CO2 Oil Minimum Miscibility Pressure Research Articles

Adsorption effects on CO2-oil minimum miscibility pressure in tight reservoirs

CO2 miscible injection holds immense potential for enhancing tight oil recovery, where achieving the minimum miscibility pressure (MMP) is pivotal. Adsorption of CO2 and oil in pores affects the CO2-oil MMP in tight reservoirs, necessitating precise nanoscale MMP calculations and understanding influencing factors. Here, we applied a modified Peng-Robinson equation of state (PR-EOS) for nanoscale MMP calculations, incorporating adsorption layers and effective molar volume to describe adsorption effects. Validation against molecular simulations and nanofluidic experiments shows a maximum deviation of 4.6 %. We found that in nanopores, achieving miscibility demands less CO2 than in bulk. The CO2-oil MMP reduces as pore size decreasing, influenced by adsorption, critical point shift and capillarity. At 5 nm, the MMP is 11.12 MPa, 27.8 % lower than the bulk value (15.4 MPa). Adsorption intensifies this reduction by curtailing free molecules and effective pore radius, and becomes more pronounced for lighter hydrocarbon mixtures. However, the nanoscale CO2-oil MMP is equal to the bulk value when rp ≥ 350 nm. Furthermore, a maximum MMP and the corresponding transition temperature exist for each pore size, and increase as pore size increasing. This method provides a valuable tool for optimizing CO2 miscible injection and carbon storage in challenging nanoscale-pore reservoirs.

Investigation of the Influence of Formation Water on the Efficiency of CO2 Miscible Flooding at the Core Scale

This study investigated the impact of formation water on the mass transfer between CO2 and crude oil in low-permeability reservoirs through CO2 miscible flooding. Formation water leads to water blocks, which affect the effectiveness of CO2 miscible flooding. Therefore, we studied the impact and mechanisms of formation water on the CO2-oil miscibility. The microscale interaction between formation water-CO2-core samples was investigated using CT scanning technology to analyze its influence on core permeability parameters. In addition, CO2 miscible flooding experiments were conducted using the core displacement method to determine the effects of formation water salinity and average water saturation on minimum miscibility pressure (MMP) and oil displacement efficiency. The CT scanning results indicate that high-salinity formation water leads to a decrease in the porosity and permeability of the core as well as pore and throat sizes under miscible pressure conditions. The experimental results of CO2 miscible flooding demonstrate that CO2-oil MMP decreases as the salinity of the formation water increases. Moreover, as the average water saturation in the core increases, the water block effect strengthens, resulting in an increase in MMP. The recovery factors of cores with average water saturations of 30%, 45%, and 60% are 89.8%, 88.6%, and 87.5%, respectively, indicating that the water block effect lowers the oil displacement efficiency and miscibility.

Nanometer-Scale CO2-Shale Oil Minimum Miscibility Pressure Calculations Based on Modified PR-EOS

CO2 flooding is recognized as an efficient method for enhancing shale oil recovery, while CO2-oil MMP (minimum miscibility pressure) in the micro-nanoscale is a crucial parameter. This paper presents a method for calculating the MMPs of pure hydrocarbons (C4H10, C6H14, C8H18, and C10H22) and CO2 systems in nanopores (3 nm to 10 nm) with temperature ranging from 290.15 K to 373.15 K. Firstly, we modify the Peng-Robinson equation of state (PR-EOS) by considering the influence of confinement effect and capillary pressure in nanopores. Secondly, the flash calculation algorithm is employed to determine whether the oil and gas phases in nanopores have reached an equilibrium state according to the equality of the fugacity of the two phases. Thirdly, we calculate the interfacial tension (IFT) between the two phases using the Macleod-Sugden equation. When the extrapolated IFT is zero, we treat the corresponding pressure as the MMP of the CO2-oil system in nanopores. Simulation results indicate that the calculated MMP using this method has a relative error of about 0.62% compared to the MMP calculated using the multiple mixing cell (MMC) method, indicating high reliability for MMP prediction. Moreover, the measured MMP at the nanoscale is generally smaller than that in the bulk phase due to the influence of the confinement effect. The MMP is positively correlated with the reservoir temperature, the carbon atom number in alkanes, and the nanopore radius.

A New Predictive Method for CO2-Oil Minimum Miscibility Pressure

Gas injection processes are among the effective methods for enhanced oil recovery. Miscible and/or near miscible gas injection processes are among the most widely used enhanced oil recovery techniques. The successful design and implementation of a miscible gas injection project are dependent upon the accurate determination of minimum miscibility pressure (MMP), the pressure above which the displacement process becomes multiple-contact miscible. This paper presents a method to get the characteristic curve of multiple-contact. The curve can illustrate the character in the miscible and/or near miscible gas injection processes. Based on the curve, we suggest a new model to make an accurate prediction for CO2-oil MMP. Unlike the method of characteristic (MOC) theory and the mixing-cell method, which have to find the key tie lines, our method removes the need to locate the key tie lines that in many cases is hard to find a unique set. Moreover, unlike the traditional correlation, our method considers the influence of multiple-contact. The new model combines the multiple-contact process with the main factors (reservoir temperature, oil composition) affecting CO2-oil MMP. This makes it is more practical than the MOC and mixing-cell method, and more accurate than traditional correlation. The method proposed in this paper is used to predict CO2-oil MMP of 5 samples of crude oil in China. The samples come from different oil fields, and the injected gas is pure CO2. The prediction results show that, compared with the slim-tube experiment method, the prediction error of this method for CO2-oil MMP is within 2%.

Chemistry of Reservoir Fluids in the Aspect of CO2 Injection for Selected Oil Reservoirs in Poland

Worldwide experiences related to geological CO2 storage show that the process of the injection of carbon dioxide into depleted oil reservoirs (CCS-EOR, Carbon Capture and Storage—Enhanced Oil Recovery) is highly profitable. The injection of CO2 will allow an increasing recovery factor (thus increasing CCS process profitability) and revitalize mature reservoirs, which may lead to oil spills due to pressure buildups. In Poland, such a solution has not yet been implemented in the industry. This work provides additional data for analysis of the possibility of the CCS-EOR method’s implementation for three potential clusters of Polish oil reservoirs located at a short distance one from another. The aim of the work was to examine the properties of reservoir fluids for these selected oil reservoirs in order to assure a better understanding of the physicochemical phenomena that accompany the gas injection process. The chemical composition of oils was determined by gas chromatography. All tested oils represent a medium black oil type with the density ranging from 795 to 843 g/L and the viscosity at 313 K, varying from 1.95 to 5.04 mm/s. The content of heavier components C25+ is up to 17 wt. %. CO2–oil MMP (Minimum Miscibility Pressure) was calculated in a CHEMCAD simulator using the Soave–Redlich–Kwong equation of state (SRK EoS). The oil composition was defined as a mixture of n-alkanes. Relatively low MMP values (ca. 8.3 MPa for all tested oils at 313 K) indicate a high potential of the EOR method, and make this geological CO2 storage form more attractive to the industry. For reservoir brines, the content of the main ions was experimentally measured and CO2 solubility under reservoir conditions was calculated. The reservoir brines showed a significant variation in properties with total dissolved solids contents varying from 17.5 to 378 g/L. CO2 solubility in brines depends on reservoir conditions and brine chemistry. The highest calculated CO2 solubility is 1.79 mol/kg, which suggest possible CO2 storage in aquifers.

Experimental Study on Reducing CO2–Oil Minimum Miscibility Pressure with Hydrocarbon Agents

CO2 flooding is an important method for improving oil recovery for reservoirs with low permeability. Even though CO2 could be miscible with oil in regions nearby injection wells, the miscibility could be lost in deep reservoirs because of low pressure and the dispersion effect. Reducing the CO2–oil miscibility pressure can enlarge the miscible zone, particularly when the reservoir pressure is less than the needed minimum miscible pressure (MMP). Furthermore, adding intermediate hydrocarbons in the CO2–oil system can also lower the interfacial tension (IFT). In this study, we used dead crude oil from the H Block in the X oilfield to study the IFT and the MMP changes with different hydrocarbon agents. The hydrocarbon agents, including alkanes, alcohols, oil-soluble surfactants, and petroleum ethers, were mixed with the crude oil samples from the H Block, and their performances on reducing CO2–oil IFT and CO2–oil MMP were determined. Experimental results show that the CO2–oil MMP could be reduced by 6.19 MPa or 12.17% with petroleum ether in the boiling range of 30–60 °C. The effects of mass concentration of hydrocarbon agents on CO2–oil IFT and crude oil viscosity indicate that the petroleum ether in the boiling range of 30–60 °C with a mass concentration of 0.5% would be the best hydrocarbon agent for implementing CO2 miscible flooding in the H Block.

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.

The impacts of gas impurities on the minimum miscibility pressure of injected CO2-rich gas\u2013crude oil systems and enhanced oil recovery potential

An effective parameter in the miscible-CO2 enhanced oil recovery procedure is the minimum miscibility pressure (MMP) defined as the lowest pressure that the oil in place and the injected gas into reservoir achieve miscibility at a given temperature. Flue gases released from power plants can provide an available source of CO2, which would otherwise be emitted to the atmosphere, for injection into a reservoir. However, the costs related to gas extraction from flue gases is potentially high. Hence, greater understanding the role of impurities in miscibility characteristics between CO2 and reservoir fluids helps to establish which impurities are tolerable and which are not. In this study, we simulate the effects of the impurities nitrogen (N2), methane (C1), ethane (C2) and propane (C3) on CO2 MMP. The simulation results reveal that, as an impurity, nitrogen increases CO2–oil MMP more so than methane. On the other hand, increasing the propane (C3) content can lead to a significant decrease in CO2 MMP, whereas varying the concentrations of ethane (C2) does not have a significant effect on the minimum miscibility pressure of reservoir crude oil and CO2 gas. The novel relationships established are particularly valuable in circumstances where MMP experimental data are not available.

Evaluation of recycle gas injection on CO2 enhanced oil recovery and associated storage performance

An enhanced oil recovery (EOR) technique comprising the alternate injection of gas (CO2) and water, commonly referred to as water alternating gas (WAG) flooding, is currently ongoing at an oil field in the northeastern Powder River Basin (PRB), where 4 million barrels (MMbbl) of oil were produced between May 2013 and September 2017. During WAG flooding, a large amount of CO2, which contains some impurities, is produced at the surface with the recovered oil and reinjected into the reservoir to minimize the amount of purchased CO2. The concentration of these impurities in the CO2, which are dominated by CH4, is a key parameter in the design of miscible CO2 flooding of an oil reservoir and in the quantitative assessment of the associated storage of CO2 that occurs in the reservoir. For a given oil and reservoir temperature, the CO2–oil minimum miscibility pressure (MMP) is strongly and adversely affected by the presence of CH4, the concentration of which varies with time and operational conditions, such as injection pressure, flooding pattern, and injection scheme (i.e., continuous CO2 injection or WAG). To capture the full range of potential variation in the MMP caused by the presence of CH4, a series of laboratory experiments were conducted to determine CO2–oil MMP with different mole percentages of CH4 in the CO2. All of the experiments were performed at reservoir temperature (108 °F) using the VIT (vanishing interfacial tension) method. Results showed that MMP increases from 1403 to 4085 psi as the mole percentage of CH4 in the CO2 increases from 0% to 100%. To assess the impact of this variation in MMP on the oil recovery performance in the field, a history-matched, compositional reservoir simulation model was used to predict the oil production performance for a WAG flood using CO2 that contained a range of CH4 concentrations. The reservoir simulations examined the effects of permeability heterogeneity, fluid crossflow, and phase behavior on the oil displacement performance in three-dimensional space over time. Simulation results indicated that CO2 floods using a limited range of CO2–CH4 mixtures could still maintain multiple-contact miscibility and result in effective EOR. In addition, the ability to reinject produced CO2–CH4 mixtures as is, without removal of the CH4, ensured that this approach to EOR would continue to be cost-effective.

Full Characterization of CO2-Oil Properties On-Chip: Solubility, Diffusivity, Extraction Pressure, Miscibility, and Contact Angle.

Carbon capture, storage, and utilization technologies target a reduction in net CO2 emissions to mitigate greenhouse gas effects. The largest such projects worldwide involve storing CO2 through enhanced oil recovery-a technologically and economically feasible approach that combines both storage and oil recovery. Successful implementation relies on detailed measurements of CO2-oil properties at relevant reservoir conditions (P = 2.0-13.0 MPa and T = 23 and 50 °C). In this paper, we demonstrate a microfluidic method to quantify the comprehensive suite of mutual properties of a CO2 and crude oil mixture including solubility, diffusivity, extraction pressure, minimum miscibility pressure (MMP), and contact angle. The time-lapse oil swelling/extraction in response to CO2 exposure under stepwise increasing pressure was quantified via fluorescence microscopy, using the inherent fluorescence property of the oil. The CO2 solubilities and diffusion coefficients were determined from the swelling process with measurements in strong agreement with previous results. The CO2-oil MMP was determined from the subsequent oil extraction process with measurements within 5% of previous values. In addition, the oil-CO2-silicon contact angle was measured throughout the process, with contact angle increasing with pressure. In contrast with conventional methods, which require days and ∼500 mL of fluid sample, the approach here provides a comprehensive suite of measurements, 100-fold faster with less than 1 μL of sample, and an opportunity to better inform large-scale CO2 projects.

Estimation of the CO2-oil minimum miscibility pressure for enhanced oil recovery

ABSTRACTThe authors' aim was to present new models based on artificial neural network (ANN) and two optimization algorithms including cuckoo optimization algorithm (COA) and teaching learning based optimization (TLBO) to predict the pure and impure CO2 MMP. Thirty-four and 11 training and testing data sets were used to develop these models with following inputs: reservoir temperature, the mole percent of volatile oil components (C1 and N2), mole percent of intermediate oil components (C2-C4, CO2, and H2S), molecular weight of C5+ fraction in oil phase (MWC5+) and mole percentage of CO2, N2, C1, C4, and H2S in the injected gas. Statistical comparisons show that although two models yield acceptable results, the ANN-TLBO model has better performance with the lower mean absolute percentage error (2.6%) and standard deviation (3.37%) and the higher coefficient of determination (0.993). Moreover, among the available correlations, the Cronquist's (1978; corrected by Sebastian et al., 1985) correlations have better performance. Finally, the sensitivity analysis on the ANN-TLBO showed that MWC5+ and reservoir temperature are the most influential parameters in determining the CO2 MMP, respectively.

Potential of carbon dioxide miscible injections into the H-26 reservoir

The H-26 reservoir in Liaohe oilfield has been subjected to water flooding for many years, and the reservoir has now reached the late water-flooding stage. At this stage, the production wells show the characteristic of high water-cut, and the oil production declines dramatically, resulting in unfavorable recovery. Therefore, improving oil recovery at this stage is a matter of concern. CO2 injection is a potential alternative method that is widely used for realizing enhanced oil recovery (EOR), while simultaneously reducing greenhouse gases by storing them in the underground formation. In order to determine the potential for CO2 injection in the H-26 reservoir, the gas–liquid phase behavior and gas–oil minimum miscibility pressure (MMP) should be tested. In this study, PVT experiments were conducted with crude oil samples from the H-26 reservoir, and the corresponding oil properties were regressed through the equation of state (EoS). Two sets of EoS parameters (EoS-5 and EoS-15) were used to determine the CO2–oil MMP. The results show that the cell-to-cell method is a convenient and accurate method to determine the MMP and verify that the H-26 reservoir can achieve miscible CO2 flooding. In addition, a case study of a typical water flooded well group was performed with a compositional model. In this case study, the characteristics of the reservoir oil component composition were found to change after water flooding, and the impacts of these changes on the CO2–oil MMP were investigated. The recovery factor of CO2 flooding was found to be 8.5% greater than that in the water-flooding scenario. This work can be used as a reference for reservoirs facing similar problems related to high water cut or low recovery.

Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO2 – Reservoir oil minimum miscibility pressure prediction

Carbon dioxide (CO2) injection into oil reservoirs is considered a mature enhanced oil recovery (EOR) technique for conventional reservoirs. The local displacement efficiency of the CO2-EOR process is highly dependent on the minimum miscibility pressure (MMP), estimating this parameter is critical to design of the CO2 injection process. Traditional empirical methods to test the CO2-oil MMP are time consuming and expensive; derived correlations are fast but not accurate. Therefore, an efficient and reliable method to determine MMP is beneficial. In this study, a mixed kernels function (MKF) based support vector regression (SVR) model was developed and used to predict the MMP for both pure and impure CO2 injection cases. Four parameters were chosen as input parameters: (1) reservoir temperature; (2) average critical temperature; (3) molecular weight of pentane plus (C5+) fraction of crude oil, and; (4) the ratio of volatile components to intermediate components in crude oil. MMP was selected as the desired output parameter to train and test this newly developed model. The performance of basic kernels function based SVR model is compared with that of this newly developed MKF-SVR model. The well-trained MFK-SVR was compared with three well-established published correlations, demonstrated the highest correlation coefficient (R of 0.9381), lowest root mean square error (RMSE of 1.9151), smallest average absolute error (AAE of 1.1406) and maximum absolute error (MAE of 4.6291). We believe that the proposed MFK-SVM model is a more reliable and stable regression model to predict MMP. In addition, a sensitivity analysis was conducted to evaluate the physical correctness. It indicates that the predicted results from the newly developed model are in excellent agreement with previous empirical work.

Integrating support vector regression with genetic algorithm for CO2-oil minimum miscibility pressure (MMP) in pure and impure CO2 streams

Accurate knowledge of the minimum miscibility pressure (MMP) is essential in successful design of any miscible gas injection process, particularly in CO2 flooding. It is however well-acknowledged that experimental measurements are expensive, time-consuming, and cumbersome. As a direct consequence, a support vector regression model combined with genetic algorithm (GA-SVR) was proposed to predict pure and impure CO2-crude oil MMP. The accuracy and reliability of the proposed model were evaluated through 150 data sets collected in the open literature and compared with approaches commonly used to estimate the MMP (Lee correlation, Shokir correlation, Orr-Jensen correlation, Yellig-Metcalfe correlation, Alston correlation, Emera-Sarma correlation, Cronquist correlation, Kamari et al. correlation, and Fathinasab-Ayatollahi correlation). The results showed that the proposed model for predicting the MMP is in excellent agreement with experimental data and outperforms all the existing methods considered in this work in prediction of pure and impure CO2-oil MMP. Furthermore, outlier diagnosis was performed on the whole data sets to identify the applicable range of all models investigated in this work by detecting the probable doubtful MMP data.

Determination of minimum miscibility pressure in CO2-IOR projects with the aid of hybrid neuro-fuzzy system

Gas injection process is one of the most efficient improved oil recovery methods for conventional oil reservoirs. The efficiency of this process is strongly dependent on minimum miscibility pressure (MMP) which is usually determined through very expensive and time-consuming laboratory tests. So, this paper is concerned with the use of hybrid approach, combining particle swarm optimization and adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means clustering technique, aimed at an estimation of injected gas-reservoir oil MMP. The hybrid proposed model makes ANFIS is practical in dealing with complex and high dimensional MMP problem. The process of model building is done by considering the reservoir temperature, crude oil composition, composition of injected CO2 as input parameters and corresponding CO2-oil MMP information as target parameter. The validity of proposed model has been successfully approved by comparing with results obtained by well-known empirical correlations in published literature. The results show that the proposed model significantly outperformed those four correlations with the lowest mean absolute error of 0.0099, the lowest root mean square error of 0.0174, and the highest coefficient of determination of 0.9823. Therefore, the proposed hybrid model can be applied as an alternative method to yield more accurate results in CO2-oil MMP estimation.

Rapid method to estimate the minimum miscibility pressure (MMP) in live reservoir oil systems during CO2 flooding

Flooding fuel-generated CO2 into oil reservoirs will lead to two advantages in both enhanced oil recovery (EOR) and reduction in atmospheric emission of carbon dioxide. The main factor for determination of the possibilities to EOR by e.g. CO2 injection, in particular miscible case, into a specific oil reservoir is the CO2–oil minimum miscibility pressure (MMP). In this communication, we present the utilization of a soft-computing technique – gene expression programming (GEP) – for developing a new symbolic equation to pursue our objective. In other words, this work presents a new approach to predict both pure and impure CO2–oil MMP in live reservoir oil systems. The parameters of the new model involve the molecular weight of C5+ fraction in crude oil, reservoir temperature, the mole percentage ratio of volatile to intermediate components of oil and critical temperature. A comprehensive error investigation is done to discuss accuracy of the recently proposed MMP model. Additionally, the results obtained by the new GEP-based model are compared with most widely-used empirically derived correlations available in the literature to show the superiority of the model. The results obtained in this study are encouraging and can propose accurate and efficient solutions for the case of CO2–oil MMP of pure and impure components in live oil systems.

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.

Modified vanishing interfacial tension (VIT) test for CO2–oil minimum miscibility pressure (MMP) measurement

Miscible gas injection is one of the most popular and applicable enhanced oil recovery method in oil fields. Minimum miscibility pressure (MMP) is an important parameter for an enhanced oil recovery process involving carbon dioxide or hydrocarbon miscible gas injection. There are many methods for measuring MMP but the most standard method for measuring MMP value in oil field gas injection process is slim tube method, which is very time consuming (4–6 weeks). Measurements of the interfacial tension of mixtures of a reservoir fluid and injection gas at various pressures have been proposed recently as an experimental method for predicting the minimum miscibility pressure (MMP) in an experiment called the vanishing interfacial tension (VIT) technique. This method is quantitative in nature and fast (2–3 days) with respect to slim tube method. VIT technique is based on the concept that the interfacial tension between the gas and crude oil phases at reservoir temperature must reduce to zero as these two phases approach the point of miscibility. The concept of zero interfacial tension at miscibility is, in turn, based on the well-accepted fact that the interface between the phases must vanish, as they become miscible with one another.Our results show the MMP value obtained from VIT method exceeds the slim tube MMP and miscibility pressure does not necessarily occur at zero IFT. Using zero IFT criteria cause MMP overestimation. In this study, by using the VIT and slim tube test, we measured the IFT in which slim tube MMP occur in VIT test for CO2–oil system for various types of reservoir oil. These IFT values are correlated by oil properties and test condition to help us measure the accurate value of MMP by using VIT method.

The Use of Optimization Procedures to Estimate Minimum Miscibility Pressure

An important factor in the design of gas injection projects is the minimum miscibility pressure (MMP). A new genetic algorithm (GA)–based correlation and two neural network models (one of them is trained by back propagation [BP] algorithm and another is trained by particle swarm optimization algorithm) have been developed to estimate the CO2–oil MMP. The correlation and models use the following key input parameters: reservoir temperature, molecular weight of C+ 5, and mole percentage of the volatiles and intermediate components (for the first time, the mole percentages are used as independent variables). Then results are validated against experimental data and finally compared with commonly used correlations reported in the literature. The results show that the neural network model trained by BP algorithm and the correlation that has been developed by GA can be applied effectively and afford high accuracy and dependability for MMP forecasting.

A CO2-Oil Minimum Miscibility Pressure Model Based on Multi-Gene Genetic Programming

Over the last decades, great attention has been devoted to the CO2injection to enhance oil recovery. Recycling CO2into oil reservoirs provides an excellent option to store this gas in subsurface formations and also improves oil recovery. Successful design of a miscible CO2flooding project mostly depends on accurate determination of Minimum Miscibility Pressure (MMP). In the present study, using multi-gene genetic programming together with a comprehensive sensitivity analysis, a model was developed that provides an accurate estimation of MMP for a wide range of reservoir temperatures and oil compositions. This model utilizes temperature, molecular weight of C5+compounds, and ratio of volatile to intermediate oil fractions as input parameters. Accuracy of correlation was tested versus experimental data and those of other correlations. Results show that the new model assumes a lower error than other published correlations. Based on the results of the present study, it can be asserted that the proposed model is able to predict CO2-oil MMP with a high accuracy when the experimental data are not available.