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Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique

Author

Listed:
  • Ahmad Al-AbdulJabbar

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

  • Salaheldin Elkatatny

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

  • Ahmed Abdulhamid Mahmoud

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

  • Tamer Moussa

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

  • Dhafer Al-Shehri

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

  • Mahmoud Abughaban

    (EXPEC Advanced Research Center (ARC), Dhahran 31311, Saudi Arabia)

  • Abdullah Al-Yami

    (EXPEC Advanced Research Center (ARC), Dhahran 31311, Saudi Arabia)

Abstract

Rate of penetration (ROP) is one of the most important drilling parameters for optimizing the cost of drilling hydrocarbon wells. In this study, a new empirical correlation based on an optimized artificial neural network (ANN) model was developed to predict ROP alongside horizontal drilling of carbonate reservoirs as a function of drilling parameters, such as rotation speed, torque, and weight-on-bit, combined with conventional well logs, including gamma-ray, deep resistivity, and formation bulk density. The ANN model was trained using 3000 data points collected from Well-A and optimized using the self-adaptive differential evolution (SaDE) algorithm. The optimized ANN model predicted ROP for the training dataset with an average absolute percentage error (AAPE) of 5.12% and a correlation coefficient (R) of 0.960. A new empirical correlation for ROP was developed based on the weights and biases of the optimized ANN model. The developed correlation was tested on another dataset collected from Well-A, where it predicted ROP with AAPE and R values of 5.80% and 0.951, respectively. The developed correlation was then validated using unseen data collected from Well-B, where it predicted ROP with an AAPE of 5.29% and a high R of 0.956. The ANN-based correlation outperformed all previous correlations of ROP estimation that were developed based on linear regression, including a recent model developed by Osgouei that predicted the ROP for the validation data with a high AAPE of 14.60% and a low R of 0.629.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1376-:d:320113
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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. 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.
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    Cited by:

    1. Maria Krechowicz & Adam Krechowicz, 2021. "Risk Assessment in Energy Infrastructure Installations by Horizontal Directional Drilling Using Machine Learning," Energies, MDPI, vol. 14(2), pages 1-28, January.
    2. Fatick Nath & Sarker Monojit Asish & Deepak Ganta & Happy Rani Debi & Gabriel Aguirre & Edgardo Aguirre, 2022. "Artificial Intelligence Model in Predicting Geomechanical Properties for Shale Formation: A Field Case in Permian Basin," Energies, MDPI, vol. 15(22), pages 1-19, November.
    3. 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.

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