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Estimation of Oil Recovery Factor for Water Drive Sandy Reservoirs through Applications of Artificial Intelligence

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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)

  • Weiqing Chen

    (College of Petroleum Engineering and Geosciences, 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

Hydrocarbon reserve evaluation is the major concern for all oil and gas operating companies. Nowadays, the estimation of oil recovery factor (RF) could be achieved through several techniques. The accuracy of these techniques depends on data availability, which is strongly dependent on the reservoir age. In this study, 10 parameters accessible in the early reservoir life are considered for RF estimation using four artificial intelligence (AI) techniques. These parameters are the net pay (effective reservoir thickness), stock-tank oil initially in place, original reservoir pressure, asset area (reservoir area), porosity, Lorenz coefficient, effective permeability, API gravity, oil viscosity, and initial water saturation. The AI techniques used are the artificial neural networks (ANNs), radial basis neuron networks, adaptive neuro-fuzzy inference system with subtractive clustering, and support vector machines. AI models were trained using data collected from 130 water drive sandstone reservoirs; then, an empirical correlation for RF estimation was developed based on the trained ANN model’s weights and biases. Data collected from another 38 reservoirs were used to test the predictability of the suggested AI models and the ANNs-based correlation; then, performance of the ANNs-based correlation was compared with three of the currently available empirical equations for RF estimation. The developed ANNs-based equation outperformed the available equations in terms of all the measures of error evaluation considered in this study, and also has the highest coefficient of determination of 0.94 compared to only 0.55 obtained from Gulstad correlation, which is one of the most accurate correlations currently available.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3671-:d:270706
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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. 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. Andres Soage & Ruben Juanes & Ignasi Colominas & Luis Cueto-Felgueroso, 2021. "The Impact of the Geometry of the Effective Propped Volume on the Economic Performance of Shale Gas Well Production," Energies, MDPI, vol. 14(9), pages 1-22, April.
    2. Miguel A. Jaramillo-Morán & Agustín García-García, 2019. "Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors," Energies, MDPI, vol. 12(23), pages 1-18, November.
    3. 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.
    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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