Intelligent Prediction of Minimum Miscibility Pressure (MMP) During CO 2 Flooding Using Artificial Intelligence Techniques

Amjed Hassan, Salaheldin Elkatatny and Abdulazeez Abdulraheem
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Amjed Hassan: 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
Abdulazeez Abdulraheem: College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

Sustainability, 2019, vol. 11, issue 24, 1-16

Abstract: Carbon dioxide (CO 2 ) injection is one of the most effective methods for improving hydrocarbon recovery. The minimum miscibility pressure (MMP) has a great effect on the performance of CO 2 flooding. Several methods are used to determine the MMP, including slim tube tests, analytical models and empirical correlations. However, the experimental measurements are costly and time-consuming, and the mathematical models might lead to significant estimation errors. This paper presents a new approach for determining the MMP during CO 2 flooding using artificial intelligent (AI) methods. In this work, reliable models are developed for calculating the minimum miscibility pressure of carbon dioxide (CO 2 -MMP). Actual field data were collected; 105 case studies of CO 2 flooding in anisotropic and heterogeneous reservoirs were used to build and evaluate the developed models. The CO 2 -MMP is determined based on the hydrocarbon compositions, reservoir conditions and the volume of injected CO 2 . An artificial neural network, radial basis function, generalized neural network and fuzzy logic system were used to predict the CO 2 -MMP. The models’ reliability was compared with common determination methods; the developed models outperform the current CO 2 -MMP methods. The presented models showed a very acceptable performance: the absolute error was 6.6% and the correlation coefficient was 0.98. The developed models can minimize the time and cost of determining the CO 2 -MMP. Ultimately, this work will improve the design of CO 2 flooding operations by providing a reliable value for the CO 2 -MMP.

Keywords: minimum miscibility pressure (MMP); CO 2 flooding; artificial intelligence; new models (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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