Minimum Miscibility Pressure Correlation Research Articles

Measurement and Modeling of Minimum Miscibility Pressure: A State-of-the-Art Review

SummaryThis paper gives a critical review of miscibility-measurement techniques published in the open literature along with recommendations and lessons learned. Many of these published methods violate the inherent assumptions for multicontact miscibility (MCM). The confusion often arises from a failure to distinguish between first-contact miscibility (FCM), in which two fluids can be mixed in all proportions without forming two phases, and MCM, in which fluid compositions that arise during the flow of two phases in a porous medium approach a specific critical point within the constraints of the MCM definition.There are many analytical, numerical, correlational, and experimental methods available to estimate the minimum miscibility pressure (MMP) for MCM flow. The numerous available methods, some of which are quite inexpensive, have caused significant misunderstandings in the literature and in practice regarding their ability to estimate MMP. Our experience has shown that the best methods are those that honor the multicontact process (MCM), in which flow interacts with phase behavior in a prescribed way. Good methods that achieve this are slimtube experiments, detailed slimtube simulations, multiple-mixing-cell calculation methods, and the method of characteristics (MOC). Techniques such as the rising-bubble-apparatus (RBA) and vanishing-interfacial-tension (IFT) (VIT) experiments are subject to significant uncertainties, although they can still provide useful information. Numerous MMP correlations have been developed. They should be used with caution for systems similar to those used to develop the correlation. Use for other fluid systems can lead to significant errors. We discuss the advantages and disadvantages of most current methods and show that various combinations of methods can reduce uncertainty.

Estimation of Minimum Miscibility Pressure for Flue Gas Injection Using Soft Experimentations

A new approach is demonstrated in which soft experimentation can be performed for MMP measurements, thus replacing the common practice of slim tube displacement laboratory experiments. Recovery potential from oil reservoirs by miscible flue gas injection was studied by slim tube and field-scale numerical simulation using two flue gases and seven crude oils sampled at different depths in three candidate reservoirs. The soft experimentations were conducted using Eclipse300TM, a three-phase compositional simulator. This study investigates minimum miscibility pressure (MMP), a significant miscible gas injection project screening tool. Successful design of the project is contingent to the accurate determination of the MMP. This study evaluates effects of important factors such as injection pressure, oil component composition, and injection gas composition on the MMP and recovery efficiency for slim tube and field-scale displacements. Two applicable MMP correlations were used for comparison and validation purposes.

CO2 MMP determination on L Reservoir by using CMG simulation and correlations

CO2 minimum miscibility pressure (MMP) is an important parameter in CO2 enhanced oil recovery (CO2-EOR) process. The CO2 injection must be conducted at the pressure equal or greater than MMP. The aim of this study is to determine CO2 MMP by using one-dimensional CMG simulation model and CO2 MMP correlations on L reservoir. To make an accurate simulation model and to choose the most satisfying correlation, it is necessary to validate by comparing the obtained CO2 MMP with experiments in the laboratory on eight type oil from various fields. The result showed that Zhang’s correlation is the best correlation which have -13.9% average percentage of errors while using the CMG simulation model shows the average percentage errors approximately -6.5 %. Using Zhang’s correlation, CO2 MMP on L reservoir is about 3166 psia and from CMG simulation, CO2 MMP equals to 3219 psia.

An improved correlation to determine minimum miscibility pressure of CO2–oil system

An accurate and reliable estimation of minimum miscibility pressure (MMP) of CO2–oil system is a critical task for the design and implementation of CO2 miscible displacement process. In this study, an improved CO2–oil MMP correlation was developed to predict the MMP values for both pure and impure CO2 injection cases based on ten influential factors, i.e. reservoir temperature (TR), molecular weight of C7+ oil components (MWC7+), mole fraction of volatile oil components (xvol), mole fraction of C2C4 oil components (xC2-C4), mole fraction of C5C6 oil components (xC5-C6), and the gas stream mole fractions of CO2 (yCO2), H2S (yH2S), C1 (yC1), hydrocarbons (yHC) and N2 (yN2). The accuracy of the improved correlation was evaluated against experimental data reported in literature concurrently with those estimated by several renowned correlations. It was found that the improved correlation provided higher prediction accuracy and consistency with literature experimental data than other literature correlations. In addition, the predictive capability of the improved correlation was further validated by predicting an experimentally measured CO2–oil MMP data, and it showed an accurate result with the absolute deviation of 4.15%. Besides, the differential analysis of the improved correlation was analyzed to estimate the impact of parameters uncertainty in the original MMP data on the calculated results. Also, sensitivity analysis was performed to analyze the influence of each parameter on MMP qualitatively and quantitatively. The results revealed that the increase of xC2-C4, xC5-C6 and yH2S lead to the decrease of MMP, while the increase of TR, MWC7+, xvol, yCO2, yC1, yHC and yN2 tend to increase the MMP. Overall, the relevance of each parameter with MMP follows the order of TR > xC5-C6 > MWC7+ >xvol > yH2S > yHC > yCO2 >yC1 >yN2 >xC2-C4.

Minimum miscibility pressure and interfacial tension measurements for N2 and CO2 gases in contact with W/O emulsions for different temperatures and pressures

In this research, the IFT behavior of two different specimens of W/O emulsion with distinctive soluble ingredients of water in contact with CO2 and N2 was examined. The Minimum Miscibility Pressure (MMP) of the mentioned gases with emulsion specimens were measured and compared with the case of the original crude oil with no water content. This study focuses on the quantity of water in the W/O emulsion specimens and its effects on IFT and MMP values. The IFT behavior of W/O emulsions was surveyed for two different cases of distinctive water soluble ingredients, i.e. sea water and reservoir brine. The tests were performed under high pressure-high temperature (HPHT) conditions utilizing Axisymmetric Drop Shape Analysis (ADSA) procedure. The equilibrium IFT values between the two distinctive specimens of W/O emulsions with different amounts and compositions of dispersed water and two types of gases, i.e. CO2 and N2, were measured. The outcomes were compared with the behavior of original crude oil, i.e. crude oil with no water content. The temperature was set in two distinctive values of 313.15 K and 333.15 K, and the pressure changes in the scope of 1 MPa–20.68 MPa. The outcomes manifested that the IFT behavior of the emulsion could be correlated directly with pressure for two distinctive variation rates, categorized in two different regions. Comparing to the original crude oil, in the case of W/O emulsion the equilibrium IFT increases, and this incremental behavior is much significant in the case of N2-emuslion system. Additionally, the outcomes of IFT versus pressure, in the case of N2-emulsion system, manifested that the slope of the first part of the curve decreases with increment in dispersed water content in the crude oil. However, the IFT values versus pressure in the second region were not affected by the water content of the emulsion. Also, Vanishing Interfacial Tension (VIT) technique was used to estimate the MMP of N2-emulsion systems. According to the outcomes, MMP was increased in the existence of water, and the incremental behavior becomes significant when the water content is increased. It is worthy to note that the outcomes of the CO2-emulsion systems manifested that both IFT behavior and MMP values were not changed significantly by water content. Finally, in light of the test information gathered in this research, various relationships were proposed to express the IFT and MMP correlations as dependants of pressure at distinctive temperatures.

Improved minimum miscibility pressure correlation for CO2 flooding using various oil components and their effects

Improved minimum miscibility pressure correlation for CO₂ flooding using various oil components and their effects

A New Simple CO2 Minimum Miscibility Pressure Correlation

The minimum miscibility pressure (MMP) is one of the most important parameter to be determined in miscible gas injection projects to ensure and maximize the displacement sweep efficiency inside the reservoir. Usually the most effective way of determining the MMP is to run slim tube experiments. However, in the early screening stage, we often relay on the published empirical correlations to estimate the MMP and identify the candidate fields for EOR gas injection projects. The main objective of this paper was to examine different published empirical CO2 MMP correlations using measured data mainly obtained from Libya and other published resources, and also to develop a new simple reliable correlation to be applied in the oil industry. The data collected covered a wide range of CO2 MMP (1544-6244 psia) and oil API gravity (28-50ºAPI). Minitab regression tool was extensively used in our study and a wide range of new constructed correlations ranging from simple to complex ones were developed and statistically evaluated. The proposed simple CO2 MMP correlation is mainly function of the measured Pb, API, T and Rsi and has very reliable degree of accuracy (SD=6.7%, ARE=0.44%, AARE=5.74%, R2=95.22%) for the examined data and has shown better performance when compared with the industry popular correlations. The new correlation was validated against 100 measured PVT variables (Pb, Rsi, T and API) obtained from Libya, and the predicted CO2 MMP results have demonstrated very reliable trend (within the measured CO2 MMP trend) with no anomalies.

An Improved CO2-Crude Oil Minimum Miscibility Pressure Correlation

Minimum miscibility pressure (MMP), which plays an important role in miscible flooding, is a key parameter in determining whether crude oil and gas are completely miscible. On the basis of 210 groups of CO2-crude oil system minimum miscibility pressure data, an improved CO2-crude oil system minimum miscibility pressure correlation was built by modified conjugate gradient method and global optimizing method. The new correlation is a uniform empirical correlation to calculate the MMP for both thin oil and heavy oil and is expressed as a function of reservoir temperature, C7+molecular weight of crude oil, and mole fractions of volatile components (CH4and N2) and intermediate components (CO2, H2S, and C2~C6) of crude oil. Compared to the eleven most popular and relatively high-accuracy CO2-oil system MMP correlations in the previous literature by other nine groups of CO2-oil MMP experimental data, which have not been used to develop the new correlation, it is found that the new empirical correlation provides the best reproduction of the nine groups of CO2-oil MMP experimental data with a percentage average absolute relative error (%AARE) of 8% and a percentage maximum absolute relative error (%MARE) of 21%, respectively.

A new correlation for calculating carbon dioxide minimum miscibility pressure based on multi-gene genetic programming

Miscible gas injection is one of the most efficient enhanced oil recovery (EOR) methods in petroleum industry. Minimum miscibility pressure (MMP) is a key parameter in any gas injection design project. Experimental Measurement of MMP is a costly and time-consuming method; so searching for a quick, not expensive and reliable method to determine gas-oil MMP is inevitable. This paper Present a fast and vigorous method using a new approach based on multi-gene genetic programming (MGGP) to determine carbon dioxide minimum miscibility pressure (CO2 MMP) for carbon dioxide injection processes. Then, new correlations for MMP calculation of both pure and impure CO2 streams using the MGGP, have been developed. Consequently, the MGGP models have been validated and compared with the other conventional model results, to evaluate different techniques. It was founded that the new developed correlations predict accurate values of CO2 MMP compare with the experimental slim-tube CO2 MMP test results, with the lowest average relative and absolute error and also higher correlation coefficient among all evaluated CO2 MMP correlation results.

Developing a Formula Based on a Hybrid Neural Genetic Algorithm for the Prediction of Minimum Miscibility Pressure

Minimum miscibility pressure is a fundamental parameter in miscible injection projects. Experimental determination of minimum miscibility pressure is very costly and time-consuming; therefore, attempts have been made to utilize artificial neural networks for determination of minimum miscibility pressure. Despite the wide range of applications and flexibility of artificial neural networks, design and structural optimization of neural networks is still strongly dependent upon the designer's experience. To mitigate this problem, this article presents a new approach based on a hybrid neural genetic algorithm to determine the minimum miscibility pressure for pure and impure carbon dioxide injections. Then, equations for minimum miscibility pressure prediction by using the optimize weights of network have been generated. With the formulas obtained, the user may use such results without running the artificial neural network software. The new model yielded the accurate prediction of the experimental slim-tube carbon dioxide minimum miscibility pressure with the lowest relative mean squared error and average absolute errors among all tested carbon dioxide minimum miscibility pressure correlations.

Application of adaptive neuro-fuzzy inference system for prediction of minimum miscibility pressure

In this study, minimum miscibility pressure (MMP) which is a key parameter in design of an efficient miscible gas injection project is aimed to be determined by means of adaptive neuro-fuzzy inference system (ANFIS). 27 features including concentrations of different components of reservoir oil and injected gas, molecular weight and specific gravity of C7+ in reservoir oil and finally reservoir temperature were taken as inputs to the ANFIS. Principal component analysis (PCA) algorithm was used to reduce the dimensionality of the data. Using the back propagation gradient descent method in combination with the least squares method, ANFIS model was trained. The model’s predictions were compared with experimental results and also the results obtained from the commonly used MMP correlations in the literature. Based on these comparisons, it was found that the proposed ANFIS model has potential in predicting MMP values and also the effect of each individual parameter on these values.

Prediction of minimum miscible pressure by using neural network based hybrid genetic algorithm and particle swarm optimization

Miscible gas injection is considered as an effective enhanced oil recovery (EOR) technique for conventional oil reservoirs. Minimum miscibility pressure (MMP) is an important parameter in design of a gas injection project as sweep efficiency from gas injection highly depends on the value of MMP. Obtaining MMP using laboratory techniques is cost, labor, and time intensive. Hence, finding a rapid, inexpensive, and robust technique to estimate the gas–oil MMP is inevitable. In this work, the model based on a feed-forward artificial neural network (ANN) optimized by Hybrid Genetic Algorithm and Particle Swarm Optimization (HGAPSO) to estimate gas–oil MMP is proposed. Genetic Algorithm is used to decide the initial weights of the neural network. The performance of the HGAPSO–ANN model is compared with experimental gas–oil MMP and the calculated results for the common gas–oil MMP correlations. The results demonstrate the effectiveness of the HGAPSO–ANN model.

An Analysis of Reservoir Production Strategies in Miscible and Immiscible Gas Injection Projects

Successful design and implementation of a miscible gas injection project depends upon the minimum miscibility pressure (MMP) and other factors such as reservoir and fluid characterization. The experimental methods available for determining MMP are both costly and time consuming. Therefore, the use of correlations that prove to be reliable for a wide range of fluid types would likely be considered acceptable for preliminary screening studies. This work includes a comparative evaluation of MMP correlations and thermodynamic models using an equation of state by PVTsim software (Schlumberger, 2001a). We observed that none of the evaluated MMP correlations studied in this investigation is sufficiently reliable. EOSbased analytical methods seemed to be more conservative in predicting MMP values. Following an acceptable estimate of MMP, several compositional simulation runs were conducted to determine the sensitivity of the oil recovery to variations in injection pressure (at pressures above, equal to and below the estimated MMP), stratification and mobility ratio parameters in miscible and immiscible gas injection projects. Simulation results indicated that injection pressure was a key parameter that affects oil recovery to a high degree. MMP determined to be the optimum injection pressure. Stratification and mobility ratio could also affect the recovery efficiency of the reservoir in a variety of ways. Key words : Reservoir production; Miscible gas injection; Immiscible gas; Minimum miscibility pressure

An Improved CO2–Oil Minimum Miscibility Pressure Correlation for Live and Dead Crude Oils

An improved CO2–oil minimum miscibility pressure (MMP) correlation has been successfully developed to more accurately determine the CO2–oil MMP for a wide range of live and dead crude oils. Experimentally, slim-tube tests have been conducted to determine the CO2–oil MMPs for four crude oil samples with high molecular weights of C7+ fraction. Theoretically, the newly developed CO2–oil MMP correlation is originated from a CO2–oil MMP database from the literature that covers 51 CO2–oil MMP data for various live and dead oil samples, especially those with high C7+ molecular weights. The new CO2–oil MMP correlation is expressed as a function of reservoir temperature, C7+ molecular weight, and mole fraction ratio of volatile components (N2 and CH4) to intermediate components (CO2, H2S, and C2–C6). Compared to nine commonly used CO2–oil MMP correlations in the literature, it is found that the new CO2–oil MMP correlation provides the best reproduction of the literature CO2–oil MMP data with a percentage average a...

CO 2–oil minimum miscibility pressure model for impure and pure CO 2 streams

CO 2 injection processes are among the effective methods for enhanced oil recovery. A key parameter in the design of CO 2 injection project is the minimum miscibility pressure (MMP), whereas local displacement efficiency from CO 2 injection is highly dependent on the MMP. From an experimental point of view, slim tube displacements, and rising bubble apparatus (RBA) tests routinely determine the MMP. Because such experiments are very expensive and time-consuming, searching for fast and robust mathematical determination of CO 2–oil MMP is usually requested. It is well recognized that CO 2–oil MMP depends upon the purity of CO 2, oil composition, and reservoir temperature. This paper presents a new model for predicting the impure and pure CO 2–oil MMP and the effects of impurities on MMP. The alternating conditional expectation (ACE) algorithm was used to estimate the optimal transformation that maximizes the correlation between the transformed dependent variable (CO 2–oil MMP) and the sum of the transformed independent variables. These independent variables are reservoir temperature ( T R), oil compositions (mole percentage of volatile components (C 1 and N 2), mole percentage of intermediate components (C 2–C 4, H 2S and CO 2), and molecular weight of C 5+ (MW C5+)), and non-CO 2 components (mole percentage of N 2, C 1, C 2–C 4, and H 2S) in the injected CO 2. The validity of this new model was successfully approved by comparing the model results to the pure and impure experimental slim-tube CO 2–oil MMP and the calculated results for the common pure and impure CO 2–oil MMP correlations. The new model yielded the accurate prediction of the experimental slim-tube CO 2–oil MMP with the lowest average relative and average absolute error among all tested impure and pure CO 2–oil MMP correlations. In addition, the new model could be used for predicting the impure CO 2–oil MMP at higher fractions of non-CO 2 components.

Improved MMP Correlations for CO2 Floods Using Analytical Gasflooding Theory

SummaryLocal displacement efficiency from CO2 gas injection is highly dependent on the minimum miscibility pressure (MMP). Correlations are sometimes used to estimate the MMP where the injected fluid may or may not contain impurities such as methane. These correlations, however, are based on a limited set of experimental data and, as such, are not widely applicable. They also do not account accurately for the more complex condensing/vaporizing (CV) displacement process.This paper presents new MMP correlations for the displacement of multicomponent oil by CO2 and impure CO2. The approach is to use recently developed analytical theory for MMP calculations from equations of state (EOSs) to generate MMP correlations for displacements by pure and impure CO2.1–8 The advantage of this approach is that MMPs for a wide range of temperatures and reservoir fluids can be calculated quickly and accurately without introducing uncertainties associated with slimtube MMPs and other numerical methods. The improved MMP correlations are based solely on the reservoir temperature, the molecular weight of C7+, and the percentage of intermediates (C2–C6)in the oil. The MMPs from the improved correlations are compared to currently used correlations and 41 experimentally measured MMPs. Correlations are also developed for impure-CO2 floods, in which the injection stream may contain up to 40% methane. The new correlations are more accurate for a wider range of conditions than the currently used correlations.

Use of genetic algorithm to estimate CO 2–oil minimum miscibility pressure—a key parameter in design of CO 2 miscible flood

A new genetic algorithm (GA)-based correlation has been developed to estimate the CO 2–oil minimum miscibility pressure (MMP)—a key parameter in design of CO 2 miscible flood to enhance oil recovery. In the order of their effects and importance, the correlation uses the following key input parameters: reservoir temperature, molecular weight of C 5+, and the ratio of volatiles (C 1 and N 2) to intermediates (C 2–C 4, H 2S, and CO 2) and has been validated against experimental data and other commonly used correlations reported in the literature, notably that of Alston et al., Glaso, Yellig and Metcalfe, Cronquist, Lee, and Holm and Josendal early correlation. Based on the Darwinian theory of evolution, it is demonstrated that the GA is particularly suited to problems with non-linearity, variable discontinuity, large search space, and all kinds of objective and constraint functions. It works by exploration and exploitation of the search space (that is, the probability of finding the global optimum increases). Moreover, GA solutions are less likely to be misleading, as the problem does not need to be ”restructured” to fit the solution method. That is, the development of the GA-based correlation does not entail any pre-correlation data manipulation, and as a consequence, all data could be utilized as reported. The GA software developed in this study uses chromosomes coded with real numbers to encode correlation coefficients in an initial random population with 100 chromosomes size. Such encoding technique enhances the GA robustness. For the selection technique, the roulette wheel method has been used. Furthermore, to produce the offspring, one-point crossover and mutation with 100% probability are used. Noteworthy advantage offered by GA-based correlation is that this correlation can be used when there is a lack of experimental data and also to design an optimal laboratory program to estimate MMP. Moreover, the correlation errors could be minimized further through a series of iterative optimisation runs. Our results suggest that a GA-based CO 2–oil MMP correlation could be superior to other correlations commonly used. For example, compared to other correlations, the GA-based correlation yielded a better match with data with an average error of 5.86% and standard deviation of 7.96%, followed by Alston et al. correlation, which yielded an average error of 8.21% with a standard deviation of 10.04%. Among the correlations tested and compared against the GA-based correlation, Yellig and Metcalfe correlation yielded an error of 14%.

A Comparative Investigation of Minimum Miscibility Pressure Correlations For Enhanced Oil Recovery

Abstract Multiple contact miscible floods involving the injection of relatively inexpensive gases into oil reservoirs represent one of the most cost effective enhanced oil recovery processes currently available. The experimental displacement procedures available for determining the optimal flood pressure, referred to as the minimum miscibility pressure (MMP), are both costly and time consuming. Hence, the use of a correlation proven reliable over a large range of conditions would likely be considered acceptable for the purposes of preliminary screening studies. This paper presents an evaluation of 15 rich gas, lean gas, and nitrogen MMP correlations published in the literature. Each method was developed using experimental data, equation-of-state (EOS) predictions, or a combination of the two. The accuracy of each correlation was evaluated by comparing predicted versus measured MMP's using a data base consisting predominantly of experimental results published in the literature. The most reliable MMP correlations were all found to have a common EOS developmental basis. These results support the applicability of EOS-based methods for accurate MMP predictions. Introduction The injection gases most commonly used for enhanced oil recovery processes are generally not miscible upon first contact with the reservoir fluids that they are displacing. However, under suitable reservoir pressure and temperature conditions, miscibility may gradually be developed between some injection-gas/reservoir-oil combinations by a mass transfer of components between the gaseous and liquid phases. Miscibility generated in such a manner is commonly referred to as multiple contact (dynamic) miscibility, and has been well described in the literature(1). The vaporizing gas drive (VGD) and the condensing gas drive (CGD) are the mechanisms through which dynamic miscibility is generally explained. Miscibility develops at the flood front during VGD processes, which are commonly referred to as lean gas (LG) drives. Dynamic miscibility in nitrogen (N2) floods similarly occurs through the VGD mechanism. During CGD processes, generally referred to as rich gas (RG) drives, miscibility develops at the injection point. Recent research has indicated, however, that miscibility in some RG drives may actually develop through a liquid extraction drive (LED) process(2,3). Similar to VGD's, miscibility develops at the flood front during a LED. An optimum displacement pressure exists for a dynamic miscible flood, commonly referred to as the minimum miscibility pressure (MMP). The object of this study is to evaluate available RG, LG, and N2 MMP correlations using a data base consisting predominantly of experimental MMP results published in the literature. Measurement and Prediction of MMP's The MMP for gas/oil mixtures has traditionally been measured by performing slim tube displacement experiments(1). These experiments involve constant temperature displacement of a live oil from the slim tube by an injection gas. The MMP has typically been accepted as the pressure at which a practical maximum recovery efficiency is observed following a series of displacements. MMP values have also been measured for many gas/oil pairs using the Rising Bubble Apparatus (RBA)(4), where a small bubble of gas is injected at the base of a column of live oil.

Miscible Displacement: Recovery Tests With Nitrogen

Summary High-pressure slim-tube tests and phase-behavior studies were performed with reservoir fluid and three-component hydrocarbon mixtures. The amount of methane and intermediates in the reservoir fluid were varied while other parameters that affect the oil recovery were kept constant. The effects of displacement velocity, temperature, and length of the packed column on slim-tube recovery were also investigated. The results obtained show that the amount of methane and intermediates in the reservoir fluid has a significant effect on minimum miscibility pressure (MMP). The results suggest very strongly that a reservoir fluid with a low methane content needs a long path length to develop miscibility with N2 (a large fraction of the oil is bypassed in the first part of the column). The slim-tube oil recovery, and hence the average microscopic displacement efficiency of the process, is shown to increase significantly with slim-tube length when injection pressure and other variables are kept constant. The results from simulation tests of phase behavior and fluid flow in the slim tube confirmed some of the conclusions made from the experimental results obtained in this work. An N2 MMP correlation was developed from displacement test data reported in the literature and in the present study.

A Thermodynamic Correlation for the Minimum Miscibility Pressure in CO2 Flooding of Petroleum Reservoirs

Summary A graphic equation-of-state (EOS)-generated correlation for the minimum miscibility pressure (MMP) of CO2/crude-oil displacements is presented, along with a brief review of 17 other MMP correlations. The MMP is estimated as the cricondenbar or first-contact miscibility pressure of the pure or impure CO2/crude-oil system in which the C5+ oil fraction is modeled as a single normal alkane of equivalent molecular weight. The correlation accounts for all the experimentally observed trends in MMP in a consistent manner and is independent of any CO2/crude-oil data base. A correction is introduced, however, to prevent a predicted increase in the MMP with decreasing temperature at low temperatures for high-molecular-weight oils. The average predicted-MMP/experimental-MMP ratio for 157 CO2/crude-oil MMP values is 1.09, with a standard deviation of 0.19. Unlike other correlations, specifically those relating MMP with CO2 density, a decrease in MMP with temperature at elevated temperatures (> 120°C [> 248°F]) is predicted.