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A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks

Author

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  • Ahmed Gowida

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

  • Tamer Moussa

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

  • Salaheldin Elkatatny

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

  • Abdulwahab Ali

    (Center of Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

Abstract

Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion design. The elastic behavior of rocks can be studied by determining two main parameters: Young’s modulus and Poisson’s ratio. Accurate determination of the Poisson’s ratio helps to estimate the in-situ horizontal stresses and in turn, avoid many critical problems which interrupt drilling operations, such as pipe sticking and wellbore instability issues. Accurate Poisson’s ratio values can be experimentally determined using retrieved core samples under simulated in-situ downhole conditions. However, this technique is time-consuming and economically ineffective, requiring the development of a more effective technique. This study has developed a new generalized model to estimate static Poisson’s ratio values of sandstone rocks using a supervised artificial neural network (ANN). The developed ANN model uses well log data such as bulk density and sonic log as the input parameters to target static Poisson’s ratio values as outputs. Subsequently, the developed ANN model was transformed into a more practical and easier to use white-box mode using an ANN-based empirical equation. Core data (692 data points) and their corresponding petrophysical data were used to train and test the ANN model. The self-adaptive differential evolution ( SADE ) algorithm was used to fine-tune the parameters of the ANN model to obtain the most accurate results in terms of the highest correlation coefficient ( R ) and the lowest mean absolute percentage error (MAPE). The results obtained from the optimized ANN model show an excellent agreement with the laboratory measured static Poisson’s ratio, confirming the high accuracy of the developed model. A comparison of the developed ANN-based empirical correlation with the previously developed approaches demonstrates the superiority of the developed correlation in predicting static Poisson’s ratio values with the highest R and the lowest MAPE. The developed correlation performs in a manner far superior to other approaches when validated against unseen field data. The developed ANN-based mathematical model can be used as a robust tool to estimate static Poisson’s ratio without the need to run the ANN model.

Suggested Citation

  • Ahmed Gowida & Tamer Moussa & Salaheldin Elkatatny & Abdulwahab Ali, 2019. "A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks," Sustainability, MDPI, vol. 11(19), pages 1-22, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5283-:d:270710
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    References listed on IDEAS

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    1. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
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    1. 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.
    2. Muhammad Nasir Khurshid & Ammad Hassan Khan & Zia ur Rehman & Tahir Sultan Chaudhary, 2022. "The Evaluation of Rock Mass Characteristics against Seepage for Sustainable Infrastructure Development," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    3. Rozmysław Mieński & Przemysław Urbanek & Irena Wasiak, 2021. "Using Energy Storage Inverters of Prosumer Installations for Voltage Control in Low-Voltage Distribution Networks," Energies, MDPI, vol. 14(4), pages 1-21, February.
    4. Xin Wei & Niaz Muhammad Shahani & Xigui Zheng, 2023. "Predictive Modeling of the Uniaxial Compressive Strength of Rocks Using an Artificial Neural Network Approach," Mathematics, MDPI, vol. 11(7), pages 1-17, March.
    5. Yan Li & Fathin Nur Syakirah Hishamuddin & Ahmed Salih Mohammed & Danial Jahed Armaghani & Dmitrii Vladimirovich Ulrikh & Ali Dehghanbanadaki & Aydin Azizi, 2021. "The Effects of Rock Index Tests on Prediction of Tensile Strength of Granitic Samples: A Neuro-Fuzzy Intelligent System," Sustainability, MDPI, vol. 13(19), pages 1-21, September.

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