Microfluidic Approach for Evaluating the Solubility of Crude Oil

Feb 19, 2016 - ... content measurements for 52 crude oil samples, spanning a range of 0–15 wt %. ..... (74) The solubility parameter of heptane is 1...
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Microfluidic Approach for Evaluating the Solubility of Crude Oil Asphaltenes Vincent J. Sieben, Asok Kumar Tharanivasan, Simon I. Andersen, and Farshid Mostowfi* DBR Technology Center, Schlumberger Canada, Limited, 9450 17th Avenue, Edmonton, Alberta T6N 1M9, Canada ABSTRACT: In this paper, we describe a microfluidic approach for measuring the solubility of asphaltenes in a sample of crude oil. The solubility parameter is an important property in assessing stability of asphaltenes in crude oils and crude mixtures, particularly when blending different oils or adding diluents. A range of solvent−precipitant mixtures are added to the crude oil, which modifies the native solubility properties, and the degree of asphaltene precipitation is monitored by the change in optical absorbance for each chosen solvent volume fraction. The microfluidic solubility profiles acquired in hours are compared to conventional gravimetric measurements obtained over days and demonstrate excellent agreement. We also show the application of the data generated for tuning solubility parameter-based thermodynamic models of asphaltene precipitation in mixtures of solvents and precipitants. The microfluidic data were used to determine the asphaltene solubility parameter that ranged from 20 to 23 MPa1/2 for the crude oils used in this study, in agreement with previous reports. The more efficient use of labor and the reduction in measurement time enabled by the microfluidic method will allow for more frequent asphaltene characterization for evaluating stability and tuning models.

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INTRODUCTION Microfluidics have been used to study fluid phenomena in the petroleum industry for decades,1−3 and recently, there has been growing interest in miniaturizing testing approaches.4 Microfluidic technology can serve as a platform for improved sensing of reservoir fluids in laboratories and at the well site, making routine characterization measurements more accessible. A variety of analytical tests on reservoir fluids have been performed successfully using microfluidic systems, including interfacial studies,5 chemical composition,6−9 process evaluation,10−12 phase behavior, and pressure, volume, and temperature (PVT) studies.13−16 One particular nuisance in the oil and gas industry is the precipitation and deposition of the asphaltene fraction of crude oil in wellbores, pipelines, and refineries. Microfluidics offer an alternate platform for the timely characterization of asphaltene behavior in both up- and downstream operations. Microfluidics offer a high-throughput approach for studying asphaltene solubility. In 2006, Bowden et al. performed one of the earliest separations of asphaltenes within a microfluidic chip.6,17 They used a H-cell microfluidic platform to fractionate hydrocarbons, where the partitioning of asphaltenes from oil was accomplished in seconds and in a continuous fashion by mixing crude oil with hexane. The microfluidic process removed high-molecular-weight species from the apolar components, and the maltenes were subsequently passed to a gas chromatography (GC) system for analysis.17 Bowden et al. evaluated the light hydrocarbon fractions of the oil (free of asphaltenes); however, they noted the potential utility of microfluidics to study asphaltene solubility. In 2009, Bowden et al. added ultraviolet−visible (UV−vis) spectroscopy to their Hcell microfluidic setup to measure the asphaltene and carboxylic acid contents.6 In 2013, Kharrat et al. published an UV−vis absorbance approach to determine the asphaltene content, comparing the optical data to the conventional ASTM D6560 (IP 143) method for 26 crude oil samples.18 The large and © XXXX American Chemical Society

geographically diverse data set showed a linear correlation between optical absorbance and asphaltene content. In 2013, Schneider et al. combined the optical technique with a microfluidic platform that incorporated on-chip asphaltene filtration.7 An asphaltene content measurement could be made in less than 30 min, as opposed to days. In 2013, Sieben et al. published a continuation of this work and showed microfluidic asphaltene content measurements for 52 crude oil samples, spanning a range of 0−15 wt %.8 In 2014, Hu and Hartman utilized microfluidics to investigate deposition of asphaltenes in porous media or packed bed reactors (PBR).9,10 By coupling optical analysis (UV-Vis) to a micro-PBR, they were able to study the influence of Reynolds number on asphaltene deposition in a rapid manner. In 2015, Sieben et al. demonstrated the measurement of the asphaltene yield data by varying tirant/oil ratios and monitoring the fractional asphaltene precipitation using a microfluidic approach.19 Over the past decade, microfluidics have been successfully applied for the separation of asphaltenes, the measurement of the asphaltene content, and, recently, the study of asphaltene aggregation and deposition behavior. The asphaltene fraction of crude oil consists of a wide distribution of molecules in terms of size and structure of alkylated polycyclic aromatic species containing heteroatoms, such as sulfur, oxygen, and nitrogen, as well as metals, such as vanadium and nickel.20,21 They are the heaviest, most high boiling, and most polar fraction of a crude oil.22,23 Asphaltenes tend to deposit onto various surfaces, showing almost irreversible adhesion to solid surfaces, such as silica and alumina; metal surfaces are also prone to strong asphaltene adhesion, potentially obstructing flow in pipelines.24 AsphalReceived: September 25, 2015 Revised: December 18, 2015

A

DOI: 10.1021/acs.energyfuels.5b02216 Energy Fuels XXXX, XXX, XXX−XXX

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species that self-associate, the enthalpy of vaporization and the molar volume are difficult to measure. Therefore, indirect measurements, such as titration techniques, can be used to estimate the solubility parameter of asphaltenes by fitting models to the experimental data. Solubility data may be obtained by carrying out a series of titration experiments on a stock tank oil using solvent− precipitant mixtures. 48−54 Many different principles of detection of solubility or precipitation have been reported, including visual observation,55 UV−vis absorption,56,57 fluorescence spectroscopy,49 near-infrared (NIR) transmittance,58 and light scattering/turbidity, along with filtration,59 acoustic resonance,60 conductivity,61 viscosity,62 and conventional gravimetric63 approaches. At the critical solubility parameter, δCR, the least soluble asphaltenes begin to precipitate in a given mixture of precipitant, solvent, and crude oil. This condition is also commonly known as the asphaltene precipitation onset. Higher percentages of precipitant induce more asphaltenes to precipitate, including the more soluble asphaltene subfractions. The critical solubility parameter is usually required to assess the type of diluent added to the oil to reduce the asphaltene precipitation issues. To calculate the solubility parameter of the mixture at onset, one uses the volume-weighted average of the individual solubility parameters as follows:32

tenes may precipitate and deposit within formations, wellbores, pipelines, and refineries. Also, asphaltenes can lead to tight water-in-oil emulsions, hindering separation of gas/oil and water in the field. Cost associated with asphaltene mitigation and remediation can be staggering, on the order of billions worldwide.25−28 Asphaltenes become unstable when the solubility or solvent power of the surrounding medium changes. This change in solubility power of the medium could be triggered by a change in the pressure and/or temperature, such as during depletion and production of a reservoir, or a change in composition arising from solvent injection for enhanced oil recovery (EOR) or gas-lift operations or via the commingling of dissimilar crudes in wellbores, pipelines, and at the front end of refineries. Optimization of flow assurance through the oil production network requires thermodynamic models that accurately describe asphaltene phase behavior to identify risk and avoid unwanted precipitation. Solubility analysis is used in the petroleum industry as a guideline to evaluate the stability and compatibility of oil constituents, often when samples are mixed with diluents or when commingled with other oil mixtures.29−32 The asphaltene fraction was initially defined using a solvent separation technique, pioneered by Boussingault in 1837 and refined by Nellensteyn in the 1920s and 1930s.33−35 At present, asphaltenes are conventionally defined as a solubility class of material, being poorly soluble in alkanes (e.g., n-heptane) and highly soluble in aromatic solvents (e.g., toluene).36 Measured asphaltene solubility profiles support thermodynamic equilibrium modeling approaches based on regular solution theory, Flory−Huggins theory, and statistical associating fluid theory (SAFT), along with cubic equations of state (EOSs).30,37−41 These thermodynamic-based approaches and models help explain asphaltene behavior when crude oils undergo physical and or chemical changes.42,43 However, because the chemical nature of asphaltenes is both ill-defined and often varies significantly across reservoirs, thermodynamic models must be calibrated to specific production scenarios and locations using at least a few accurate and specific measurements. Consistent and reliable measurement techniques that report asphaltene solubility profiles are critically important in managing flow assurance issues and planning production processes, such as gas and solvent injection. Solvent separation techniques coupled with regular solution theory describe the solubility of asphaltenes and characterize the conditions of instability, through the application of the Hildebrand solubility parameter.42,44,45 The definition of the solubility parameter is given by Hildebrand46 in terms of cohesive energy of the liquid and molar volume. Physically, the extent of miscibility of two components depends upon the closeness of the solubility parameters. At temperatures below the normal boiling point and low pressures, the expression for the solubility parameter is given by47 δ=

ΔH − RT ν

δCR =

VPδ P + VSδS + VOδO VP + VS + VO

(2)

where VP, VS, and VO are the volumes of precipitant, solvent, and oil required at the precipitation onset, with δP, δS, and δO being the associated Hildebrand solubility parameters, respectively. If the volume of solvent plus precipitant far exceeds the volume of oil (VP + VS ≫ VO), such as the 40:1 ratio that we use in this paper, the critical solubility parameter of the mixture can be calculated in terms of the solvent volume fraction in the solvent−precipitant mixture (φ) as in eqs 3 and 4. An asphaltene solubility profile can be generated by measuring the amount of asphaltene precipitation versus the solvent volume fraction, ranging from φ = 1 (pure solvent) to φ = 0 (pure precipitant). δCR = φ(δS − δ P) + δ P

φ=

VS VP + VS

(3)

(4)

The experimental effort necessary to determine an asphaltene solubility profile through multiple measurements of the asphaltene content may not always be practical or economical. Several conventional measurement strategies have evolved to acquire asphaltene solubility profiles.43,51−54 A typical experiment will involve manual preparation of solvent/precipitant mixtures and manual mixing with crude oil or asphaltenes. A frequently reported procedure is the precipitation of asphaltenes from whole crude oil by the addition of a mixed solvent (e.g., heptane−toluene). The steps involved are similar to those used in a modified IP 143 method or ASTM D656036 for quantifying the asphaltene content but substituting a mixed solvent in place of heptane.48−50 The separation and filtration aspects for partitioning solid asphaltenes as well as the recording of fractional precipitation (e.g., gravimetrically, optically, etc.) are also manual operations. Asphaltene separation and quantification are highly dependent upon the procedure. Subtle differences in methodology, for example,

(1)

where ΔH, R, T, and v represent the enthalpy of vaporization, the universal gas constant, the absolute temperature, and the molar volume, respectively. For a pure component, such as simple hydrocarbons, the solubility parameter can be easily calculated, because all parameters in eq 1 are known. However, for a complex mixture of high boiling compounds, such as asphaltenes, which encompass thousands of different molecular B

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Figure 1. (a) Conceptual diagram for measuring asphaltene solubility in solvent mixtures using an automated microfluidic apparatus. The symbols V and P represent valves and pressure sensors. (b) Photograph of the system. (c) Computer-rendered microfluidic chip setup in exploded view. When assembled, the porous membrane is compressed between the filtration microfluidic chip and the chip holder collection channels.

previous work7,8 and by others,65 where the coloration of the asphaltene−solvent mixture has been linearly correlated with the asphaltene weight content. In a typical microfluidic solubility measurement, we use toluene as the solvent and nheptane as the precipitant. Toluene and n-heptane mixtures can be rapidly generated and combined with the oil sample, filtering out the fraction of precipitated asphaltenes and enabling precipitation yields to be profiled. The rapid and repeatable results obtained by this automated system validate that microfluidic devices can improve routine characterization of the asphaltene solubility behavior. These data can then be used in the development of a thermodynamic model for a given petroleum system to tune the properties of the asphaltene component. We will show the use of this technology to generate solubility curves for asphaltenes and demonstrate the application of these data for tuning two thermodynamic models: the simple Flory−Huggins approach initially proposed by Hirschberg et al.42 as well as a more elaborate multicomponent model described by Yarranton et al.53 The two models are described in the Theory section, along with their

which solvents are used, the ratio between solvent and oils, the time involved in precipitation and aggregation, and the type and extent of washing,63,64 can generate notable differences in measurements. Often modifications are required in washing the precipitate, which cannot be performed by reflux in blends of solvent/precipitants with different boiling points. Therefore, creating a precipitation versus solvent composition profile comprised of 10−20 discrete points can easily take days or weeks to complete, testing solvent volume fractions that range from φ = 1 (pure solvent) to φ = 0 (pure precipitant). Furthermore, these experiments usually require knowledgeable specialists and consume dozens of liters of solvent and a substantial amount of crude oil. In this paper, we show that microfluidic approaches can be used to address the experimental bottleneck associated with repetitive precipitation experiments, which tend to be long and tedious. The process of measuring asphaltene solubility in various solvent mixtures is shown in Figure 1 and is described more in the Materials and Methods section. The underlying principles of optical detection have been addressed both in our C

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where f values are the fugacities, superscript 0 refers to the standard states, superscript H refers to the heavy phase, and superscript L refers to the light phase. Assuming that only asphaltenes are partitioned to the heavy phase, then ϕH1 = 0 and ϕH2 = 1 and the left sides of eqs 9 and 10 are reduced to zero. Equation 9 can be further simplified to the original Hirschberg equation by redefining the component properties as

asphaltene solubility parameters, which are tuned to match experimental data acquired using our microfluidic approach.

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THEORY Hirschberg Model. The Hirschberg approach is one of the earliest and most cited thermodynamic models to describe asphaltene solubility in hydrocarbons.42 The model is based on the Flory−Huggins theory (eq 5) combined with the regular solution or Scatchard−Hildebrand theory (eq 6).66−69 The Flory−Huggins theory was originally proposed to describe mixtures with a considerable size difference between molecules of solute (polymers) and solvent, a reasonable assumption with asphaltenes in hydrocarbons. To use this theory for the solutions of asphaltenes in hydrocarbon mixtures, the asphaltenes are treated as a separate liquid phase, which is consistent with models developed for amorphous solids with negligible enthalpy of fusion.43 According to the Flory− Huggins solution theory, the free energy change upon mixing is20 ΔGmix = RT (n1 ln ϕ1 + n2 ln ϕ2 + n1ϕ2χ ) χ=

ν1 (δ1 − δ2)2 RT

⎧ν ⎡ ⎤⎫ ν ν (ϕ2L)max = (ϕa)max = exp⎨ a ⎢1 − L − L (δa − δ L)2 ⎥⎬ RT νa ⎦⎭ ⎩ νL ⎣

(12)

νL = x1Lν1L + x 2Lν2L = (xSνS + x PνP + xmνm) + xaνa

(8)

Hirschberg assumed a liquid−liquid asphaltene equilibrium exists between an asphaltene-rich phase and a solvent-rich phase. We call the asphaltene-rich phase the “heavy phase” and the solvent-rich phase the “light phase”. When the mixture is in equilibrium, the fugacities of each component in the existing phases are equal. Also, in liquid−liquid equilibrium, the activities can be equated conveniently using the same standard state fugacity for both heavy and light phases.71 For the asphaltene component ln

f2H f 20H

= ln

f2L f 20L

νL = xSνS + x PνP = xSνS + (1 − xS)νP xS =

(14)

φ φ+

νS (1 νP

− φ)

(15)

Molar volumes for the solvents and precipitants were acquired from tabulated data sets.73 The molar volume of heptane is 147.45 cm3/mol at 25 °C and 148.38 cm3/mol at 30 °C. The molar volume of pentane is 117.16 cm3/mol at 30 °C. The molar volume of toluene is 107.44 cm3/mol at 30 °C, and the molar volume of dichloromethane (DCM) is 64.54 cm3/mol at 25 °C. The solubility parameter of the light liquid phase is determined from the volumetric mixing rule in eq 3. Heptane, pentane, toluene, and DCM solubility parameters can be directly acquired from tabulated values with appropriate

→ ln a 2H = ln a 2L (9)

⎛ νH ⎞ νH ln ϕ2H + ⎜1 − 2H ⎟ϕ1H + χ H 2H (ϕ1H)2 ν1 ⎠ ν1 ⎝ ⎛ νL ⎞ νL = ln ϕ2L + ⎜1 − 2L ⎟ϕ1L + χ L 2L (ϕ1L)2 ν1 ⎠ ν1 ⎝

(13)

where νa is the average molar volume of asphaltenes, νL is the average molar volume of the entire light liquid phase, δa is the average solubility parameter of the asphaltenes, and δL is the average solubility parameter of the entire light liquid phase. Subscript S refers to added solvent, and subscript P refers to added precipitant, both of which are predetermined according to the solvent volume fraction φ in eq 4. Subscript m refers to maltenes (oil-component-less asphaltenes), and subscript a refers to asphaltenes. In eq 13, x is the mole fraction of the various components. For our experiments, the added volume of solvent and precipitant mixture is always 40 times greater than the oil volume (maltenes and asphaltenes). Thus, eqs 12 and 13 can be simplified as δL ≈ ϕSδS + ϕPδP (equivalent to eq 3) and νL ≈ xSνS + xPνP. Using eq 11, the volume fraction of soluble asphaltenes can now be determined from four average parameters: the molar volume and solubility parameter of the asphaltenes and the molar volume and solubility parameter of the light liquid phase. At the measured onset point, the volume fraction of asphaltenes in the light phase is at the maximum. Therefore, the solubility parameter of asphaltenes at the onset can be calculated by assuming (ϕa)max is related to the total content of asphaltenes in the mixture.42 Then, eq 11 can be used to calculate the average solubility parameter of asphaltenes as a function of the solvent volume fraction based on an estimate of the otherwise unknown average molar volume of the asphaltenes, νa. We assume an arbitrary average molar volume of 1000 cm3/mol for asphaltenes,72 while Hirschberg et al. assumed 4000 cm3/mol.42 The average molar volume of the light liquid phase is determined as a function of the solvent volume fraction, as follows:

where n, ϕ, v, and δ represent the number of moles, the volume fraction, the molar volume, and the solubility parameter, respectively. The subscript 1 refers to the solvent (maltenes plus added solvents in our case), and the subscript 2 refers to the polymer (asphaltenes in our case). The Flory−Huggins interaction parameter shown in eq 6, χ, is based on the Scatchard−Hildebrand approximation, which assumes that there are no specific interactions between components. Physically, this parameter determines the phase separation of asphaltenes. Through partial differentiation of eq 5 with respect to the components, the activities of the solvent (eq 7) and polymer (eq 8) are70,71

⎛ ν ⎞ ν ln a 2 = ln ϕ2 + ⎜1 − 2 ⎟ϕ1 + 2 χϕ12 ν1 ⎠ ν1 ⎝

⎪

δ L = ϕ1Lδ1L + ϕ2Lδ2L = (ϕSδS + ϕPδ P + ϕmδm) + ϕδ a a

(6)

(7)

⎪

⎪

(11)

(5)

⎛ ν ⎞ ln a1 = ln ϕ1 + ⎜1 − 1 ⎟ϕ2 + χϕ2 2 ν2 ⎠ ⎝

⎪

(10) D

DOI: 10.1021/acs.energyfuels.5b02216 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels Table 1. Composition and Densities of Crude Oils Used in This Study (1 atm and 20 °C) composition (wt %) sample crude crude crude crude

oil oil oil oil

API gravity (deg)

saturates

aromatics

resins

n-C7 asphaltenes

recovery

28.5 31.1 33.5 29.4

54.4 56.7 59.1 50.3

21.9 25.3 23.0 24.8

18.8 14.6 15.7 22.6

4.3 2.1 1.6 1.1

99.4 98.7 99.4 98.8

A B C D

temperature corrections, or they can be calculated using eq 1 with well-established enthalpy of vaporization correlations. We used eq 1, acquiring the enthalpy of vaporization from the correlations of Majer and Svoboda.74 The solubility parameter of heptane is 15.20 MPa1/2 at 25 °C and 15.08 MPa1/2 at 30 °C. Similarly, the solubility parameter of pentane is 14.20 MPa1/2 at 30 °C, and the solubility parameter of toluene is 18.10 MPa1/2 at 30 °C. The solubility parameter of DCM was calculated as 20.27 MPa1/2 at 25 °C using an enthalpy of vaporization of 29.0 kJ/mol. With these data, therefore, the molar volume and solubility parameter of the light liquid phase for each predefined solvent volume fraction can be calculated. Because three of these four values are known, the asphaltene solubility parameter at the onset can be determined. Yarranton Model. Extended versions of the Flory− Huggins model have had reasonable success predicting asphaltene solubility profiles in dilute solvent mixtures, such as those developed by Yarranton et al.,43,53 which are based on a distribution of asphaltene subfractions instead of a single asphaltene component representation. Yarranton et al. divide the asphaltene fraction into 30 asphaltene subfractions with a gamma distribution, in which both the molar volume and solubility parameter for each subfraction are calculated from empirical correlations.75 The shape of the gamma distribution is determined by the parameter β. The remaining oil fractions, saturates, aromatics, and resins, are also incorporated with the model, along with their respective solubility parameters and molar volumes (which are determined empirically). A liquid− liquid equilibria (LLE) is assumed between a heavy phase and a light phase, as described above. However, this model allows for resins to partition into the asphaltene heavy phase. For each solvent volume fraction, LLE calculations are performed and the mole fractions of saturates, aromatics, resins, 30 asphaltene components, and the added solvents/precipitants are determined for the heavy and light phases. In the LLE calculations, the fugacity of each component in either liquid phase is given by43 ⎡ fi = γixif i0 exp⎢ ⎣

∫0

p

νi dP ⎤ ⎥ RT ⎦

K iHL = +

=

xiH xiL

⎛ γ L ⎞⎛ f 0L ⎞ ⎡ = ⎜⎜ iH ⎟⎟⎜⎜ i0H ⎟⎟exp⎢ ⎝ γi ⎠⎝ f i ⎠ ⎣

■

∫0

⎪

⎪

⎫ νiL L νH (δi − δ LL)2 − i (δiH − δ LH)2 ⎬ RT RT ⎭

⎪

⎪

(18)

MATERIALS AND METHODS

Four crude oil samples were used in this study with n-heptaneprecipitated asphaltene contents ranging from 1 to 4 wt %, as listed in Table 1. The saturate, aromatic, resin, and asphaltene (SARA) fractions for crude oils A, B, C, and D were measured using a modified ASTM D6560 (IP 143) method and a modified ASTM D4124 method described elsewhere.18,76 When converting asphaltene weight percentages into asphaltene volume fractions, we used an asphaltene density of 1.2 g/cm3 and the measured oil densities in Table 1. Highperformance liquid chromatography (HPLC)-grade toluene (CAS Registry Number 108-88-3), DCM (CAS Registry Number 75-09-2), n-heptane (CAS Registry Number 142-82-5), and n-pentane (CAS Registry Number 109-66-0) were purchased from Fisher Scientific (Fair Lawn, NJ). Conventional Precipitation Yield Measurements. The asphaltene content was determined using a modified ASTM D6560 (IP 143) standard method36 briefly described here. The oil sample was added to 40-fold excess of HPLC-grade n-heptane, or if solvent blends were used, 400 mL of the toluene−heptane mixture was combined with 10 mL of oil. The mixture was sonicated for 1 min and then left at room temperature (approximately 24 °C) for 48 h. The precipitated asphaltenes were recovered with a 0.2 μm polytetrafluoroethylene (PTFE) filter. Rinses with n-heptane or the toluene/n-heptane mixture at that solvent volume fraction were completed to remove the nonasphaltenic materials, maltenes, that may have been entrapped with the asphaltenes on the filter. The washing was performed at room temperature, “cold rinsing”, until the solvent was visually clear. In our typical modified ASTM D6560 method, such as for the asphaltene contents listed in Table 1, refluxing heptane is used for the washing step, where the temperature of the heptane rinse ranges from 60 to 70 °C, “hot rinsing”. We did not perform reflux rinsing for our solubility measurements because we used blended solvent mixtures where toluene and heptane have differing boiling points. Finally, the coldrinsed and retained asphaltenes were dissolved by flowing DCM over the filter to leave behind inorganic solids entrapped with the asphaltenes or retained by the filter membrane. The DCM solvent was evaporated from the permeate, and the solid asphaltenes were stored under nitrogen for subsequent mass measurement.

(16)

p

xiL

⎧ νH ⎛ νL ⎞ ⎛ νH ⎞ νL = exp⎨ i H − i L + ln⎜ i L ⎟ − ln⎜ i H ⎟ νL ⎝ νL ⎠ ⎝ νL ⎠ ⎩ νL

where xHi and xLi are the heavy and light liquid-phase mole fractions, vi and δi are the molar volume and solubility parameter of component i in either the light liquid phase (L) or the heavy liquid phase (H), and vL and δL are the molar volume and solubility parameter of either the entire light liquid phase or the entire heavy liquid phase depending upon the superscript. Terms containing only molar volumes represent entropic contribution, and the terms containing solubility parameters represent enthalpic contribution, same as in the simple Flory−Huggins model. The amount of insoluble asphaltenes at any solvent volume fraction can be determined using eq 18 in a calculation workflow described elsewhere.43,76

where γi is the activity coefficient (γi = ai/xi), xi is the mole fraction, f 0i is the standard state fugacity, and νi is the molar volume of component i. The equilibrium ratio is determined by equating the fugacities for each component in both phases and equating the standard state fugacities as in LLE. Also, negligible volume changes from mixing are assumed. Thus, the equilibrium ratio, KHL i , for any particular component is given by K iHL

xiH

L Δνi dP ⎤ ⎛ γi ⎞ ⎜ ⎟ ⎥≈ RT ⎦ ⎜⎝ γi H ⎟⎠

(17)

Using the activity definition from eq 8, it can be shown that E

DOI: 10.1021/acs.energyfuels.5b02216 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels

and dark reference spectra were acquired with toluene-saturated flow cells. The absorbance of the oil was measured by mixing toluene and oil at a ratio of 40:1. If the oil sample was too dark and insufficient light reached the spectrometers, the sample was further diluted 80:1. Diluted oil passed through the filtration membrane into the second optical flow cell, where its spectrum was acquired. Flow conditions were maintained until a stable absorbance spectrum could be measured, which was preset to 5 min. Next, toluene was flushed through the system to displace residual diluted oil, and the system was primed with the solvent−precipitant mixture at the user-set solvent volume fraction. Bright and dark reference spectra were acquired with flow cells filled with a toluene−heptane blend. This mixture and crude oil were injected into the reactor chip and mixed at a ratio of 40:1, causing a fractional amount of asphaltenes to precipitate. Asphaltenes were filtered out on the filtration chip and the diluted deasphalted oil passed through the second flow cell, where the absorbance spectrum was acquired. An automated cleaning cycle redissolved the asphaltenes and eliminated sample residues from the system. The PTFE filter membrane was manually changed, and another solvent volume fraction experiment was performed. The first flow cell (pre-filtration) was used to detect the onset of asphaltene precipitation via scattering and to ensure correct system operation during diluted oil runs. We typically executed this entire sequence 20 times to evaluate solvent volume fractions from φ = 1 (pure solvent) to φ = 0 (pure precipitant) at decrements of φ = 0.05. At φ = 1, the oil was diluted with toluene, as shown on the left side of Figure 1a. This spectrum represents the minimum precipitation, because we assume asphaltene solids were fully dissolved. Next, at φ = 0, the oil was mixed with nheptane and the solution was filtered, thereby removing the precipitated solid asphaltenes. The optical absorbance spectrum was acquired and stored as the maximum asphaltene precipitation, as shown on the right side of Figure 1a. The center sequence in Figure 1a, labeled “sweeping solubility range”, spans several mixtures of toluene and n-heptane, starting at a solvent fraction in which almost no precipitation occurs. For instance, a high solvent volume fraction of 0.95 would indicate a mixture comprised of 95 volume percent toluene and 5 volume percent n-heptane. The accuracy of the solvent volume fraction, φ, is ±0.00116 (from 0.1% at φ = 0.95 to 2.3% at φ = 0.05) based on the 0.21% syringe pump accuracy (full stroke) and the syringe tolerance of 1% total volume. Next, the asphaltene spectra, ranging from a solvent volume fraction of φ = 0 (pure precipitant) to φ = 1 (pure solvent), were processed, plotted, and evaluated to extract relevant model tuning parameters. This workflow established the fractional asphaltene precipitation across a range of solubility parameters, bounded by the solvent and the precipitant. The baseline-corrected optical absorbance for the asphaltenes at a particular solvent volume fraction was calculated using eq 19 as

Microfluidic System. For microfluidic measurements, our design uses two glass microfluidic chips along with a PTFE membrane filter and a custom stainless-steel holder with integrated optical flow cells. The first microfluidic chip, the reactor chip shown in Figure 1, introduces the oil sample to the solvent−precipitant mixture. Two inlets lead to individual serpentines, which thermally equilibrate the fluids before they are combined. The sample flow rate is 10 μL/min, and the solvent−precipitate flow rate is 400 μL/min. In the asphaltene microfluidic system discussed here, the fluid travels through PTFE tubes, microchannels etched in glass, and micro-/minichannels in stainless steel. The Reynolds number ranges from 50 to 180 for toluene flowing through the reactor chip. The fluids are brought together using a y-junction and combined using chaotic micromixers (Dolomite, Royston, U.K.). Fluid mixing occurs over 12 individual micromixer units, which implement laminar flow folding to yield smaller diffusion lengths. The well-mixed fluid enters a long serpentine channel that introduces a sufficient time delay for reaction kinetics to take place, which permit asphaltene flocculates to form. At low precipitant/oil ratios (1:1, 50% crude and 50% precipitant), asphaltenes can take hours or even days to form aggregates that are greater than 1 μm.77 At high dilution ratios, such as the 40:1 ratio used here, the precipitation occurs within seconds. We have shown in previous work7 that the optical measurements after 0.5−5 s are within 10% of the optical value recorded after a wait time of 5000 s (off-chip). The residence time in the reactor is 5.8 s, which is sufficient time for asphaltene precipitation and aggregate formation. When fluid exits the reactor chip, it is coupled to the stainless-steel holder. The sample−solvent mix passes through the first optical flow cell (3.0 mm path length), where absorption is recorded. Then, fluid is routed into the second microfluidic chip, called the filtration chip, where an open-face filtration channel interfaces with a porous membrane for asphaltene separation. The fluid is filtered and collected by the stainless-steel holder channel (a mirror image of the open-face filtration channel on the filtration chip). Then, filtered fluid is routed through to a second optical flow cell (3.0 mm path length), where absorption is recorded again. The lower portion of Figure 1 shows the microfluidic system and the chip configuration in an exploded view. These microfluidic chips were fabricated using conventional microfabrication techniques, such as photolithography, isotropic etching, and temperature annealing. Nominal cross-sectional dimensions (depth × width) are as follows: The mixer was 125 × 350 μm (large channels) and 50 × 125 μm (small channels). The reactor was 175 μm × 450 μm × 500 mm (40 μL). The inlet and interconnecting channels were 175 × 350 μm. The filtration channel was 200 × 600 μm and was open-faced (i.e., not capped or sealed). The absorption spectra were measured using 3.0 mm optical pathlength flow cells coupled to a HL-2000 tungsten halogen white-light source (Ocean Optics, Dunedin, FL) with a usable range of 360−2400 nm and an USB2000+UV−vis spectrophotometer (Ocean Optics, Dunedin, FL) with a usable range of 200−850 nm. Fluid flow was achieved with three Mitos Duo XS syringe pumps (Dolomite, U.K.) equipped with 5 mL syringes for toluene and heptane solvents and a 0.25 mL syringe for pushing sample. Two pressure sensors, PR-35X (Keller, Switzerland), were used to ensure correct operation and to detect membrane clogging, fouling, and aging. Three Cheminert C223186EH (VICI-Valco, Houston, TX) rotary valves were used: one for sample loading and injecting and two for solvent control. Sample and solvents were filtered using 10 and 2 μm in-line frit cartridges, respectively. All subsystems were connected with fluorinated ethylene propylene (FEP) tubing with a 250 μm inside diameter. The system was controlled and automated via a personal computer running LabVIEW 2011 (National Instruments, Austin, TX) and custom electronics. The program also captured and analyzed the absorbance spectrum over time for data processing and presentation. Microfluidic Precipitation Yield Measurements. For each solvent fraction, we manually introduced a 200 μL crude oil sample with a syringe into the system, where it was stored in a sample loop. Then, we executed an automated protocol. Initially, the system was flushed with toluene to purge air from the tubes, chips, filter membrane, and optical flow cells. Spectrometer calibration of bright

AAφ = [A s(600 nm) − A s(800 nm)] − [A mφ(600 nm) − A mφ(800 nm)]

(19)

where As values are the absorbance values at 40:1 dilution for the toluene-diluted oil and Amφ values are the absorbance values at 40:1 dilution for the partially deasphalted oil mixture (toluene−heptane mixture at a solvent volume fraction, φ) at particular wavelengths. When the 80:1 ratio was used for the toluene-diluted oil, absorbance values were converted to the 40:1 dilution equivalent using a ratio of 81:41 ≈ 1.976. Selection of the absorbance wavelengths was described in a previous study,18,56 in an effort to (a) target the visible absorbance of asphaltenes diluted in solvent (600 nm), (b) fit within the dynamic range of the spectrometer, and (c) account for baseline variations and drift caused by optical noise and fluid fluctuations (800 nm). Raw asphaltene absorbance values from eq 19 and fractional precipitation are shown below. Fractional precipitation was calculated for each measurement point by scaling to the maximum precipitation run at φ = 0 or pure precipitant. It is possible to convert the measured optical absorbance values to weight percentages; however, there may be error arising from different petroleum chemistries (aromaticity, heteroatom and metal contents, etc.). The “average” molecular extinction coefficients or response F

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Energy & Fuels factors could be different from crude oil to crude oil. However, in our previous paper, we showed a linear correlation between the asphaltene optical absorbance and weight percentage for 52 oils spanning a range of geographic sources.7,8,18 In this study, the 10−20 individual asphaltene solubility measurements performed on a single crude oil are relative to the same petroleum chemistry. One could perform the optical measurements of a particular crude oil and then calibrate to a single gravimetric point. However, this may also have error in relating optical asphaltene absorbance to weight percentage, because the “average” response factor (optical-to-mass), even from a single crude oil, will change as the precipitant is varied. The asphaltene fraction of the crude oil would be partitioned differently with different solvent− precipitant mixtures. We anticipate from our historical and published data that these variabilities would be minor and that the optical absorbance measurements are well-suited for deducing solubility data.

■

RESULTS AND DISCUSSION Microfluidic Technique Compared to the Conventional Method. Figure 2 shows two solubility profiles of the crude oil A sample (n-C7 asphaltene content of 4.3 wt %): one acquired using the conventional wet chemistry method and the other acquired using the microfluidic approach. In the top plot of Figure 2, the empty square data points are optical absorbance measurements acquired using the microfluidic system and plotted using the left vertical axis versus the solvent volume fraction. Filled circle data points are gravimetric measurements acquired using the conventional cold rinse method and plotted as a weight percent using the right vertical axis versus the solvent volume fraction. The maximum asphaltene content was 5.1 wt % at φ = 0, higher than the 4.3 wt % reported by the ASTM D6560 procedure used in Table 1. This is because no reflux with hot heptane (60−70 °C) was performed for the solubility measurements (cold rinse), whereas hot heptane reflux was used when oil compositions were measured for the asphaltene contents listed in Table 1 (hot rinse). In the bottom plot of Figure 2, the fractional precipitation obtained from both methods is plotted versus the solvent volume fraction. The solubility profiles of the two methods show similar linear trends within the experimental error of each technique. The solvent volume fraction at asphaltene precipitation onset can be determined by fitting data within the solvent volume fraction range of 0−0.45. The x-axis intercept of the fitted lines yields an onset at a toluene solvent volume fraction of 0.502 for the microfluidic measurement and 0.503 for the conventional measurement. The corresponding critical solubility parameters are 16.75 MPa1/2 (at 24 °C) for the conventional approach and 16.60 MPa1/2 (at 30 °C) for the microfluidic method. Therefore, the two methods show nearly identical asphaltene onset points, in which the solvent volume fraction agrees within 0.2% and the critical solubility parameter agrees within 0.15 MPa1/2, although the precipitation temperatures were slightly different. We performed temperature studies, shown below, where an increase in the temperature from 30 to 50 °C yielded an onset decrease of 0.007. When the 6 °C temperature difference in Figure 2 is taken into account, we anticipate the conventional approach solvent volume fraction onset to be approximately 0.505, which still compares well with 0.502, as measured by the microfluidic method, within 0.6%. Linear extrapolation may not be the optimal fitting approach. However, in this study, it appears to fit the data well. Crude oil A showed unexpected behavior for the microfluidic measurement near asphaltene precipitation onset between solvent volume fractions of 0.3−0.4. It was the only sample of the four samples used in this study that exhibited this

Figure 2. Comparison of asphaltene solubility profiles obtained from conventional and microfluidic techniques for crude oil A.

behavior. From φ = 0 to 0.3, the data followed the expected trend, where the amount of precipitated asphaltenes decreased with increased solvent volume fraction. However, from φ = 0.33 to 0.37 an anomalous increase in the asphaltene absorbance occurred, apparently indicating that more asphaltenes were precipitated and filtered. At solvent volume fractions greater than φ = 0.38, the expected behavior resumed. That is, asphaltene precipitation decreased as the solvent content increased. We repeated the experiment to confirm this behavior, obtaining similar results in both cases. Our hypothesis is that the asphaltene aggregates formed in this range of solvent volume fractions created a cake layer on the membrane, which increased the level of filtration beyond the nominal pore size of 0.2 μm. We observed that the transmembrane pressure for all solvent volume fractions between φ = 0.33 and 0.37 exceeded 4.5 bar during asphaltene precipitation, usually reaching the upper limit of the system of 6 bar. For all other solvent volume fractions, the pressure did not exceed 1.2 bar during asphaltene precipitation, even at φ = 0, with maximum precipitation of asphaltenes. The relatively highpressure buildup in the problematic region suggests that a more stringent filtration process occurred, probably as a result of the asphaltene cake layer on the filter membrane. Although microfluidic data for crude oil A showed unexpected behavior near the asphaltene precipitation onset, other data points G

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Table 2. Comparison of Crude Oil B Asphaltene Solubility Parameters at the Precipitation Onset for Different Solvent Mixtures solvent and precipitant

parameter

φCR = φx‑int

δL = δCR (MPa1/2)

linear fit

toluene−heptane

0.348

16.13

linear fit

DCM−heptane

0.230

16.37

Hirschberg fit

toluene−heptane

0.348

16.13

Hirschberg prediction

DCM−heptane

y = −2.90x + 1.01 R2 = 0.994 y = −4.43x + 1.02 R2 = 0.997 νa = 1000 cm3/mol δa = 22.18 MPa1/2 νa = 1000 cm3/mol δa = 22.18 MPa1/2

0.168

16.05

model

closely matched the conventional data, showing similar precipitation onsets and yields. Solvent Effect and Simplified Flory−Huggins Modeling. Figure 3 shows the solubility profile for crude oil B, acquired on the microfluidic system using different solvents. We used a variety of solvent and precipitant combinations with different overall solubility parameters to measure fractional asphaltene solubility. This example shows two microfluidic experiments. The oil sample was mixed with toluene−heptane mixtures in one experiment, shown by the empty squares. In another experiment, the oil sample was mixed with DCM− heptane mixtures, shown by the filled circles. The measurements revealed similar critical onset solubility parameters when we used a linear fit: 16.13 MPa1/2 for toluene−heptane mixtures and 16.37 MPa1/2 for DCM−heptane mixtures, as summarized in Table 2. The apparent increase in asphaltene absorbance between φ = 0.4 and 0.5 for the toluene and heptane experiment is within measurement error; the absorbance measurements have a typical standard deviation of 0.005− 0.015 au.19 Figure 3 also shows the Hirschberg model for both the toluene−heptane and DCM−heptane mixtures. The data from the toluene−heptane experiment was used to tune the Hirschberg approach at the precipitation onset; the average solubility parameter of asphaltenes was 22.18 MPa1/2 for crude oil B. The value is in reasonable agreement with values listed in the literature, which range from 20 to 22 MPa1/2.37 Next, using this value and eq 11, the insoluble asphaltenes for other toluene−heptane volume fractions were calculated, as shown in Figure 3. As expected, the model poorly predicts the asphaltene yield or degree of precipitation, a shortcoming noted by Hirschberg et al. in the initial work.42 This lack of accuracy arises from the simplifications of using a two-component system with a single asphaltene solubility parameter as well as an arbitrary molar volume of asphaltenes. The fixed asphaltene solubility parameter at precipitation onset represents only the most insoluble subfraction. As the solvent volume fraction decreases, the other more soluble asphaltene subfractions precipitate and the average solubility parameter decreases. It is well-known37 that this approach cannot accurately predict asphaltene fractional precipitation without incorporating interaction parameters or multiple asphaltene components. However, while the model poorly predicts yield, it should be able to predict the asphaltene precipitation onset with other solvents, because the most insoluble asphaltenes are captured by the solubility parameter at the onset. The asphaltene solubility parameter acquired from the toluene−heptane experiments was then used as input to the DCM−heptane Hirschberg model to predict the asphaltene precipitation onset point using a different solvent. The linear fit shows that the precipitation onset occurred at a solvent volume

Figure 3. Asphaltene solubility profiles for crude oil B obtained using two different solvent mixtures reveal similar solubility parameters at the precipitation onset.

fraction of approximately 0.230 for the DCM−heptane experiments, while the Hirschberg model predicted the onset at a solvent volume fraction of 0.168, assuming the same asphaltene solubility parameter of 22.18 MPa1/2. To close that gap, the asphaltene solubility parameter would need to be 22.61 MPa1/2 for the Hirschberg approach to report the asphaltene precipitation onset at a DCM−heptane solvent volume fraction of 0.230. Thus, the solvent volume fraction difference of 0.062 (6.2%) between the linear fit and the Hirschberg approach is significant. It is probably due to the simplified representation of H

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Mannistu et al.51 used DCM−hexane mixtures to measure a precipitation onset at φ = 0.33−0.34 for Athabasca bitumen asphaltenes. The single-component solubility parameter model determined that the asphaltene onset was φ = 0.24, whereas the three-component solubility parameter model determined that it was φ = 0.27.51 Therefore, part of the deviation in our experiment above is likely due to the simplified singlecomponent solubility model. To minimize the impact of solvent properties on the predictive capability of the Hirschberg approach, we analyzed crude oil C with two normal alkanes commonly used for asphaltene precipitation studies: n-pentane and n-heptane. Figure 4 shows the solubility profile for crude oil C, acquired on the microfluidic system using different precipitants. In one experiment, the oil sample was mixed with toluene−heptane mixtures, shown by the empty squares. In another experiment, the oil sample was mixed with toluene−pentane mixtures, shown by the filled circles. The measurements revealed similar critical onset solubility parameters using a linear fit: 16.38 MPa1/2 for toluene−heptane mixtures and 16.18 MPa1/2 for toluene−pentane mixtures, summarized in Table 3. Using the Hirschberg approach, the average solubility parameter of asphaltenes at the precipitation onset was 22.53 MPa1/2 for crude oil C using toluene−heptane mixtures. Then, the asphaltene solubility parameter acquired from the toluene− heptane experiments was used as input to the toluene−pentane Hirschberg approach to investigate the ability to determine the asphaltene precipitation onset using a different precipitant. The linear fit showed that the precipitation onset occurred at a solvent volume fraction of approximately 0.506 for the toluene−pentane experiments, while the Hirschberg approach predicted the onset at a solvent volume fraction of 0.502 (the asphaltene solubility parameter was 22.53 MPa1/2). The 0.004 (0.4%) difference was much smaller than the DCM/toluene experiments of Figure 3. The asphaltene solubility parameter would need to be 22.55 MPa1/2 for the Hirschberg approach to report the asphaltene precipitation onset at a toluene−pentane solvent volume fraction of 0.506. This very small difference between the asphaltene solubility parameters (0.02 MPa1/2) suggests that the Hirschberg approach can predict the precipitation onset for similar classes of solvents. However, the large difference of 0.43 MPa1/2 observed in Figure 3 indicates that one should exercise caution when attempting to accurately predict asphaltene onset with any diluent.29 In either case, our microfluidic platform can aid in the calibration of such models with the ability to rapidly acquire experimental data. Temperature Effect and Modified Regular Solution Modeling. In this section, we use asphaltene solubility data obtained from our microfluidic method at various temperatures to tune the Hirschberg approach and also more recent

asphaltene subfractions and the simplified representation of the solvent properties. Mannistu et al.51 demonstrated that more polar solvents, such as DCM, could be better matched to experimental data by incorporating Hansen solubility parameters with the Flory−Huggins interaction parameter. Hansen parameters account for different types of interactions: dispersive, polar, and hydrogen bonding.47 However, even after accounting for these interactions, the improvement in matching with experimental data was marginal. For example,

Figure 4. Asphaltene solubility profiles for crude oil C obtained using two different solvent mixtures reveal equivalent solubility parameters at the precipitation onset.

Table 3. Comparison of Crude Oil C Asphaltene Solubility Parameters at the Precipitation Onset for Different Solvent Mixtures solvent and precipitant

parameter

φCR = φx‑int

δL = δCR (MPa1/2)

linear fit

toluene−heptane

0.429

16.38

linear fit

toluene−pentane

0.506

16.18

Hirschberg fit

toluene−heptane

0.429

16.38

Hirschberg prediction

toluene−pentane

y = −2.38x + 1.02 R2 = 0.984 y = −2.09x + 1.06 R2 = 0.988 νa = 1000 cm3/mol δa = 22.53 MPa1/2 νa = 1000 cm3/mol δa = 22.53 MPa1/2

0.502

16.16

model

I

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Figure 5. Asphaltene solubility profiles of crude oil D measured at different temperatures demonstrate the sensitivity of the technique.

thermodynamic models, namely, the Yarranton approach. The microfluidic system enables temperature control of the fluid samples during the entire automated process, including mixing, reacting, filtering, and measurement. Figure 5a shows the solubility profiles in terms of asphaltene absorbance versus the solvent volume fraction for crude oil D at different temperatures. In Figure 5a, the trend for all solvent volume fractions agrees with the literature,49 in which, as the temperature increases, typically an increase in asphaltene solubility occurs; hence, there is less fractional precipitation. In panels b−d of Figure 5, data points have been scaled to the maximum as fractional precipitation at each temperature. That is, they have been normalized to the solvent volume fraction of φ = 0. In panels b−d of Figure 5, the asphaltene precipitation onset solvent volume fraction can be determined by fitting data within the solvent volume fraction range of 0−0.35, shown by dashed lines. The x-axis intercept of the fitted lines yield onsets at a toluene solvent volume fraction of 0.374 for the 30 °C profile, 0.367 for the 50 °C profile, and 0.355 for the 70 °C profile. The corresponding critical solubility parameters are 16.21 MPa1/2 for 30 °C, 15.67 MPa1/2 for 50 °C, and 15.11 MPa1/2 for 70 °C. The x-axis intercepts from the linear fits for various temperatures was used to determine asphaltene solubility parameters using the Hirschberg approach, summarized in Table 4. Assuming an asphaltene molar volume of 1000 cm3/ mol and density of 1.2 g/cm3, the asphaltene solubility parameters at the precipitation onset were 22.41 MPa1/2 for the 30 °C profile, 22.03 MPa1/2 for the 50 °C profile, and 21.61

Table 4. Comparison of Crude Oil D Asphaltene Solubility Parameters at the Precipitation Onset for Different Temperatures Using the Hirschberg Approach temperature (°C)

φCR = φx‑int

νL (cm3/mol)

δL = δCR (MPa1/2)

νa (cm3/mol)

δa (MPa1/2)

30

0.374

129.86

16.21

50

0.367

133.38

15.67

70

0.355

137.37

15.11

1000 4000 1000 4000 1000 4000

22.41 21.13 22.03 20.69 21.61 20.22

MPa1/2 for the 70 °C profile. Assuming an asphaltene molar volume of 4000 cm3/mol, the asphaltene solubility parameters at the precipitation onset were 21.13 MPa1/2 for the 30 °C profile, 20.69 MPa1/2 for the 50 °C profile, and 20.22 MPa1/2 for the 70 °C profile. In the Hirschberg et al.42 paper, they assumed a molar volume of 4000 cm3/mol and noted an empirical temperature dependence for the asphaltene solubility parameter as δa = 20.04 [1−1.07 × 10−3T (°C)] MPa1/2. In this paper, when we assume a molar volume of 4000 cm3/mol, we note the temperature dependence for the asphaltene solubility parameter for crude oil D as δa = 21.81 [1−1.04 × 10−3T (°C)] MPa1/2. In this case, the asphaltene solubility temperature dependence shows less than a 6% difference (0.0227 MPa1/2/ °C versus 0.0214 MPa1/2/°C) and highlights the ability of the microfluidic approach to rapidly conduct temperature studies. Panels b−d of Figure 5 also show the Yarranton et al. model tuned to the experimental data. Using the Yarranton model, the J

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asphaltene molecules at 50 °C, and 6−7 asphaltene molecules at 70 °C. These numbers agree with the literature and are consistent with the expected aggregate size as temperatures increase. The decrease in the aggregation number or aggregate molecular mass also matches the increasing dissociation of the aggregate as temperatures rise, a phenomenon observed in wellcharacterized associating systems. Figure 6b shows the solvent volume fraction at the asphaltene precipitation onset versus the temperature for the linear fit approach and for the tuned Yarranton model. The expected downward trend is similar for both approaches. However, the asphaltene precipitation onset occurs at a 5−6% higher solvent volume fraction for the Yarranton model. This increase is a result of the multi-component asphaltene subfractions, in which high-molecular-mass asphaltene aggregates exist in very small percentages by weight. As a result, the model more closely matches the experimental data near the onset. In most of our microfluidic solubility profiles, a gradual transition occurs near the onset to the “completely” soluble asphaltene region. Data generated from the microfluidic system will enable more accurate tuning of models, such as Yarranton’s, particularly near the asphaltene precipitation onset. Microfluidic Improvements. We have shown that an automated microfluidic system can rapidly generate asphaltene yield profiles and derive properties relevant to thermodynamic model tuning, such as solubility parameters. The system works well with a variety of solvent−precipitant combinations over multiple temperature ranges, which will make it possible to acquire asphaltene solubility properties in dilute solvent solutions more frequently and accurately. A single asphaltene measurement takes 35 min on the microfluidic apparatus, while the conventional gravimetric approach takes 1−2 days per experiment. This difference in experimental time is even greater when profiling different solvent volume fractions to measure solubility parameters, as described above. For example, we completed a solubility analysis with 20 experimental points in just 12 h with this method, while a similar analysis with 14 gravimetric points (Figure 2) would have taken a minimum of 10 days using standard laboratory procedures. Furthermore, the traditional measurement approach is considerably more labor-intensive. A technician performs most of the protocol steps manually, with little to no automation.36 The microfluidic approach, on the other hand, is highly automated. It requires only 5 min of setup time, which significantly reduces active labor time and improves reproducibility.7 Finally, the microfluidic system consumes ∼0.3 g of crude oil per run and ∼3−6 g per solubility analysis. The traditional ASTM D6560 method requires 2−10 g per run and 20−200 g per solubility analysis. At solvent volume fractions immediately following the onset of asphaltene precipitation (often detected with optical turbidity titration), an input sample mass closer to the upper limit tends to yield more valid results. This is because small amounts of asphaltenes precipitate and much larger samples of crude oil are required for conventional mass balances to measure solid precipitate. Microfluidic chips reduce effective sample use by an order of magnitude or more. They also use solvents more efficiently. The microfluidic system consumes 0.0192 L of solvent per run and ∼0.2−0.4 L per solubility analysis compared to 0.5−1 L of solvent per gravimetric measurement and 5−10 L per solubility analysis. Thus, the use of microfluidics substantially reduces the

onsets occur at a toluene solvent volume fraction of 0.435 for the 30 °C profile, 0.420 for the 50 °C profile, and 0.410 for the 70 °C profile. The onset was determined when the fractional precipitation exceeded 0.01 wt %. The corresponding critical solubility parameters are 16.40 MPa1/2 for 30 °C, 15.83 MPa1/2 for 50 °C, and 15.27 MPa1/2 for 70 °C. Figure 6a shows molar

Figure 6. (a) Fitted molar mass distribution of asphaltene aggregates in crude oil D at various temperatures used in the Yarranton model for the experiments in Figure 5. (b) Asphaltene precipitation onsets obtained from the Yarranton model and linear fit. The linear fit onset was determined using the x intercepts, and the Yarranton model onset was determined when the fractional precipitation exceeds 0.01 wt %.

mass distributions of the asphaltene aggregates, derived from tuning of the Yarranton approach to the microfluidic data in panels b−d of Figure 5. At 30 °C, the model was tuned to β = 1.3 and an average effective molecular mass of 5150 g/mol. At 50 °C, the model was tuned to β = 1.2 and an average effective molecular mass of 4600 g/mol. At 70 °C, the model was tuned to β = 1.1 and an average effective molecular mass of 4200 g/ mol. The term “effective molecular mass” may describe selfassociated stacks of asphaltene molecules as in the original Yen model,78 also described by Mullins et al.79 Alternative explanations may also be possible to capture associative interactions. It is important to note that average effective molar masses are only fitting parameters for the modified regular solution model. Wu et al. showed that asphaltene molecular masses range from 580 to 700 g/mol and determined that petroleum asphaltenes form aggregates, which typically contain 6−8 molecules.80 Crude oil D aggregates would contain approximately 7−9 asphaltene molecules at 30 °C, 6−8 K

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environmental impact of laboratory tests while producing highquality solubility data in less time, with fewer resources.

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CONCLUSION Using a microfluidic method and apparatus, we accurately measured the solubility profile of asphaltenes in solvent− precipitant mixtures. The asphaltene yield was measured using UV−vis spectroscopy as the solubility parameter of the solvent−precipitant mixture was changed gradually at constant volume conditions. All mixing, precipitation, filtration, and optical measurements took place within the fully integrated microfluidic device. We corroborated the results of microfluidic measurements by comparison to results of the conventional gravimetric method. To illustrate its applicability in the oil industry, we applied two thermodynamic models based on regular solution theory and tuned them to the experimental data, investigating differences between the models when applied to difference scenarios. Automated microfluidic measurements reduced the usual measurement time from days to mere hours while also improving data quality. In addition, obtaining these measurements required only a fraction of the valuable crude oil sample and used substantially less solvent required by traditional laboratory analyses, effectively reducing the environmental impact of solvent waste for such laboratory tests.

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AUTHOR INFORMATION

Corresponding Author

*Telephone: +1-617-768-2152. E-mail: fmostowfi@slb.com. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors acknowledge Julien Brunet for running experiments and Dr. John Ratulowski for many interesting asphaltene discussions. The authors also thank Joe Baddeley, Andrew Lovatt, Richard Gray, and Phil Homewood from Dolomite for their technical contributions.

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REFERENCES

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