CO2–oil minimum miscibility pressure model for impure and pure CO2 streams

Shokir, E.M. El-M.

doi:10.1016/j.petrol.2006.12.001

Cited by 126 publications

(52 citation statements)

References 35 publications

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“…Among all the models, the GA-SVR outperforms all the other correlations considered in this work and aimed at predicting the MMP. Tables 2 and 3 highlight that (i) the proposed model predictions are in excellent agreement with experimental data with AARD of 4.75% for pure CO 2 streams and 7.69% for impure CO 2 streams, respectively, while AARD for the best openly published correlation in the present work is 7.11% (Shokir correlation [27]) for pure CO 2 streams and 8.38% (Kamari et. al.…”

Section: Accuracy Of the Modelsupporting

confidence: 78%

“…The major factors influencing CO 2 -oil MMP involve reservoir temperature, reservoir oil composition, and composition of injected gas [22][23][24]26,27,[65][66][67][68][69]. The reservoir temperature is always regarded as a critically important factor that affects CO 2 -oil MMP.…”

Section: A Literature Review Of the Factors Affecting The Co 2 -Oil Mmpmentioning

confidence: 99%

“…Emera and Sarma (2005) [26] firstly employed a genetic algorithm (GA) to express the proper relationship between the CO 2 -oil MMP and the parameters that affect its value. Similarly, Shokir [27] developed a new correlation to determine the MMP of pure and contaminated CO 2 by applying alternative conditional expectation (ACE) algorithm. Using genetic programming combined with constrained multivariable search methods, Fathinasab and Ayatollahi [28] developed a correlation to calculate both pure and impure CO 2 -oil MMP in live reservoir oil.…”

Section: Introductionmentioning

confidence: 99%

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

Bian

Han

et al. 2016

Fuel

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Section: Accuracy Of the Modelsupporting

confidence: 78%

Section: A Literature Review Of the Factors Affecting The Co 2 -Oil Mmpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Bian

Han

et al. 2016

Fuel

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“…While those correlations reproduce MMP reasonably well for the experimental observations on which they are fitted, they may result in considerable errors when injected gas or oil compositions are substantially different from those were used to build the correlations (Orr and Silva 1987;Wang and Orr 1997). Furthermore, data-based techniques have been paid more and more attention in predicting MMP between pure or impure CO 2 and crude oil, such as artificial neural network (Huang et al 2003), genetic algorithm (Emera and Sarma 2005), alternating conditional expectation algorithm (Shokir 2007), hybrid neural genetic algorithm (Dehghani et al 2008), and least-squares support vector machine (Shokrollahi et al 2013). A detailed review on empirical and data-driven models has been published elsewhere (Shokrollahi et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Application of PC-SAFT Equation of State for CO2 Minimum Miscibility Pressure Prediction in Nanopores

Wang

Chen

2016

All Days

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CO 2 miscible injection is generally one of the most efficient enhanced oil recovery (EOR) methods and widely used in the conventional oil reservoirs. The applicability of CO 2 EOR technology for unlocking the resources from unconventional tight and shale formations and the mechanisms of miscible flooding in these reservoirs still remain unclear. An important parameter used to evaluate the feasibility of CO 2 miscible flooding is the minimum miscibility pressure (MMP). Even though experimental approaches, empirical correlations and theoretical methods have performed well in measuring or predicting MMP between CO 2 and crude oil in conventional reservoirs, they may not be suitable for unconventional formations as phase behavior and MMP can be significantly affected by confinement effect in small pores (e.g., nanopores) in such formations.In this study, a new MMP prediction model based on the modified Parachor Model associated with the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) is developed to determine CO 2 MMP both in the bulk phase and nanopores. The Parachor Model is modified to account for the confinement effect of nanopore walls on the equilibrium interfacial tension (IFT). The Equilibrium IFT reduction in nanopores is related to a temperature-dependent and slit pore width-dependent modification term. The parameters of the new Parachor Model are determined by matching the vapor-liquid surface tension values for CH 4 , C 2 H 6 , C 3 H 8 , n-C 4 H 10 , and n-C 8 H 18 in nanopores, respectively. The prediction ability of the new model is verified by comparing the predicted MMP in the bulk phase with the results of other theoretical approaches and slim-tube experimental data. Finally, the new model is applied to estimate the MMP between Bakken oil and CO 2 stream. The effect of temperature, slit pore width, and impure components in the injected CO 2 on the MMP are also studied. The newly developed model successfully reproduces MMP in bulk phase as compared with both other methods and experimental data. The overall average absolute relative deviation (AARD) for MMP is within 8 %. The calculated equilibrium IFT for liquid-vapor phase has a good agreement with molecular simulation results. For Bakken oil-CO 2 system, if the slit pore width is larger than 10 nm, MMP is independent on pore width; otherwise, it decreases significantly with the decrease of the pore width. If pore width decreases to 3 nm, 67.5 % decrease in the IFT is observed and 23.5% reduction is achieved for MMP between Bakken oil and CO 2 stream, indicating that it is easier to reach miscibility in nanopores, and CO 2 miscible flooding might be a promising enhanced oil recovery (EOR) technology for tight oil and shale oil reservoirs. Furthermore, MMP increases with an increase of temperature in bulk phase, whereas IFT and MMP decrease with an increase of temperature in nanopores.

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“…In the petroleum industry, the determination of the MMP between the CO2 and crude oils is usually accomplished by techniques such as the slim tube method [7], rising bubble method [8], of the vanishing interfacial tension technique [9]. Numerous MMP studies have been published, such as those in references [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Experimental and modeling study of the phase behavior of synthetic crude oil+CO2

Ghafri

Maitland

Trusler

2014

Fluid Phase Equilibria

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A full understanding of the phase behavior of CO2-hydrocarbon mixtures at reservoir conditions is essential for the proper design, construction and operation of carbon capture and storage (CCS) and enhanced oil recovery (EOR) processes. While equilibrium data for binary CO2-hydrocarbon mixtures are plentiful, equilibrium data and validated equations of state having reasonable predictive capability for multi-component CO2-hydrocarbon mixtures are limited. In this work, a new synthetic apparatus was constructed to measure the phase behavior of systems containing CO2 and multicomponent hydrocarbons at reservoir temperatures and pressures. The apparatus consisted of a thermostated variable-volume view cell driven by a computer-controlled servo motor system, and equipped with a sapphire window for visual observation. Two calibrated syringe pumps were used for quantitative fluid injection. The maximum operating pressure and temperature were 40 MPa and 473.15 K, respectively. The apparatus was validated by means of isothermal vapor-liquid equilibrium measurement on (CO2 + heptane), the results of which were found to be in good agreement with literature data.In this work, we report experimental measurements of the phase behaviour and density of (CO2 + synthetic crude oil) mixtures. The 'dead' oil contained a total of 17 components including alkanes, branched-alkanes, cyclo-alkanes, and aromatics. Solution gas (0.81 methane + 0.13 ethane + 0.06 propane) was added to obtain live synthetic crudes with gas-oil ratios of either 58 or 160. Phase equilibrium and density measurements are reported for the 'dead' oil and the two 'live' oils under the addition of CO2. The measurements were carried out at temperatures of (298.15, 323.15, 373.15 and 423.15) K and at pressures up to 36 MPa, and included vapor-liquid, liquid-liquid and vapor-liquid-liquid equilibrium conditions.The results are qualitatively similar to published data for mixtures of CO2 with both real crude oils or and simple hydrocarbon mixtures containing both light and heavy components. The present experimental data have been compared with results calculated with two predictive 2 models, PPR78 and PR2SRK, based on the Peng-Robinson 78 (PR78) and Soave-RedlichKwong (SRK) equations of state with group-contribution formulae for the binary interaction parameters. Careful attention was paid to the critical constants and acentric factor of high molar-mass components. Since the mixture also contained several light substances with critical temperatures below some or all experimental temperatures, we investigated the use of the Boston-Mathias modification of the PR78 and SRK equations. The results showed that these models can predict with reasonable accuracy the vapor-liquid equilibria of systems containing CO2 and complex hydrocarbon mixtures without the need to regress multiple binary parameters against experimental data.

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CO2–oil minimum miscibility pressure model for impure and pure CO2 streams

Cited by 126 publications

References 35 publications

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

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

Application of PC-SAFT Equation of State for CO2 Minimum Miscibility Pressure Prediction in Nanopores

Experimental and modeling study of the phase behavior of synthetic crude oil+CO2

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