Gravitational Gradient of Asphaltene Molecules in an Oilfield

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Gravitational Gradient of Asphaltene Molecules in an Oilfield Reservoir with Light Oil Soraya S. Betancourt, Yngve Bolstad Johansen, Julia C. Forsythe, Joachim Rinna, Kjell Christoffersen, Pål Skillingstad, Vladislav Achourov, Jesus A Canas, Li Chen, Andrew E Pomerantz, Julian Y. Zuo, and Oliver C. Mullins Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b00256 • Publication Date (Web): 02 Mar 2018 Downloaded from http://pubs.acs.org on March 3, 2018

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Gravitational Gradient of Asphaltene Molecules in an Oilfield Reservoir with Light Oil Soraya S. Betancourt1, Yngve Bolstad Johansen2, Julia C. Forsythe3, Joachim Rinna2, Kjell Christoffersen2, Pål Skillingstad2, Vladislav Achourov4, Jesus Canas1, Li Chen1, Andrew E. Pomerantz,3 Julian Y. Zuo1, Oliver C. Mullins3 1. 2. 3. 4.

Schlumberger SIS, San Felipe St., Houston TX, 77056 AkerBP, Trondheim, Schlumberger-Doll Research, Cambridge MA, 02139 Schlumberger, Stavanger, Norway

Abstract In toluene, asphaltenes are dispersed as molecules at low concentrations, as nanoaggregates at moderate concentrations and as clusters of nanoaggregates at high concentrations. These three asphaltene species are codified in the Yen-Mullins model. For reservoir crude oils, equilibrated asphaltene gradients can be modeled with the Flory-Huggins-Zuo Equation of State (FHZ EoS). The gravity term and other terms depend on the particle sizes of the asphaltenes which are given in the YenMullins model; these different asphaltene species (molecular and two nanocolloidal species) have been identified in gravity gradients in various reservoir studies. Here, the asphaltene gradient in a large reservoir is examined and found to be consistent with a molecular dispersion of asphaltenes in the crude oil. A variety of fluid and reservoir properties are evaluated to assure validity of the analysis, particularly of thermodynamic equilibrium of the reservoir fluid. For crude oil samples throughout the reservoir, downhole fluid analysis (DFA), gas chromatography (GC) and two-dimensional gas chromatography (GCxGC) with cubic EoS and geochemical interpretation are consistent with fluid equilibration. Pressure measurement and production results are also consistent with fluid equilibration. This analysis is applicable to other reservoirs; molecular dispersions of asphaltenes are expected for other light oil reservoirs. I.

Introduction

Much of the basic chemistry of asphaltenes has been largely resolved.[1-3] Asphaltene molecular weight is approximately 750 g/mole as shown by a variety of molecular diffusion [4-8] and mass spectral methods.[9-16] The dominance of ‘island’ molecular architecture has been shown by time-resolved fluorescence depolarization (TRFD) molecular diffusion measurements,[4-6] unimolecular decomposition utilizing two-step laser desorption, laser ionization mass spectrometry (L2MS),[13] and ultrahigh resolution molecular imaging.[17,18] Island molecular architecture has a single polycyclic aromatic hydrocarbon (PAH) per molecule, often with peripheral alkane substitution. The TRFD measurements require the least sample preparation, only sample dilution. The L2MS experiments used 25 model compounds with half of them traditional archipelagos compounds. Traditional archipelagos have two or more PAHs crosslinked with alkane bridges as opposed to direct aryl linkage of two or more PAHs. All traditional archipelago compounds fragmented at high ionization laser energy while none of the island compounds, nor asphaltenes fragmented under the same conditions.[13] This instability of archipelago compounds may be why they are not present in asphaltenes. Figure 1 shows an image of an asphaltene molecule.[17] Atomic force microscopy (AFM) is used to obtain an image of the atoms and bonds and scanning tunneling microscopy (STM) is used to obtain an

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image of a single molecular orbital, here the lowest unoccupied molecular orbital (LUMO).[17] These and the many other asphaltene images are very compelling to establish asphaltene molecular architecture.[17-19]

Figure 1. (A & B) AFM images of an asphaltene molecule. 8 aromatic rings are evident with a single methyl group substitution. (C) STM image of the lowest occupied molecular orbital (LUMO). (D) Molecular orbital calculations (with phase represented by red and blue) matching closely with the measurement in (C).[17]

In an exhaustive study, ten asphaltene samples were acquired from a large variety of source materials from ExxonMobil, Chevron, Shell and Schlumberger.[17,18] In the hundreds of molecular images obtained, not a single traditional archipelago asphaltene molecule was obtained.[17,18] In critical experiments to confirm that the heated filament transfer procedure employed in the imaging experiment is valid for asphaltenes, a series of archipelago model compounds were imaged with the same transfer process as for the asphaltenes.[19] One of the archipelago compounds, 1,2-di(pyren-1yl)ethane, was successfully imaged with no fragmentation at all despite the presence in this molecule of one of the weakest C–C bonds, the ethane linkage with bond strength 65.2 kcal/mole.[19] If any archipelago would fragment under these sample transfer conditions, this one should. The imaging experiments found no traditional archipelagos in various asphaltenes evidently because there are not present.[17,18] A small percent of archipelagos with a direct aryl linkage between two PAHs was found in a few samples.[17,18] At about 10-4 mass fraction in toluene, asphaltenes molecules for nanoaggregates. Many techniques have been used to show the critical nanoaggregate concentration including high-Q ultrasonics,[20-22] DC-conductivity,[23,24] AC-conductivity,[25] NMR,[26,27] centrifugation,[28] and mass spectrometry.[29] The aggregation number of the nanoaggregate of about 6 has been measured by surface assisted, laser desorption ionization mass spectrometry (SALDI-MS) coupled with L2MS.[30,31] All measurements involving the increase of Stokes drag upon nanoaggregate formation versus molecules are consistent with this aggregation number of 6.[23,24,26-28] The nanoaggregates survive a mild desorption process only at very low laser power with indirect heating in the SALDI-MS experiments; at somewhat higher laser powers, the nanoaggregates break into trimers and dimers.[30,31] Weak asphaltene nanoaggregate binding energy implied in the SALDI MS studies is consistent with the critical nanoaggregate concentration (CNAC). Using the standard approximation for the free energy of formation of nanoaggregates (or micelles), ∆G ∼ RTln(cmc) or ∆G ∼ RTln(CNAC),[32,33] the resulting


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estimate is ∆GCNAC ∼ -6kcal per mole of nanoaggregate. This small binding energy is consistent with the fragile nature of the nanoaggregate obtained from SALDI-MS.[30,31] Moreover, there is a significant entropic component favoring nanoaggregate formation as gleaned from the small temperature dependence of the CNAC, thus the enthalpic component is not large.[24,26] Entropy also dominates the formation of many aqueous micelles.[33] The structure of the nanoaggregate is given by the divergence and analysis of combined small angle xray scattering (SAXS) and small angle neutron scattering (SANS), [34,35] which is consistent with molecular dynamics modeling results.[36] These measurements and modeling show that the core of the nanoaggregate is concentrated in aromatic carbon (with its high atomic number) and the periphery is enriched in alkane with its high hydrogen content.[34-36] The mass spectrometry study employing atmospheric pressure ionization obtained a CNAC of ~10-4 mass fraction as noted above,[29] is consistent with other studies. However, the aggregation number determined in this study was much higher, on order 20 depending on conditions.[29] Interestingly, this nanoaggregate could not be disaggregated,[29] in spite of the known small binding energy of asphaltene nanoaggregates as discussed above. Moreover, related experiments employing atmospheric ionization mass spectrometry appear to yield a substantial fraction of archipelagos, although there are unexplained, large differences of cross section (factor of 50) between island versus (interpreted) archipelago compounds.[37] Perhaps the excessive nanoaggregate binding energy, the excessive aggregation number, very small cross section, and apparent finding of archipelago molecules are related. The large aggregate could be acting somewhat similar to bulk asphaltene; it is known that bulk decomposition of island model compounds can result in the synthesis of archipelago compounds.[38] At about 10-3 mass fraction in toluene, asphaltene nanoaggregates form clusters of nanoaggregates as shown by aggregation kinetics,[39,40] DC-conductivity,[41] centrifugation,[41] and NMR.[42-44] An aggregation number of about 8 is consistent with all measurements including the combined SAXS and SANS study.[34,35] Yen-Mullins Model. These three species, the asphaltene molecule, nanoaggregate and cluster are codified in the Yen-Mullins model as shown in Fig. 2.[2,3]

Figure 2. Yen-Mullins model.[2,3] Asphaltenes are dispersed in crude oils as molecules at low concentrations (light oils), as nanoaggregates at moderate concentrations (black oils), and as clusters at high concentrations (heavy oils).[2,3,45]

Flory-Huggins-Zuo Equation of State (FHZ EoS). A simple theory is highly desirable for oilfield application; this can be achieved by adding the gravity term to the Flory-Huggins Theory. The resulting Flory-Huggins-Zuo Equation of state is obtained as shown in Eq. 1 which is applicable in the oilfield.[45,46] For oilfield applications, it is important to specify the asphaltene content as a function of


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height hi in the oil column. For an equilibrated oil column (for which an Equation of State applies), there is no lateral fluid variation.

[

2 2 Ca (h 2 ) OD(h 2 )  v g∆ρ (h 2 − h1 )    v a   v    v a (δ a − δ )h 2 − (δ a − δ )h1 = = exp − a exp   −  a  exp − C a (h1 ) OD(h1 ) RT RT     v  h 2  v  h1  

]  

1.

Where Ca(hi) is the asphaltene concentration at height hi, and OD(hi) is the optical density of the oil in a color channel, oil color in the visible-near-infrared (electronic absorption) has been established as linear in asphaltene content.[47] va is the molar volume of the asphaltene species of interest from the YenMullins model, v is the effective molar volume of the crude oil, g is earth’s gravitational acceleration, ∆ρ is the density difference between asphaltenes and the crude oil, R is the gas constant, T is temperature, δa and δ are the asphaltene and crude oil Hildebrand solubility parameters respectively. The first exponential term in Eq 1 is the gravity term which incorporates Archimedes buoyancy in the Boltzmann distribution. For asphaltene molecules, the gravity term is small. For nanoaggregates, the gravity term yields measurable gradients for heights exceeding hundreds of feet. For clusters, the gravity term is large often accounting for a doubling or more of asphaltene content in 20 meters of height depending on the crude oil density. The second exponential term in Eq. 1 is the Flory-Huggins entropy term is generally small in Eq. 1.[48] However, for cases of small GOR gradients for molecular dispersions, the various terms can be comparable. The final exponential term in Eq. 1 is the solubility term which is dependent primarily on the gas-oil ratio (GOR) of the oil. For low GORs, e.g. less than 500 scf/bbl (standard cubic feet of gas per barrel of oil at one atmosphere and 60 degF), the variation of GOR is small and does not impact the asphaltene gradients.[48] Consequently, lower pressure reservoirs, where much of the solution gas evolves as a separate phase, often have small GOR gradients. As the GOR increases, the GOR gradients increase and contribute to the asphaltene gradients.[48] The chemistry axiom “like dissolves like” applies; as the GOR increases, the asphaltene solubility decreases and is accounted for by the solubility term in Eq. 1. Equilibrated Nanoaggregates. Many examples of the successful application of the FHZ EoS with the YenMullins model are given elsewhere;[45] here we show a few examples. Figure 3 shows a reservoir with two wells, with each well intersecting two stacked sands, the upper sand and the lower sand. In each sand, the asphaltenes are equilibrated, but there is a small offset in oil color or asphaltene content between the crude oils in the upper and lower sands.[49] The implication is that each sand is laterally connected between the two wells; however, there is limited vertical connectivity.[45] Connectivity means that there is fluid flow communication, and therefore fewer wells are needed to drain the reservoir.


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Figure 3. A deep water reservoir with two stack sands, for the most part separated by an laterally extensive shale layer.[49] Two wells were drilled in this reservoir separated by roughly one kilometer. Track 6 (DFA: Asphaltenes) shows each sand has equilibrated asphaltenes implying flow connectivity across the field. The two asphaltene curves are offset indicating the intervening shale layer is laterally extensive. Track 1; the natural gamma ray log (GR) of Well 2 which identifies sand formations (yellow), indicated as upper and lower. Track 2; horizontal bars with locations of DFA and sampling depths in Well 2. Green is oil, blue is water, yellow is methane, orange is other hydrocarbon gases. Track 3; the natural gamma ray log (GR) of Well 1 which identifies two sand formations (yellow). Track 4; horizontal bars with locations of DFA and sampling depths in Well 1.[49] Track 5; depth (the leading xx’s are to conceal the absolute reservoir depth). Track 7; GOR (from the lab), Track 8; IFA-measured density, Track 9, IFAmeasured viscosity. Track 10, thermal maturity biomarker ratio Ts/(Ts+Tm).[49]

Figure 3 shows how downhole fluid analysis (DFA) measurements [50] of asphaltenes gradients are used to understand reservoirs. Track 6 of Fig. 3 shows that the asphaltene gradients in the upper sand and lower sand are consistent with an equilibrium distribution of 2nm asphaltene nanoaggregates.[49] Nevertheless, these two asphaltene gradients are offset indicating poor vertical flow communication. At the top of the two sands, the fluids appear to be the same indicating some limited vertical communication towards the crest. This analysis of lateral connectivity but limited vertical connectivity is consistent with the seismic interpretation, and with all production data commencing one year ago.[49] The GOR and density gradients in Fig. 3 are small and consistent with equilibration.[49] The ratio Ts/(Ts+Tm), the hopanoid thermal maturity markers,[51] show no gradient; the fluid gradients in this reservoir are not due to a variation of thermal maturity of the oils.


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Figure 4. (A) Large asphaltene gradient in a heavy oil column matching a gravity gradient of 5nm asphaltene clusters.[52] (B) invariant saturation pressure. (C) nearly invariant ratio of hopanoid thermal maturity biomarkers Ts and Tm.[53] Figure 4 shows the results from a large, anticlinal oilfield in Saudi Arabia. Around the 100-kilometer base of the field there is a heavy oil column. The factor of ten, vertical asphaltene gradient in this heavy oil column matches the FHZ EoS with 5 nm asphaltene clusters over the 100 kilometers.[52] Deviations from points of the equilibrium curve (e.g. circled three points in Fig. 4A) are understood in terms of the processes that led to this asphaltene equilibration distribution over these enormous distances.[52] For heavy oil with their low GOR, the only significant surviving term to establish asphaltene gradients is the gravity term, making application of the FHZ EoS very simple.[52] Moreover, the application of the YenMullins model presumes chemical invariance of the asphaltenes in the oil column; it has been established that the asphaltene molecular weight,[31] nanoaggregate weight,[31] and sulfur chemistry [54] are invariant for samples throughout this oil column. Any application of the cubic EoS for this oil column would require many different asphaltene psuedocomponents in contradiction to the uniformity of chemical measurements of these asphaltenes. Finally, the ratio of the hopanoid thermal maturity markers Ts/(Ts+Tm) are nearly invariant (cf. Fig. 4C); this asphaltene gradient is not due to a thermal maturity variation. This is expected as the underlying tar demonstrates; a maturity gradient would require the tar to migrate first into the reservoir then displace downward to its current location by subsequent reservoir filling. This tar mat which is immobile did not slide 30 kilometers from the crest. A recent oilfield case study showed the coexistence of both asphaltene nanoaggregates and asphaltene clusters. Figure 5 shows the DFA-measured asphaltene gradients with the (large) clusters accumulating at the base of the oil column.[55] The origin of the formation of clusters was the destabilization of asphaltene nanoaggregates due to the admixture of primary biogenic methane to the oil reservoir over geologic time.[55] Nevertheless, the instability was not great enough to cause bulk phase instability nor to convert all asphaltene nanoaggregates to clusters.[55]


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Figure 5. Well data showing the coexistence of asphaltene nanoaggregates and clusters.[55] Track 1; true vertical depth subsea (the leading depth numbers are proprietary). Track 2; vertical and horizontal resistivity. Anisotropy is high in shales. Track 3 continuous permeability from NMR logging and discrete permeability by MDT pressure build-up curves. Track 4; horizontal bars represent DFA measurement stations. Green is oil, Blue is water. Track 5; DFA asphaltene gradients. Track 6; lab GOR measurements. Track 7. Fluid density measured by the IFA. Track 8. Fluid viscosity measured by the IFA. Track 9; methane isotope in ‰. Analysis of many pressure and fluid properties including GOR modeling, methane isotope ratios, extent of biodegradation, and ratios of several thermal maturity biomarkers all establish that the fluid column is equilibrated.[55] Use of the FHZ EoS is then justified; clearly the asphaltene gradient is bimodal as shown in Fig. 5, Track 5 and in Fig. 6.[55]


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Figure 6. Coexistance of asphaltene nanoaggregates and clusters in the oil column shown in Fig. 5. The FHZ EoS with the Yen-Mullins model accounts for this asphaltene gradient.[55] A schemtic shows the accumulation of asphaltene clusters towards the base of the oil column, while the smaller nanoaggregates are dispersed more unifromly throughout the column. Figure 6 shows the FHZ EoS analysis of the asphaltene gradient using 2 nm nanoaggregates and 5 nm clusters. The only variable is the mass fraction of each species; here, the split is 61% nanoaggregates, 39% clusters by mass. In

the laboratory, the formation of asphaltene clusters is generally associated with high asphalltene concentration; this condition also applies to the oilfield as shown in Fig. 4. Fig. 6 shows the other primary reason to form asphaltene clusters from nanoaggregates is the reduction in solvent quality. Addition of solution gas to the oil reduces the Hildebrand solubility paramter of the solvent making it a worse solvent for asphaltenes. The addition of primary biogenic methane to the oil in Figs. 5 and 6 caused cluster formation.[55] In this paper, an oilfield containing a light oil is examined. The DFA-measured asphaltene gradient is found to be uniform in several wells across the oilfield. Many fluid and pressure measurements are consistent with fluid equilibration. Application of the FHZ EoS to this asphaltene gradient is consistent with the molecular dispersion of asphaltenes in this light oil. The application of the FHZ EoS and the YenMullins model continues to be successful. II.

Experimental Methods.

Downhole Fluid Analysis. The preferred means of measuring asphaltene and some other fluid gradients is using downhole fluid analysis (DFA).[50] One reason is that fluid complexities can be identified during the sample acquisition job; more DFA stations can be added when complex fluid columns are encountered. In DFA, systematic errors often cancel giving excellent gradient data. Nevertheless, it is highly desirable to validate DFA measurements with subsequent laboratory measurements. DFA is part of the “wireline” survey of a well, which is performed shortly after a well is drilled. Logging tool packages lowered into the well on a wireline cable measure various attributes of subsurface formations.[56] For example, measurement of γ-ray scattering in subsurface formations identifies formations of low mass density, measurement of neutron scattering identifies formations of high hydrogen content and measurement of electrical conductivity identifies resistive formations.[56] Oil-bearing formations are often low mass density, high hydrogen content and resistive.[56] Natural gamma ray well logs often


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differentiate shales from sandstones, as shales are enriched in potassium (with its radioactive uranium and thorium.[56]

40

K),

A wireline tool, the Modular Formation Dynamics Tester (MDT) can acquire formation fluid samples long before a well is put into production.[57,58] The MDT has various probes (cf. Fig. 7) which make hydraulic communication with permeable zones. After setting the probes at the desired depth location in the well, formation fluids are then pumped into the tool for analysis by various DFA tools and for acquisition in high pressure sample bottles for subsequent laboratory analysis. The Schlumberger In-situ Fluid Analyzer (IFA), a DFA tool, has both filter and grating optical spectrometers to determine (relative) asphaltene content, GOR, some light hydrocarbon compositional analysis and CO2 analysis.[59] In addition, vibrating-object sensors in the IFA measure density and viscosity of the formation fluids.[59]

Figure 7. The MDT, a tool for formation fluid sample acquisition and downhole fluid analysis (DFA).[57,58] (A) Schematic of the tool including the probe to make hydraulic communication with permeable formations, pumps to extract formation fluids into the tool, DFA tools with optical spectrometers, and sample bottles for fluid acquisition. (B) Photograph of a single probe module. (C) Photograph of a radial probe in a packer element.

Oil Based Mud. A possible complication is potential contamination of the formation crude oil samples by the liquid or “filtrate” component of the drilling fluid. For water based muds (WBM), there is no miscibility with crude oil. For oil based muds (OBM), the base fluid is miscible with crude oil and can complicate sample analysis. In this study, some wells had OBM while others had WBM; all lab samples used in this study had zero or very low contamination levels. Laboratory Gas-Oil Ratio. An accurately measured volume of the single-phase fluid is isobarically displaced into a pycnometer where its mass is measured. The pycnometer is connected to a GOR apparatus where the oil is flashed to ambient pressure and temperature conditions. The evolved gas phase is then circulated through the residual liquid for a period of time to achieve equilibrium between the two phases. Following circulation, the volume of equilibrium vapor and the mass of liquid remaining in the pycnometer are measured. The vapor phase is collected and analyzed to C12, while the residual liquid is analyzed to C30+. Laboratory Compositional Analysis. The compositional analysis of gaseous mixtures is performed using two separate gas analysis detectors: one with a natural gas configuration and the other with an


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extended gas configuration. The natural gas configuration consists of packed columns, a Thermal Conductivity Detector (TCD) detector and uses helium as a carrier gas. The detection range covers N2, CO2, H2S, C1 to nC4. The extended gas configuration consists of a capillary column, a Flame Ionization Detector (FID) and uses helium as a carrier gas. The temperature programming is non-isothermal (ramping to 464 °F) and the detection range covers C1 to C12 including the associated common isomers. The compositional analysis of liquid samples is performed on a temperature programmed GC equipped with a different capillary column, an FID detector and uses helium as a carrier gas. The temperature programming is also non-isothermal (ramping to over 572 °F), and the detection range covers C3 to C29 and a lumped C30+. The analysis includes the associated common isomers. GCxGC. GCxGC methods utilized here are discussed elsewhere.[60] A Leco Pegasus 4D GCxGC system was used in this study, Agilent Model 7890 GC coupled with a FID was configured with split/splitless autoinjectors (7683B series) and Leco dual-stage cryogenic modulators. The modulator operates with a cold gas jet consisting of dry N2 cooled with liquid N2 and a dry air hot gas jet operated at 20 degC above the temperature of the main oven. Two capillary GC columns were connected with Restek PressTight connectors. The first dimension (1D) column was a nonpolar Rxi-1 MS (30 m x 0.25 mm ID, 0.25 µ film thickness and the second dimension (2D) column was a BPX-50 midpolar 50% phenyl polysilphenylene-siloxane column (1.0 m x 0.10 mm ID, 0.1 µ film thickness) (SGE Scientific). For GC x GC-FID analysis, 1 mL of each sample solution was injected into a 300 degC splitless injector with a purge time of 1.0 min. Ultra-high purity helium was used as the carrier gas and was maintained at a constant flow rate of 1.50 mL/min. The temperature program of the main oven started isothermal at 40 degC for 1 min and was then ramped to 335 degC at a rate of 1.5 degC/min. The modulation period was 10 seconds with a hot jet pulse at 2.5 s and a cold period of 2.5 s between stages. The oven for the second column was maintained at +10 degC above the main oven. The FID signal was sampled at 100 Hz. Table 1 lists the samples used in the analysis of the reservoir. The corresponding SAMPLE number appears in various figures. The contamination levels of the samples were either low (a few %) or lower, in some cases undetectable. Consequently, contamination was not a concern for these samples. Table1. The fluid samples from specific wells and depths used for this study. The file number (with heavy outline) is used in subsequent figures for sample designation.


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III.

Results and Discussions

The Ivar Aasen reservoir is discussed in this paper. Figure 8 shows the overall location of this field and shows the wells that are utilized for analysis herein.

Figure 8. The location of the oilfield Ivar Aasen, in the Norwegian North Sea, and the specific wells used for analysis in this study.

Asphaltene Gradient Analysis with the FHZ EoS. Figure 9 shows the FHZ EoS analysis of the asphaltene gradient; samples 5 and 15 are outliers in the asphaltene gradient plot and are not used in the FHZ EoS fitting. The origin of the fluid differences for these two samples is discussed below. The asphaltene gradient is nearly vertical; the gradient is very small. This is in contrast to asphaltene gradients depicted in Figs. 3 and 4. The optical channel of choice to measure this gradient is 815nm indicating that the oils are lightly colored and the asphaltene content is not high. One sample had an asphaltene content measured to be 0.9% by mass for the pentane asphaltene fraction. The heptane asphaltene fraction might be 2/3 of this number, or 0.6%.


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Figure 9. Asphaltene gradient in Ivar Aasen measured by DFA. Each color for the data points represents a different well (color coded with Fig. 8). The points lie close to the FHZ EoS modeling curve with a size of 1.05 nm for the asphaltene particle. This represents an equilibrium distribution of asphaltene molecules. The applicability of the FHZ EoS to this oil column is the subject of this paper. (Points 5 and 15 are excluded as they are not equilibrated with the main reservoir body.)

Figure 9 shows the fit of the asphaltene gradient with the FHZ EoS excluding two points, samples, 5 and 15, that fall off the curve. As discussed below, fluid equilibrium and the corresponding reservoir connectivity is consistent with all other fluid and pressure measurements validating applicability of FHZ EoS modeling with the presumption asphaltene thermodynamic equilibrium. Fig. 9 shows that the asphaltene gradient is rather small indicating that the asphaltene ‘particle’ is small. The best fit for the asphaltene size is 1.05 nm diameter; this corresponds to asphaltene molecules meaning that the asphaltenes are dispersed in this crude oil as a true molecular solution. A molecular dispersion of asphaltenes is expected for light crude oils where the asphaltene fraction is well under 1% by mass. In addition, this crude oil is partially degassed (there is a gas cap). The loss of gas further increases the solvency of the crude oil for asphaltenes. Moreover, mild biodegradation removed significant fractions of the light n-alkanes further stabilizing the asphaltenes. The asphaltene molecular size is the effective diameter for a sphere and is smaller than the long axis of the oblate spheroid molecules depicted in Fig. 2. The small size of 1.05 nm for the effective diameter of a sphere to represent the asphaltene molecule is somewhat smaller than expected. Perhaps for this light oil with its low asphaltene content, there might be some contribution to the oil color from resin molecules, which are generally smaller than asphaltene molecules. In any event, this size of 1.05 nm represents a reasonably large molecule consistent with asphaltenes, yet is not representative of asphaltene nanoaggregates as seen for example in Fig. 3. Moreover, using 2 nm for the nanoaggregate (cf. Fig. 3), and 1.05 nm for the molecule, one obtains a nanoaggregate aggregation number of 7 which is the same as that measured by SALDI-MS.[14,30,31]


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Table 2. The magnitude of the three terms of the FHZ EoS for 1.05 nm molecules and for 2 nm nanoaggregates.

Asphaltene Ratio Ratio Ratio Ca(h1)/Ca(h2) Size (gravity) (entropy) (solubility) (total) 1.05 nm

1.050

0.937

1.199

1.180

2.0 nm

1.398

0.637

3.514

3.133

The FHZ EoS (Eq. 1) shows that the ratio of asphaltene concentrations (Ca) at height h2 vs. height h1 in the oil column is represented by a product of three exponential terms representing gravity, entropy and solubility. Table 2 shows the magnitude of each of the three exponential factors of the FHZ EoS for a true molecular solution (1.05 nm diameter molecules) applied to the data in Fig. 9. The vertical height difference is 82 meters as shown in Fig. 9. Note that in Eq. 1, the argument of the exponential of each exponential includes the factor va, the molar volume of the asphaltene species in question. Consequently, all factors, gravity, entropy and solubility, have a dependence on the asphaltene size. The ratio of asphaltene concentrations from the bottom to the top of the column over the 82 meters interval is calculated to be 1.18. In addition, Table 1 shows the magnitude of the ratio for asphaltenes for nanoaggregates (2nm diameter), all other parameters being fixed. The asphaltene ratio (and OD ratio) for nanoaggregates is 3.133 which is much larger than the field data in Fig. 9. That is, a nanoaggregate colloidal dispersion is very incompatible with this field data. Samples 5 and 15 are clearly off the asphaltene gradient by far more than error which is roughly 0.06 OD. Sample 5 is a crude oil with less than ½ the asphaltene content of any other sample. Likewise, sample 15 has about 3 times more asphaltene than any other. The very different fluids are likely in isolated sand bodies that are not connected to the main reservoir. The origin of these differences is examined in the GCxGC Analysis and Thermal Maturity section. In particular, sample 15 with most asphaltenes is from the eastern flank of the field farthest from the charge direction, and sample 5 with the least asphaltenes is on the western flank nearest to the charge direction.


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Figure 10. Excess pressure (pressure at each depth vs a reference pressure and pressure gradient from Well 16/1-16. All wells except 16/1-9 overlay; the pressure offset of well 16/1-9 is within uncertainty. The pressure measurements are consistent with reservoir connectivity, thus is consistent with equilibrated reservoir fluids. Figure 10 shows that all pressure measurements fall on a single pressure gradient within error supporting the idea of a single connected reservoir. (Each symbol color represents a different well as indicated.) Pressure gradients from all but one well are exactly matching consistent with connectivity. One well, the 16/1-9 well, is of by about 1 bar or about 14.5 psi. Nevertheless, this is within error of the pressures in the other wells. One published case showed a pressure difference between two wells of 20 psi, yet the asphaltenes were equilibrated across the field indicating connectivity.[61] In that case, pressure interference between the wells in production proved connectivity; the 20psi difference was within error and the equilibrated asphaltenes correctly predicted reservoir connectivity.[61] Cubic EoS Modeling. It is important to consider other fluid properties to validate the use of equilibrium asphaltene modeling of the reservoir fluids. A variety of fluid properties can be investigated. Fig. 11 shows the GOR gradient along with cubic EoS modeling.


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Figure 11. GOR measurement and cubic EoS modeling. The GOR gradient is consistent with equilibrium of the reservoir fluid within measurement error. The modeling correctly predicts the gas cap at the gas-oil contact (GOC).

Figure 11 shows that the DFA-measured GOR and lab-measured GOR are consistent with equilibration of the reservoir fluid within the considerable error of GOR measurements. Indeed, the reservoir contains both oil and gas. Consequently, the GOR is limited by this constraint; excess solution gas will phase separate as gas. The relatively large error in GOR measurement coupled with the limited range of GOR due to this phase separation makes it difficult to use GOR to identify outliers in contrast to asphaltene content for example for samples 5 and 15 (cf. Fig. 9).

Figure 12. Compositional analysis from DFA and lab measurement. (A) Cubic EoS modeling shows that compositional analysis of C1, C2-C5 and C6+ are consistent with equilibrated reservoir fluids. (B) The small quantity of CO2 is also consistent with equilibrated reservoir fluids.

Figure 12A shows compositional analyses of C1, C2-C5 and C6+ from both DFA and lab measurements are accounted for by the cubic EoS; this indicates that the reservoir fluids are equilibrated at least for these components. Figure 12B shows that the small quantity of CO2 is also equilibrated.


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GC Analysis and Biodegradation. All fluid measurements discussed are consistent with equilibrated reservoir fluids. A more detailed look at some fluid samples can provide a more stringent test of equilibration. Eight samples from five wells were selected for further evaluation including the two outlier samples, 5 and 15, from Fig. 9. Table 1 lists these selected samples.

Figure 13. GC chromatograms of the eight samples in the study showing very mild biodegradation. Figure 13 shows the gas chromatograms (GCs) for the liquid phases of the samples. The GCs are very similar consistent with equilibration. The moderate loss of light n-alkanes indicates mild biodegradation.[51] Sample 8, the sample closest to the oil-water contact shows slightly higher levels of biodegradation.


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Figure 14 shows the GC of sample 11 on an expanded scale. n-alkanes are prominent but the light nalkanes are reduced compared to other compounds indicating mild biodegradation; roughly PetersMoldowan rank 1.[51]

Figures 13 and 14 show that the crude oil has undergone mild biodegradation. The greater peak heights of cyclohexane and methylcyclohexane above those of n-hexane, n-heptane and n-octane are a clear indicator of biodegradation. Present day reservoir temperature is 95 degC; this is far beyond the temperature limit for biodegradation of 80 degC.[51] Consequently, the biodegradation had to take place prior to subsidence of the reservoir. In-reservoir biodegradation gives rise to large biodegradation gradients,[62] while reservoirs charging with biodegraded oils in a spill-fill mechanism of reservoir charging can give rise to small biodegradation gradients.[60] In addition, the lack of a biodegradation gradient as seen in Fig. 13 could have been removed by diffusion and could have happened subsequent to the termination of biodegradation with subsidence and reservoir heating. The time duration required for diffusion over the 80 meter height of this oil column is on order 1 million years.[49] The existence of CO2 in this reservoir is consistent with in-reservoir biodegradation.[63] The samples in Fig. 14 show mild biodegradation and little biodegradation gradient. This can be quantified. Figure 15 shows the ratios of isoprenoids pristane and phytane and n-alkanes, n-heptadecane (n-C17) and n-octadecane (n-C18). Since the peak height is large for all of these components, the ratios tend to be robust. The different plots in Fig. 12 show little variation, ratios of these similar liquid components should be vertical at equilibrium; thus, Fig. 14 is consistent with fluid equilibrium throughout the reservoir. Moreover, the magnitude of ratios pristane/n-C17 and phytane/n-C18 in the range of 0.45 to 0.55 are in the range expected for crude oils sourced from the Kimmeridge clay and with no biodegradation in this range of carbon number.


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Figure 15. Peak ratios involving pristane and phytane and n-C17 and n-C18. There is little variation of these peak ratios consistent with thermodynamic equilibration of the reservoir fluid. The values of pristane/n-C17 and phytane/n-C18 are typical of no biodegradation in this range of carbon number. Lower carbon number n-alkanes are more susceptible to biodegradation.

Figure 16. Halpern indices of biodegradation for seven carbon compounds. Increasing biodegradation corresponds to small x-axis values for the plots. (A) The “TR2” index, n-C7/1,1-dimethylcyclopentane, shows mild biodegradation and some differences amongst the samples. (B) The TR3 index, 3methylhexane/1,1-dimethylcyclopentane and (C) TR4 index, 2-methylhexane/1,1-dimethylcyclopentane, show smaller, consistent trends. The deepest crude oil, 8, shows the greatest biodegradation as expected by examination of GCs in Fig. 13.

Examination of the seven carbon compounds can reveal biodegradation. The microbes first consume nalkanes especially in this carbon number range. Fig. 16A shows some variability in the Halpern index TR2


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which is n-heptane divided by 1,1-dimethylcyclohexane. Microbes preferentially consume n-alkanes, so this index becomes smaller with increasing biodegradation. The microbes live in the water; the deepest sample, 8, is nearest to the oil-water contact (OWC) and shows the greatest biodegradation. In addition, the petrophysical evaluation of this reservoir shows that sample 8 is in a location with many shale layers thereby somewhat impeding diffusive equilibration. However, sample 14 shows more apparent biodegradation than sample 15 in the same well despite of its being much further from the OWC. The implication is that there are other factors influencing the value of TR2 than biodegradation. Figure 16B, the Halpern index TR3 which is 3-methylhexane/ 1,1-dimethylcyclohexane, and Fig. 16C, the Halpern index TR4 which is 2-methylhexane/ 1,1-dimethylcyclohexane, show smaller yet consistent trends compared to TR2. While there are some differences in these ratios, the variation is not large, thus does not imply any significant disequilibrium. Figure 9 shows the asphaltene gradient in this reservoir. Two crude oil samples lie significantly off trend. Sample 5 has much less than half of the asphaltene content and sample 15 has three times the asphaltene content compared to the trend associated with all other samples. It is well known than biodegradation can concentrate the asphaltenes thereby increasing crude oil color.[62] Figure 13 establishes that the large differences of asphaltene concentration in samples 5 and 15 compared to the other samples are not due to biodegradation. Moreover, the sample which shows slightly more biodegradation than the other samples, sample 8, is precisely on trend with regard to coloration seen in Fig. 9. The implication is that the slight difference of biodegradation between sample 8 versus other samples seen in Fig. 13, did not result in any significant volumetric change in the oil. Such volumetric change can result in a concentration of the asphaltenes, [62] but is not impacting the crude oils here. For light, small carbon number components, possible differences can result from flash differences of the samples. Especially because the TR2 index, and to a lesser extent, the TR3 and TR4 index show some variability, other seven carbon indices are explored. The Halpern index C1, C2 and C3 are examined; these indices are associated with different crude oil source materials. The definitions of these indices are provided in Fig. 17.


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Figure 17. The Halpern C1, C2 and C3 indices. Little variation is seen in any of these indices consistent with fluid equilibrium. No differences in crude oil source materials or even live sample flashing are evident. The homogeneity of ratios of similar compounds is consistent with fluid equilibration.

Figure 17 examines other seven carbon indices showing no variation. These ratios of very similar compounds should be the same for equilibrated crude oil. Thus, these indices are consistent with an equilibrated reservoir crude oil. No effect of flash differences in the samples is seen in these seven carbon compounds. This is expected as the volatility of these compounds is fairly similar compared to those in the Halpern indices. GCxGC Analysis and Thermal Maturity. GCxGC was performed particularly for hopane, sterane and diasterane biomarker analysis in these crude oils. Figure 18 shows the corresponding biomarker region of a typical GCxGC chromatogram of these crude oils. Molecular specificity of these biomarkers is evident.

Figure 18. The biomarker region of the GCxGC chromatogram. Ratios of related compounds provide valuable information about the crude oils.

The ratio of Ts/(Ts+Tm) gives one of the best measures of the maturity of the crude oil.[51] Figure 19 shows two maturity indices for these oils, one hopane index, C27 Ts/(Ts+Tm), and one sterane index C32 22S/(22S+22R).


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Figure 19. Maturity indices. (A) Ts/(Ts+Tm) is generally the preferred maturity index.[51] Sample 5 is the most maturity accounting for its low asphaltene content and sample 15 is the least mature accounting for its high asphaltene content (cf. Fig. 9). Other samples are within error. (B) The maturity of the crude oils is high and at the equilibrium value of the C32 ratio.

Crude oil samples 5 and 15 are clearly off the asphaltene gradient trend in Fig. 9; in the reservoir, they are isolated and not equilibrated with the rest of the reservoir. Figure 19 shows that these samples have different maturities than the other crude oils; sample 5 is higher maturity and lower asphaltene content and sample 15 is lower maturity and higher asphaltene content. Nevertheless, these maturity differences are not large. For example, a lower maturity asphaltic black oil in Saudi Arabia showed a Ts(Ts+Tm) value of 0.2;[53] the crude oil in this reservoir is much higher maturity and the outlier points 5 and 15 are not that different. The least mature sample, 15, is found on the eastern flank of the field. The most mature sample, 5, is found on the western flank of the field. The likely direction of charge is from the west. In a normal basin subsidence sequence, the least mature oil would charge first. Evidently, an isolated pocket of this charge, sample 15, is found on the eastern flank and did not mix with subsequent lighter charge. In this subsidence scenario, the most mature charge would charge last. An isolated pocket of this latest, most mature charge, sample 5, is in an isolated pocket in the western flank likely near the reservoir charge point. The fact the all the rest of the oil in the field is similar in thermal maturity indicates mixing which requires connectivity. This is consistent with the interpretation of the asphaltene gradients. The two outliers, samples 5 and 15 are also understood within the overall framework for the oilfield. IV.

Conclusions

Once again, the asphaltene gradients in a reservoir crude oil can be understood with thermodynamic modeling with the Flory-Huggins-Zuo Equation of State utilizing the Yen-Mullins model of asphaltenes. In this light crude oil, with its small asphaltene content, the asphaltenes are dispersed as a true molecular


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solution. This is in contrast to typical black oils with their moderate asphaltene content, where asphaltenes are dispersed as nanoaggregates, and to heavy oils with their high asphaltene content, where asphaltenes are dispersed as clusters of nanoaggregates. The preferred methodology shown here combines both a thermodynamic analysis especially of DFA data with a detailed compositional analysis with geochemical interpretation. Confluence of all fluid, pressure and production data indicate equilibrated reservoir fluids thereby providing a robust analysis. In turn, this analysis at the kilometers length scale reinforces the determination of the asphaltene molecular size approximately 12 orders of magnitude smaller. Detailed reservoir studies have confirmed all three species of the Yen-Mullins model. Moreover, the ability to identify equilibrated reservoir fluids with a simple powerful formalism also enables identification of disequilibrium. This enables the ability to identify reservoir fluid geodynamic processes which can then be used to address a myriad of reservoir production concerns. V.

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Energy & Fuels

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