Minimum Miscibility Pressure Prediction With Equations of State

Benmekki, E. H.; Mansoori, G. Ali

doi:10.2118/15677-pa

Cited by 22 publications

(9 citation statements)

References 12 publications

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“…Generally many compounds are known to be present in petroleum fluids. The number of carbon atoms in the hydrocarbons and other organic compounds present in petroleum fluids can vary from one (as in methane) to over a hundred (as in asphaltenes and heavy paraffins) (Berkowitz, 1997;Firoozabadi, 1999).…”

Section: Naturally Occurring Petroleum Fluidsmentioning

confidence: 99%

A unified perspective on the phase behaviour of petroleum fluids

Mansoori

2009

IJOGCT

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We present a coherent and unified group of general explanations for the various phase transitions, which may occur in the seven naturally occurring petroleum fluids with emphasis on their heavy organics. The seven petroleum fluids include, in the order of their fluidity, natural gas, near-critical gas-condensate, light crude, intermediate crude, heavy oil, tar sand and oil shale. At first the nature of every petroleum fluid is presented including their constituents and their heavy fractions. Then their main families of constituents are presented. The generalised petroleum fluid phase behaviour is introduced in light of the well-known theory of phase transitions. The effects of variations of composition, temperature and pressure on petroleum fluids phase behaviour are introduced. Their 11 distinct phase-transition points are presented and their relation with state variables and constituents are identified. This report is the basis for development of a unified phase behaviour prediction model of all the petroleum fluids. [

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Section: Naturally Occurring Petroleum Fluidsmentioning

confidence: 99%

A unified perspective on the phase behaviour of petroleum fluids

Mansoori

2009

IJOGCT

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“…This package is based on a combination of solubility model, suspension model, and the FRACf AL aggregation and growth of organics suspensions model [16]. The following principles have been used iii its development: Statistical mechanical mixing rules and conformal solution equations of state, polydisperse polymer solution theories, continuous thermodynamics arid various phase equilibrium algorithms, electrokinetic phenomena, transport phenomena, colloidal solution theory, and FRACTAL kinetics of aggregation theories [16,18,24,27,[30][31][32][33][34][35][36][37]. This package is general enough to predict the heavy organics-oil-wall interaction problems wherever they may occur during oil production, transportation or processing.…”

Section: Modeling Of Heavy Organic Depositions From Petroleum Fluidsmentioning

confidence: 99%

“…However, when a mixture SO/SO vol% of these two crude oils was subjected to the same test, the percentage of sedimentation This seems to be the main factor one has to focus on when trying to find a solution to the asphaltene deposition problem in the Campeche marine platform. In order to prevent the asphaltene deposition in mixing of large amounts of light crude oils with heavier ones it is necessary to study the phenomena from the fundamental principles in order to develop predicting techniques [31][32][33][34][35][36]. It is also necessary to study and predict the optimum conditions at which light and heavy crudes can be mixed together without getting to the onset of asphaltene flocculation.…”

Section: Heavy Organic Deposition In the Campeche Marine Platformmentioning

confidence: 99%

Heavy Organic Deposition and Plugging of Wells (Analysis of Mexico's Experience)

Escobedo

Mansoori

1992

Proceedings of SPE Latin America Petroleum Engineering Conference

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The pipeline transportation of petroleum fluids can be significantly affected by flocculation, deposition, and plugging of asphaItene, paraffin/wax, and/or diamondoid in the transfer pipelines, tubulars, pumps, and other equipment. The economic implications of the problem of heavy organic depositions in such processes are tremendous.In this report Mexico's experience with the pipeline plugging due to heavy organic deposition is reviewed and analysed based on the present state of knowledge. The modeling basis of a comprehensive estimation and prediction technique is presented. This technique is based on observed field and laboratory data, statistical mechanical theories, polydisperse polymer solution theory, continuous thermodynamics, electrokinetics and transport phenomena, colloidal solution theory, and the FRACTAL aggregation theory. INTRODUCTIONA number of problems could arise during the production of petroleum which would drastically increase the production costs.

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“…MMP is usually defined as the lowest pressure where oil and solvent develop miscibility dynamically. In other words, the injected gas can reach dynamic miscibility with the reservoir oil at this particular pressure 2–6. The displacement is assumed to be a piston‐like process, considering a 1D, two phase dispersion‐free flow regime for the displacement process at the MMP.…”

Section: Introductionmentioning

confidence: 99%

“…The displacement is assumed to be a piston‐like process, considering a 1D, two phase dispersion‐free flow regime for the displacement process at the MMP. In this case, the oil production performance is expected to be 100% at 1 PV of the gas injected 2, 4, 5…”

Section: Introductionmentioning

confidence: 99%

A developed smart technique to predict minimum miscible pressure—eor implications

et al. 2013

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Miscible gas injection (MGI) processes such as miscible CO2 flooding have been in use as attractive EOR options, especially in conventional oil reserves. Optimal design of MGI is strongly dependent on parameters such as gas–oil minimum miscibility pressure (MMP), which is normally determined through expensive and time‐consuming laboratory tests. Thus, developing a fast and reliable technique to predict gas–oil MMP is inevitable. To address this issue, a smart model is developed in this paper to forecast gas–oil MMP on the basis of a feed‐forward artificial neural network (FF‐ANN) combined with particle swarm optimisation (PSO). The MMP of a reservoir fluid was considered as a function of reservoir temperature and the compositions of oil and injected gas in the proposed model. Results of this study indicate that reservoir temperature among the input parameters selected for the PSO–ANN has the greatest impact on MMP value. The developed PSO–ANN model was examined using experimental data, and a reasonable match was attained showing a good potential for the proposed predictive tools in estimation of gas–oil MMP. Compared with other available methods, the proposed model is capable of forecasting oil–gas MMP more accurately in wide ranges of thermodynamic and process conditions. All predictive models used other than the PSO–ANN model failed in providing a good estimate of the oil–gas MMP of the hydrocarbon mixtures in Azadegan oilfield, Iran. © 2013 Canadian Society for Chemical Engineering

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Minimum Miscibility Pressure Prediction With Equations of State

Cited by 22 publications

References 12 publications

A unified perspective on the phase behaviour of petroleum fluids

A unified perspective on the phase behaviour of petroleum fluids

Heavy Organic Deposition and Plugging of Wells (Analysis of Mexico's Experience)

A developed smart technique to predict minimum miscible pressure—eor implications

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