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A Self-Adaptive Artificial Intelligence Technique to Predict Oil Pressure Volume Temperature Properties

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  • Salaheldin Elkatatny

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

  • Tamer Moussa

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

  • Abdulazeez Abdulraheem

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

  • Mohamed Mahmoud

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

Abstract

Reservoir fluid properties such as bubble point pressure ( Pb ) and gas solubility ( Rs ) play a vital role in reservoir management and reservoir simulation. In addition, they affect the design of the production system. Pb and Rs can be obtained from laboratory experiments by taking a sample at the wellhead or from the reservoir under downhole conditions. However, this process is time-consuming and very costly. To overcome these challenges, empirical correlations and artificial intelligence (AI) models can be applied to obtain these properties. The objective of this paper is to introduce new empirical correlations to estimate Pb and Rs based on three input parameters—reservoir temperature and oil and gas gravities. 760 data points were collected from different sources to build new AI models for Pb and Rs . The new empirical correlations were developed by integrating artificial neural network (ANN) with a modified self-adaptive differential evolution algorithm to introduce a hybrid self-adaptive artificial neural network (SaDE-ANN) model. The results obtained confirmed the accuracy of the developed SaDE-ANN models to predict the Pb and Rs of crude oils. This is the first technique that can be used to predict Rs and Pb based on three input parameters only. The developed empirical correlation for Pb predicts the Pb with a correlation coefficient (CC) of 0.99 and an average absolute percentage error (AAPE) of 6%. The same results were obtained for Rs , where the new empirical correlation predicts the Rs with a coefficient of determination ( R 2 ) of 0.99 and an AAPE of less than 6%. The developed technique will help reservoir and production engineers to better understand and manage reservoirs. No additional or special software is required to run the developed technique.

Suggested Citation

  • Salaheldin Elkatatny & Tamer Moussa & Abdulazeez Abdulraheem & Mohamed Mahmoud, 2018. "A Self-Adaptive Artificial Intelligence Technique to Predict Oil Pressure Volume Temperature Properties," Energies, MDPI, vol. 11(12), pages 1-14, December.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3490-:d:190568
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    Citations

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    Cited by:

    1. Ahmed Gowida & Salaheldin Elkatatny & Emad Ramadan & Abdulazeez Abdulraheem, 2019. "Data-Driven Framework to Predict the Rheological Properties of CaCl 2 Brine-Based Drill-in Fluid Using Artificial Neural Network," Energies, MDPI, vol. 12(10), pages 1-17, May.
    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. Saad Alatefi & Abdullah M. Almeshal, 2021. "A New Model for Estimation of Bubble Point Pressure Using a Bayesian Optimized Least Square Gradient Boosting Ensemble," Energies, MDPI, vol. 14(9), pages 1-21, May.

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