Prediction of Asphaltene Precipitation in Reservoir Model Oils in the

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Thermodynamics, Transport, and Fluid Mechanics

Prediction of Asphaltene Precipitation in Reservoir Model Oils in the Presence of Fe3O4 and NiO Nanoparticles by Cubic Plus Association Equation of State Amir Varamesh, and Negahdar Hosseinpour Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b05432 • Publication Date (Web): 19 Feb 2019 Downloaded from http://pubs.acs.org on February 19, 2019

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Industrial & Engineering Chemistry Research

Prediction of Asphaltene Precipitation in Reservoir Model Oils in the Presence of Fe3O4 and NiO Nanoparticles by Cubic Plus Association Equation of State Amir Varamesh, Negahdar Hosseinpour* Institute of Petroleum Engineering, School of Chemical Engineering, College of Engineering, University of Tehran, P.O. Box: 11155/4563, Tehran, Iran

Keywords: Asphaltene onset; Asphaltene inhibitor; Nanoparticles; In-situ oil upgrading; CPA. ABSTRACT Nanoparticle-technology was employed to address the challenges of asphaltene precipitation during oil production. Fe3O4 and NiO nanoparticles were synthesized and characterized. Asphaltene was extracted and dissolved in toluene to prepare reservoir model oil. The asphaltene precipitation onset in the presence and absence of the nanoparticles by the addition of n-heptane was measured using dynamic light scattering. Cubic plus association equation of state (CPA EoS) was employed to predict the asphaltene precipitation in the presence and absence of the nanoparticles. The CPA EoS determines the precipitate amount of the asphaltene by knowing the asphaltene average aggregate size in the liquid. In the presence of the nanoparticles, the asphaltene self-association energy is inferred to be a function of the molar density of the surface sites of the nanoparticles with an exponential relationship. The CPA EoS can be employed for designing chemical inhibitors of the asphaltene precipitation by the metal oxides nanoparticles. 1. INTRODUCTION Reservoir oil is described as a dispersion of molecules and colloidal aggregates in a mixture of hydrocarbons and is divided commonly into four solubility classes, including saturate, aromatic, resin and asphaltene. Asphaltenes are the most polar and heaviest constituents of reservoir oils.1, 2ASTM D6560 and IP 143standards define asphaltenes as a solubility class, soluble in light aromatics such as toluene but insoluble in low-molecular-weight paraffins such as n-heptane.3, 4Since asphaltenes have a complex molecular structure, low volatility, and high tendency to self-association, identification of their actual chemistry is difficult.5Generally, asphaltene is considered as aggregates poly-dispersed in reservoir oils. The asphaltene

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molecules are composed of a polynuclear aromatic sheet, aliphatic chains, and alicyclic moieties with small amounts of metallic and heteroatoms (Ni, V, Fe, O, N and S).6During oil production, asphaltenes strong self-association tendencies through van der Waals, electrostatic, charge transfer, and hydrogen bonding forces cause asphaltenes aggregation, flocculation, and finally precipitation.7-9The precipitation and deposition of asphaltenes are one of the challenges in flow assurance issues. In addition to precipitation/deposition in porous media, asphaltene is precipitated out of the oil phase in the production wells, wellheads, safety valves, pipeline transportation and downstream process plants. Due to strong attractive interactions among their aggregates, asphaltenes can significantly increase the oil viscosity.10 In addition, asphaltenes are adsorbed onto various mineral surfaces, leading to wettability alteration of reservoir rocks, permeability impairment of the porous media, and even plugging the oilwells.11, 12 To prevent the detrimental effects of asphaltene on oil production, several methods have been proposed to inhibit asphaltene precipitation or remove the deposited asphaltenes. The most effective way is to prevent asphaltene precipitation by keeping the operational conditions outside the asphaltene precipitation envelop.13However, this is not always feasible since a large drawdown occurs in the near wellbore region which causes the reservoir pressure becomes lower than the asphaltene upper onset pressure.13Mechanical treatment techniques such as rod and wireline scrappers, mechanical vibration, pigging, and manual stripping can be used for the removal of the deposited asphaltene. In addition, chemical treatment techniques including the injection of dispersants, flocculants, coagulants, solvents, and co-solvents into the wellbore and near-wellbore region are proposed to inhibit the asphaltene precipitation.14However, none of these conventional methods has permanent effects on prevention of the asphaltene precipitation and deposition. Recent advances in nanotechnology made it possible to overcome some of the mentioned limitations of the conventional methods, by the application of nanoparticle-technology for addressing the flow assurance challenges. Because of their ultra-small size, smaller than 100 nm, nanoparticles provide superior characteristics including large specific surface area, functionalizable surface, excellent dispersibility, longterm stability, and high transportability in porous media.15, 16Through this unique properties, nanoparticles are able to quickly adsorb suspended asphaltenes in the oil and inhibit their precipitation and also able to remove the deposited asphaltenes from the surface.17, 18Nanoparticles, via electrostatic and acid-base interactions with asphaltenes, have the potential to reduce asphaltenes self-association tendencies, inhibiting asphaltenes precipitation.16In the recent years, several studies have been conducted on the application of metal oxides nanoparticles as adsorbents/catalysts for in-situ upgrading of reservoir oils.16, 17, 19-25

The adsorption isotherms of asphaltenes onto different acid/base metal oxides nanoparticles,

including NiO, Fe2O3, WO3, MgO, CaCO3, ZrO2, were found to follow the Langmuir-type behavior with the adsorption capacity of 1.23−3.67 mg/m2.16Nassar et al. reported the rapid adsorption of asphaltenes


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aggregates from asphaltenes/toluene solutions onto γ-Al2O3 nanoparticles.17In addition, the affinity of transition metal oxides nanoparticles for the adsorption of asphaltenes decreases in the order of NiO > Co3O4> Fe3O4, as reported in the literature.19 Furthermore, as reported by Igder et al., NiO and Fe3O4 nanoparticles showed significant delays in the asphaltenes precipitation onset, indicating the activity of the nanoparticles for the control of the asphaltene aggregation during oil production.25 The study of thermodynamic phase behavior of asphaltene precipitation is a topic with continuous importance in the area of fluid property related flow assurance issues. In addition to the disagreement on stabilization mechanism of asphaltene in the reservoir oil, the complex structure of asphaltene molecules has resulted in a large variety of models for describing the thermodynamics of asphaltene precipitation. Generally, the models are categorized briefly into two types: molecular solution (true solution or lyophilic) approach and micellar (colloidal or lyophobic) approach.5, 6, 26Micellar approach assumes that asphaltenes are stabilized within the reservoir oil by resin molecules. Therefore, as opposed to a true solution, asphaltenes are maintained in a colloidal dispersion within the crude oil surrounded by resin molecules. Micellar approach is based on the viewpoint that asphaltenes and resins are the most polar fractions of crude oil since they are composed of highly polar functional groups originating from different heteroatoms.6Resins cross-associate with asphaltene molecules through dipole-dipole interactions and hydrogen bonding, forming micelles with asphaltenes. The higher the ratio of the resin to asphaltene concentration in reservoir oils, the higher is the asphaltene stability. On the contrary, the molecular solution approach assumes that the asphaltene is in a true solution with other oil constituents.26 This approach is based on the molecular size and dispersion attractions, which control the asphaltene phase behavior in reservoir oils. Asphaltene precipitation occurs when the solvent power of the oil decreases and the asphaltene solubility is diminished in the hydrocarbon fluid. Asphaltene precipitation can be modeled as a liquid-liquid equilibria or a solid-liquid equilibria.6, 26The precipitated asphaltene phase can be considered as either a pure liquid asphaltene phase or as a liquid phase contains all of the oil components with asphaltenes as its dominate constituent.5 Thermodynamic models which are based on the true solution approach include solubility models,27, 28cubic equations of state (CEoS),29, 30 perturbed chain statistical association fluid theory equation of state (PC-SAFT EoS),31-33and cubic plus association equation of state (CPA EoS) 5, 26, 34-42.Among all of the available models, molecular-based EoS models, such as CPA EoS, have gained lots of attention and application. CPA has many advantages over its counterparts. For instance, it is a fully compositional model and able to predict quickly and accurately the effects of pressure, temperature, and asphaltene polydispersity on asphaltene phase behavior. In addition, CPA can simultaneously model all of the phases in equilibrium with the asphaltene phase within a unified framework. Furthermore, when compared to molecular simulations, CPA has a high convergence speed, making its integration into the commercial reservoir simulators readily possible. The CPA EoS was originally



developed for modeling complex phase equilibria of mixtures containing strongly polar or highly associating compounds. CPA EoS is comprised of a physical part which is a cubic equation of state such as Soave-Redlich-Kwong (SRK)43or Peng-Robinson (PR)44and an association part originating from statistical association fluid theory (SAFT).45, 46The aim of the development of the CPA EoS was to develop a model based on the SRK (or other cubic) EoS and the SAFT theory for modeling the behavior of complex associating mixtures. In addition to considering physical interactions, the CPA EoS is able to account for hydrogen bonding between molecules of the same kind (self-association) and hydrogen bonding between two different molecules (cross-association and solvation). Over the past years, various researchers have utilized CPA EoS to model asphaltene precipitation with different modeling approaches.5, 26, 34-42 The aim of this work is to develop a robust and simple approach based on CPA EoS to model the asphaltene precipitation in the presence of Fe3O4 and NiO nanoparticles. Fe3O4 and NiO nanoparticles are synthesized and their surface and structural properties are characterized. Asphaltene is extracted and its molecular structure and functional groups are characterized. Experimental tests are conducted to find the activity of the nanoparticles for the control of the asphaltene aggregation and precipitation. Then, a new approach based on the asphaltene molecular structure and functional groups is developed to predict the asphaltene precipitation in the absence of the nanoparticles by the CPA EoS. The model is then extended to show that the CPA EoS is able to accurately model the asphaltene precipitation in the presence of the nanoparticles. The reliability and accuracy of the presented approach is proved by comparing the predictions of the CPA EoS with the collected experimental data in the presence and absence of the nanoparticles. 2. EXPERIMENTAL AND MODELING 2.1. EXPERIMENTAL NiO and Fe3O4 nanoparticles were synthesized via a simple precipitation from aqueous solutions containing 0.05 M Ni(NO3)2ꞏ6H2O or Fe(NO3)3ꞏ9H2O by a dropwise addition of 0.5 M urea or ammonia solution to set the pH at 10. The precipitated solids were washed with plenty of de-ionized (DI) water and dried at 60oC for 12 h. Finally, the samples were calcined at 700oC for 3 h in air. The thus-obtained nanoparticles were characterized by X-ray powder diffraction (XRD), N2-adsorption-desorption (for surface area measurement), NH3-temperature-programed desorption (TPD) and CO2-TPD, Fourier transform infrared (FTIR) transmission spectroscopy, and field-emission scanning electron microscopy (FESEM) techniques. The details of the synthesis and characterization of the NiO and Fe3O4 nanoparticles were reported in our previous work.25 Asphaltene was extracted from a dead sample of an Iranian heavy-oil reservoir by precipitation in excess of n-heptane following the modified ASTM 2007-03 standard procedure, as described in our previous


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work.22 The solid black shiny asphaltene was subjected to FTIR spectroscopy analysis to characterize the functional groups in its molecular structure. The acid number and base number of the asphaltene were measured by a titration method detailed in our previous report.16 Asphaltene forms nanoaggregates of the average size of 5-20 nm in reservoir oils depending on the reservoir conditions and oil composition.47, 48Therefore, the asphaltene was dissolved in toluene (99.8%, Merck) with the concentration of 400 mg/L to prepare a model oil with the asphaltene aggregate sizes similar to those in reservoir oils. Dynamic light scattering (DLS) was employed to determine the size distribution of the asphaltene aggregates in the model oil. DLS analysis was done by Malvern ZS Nano analyzer-Malvern Instrument Inc, UK. In order to simulate the effects of pressure depletion on the asphaltene aggregation and precipitation, different volume ratios of n-heptane (99%, Merck) were added to 20 mL of the model oil in the absence and presence of different mass ratios of the nanoparticles. After shaking the mixtures for 24 h at 20oC and 1 bar to approach equilibrium, the samples were centrifuged and the average size of the asphaltene aggregates in the supernatant liquids was determined by DLS analysis. The average aggregate size of 500 nm was considered as the precipitation onset point of the asphaltene. The volume ratios of the added n-C7 to the model oil were converted to the molar compositions of the system to be used for the calculation of the parameters of the CPA EoS. 2.2. MODELING 2.2.1. CPA EOS The CPA equation of state can be written for mixtures in terms of pressure by adding an association part to the SRK EoS as given by Michelsen and Hendriks49: 𝑃

𝑅𝑇 𝑉

𝑏

𝑎 𝑇 𝑉 𝑉 𝑏

1 𝑅𝑇 1 2𝑉

𝜌

𝜕 𝑙𝑛 𝑔 𝜕𝜌

𝑥

1

𝑋

(1)

where 𝑅 is the universal gas constant, 𝑇 is the absolute temperature, 𝑎 𝑇 is the energy parameter, 𝑏 represents the co-volumeparameter, 𝑉

is the molar volume, 𝜌 is the molar density (

demonstrates the mole fraction of component 𝑖 and 𝑋

1⁄𝑉 ), 𝑥

stands for the mole fraction of site 𝐴 on the

molecules of 𝑖 not bonded to other sites. It is worth to mention that the original CPA was developed by Kontogeorgis et al.50 uses the derivative of 𝑋 with respect to molar density in the associate term. Michelsen and Hendriks49 proposed another identical term for the association contribution (as presented in eq 1), which is much simpler, thus substantially improved the computational time of the CPA EoS. The mixture energy and co-volume parameters of the CPA EoS are calculated by the classical van der Waals one-fluid mixing and combining rules (eq 2andeq 3). The energy parameter of the component 𝑖



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(𝑎 𝑇 ) is given by a Soave-type temperature dependency,43 while co-volume parameter of the component 𝑖 (𝑏 ) is temperature independent. These parameters are calculated with the conventional method using critical properties and acentric factor as proposed in the SRK equation of state (eq 4 and eq 5).

a    n i n j aij , i

where

aij  ai a j 1  k ij



j

b   ni bi

(2) (3)

i

ai  a0   (T ) R 2T ci2 Pci

a0  0.42747

(4)





 (T )  1  c 1i 1  T T ci  

2

c 1i  0.48508  1.55171  0.15613 2

bi  0.08664

RTci Pci

(5)

where 𝑘 is the binary interaction parameter between component 𝑖 and 𝑗; 𝑇 , 𝑃 and 𝜔 are the critical temperature, critical pressure and acentric factor of component 𝑖, respectively. The mole fraction of site 𝐴 in molecules of component 𝑖not bonded to other sites (𝑋 ) is the key parameter of the association term in the CPA EoS. The way that the associating molecules self-associate with the molecules of the same kind or cross-associate with the molecules of the other kinds affect the value of 𝑋 , hence it is crucially important to correctly determine the number and type of association sites (association scheme) for associating molecules prior to the implementation of CPA EoS. Haung and Radosz51 categorized the different association schemes and proposed expressions for the calculation of 𝑋 for each of the presented schemes. Generally, within the CPA framework, 𝑋 can be obtained as follows:

X Ai 

1

1  n j  X B j  j

where ∆

(6)

Ai B j

Bj

is the association (binding) strength between site Aon molecules𝑖 and site B on molecules𝑗,

expressed as follows:



Ai B j

   Ai B j  g    exp   RT 

  Ai B j   1 bij   

(7)


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where 𝑏

𝑏

𝑏 ⁄2 and 𝑔 𝜌 is the radial distribution function calculated by the simplified radial

distribution function proposed by Kontogeorgis et al.50 as below:

g  

1 , 1  1.9

where

In the expression for the ∆

1 4

  b

(eq 7), the 𝜀

(8)

, and 𝛽

stand for the association energy and association

volume between site A on molecules𝑖 and site B on molecules𝑗, respectively. These two parameters are nonzero for associating compounds and they are equal to zero for non-associating compounds. 2.2.2. MODELING APPROACH The asphaltene precipitated out of the oil phase is considered as a liquid phase at reservoir conditions.52Experimental studies by Kokal et al.53 Hireschberg et al.54 and Godbole et al.55showed that the precipitated asphaltene is a black-liquid mixture at high temperatures. Therefore, the modeling of asphaltene precipitation is done by considering the precipitation of asphaltene as a liquid-liquid phase separation in the literature.5, 26, 34, 35, 37-41In this work, a liquid-liquid equilibrium was assumed to model the asphaltene precipitation. The asphaltene-rich phase, which is a liquid-dense phase, and the oil-rich phase, which behaves as a solvent for the asphaltene, were considered as the two liquid phases in equilibrium. The asphaltene-rich phase was assumed to be the pure asphaltene, concomitant with the reports in the literature.31, 42, 56, 57 This assumption is consistent with the definition of asphaltene as a solubility class insoluble in low-molecular-weight paraffins. Therefore, all those precipitated by the addition of n-C7 in the experiments were considered as a pure asphaltene phase. In addition, the precipitation was considered as a thermodynamically reversible process. These assumptions simplify and speed up the chemical equilibrium calculations by the CPA EoS using the true solution approach. At the liquid-liquid equilibrium conditions, the fugacity of each component in both of the liquid phases must be equal: 𝑓 where 𝑓

𝑓

(9) and 𝑓

are the fugacity of component 𝑖 in the first and second liquid phases (𝐿 𝑎𝑛𝑑 𝐿 ),

respectively. In our approach, it was assumed that, at the beginning, there is only a liquid phase, i.e. the oilrich phase. By changes in the thermodynamic conditions, the second liquid phase, i.e. the asphaltene-rich phase, must be formed under definite circumstances to make the system stable.



@ constant T & P Asphaltene Fugacity

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Asphaltene Fugacity in Oil-Rich Phase Asphaltene Fugacity in Asphaltene-Rich Phase

Asphaltene precipitation No precipitation

Equilibrium Point Asphaltene overal mole fraction in the system Figure 1. Schematic representation of the asphaltene stability in the liquid-liquid equilibria.

In order to decide whether the asphaltene precipitates out of the oil-rich phase at each thermodynamic condition, a stability check was performed by comparing the asphaltene fugacity in the oil-rich phase (𝑓

) with that in the asphaltene-rich phase (𝑓

). If 𝑓

𝑓

in the system. On the contrary, the asphaltene precipitates if 𝑓

, no asphaltene precipitation occurs 𝑓

, indicating that the system

becomes stable via the generation of an additional phase called the asphaltene-rich phase. The stability condition of the model oil is depicted schematically in Fig.1. According to this stability condition, when the calculated fugacity of asphaltene in both the oil-rich phase and the asphaltene-rich phase become equal for the first time, the asphaltene starts to precipitate out of the oil-rich phase. This criterion defines the onset point of the asphaltene precipitation in the system. If it is assumed imaginarily that there is no asphaltene precipitation in the system, the fugacity of asphaltene in the oil-rich phase will constantly increase with the asphaltene concentration in the system and becomes higher than that in the asphaltene-rich phase. This means that the system is unstable and the asphaltene must precipitate out of the oil-rich phase in order for the total Gibbs energy of the system to be at the global minimum. As illustrated in Fig. 1, when the asphaltene fugacity in the oil-rich phase becomes higher than that in the asphaltene-rich phase, i.e.𝑓 𝑓

, the excess mol of the asphaltene must precipitate out of the oil-rich phase in order for the fugacity

values become equal. This was the criterion for the calculation of the composition of the oil-rich phase as well as the asphaltene precipitate amount at all possible equilibrium conditions. The CPA equation of state was first employed to model the asphaltene precipitation in the model oil solution in the absence of the nanoparticles. Then, the CPA EoS extended to predict the asphaltene precipitation in the presence of the nanoparticles. The experimental data of the onset point of the asphaltene precipitation


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in the presence and absence of the nanoparticles were used. By the addition of definite volumes of n-C7 to the asphaltene-toluene model oil, the average aggregate size of the asphaltene reaches 500 nm in the presence and absence of the nanoparticles and the asphaltene starts to precipitate. Therefore, the asphaltene onset was defined as the n-C7 volume required for the initiation of the asphaltene precipitation. In the modeling approach, the critical properties (Pc and Tc), acentric factor (ω), and molecular weight (MW) of the pure compounds, i.e. n-heptane and toluene, were taken directly from the DIPPR database reported in the literature.58 In addition, n-heptane and toluene were considered to have no self-association behavior. Therefore, their self-association parameters were fixed to be zero. The molecular weight and specific gravity of the asphaltene were considered to be 750 Da and 1.035, respectively, similar to those reported in the literature.40,

59

The asphaltene physical properties including the critical pressure (𝑃 ), critical

temperature (𝑇 ) and acentric factor (𝜔) were fixed to be 𝑃

15.4 𝑏𝑎𝑟, 𝑇

1040 𝐾, 𝜔

1.54.37, 39In

addition, potentiometric titration experiments revealed that the molar concentration of basic functional groups in the structure of the asphaltene molecules is about 4.5 times higher than that of acidic functional groups, as reported in our previous work.16This means that there are four basic sites per each acidic site in the molecular structure of the asphaltene. Therefore, in our modeling approach, it was assumed that the asphaltene molecules have five associating sites, inline with the potentiometric titration results. Commonly, four associating sites have been considered for asphaltene molecules when modeling asphaltene precipitation by CPA EoS in the literature.26, 35, 37-42 As stated by Li and Firoozabadi35, the consideration of four associating sites for asphaltene may not reflect the asphaltene molecular reality. Furthermore, it was assumed that the associating behavior of all of these sites are similar, indicating that their association energy with the associating sites of other asphaltene or toluene molecules is the same. Two associating sites were assigned to toluene molecules. One, two, or four associating sites are considered for toluene or resin in the literature.5,26,37 Franco et al. showed that the active sites of asphaltenes can be more than two times of that of resins.60 For n-C7 molecules, it was assumed that there is no association behavior. In our modeling approach, it was assumed that the asphaltene self-associate with each other and cross-associate with toluene. However, no self-association was considered between toluene molecules. This means that active sites of the toluene cannot interact with each other, while they can interact with all active sites of the asphaltene molecules. In the framework of the CPA EoS, cross-association and self-association are two competitive mechanisms.5When dealing with the asphaltene precipitation, the asphaltene self-association has a higher impact compared to the asphaltene cross-association since the precipitated phase contains mainly asphaltene.61 In other words, the asphaltene self-association is the controlling mechanism for precipitation. As reported by Edmonds et al.62, asphaltene precipitation envelope can be reproduced accurately by assuming an appropriate constant value for asphaltene-asphaltene association parameters and tuning the asphaltene-heavy hydrocarbon cross-association parameters. In the literature, an accurate prediction of the



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precipitation of asphaltene was reported by considering the universal value of 3000 K and 0.05 for the asphaltene-asphaltene association energy (𝜀

⁄𝑅 ) and association volume (𝛽

), respectively.5, 34, 62In our

modeling approach, in the absence of the nanoparticles, the association energy of the asphaltene-asphaltene ⁄𝑅 ), the association volume of the asphaltene-asphaltene (𝛽

(𝜀

asphaltene-toluene (𝛽

), and the association volume of the

) were fixed at 3000 K, 0.05, and 0.05, respectively. Therefore, the association

energy between asphaltene and toluene (𝜀

) was considered as the only tuning parameter of the model in

the absence of the nanoparticles. The tuning parameter was obtained using the experimental conditions of the asphaltene onset. Then, the CPA EoS was employed to predict the precipitate amount of the asphaltene at n-heptane volume ratios higher than the onset point. For the extension of the CPA EoS to predict the asphaltene precipitation in the presence of the nanoparticles, it was assumed that the nanoparticles surface sites affect the self-association of asphaltene molecules and do not have any impact on asphaltene and toluene cross-association. This assumption is based on the concept that the asphaltene precipitation is controlled mostly by the asphaltene self-association. Therefore, the surface sites of the nanoparticles interact with the asphaltenes, modifying the asphaltene self-association behavior. As a result, the self-association energy of the asphaltene molecules (𝜀

⁄𝑅 ) was considered as

the only tuning parameter of the model in the presence of the nanoparticles. The cross-association energy between asphaltene and toluene (𝜀

⁄𝑅) was fixed at the value that obtained already by tuning the model

in the absence of the nanoparticles. The values of the association volumes, i.e.𝛽

and 𝛽

, were

⁄𝑅 was tuned using the experimental data of the asphaltene onset

considered to be 0.05. The value of 𝜀

point in the presence of only three mass ratios of each of the nanoparticles. The tuned values of 𝜀

⁄𝑅 was

found to be dependent on the mol number of the surface sites added to the system by the addition of the nanoparticles. Therefore, this dependency was represented by linear, quadratic, and exponential functions as shown by eqs. 10-12.

 AA R

 AA R

 AA R

 A  BW N N N

 A  BW N N N  C W N N N

(10)



2

(11)

 A  B exp(CW N N N )

(12)

In the above equations,𝑊 is the mass of the added nanoparticles (g), 𝑁 is the molar density of the surface sites of the nanoparticles (mol/g), and 𝐴, 𝐵, and 𝐶 are the constants obtained by matching the model predictions using only three of the asphaltene onset data. For each type of the nanoparticles, the three


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experimental data, matched for tuning the model, are the asphaltene onset point in the absence of the nanoparticles, in the presence of the lowest content of the nanoparticles, and in the presence of the highest content of the nanoparticles, as discussed in section 3.2. Then, the asphaltene onset point at the other concentrations of the nanoparticles, which have not been used previously for tuning the model, are predicted by the CPA EoS. The values of 𝑁 were determined via the nanoparticles characterization by NH3- TPD and CO2-TPD techniques. Finally, the CPA-EoS was employed to predict the precipitate amount of the asphaltene at the n-heptane volume ratios higher than the onset points. 3. RESULTS AND DISCUSSION 3.1. Asphaltene precipitation in the absence of the nanoparticles Table 1 summarizes the average aggregate size of the asphaltene in the absence of the nanoparticles as a function of the vol% of the n-C7 in the model oil. The average aggregate size becomes 500 nm when the n-heptane vol% reaches 21 in the system, indicating the asphaltene onset point in the absence of the nanoparticles. Table 1.The asphaltene average aggregate size as a function of the nC7 added to the model oil in the absence of the nanoparticles. Asphaltene average aggregate size (nm)

n-C7 vol%

18

0

38

5

89

10

167

15

465

20

500*

21*

778

25

1080

30

1318

35

1571

40

* The asphaltene onset point

In order to match the experimental value of the asphaltene onset point by the CPA EoS, the asphaltenetoluene cross-association energy (𝜀 energy (𝜀

⁄𝑅) was tuned by fixing the asphaltene-asphaltene self-association

⁄𝑅 ), asphaltene-asphaltene self-association volume (𝛽

association volume (𝛽

), and asphaltene-toluene cross-

) at 3000 K, 0.05, and 0.05, respectively. The tuned value of 𝜀

⁄𝑅 is obtained

equal to 1068.73 K. The CPA EoS with the tuned parameter values were employed to predict the fractional



precipitation of the asphaltene at the n-heptane concentrations higher than that of the onset point. The fractional precipitation is defined as the mass of the asphaltene precipitated per total mass of the asphaltene present in the two-phase system. Fig. 2 illustrates the model prediction of the asphaltene fractional precipitation as a function of the n-C7 concentration in the system. The asphaltene precipitation starts at 21 vol% of n-heptane and the fractional precipitation approaches unity when the n-C7 concentration reaches 70 vol%. As expected, the mass of the precipitated asphaltene increases by the addition of n-C7 as a precipitant of the asphaltene to the system. The accuracy of the fractional precipitation predicted by the model was studied by centrifuging the equilibrium model oil containing 400 mg/L of asphaltene in 65 vol% of n-C7 and 35 vol% of toluene, followed by UV-vis spectroscopy analysis of the supernatant liquid. The experimental result indicates that the fractional precipitation of the asphaltene is 0.93 which is in close agreement with the model prediction. As a conclusion, the CPA EoS with the tuned parameter values predicts accurately the general trend of the fractional precipitation of the asphaltene as a function of the nheptane concentration in the system. 1 Asphaltene fractional precipitation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 23

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

10

20

30

40

50

60

70

80

90

100

Volume percent of the added n-C7 Figure 2.Fractional precipitation of the asphaltene in the absence of the nanoparticles versus the concentration of nC7 in the model oil.

Considering both the experimental data reported in Table 1 and the model predictions depicted in Fig. 2, the fractional precipitation of the asphaltene can be calculated by knowing its average aggregate size in the oil-rich phase in equilibrium with the precipitated asphaltene, i.e. the asphaltene-rich phase. This leads to obtain further details not able to be collected by the DLS analysis. For instance, the asphaltene fractional precipitation is 0, 0.33, 0.60, and 0.76 when the average aggregate size of the asphaltene is 500, 778, 1080, and 1571 nm, respectively.


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3.2. Asphaltene precipitation in the presence of the nanoparticles The results of the XRD and FTIR analyses confirm that the synthesized nanoparticles are crystalline Fe3O4 and NiO with the cubic (JCPDS 01-1111) and rhombohedral (JCPDS 22-1189) structure, respectively. In addition, the FESEM images of the prepared nanoparticles show that the particle size of the Fe3O4 and NiO nanoparticles is in the range of 20-45 nm and 50-90 nm, respectively. Furthermore, the Brunauer-EmmettTeller surface area measurement along with the NH3-TPD and CO2-TPD data reveal that the molar density of the surface sites, i.e 𝑁 12.375

10 and 8.3160

in eqs. 10-12, of the synthesized Fe3O4 and NiO nanoparticles is 10

mol/g, respectively. Further details of the characterizations of the

nanoparticles have been reported in our previous works.16, 25 The experimental data of the asphaltene precipitation onset in the model oil as a function of the nanoparticles content of the system are summarized in Table 2. The nanoparticles content of the system is defined as the mass of the nanoparticles added initially to 20 mL of the asphaltene-toluene model oil prior to the addition of nC7. The experimental data indicates significant delays in the asphaltene onset by the addition of the nanoparticles. As the nanoparticles content of the system increases, the asphaltene become more stable in the system, indicating the activity of the nanoparticles to be utilized as the asphaltene precipitation inhibitor. The asphaltene precipitation onset is at 21, 29, and 45 vol% of n-C7 in the absence of the nanoparticles, in the presence of 20 mg of NiO, and in the presence of 14 mg of Fe3O4 nanoparticles, respectively. Compared to NiO, the Fe3O4 nanoparticles exhibit a higher activity for the control of the asphaltene aggregation and precipitation. This may be attributed to the number and strength of the interactions of the asphaltene with the surface sites of the nanoparticles. Table 2. Experimental data of the asphaltene precipitation onset in the presence of the Fe3O4 and NiO nanoparticles. Nanoparticles content (g)

n-C7 vol% at the onset point

Fe3O4 nanoparticles 0.014 0.015 0.016 0.017 0.018 0.019 0.020 0.021 0.022

45.0 46.0 48.2 50.7 53.2 56.7 60.6 65.9 72.4 NiO nanoparticles

0.020 0.022

29.0 30.5



0.025 0.028 0.030 0.032 0.035

Page 14 of 23

32.7 35.0 38.0 42.2 49.8

In this study, for the first time, the CPA equation of state is utilized to model the asphaltene precipitation in the presence of nanoparticles. For modeling the asphaltene precipitation in the presence of the NiO and Fe3O4 nanoparticles, the self-association volume between the asphaltene molecules (𝛽 association volume between the asphaltene and toluene molecules (𝛽

) and cross-

) were considered constant and

equal to 0.05. The cross-association energy of the asphaltene-toluene, i.e.𝜀

⁄𝑅,was fixed at 1068.73 K,

the same as the value tuned for the prediction of the asphaltene onset in the absence of the nanoparticles. ⁄𝑅 , was the only adjustable parameter for the

Therefore, the asphaltene self-association energy, 𝜀

prediction of the asphaltene precipitation onset in the presence of the nanoparticles. The 𝜀

⁄𝑅 values are

expected to be dependent on the mol number of the surface sites of the added nanoparticles. In order to find the dependency of the 𝜀

⁄𝑅 values to the mol number of the surface sites of the nanoparticles, the

experimental onset points in the presence of 0, 14, and 22 mg of Fe3O4 as well as 0, 20, and 35 mg of NiO were used. These three experimental data are the only data matched for tuning the model in the presence of each type of the nanoparticles. These are the experimental asphaltene onset point in the absence of the nanoparticles, in the presence of the lowest nanoparticles content, and in the presence of the highest nanoparticles content, as reported in Table 2. Then, the tuned values of 𝜀

⁄𝑅 were correlated with the mol

number of the surface sites of the added nanoparticles using linear, quadratic, and exponential relations, i.e. eqs. 10-12, as summarized in Table 3. Table 3. The association parameters of the CPA EoS for the prediction of the asphaltene onset in the presence of the Fe3O4 and NiO nanoparticles. 𝜀

/𝑅 (K)

𝛽

𝜀

/𝑅 (K)

𝛽

Fe3O4 nanoparticles Exponential: 5.639 Quadratic: 3000 Linear: 3044

10

8.086

5.24

5.642 10 𝑊 𝑁

10 exp 92.85𝑊 𝑁 1.09

10

𝑊 𝑁

0.05

1068.73

0.05

0.05

1068.73

0.05

10 𝑊 𝑁 NiO nanoparticles

Exponential: 4.417 Quadratic: 3000 Linear: 3019

10

4.867

3.944

4.420 10 𝑊 𝑁

10 exp 89.21𝑊 𝑁 3.248

10

𝑊 𝑁

10 𝑊 𝑁


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In general, the addition of the nanoparticles to the model oil increases the value of 𝜀

⁄𝑅 . This indicates

that the asphaltene precipitation needs much stronger self-association interactions in the presence of the nanoparticles when compared to the precipitation in the absence of the nanoparticles. This is an indication of the underlying mechanism of the inhibition of the asphaltene precipitation by the nanoparticles. In addition, in the presence of the Fe3O4 nanoparticles, the self-association energy is higher than that in the presence of NiO. The higher values of 𝜀

⁄𝑅 in the presence of the Fe3O4 nanoparticles may be ascribed

to the more active sites provided by the Fe3O4 nanoparticles, which readily control the asphaltene precipitation even when the content of the nanoparticles is very low as observed in the experiments. The CPA EoS with the tuned parameter values was employed to predict the precipitation onset of the asphaltene in the presence of different quantities of the Fe3O4 and NiO nanoparticles, the experimental data of which have not been previously used to develop eqs. 10-12. The predictions of the CPA EoS are compared with the experimental data reported in Table 2 to find the actual dependency of the asphaltene self-association energy to the mol number of the nanoparticles surface sites. In order to assess the accuracy of the correlations found for the 𝜀

⁄𝑅 parameter, i.e eqs. 10-12, the absolute relative error (ARE) and the

average absolute relative error (AARE) in the prediction of the asphaltene onset point are defined as per eq 13 and eq 14, respectively.  AOPCPA  AOPexp  ARE  100   AOPexp   100 N   AOPi CPA   AOPi exp  AARE    N i 1   AOPi exp  

(13) (14)

where AOP is the asphaltene onset point, the subscript exp denotes the experimental data, and N is the number of the data points. Table 4 summarizes the ARE and AARE of the CPA EoS predictions of the asphaltene onset in the presence of the Fe3O4 nanoparticles. It is worth mentioning that the onset data in the absence of the Fe3O4 nanoparticles, in the presence of the lowest content of the Fe3O4 nanoparticles, i.e. 14 mg, and in the presence of the highest content of the Fe3O4 nanoparticles, i.e. 22 mg, are not considered for the calculation of AARE since these data have been matched previously by the model to develop eqs. 1012. The results show that the CPA EoS is able to predict the asphaltene onset point in the presence of the Fe3O4 nanoparticles with an acceptable accuracy. The value of AARE is 6.57, 8.95, and 7.23% for the exponential, quadratic, and linear dependency of the asphaltene self-association energy (𝜀

⁄𝑅 ) to the mol

number of the nanoparticles surface sites. It can be inferred from the AARE values that the effects of the surface sites of the Fe3O4 nanoparticles on the asphaltene self-association energy may have an exponential



Page 16 of 23

form. That is why the asphaltene precipitation is inhibited by the Fe3O4 when the nanoparticles content of the model oil passes a definite limit. Table 4. The absolute relative error (ARE) and average absolute relative error (AARE) in the prediction of the asphaltene onset point in the presence of the Fe3O4 Nanoparticles by the CPA EoS. Exponential

Quadratic

Linear

Nanoparticles content (g)

Experimental

CPA

ARE (%)

CPA

ARE (%)

CPA

ARE (%)

0.015

46.0

38.99

15.27

48.59

5.60

41.45

9.90

0.016

48.2

42.69

11.40

52.25

8.43

45.52

5.52

0.017

50.7

46.93

7.37

55.92

10.36

50.14

1.05

0.018

53.2

51.72

2.70

59.54

12.02

55.29

4.02

0.019

56.7

57.04

0.56

63.06

11.16

60.97

7.47

0.020

60.6

62.88

3.74

66.40

9.57

67.12

10.75

0.021

65.9

69.17

4.99

69.54

5.55

73.70

11.86

AARE= 6.57%

AARE= 8.95%

AARE= 7.23%

The ARE and AARE of the CPA EoS predictions of the precipitation onset of the asphaltene in the presence of the NiO nanoparticles are reported in Table 5. It should be noted that the onset data in the absence of the NiO nanoparticles, in the presence of the lowest content of the NiO nanoparticles, i.e. 20 mg, and in the presence of the highest content of the NiO nanoparticles, i.e. 35 mg, are not considered for the calculation of AARE since these data have been matched previously by the model to develop eqs. 10-12. In general, the ARE values are smaller than that of the predictions in the presence of the Fe3O4 nanoparticles. This may indicate better predictions of the asphaltene onset in the presence of NiO compared to that in the presence of Fe3O4. In addition, the AARE values of the CPA EoS predictions is 3.21, 6.03, and 4.30% for the exponential, quadratic, and linear relations of the asphaltene self-association energy with the mol number of the NiO surface sites. As a result, the exponential form of the dependency of the asphaltene selfassociation energy to the mol number of the surface sites of the both types of the nanoparticles exhibits the best predictions. Therefore, in the following, this exponential dependency is used for the prediction of the asphaltene onset in the presence of any amount of the nanoparticles. Table 5.The absolute relative error (ARE) and average absolute relative error (AARE) in the prediction of the asphaltene onset point in the presence of the NiO Nanoparticles by the CPA EoS. Exponential


Quadratic

Linear

Page 17 of 23

Nanoparticles content (g)

Experimental

CPA

ARE (%)

CPA

ARE (%)

CPA

ARE (%)

0.022

30.5

29.52

3.36

31.38

2.76

30.09

1.49

0.025

32.7

32.19

1.59

34.21

4.56

32.91

0.59

0.028

35.0

35.52

1.38

37.48

6.96

36.40

3.89

0.030

38.0

39.60

4.24

41.19

8.43

40.66

7.05

0.032

42.2

44.49

5.50

45.30

7.42

45.75

8.50

AARE= 3.21%

AARE= 6.03%

AARE= 4.30%

Figure 3 illustrates the CPA EoS predictions and experimental values of the asphaltene onset points in the presence of the Fe3O4 and NiO nanoparticles. It is worthy to mention that the onset data that have previously been matched by the model to develop eqs. 10-12 are not presented in Fig. 3. As observed, the CPA EoS predictions are in good agreement with the corresponding experimental data. Therefore, the exponential dependency of the asphaltene self-association to the concentration of the nanoparticles surface sites reproduces the experimental data of the asphaltene onset with a reasonable accuracy. The accuracy of the predictions is worsened when the linear or quadratic form of the dependency is considered for the asphaltene self-association. 100 90 (a) 80 70 60 50 40 30 20 10 0 0.014 0.016

Experimental data CPA predictions

100 90 (b) Experimental data 80 CPA predictions 70 60 50 40 30 20 10 0 0.02 0.0225 0.025 0.0275 0.03 0.0325 0.035

Heptane vol.%

Heptane vol.%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.018

0.02

0.022

Fe3O4 nanoparticles content (g)

NiO nanoparticles content (g)

Figure 3. Comparison of the experimental values of the asphaltene precipitation onset with the CPA EoS predictions in the presence of (a) the Fe3O4 and (b) the NiO nanoparticles.

Finally, the CPA EoS was applied to predict the asphaltene onset point over a broad range of nanoparticles concentrations and the results are shown in Fig. 4. At low mass ratios of both the nanoparticles, there is no significant impact on the inhibition of the asphaltene precipitation. However, when the content of the Fe3O4 nanoparticles in the model oil reaches 10 mg, an effective control of the asphaltene onset is observed. In



addition, as observed in Fig. 4, the addition of 25 mg of the Fe3O4 prevents the asphaltene precipitation since around 100 vol% of n-C7 is required for the model oil to approach the asphaltene onset point. In the presence of NiO, the control of the asphaltene precipitation is done effectively when the nanoparticles content of the model oil is higher than 20 mg. In addition, the control of the asphaltene precipitation onset by the addition of the NiO nanoparticles is done with a slow pace. For instance, around 50 mg of the NiO are required to prevent the asphaltene precipitation in the model oil with 100 vol% of the n-heptane. This is another confirmation of the higher activity of the Fe3O4, compared to that of the NiO, for the asphaltene precipitation control. As a conclusion, the CPA EoS is capable of predicting the asphaltene precipitation onset in the presence of the nanoparticles when the molar density and strength of the surface sites of the nanoparticles are characterized. Therefore, the CPA EoS has the potential to be employed for designing tailor-made chemical inhibitors for asphaltene precipitation using the nanoparticles. 100

100

(a) Heptane vol. %

80 Heptane vol.%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 23

60 40 20

(b)

80 60 40 20

0

0 0

0.01

0.02

0.03

0

Fe3O4 nanoparticles content (g)

0.02

0.04

0.06

NiO nanoparticles content (g)

Figure 4. The prediction of the asphaltene precipitation onset with the CPA EoS in the presence of (a) the Fe3O4 and (b) the NiO nanoparticles.

4. CONCLUSIONS Cubic plus association equation of state (CPA EoS) was employed to model the asphaltene precipitation in reservoir model oils in the presence and absence of the Fe3O4 and NiO nanoparticles. In the absence of the nanoparticles, the asphaltene-toluene across-association energy is tuned by matching the experimental data of the asphaltene precipitation onset. The CPA EoS, with the tuned parameters, determines the precipitate amount of the asphaltene by knowing the asphaltene average aggregate size in the oil, determined by dynamic light scattering. Then, the CPA EoS was extended to predict the asphaltene precipitation in the presence of the Fe3O4 and NiO nanoparticles as the candidates of the asphaltene precipitation inhibitor. The nanoparticles have a significant impact on the asphaltene self-association energy. Therefore, the


Page 19 of 23 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


dependency of the self-association energy of the asphaltene on the molar density of the surface sites of the nanoparticles was inferred through matching the experimental data of the asphaltene onset in the presence of the nanoparticles. The results indicate that the asphaltene self-association energy changes with the molar density of the surface sites of both the nanoparticles by exponential relationships. As the mass ratio of the nanoparticles to the asphaltene in the model oil passes the values of 0.0625 for Fe3O4 and 0.125 for NiO, no asphaltene precipitation occurs in the system. The average absolute relative error of the CPA EoS predictions of the asphaltene onset in the presence of the Fe3O4 and NiO nanoparticles is 6.57 and 3.21%, respectively. This indicates the capability of the CPA EoS for the prediction of the asphaltene behavior in the presence of the nanoparticles. As a conclusion, via the proposed approach, the CPA EoS can be employed for designing chemical inhibitors of the asphaltene precipitation by the metal oxides nanoparticles. AUTHOR INFORMATION *Corresponding Author E-mail address: [email protected] (N. Hosseinpour) Tel: +98 (21) 61114712, Fax: +98 (21) 88632976 REFERENCES (1) Headen, T. F.; Boek, E. S.; Jackson, G.; Totton, T. S.; Müller, E. A., Simulation of Asphaltene Aggregation through Molecular Dynamics: Insights and Limitations. Energy & Fuels 2017, 31, (2), 11081125. (2) Hemmati-Sarapardeh, A.; Dabir, B.; Ahmadi, M.; Mohammadi, A. H.; Husein, M. M., Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling. 0, (0). (3) Speight, J., Petroleum Asphaltenes-Part 1: Asphaltenes, resins and the structure of petroleum. Oil & gas science and technology 2004, 59, (5), 467-477. (4) Hemmati-Sarapardeh, A.; Dabir, B.; Ahmadi, M.; Mohammadi, A. H.; Husein, M. M., Toward mechanistic understanding of asphaltene aggregation behavior in toluene: The roles of asphaltene structure, aging time, temperature, and ultrasonic radiation. Journal of Molecular Liquids 2018, 264, 410-424. (5) Zhang, X.; Pedrosa, N.; Moorwood, T., Modeling Asphaltene Phase Behavior: Comparison of Methods for Flow Assurance Studies. Energy & Fuels 2012, 26, (5), 2611-2620. (6) Mullins, O. C.; Sheu, E. Y.; Hammami, A.; Marshall, A. G., Asphaltenes, heavy oils, and petroleomics. Springer Science & Business Media: 2007. (7) Merino-Garcia, D., Calorimetric investigations of asphaltene self-association and interaction with resins. Technical University of Denmark, Department of Chemical EngineeringTechnical University of Denmark, Department of Chemical Engineering 2004. (8) Siffert, B.; Kuczinski, J.; Papirer, E., Relationship between electrical charge and flocculation of heavy oil distillation residues in organic medium. Journal of colloid and interface science 1990, 135, (1), 107-117. (9) Wiehe, I.; Liang, K., Asphaltenes, resins, and other petroleum macromolecules. Fluid Phase Equilibria 1996, 117, (1-2), 201-210.



(10) Ghanavati, M.; Shojaei, M.-J.; S. A, A. R., Effects of Asphaltene Content and Temperature on Viscosity of Iranian Heavy Crude Oil: Experimental and Modeling Study. Energy & Fuels 2013, 27, (12), 7217-7232. (11) Yan, J.; Plancher, H.; Morrow, N., Wettability changes induced by adsorption of asphaltenes. SPE Production & Facilities 1997, 12, (04), 259-266. (12) Kamath, V.; Yang, J.; Sharma, G. In Effect of asphaltene deposition on dynamic displacements of oil by water, SPE Western Regional Meeting, 1993; Society of Petroleum Engineers: 1993. (13) Fanchi, J. R., General Engineering. In Petroleum Engineering Handbook, Lake, L. W., Ed. Society of Petroleum Engineers: 2006; Vol. I. (14) Mansoori, G. A., Remediation of asphaltene and other heavy organic deposits in oil wells and in pipelines. Socar proceedings 2010, 2010, (4), 12-23. (15) Hemmati-Sarapardeh, A.; Varamesh, A.; Husein, M. M.; Karan, K., On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment. Renewable and Sustainable Energy Reviews 2018, 81, 313-329. (16) Hosseinpour, N.; Khodadadi, A. A.; Bahramian, A.; Mortazavi, Y., Asphaltene Adsorption onto Acidic/Basic Metal Oxide Nanoparticles toward in Situ Upgrading of Reservoir Oils by Nanotechnology. Langmuir 2013, 29, (46), 14135-14146. (17) Nassar, N. N., Asphaltene adsorption onto alumina nanoparticles: kinetics and thermodynamic studies. Energy & Fuels 2010, 24, (8), 4116-4122. (18) Nassar, N. N.; Hassan, A.; Pereira-Almao, P., Application of Nanotechnology for Heavy Oil Upgrading: Catalytic Steam Gasification/Cracking of Asphaltenes. Energy & Fuels 2011, 25, (4), 15661570. (19) Nassar, N. N.; Hassan, A.; Pereira-Almao, P., Comparative oxidation of adsorbed asphaltenes onto transition metal oxide nanoparticles. Colloids and surfaces A: Physicochemical and Engineering aspects 2011, 384, (1-3), 145-149. (20) Nassar, N. N.; Hassan, A.; Pereira-Almao, P., Metal oxide nanoparticles for asphaltene adsorption and oxidation. Energy & Fuels 2011, 25, (3), 1017-1023. (21) Nassar, N. N.; Hassan, A.; Pereira-Almao, P., Effect of surface acidity and basicity of aluminas on asphaltene adsorption and oxidation. Journal of colloid and interface science 2011, 360, (1), 233-238. (22) Hosseinpour, N.; Mortazavi, Y.; Bahramian, A.; Khodatars, L.; Khodadadi, A. A., Enhanced pyrolysis and oxidation of asphaltenes adsorbed onto transition metal oxides nanoparticles towards advanced in-situ combustion EOR processes by nanotechnology. Applied Catalysis A: General 2014, 477, 159-171. (23) Franco, C.; Patiño, E.; Benjumea, P.; Ruiz, M. A.; Cortés, F. B., Kinetic and thermodynamic equilibrium of asphaltenes sorption onto nanoparticles of nickel oxide supported on nanoparticulated alumina. Fuel 2013, 105, 408-414. (24) Biyouki, A. A.; Hosseinpour, N.; Bahramian, A.; Vatani, A., In-situ upgrading of reservoir oils by in-situ preparation of NiO nanoparticles in thermal enhanced oil recovery processes. Colloids and Surfaces A: Physicochemical and Engineering Aspects 2017, 520, 289-300. (25) Igder, M.; Hosseinpour, N.; Biyouki, A. A.; Bahramian, A., Control of Asphaltene Aggregation in Reservoir Model Oils along the Production Streamline by Fe3O4 and NiO Nanoparticles. Energy & Fuels 2018. (26) Li, Z.; Firoozabadi, A., Cubic-plus-association equation of state for asphaltene precipitation in live oils. Energy & Fuels 2010, 24, (5), 2956-2963. (27) Powers, D.; Sadeghi, H.; Yarranton, H.; Van Den Berg, F., Regular solution based approach to modeling asphaltene precipitation from native and reacted oils: Part 1, molecular weight, density, and solubility parameter distributions of asphaltenes. Fuel 2016, 178, 218-233. (28) Yarranton, H.; Powers, D.; Okafor, J.; van den Berg, F., Regular solution based approach to modeling asphaltene precipitation from native and reacted oils: Part 2, molecular weight, density, and solubility parameter of saturates, aromatics, and resins. Fuel 2018, 215, 766-777.


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(29) Sabbagh, O.; Akbarzadeh, K.; Badamchi-Zadeh, A.; Svrcek, W.; Yarranton, H., Applying the PREoS to asphaltene precipitation from n-alkane diluted heavy oils and bitumens. Energy & fuels 2006, 20, (2), 625-634. (30) Vafaie-Sefti, M.; Mousavi-Dehghani, S. A.; Mohammad-Zadeh, M., A simple model for asphaltene deposition in petroleum mixtures. Fluid Phase Equilibria 2003, 206, (1-2), 1-11. (31) Sabeti, M.; Rahimbakhsh, A.; Nikookar, M.; Mohammadi, A. H., Estimation of asphaltene precipitation and equilibrium properties of hydrocarbon fluid phases using the PC-SAFT equation of state. Journal of Molecular Liquids 2015, 209, 447-460. (32) Meighani, H. M.; Ghotbi, C.; Behbahani, T. J.; Sharifi, K., Evaluation of PC-SAFT model and Support Vector Regression (SVR) approach in prediction of asphaltene precipitation using the titration data. Fluid Phase Equilibria 2018, 456, 171-183. (33) Mohebbinia, S.; Sepehrnoori, K.; Johns, R. T.; Korrani, A. K. N., Simulation of asphaltene precipitation during gas injection using PC-SAFT EOS. Journal of Petroleum Science and Engineering 2017, 158, 693-706. (34) AlHammadi, A. A.; Vargas, F. M.; Chapman, W. G., Comparison of cubic-plus-association and perturbed-chain statistical associating fluid theory methods for modeling asphaltene phase behavior and pressure–volume–temperature properties. Energy & Fuels 2015, 29, (5), 2864-2875. (35) Li, Z.; Firoozabadi, A., Modeling asphaltene precipitation by n-alkanes from heavy oils and bitumens using cubic-plus-association equation of state. Energy & Fuels 2010, 24, (2), 1106-1113. (36) Shirani, B.; Nikazar, M.; Mousavi-Dehghani, S. A., Prediction of asphaltene phase behavior in live oil with CPA equation of state. Fuel 2012, 97, (Supplement C), 89-96. (37) Arya, A.; von Solms, N.; Kontogeorgis, G. M., Determination of asphaltene onset conditions using the cubic plus association equation of state. Fluid Phase Equilibria 2015, 400, (Supplement C), 8-19. (38) Arya, A.; Liang, X.; von Solms, N.; Kontogeorgis, G. M., Prediction of Gas Injection Effect on Asphaltene Precipitation Onset Using the Cubic and Cubic-Plus-Association Equations of State. Energy & Fuels 2017, 31, (3), 3313-3328. (39) Arya, A.; Liang, X.; von Solms, N.; Kontogeorgis, G. M., Modeling of Asphaltene Onset Precipitation Conditions with Cubic Plus Association (CPA) and Perturbed Chain Statistical Associating Fluid Theory (PC-SAFT) Equations of State. Energy & Fuels 2016, 30, (8), 6835-6852. (40) Arya, A.; von Solms, N.; Kontogeorgis, G. M., Investigation of the Gas Injection Effect on Asphaltene Onset Precipitation Using the Cubic-Plus-Association Equation of State. Energy & Fuels 2015, 30, (5), 3560-3574. (41) Arya, A.; Liang, X.; von Solms, N.; Kontogeorgis, G. M., Modeling of Asphaltene Precipitation from Crude Oil with the Cubic Plus Association Equation of State. Energy & Fuels 2017, 31, (2), 20632075. (42) Sattari, M.; Abedi, J.; Zirrahi, M.; Mehrotra, A. K. In Modeling The Onset Of Asphaltene Precipitation In Solvent-Diluted Bitumens Using Cubic-Plus-Association Equation Of State, SPE Canada Heavy Oil Technical Conference, 2016; Society of Petroleum Engineers: 2016. (43) Soave, G., Equilibrium constants from a modified Redlich-Kwong equation of state. Chemical Engineering Science 1972, 27, (6), 1197-1203. (44) Peng, D.-Y.; Robinson, D. B., A new two-constant equation of state. Industrial & Engineering Chemistry Fundamentals 1976, 15, (1), 59-64. (45) Chapman, W. G.; Gubbins, K. E.; Jackson, G.; Radosz, M., New reference equation of state for associating liquids. Industrial & Engineering Chemistry Research 1990, 29, (8), 1709-1721. (46) Chapman, W. G.; Gubbins, K. E.; Jackson, G.; Radosz, M., SAFT: Equation-of-state solution model for associating fluids. Fluid Phase Equilibria 1989, 52, 31-38. (47) Gray, M., From Black to Gold: Nanotechnology in Upgrading of Heavy Asphaltic Crude Oils. In SPE Distinguished Lecturer Program, 2010. (48) Mullins, O. C.; Seifert, D. J.; Zuo, J. Y.; Zeybek, M., Clusters of Asphaltene Nanoaggregates Observed in Oilfield Reservoirs. Energy & Fuels 2013, 27, (4), 1752-1761.



(49) Michelsen, M. L.; Hendriks, E. M., Physical properties from association models. Fluid phase equilibria 2001, 180, (1-2), 165-174. (50) Kontogeorgis, G. M.; Voutsas, E. C.; Yakoumis, I. V.; Tassios, D. P., An equation of state for associating fluids. Industrial & engineering chemistry research 1996, 35, (11), 4310-4318. (51) Huang, S. H.; Radosz, M., Equation of state for small, large, polydisperse, and associating molecules. Industrial & Engineering Chemistry Research 1990, 29, (11), 2284-2294. (52) Firoozabadi, A., Thermodynamics of hydrocarbon reservoirs. 1st ed.; McGraw-Hill: USA, 1999. (53) Kokal, S. L.; Najman, J.; Sayegh, S. G.; George, A. E., Measurement and correlation of asphaltene precipitation from heavy oils by gas injection. Journal of Canadian Petroleum Technology 1992, 31, (04). (54) Hirschberg, A.; DeJong, L.; Schipper, B.; Meijer, J., Influence of temperature and pressure on asphaltene flocculation. Society of Petroleum Engineers Journal 1984, 24, (03), 283-293. (55) Godbole, S.; Thele, K.; Reinbold, E., EOS modeling and experimental observations of threehydrocarbon-phase equilibria. SPE Reservoir Engineering 1995, 10, (02), 101-108. (56) Gonzalez, K.; Nasrabadi, H.; Barrufet, M., Modeling asphaltene precipitation in a compositional reservoir simulator using three-phase equilibrium. Journal of Petroleum Science and Engineering 2017, 154, 602-611. (57) Nghiem, L. X.; Coombe, D. A., Modelling asphaltene precipitation during primary depletion. SPE Journal 1997, 2, (02), 170-176. (58) Smith, J. M.; Van Ness, H. C.; Abbott, M. M., Introduction to Chemical Engineering Thermodynamics. McGraw-Hill: 2004. (59) Badre, S.; Carla Goncalves, C.; Norinaga, K.; Gustavson, G.; Mullins, O. C., Molecular size and weight of asphaltene and asphaltene solubility fractions from coals, crude oils and bitumen. Fuel 2006, 85, (1), 1-11. (60) Franco, C. A.; Lozano, M. M.; Acevedo, S.; Nassar, N. N.; Cortés, F. B., Effects of Resin

I on Asphaltene Adsorption onto Nanoparticles: A Novel Method for Obtaining Asphaltenes/Resin Isotherms. Energy & Fuels 2016, 30, (1), 264-272. (61) Buenrostro‐Gonzalez, E.; Lira‐Galeana, C.; Gil‐Villegas, A.; Wu, J., Asphaltene precipitation in crude oils: Theory and experiments. AIChE Journal 2004, 50, (10), 2552-2570. (62) Edmonds, B.; Moorwood, R.; Szczepanski, R.; Zhang, X.; Heyward, M.; Hurle, R. In Measurement and prediction of asphaltene precipitation from live oils, 3rd International Symposium on Colloid Chemistry in Oil Production, Asphaltenes and Waxes Deposition, 1999; Citeseer: 1999.


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Table of Contents (TOC) Graphic

Nanoparticles (the surface sites of the nanoparticles interact with the asphaltenes, modifying the asphaltene self-association)

Toluene

Asphaltene

(two association sites)

(five association sites)

Toluene-Asphaltene Cross-Association


Prediction of Asphaltene Precipitation in Reservoir Model Oils in the

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