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Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations

Author

Listed:
  • 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)

  • Dhafer Al Shehri

    (College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

Abstract

Prediction of the mechanical characteristics of the reservoir formations, such as static Young’s modulus (E static ), is very important for the evaluation of the wellbore stability and development of the earth geomechanical model. E static considerably varies with the change in the lithology. Therefore, a robust model for E static prediction is needed. In this study, the predictability of E static for sandstone formation using four machine learning models was evaluated. The design parameters of the machine learning models were optimized to improve their predictability. The machine learning models were trained to estimate E static based on bulk formation density, compressional transit time, and shear transit time. The machine learning models were trained and tested using 592 well log data points and their corresponding core-derived E static values collected from one sandstone formation in well-A and then validated on 38 data points collected from a sandstone formation in well-B. Among the machine learning models developed in this work, Mamdani fuzzy interference system was the highly accurate model to predict E static for the validation data with an average absolute percentage error of only 1.56% and R of 0.999. The developed static Young’s modulus prediction models could help the new generation to characterize the formation rock with less cost and safe operation.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1880-:d:327312
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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. Zhi-Hua Xu & Guang-Liang Feng & Qian-Cheng Sun & Guo-Dong Zhang & Yu-Ming He, 2020. "A Modified Model for Predicting the Strength of Drying-Wetting Cycled Sandstone Based on the P-Wave Velocity," Sustainability, MDPI, vol. 12(14), pages 1-17, July.
    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.

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