Gas Rocks by Hydrocarbon Generation Zones," Energies, MDPI, vol. 15(4), pages 1-14, February.
    2. Miltiadis D. Lytras & Anna Visvizi, 2021. "Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making," Sustainability, MDPI, vol. 13(7), pages 1-3, March.
    3. Jiangtao Sun & Wei Dang & Fengqin Wang & Haikuan Nie & Xiaoliang Wei & Pei Li & Shaohua Zhang & Yubo Feng & Fei Li, 2023. "Prediction of TOC Content in Organic-Rich Shale Using Machine Learning Algorithms: Comparative Study of Random Forest, Support Vector Machine, and XGBoost," Energies, MDPI, vol. 16(10), pages 1-26, May.
    4. Ahmed Abdulhamid Mahmoud & Salaheldin Elkatatny & Dhafer Al Shehri, 2020. "Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
    5. Ahmad Al-AbdulJabbar & Salaheldin Elkatatny & Ahmed Abdulhamid Mahmoud & Tamer Moussa & Dhafer Al-Shehri & Mahmoud Abughaban & Abdullah Al-Yami, 2020. "Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique," Sustainability, MDPI, vol. 12(4), pages 1-19, February.

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