New empirical correlations for predicting Minimum Miscibility Pressure (MMP) during CO₂injection; implementing the Group Method of Data Handling (GMDH) algorithm and Pitzer’s acentric factor

Abstract

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%.

Direct Measurement of Minimum Miscibility Pressure of Decane and CO2 in Nanoconfined Channels

“…In addition to EOS, empirical correlation is another widely adopted approach predicting the MMP based on adequate data of gas and oil properties, including but not limited to temperature, oil composition, gas composition, and initial gas–oil ratio. − Furthermore, molecular simulation is another powerful approach to determining the MMP by simulating the interactions between oil and gas molecules . Beneficial from the rapid development in computer science, a variety of data processing algorithms have been developed and applied to calculate the MMP, such as group method of data handling, , support vector machine , and support vector regression, fuzzy modeling, , genetic programming, − and artificial neural network. − In summary, theoretical methods are effective and powerful tools in determining MMP values and have gained increasing attention. It is equally important to validate these theoretical models by reliable experimental data.…”

“…11−16 Furthermore, molecular simulation is another powerful approach to determining the MMP by simulating the interactions between oil and gas molecules. 17 Beneficial from the rapid development in computer science, a variety of data processing algorithms have been developed and applied to calculate the MMP, such as group method of data handling, 18,19 support vector machine 18,20 and support vector regression, 21 fuzzy modeling, 22,23 genetic programming, 24−28 and artificial neural network. 29−32 In summary, theoretical methods are effective and powerful tools in determining MMP values and have gained increasing attention.…”

Carbon Dioxide-Oil Minimum Miscibility Pressure Methods Overview

“…It is beneficial for quick screening reservoirs for potential carbon dioxide (CO 2 ) flooding. Various empirical correlations for estimating minimum miscibility pressure (MMP) have been calculated from regression data analysis of slimtube data [28]. Generally, empirical correlations for the predicting of minimum miscibility pressure (MMP) reservoir temperature, the (C2-C6) content of reservoir fluid, and API (oil gravity) as input parameters [29].…”

Utilizing Artificial Intelligence Techniques for Modeling Minimum Miscibility Pressure in Carbon Capture and Utilization Processes: A Comprehensive Review and Applications

“…Their findings claimed the superiority of the implemented RBF model in predicting the MMP values with an AARD of 5.6%. Two different explicit correlations based on GMDH for pure and impure CO 2 – oil systems were proposed by Delforouz et al The authors utilized 252 measurements of MMP. The developed GMDH-explicit-based correlations showed acceptable prediction performance.…”