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

Salaheldin Elkatatny, Tamer Moussa, Abdulazeez Abdulraheem and Mohamed Mahmoud
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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

Energies, 2018, vol. 11, issue 12, 1-14

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.

Keywords: self-adaptive differential evolution; artificial intelligence (AI); bubble point pressure correlation; gas solubility correlation; pressure volume temperature (PVT) properties prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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