Prediction of optimum salinity and solubilization ratio for microemulsion phase behavior with live crude at reservoir pressure

Roshanfekr, Meghdad; Johns, Russell T.

doi:10.1016/j.fluid.2011.02.004

Cited by 56 publications

(39 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The EACN concept thus provides a useful tool to simplify the characterization of the crude oil even without sophisticated compositional analysis to determine its hydrophobicity for the screening tests. [19][20][21] Due to their reasonable low cost and less adsorptive losses onto the sandstone rocks, anionic surfactants are typically better choices than most nonionic or cationic surfactants for chemical flooding, and thus, several anionic candidates were investigated in this effort. With a successful description of qualitative effects of reservoir salinity (of brine), types of crude oils and surfactants, Salager et al developed a semi-empirical equation to address systems containing various hydrocarbons, anionic surfactants, co-surfactant (or alcohols), and salinity: [22] T ln S* K EACN f A T (1) Where S* = Optimal salinity as wt % NaCl, K = a characteristic parameter of the anionic The EACN of a mixture of alkanes or alkanes and non-alkyl hydrocarbons have been determined in many earlier studies based on Eqs.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Crude Oil Equivalent Alkane Carbon Number (EACN) for Surfactant Flooding Design

Wan

Zhao

Harwell

et al. 2014

Journal of Dispersion Science and Technology

View full text Add to dashboard Cite

EACN of crude oil reflects the hydrophobicity of the crude oils, thus it would be helpful to estimate the EACN of crude oil before fine tuning the surfactant selection for enhanced oil recovery (EOR) field application. In this study, we used the titration methodology to quantify the EACN values for several crude oil samples retrieved from the targeted mature oil fields. The results showed the method of using extended surfactant was simpler but reliable to determine the EACN of crude oils. The resulting EACN for these crude oils was found to be in the range of 6.0-11.3.

show abstract

Section: Introductionmentioning

confidence: 99%

Characterization of Crude Oil Equivalent Alkane Carbon Number (EACN) for Surfactant Flooding Design

Wan

Zhao

Harwell

et al. 2014

Journal of Dispersion Science and Technology

View full text Add to dashboard Cite

show abstract

“…The optimum solubilization ratio, however, was not monotonic along this curve. More recently, Roshanfekr and Johns (2011) showed how to predict the optimum salinity and solubilization ratio for live oils from its measured dead-oil data using the EACN concept. Knudsen et al (1994) attempted to develop a thermodynamic model to explain the effect of pressure on microemulsion phase behavior using a nonionic surfactant.…”

Section: Introductionmentioning

confidence: 99%

Simulation of the Effect of Pressure and Solution Gas on Oil Recovery From Surfactant/Polymer Floods

et al. 2012

View full text Add to dashboard Cite

Surfactant/polymer (SP) and alkali/surfactant/polymer flooding is of current interest because of the need to recover residual oil after primary and secondary recovery. If designed properly, these enhanced-oil-recovery processes can give very high oil recoveries. Microemulsion phase behavior plays a central role in process performance and is typically measured by performing salinity scans in glass pipettes at atmospheric pressure and reservoir temperature using dead crude oil from the reservoir of interest. There have been only a few experiments reported in the literature on live oil at reservoir pressure and temperature, and the importance of those experimental results is conflicting.This paper investigates the effect of pressure and solution gas on microemulsion phase behavior and its impact on oil recovery. We examine previous data reported in the literature, and report new measurements with live oil to show that the optimum parameters can change significantly. The experiments show that while pressure induces a phase transition from upper microemulsion (Winsor Type IIþ) to lower microemulsion (Winsor Type II-), solution gas does the opposite. An increase in pressure decreases the optimum solubilization ratio and shifts the optimum salinity to a larger value. Adding methane to dead oil at constant pressure does the reverse. Thus, these effects are coupled and both must be taken into account. Using a numerical simulator, we show that these changes in the optimum conditions can significantly impact oil recovery if not accounted for in the SP design.

show abstract

“…Several groups recently modeled live oil behavior including reservoir P and T conditions and gas solution [10,11,13,14,22]. This formulation of Equation (3) has been revisited by Trouillaud et al who expressed the gas molar fraction in terms of ratio of molar gas and oil volumes, and gas to oil ratio [13].…”

Section: Theoretical Sectionmentioning

confidence: 99%

“…chemistry of surfactants, types of salts, TDS -the amount of total dissolved solids -etc.). Investigations of gas dissolution, pressure and temperature effects on the {brine/formulation/ live oil} microemulsion have already been performed indicating competing effects [10][11][12][13]. Several recent works proposed reviews of these competing effects, for instance when increasing the gas to oil ratio and/or the pressure [10,13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Investigations of gas dissolution, pressure and temperature effects on the {brine/formulation/ live oil} microemulsion have already been performed indicating competing effects [10][11][12][13]. Several recent works proposed reviews of these competing effects, for instance when increasing the gas to oil ratio and/or the pressure [10,13,14]. Works report that optimal salinity decreases as gas is added to a crude oil at high pressure [12,15] while authors, as Cottin et al indicated slight increase of S* [16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Equivalent Alkane Carbon Number of Live Crude Oil: A Predictive Model Based on Thermodynamics

Creton

Mougin

2016

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

View full text Add to dashboard Cite

-We took advantage of recently published works and new experimental data to propose a model for the prediction of the Equivalent Alkane Carbon Number of live crude oil (EACNlo) for EOR processes. The model necessitates the a priori knowledge of reservoir pressure and temperature conditions as well as the initial gas to oil ratio. Additionally, some required volumetric properties for hydrocarbons were predicted using an equation of state. The model has been validated both on our own experimental data and data from the literature. These various case studies cover broad ranges of conditions in terms of API gravity index, gas to oil ratio, reservoir pressure and temperature, and composition of representative gas. The predicted EACNlo values reasonably agree with experimental EACN values, i.e. determined by comparison with salinity scans for a series of n-alkanes from nC 8 to nC 18 . The model has been used to generate high pressure high temperature data, showing competing effects of the gas to oil ratio, pressure and temperature. The proposed model allows to strongly narrow down the spectrum of possibilities in terms of EACNlo values, and thus a more rational use of equipments.Résumé -Développement d'un modèle pour prédire l'alcane équivalent d'une huile vive -En se basant sur de récents travaux de la littérature ainsi que sur de nouvelles données expérimentales, nous proposons un modèle permettant de prédire l'EACNlo (Equivalent Alkane Carbon Number of live crude oil) -alcane équivalent d'une huile vive (pétrole brut contenant des gaz dissous) -dans des contextes de recherche de tensioactifs pour des activités EOR. Pour mettre en oeuvre ce modèle, il faut s'assurer de la connaissance des conditions de pression et de température dans le réservoir et du rapport gaz sur huile. En outre, des propriétés volumétriques d'hydrocarbures nécessaires pour alimenter le modèle, sont estimées par l'utilisation d'équations d'état. La validation de notre modèle a été réalisée à partir de données expérimentales de la littérature et nos propres données obtenues pour des pétroles bruts. Ces différents cas d'études couvrent de larges gammes en termes de degrés API, de rapports gaz sur huile, de pressions et températures en condition de fond (réservoir) et de compositions pour le gaz représentatif. L'utilisation du modèle conduit à des valeurs d'EACNlo en accord avec les valeurs expérimentales, i.e. obtenues par comparaisons avec des analyses de salinités sur des séries d'alcanes linéaires de nC 8 à nC 18 . Le modèle a ensuite été utilisé pour générer des prédictions et ainsi étudier l'impact sur les prédictions des conditions de pressions et de températures et de la nature du gaz. Le modèle permet de restreindre la gamme de valeurs d'EACNlo et ainsi une meilleure utilisation des équipements expérimentaux.

show abstract

Prediction of optimum salinity and solubilization ratio for microemulsion phase behavior with live crude at reservoir pressure

Cited by 56 publications

References 14 publications

Characterization of Crude Oil Equivalent Alkane Carbon Number (EACN) for Surfactant Flooding Design

Characterization of Crude Oil Equivalent Alkane Carbon Number (EACN) for Surfactant Flooding Design

Simulation of the Effect of Pressure and Solution Gas on Oil Recovery From Surfactant/Polymer Floods

Equivalent Alkane Carbon Number of Live Crude Oil: A Predictive Model Based on Thermodynamics

Contact Info

Product

Resources

About