A new correlation for estimation of minimum miscibility pressure (MMP) during hydrocarbon gas injection (original) (raw)
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Journal of Canadian Petroleum Technology, 2007
Two new genetic algorithm (GA)-based correlations were proposed for more reliable prediction of minimum miscibility pressure (MMP) between reservoir oil and CO2 or flue gas. Both correlations are particularly useful when experimental data are lacking and also in developing an optimal laboratory program to estimate MMP. The key input parameters in a GA-based CO2-oil MMP correlation, in order of their impact, were: reservoir temperature, MW of C5+, and volatiles (C1 and N2) to intermediates (C2-C4, H2S and CO2) ratio. This correlation, which has been successfully validated with published experimental data and compared to common correlations in the literature, offered the best match with the lowest error (5.5%) and standard deviation (7.4%). For a GA-based flue gas-oil MMP correlation, the MMP was regarded as a function of the injected gas solvency into the oil which, in turn, is related to the injected gas critical properties. It has also been successfully validated against published ...
A CO2-oil minimum miscibility pressure model based on multi-gene genetic programming
Energy, Exploration & Exploitation, 2013
Over the last decades, great attention has been devoted to the CO2 injection to enhance oil recovery. Recycling CO2 into oil reservoirs provides an excellent option to store this gas in subsurface formations and also improves oil recovery. Successful design of a miscible CO2 flooding 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 assert...
Journal of Petroleum Science and Engineering, 2005
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 2 S, 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 nonlinearity, 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 QrestructuredQ 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
2017
Enhanced Oil Recovery (EOR) is defined as “the recovery of oil by injection of a fluid that is not already produced from the reservoir”. There are different methods for the EOR. Among all EOR techniques, the miscible displacement process has the highest potential. It involves the injection of fluids that are capable to generate miscibility with reservoir fluid at certain conditions of pressure and reservoir temperature. The minimum miscibility pressure (MMP ) is defined as the pressure required for the injection fluid to generate a miscible front that is completely miscible with the reservoir fluid. There are many available correlations in literature to calculate the MMP for a given injection and reservoir fluids and reservoir conditions. Choosing the best accurate method of calculating the MMP is very important to determine accurately the MMP value. The objective of this study is to determine the best accurate correlation to determine the MMP for Libyan oils. Six correlations were ...
Journal of King Saud University - Engineering Sciences, 2012
Equilibrium ratios play a fundamental role in understanding the phase behavior of hydrocarbon mixtures. They are important in predicting compositional changes under varying temperatures and pressures in the reservoirs, surface separators, and production and transportation facilities. In particular, they are critical for reliable and successful compositional reservoir simulation. Several techniques are available in the literature to estimate the K-values. This paper presents a new model for predicting K values with genetic programming (GP). The new model is applied to multicomponent mixtures. In this paper, 732 high-pressure K-values obtained from PVT analysis of 17 crude oil and gas samples from a number of petroleum reservoirs in Arabian Gulf are used. Constant Volume Depletion (CVD) and Differential Liberation (DL) were conducted for these samples. Material balance techniques were used to extract the K-values of crude oil and gas components from the constant volume depletion and differential liberation tests for the oil and gas samples, respectively. These K-values were then used to build the model using the Discipulus software, a commercial Genetic Programming system, and the results of K-values were compared with the values obtained from published correlations. Comparisons of results show that the currently published correlations give poor estimates of K-values for all components, while the proposed new model improved significantly the average absolute deviation error for all components.
Gas–oil ratio correlation (R s ) for gas condensate using genetic programming
Journal of Petroleum Exploration and Production Technology, 2014
A new correlation for solution gas-oil ratio (R s) for gas condensate reservoir was developed in this paper by using genetic programming algorithm of a commercial software (Discipulus) program. Matching PVT experimental data with an equation of state model, a commercial simulator (Eclipse simulator) was used to calculate the solution gas-oil ratio (R s) values used in this study. More than 1,800 solution gas-oil ratio (R s) values obtained from the analysis of eight gas condensate fluid PVT laboratory reports, selected under a wide range of reservoir temperature and pressure, composition and condensate yield, were used. Comparisons of the results showed that currently published correlations of gas-oil ratio (R s) for gas condensate gave poor estimates of its value (the average absolute error for Standing correlation was 63.48 with a standard deviation (SD) equal to 0.724, the average absolute error for Glaso correlation was 61.19 % with a SD equal to 0.688, the average absolute error for Vasques and Beggs correlation was 52.22 % with a SD equal to 0.512, the average absolute error for Marhoun correlation was 56.34 % with a SD equal to 0.519 and the average absolute error for Fattah et al. correlation was 18.6 % with a SD equal to 0.049). The proposed new correlation improved extensively the average absolute error for gas condensate fluids. The average absolute error for the new correlation was 10.54 % with a SD equal to 0.035. Also, the hit-rate (R 2) of the new correlation was 0.9799 and the fitness variance was 0.012. The importance of the new correlation comes from depending only on readily available production data in the field and can have wide applications when representative PVT lab reports are not available.
Genetic Programming (GP)-Based Model for the Viscosity of Pure and Hydrocarbon Gas Mixtures
Energy & Fuels, 2009
Accurate determination of the viscosity and phase behavior of pure hydrocarbon gases and hydrocarbon gas mixtures is essential for reliable reservoir characterization and simulation and, hence, for optimum usage and exploitation. The variety of possible hydrocarbon gas mixtures at different conditions of interest preclude obtaining the relevant data by experimental means alone; thus, the development of prediction methods is required. Many pure hydrocarbon gas viscosity correlations are available. However, a wide-ranging and accurate viscosity correlation of gas mixtures associated with heavier hydrocarbon components and impurity components, such as carbon dioxide, nitrogen, helium, and hydrogen sulphide, is not available. Therefore, this paper presents a new pure hydrocarbon gas and gas mixture viscosity model over a wide range of temperatures and pressures as a function of gas density, pseudo-reduced temperature, pseudo-reduced pressure, and the molecular weight of pure and hydrocarbon gas mixtures. The new model is designed to be simpler and eliminate the numerous computations involved in any equation of state (EOS) calculation. The developed new model yields a more accurate prediction of the pure gas and gas mixture viscosity compared to the commonly used correlations.
Advances in Geo-Energy Research
Minimum miscibility pressure (MMP) is a key variable for monitoring miscibility between reservoir fluid and injection gas. Experimental and non-experimental methods are used to estimate MMP. Available miscibility correlations attempt to predict the minimum miscibility pressure for a specific type of gas. Here an artificial neural network (ANN) model is applied to a dataset involving 251 data records from around the world in a novel way to estimate the gas-crude oil MMP for a wide range of injected gases and crude oil compositions. This approach is relevant to sequestration projects in which injected gas compositions might vary significantly. The model is correlated with the reservoir temperature, concentrations of volatile (C1 and N2) and intermediate (C2, C3, C4, CO2 and H2S) fractions in the oil (Vol/Inter), C5+ molecular weight fractions in the oil and injected gas specific gravity. A key benefit of the ANN model is that MMP can be determined with reasonable accuracy for a wide range of oil and gas compositions. Statistical comparison of predictions shows that the developed ANN model yields better predictions than empiricalcorrelation methods. The ANN model predictions achieve a mean absolute percentage error of 13.46%, root mean square error of 3.6 and Pearson's correlation coefficient of 0.95. Sensitivity analysis reveals that injected gas specific gravity and temperature are the most important factors to consider when establishing appropriate miscible injection conditions. Among the available published correlations, the Yellig and Metcalfe correlation demonstrates good prediction performance, but it is not as accurate as the developed ANN model.
Journal of King Saud University - Engineering Sciences, 2016
In this paper, two correlations for oil formation volume factor (B o) for volatile oil reservoirs are developed using non-linear regression technique and genetic programming using commercial software. More than 1200 measured values obtained from PVT laboratory analyses of five representative volatile oil samples are selected under a wide range of reservoir conditions (temperature and pressure) and compositions. Matching of PVT experimental data with an equation of state (EOS) model using a commercial simulator (Eclipse Simulator), was achieved to generate the oil formation volume factor (B o). The obtained results of the B o as compared with the most common published correlations indicate that the new generated model has improved significantly the average absolute error for volatile oil fluids. The hit-rate (R 2) of the new non-linear regression correlation is 98.99% and the average absolute error (AAE) is 1.534% with standard deviation (SD) of 0.000372. Meanwhile, correlation generated by genetic programming gave R 2 of 99.96% and an AAE of 0.3252% with a SD of 0.00001584. The importance of the new correlation stems from the fact that it depends mainly on experimental field production data, besides having a wide range of applications especially when actual PVT laboratory data are scarce or incomplete.