New empirical correlations for predicting Minimum Miscibility Pressure (MMP) during CO2 injection; implementing the Group Method of Data Handling (GMDH) algorithm and Pitzer’s acentric factor
Amirkabir University of Technology, Hafez Ave., No. 424, P.O. Box: 15875-4413 Tehran, Iran
* Corresponding author: email@example.com
Accepted: 20 May 2019
Miscible injection of carbon dioxide (CO2) with ability to increase oil displacement as well as to reduce greenhouse effect has become one of the pioneering methods in Enhanced Oil Recovery (EOR). Minimum Miscibility Pressure (MMP) is known as a key indicator to ensure complete miscibility of two phases and maximum efficiency of injection process. There are various experimental and computational methods to calculate this key parameter. Experimental methods provide the most accurate and valid results. However, such methods are time consuming and expensive leading researchers to use mathematical methods. Among computational methods, empirical correlations are the most straight-forward and simple tools to precisely estimate MMP, especially for gases with impurities.
Furthermore, in predicting the miscibility state of oil–gas system, phase behavior is a vital issue which should be taken into account to achieve reliable results. In this regard, equations of state have an indisputable role in predicting the phase behavior of reservoir fluids. Remarkable improvements have been introduced to elevate performance of equations of state, based on Pitzer’s acentric factor. Hereupon, this study aims to enumerate acentric factor of injected gas (impure CO2) as a correlating parameter alongside conventional parameters including reservoir temperature, oil constituents (molecular weight of C5+, ratio of volatiles to intermediates) and critical properties of injected gas (pseudo-critical pressure & temperature).
Thus, in this study an effective empirical correlation is created, implementing the Group Method of Data Handling (GMDH) algorithm along with including the acentric factor of injected gas, which eventuated to precise predictions of MMP for impure CO2 injection. The GMDH is one of the most robust mathematical modeling methods for predicting physical parameters using linear equations.
A comparison with well-known correlations, demonstrated at least 2% improvement in average absolute error with enumerating the acentric factor and the final error was equal to 12.89%.
© F.B. Delforouz et al., published by IFP Energies nouvelles, 2019
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