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Reservoir Fluid Geodynamics: the Chemistry and Physics of Oilfield Reservoir Fluids After Trap Filling Oliver C. Mullins, Julian Y. Zuo, Andrew E Pomerantz, Jerimiah C. Forsythe, and Kenneth E. Peters Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b02945 • Publication Date (Web): 13 Nov 2017 Downloaded from http://pubs.acs.org on November 13, 2017

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Reservoir Fluid Geodynamics: the Chemistry and Physics of Oilfield Reservoir Fluids After Trap Filling Oliver C. Mullins, Julian Y. Zuo, Andrew E. Pomerantz, Julia C. Forsythe, Kenneth Peters, Schlumberger Abstract Oilfield reservoirs exhibit a wide array of complexities that have great impact on the efficiency of oil production. Major challenges include delineating overall reservoir architecture and the distributions of the contained fluids. Reservoir crude oils consist of dissolved gases, liquids and dissolved solids (the asphaltenes); the corresponding compositional variations and phase transitions within reservoirs greatly impact production strategies and economic value. Standard workflows for understanding reservoir (rock) architecture are subsumed in the discipline “geodynamics” which incorporates the initial rock depositional setting and subsequent alterations through geologic time to yield the present day reservoir. However, reservoir fluids are not generally treated in such a systematic manner. Petroleum system modeling provides the timing, type, and volume of hydrocarbon fluids that charge into reservoirs. However, there is little treatment of how these fluids change after filling the reservoir. A significant limitation had been the lack of thermodynamic treatment of asphaltenes in reservoir crude oils. Consequently, projecting reservoir fluid properties away from the wellbore has been problematic. “Reservoir fluid geodynamics” (RFG) is the newly formalized discipline that incorporates changes in the distributions of reservoir fluids and phase transitions during geologic time. A key enabling advance is the recently developed ability to treat asphaltene gradients in oilfield reservoirs using the Flory-Huggins-Zuo equation of state (FHZ EoS) with its reliance on the Yen-Mullins model of asphaltenes. In addition, insitu downhole fluid analysis in oil wells provides accurate vertical and lateral fluid gradients in reservoirs in a cost effective manner. Thermodynamic equilibrium can now be recognized; equilibrated fluids imply connected reservoirs, meaning a single flow unit. Disequilibrium fluid gradients imply ongoing or recent fluid processes in geologic time. The analysis of 35 oilfields (with more than 100 oil reservoirs) has allowed identification of various reservoir fluid geodynamic processes. Some processes, such as biodegradation, have long been studied; nevertheless, even in these cases, inclusion of the thermodynamic modeling yields accurate predictions of distributions of key fluid attributes. Many other RFG processes are elucidated herein and are shown to impact major reservoir concerns for production. The resulting fundamental understanding of the physics and chemistry of these RFG processes enables measurements made at the wellbore to be used as a basis for accurate prediction of fluid properties throughout the reservoir. 1. Introduction Oilfield Rock and Fluid Complexities. Oilfield reservoirs exhibit a wide variety of complexities both in rock and fluid structures that must be reasonably well understood if the oil is to be produced economically. The task is challenging; reservoirs are often large (kilometers to tens of kilometers in lateral dimension), and commonly only a few wells are drilled by the time major production decisions are made. To reach those decisions, operators take measurements made in the vicinity of those wellbores and then must project properties in regions of the reservoir far from the wellbore. For example, understanding large scale structure of oilfield reservoirs is of keen interest. One major risk factor is whether the reservoir is connected, that is, that fluids can flow relatively easily over large distances, or is composed of many smaller compartments created by geological layers or faults that restrict the flow the fluids. Compartmentalized reservoirs require more wells to drain, greatly increasing cost. For example, 1 ACS Paragon Plus Environment

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reservoirs can be well connected like kitchen sponges, where all permeable, oil-filled zones are connected. In contrast, reservoirs can be like a spool of bubble wrap with horrible connectivity and described as highly compartmentalized.[1] A knitting needle going through the spool of bubble wrap represents the well and would only intersect a relatively small volume. Figure 1 shows a large reservoir; the extent of reservoir connectivity is always a key concern.

Figure 1. An image of the upper and lower horizons (surfaces) of a Gulf of Mexico reservoir,[1,2] at a depth greater than 20,000 feet below sea level with water depths exceeding 4000 feet. The reservoir is tilted due to basin deformation and faults are observed in seismic imaging. A significant risk for oil production from the reservoir is whether the faults act as sealing barriers that prevent fluid flow.[1,2] Wells are drilled with spacings of kilometers to establish properties of the reservoir, and in favorable circumstances, production proceeds.

Oil reservoirs are constructed of different rock layers that combine to give an overall reservoir architecture as pertains to the contained fluids. The (fluid) volumetric size of the reservoir is naturally a major concern. In addition, the extent of connectivity, the ability of fluids to flow throughout the reservoir, is a major concern, particularly in high cost settings. Seismic imaging is generally used to understand the reservoir architecture or “geologic model” of the reservoir. In addition, seismic imaging is interpreted in terms of the depositional setting of the rock formations as well as their evolution in geologic time. This evolution is treated within the discipline “geodynamics”.[3] By conventional definition, the term geodynamics does not incorporate the evolution of contained hydrocarbons in the reservoir. (Note that the academic focus of geodynamics is more on processes in the mantel and involve igneous and metamorphic rock, while the oil industry is more focused on sedimentary rock on the surface of the crust.[4]) A variety of data is used to develop understanding of the geology, the depositional setting and the relevant geodynamics including core data, wellbore image data and petrophysical data. Inclusion of geodynamics significantly improves development of the geologic model.[3] A second major complexity associated with reservoirs is their contained hydrocarbons. Reservoir hydrocarbons can vary considerably in composition--from dry gas to tar, even within single reservoirs. Consequently, every aspect of exploitation of reservoirs depends on the type of contained hydrocarbon. Figure 2 shows a series of “dead” oil samples from a single reservoir from deepwater in the Gulf of Mexico.[5] This figure shows clear differences in fluid colors, with lighter colors near the top of the 2 ACS Paragon Plus Environment

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reservoir and darker colors near the bottom. The color difference reflects large differences in asphaltene content of the fluids. In addition, gas-oil-ratios (GORs) of the corresponding live oil samples vary enormously from 8000 to 1800 scf/bbl (standard cubic feet of gas per barrels of oil at 1 atm and 60degF). further demonstrating the wide range of fluid composition encountered in a single reservoir.[5,6] (Dead crude oils have been flashed to one atmosphere thereby losing dissolved gases; live crude oil contains its dissolved gases as in the reservoir at elevated pressures.)

Figure 2. (Left) Huge color and GOR gradients in crude oil composition are observed in oil samples within a single connected reservoir, deepwater Gulf of Mexico.[5] (Right) The areal map of the reservoir is shown with contour lines which also represent different gas-oil ratios (GORs). Four well locations are shown. The methane stable carbon isotope ratios are thought to indicate more thermogenic gas lower in the reservoir and more biogenic gas toward the top.[5]

Figure 2 shows that the carbon isotope varies throughout the reservoir, indicating that there are two sources of methane in this reservoir and that biogenic methane in this reservoir is substantial. (Primary) biogenic methane is generally enriched in 12C typically about δ13C=-68‰ while thermogenic methane resulting from the catagenesis of kerogen is typically in the range of δ13C=-40‰.[7] Consequently, this reservoir experienced a mixture of biogenic methane and crude oil. (Primary biogenic methane corresponds to biodegradation of organic matter in sediments; secondary biogenic methane is from biodegradation of crude oil.) Understanding the type and variations of reservoir hydrocarbons is essential for efficient oil and gas production. Oil samples are acquired from wellbores at known depths shortly after wells are drilled and long before production commences. Specification of production facilities depends on the nature of the produced hydrocarbons; therefore, reservoir fluid samples are required prior to production. Moreover, delineating reservoir hydrocarbon type requires fluid samples; there is no substitute.[1] The inability of seismic imaging to address hydrocarbon type (with the possible exception of showing gas accumulations) accentuates the importance of wellbore data for this determination. A workflow was developed for reservoir fluid characterization that is fundamentally different than that used to characterize the geologic model. The primary focus amongst reservoir technology teams for fluid characterization is on acquired fluid samples from the wellbore where accurate measurements can be obtained. However, this focus does not reduce the large uncertainty when projecting or extrapolating fluid properties away from wellbore. 3 ACS Paragon Plus Environment

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Petroleum system modeling is a widely used exploration tool and provides a model of the timing, type and volume of fluids entering the reservoir, among other objectives.[7,8] Petroleum system models incorporate the deposition of petroleum source rock and other formations, the evolution of the basin in geologic time, the catagenesis of kerogen, the migration of hydrocarbons, and trap filling.[7,8] However, for known reservoirs (after discovery), few reservoir teams use petroleum system modeling to improve production because of its inherent limitations. First, the very broad focus of petroleum system modeling over large physical distances and over geologic time limits resolution. In some cases, the reservoir is handled as a single or a few voxels and uncertainty remains regarding hydrocarbon type in the reservoir. Second, petroleum system modeling only provides the type of fluids that enter the reservoir but is not designed to model or predict changes of fluid distributions or phase transitions in the reservoir over geologic time. The time between reservoir charge and present day can be significant. In the presalt reservoirs in Brazil, the gap between reservoir charge and present day can be 100 million years.[9] In the continental shelf of Norway, this gap can be one million years.[10] In both cases, huge changes can take place in the distribution and phases of the reservoir fluids. Consequently, since these fluid processes are not clarified, there is reduced utility in predicting the fluids that were originally present. Advances Enabling Reservoir Fluid Geodynamics. Recently, technical advances have led to new formalization of “reservoir fluid geodynamics” (RFG).[11-16] The developments are either entirely or largely within a chemistry context. Therefore, it is appropriate to provide an overview of RFG with an emphasis on chemistry and its utility in the oilfield. First, the measurement of fluid gradients by “downhole fluid analysis” (DFA) using wellbore tools has become routine. Specifically, visible-nearinfrared spectroscopy is utilized to measure the solution gas or GOR of crude oil and the relative asphaltene content.[1] Fluid density and viscosity are also routinely measured downhole.[17-19] Complex fluid columns can be identified in real time with DFA tools in the well. The measurement program can be optimized to allow elucidation of these fluid complexities. In principle, many fluid samples can be acquired from many points in the well, with subsequent analysis in the laboratory. In practice, this approach is limited because ‘simple’ oil columns are then oversampled which is costly and inefficient. Without real-time DFA analysis, oil columns may be undersampled, thereby restricting RFG analyses. The second enabler for RFG has been the development of the industry’s first equation of state for asphaltene gradients, the Flory-Huggins-Zuo Equation of State (FHZ EoS) [20-25] and its reliance on the Yen-Mullins model of asphaltenes.[26,27]. The FHZ EoS can model the gradient of asphaltene content of crude oil within a reservoir if the fluid has achieved thermodynamic equilibrium. Equilibrium indicates the fluid chemical composition is no longer changing with time, which generally occurs long after reservoir charging is complete; reservoir charging refers to the entry of new fluids into the reservoir. Asphaltene gradients can be measured very precisely with DFA allowing identification of equilibrated oil columns. Equilibrated asphaltenes imply connected reservoirs; it is hard to imagine asphaltenes equilibrating across sealing barriers. This addresses one of the biggest risks in production, reservoir connectivity. If the oil column exhibits a gradient that is not equilibrated, then ongoing RFG processes are indicated and can be elucidated. Often, the cause of the lack of equilibrium has important consequences in production. This thermodynamic approach has been vital in the development of RFG. Indeed, in light oil reservoirs where large gradients of GOR are expected, a similar approach is used by measuring GOR gradients and analyzing these with the cubic EoS. However, this approach is somewhat limited because GOR gradients are difficult to measure accurately, whether downhole or in the lab, yielding significant error bars. Moreover, crude oils of low GOR have small GOR gradients that are often smaller than measurement error rendering thermodynamic analysis difficult. Nevertheless, analysis of GOR gradients is complementary to analysis of asphaltene gradients.

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The third enabling development has been our studies of approximately 35 oilfields, each generally with many oil reservoirs, utilizing an RFG perspective. These studies reveal all manner of fluid and reservoir processes and complexities. Systematics in RFG processes are evident, and it is now routine to use an RFG perspective to evaluate reservoirs. Published case studies include reservoir compartmentalization, [28,29] and its inverse, reservoir connectivity and fluid equilibration,[1,2,21,30-32] diffusive gradients in oilfields,[33-37] asphaltene migration via density inversions,[33,34,35-37], heavy oil and tar formation,[33,34,35-39] heavy oil equilibration,[32,40-42] biodegradation with a single reservoir charge of oil,[43] biodegradation with multiple charges of oil,[44,45] biodegradation with spill-fill reservoir filling,[46] and water washing of crude oil,[45,46] among many other concerns.[16] A complementary discipline to the thermodynamic approach of RFG is organic geochemistry [cf. 47]. If a process is indicated, for example, a condensate charge into a black oil reservoir, then there should be evidence of this in the thermodynamic treatment as well as in the detailed compositional analysis of the crude oil, especially within a geochemical interpretation. If the thermodynamic, analytical and geochemical treatments of a reservoir are in accord, then the corresponding reservoir understanding is more robust. 2. Experimental Methods and Interpretation 2.1 Downhole Fluid Analysis (DFA) This section is meant to provide an overview, not a detailed description of downhole fluid analysis. More detailed discussions of DFA can be found elsewhere.[1] The characterization of reservoir fluids is key for optimal production planning. In general, reservoirs fluids consist of dissolved gases, liquids, and dissolved (or stable nanocolloidally suspended) solids, the asphaltenes. Reservoirs can contain virtually 100% methane, or tar with high asphaltene content, or all manner of crude oils with different ratios of dissolved gases, liquids, and asphaltenes (within the limits that higher dissolved gas content reduces the solubility of asphaltenes). Moreover, huge gradients of fluid properties can occur in reservoirs vertically and/or laterally. For field development planning, it is of paramount importance to determine these fluid properties and their variations. No remote sensing can determine such properties; it is essential to acquire fluid samples to perform chemical analysis. Crude oils from potential producing formations must be analyzed immediately after a well is drilled; at this stage, the well is in so-called “open-hole” conditions prior to placement of steel casing in the well (cased hole) for subsequent perforation and production. Crude oil samples are needed to determine whether and how oil production will take place. In open-hole conditions, the well is filled with drilling mud which maintains higher borehole pressure than that in penetrated permeable formations, thereby preventing blowouts, the uncontrolled entry of formation fluids into the well. The drilling fluid contains clay, thus its name drilling mud, which acts to form a mud cake on permeable formations thereby preventing excessive fluid loss from the wellbore into the formations. Nevertheless, some drilling fluid “filtrate” leaks into permeable zones thereby contaminating formation crude oils. Oil based muds (OBM) are commonly used and OBM filtrates are miscible with reservoir crude oils making OBM filtrate contamination a significant concern. Water based muds are much less problematic for acquisition of crude oil samples.


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Figure 3. (A) Schematic of the MDT fluid acquisition and analysis tool for open-hole wells. Probes interface with permeable formations for fluid extraction using pumps. Optical fluid analyzers (DFA tools) and high-pressure sample bottles are depicted.[48-50] (B) A photograph of a “probe” with a protruding steel tube that is pressed firmly against the borehole wall to make hydraulic communication with permeable zones in the oil well to extract formation fluids. (C) Photograph of the Schlumberger radial Saturn probe where fluid entry occurs at four symmetrically placed ports in the middle of a rubber packer, which contact the borehole wall when deployed.[51]

To acquire a sample of formation fluid in open-hole, a tool is lowered into the well on a wireline cable; Fig. 3 depicts the Schlumberger MDT (Modular Formation Dynamics Tester) used for this purpose.[4851] DFA tools are mounted onboard the MDT and enable accurate assessment of fluid properties and their variations within the oilfield.[1] First, DFA identifies the presence or absence of drilling fluid filtrate that can leak into permeable zones and contaminate reservoir crude oil.[1] Pumping fluid out of the formation for extended times by the MDT, for example a few hours, is frequently required to greatly reduce or eliminate contamination of the crude oil by OBM filtrate. DFA is used to monitor the extent of OBM contamination; crude oils generally have solution gas and/or asphaltenes, while OBM filtrate generally has neither.[1,52-56] This distinction along with knowledge of the typical time rate of change of contamination allows real-time determination of contamination during the pumping process.[1,52-56] Many different probe types are available that make contact with the borehole wall. This enables sample acquisition and DFA to be performed in a large variety of formations including unconsolidated sands, low permeability formations, and formations with extensive drilling filtrate invasion.[19,51] DFA is performed on a fluid flow stream in the MDT flowline (cf. Fig. 3); visible and near infrared spectroscopy are the methods of choice for downhole measurement of crude oil composition.[1] Reservoir and MDT flowline pressures can exceed 30,000 psi; but this pressure is readily handled in high pressure optics cells (with sapphire windows). Reservoir temperatures can be 175 degC or more; the primary impact of this temperature on DFA measurements is on optical detectors. Use of InGaAs detectors with bandgaps corresponding to a wavelength of 1.7 microns or slightly longer limits the thermal noise to acceptable levels. Aside from affecting fluid density, reservoir pressure and temperature do not impact the fluid spectra significantly.[57] To keep optical density values within a good range for crude oils, a pathlength of 2 mm is typically used in DFA tools.[1] Asphaltene content is determined in DFA by measuring the electronic absorption of the oil in the visible and near infrared spectral ranges.[58,59] Figure 4 shows the optical absorption spectra of many dead crude oils. Plotting optical density (OD) on a log plot vs. photon energy shows that all crude oils exhibit the same slope. This behavior is reminiscent of the Urbach tail from solid state physics and relates to the constant ratio of small to large band gap components in all crude oils.[59] 6 ACS Paragon Plus Environment

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Figure 4. (A) the optical absorption spectra of dead crude oils (at one atmosphere). There is a huge and systematic variation of crude oil color amongst different crude oils. (B) Optical density on a logarithm plot vs. photon energy yields parallel, straight electronic absorption edges, which is reminiscent of the Urbach tail.[58,59] The coloration of an oil is a linear function of asphaltene content[2,60]


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Figure 5. (A) Two-stretch overtone for selected hydrocarbons showing largely separable peaks dominated by methane (1650 nm), methyl (1700 nm), and methylene (1725 nm) groups.[61] These peaks add linearly in compositional mixtures.[63] (B) Spectra of methane, live crude oils (with dissolved gases), and dead oil.[55] (C) The optical density (OD) ratio of the oil peak to methane peak correlates with gas-oil ratio.[55] This spectral analysis is used to estimate GOR downhole using various algorithms.[55,61]

The two-stretch overtone region provides the opportunity to estimate the gas-oil ratio (GOR) of live crude oils.[55,61-63] Figure 5A shows linearity of the spectra of methane, heptane, and a mixture of the two. The NIR peaks of methane, methyl and methylene groups are readily resolvable using optical hardware compatible with the downhole environment.[55,61-63] Figure 5B shows the CH two-stretch overtone region of methane, a series of live crude oils, and a dead crude oil. Large spectral differences are seen in both the methane peak and “oil” peak for the crude oils. Figure 5C shows the correlation of GOR with OD ratios for many crude oils. The data in Fig. 5C can be used to obtain GOR. Evaluation of 25 crude oils shows that GOR can be obtained from:[55]

(1) ෡ , the signal vector ሬࡿԦ and the mass fraction vector For example, it is useful to define the response matrix ࡮ ሬሬሬԦ as: ࢓

;

;

(2)

Where bm_m and bm_o are the response factors for the methane channel to methane and dead oil, respectively, bo_m and bo_o are the response factors for the oil channel to methane and dead oil respectively, mm and mo are the mass fraction of methane and dead oil respectively, and sm and so are the optical density response of the methane and oil channels to live crude oil. Equations 1 and 2 can be combined to obtain a spectroscopic determination of GOR.[55,61]


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Incorporation of other peaks including the those at ~1700 nm which are dominated by methyl groups enriched in hydrocarbon gases other than methane helps improve the GOR estimate.[55] Other analytes can also be measured such as CO2 and some specific hydrocarbon gas compositions.[64,65] DFA measurements also include density and viscosity measurements using the response to some form of mechanical vibrating object that interacts with the MDT flowline fluid. For example, the ringdown (loss) of a mechanical oscillator in fluid is greatly impacted by fluid viscosity,[17,18] and the reduction of resonant frequency of a mechanical oscillator is greatly impacted by the surrounding fluid density.[17,18] Figure 6 provides a schematic of the In-situ Fluid Analyzer (IFA) which is a module of the MDT, that performs advanced DFA.[19]

Figure 6. The In-situ Fluid Analyzer (IFA), an advanced DFA tool that is a module of the MDT.[19] This tool performs near infrared and visible spectroscopy with a filter spectrometer and a grating spectrometer. Gas detection and fluorescence detection are also performed. Fluid density, viscosity (not shown), pressure and temperature are also measured.

2.2 Two-Dimensional Gas Chromatography (GC×GC) DFA provides the concentrations of dissolved gases, liquids, and dissolved solids of reservoir crude oils. The corresponding gradients are very amenable to a thermodynamic treatment. It is desirable to perform a detailed compositional analysis of the crude oils, especially with a geochemical interpretation, to compare with the thermodynamic analysis. Gas chromatography (GC) is a powerful tool to separate complex molecular mixtures for compound identification and quantification. Conventional GC uses a single capillary column coated with a stationary phase that influences how compounds are separated by a combination of properties such as molecular weight, volatility, polarity, or polarizability. GC has been very useful for analyzing complex mixtures, such as crude oil, and for resolving many individual compounds of interest. However, conventional GC has limited utility for unresolved complex mixtures (UCMs) in weathered and biodegraded oils and overlapping or interfering peaks that impede compound identification and analysis. In contrast, comprehensive two-dimensional gas chromatography (GC×GC) adds a second column after the primary column with a different stationary phase to separate compounds according to two combinations of physicochemical attributes. GC×GC offers advantages over conventional GC in separating highly complex mixtures, such as crude oil, into thousands of fully resolved individual compound peaks and with low variability in response factors between compounds.[66-68] Compound peaks in GC×GC are less affected by coeluting components due to higher peak capacity and increased resolving power.[69] In a common configuration of columns, GC×GC separated components according to their molecular weight (first axis) and polarizability (second axis). Those properties are particularly diagnostic of RFG processes such as biodegradation, water washing, and thermal maturity, making GC×GC a natural complement to DFA for RFG analysis.[70]


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GC and GC×GC with flame ionization detection (FID) offer advantages of quantitative detection of peak abundances, reproducibility, and increased sensitivity. GC-FID is used to examine the overall n-alkane distribution followed by GC×GC-FID for simultaneous detection of saturated and aromatic compounds plus establishing chemical retention maps. Compounds in GC and GC×GC-FID are identified through use of standard materials, retention times, and comparison with literature. To fully understand the identity of unknown compounds in chromatograms, time-of-flight mass spectrometry (TOFMS) is coupled with GC×GC permitting identification of compounds with fragmentation patterns matching with known components in various compound libraries. GC×GC-TOFMS is used to assign retention times to compounds that are then used in GC×GC-FID measurements for quantitative analysis of individual peaks. Details of the GC×GC with flame ionization detection and GC×GC with mass spectrometry for peak assignments are provided elsewhere.[42,46] Figure 7 is an example of GC×GC analysis is shown below for a crude oil acquired in the Llanos Basin, Colombia.[45]

Figure 7. GC×GC chromatogram of a Colombian oil from the Llanos basin.[45] The excellent resolving power allows chemical specificity of many peaks. Here, n-alkanes are dominant indicating no biodegradation. In addition, 25-norhopanes are much more prominent than the corresponding hopanes, indicating severe biodegradation. Two crude oil charges entered this reservoir, the first was severely biodegraded after emplacement. The second lighter charge entered the reservoir after subsidence and heating of the reservoir beyond 80 degC, thus deactivating and killing the microbes. Petroleum system modeling gives concurring results.[45] The loss of naphthalene is due to water washing of the crude oil; the aquifers in the Llanos basin are extremely active.[45]

3. Modeling Asphaltene Gradients 3.1 General Considerations Crude oils in reservoirs can exhibit significant vertical and lateral compositional gradients. In general, the most important gradients relate to GOR and asphaltene content. GOR gradients are important because many production parameters relate to this variable. Economic value depends critically on GOR; in certain oilfield settings, gas cannot be transported so is not economic. In production, handling high pressure gas 10 ACS Paragon Plus Environment

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is a major concern while liquids are relatively easy to handle; again, GOR is key. Frequently, it is desirable to maintain reservoir pressure in excess of the saturation pressure of the oil because the appearance of newly formed gas bubbles in porous media results in a decline of the formation oil relative permeability. In addition, because gas has much lower viscosity than liquid, it can flow preferentially, leaving valuable liquids behind in the formation. The saturation pressure of an oil is closely related to its GOR. For these and other reasons, it is important understand and model GOR gradients. Various socalled cubic equations of state (EoS) such as the Peng-Robinson equation are very effective at modeling GOR gradients as well as the gas-liquid phase behavior of crude oils and have been in use for 40 years.[71] The various cubic EoSs that are used for live crude oils are variants of the familiar Van der Waals EoS. There are many excellent treatments of the cubic EoS applied to crude oil.[71-73] Of course, equation of state modeling applies only for crude oil in thermodynamic equilibrium, and many reservoirs are not equilibrated. Nevertheless, the cubic EoS provides a good starting point. For equilibrated crude oils, large GOR gradients are expected only for high GOR crude oils because they are compressible.[71-73] Hydrostatic head pressure of the oil column creates higher pressure towards the base of the column. For compressible crude oils, this higher pressure causes higher density toward the base of the column. The density gradient causes lower density components such as methane to accumulate towards the top of the column. Thus, the density gradient creates a compositional gradient. In contrast, low GOR crude oils are not very compressible; the hydrostatic head pressure of the oil column does not induce a density gradient. Consequently, equilibrated low GOR crude oils have fairy homogeneous GORs.[72,73] For so-called black oils with their limited GORs, and for heavy oils with low GORs, the cubic EoS has limited application. Indeed, the properties of black oils and heavy oils are defined in great measure by their asphaltene content, not their GOR. It is important to employ an EoS for asphaltene gradients to understand reservoirs and contained fluids. 3.2 The Yen-Mullins Model The development of an EoS for asphaltene gradients requires knowledge of the size of asphaltene structure in reservoir crude oil. For example, Newton’s 2nd law of motion, F=mg in a gravitational field, requires the mass of the object. For asphaltenes, this was previously unknown, thus precluding a 1stprinciples EoS for asphaltene gradients in reservoirs. Indeed, even the molecular weight of asphaltenes was unresolved over several orders of magnitude.[74] The first measurement of molecular diffusion of asphaltenes utilized time-resolved fluorescence depolarization (TRFD), and showed that asphaltenes are small molecules, with predominantly one polycyclic aromatic hydrocarbon (PAH) per molecule.[75,76] The TRFD measurements only involve dissolution of the asphaltenes under very mild conditions. Many measurements confirm the relatively small molecular weights of asphaltenes.[26,27] Moreover, laser desorption, laser ionization mass spectrometry (L2MS) compared 23 ‘island’ model compounds (one PAH per molecule) and ‘traditional archipelago’ model compounds (multiple PAHs per molecule, linked by alkane chains) with asphaltenes and confirmed asphaltenes are dominated by island molecular structure.[77] More recently, a group at IBM Zurich obtained ultrahigh-resolution images of asphaltenes.[78,79] The analysis of hundreds of molecular image in 10 diverse samples with all kinds of asphaltenes showed the lack of even one traditional archipelago molecule.[78,79] In addition, this group proved the ability to image a variety of traditional archipelago molecules.[80] Evidently, traditional archipelagos are not observed in asphaltenes because they are not present. At low concentrations, the asphaltenes form a true molecular solution in toluene. With increasing concentration, nanoaggregates form. The critical nanoaggregate concentration (CNAC) has been observed by a variety of methods including high-Q ultrasonics,[81,82] NMR diffusion,[83] DC-conductivity,[84] AC-conductivity,[85] and centrifugation.[86] The CNAC is about 100 mg/liter in toluene. In addition, 11 ACS Paragon Plus Environment

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several of these methods that are sensitive to the change in Stokes drag upon nanoaggregate formation (NMR, DC-conductivity, centrifugation) verify that the aggregation number is quite small. Surface assisted laser desorption ionization mass spectrometry (SALDI-MS) has been used to measure the aggregate mass, and knowing the molecular weight from L2MS, the aggregation number is obtained. The resulting aggregation number for asphaltenes is approximately 7.[41,87,88] Combined small-angle x-ray scattering (SAXS) and small angle neutron scattering (SANS) confirm the small size of the nanoaggregate.[89-91] In addition, the divergence of the SANS and SAXS cross sections showed that the core of the nanoaggregate is enriched in aromatic carbon (high Z) while the periphery is enriched in hydrogen (lowest Z), thus alkane.[89-91] All data are consistent with the early proposed nanoaggregate with a disordered PAH stack in the core.[81,82] With a further increase in concentration, nanoaggregates form clusters. The critical concentration of cluster formation (CCC) was shown from the aggregation kinetics,[92,93] and DC-conductivity,[94,95] centrifugation;[94,95] the CCC is roughly 3 grams per liter in toluene. Small cluster aggregation numbers are shown in several NMR studies.[96-99]. In addition, the combined SAXS and SANS study showed the existence of a fractal cluster.[89-91] All these techniques are consistent with small aggregation numbers of roughly 8 nanoaggregates for the cluster. Detailed accounts of asphaltenes, and several reviews of asphaltene science are available.[100-102] The molecular structures and hierarchical nanocolloidal structures, the nanoaggregate and cluster, are represented in Fig. 8 known as the Yen-Mullins model.[26,27] With the size of asphaltene structures resolved, the gravity and other terms can be developed in an equation of state for asphaltene gradients.

Figure 8. The Yen-Mullins model of asphaltenes.[26,27] The dominant molecular structure with a single PAH (“island” architecture) is shown on the left. In dilute solution, asphaltenes are dispersed as a true molecular solution. At higher concentrations, asphaltenes form nanoaggregates with aggregation number of about 6. At yet higher concentrations, clusters of nanoaggregates form with an aggregation number of about 8.

3.3 Flory-Huggins-Zuo Equation of State (FHZ EoS) A good theory for asphaltenes in crude oil is the Flory-Huggins theory, a simple polymer solution theory. The theory accounts for the chemistry axiom “like dissolves like”. The Flory-Huggins theory employs the Hildebrand solubility parameters δ for the solute and solvent to gauge solubility. Small differences between solute and solvent of the Hildebrand parameters indicate solubility, large differences indicate the lack of solubility. The Hildebrand solubility parameter is defined as: δ = ∆Hvap/V

(3)


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where ∆Hvap is the enthalpy of vaporization and V is the molar volume. Thus, the Hildebrand solubility parameter is the cohesive energy density of a compound. A more generalized approach is to perform a vector projection of the Hildebrand solubility parameter in the three Hansen solubility components, polarizability, polarity, and H-bonding.[103] The solubility parameter of crude oils is dominated by the polarizability term and is similar to that of alkanes. Fortunately, the Hansen solubility parameters for asphaltenes are also dominated by polarizability.[104] Consequently, the Flory-Huggins theory can be applied for asphaltenes and crude oils with a single solubility parameter for each.[24,104] It is essential to incorporate the effect of gravity when analyzing fluid gradients in reservoirs. For a colloidal suspension, the gravity term is accounted for using Archimedes buoyancy in the Boltzmann distribution.[2] By adding the gravity term to the Flory-Huggins theory, the so-called Flory-Huggins-Zuo Equation of State (FHZ EoS) is obtained. This equation can be used with the downhole measurement of oil color (which is linear in the asphaltene content) as shown in Eq. 4.[20-22] By using a ratio of optical densities (OD) of the reservoir crude oil at two heights, h1 and h2, one is left with a ratio of asphaltene content.

 v g∆ρ (h − h )  v  OD(h 2 ) 2 1 = exp a + a   OD(h1 ) kT  v h 2 

[

v a (δ a − δ)h 2 − (δ a − δ)h1 v  − a  − kT  v  h1 2

2

]  

(4)

where va is the molar volume of asphaltene, v is the effective molar volume of the reservoir crude oil, g is earth’s gravitational acceleration, k is Boltzmann’s constant, T is temperature, and δa and δ are the Hildebrand solubility parameters of the asphaltene and reservoir crude oil respectively. The term on the left in the argument of the exponential is the gravity term, the two terms in the middle are the FloryHuggins entropy term, and the term on the right is the solubility term involving the Hildebrand solubility parameters. The molar volume of asphaltenes depends on the specific species of asphaltene as described by the Yen-Mullins model in Fig. 8. The asphaltene solubility parameter is essentially constant except for a weak temperature dependence.[23,105] A major contributor to the magnitude of the crude oil solubility parameter is the GOR.[20-22] For asphaltene gradients versus height in the oil column, the solubility term depends significantly on GOR. For high GOR crude oils, the GOR gradient tends to be large as described by the cubic EoS (if equilibrated) and gives a significant change in the solubility parameter with height in the column. Consequently, the solubility term contributes to the asphaltene gradient. For low GOR crude oils, the GOR gradient is minimal, consequently, for these crude oils, the solubility term does not add much to the asphaltene gradient. The entropy term is small and counterbalances possible small contributions from the solubility term for low GOR crude oils. That is, entropy tends to homogenize the asphaltene distributions, while the solubility term for low GOR crude oils might have a small component of increasing the asphaltene gradient due to slight concentration increases of heavy liquids towards the base of the column. For low GOR crude oils, the gravity term dominates. In particular, for heavy oils with asphaltene clusters, the gravity term is dominant and unmistakable. Eq. 5 isolates the gravity term and can be applied very rapidly to oilfield data for heavy crude oils which, if equilibrated, have asphaltenes dispersed as clusters. In general, for heavy oils (with clusters), Eq. 5 predicts that the asphaltene content is doubled for each 20 meters of height. For clusters in light crude oils, the asphaltene gradients are even larger due both to a 13 ACS Paragon Plus Environment

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large density contrast between the crude oil and asphaltenes and because of a contribution from the GOR gradient.

OD(h 2 )  v g∆ρ (h 2 − h 1 )  = exp a  OD(h 1 ) kT  

(5)

4. Modeling for Reservoir Fluid Geodynamics Figure 9 illustrates a typical schematic diagram of a 1D multicomponent system in a 2D cartoon. The gas cap is sealed by cap rock at the top of the formation and the aquifer is below the oil column. The oil-water contact (OWC) is selected as the coordinate origin. It is assumed the cap rock and the aquifer are incompressible and impermeable for diffusion of hydrocarbon components. The vertical height can be obtained by h = z sin(θ) and the lateral distance is given by z cos(θ), where θ is the dip angle and z is the incline distance (1D diffusion path).

Figure 9. Schematic diagram of 1D diffusion for gas charge into an oil column reservoir. Homogeneous initial conditions are assumed (left); at any time t, gas diffuses down, gas-oil contact (GOC) moves up, and asphaltene migrates down (right). t, θ, h, and z are the time, dip angle, true vertical depth (height), and 1D diffusion path, respectively.

If ignoring convection, sink and source, we have the 1D conservation equation:

∂Ci + ∇(J i ) = 0, ∂t

i = 1,2,..., N

(6)

where Ci and Ji are the molar concentration and molar flux of component i, and t stands for the time. If it is presumed that the partial molar volume of each component remains constant, the mole fraction of component i (xi) can be calculated by:

xi = CiV =

CiVVN = VN

CiVVN

=

N −1

CiVN N −1

V − ∑ xk (Vk − VN ) 1 − ∑ Ck (Vk − VN ) k =1

(7)

k =1

where the molar volume (V) is associated with the component partial volume by: N

N −1

k =1

k =1

V = ∑ xkVk = ∑ xk (Vk − VN ) + VN

(8)


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If a dip angle (θ) is taken into account and it is assumed that mechanical equilibrium is reached in the system, the molar flux vector in Eq. 6 at isothermal conditions is then expressed as:[37]

xiVi ∆ρg sin (θ ) (9) RT where ∆ρ = ρ – ρi, and ρi denotes the mass density of component i. Ct, g, R, and T stand for the total molar concentration, the gravitational acceleration, the gas constant, and the temperature, respectively. The first term of Eq. 9 is the molecular diffusion flux as defined in previously.[35] The [B] matrix of the drag effects can be derived from the Maxwell–Stefan diffusivities:[106] J = J mol + J grav = −Ct [ B ]−1[ Γ ]∇x − Ct [ B ]−1

Bii =

N xi x +∑ k , DiN k =1 Dik k ≠i

 1 1  Bij (i≠ j ) = xi  − , D  D iN ij  

i, j = 1,2,..., N − 1

(10)

The [Γ ] matrix represents the thermodynamic nonideality of the mixture, which can be estimated by the derivatives of the activity coefficients:[106]

Γij = δ ij + xi

∂ ln γ i ∂x j

i, j = 1,2,..., N − 1

(11) where δij is the Kronecker delta function and γ denotes the activity coefficient. If the thermodynamic nonideality of the mixture is negligible, the [Γ ] matrix becomes the identity matrix. The second term of Eq. 9 is the molar flux from the gravitational contribution. Asphaltenes take different forms in oil during gas charge.[37] They are often dispersed in oil as nanoaggregates (which are nanocolloidal) before gas charge. However, gas charge gives rise to increasing asphaltene concentrations at some locations owing to decreasing oil solvency capability to dissolve the asphaltene. As such, some asphaltenes form asphaltene clusters of nanoaggregates that are stably suspended in oil as second nanocolloidal particles. The gravitational contribution in Eq. 9 must be modified to accommodate this multimorphism. The exact ratio of nanoaggregate to cluster dispersion in the crude oil depends on the extent of increase of solution gas and also the concentration of asphaltenes in the oil. At present, detailed prediction of this ratio is difficult and is the subject of current research; nevertheless, forward models can assume justifiable ratios.[37] In principle, the gravitational flux of asphaltene clusters is much higher than that of nanoaggregates because the former is much larger. Thus, asphaltenes are simply divided into two groups in our model: nanoaggregates and clusters, with fractions of (1 – ϕclusters) and ϕclusters, respectively. Correspondingly, the asphaltene molar flux is also divided into two parts: a contribution of nanoaggregates which is estimated by the Maxwell–Stefan gravitational term in Eq. 9; the gravitational diffusion flux can be represented as:[37]

J agrav = −Ct [ B ]−1

xa ∆ρg sin (θ ) Vaggregates (1 − ϕ clusters ) + Vclusterϕclusters RT

[

]

A similar expression using the simplified Stokes law is given by:[107]


(12)

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Jagrav = −Ct [ B]−1

xaVaggregates∆ρg sin(θ ) RT

(1 − ϕclusters) + Ct xauclustersϕclusters

Page 16 of 60

(13)

where subscript a stands for total asphaltenes and Vaggregates Vclustersare the partial molar volume of nanoaggregates and clusters, respectively. It is presumed that asphaltene density remains unchanged in either form. The Stokes sedimentation velocity is simply calculated by Stokes’ law:

u clusters =

1 2 gd clusters (ρ a − ρ )sin (θ ) 18η

(14)

where η is the viscosity of the reservoir fluid and dclusters is the average particle diameter of asphaltene clusters. To solve the aforementioned differential equations, initial and boundary conditions are required. Two scenarios are considered: (1) only the oil column is taken into account, which gives rise to a moving boundary problem,[37] and (2) both gas cap (light oil) and oil column are taken into account simultaneously. The latter means the single thermodynamic model can be used in all the phases including near critical fluids without vapor-liquid equilibrium (separation) calculation. Initial conditions are set to be homogeneous compositions in the gas cap and oil column, respectively, i.e.,

Ci (0, z ) = Ci0_ oil ,

z ≤ Z oil ,

i = 1,2 ,..., N

Ci (0, z ) = Ci0_ gas ,

z > Z oil ,

i = 1,2 ,..., N

(15)

where Zoil is the 1D distance of the initial oil column. At the base of the oil column (z = 0), i. e., the OWC, if no components are flocculated, impermeable diffusion boundary conditions are given by:

∂Ci (t ,0 ) = 0, ∂z

i = 1,2 ,..., N

(16)

For the asphaltene component, if its instability in the oil is detected,[36] a simple deposition flux is presumed for considering asphaltene flocculation at the base of the oil column (OWC):

∂Ca (t ,0) = kaCa (t ,0 ) ∂z

(17)

where ka is the asphaltene deposition constant, which is treated as an adjustable parameter. ka = 0 indicates that there is no asphaltene flocculation at the base of the oil column (reducing to Eq. 16).


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If the gas cap and oil column are simultaneously considered, at the top of the gas cap (z = Ztop), impermeable diffusion boundary conditions are similarly given by:

∂Ci (t, Ztop ) ∂z

= 0,

i = 1,2 ,..., N

(18)

If only the oil column is taken into consideration, because we assume that the gas component is always in equilibrium with the oil column at the GOC and no asphaltene component moves up from the oil column to the gas cap, we have the following boundary conditions at the GOC.

C g = C gsat ,

and

∂Ci (t , GOC ) = 0, ∂z

i = 1,2,..., N

(19)

where superscript sat refers to the saturated (equilibrium) condition at the GOC and subscript g stands for the gas component. Because gas addition into the oil column results in swelling of the oil column, the GOC variations can be projected by

J gGOC

t

z = z0 + ∫ 0

C gGOC − O − C gGOC − G

dτ

(20)

where z0 is the initial depth of the GOC. Jg and Cg are the gas flux and molar concentration at the GOC. If the asphaltene flocculation occurs at the base of the oil column, we can calculate asphaltene loss in mole at any time t by

Z

Z

0

0

Qloss (t ) = ∫ C 2 (t , z )Adz − ∫ C 2 (0, z )Adz

(21)

where A is the sectional area. The thickness of asphaltene deposition is then estimated by

∆z s =

Qloss (t ) = AC s

Z

Z

0

0

∫ C 2 (t , z )dz − ∫ C 2 (0, z )dz (22)

Cs

where Cs is the average molar concentration of asphaltenes in the asphaltene deposition phase. As mentioned above, an activity coefficient model is required to describe nonideality in oil or/and gas. The activity coefficient can be calculated by the Flory–Huggins regular solution model.[35-37]

ln γ i = ln

Vi V V +1− i + i V V RT

∑∑φ φ [(δ N

N

j k

− δ j ) + 2lijδ iδ j − 0.5(δ j − δ k ) − l jkδ jδ k 2

i

2

j =1 k


]

(23)

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where δ is the solubility parameter and ljk is the binary interaction parameter between components j and k. For pure component j, ljj = 0. If ljk = 0, the Eq. 23 is reduced to: Vi V V (δ − δ ) +1− i + i i V V RT

2

ln γ i = ln

(24)

N

δ = ∑φkδ k

(25)

i =1

φi =

xiVi

(26)

N

∑ xkVi i =1

If the system reaches a thermodynamic equilibrium (or stationary state), we have ∇ J i = 0,

i = 1, 2 ,..., N

(27)

 V ∆ρg∇z sin (θ )  ∇T ,P ln xiγ i +  i  = 0 RT  

(28)

If we further assume that a reservoir fluid has only two-pseudocomponents (asphaltene and maltene), Eq. 28 combined with Eq. 24 is reduced to the Flory–Huggins–Zuo EoS (Eq. 4) for asphaltene gradients in hydrocarbon reservoirs.[20-22]

4.1 Applications of the 1D RFG Model The RFG diffusive model can be successfully tailored to predict asphaltene gradients in diverse RFG processes such as biodegradation in reservoirs [43] and late gas charge into oil reservoirs using a two pseudocomponent mixture coupled with the FHZ EoS.[107] For biodegradation, it is presumed that microbes consume the alkanes rapidly in comparison with diffusion times of the components to the oilwater contact. The rate-limiting step is presumed to be alkane diffusion down to the oil-water contact. The so-called dynamic FHZ EoS with two pseudocomponents can be used to determine the time required for equilibrium asphaltene distributions in oil reservoirs without influxes and outfluxes at different initial conditions.[25] Equilibrium fluid distributions are shown to be independent of initial conditions and always converge to the Gibbs sedimentation criterion characterized by the FHZ EoS. A 1D multicomponent diffusion model for gas charges into oil reservoirs was developed by considering a moving boundary problem.[34] This diffusion model presumes constant total molarity regardless of concentration variations. A mathematical transformation of the complex moving boundary problem into a fixed boundary problem with simple boundary conditions was developed by introducing a new space variable.[35] Nevertheless, this transformation is difficult to extend to 2D/3D problems. To overcome the deficiency of a presumption of constant total molarity, a formula has been derived to obtain component mole fractions based on component concentrations.[6] In addition, a comparison of the three diffusion models found that similar results were obtained in all cases.[6] More importantly, density inversion can be simulated by the three models.[6,34,35] As gas diffuses into the oil, the asphaltenes accumulate at the base of the gas front. Accumulation of asphaltene here can 18 ACS Paragon Plus Environment

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increase density of the oil beyond the original concentration increasing the density likewise beyond the original density. This density inversion can induce convective flow that accelerates asphaltene migration downward to the base of the oil column. Furthermore, algorithms have been developed to calculate spinodal and binodal phase boundaries using the same thermodynamic model as in the diffusion model and then to analyze asphaltene instability during gas charge into oil reservoirs.[36] Figure 10 depicts the simulated density changes with true vertical depth for the vertical reservoirs of two different thicknesses of 100 m, (Figs. 10a–10c), and 20 m, (Figs. 10d–10f) in the absence and presence of the gravitational contribution during gas charge. Without the gravitational contribution (molecular diffusion only), density inversion can be created before the gas front reaches the base of the oil column in Figs. 10a and 10d. After large amount of gas arrives at the base over 0.7 million years (Mya), the enriched gas in the oil results in a reduction of fluid density, as shown in Fig. 10d for the thin oil reservoir. With the molecular diffusion and gravitational contribution, a small density inversion can be generated in early time at the top of the oil reservoir. With inclusion of gravity and with formation of the (larger) asphaltene clusters, large density crude oils can be formed at the base of the oil column. Fluid density at the base of the diffusive gas front can easily be greater than the density of the original oil as shown in Figs. 10b–10f. This high density oil can form at the base of the crest giving rise to (convective) gravity currents moving asphaltenes down the base of the permeable formation to the base of the reservoir as shown in Fig. 11.

Figure 10. Simulated fluid density changes with true vertical depth for the vertical reservoir of two different thicknesses during gas charge into oil reservoirs with and without the gravity terms. (a) molecular diffusion only in 100 m oil column; (b) molecular diffusion and gravity terms in 100-m oil column with zero cluster fraction; (c) molecular diffusion and gravity terms in 100-m oil column with 0.02 cluster fraction; (d) molecular diffusion only in 20-m oil column; (e) molecular diffusion and gravity terms in 20-m oil column with zero cluster fraction; (f) molecular diffusion and gravity terms in 20-m oil column with 0.02 cluster fraction. Fluid density inversion can be easily generated at the bottom of sealing rock in a short time


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period, which in turn induces gravity currents, thus speeding up to transfer asphaltenes from the top to the base of the oil column.

Figure 11 shows a schematic of asphaltene accumulation at the base of the oil column upstructure because of a short vertical distance from the gas-oil contact due to the base of the crest of the permeable formation. This asphaltene accumulation creates higher density crude oil at the base of the crest; this can then convect to the base of the reservoir as shown in Fig. 11b.

Figure 11. (a) Schematic of asphaltene accumulation at the base of a permeable interval upstructure in an anticline reservoir; (b) Enlarged view of reservoir upstructure and gravity currents induced by density inversion.

The characteristic buoyancy convective velocity (ub) induced by the density inversion is estimated in Eq. 25.[12]

ub =

∆ρgk sin θ Φµ

(25)

where θ is dip angle, k is the permeability, g denotes the gravitational acceleration, Φ is porosity, and µ is viscosity. Figure 12 shows the fluid velocities for a reservoir with the following parameters; k = 300 md, Φ = 0.2, µ = 1 cP, and for different dip angles at different density inversions ∆ρ. The greater density inversion, the higher buoyancy convective velocity. Flat reservoirs have no buoyancy convective velocity whereas vertical reservoirs have the largest buoyancy convective velocity. It is also seen that the buoyancy convective velocities are much greater than the diffusion velocity (~0.1 km/MYa) under most conditions. As such, density inversions significantly accelerate asphaltene migration to the base of the oil column over geological time.


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800 dRho = 0.1 kg/m3 700

Buoyancy velocity, km/MY

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dRho = 0.5 kg/m3

600

dRho = 1.0 kg/m3

500

dRho = 1.5 kg/m3

400 300 200 100 0 0

20

40

60

80

100

Dip Angle, Degree Figure 12. Buoyancy convective velocity (kilometers/ million years) with dip angle at different density 3 inversion (∆ρ = 0.1, 0.5, 1.0 and 1.5 kg/m ).

5. (Rock Formation) Geodynamics and Reservoir Fluid Geodynamics There are innumerable reservoir complexities associated with rock formations, fluid compositional distributions (or fluid structures), and their interactions; optimal reservoir development requires understanding these many complexities. (Remote) seismic imaging is generally performed to obtain an image of the layers associated with the subsurface. However, the access for direct measurement of reservoir properties is limited to oil wells. Oil wells provide the opportunity to measure all manners of properties; whole cores can be acquired, many fluid samples can be acquired and DFA performed by the MDT,[1] and formation pressure surveys are obtained. Petrophysical logs can be run and include high resolution imaging, nuclear magnetic resonance, neutron scattering, gamma ray scattering, phase and amplitude response at many electromagnetic frequencies, and phase and amplitude response at many acoustic frequencies;[108] all providing a wealth of information primarily near the wellbore. However, the coverage of wells in a field is generally very limited at the very times when significant capital investment must be made. For example, deepwater oilfields can be one to several kilometers or more in lateral extent and generally have fewer than ten wells, often fewer than five wells, when production plans are developed and expensive sea floor facilities for fluid handling are designed and installed. With such limited coverage, it is essential to project fluid and formation properties away from the wellbore. For understanding rock formations, there is a well-worn workflow; the concept is simple, to understand how to project formation properties away from the wellbore, it is essential to understand how the formation came to be. All available information is used to understand the depositional setting of the formations (e.g. turbidite fans, aeolian sands, near shore facies etc). In addition, an understanding is developed for the evolution of these formations in geologic time; this is called geodynamics (and does not include the reservoir fluids). The term “geodynamics” is defined as “the branch of geology concerned with forces and processes, especially large scale, of the earth’s interior particularly as regards to their effects on the crust or lithosphere”.[3] These concepts have been adopted wholeheartedly by the oil industry for applications to oil reservoirs. It is now considered simplistic to prepare a geologic model of a reservoir without an understanding the geodynamics that led to that geologic model. Figure 13 below


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shows a geodynamic analysis from the middle Eocene Epoch (~45 Mya) to present day that is tied into seismic image interpretation of the basin.[109]

Figure 13. An example of a geodynamic evaluation of a basin.[109]. Schematic restoration of a simplified geologic cross section across the western Gulf of Mexico, illustrating fold-belt evolution. (A) Initially, extension and shortening were detached on the deep-salt layer. (B) Following the emplacement of a continuous salt canopy in the Eocene, translation was detached on allochthonous salt, forming the East Breaks fold belt near the downdip end of the welded canopy. (C) Oligocene–Holocene craton uplift has tilted the margin, reactivating parts of the deep-salt detachment and shifting shortening to the Perdido fold belt at the seaward end of the margin. Geology on the cross section is simplified from a regional seismic section.[109] (VE = vertical exaggeration)

In contrast to the treatment of rock, the oil industry does not have a comparable workflow for oil. There is a large missing component in the treatment of reservoir crude oils; nevertheless, it is at least equally important to project oil (and tar) properties away from the wellbore as it is for rock. The workflow for understanding oilfield reservoirs generally starts with petroleum system modeling (PSM).[7,8] PSM accounts for kerogen catagenesis and oil generation, oil migration and trap filling. Of course, such modeling represents an enormous challenge; consequently, PSM is not overly deterministic but rather provides general guidance for understanding oil reservoirs. PSM is not designed to predict the distribution of fluids within a reservoir. The requisite diffusive, convective, and phase change processes for understanding such reservoir fluid distributions are not part of PSM. Consequently, the processes that occur within the oil in reservoirs after trap filling are not properly treated.[11-16] Modeling then picks up again for treating present day oil reservoirs for production simulation. Reservoir fluid geodynamics (RFG) is the name given to account for processes which hydrocarbon reservoir fluids undergo after (and during) reservoir trap filling.[11-16] (As noted earlier, “geodynamics” has been used by geologists to describe evolution of the formations, and “fluid geodynamics” has been used to account for magma flows in the mantel within geodynamic treatments.) RFG includes processes long studied by geochemists including biodegradation and water washing.[47] Nevertheless, as shown below, even for these processes, use of equation of state modeling greatly clarifies a detailed spatial accounting of fluid properties and their variations within the reservoir. In particular, incorporation of the 22 ACS Paragon Plus Environment

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FHZ EoS and its reliance on the Yen-Mullins model has been very revealing of all manners of complexities in reservoir as established below in many reservoir case studies. Figure 14 shows a schematic of a time line with different disciplines indicated. Petroleum system modeling provides the timing, type and volume of fluids entering the reservoir. Reservoir fluid geodynamics as proposed herein accounts for the many processes that can redistribute and alter the fluids and cause phase transitions such as tar formation. Simulation modeling then accounts for production in a present day setting.

Figure 14. A timeline showing the different disciplines. Petroleum system modeling accounts for the timing, type and volume of fluids that enter the reservoir. Reservoir fluid geodynamics, a newly formalized discipline, accounts for the many processes that redistribute fluids within the reservoir and that account for phase transitions such as tar formation.[11-16] Simulation then accounts for reservoir production in present day. 6. Application of Reservoir Fluid Geodynamics to Reservoirs 6.1 Compartmentalization and Connectivity One of the most important concerns is the extent of reservoir “connectivity” or its inverse, compartmentalization.[1] A compartment must be intersected by a well for drainage. A reservoir might consist of one or a few large compartments. For reservoirs that exhibit good connectivity, few wells are required. For reservoirs consisting of many small compartments, many wells are required to drain. When well costs are high, compartmentalized reservoirs can be uneconomic. An industry study concluded that 75% of deepwater reservoirs in the Gulf of Mexico underperformed in both rate of production and ultimate oil recovery, primarily due to unrecognized compartmentalization.[110] A large variety of sealing barriers separating compartments can be present in oilfield reservoirs. Some barriers correspond to (sealing) faults that are observed in seismic imaging. Seismic imaging is inherently low resolution so that layers or objects that are much smaller than 10 meters thick are difficult to resolve. Other sealing barriers are not only invisible to seismic imaging, they can even be invisible to wireline logging measurements performed in oil wells which often have a resolution of centimeters.[111] Indeed, such ‘invisible’ barriers can even hold off 2000 psi of depletion pressure differential.[111] One method to reveal compartmentalization is to perform pressure surveys. In an oil column, the pressure increases with depth according to the fluid density, so a pressure gradient is always obtained. In a new, 23 ACS Paragon Plus Environment

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unproduced field, if two formations are not in pressure communication, then they are not in flow communication and represent two separate compartments. Similarly, if a well intersects two oil-bearing formations, and the oil in the upper formation has a higher concentration of asphaltenes, then the formations are likely not connected as asphaltenes are negatively buoyant in crude oil.

Figure 15. Well log data from deepwater Gulf of Mexico.[28,29] The exact depth is confidential, thus the leading xy in depth. Six stacked sandstone formations are intersected (A,B,C,D,E and F) with intervening shale zones with slightly higher gamma ray signal (green curve). Formation pressures with gradients are shown. DFA-measured coloration at 815nm for five sampling points are shown. Fluorescence is also shown and is higher for crude oils of lower color (asphaltene content). Both pressure and DFA color measurements show significant compartmentalization; the oil company ceased any activity in this reservoir due to this conclusion.

Compartmentalization. Figure 15 shows an example of a highly compartmentalized series of oil-bearing sandstone layers (sands) in a single well. Pressure measurements show Sand A is isolated from Sand B, Sand C is isolated from Sand D, and Sand E is isolated from Sand F. DFA color measurements show the oil in Sand A has three times the asphaltene content as the oil in Sand B below it; clearly these are not connected. The oil in Sand B has more asphaltene than the oil in Sand C. The asphaltene difference is large between Sand C and Sand D, so lack of connectivity is plausible. Sand D has more asphaltene than Sand E below it, thus they are isolated. Combining the pressure and DFA color data shows the sands are all isolated from each other; the oil company ceased all activities in the field due to this conclusion.[28,29] Dead crude oils were obtained from most of these zones to look for detailed compositional differences. No significant differences could be detected in the heavy ends using ultrahigh resolution mass spectroscopy,[28] nor in the liquid phases using comprehensive two dimensional gas chromatography (GC×GC).[29] The crude oils have very similar chemical components, but differ mostly in their asphaltene fractions.[28,29] That is, the chemical constituents in each phase are very similar as shown by high resolution analytical chemistry. What differs is the overall concentrations of the different gas, liquid and solid constituents; DFA measurements are well suited to analyze these fractions. With DFA measurements, there is minimal sample handling reducing sample transfer problems that can arise, especially in remote, offshore locations. In addition, the DFA tools within a well have the same time,


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temperature, calibration, and baselines, such that many systematic errors cancel in establishing fluid gradients. Nevertheless, the more important issue is to identify the good news of connectivity (better economic value), not just the bad news of compartmentalization (lower economic value). While pressure measurement can conclusively demonstrate the bad news of compartmentalization, it is not definitive to establish connectivity. Pressure connectivity is a necessary but insufficient condition to establish flow connectivity in production time frames (years to ten years).[1] Indeed, in the industry-wide study showing the huge problem with unrecognized compartmentalization in deepwater Gulf of Mexico, all of the fields had extensive pressure surveys;[110] pressure measurement is grossly inadequate to establish reservoir connectivity. The cause of the shortcoming is simple: very little mass transfer between two compartments is needed to equilibrate pressure. A leaky seal between two compartments can act as a conduit for a little mass transfer equilibrating pressure in geologic time, but leaky seals are not adequate to handle significant oil flow in production time. Connectivity and Equilibrated Asphaltenes. The equilibration of asphaltenes in a reservoir requires extensive mass flow, likely both during and post trap filling. Consequently, if asphaltenes are equilibrated across a field, the field is likely connected. Many oilfield case studies have established that equilibrated asphaltenes imply connectivity in a production time frame.[2,31,32] Figure 16 shows examples of equilibrated asphaltenes involving all three structures in the Yen-Mullins model as well as heavy resins for a light condensate.[21]

Figure 16. Equilibrated asphaltenes imply reservoir connectivity as shown in five oilfields examples; in all cases, production proved the connectivity prediction to be correct.[21] These five examples also involve the three species of the Yen-Mullins model as well as heavy resins for a light condensate. The “black oil” reservoirs C and D with moderate concentration of asphaltenes and moderate GORs are discussed below in greater detail.

Figure 16A shows a heavy end gradient in a light condensate.[112] In this case, the DFA color measurement was close to zero throughout the visible spectral range; thus, the DFA fluorescence measurement was used.[112] For low concentrations of fluorophores and chromophores, the fluorescence intensity is linear in concentration of fluorophores,[113] which in this case are the heavy ends of a resin. For another light condensate, the predominant fluorophore was identified as perylene,[114] and the corresponding molecular dimension of perylene is used in the FHZ EoS analysis in Fig 16A. Indeed,


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peryelene fluorescence gave this oil a blue appearance under normal illumination; a photograph of this condensate under visible illumination is shown in Fig. 16A as an inset. Fig. 16B is a heavier condensate with a molecular dispersion of asphaltenes. The asphaltenes across the field were shown to be equilibrated, however, the dissolved gas was not equilibrated across the field. The reservoir had two separate anticlinal structures, each with a gas cap and with different GOCs.[115] The field was put into production and was proven to be connected; equilibrated asphaltenes again are associated with reservoir connectivity. The GOR was not equilibrated due to a late gas charge (entry of natural gas into a reservoir that has previously been filled with oil) filling different amounts of gas into the two anticlines. Equilibration of the two GOCs by diffusive methane transport across the field is a very slow process. In contrast, the asphaltene content of saturated crude oils is not impacted by further gas phase addition to the reservoir. (Saturated crude oils are at their bubble point and cannot hold more solution gas.) Figures 16C and 16D show two oilfields with black oils; black oils have moderate GORs and moderate asphaltene content. Black oils almost always have asphaltenes dispersed as nanoaggregates.[2,30,31,116,117] These two black oil case studies are discussed in greater detail below. Figure 16E shows a heavy oil; heavy oils have asphaltenes dispersed as clusters.[32,118,119] A detailed review of a heavy oil column is presented below in the subsection entitled “Heavy Oil Formation and Equilibrated Asphaltenes.”

Figure 17. An expanded view of the reservoir shown in Fig. 16D. DFA color measurement from across a large oilfield versus (true vertical depth subsea).[2] The exact depth is confidential, thus the leading “x” in the depth. The reservoir is shown in Fig. 1 and is tilted thereby giving large differences in depth across the field. Sand A and Sand B are offset in pressure, thus not connected. Sand A, Sand B and Sand B North independently fit the FHZ EoS; the asphaltenes are equilibrated in each sand; thus lateral connectivity is predicted in each sand. The development well data designated DW was drilled many years after that other wells. All connectivity and compartmentalization assessments from DFA color measurement were confirmed more than a year later in production.[2]


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Figure 17 shows an expanded view of the data shown in Fig. 16D. This figure shows DFA color data for a reservoir with a moderately low GOR crude oil (~600 scf/bbl). Sand A and Sand B are at significantly different pressures (not shown) so are not connected.[2] The DFA color gradients in each sand are fit using the gravity term only of the FHZ EoS and using the asphaltene nanoaggregate here with a particle size of 1.6 nm for all sands.[2] The low GOR precludes appreciable GOR gradients; the predominant contribution to asphaltene gradients is the gravity term. At times, there is contradictory data in well log analysis; often the asphaltene gradients as measured by DFA are the most reliable. Fig. 18 shows an expanded view of the case study presented in Fig. 16C. In this reservoir, two wells intersected the same sand body across a field, and the connectivity of this sand needed to be established. The pressure measurements across the field showed an offset of about 20 psi and indicate lack of connectivity; however, this pressure difference is within error of the measurement made at depth in the well considering reservoir pressures in the Gulf of Mexico can exceed 30,000 psi.[31]

Figure 18. An expanded view of the reservoir presented in Fig. 16C. Well log data from two wells across an oilfield. DFA color data is fit with the FHZ EoS using 2 nm asphaltene nanoaggregates indicating equilibrated asphaltenes across the field. Connectivity is implied and was proven in production; the pressure disturbance by production in one well was measured in the second well.[31] Other fluid properties are more ambiguous regarding connectivity, and the pressure measurement incorrectly indicates compartmentalization, but is within error.[31]

Fig. 18 shows that the DFA color gradient is fit with the FHZ EoS using a 2nm asphaltene nanoaggregate thereby indicating equilibrated asphaltenes and connectivity. When production was initiated in one of the wells depicted in Fig. 18, there was “pressure interference” or a pressure disturbance measured in the other well. This clearly establishes connectivity and validates the predictions from equilibrated asphaltenes.[31] A recent oilfield case study is again in deepwater, Gulf of Mexico. This is a middle Pliocene reservoir rock, so very recent relative to typical oil reservoirs. Two wells intersected the two reservoir sands denoted upper sand and lower sand in Fig. 19.[120] Each sand shows equilibrated asphaltenes matching the FHZ EoS with 2nm nanoaggregates, so connectivity is indicated. The offset in the asphaltene gradients between the two sands matches the shale break observed seismic imaging. Fig. 19 shows a large shale barrier or shale “break” between the sands covering most, not all, of the oilfield. This young reservoir is relatively shallow and not covered by a salt diaper; consequently, the seismic imaging is excellent. Data from twelve months of production and from corresponding production simulations are consistent with all connectivity predictions from DFA in this oilfield.[120]


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Figure 19. Two wells intersect the upper and lower sands which are separated by a shale break across most, not all of the oilfield.[120] Each sand shows equilibrated asphaltenes (2nm nanoaggregates in the FHZ) indicating connectivity. The offset in the asphaltene curves between the upper and lower sand is consistent with the extensive shale break which precludes vertical equilibration of asphaltenes over much of the oilfield.

Figure 20. Well log and laboratory fluid data in the reservoir shown in Fig. 19.[120] All fluid data is consistent with the DFA color gradient. The high GOR crude oil shows a gradient that contributes to the


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asphaltene gradient via the solubility term of the FHZ EoS. There is no discernable thermal maturity variation as shown be the ratio Ts/(Ts+Tm).[120]

The oilfield examples analyzed up to this point have been associated with equilibrated “vanilla” crude oils that have not been significantly impacted by possible complex fluid processes. This statement is supported in part by extensive fluid properties measurements using GC×GC with geochemical analysis.[31,120] The C27 hopanoids Ts (18α(H)-22, 29, 30-trinorneohopane) and Tm (17α(H)-22, 29, 30-trinorhopane) are used as thermal maturity markers particularly for crude oils from the same (kerogen containing) source rock.[47] At higher kerogen maturation temperatures, there is a decreasing ratio of metastable Tm versus the stable Ts (m for metastable, s for stable giving a mnemonic for remembering this).[47] Figure 20 shows general consistency of the thermal maturity markers Ts and Tm, indicating relatively minor variations in thermal maturity at present day. Incompatible Reservoir Charges and Asphaltene Instability. A fairly common occurrence in reservoir filling or “trap” filling is for different crude oils or gas and crude oil to be (separately) charged into reservoirs. Gas charge into a crude oil reservoir (or crude oil charge into a gas reservoir) is easy to observe because gas and oil have such different properties. A celebrated case of such a charge scenario is shown in Fig. 21.[121] The reservoir is a so-called tilted sheet sand reservoir and is shown in Fig. 2.[21] With this charge of gas into oil (or vice versa), the reservoir fluids density stack without mixing,[122] except in and near charge points of the reservoir.[123,124] If the reservoir pressure is higher than the saturation pressure (or bubble point) of the crude oil, then gas will diffuse into the crude oil increasing solution gas at and near the GOC as measured by GOR. The GOR will then reflect a diffusive gradient that reaches lower into the oil column as geologic time progresses. Figure 21A shows a schematic of this process and Fig. 21D shows the corresponding diffusive gradient of GOR over the top half of the oil column.

Figure 21. Mixing of separate gas and oil charges into the reservoir.[121] Lab data and DFA data are in close agreement. (A) a schematic showing the gas diffusing into the oil column increasing solution gas and expelling asphaltene. (B) A series of dead crude oils associated with this reservoir, the missing asphaltenes at the top of the column are evident in the very light coloration of these oils. (C) The expelled asphaltenes are migrating to the base of the column. At the base, the asphaltenes are dispersed as clusters and approach an equilibrium distribution of asphaltene clusters. (D) Gradients in GOR and saturation pressure: near the top of the column, these gradients are diffusive, gigantic and not nearly


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equilibrated. Towards the base of the column, these gradients are much closer to equilibrium. Diffusion has not had time to transport much solution gas to the base of the oil column.[121]

This increase in GOR towards the top of the oil column will cause the solubility parameter of the oil to decrease both because methane has a small solubility parameter and because the density of the crude oil decreases with increasing GOR. This decreased solubility parameter leads to asphaltene instability. There are a variety of fates of unstable asphaltenes: clusters can form from nanoaggregates causing asphaltenes to accumulate at the base of the permeable interval and enabling long distance transport of asphaltenes to the base of the oil column,[125-128] they can deposit locally;[129] they can migrate a short distance, then deposit;[124] or they can migrate to the base of the reservoir even if 30 kilometers away.[32,125-128] The differing fates depend both on the nature of the two fluids being mixed as well as on the detailed geometry of the mixing.[124,128,129] In the oilfield depicted in Fig. 21, the asphaltenes are clearly absent from the top of the oil column as gleaned by the light yellow color of the highest oil depicted in Fig. 21B. Fig. 21C shows that the asphaltene concentration and oil coloration are consistent with the asphaltenes being expelled from the top of the column where GOR is high and are accumulating near the base of the oil column in the oil phase where the GOR is relatively low.

Figure 22. (A) The GOR gradient in Fig. 21 is accounted for by diffusion of solution gas components from the top of the oil column.[125] The massive GOR gradient at the top and small GOR gradient towards the base of the oil column is reproduced. (B) The initial asphaltene distribution is nearly vertical. The asphaltenes expelled from the top of the column accumulate at the base.

Figure 22 presents time-dependent models of the GOR gradient for the reservoir presented in Fig. 21.[125] The reservoir is a tilted sheet sand; most of the diffusive distance is lateral while there are small vertical distances indicated. The timeline that matches best is about 8 million years; the mixing of these reservoir fluids is predicted to have occurred in the upper Miocene which is quite reasonable for this Gulf of Mexico reservoir. In addition, the asphaltene gradient is reproduced; asphaltenes are expelled from the top of the column and migrate towards the base where they set up a gravitational gradient of clusters.[125] Heavy Oil Formation and Equilibrated Asphaltenes. Figure 23 shows a reservoir where this process of asphaltene instability, migration and equilibration has come to completion.[32,126,127] This is a massive reservoir, a four way sealing anticline, and is 50 kilometers long. Asphaltenes were destabilized near the crest of the field and migrated in convective currents to the base of the reservoir. The high asphaltene concentration at the base of the column assured clusters formed in that region. The asphaltene gradient at the base of the 100 kilometer rim of this field matches the gravity term alone in the FHZ EoS with no 30 ACS Paragon Plus Environment

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adjustable parameters. Figure 23 shows that the cluster size in this field is 5.1nm,[32] matching the nominal cluster size of 5.0nm of the Yen-Mullins model published many years before this study.[26] Towards the crest of the field where the asphaltene concentration is smaller, the asphaltenes are dispersed as nanoaggregates.[32] The asphaltene gradient is a factor of ten over 60 meters of height and over the 100 kilometer perimeter of this oilfield; this is a stringent and successful test of the FHZ EoS and the Yen-Mullins model. This is a remarkable occurrence, where reservoir measurements at the 100 kilometer length scale validate asphaltene nanoscience determinations 14 orders of magnitude smaller.[32]

Figure 23. (A) An oilfield exhibits a gravitation gradient of asphaltene clusters over its 100 kilometer rim. The asphaltene gradient matches the gravity term of the FHZ EoS with no adjustable parameters.[32] The 10x asphaltene gradient creates a 1000x gradient in viscosity, an enormous concern in production. (B) The saturation pressure (Psat) is invariant and carries no signature of this huge viscosity gradient. (C) The maturity index Ts/(Ts+Tm) shows no gradient, demonstrating this giant asphaltene and viscosity gradient is not from a maturity gradient of the oil.

Convective flow of asphaltene enriched currents must have occurred to achieve this equilibrium; diffusion across this length scale would take a trillion years. In addition, the crest of the field is in the south and the steeper dip angle away from crest is also towards the south. Consequently, the asphaltene-enriched convective flows caused more accumulation of asphaltene in the south and less in the north. Three points that deviate towards lower asphaltene content are in the north of the field as shown in Fig. 23.[127] The Yen-Mullins model presumes that there is no change in the chemistry of the asphaltenes in this oil column; the 10x gradient is due to a gravitation gradient of nominally identical asphaltene clusters. Any theoretical treatment of asphaltene gradients with variants of the cubic EoS starts with a substantial deficiency in treating colloidal solids with a theory developed for high pressure gases and gas-liquid equilibria. Nevertheless, one method of treating asphaltene gradients with the cubic EoS is to use many pseudo-components for the asphaltenes, each with its own interaction parameters and its own pseudocritical constants. This type of treatment is opposite to the application of the Yen-Mullins model which presumes a single class and chemistry of asphaltenes. The distinction in the two treatments is readily checked (Fig 24 and 25).


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Figure 24. Molecular weight and nanoaggregate aggregation number for asphaltenes obtained from the heavy oil column from Fig. 23 (in blue) as well as two asphaltene samples from a separate stacked reservoir in this field.[41]. The asphaltene samples are essentially the same validating use of the YenMullins model in the FHZ EoS to account for the asphaltene gradient in Fig. 23.

Figure 24 shows that the asphaltene molecular weights and nanoaggregate weights are invariant in the large reservoir shown in Fig. 23. The molecular weights are obtained by L2MS,[41,130] and the nanoaggregate weights are determined by SALDI-MS,[41,131] allowing the aggregation number to be determined. The samples were acquired from the top to the bottom of the heavy oil column. The invariance of the molecular weight and aggregation number validates use of the Yen-Mullins model. In contrast, use of many different pseudo-components in a cubic EoS treatment is contradicted by these results.

Figure 25. The sulfur speciation for asphaltenes obtained from the heavy oil column from Fig. 23 (in blue) as well as two asphaltene samples from a separate stacked reservoir in this field.[40]. The asphaltene


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samples are essentially the same with thiophene sulfur dominating thereby validating use of the YenMullins model in the FHZ EoS to account for the asphaltene gradient in Fig. 23.

Figure 25 shows that the sulfur speciation of the asphaltenes from oil samples throughout the heavy oil column from Fig. 23 is essentially the same within error, with thiophene sulfur dominating in all these samples.[40] Sulfur is more chemically labile than other elements in asphaltenes; thus, analysis of sulfur chemistry is particularly sensitive to possible different chemistries of these asphaltene samples. Nevertheless, the samples are very similar validating again that the asphaltene chemistry is very similar for all samples in the oil column with a 10x gradient of asphaltenes. Certainly, Fig. 25 does not show any systematic trends of the sulfur chemical speciation in this column. Use of the Yen-Mullins model is validated with its presumption of the same asphaltene chemistry within a column of crude oil. In contrast, the cubic EoS approach utilizing many pseudo-components with the cubic EoS is shown to be invalid. Tar Mats. The instability process that led to this massive accumulation of asphaltenes at the base of the oil column in Fig. 23 caused the asphaltene content to exceed 35% in the crude oil. In this crude oil (and many others), 35% asphaltene content represents the limit of solubility. Once the asphaltene (clusters) exceed this concentration, the asphaltenes undergo phase separation yielding a carbonaceous phase (a tar mat) that is likely about 60% asphaltene in this case. This carbonaceous material coats pore throats, reducing permeability to negligible values. Further instability towards the crest causes more asphaltene accumulation at the base, and the tar mat builds in height. In this reservoir there is a 10 meter vertical tar mat that seals off any contact of the aquifer with the oil column; there will be no aquifer sweep of this crude oil reservoir.[32,126,127] In addition to having well data from 8 wells in the oil column as depicted in Fig. 23, there is also data from 5 wells drilled in the tar zone.[32,126,127] Whole core was acquired in these tar wells and small core plugs at known depths were cut from whole core. Then all organics were extracted and the SARA (saturates, aromatics, resins, asphaltenes) concentrations were determined for these organics.

Figure 26. Analysis of extracted organics from core plugs at specific depths in the tar zone.[32,126,127] (A) Each symbol is a different tar well. There is no gravitation gradient of clusters in the tar zone even over a few meters of height. The tar zone is not just a viscous crude oil; instead the tar zone has phase separated asphaltenes. The asphaltene phase (~60% here) coats pore throats reducing permeability to negligible values. Core plugs that have more oil than asphaltene phase trend towards 30% asphaltene, core plugs with more asphaltene coat trend towards 60% asphaltene. (B) Plotting normalized ratios of asphaltenes, resins and aromatics shows that the asphaltene-enriched phase is not enriched in aromatics nor resins.

Analysis of the tar wells shows that there is no hint of asphaltene equilibration in the tar zone (shotgun pattern in Fig 26a) even within a few meters, while the viscous oil above the tar zone is equilibrated over 33 ACS Paragon Plus Environment

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a lateral distance of 100 kilometers.[32,126,127] This indicates the tar mat is not simply a viscous oil. Instead, the tar mat contains a phase separated asphaltene-enriched phase. The asphaltene enriched phase is likely around 60% asphaltene here as gleaned from the highest asphaltene content in core plugs shown in Fig. 26A. In addition, plotting ratios of crude oil components, aromatics, resins and asphaltenes vs. paraffins (or saturates) shown in Fig. 26B indicates that the asphaltene phase is not preferentially enriched in resins nor aromatics. The 10 meters (vertical) thick tar mat shows that the asphaltene accumulation at the base of this large field cannot be from a maturity variation of the crude oil originally charged into the reservoir. The least mature crude oil with the most asphaltene content generally charges first into the reservoir in a normal subsidence sequence. For the field in Fig. 23, this means the tar mat would have had to charge first into the crest of the field, then slide downwards and laterally over 30 kilometers to its present position. This tar mat is not movable and forms an excellent seal precluding aquifer pressure support; the field was initially produced then shut in, and has not yet returned to virgin pressure more than 20 years later. Nevertheless, it is important to check for maturity variations in the crude oil to rule out maturity variations as a source of the giant asphaltene gradient. The GC×GC chromatograms were obtained for many of these oils and the biomarker region was analyzed.[42] The ratio Ts/(Ts+Tm) is one of the most widely used thermal maturity indicators,[47] and as see in Fig. 23, there is only a slight variation of this thermal maturity marker. Likewise, several other thermal maturity markers shows little variation,[42] confirming that the gradient in Fig. 23 is not due to a thermal maturity variation. Validation of Reservoir Fluid Geodynamics. Another oilfield study showed a variety of complexities with associated with a late gas charge into oil reservoirs. Indeed, this study proves the validity of RFG because three reservoirs exhibit totally different reservoir realizations even though they all had the same petroleum system process of a gas charge into oil.[124,132] Figure 27A shows the areal contour map on the continental shelf of Norway of three adjacent fault blocks, each with one well. Fault blocks are very large blocks of rock, often many kilometers in extent, created by tectonic and localized stresses in the Earth's crust. Fault blocks are often seen in seismic imaging.


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Figure 27. (A) Areal contour map of the Norwegian continental shelf showing three adjacent fault blocks, each with one well.[124,132] (B) The vertical well in Fault Block 1 in has both a gas-oil contact (GOC) and an oil-water contact (OWC); thus, all fluid migration is vertical and measurable within this well. The asphaltene gradient is very large and in disequilibrium; the data points do not match the FHZ EoS. In addition, the asphaltene content in the oil at the base of the column is 35%; this is a heavy oil. The saturation pressure (Psat) and GOR are exhibit large, disequilibrium gradients; they do not match the cubic EoS.

Figure 27B shows that the oil column is in disequilibrium in Well 1.[124,132] The asphaltene gradient is very large and does not match the FHZ EoS with clusters; there are no larger stable colloidal particles in crude oil. In addition, the saturation pressure and GOR also exhibit large, disequilibrium gradients. The crude oil at the bottom of the column has a high asphaltene concentration of 35%, thus is heavy. Well 1 has both a GOC and an OWC; thus, all fluid migrations are vertical and observable within this vertical well. This contrasts the frequent occurrence of tilted reservoirs, where the GOC is not only vertically displaced but also laterally displaced from the OWC so vertical wells often insect only one of these contacts. Nevertheless, this oil column is somewhat similar to the reservoir in Fig. 21 with a diffusive GOR gradient and asphaltene migration to the base of the reservoir. The issue arises as to why this column is in disequilibrium considering the very small vertical distances that are involved, just tens of meters. In all three fault blocks that late gas charge is thought to be Pleistocene, limiting the time duration for establishing equilibrium. To address this question further, it is best to also consider the adjacent Fault Block 2.


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Figure 28. (A) The initial condition after charge of a density-stacked gas on top of an undersaturated oil.[124,132] (B) Fault Block 1; schematic of gas diffusing into oil increasing GOR at the top of the oil column; this causes asphaltenes migration to the base of the column. (C) Schematic of the completion of the RFG processes of gas diffusion and asphaltene migration down. The solution gas is high and equilibrated in Well 2. The asphaltene content is very small and equilibrated in Well 2. The asphaltenes migrated to the base of the oil column and underwent phase separation as seen in analysis of core extracts.[124,132]

”Movie” of Tar Mat Formation. Figure 28 shows that Well 1 is undergoing the process of gas diffusion into the oil column from the top, while the asphaltenes are migrating down away from high solution gas oil.[124,132] Well 2 shows the completion of this process in geologic time. The asphaltenes migrated to the base of the column increasing their concentration. Gas diffused to the base of the column increasing solution gas. Both fault blocks had this process initiate at about the same time; the question arises what slowed equilibration in Fault Block 1. The answer is obtained in measurement of both vertical permeability and oil production rates. Well 1 was shown to have much lower vertical permeability by pressure interference testing.[132] In addition the production rates in Well 1 are ten times lower than Well 2 after accounting for viscosity differences of the oils.[132]

Figure 29. Scanning electron micrograph obtained from the tar zone in Well 2.[38] The asphalteneenriched phase is a smooth, cracked material that is conformally attached to rock surfaces. Void space that is oil filled in the reservoir is also evident. The tar mat consists of two organic phases; once the asphaltene phase seals sufficient numbers of pore throats, permeability becomes negligible.[38]


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Most of the asphaltenes at the base of the oil column in Well 2 have undergone phase separation. Figure 29 shows an SEM from this tar zone showing an asphaltene-enriched phase and a void space that is filled by oil in the reservoir; the tar mat consists of two organic phases.[38] The asphaltene phase adheres conformally to the rock surface which is consistent with heterogeneous nucleation. However, asphaltenes are nanocolloidal with huge surface area; thus, there is no standard homogeneous nucleation difficulty associated with formation of new surface energy in single-phase true molecular solutions. Instead, a plausible explanation for the heterogeneous nucleation observed here is due to the slow kinetics of reaction limited aggregation known for floc formation from asphaltene clusters,[92,93] vs. the relatively rapid rate of collisions with pore walls. Figure 30 shows the application of a single RFG model to Wells 1 and 2.[37] The single model accounts for the disequilibrium gradients of both GOR and asphaltenes in Well 1. The same RFG model also accounts for the equilibrated GOR and asphaltene gradients as well as the thick tar mat in Well 2. The major difference between the wells (and fault blocks) is the vertical permeability in the model. Well 1 is known to have low vertical permeability and Well 2 is known to have higher vertical permeability in accordance with vertical pressure interference testing.[132] Moreover, much higher production rates were obtained from Well 2 than Well 1 in accord with the higher permeability of Well 2.[132] From a production standpoint, this RFG model is addressing many important concerns including the spatial distribution of asphaltenes and solution gas, thus oil viscosity and the presence of absence of a tar mat which has a huge impact on pressure support and aquifer sweep. Understanding the evolution of the fluid column greatly improves the understanding of present day fluid and tar distributions. This understanding is essential for optimal field development planning.

Figure 30. A single RFG model accounts for present day fluid and tar distributions in two adjacent fault blocks in an oilfield (cf. Figs. 27 and 28).[37] The initial condition of a late gas charge into an undersaturated oil applies to both fault blocks. The RFG model incorporates gas diffusion into the oil, asphaltene migration away from high solution gas downwards in the oil column, and asphaltene phase change. Well 1 is in the middle of these processes in geologic time, thus not equilibrated. Well 2 shows the final stage of this process.[37]

Well 3 in Fault Block 3 in this oilfield exhibits a very complex behavior of deposited asphaltenes. At this point, modeling can only account for the observations above the shale break as shown in Fig. 31.


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Figure 31. Well 3 log data.[124] (A) Schematic of the processes that led to complex asphaltene deposition observed in core extracts. Above the shale baffle, the same process occurs as in Well 2. Methane diffused down driving the asphaltene onto the shale baffle as a phase separated material. Below the shale baffle, methane diffusion from above is impeded and lateral gas sweep causes local deposition of asphaltene throughout the core with no tar mat at the base.[124] (C) Little asphaltene remains in the oil due to the high GOR (~1200 scf/bbl). The asphaltenes, GOR (not shown) and pressure (not shown) are equilibrated from top to bottom in the oil column; the shale layer is a baffle, not a seal.[38]

Figure 31 shows a complex distribution of deposited asphaltenes for Well 3 in Fault Block 3.[124] The fluids and pressures are equilibrated above and below the shale layer, thus, it is likely the shale is not a sealing layer but is a baffle and pinches out away from the wellbore. Above the shale layer, the late charge of gas density-stacked above the liquid and diffused down into the oil column. This caused the asphaltenes to migrate onto the shale break where they became trapped (ponded as in a shale bowl) and underwent phase separation to form a thin tar mat. The tar mat is thin because there is only a small oil column above this shale break, thus little asphaltene was available to accumulate.[124] Below the shale break, gas could not diffuse from above. In this section of the well, the asphaltene is deposited throughout the column; moreover, there is no tar mat because there was no vertical migration of asphaltenes. This has been attributed to lateral gas sweep into the reservoir.[16] Lateral gas sweep means that a significant lateral variation of fluid properties exists within a permeable formation. The origin of such variations is often associated with recent entry of new hydrocarbons, potentially gas, into the reservoir from the flank of a field.[123,124] With lateral migration, the asphaltenes cannot migrate downwards away from the gas front; they become trapped locally and undergo phase transition. Lateral sweep of charge fluids has been incontrovertibly established by a black oilfield with a late gas charge.[123] Lateral sweep of charge fluids is particularly likely if the well is located near the charge point of the reservoir, as is the case here for Well 3.[16,123,124] Connectivity, Compartments and Baffles. Figure 32 shows a reservoir that exhibits many of the complexities that have been described above. The DFA color data is plotted along with fluid density, GOR, and pressure. The pressure curve shows none of the complexities which are evident in the DFA color data. The GOR values corroborate the asphaltene gradient data and interpretation. The fluid density measurements are not very sensitive; the best data to analyze is the asphaltene gradient data.[16]


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Figure 32. A reservoir with five wells exhibiting many different complexities.[116] Well 1 intersects the formation at the lowest elevation and has among the lightest crude oils in the field (that is, least asphaltene content); it does not appear to be connected to the rest of the formations in the field. The upper sand in Well 2 appears to have equilibrated asphaltenes with Well 3; this indicates connectivity. Well production tests showed high rates consistent with connectivity.[116] Well 4 is at the crest of the field where formation deformation is often worse. The asphaltenes are not equilibrated even over a short distance of this well and poor production was obtained (the black line of Well 4 is simply to guide the eye and is not a thermodynamic fitting). In Well 5, the deeper sands contain successively lighter crude oil; these zones are compartmentalized and poor production was obtained.[116]

Well 1 in Fig. 32 insects the target sands the deepest, yet contains the lightest crude oils; the formation is likely not connected to other wells in the field. The upper sand in Well 2 contains heavier crude oil than the lower sands; these two zones are not connected.[116] The upper sand in Well 2 and the sand in Well 3 appear to have equilibrated asphaltenes matching the FHZ EoS with 2nm nanoaggregates. These two wells are towards the flank where reservoir distortion and compartmentalization are less (still relatively deep). Indeed, production from Well 3 was quite good and consistent with connectivity.[116] Well 4 is in the crest of the field where the formation was tilted past 90 deg; this is called formation overturn. Often formation distortion is significant in and near the crest. Well 4 is a vertical well that entered the sand from the bottom, went through a long distance then came out the bottom of the sand; the sand has been rotated to slightly beyond 90 degrees tilt during the process of basin distortion. Well 4 intersected predominantly sand; however, the asphaltenes are far from equilibrium. This gradient (Well 4, Fig. 32) does not match the FHZ EoS with either nanoaggregates, or clusters. Likewise, production was very poor from this well. The origin of the slow equilibration rate of asphaltenes and poor production is “deformation banding” that was evident even in many sidewall core samples. Deformation banding corresponds to powderized sandstone associated with severe formation distortion. Deformation bands have very low permeability, thus hinder fluid flow and also hinder fluid equilibration. These deformation bands are not pressure seals; they are not barriers, they are baffles. Baffles are one of the most difficult formation difficulties to analyze,[133] yet they are evident in the DFA color data. When part of a field shows equilibrated asphaltenes, and another part does not show equilibrated asphaltenes, then the part without equilibrated asphaltenes often suffers low production rates.[116] Well 5 shows three sands with 39 ACS Paragon Plus Environment

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successively less asphaltene for the deeper formations. This is a strong indicator of compartmentalization, and low production rates were obtained.[116] Reservoir Fluid Geodynamics and Geochemistry. The combination of physical chemical analysis and geochemical analysis of a single reservoir can often yield powerful results. A reservoir consisting of five stacked sands is shown in Fig. 33 in the Rajasthan Basin in India.[43] The reservoir is at a depth of 300 meters, has a relatively low temperature of 60 degC and is susceptible to biodegradation. Microbes which digest oil survive up to a temperature of 80 degC.

Figure 33. (A) Upper and lower surfaces of an oilfield consisting of five stacked sands. Location of four wells is indicated. (B) DFA Color vs. true vertical depth. Embedded GCs are shown for the samples. The wells were drilled with oil-based muds (OBM), a GC of the mud filtrate is shown. The upper half of the oil column shows a small asphaltene gradient, and the GC’s show that the oils in the upper half of the column contain their n-alkanes (large spikes in the chromatogram). The lower half of the oil column shows a large gradient and decreasing n-alkane population in the oils towards the OWC.[43] The large gradient is diffusive associated with alkanes diffusing to the OWC where they are consumed by microbes.

Figure 33 shows an ongoing process impacting the lower half of the oil column of alkanes diffusing to the OWC and being consumed by microbes that live in the water (not the oil). The rate limiting step is diffusion. The upper half of the oil column is not affected much at this point in time.[43,125] The deepest oil samples are the most contaminated with OBM filtrate; this is due to the higher viscosity of these crude oils resulting from their higher asphaltene content.


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Figure 34. Modeling of the asphaltene gradient in the five stacked sand reservoirs in the oilfield presented in Fig. 33.[43] (A) Schematic of the diffusion and biodegradation processes with a resultant increase in asphaltene concentration lower in the oil column. (B) The DFA color data from these five reservoirs overlays, all sands have experienced the same history. An equilibrium model of asphaltene nanoaggregates in the FHZ EoS fits the upper half of the oil column. A diffusive model with alkane consumption at the OWC incorporating the FHZ EoS fits the entire column. The alkane consumption concentrates the asphaltenes.

The microbes that consume the crude oil live in the water, not the oil. Thus, oil components must diffuse to the OWC in order for the microbes to digest them. The Peters-Moldowan scale (PM Scale) is widely used to characterize the order and preference exhibited by the microbes for specific chemical constituents of crude oil.[47] The microbes preferentially consume n-alkanes which are particularly easy to measure in GC; the n-alkanes appear as tall spikes in a crude oil chromatogram (cf. Fig 33). Here, the rate limiting step is diffusion; most of the diffusive distance is lateral, not vertical yielding long timelines. Using a set of parameters consistent with the oilfield in terms of the reservoir size, dip angle, permeability, and viscosity, this ongoing process can be modeled as shown in Fig. 34B. The timeline for this process of diffusion and biodegradation is approximately 50 million years,[43] which is in agreement with petroleum system analysis of this basin.[134] In this oil column, there is a huge gradient in biodegradation going from very mild or unbiodegraded at the top of the column to severe biodegradation at the bottom of the column.[43] Figure 34 shows that the asphaltene content is increased a factor of three more than an equilibrium curve. Biodegradation can result in consumption of about ⅔ of the oil for severe biodegradation (PM~6).[135] In that process, the asphaltenes are generally preserved while the rest of the oil is removed;[135] hence, this process accounts for the tripling of the asphaltene concentration beyond the equilibrium concentration at the bottom of the oil column as seen in Fig. 34. This asphaltene variation in Fig. 34 corresponds to a factor of eight in viscosity making this modeling of significant relevance to oil production. Linking geochemistry to thermodynamic analysis used for DFA data is seen to be very effective. Biodegradation can result in different scenarios than that seen in Fig. 33 and 34. A series of seven reservoirs are shown in Fig. 35. These reservoirs are thought to be subject to the spill-fill mechanism in trap filling.[46] The deepest reservoir fills first; subsequent charge overfills the reservoir and crude oil spills out and migrates up to the next reservoir. As trap filling continues, the second reservoir overfills 41 ACS Paragon Plus Environment

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and crude oil spills out of that reservoir into another shallower reservoir. In the Catcher Area oilfield depicted in Fig. 35, this spill-fill mechanism is thought to account for oil in all seven reservoirs. (The Catcher reservoir is the deepest of the seven Catcher Area reservoirs.)

Figure 35. The Catcher Area Reservoirs; seven reservoirs thought be related by a spill-fill sequence with crude oil overfilling deeper reservoirs and spilling into shallower reservoirs.[46] The viscosity profiles of the crude oils in these reservoirs and reservoir connectivity are of significant interest. Other complicating factors include biodegradation, water washing and crude oil maturity variations.[46]

The reservoirs are below 80 degC, thus biodegradation is an expected complicating factor. Water-washing is another possible complicating factor and needs to be considered. Moreover, since seven reservoirs are involved in overfilling reservoirs, it is also possible to have thermal maturity variations of oils filling different reservoirs. The combination of DFA and geochemistry analysis of GC×GC data can identify these processes. Figure 36 shows the DFA data from crude oils in the reservoirs showing a large variation of color, thus asphaltenes amongst the different reservoirs, and mild gradients of color within individual reservoirs.[46] This is in contrast to the observations in Fig. 34 with a large color variation within a single reservoir.


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Figure 36. (Top) DFA color measurements for the different reservoirs. Large color differences are observed between reservoirs and mild gradients are seen within individual reservoirs.[46] The trend is increasing asphaltene content for shallower reservoirs; this is consistent with heavy oils spilling out of deeper reservoirs filling shallower reservoirs. The curve fitting lines correspond to the FHZ EoS but since these reservoirs have active biodegradation, these fitting lines are only qualitative. (Bottom) A schematic showing a spill-fill sequence; the most dense oil in a reservoir is at the bottom of the oil column at the OWC and spills out and up when the reservoir is overfilled.


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Figure 37. GC×GC chromatograms showing the n-alkanes and isoprenoids including farnesane, norpristane, pristane and phytane.[46] A) Carnaby (TVDss = 3513 ft), B) Burgman (TVDss = 3756 ft), and C) Catcher (TVDss = 4640 ft). The oils have distinctly different levels of biodegradation as given by the Peters-Moldowan rank (PM) that increase with decreasing depth. The Catcher sample has small nalkane peaks and large isoprenoid peaks indicating mild biodegradation. The Burgman sample has lost nalkanes but retains isoprenoids indicating moderate biodegradation (PM=4). The Carnaby sample has neither n-alkanes nor isoprenoids except for those from the drilling mud filtrate indicating PM=6. The Carnaby oil sample is more contaminated with OBM filtrate because this oil, with its higher asphaltene content, is more viscous than the others.

Figure 37 shows that the crude oils in the deeper reservoirs are less biodegraded. The preferential consumption of n-alkanes over branched alkanes such as isoprenoids is evident in the GC×CG chromatograms; the reduction of these compounds helps establish the Peters-Moldowan rank of biodegradation.[47] The assignment of PM=6 for Carnaby oils was confirmed by GC×GC-MS (mass spectrometry) with the identification of specific 25-norhopanes, which are generated as byproducts upon reaching severe levels of biodegradation of PM~6.[46,47] Biodegradation occurs at the OWC where the oils spill out of the reservoirs. Biodegradation also increases the density of the oil assuring this oil does not rise within a reservoir. Thus, shallower reservoirs fill with more biodegraded oils as validated in Fig. 37. Specifically, all seven reservoirs are consistent with the spill-fill model with biodegradation occurring increasingly in shallower reservoirs.[136]


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Figure 38. Naphthalenes (left) and phenanthrenes and dibenzothiophenes (DBT) (right) from A) Carnaby, B) Burgman, and C) Catcher reservoirs.[46] Depths are recorded in TVDss in feet. Progressive removal of compounds by their water solubility is observed. The Catcher sample (C) has a full complement of naphthalenes and phenanthrenes. The Burgman sample (B) has lost naphthalene, the methylnaphthalenes (1-Me-Naph and 2-Me-Naph), and some C2-naphthalenes (C2-Naphs) indicating moderate water washing. The Carnaby sample has lost naphthalene and increasingly alkylated naphthalenes to C5-naphthalenes, and lost phenanthrene, C1-phenanthrenes (C1-Phens), and even lost some C2-phenanthrenes (C2-Phens); only the C3-phenanthrenes (C3-Phens) are preserved unaltered, indicating severe water washing.[46]

Water washing is also confirmed in these samples. Aromatic compounds are more water soluble than alkanes; the extent of water solubility of aromatics decreases with increasing numbers of fused rings and increasing alkylation. Figure 38 shows the naphthalene series identified in the figure caption. Naphthalene is present in the Catcher crude oil indicating mild water washing at the most. Naphthalene and the two methylnaphthalenes are missing in the Burgman crude oil. In addition, some two-carbon substituted naphthalenes (C2-naphthalenes) are missing in the Burgman crude oil indicating moderate water washing. The naphthalenes, phenanthrene and methylphenanthrenes are missing in the Carnaby crude oil indicating severe water washing. The data suggests that there is a biodegradation-assisted water washing process taking place;[46] this is under investigation. Figure 39 shows the analysis of the thermal maturity marker Ts/(Ts+Tm) in these crude oils; more mature (less dense) crude oils are in deeper reservoirs and less mature (more dense) crude oils in shallower reservoirs. Nevertheless, the range of maturities is not that large, so this is a secondary factor in establishing gradients of oil properties.[46] Other thermal maturity markers show similar trends; nevertheless, there is some variability in these ratios thus, only general trends are considered valid.[46]


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Figure 39. Thermal maturity ratio Ts/(Ts+Tm) showing the expected thermal maturity variation for a spillfill sequence for Catcher Area reservoirs.[46] Nevertheless, the variation in thermal maturity is not large. Other thermal maturity indices show the same general trend for these crude oils.[46] The most mature crude oil in the reservoirs are the Catcher and Catcher North crude oil and the least mature are the Carnaby crude oil.

Figure 40. Catcher in-reservoir gradients.[46] (A) DFA asphaltene gradient is not large, but largest at the OWC. (B) Biodegradation; Halpern index TR2 which is the ratio: n-heptane/1,1-dimethylpentane. With no biodegradation, the value of this index can be 20 or 30; here TR2 is less than 1 indicating biodegradation. (C) Thermal maturity marker Ts/(Ts+Tm). There is little detectable thermal maturity gradient within the Catcher reservoir.[46]

Figure 40 explores the in-reservoir gradient in the deepest reservoir, Catcher. Gradients are present but are not as large as other reservoirs with an initial unbiodegraded charge with ongoing biodegradation (cf. Fig. 34).[43] The overfilling of the Catcher reservoir continuously spilled the most biodegraded oil at the OWC out of the reservoir, thereby reducing the gradient. That is, spill-fill sequences can yield small in46 ACS Paragon Plus Environment

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reservoir gradients but large differences in asphaltene content across the different fields, whereas a single charge into a shallow reservoir can give rise to large in-reservoir gradients from biodegradation. The Catcher Area reservoirs evidently have undergone a spill-fill sequence of reservoir filling. The crude oils in the deepest reservoirs are the lowest in asphaltene content, the least water washed and the most mature. Consequently, this reservoir is attractive. In addition, the fact that incoming, less biodegraded crude oil caused excellent flushing of the more biodegraded, resident oil out of the Catcher reservoir is a strong indication of excellent reservoir connectivity; thereby addressing the other major concern in addition to viscosity profiling.[46,136] Fluid Inclusions and the Sequence of Charge. Many fluid complexities are associated with gas and oil charging into the same reservoir. It is desirable to know which reservoir fluid arrived first, gas or oil. This can be important especially in understanding complexities associated with a late lateral charge and asphaltene phase change.[123,124] Analysis of microscopic fluid inclusions can be very useful for determining the sequence of charge among other concerns.[137] A recent study showed both oil and gas charged into the same sand causing formation of asphaltene clusters from nanoaggregates.[120] The sequence of charge in this case is indicated by examining fluid inclusions in sand grains or cement in both the reservoir of interest and in a separate, nearby stacked gas reservoir.

Figure 41. Fluid inclusion obtained from core plugs in the oil zone of a reservoir.[128] The petroleum inclusion appears dark with visible illumination and fluoresces strongly with UV illumination. The oil zone has a high concentration of such inclusions.


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Figure 42. Fluid inclusion analysis from a gas-filled sandstone 500 feet deeper than the oil reservoir in Fig. 41.[128] A fluorescing oil filled inclusion (and many others not shown) indicate that this gas zone was once filled with oil. An asphaltene precipitate is also identified consistent with expectations of gas entry into this former oil zone. Examinations of microscopic fluid inclusions in sand grains or interstitial cement in the gas reservoir indicates that this sand was once oil filled.[128] The implication is that a late gas charge entered the reservoir and spilled the oil out.[128] The oil reservoir of Fig. 41 also had the same gas charge into it, thus the implied sequence is that the oil reservoir in Fig. 41 first had an oil charge followed by a gas charge. Conclusions The development of downhole fluid analysis has enabled measurement of vertical and lateral fluid gradients in oilfields in a cost effective manner. The recent development of the Flory-Huggins-Zuo equation of state and its reliance on the Yen-Mullins model has enabled interpretation of these fluid gradients in a thermodynamic context. Reservoir crude oils with equilibrated asphaltenes are likely in connected reservoirs representing a single flow unit, addressing one of the most important risks in oilfield development. Reservoirs with fluid gradients that are not equilibrated often have processes taking place which fall within the newly formalized discipline “reservoir fluid geodynamics” (RFG). Approximately 35 oilfields have been examined within this perspective, thereby elucidating many different RFG processes. A wide variety of reservoir concerns have been addressed in RFG studies including reservoir connectivity, flow baffling, fault block migration, lateral sweep of reservoir fluids, viscosity gradients, heavy oil and tar formation, mobile bitumen, GOR gradients, and asphaltene onset pressure. Many processes such as biodegradation have been treated in extensive literature within a geochemical perspective; these processes are subsumed within RFG. Indeed, combining a thermodynamic and geochemical perspective into a single treatment can increase the depth of understanding of reservoirs and their contained fluids. While this new discipline of RFG incorporates many recent scientific advances, corresponding workflows are well suited to be used by operating units of oil companies in the quest to produce oil more efficiently.


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29. Mullins, O.C.; Ventura, G.T.; Nelson, R.L.; Betancourt, S.S.; Raghuraman, B.; Reddy, C.M.; Oil Reservoir Characterization by coupling Downhole Fluid Analysis with Laboratory 2D-GC Analysis of Crude Oils, Energy & Fuels, 22, 496-503, (2008) 30. Betancourt, S.S.; Ventura, G.T.; Pomerantz, A.E.; Viloria, O.; Dubost, F.X.; Zuo, J.Y.; Monson, G.; Bustamante, D.; Purcell, J.M.; Nelson, R.K.; Rodgers, R.P.; Reddy, C.M.; Marshall, A.G.; Mullins, O.C.; Nanoaggregates of Asphaltenes in a Reservoir Crude Oil, Energy & Fuels, 23, 1178–1188, (2009) 31. Dong, C.; Petro, D.; Pomerantz, A.E.; Nelson, R.L.; Latifzai, A.S.; Nouvelle, X.; Zuo, J.Y.; Reddy, C.M.; Mullins, O.C.; New Thermodynamic Modeling of Reservoir Crude Oil, Fuel, 117, 839-850, (2014) 32. Mullins, O.C.; Zuo, J.Y.; Seifert, D.; Zeybek, M.; Clusters of Asphaltene Nanoaggregates Observed in Oilfield Reservoirs, Energy & Fuels, 27, 1752–1761, (2013) 33. Zuo, J.Y.; Elshahawi, H.; Mullins, O.C.; Dong, C.; Zhang, D.; Jia, N.; Zhao, H.; Asphaltene Gradients and Tar Mat Formation in Reservoirs under Active Gas Charging, Fluid Phase Equilibria, 315, 91 – 98, (2012) 34. Zuo, J.Y., Chen, Y., Pan, S., Wang, K., and Mullins, O.C. Investigation of density inversion induced by gas charges into oil reservoirs using diffusion equations. Energy, 100, 199 – 216 (2016) 35. Pan, S.; Zuo, J.Y.; Wang, K.; Chen, Y.; Mullins, O.C.; A Multicomponent Diffusion Model for Gas Charges into Oil Reservoirs, Fuel, 180, 384-395, (2016) 36. Zuo, J.; Pan, S.; Wang, K.; Mullins, O.C.; Dumont, H.; Chen, L.; Mishra, V.; Canas, J.; Analysis of Asphaltene Instability Using Diffusive and Thermodynamic Models during Gas Charges into Oil Reservoirs; Energy & Fuels, 31, (4), 3717-3728, (2017) 37. Zuo, J.Y.; Mullins, O.C.; Achourov, V.; Pfeiffer, T.; Pan, S.; Wang, K.; Terje Kollien, T.; Di Primio, R.; Fluid Distributions during Light Hydrocarbon Charges into Oil Reservoirs Using Multicomponent Maxwell-Stefan Diffusivity in Gravitational Field, Fuel, 209, 211-223, (2017) 38. Pfeiffer, T.; Di Primio, R.; Vladislav Achourov, V.; Mullins, O.C.; Scanning Electron Micrographs of Tar Mat Intervals Formed by Asphaltene Phase Transition, Petrophysics, 58, 141-152, (2017) 39. Zuo, J.Y.; Mullins, O.C.; Mishra, V., Garcia, G.; Dong, C.; Zhang, D.; Pang, J.; Asphaltene Grading, Flow Assurance and Tar Mats in Oil Reservoirs, Energy & Fuels, 26 (3), pp 1670–1680, (2012) 40. Pomerantz, A.E.; Bake, K.D.; Craddock, P.R.; Qureshi, A.; Zeybek, M.; Mullins, O.C.; Kodalen, B.G.; Mitra-Kirtley, S.; Bolin, T.B.; Seifert, D.J.; Sulfur Speciation in Asphaltenes from a Highly Compositionally Graded Oil Column, Energy & Fuels, 27, 4604–4608, (2013) 41. Wu, Q.; Seifert, D.J.; Pomerantz, A.E.; Mullins, O.C.; Zare, R.N.; Constant Asphaltene Molecular and Nanoaggregate Mass in a Gravitationally Segregated Reservoir, Energy & Fuels, 28, 3010−3015, (2014) 42. Forsythe, J.C.; Pomerantz, A.E.; Seifert, D.J.; Wang, K.; Chen, Y.; Zyo, J.Y,; Nelson, R.K.; Christopher M. Reddy, C.M.; Schimmelmann, A.; Sauer,P.; Peters, K.E.; Mullins, O.C.; A Geological Model for the Origin of Fluid Compositional Gradients in a Large Saudi Arabian Oilfield: An


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Investigation by Two-Dimensional Gas Chromatography and Asphaltene Chemistry, Energy & Fuels, 29 (9), 5666–5680, (2015) 43. Zuo, J.Y.; Jackson, R.; Agarwal, A.; Herold, B.; Kumar, S.; De Santo, I.; Dumont, H.; Beardsell, M.; Mullins, O.C.; A diffusion model coupled with the Flory-Huggins-Zuo Equation of State and YenMullins model accounts for large viscosity and asphaltene variations in a reservoir undergoing active biodegradation, Energy & Fuels, 29, 1447 −1460, (2015) 44. Pomerantz, A.E.; Ventura, G.T.; A.M McKenna, J.A. Cañas, J. Auman, K. Koerner, D. Curry, Nelson, R.L.; Reddy, C.M.;Rodgers, R.P.; Marshall, A.G.; K.E. Peters, Mullins, O.C.; Combining Biomarker and Bulk Compositional Gradient Analysis to Assess Reservoir Connectivity, Org. Geochem. 41 (8), 812-821, (2010) 45. Bartha, A.; De Nicolais, N.; Sharma, V.; Roy, S.K.; Srivastava, R.; Pomerantz, A.E.; Sanclemente, M.; Perez, W.; Nelson, R.K.; Reddy, C.M.; Gros, J.; Arey, J.S.; Lelijveld, J.; Dubey, S.; Tortella, D.; Hantschel, T.; Peters, K.E.; Mullins, O.C.; Combined petroleum system modeling and comprehensive two dimensional gas chromatography to improve understanding of the crude oil chemistry in the Llanos Basin, Colombia, Energy & Fuels. 29, 8, 4755–4767, (2015) 46. Forsythe, J.C.; Martin, R.; De Santo, I.; Tyndall, R.; Arman, K.; Pye, J.; De Nicolais, N.; Nelson, R.K.; Pomerantz, A.E.; Kenyon-Roberts, S.; Zuo, J.Y.; Reddy, C.; Peters, K.E.; Mullins, O.C.; Integrating Comprehensive Two-Dimensional Gas Chromatography and Downhole Fluid Analysis to Validate a Spill-Fill Sequence of Reservoirs with Variations of Biodegradation, Water Washing and Thermal Maturity, Fuel, 191, 538-554, (2017) 47. Peters, K.E.,Walters, C.C., Moldowan, J.M.; The Biomarker Guide, second ed. Cambridge University Press, Cambridge, U.K., (2005) 48. Zimmerman, T.H., Pop, J.J., Perkins, J.L.: “Down Hole Method for Determination of Formation Properties,” US Patent No. 4,936,139 (1990). 49. Zimmerman, T.H., Pop, J.J., Perkins, J.L.: “Down Hole Tool for Determination of Formation Properties,” US Patent No. 4,860,581 (1989). 50. Zimmerman, T.H.; MacInnis, J.; Hoppe, J.; Pop, J.; Application of Emerging Wireline Formation Testing Technologies, Offshore, South East Asia Conference, OSEA 90105, (1990) 51. Kazakevich, E.; Abram, P.; Wichers, W.; Weinheber, P.; Makhmotov, A.; Kazuho, S.; El-Battawy, A.; The first application of the wireline 3D radial probe for determination of permeability and mobile fluid type in hishly water saturated rocks in the Piltun-Astokhskoye field, SPE 182559, Ann. Casp. Tech. Conf. Exhib., Kazalhstan, (2016) 52. Mullins, O.C.; J. Schroer, Real-time Determination of Filtrate Contamination During Openhole Wireline Sampling by Optical Spectroscopy, SPE ATCE 63071, Dallas, TX, (2000) 53. Mullins, O.C.; J. Schroer, G. Beck, Real-time Quantification of OBM Filtrate Contamination in the MDT using OFA data, SPWLA 41st Annual Symposium, Houston, Texas, Paper SS, (2000)


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54. Zuo, J.Y.; Dumont, H.; Dubsot, F.X.; Pfeiffer, T.; Mishra, V.K.; Chen, L.; Mullins, O.C.; A Breakthrough in Accurate Downhole Fluid Sample Contamination Prediction in Real Time, Petrophysics, 56, 3, 251–265, (2015) 55. Dong, C.; Mullins, O.C.; Hegeman, P.S.; R. Teague, Kurkjian, A.; Elshahawi, H.; In-situ contamination monitoring and GOR measurement of formation samples, SPE 77899, SPE Asia Pacific Meeting, Melbourne, Australia, (2002)

56. Zuo, J.Y.; Gisolf, A.; Pfeiffer, T.; Achourov, V.; Chen, L.; Mullins, O.C.; Edmundson, E.; Partouche, A.; Advances in quantification of miscible contamination in hydrocarbon and water samples from downhole to surface laboraties; SPWLA Ann. Symp., Oklahoma, (2017) 57. Fujisawa, G.; Van Agthoven, M.A.; Rabbito, P.; Mullins, O.C.; Near-Infrared Compositional Analysis of Gas and Condensate Reservoir Fluids at Elevated Pressures and Temperatures, Applied Spec. 56, 1615, (2002) 58. Mullins, O.C.; Mitra-Kirtley, S.; Zhu, Y.; Electronic absorption edge of petroleum, Appl. Spectros. 46, 1405 (1992a) 59. Mullins, O.C.; Zhu, Y.; First observation of the Urbach tail in a multicomponent organic system, Appl. Spectros., 46, 354 (1992b) 60. Kharrat, A.M.; Indo, K.; Mostowfi, F.; Asphaltene Content Measurement Using an Optical Spectroscopy Technique, Energy & Fuels, 27 (5), 2452–2457, (2013) 61. Mullins, O.C.; Daigle, T.; Crowell, C.; Groenzin, H.; Joshi, N.B.; Gas-Oil Ratio of Live Crude Oils Determined by Near-Infrared Spectroscopy, Applied Spectros., 55, 197, (2001) 62. Mullins, O.C.; Beck, G.; Cribbs, M.Y.; Terabayshi, T.; Kegasawa, K,; Downhole determination of GOR on single phase fluids by optical spectroscopy, SPWLA 42nd Annual Symposium, Houston, Texas, Paper M, (2001) 63. Mullins, O.C.; Joshi, N.B.; Groenzin, H.; Daigle, T.; Crowell, C.; Joseph, M.T.; Jamaluddin, A.; Linearity of alkane near-infrared spectra, Appl. Spectros. 54, 624, (2000) 64. Van Agthoven, M.A.; Fujisawa, G.; Rabbito, P.; Mullins, O.C.; Near-Infrared Spectral Analysis of Gas Mixtures, Applied Spectroscopy 56, 593, (2002) 65. Daungkaew, S.; Johan, Z.J.; Lehne, E.; Zuo, J.Y.; Mullins, O.C.; Pfeiffer, T.; Lin, T.G., Sun, B.; Muthalib, T.I.B.T.A.; Hong, T.Y.; Reservoir Connectivity and Compartmentalization with the CO2 Compositional Gradient and Mass Transportation Simulation Concepts, SPE IPTC 14398, (2011) 66. Gaines, R. B.; Frysinger, G. S. Temperature requirements for thermal modulation in comprehensive two-dimensional gas chromatography, J. Sep. Sci. (2004), 27 (5-6), 380-388. 67. Gaines, R. B.; Frysinger, G. S.; Reddy, C. M.; Nelson, R. K., 5 - Oil spill source identification by comprehensive two-dimensional gas chromatography (GC × GC) A2 - Wang, Zhendi. In Oil Spill Environmental Forensics, Stout, S. A., Ed. Academic Press: Burlington, pp 169, (2007)


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68. Nelson, R. K.; Aeppli, C.; Arey, J. S.; Chen, H.; de Oliveira, A. H. B.; Eiserbeck, C.; Frysinger, G. S.; Gaines, R. B.; Grice, K.; Gros, J.; Hall, G. J.; Koolen, H. H. F.; Lemkau, K. L.; McKenna, A. M.; Reddy, C. M.; Rodgers, R. P.; Swarthout, R. F.; Valentine, D. L.; White, H. K., 8 - Applications of comprehensive two-dimensional gas chromatography (GC × GC) in studying the source, transport, and fate of petroleum hydrocarbons in the environment A2 - Stout, Scott A. In Standard Handbook Oil Spill Environmental Forensics (Second Edition), Wang, Z., Ed. Academic Press: Boston, pp 399-448, (2016) 69. Frysinger, G.S.; Gaines, R.B. Separation and identification of petroleum biomarkers by comprehensive two-dimensional gas chromatography, J. Sep. Sci. 24 (2), 87-96, (2001) 70. Nelson, R. K.; Kile, B. M.; Plata, D. L.; Sylva, S. P.; Xu, L.; Reddy, C. M.; Gaines, R. B.; Frysinger, G. S.; Reichenbach, S. E. Tracking the weathering of an oil spill with comprehensive two-dimensional gas chromatography, Environmental Forensics, 7 (1), 33-44, (2006) 71. Peng, D.Y.; Robinson, D.B.; A New Two-Constant Equation of State, Industrial and Engineering Chemistry: Fundamentals. 15, 59–64, (1976) 72. Høier, L.: “Miscibility Variations in Compositionally Grading Petroleum Reservoirs,” PhD thesis, Norwegian University of Science and Technology, Trondheim, Norway (1997). 73. Høier, L.; Whitson, C.H.; Compositional Grading—Theory and Practice, SPE Reservoir Evaluation & Engineering, 4, 6, 525–535; also presented as paper SPE 63085 at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, USA, (2001) 74. Mullins, O.C.; B. Martinez-Haya, Marshall, A.G.; Contrasting perspective on asphaltene molecular weight; this Comment vs. the Overview of A.A. Herod, K.D. Bartle, R. Kandiyoti, Energy & Fuels, 22, 1765-1773, (2008) 75. Groenzin, H.; Mullins, O.C.; Asphaltene Molecular Size and Structure, J.Phys. Chem. A., 103, 1123711245, (1999) 76. Groenzin, H.; Mullins, O.C.; Molecular sizes of asphaltenes from different origin, Energy & Fuels, 14, 677 (2000) 77. Sabbah, H.; Morrow, A. L.; Pomerantz, A. E.; Zare, R. N. Evidence for island structures as the dominant architecture of asphaltenes. Energy Fuel, 25, 1597−1604, (2011) 78. Schuler, B.; Meyer, G.; Pena, D.; Mullins, O.C.; Gross, L.; Unraveling the molecular structures of asphaltenes by atomic force microscopy, J. Amer. Chem. Soc., 137 (31), 9870–9876, (2015) 79. Schuler, B.; Fatayer, S.; Meyer, G.; Rogel, E.; Moir, M.; Zhang, Y.; Harper, M.R.; Pomerantz, A.E.; Bake, K.; Witt, M.; Pena, D.; Kushnerick, J.D.; Mullins, O.C.; Ovalles, C.; van den Berg, F.G.A.; Gross, L.; Heavy oil mixtures of different origins and treatments studied by AFM, Energy & Fuels, (2017) 80. Schuler, B.; Zhang, Y.; Collazos, S.; Fatayer, S.; Meyer, G.; Perez, D.; Guitián, E.; Harper, M. R.; Kushnerick, J. D.; Peña, D.; Gross, L. Chem. Sci., 8, 2315−2320, (2017) 81. Andreatta, G.; Bostrom, N.; Mullins, O.C.; High-Q Ultrasonic Determination of the Critical Nanoaggregate Concentration of Asphaltenes and the Critical Micelle Concentration of Standard Surfactants, Langmuir, 21, 2728, (2005) 55 ACS Paragon Plus Environment

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82. Andreatta, G.; Goncalves, C.C.; Buffin, G.; Bostrom, N.; Quintella, C.M.; Arteaga-Larios, F.; Perez, E.; Mullins, O.C.; Nanoaggregates and Structure-Function Relations in Asphaltenes, Energy & Fuels, 19, 1282-1289, (2005) 83. Freed, D.E.; Lisitza, N.V.; Sen, P.N.; Song, Y.Q. A study of asphaltene nanoaggregation by NMR. Energy Fuels, 23, 1189−1193. (2009) 84. Zeng, H.; Y.Q. Song, D.L. Johnson, Mullins, O.C.; Critical nanoaggregate concentration of asphaltenes by low frequency conductivity, Energy & Fuels, 23, 1201–1208, (2009) 85. Sheu, E. Y.; Long, Y.; Hamza, H.; Asphaltene self-association and precipitation in solvents, ACconductivity measurements. In Asphaltene, Heavy Oils and Petroleomics; Mullins, O. C., Sheu, E. Y., Hammami, A., Marshall, A. G., Eds.; Springer: New York, 2007; Chapter 10. 86. Mostowfi, F.; Indo, K.; Mullins, O.C.; McFarlane, R.; Asphaltene Nanoaggregates and the Critical Nanoaggregate Concentration from Centrifugation, Energy & Fuels, 23, 1194–1200, (2009) 87. Wu, Q.; Pomerantz, A.E.; Mullins, O.C.; Zare, R.N.; Laser-based Mass Spectrometric Determination of Aggregation Numbers for Petroleum- and Coal-Derived Asphaltenes, Energy & Fuels, 28, 475−482, (2014) 88. Pomerantz, A.E.; Wu, Q.; Mullins, O.C.; Zare, R.N.; Laser-Based Mass Spectroscopic Assessment of Asphaltene Molecular Weight, Molecular Architecture and Nanoaggregate Number; Energy & Fuels, 29, 2833−2842, (2015) 89. Eyssautier, J.; Levitz, P.; Espinat, D.; Jestin, J.; Gummel, J.; Grillo, I.; Barré, L.; Insight into asphaltene nanoaggregate structure inferred by small angle neutron and X-ray scattering. J. Phys. Chem. B 115, 6827−6837, (2011) 90. Eyssautier, J.; Henaut, I.; Levitz, P.; Espinat, D.; Barré, L.; Organization of asphaltenes in a vacuum residue: A small-angle X-ray scattering (SAXS)−viscosity approach at high temperatures, Energy & Fuels 26 (5), pp 2696–2704, (2012) 91. Eyssautier, J.; Espinat, D.; Gummel, J.; Levitz, P.; Becerra, M.; Shaw, S.; Barré, L. Mesoscale organization in a physically separated vacuum residue: Comparison to asphaltenes in a simple solvent, Energy Fuels 26 (5), pp 2680–2687, (2012) 92. Anisimov, M.A.; Yudin, I.K.; Nikitin, V.; Nikolaenko, G.; Chernoutsan, A.; Toulhoat, H.; Frot, D.; Briolant, Y. Asphaltene aggregation in hydrocarbon solutions studied by photon correlation spectroscopy. J. Phys. Chem. 99 (23), 9576−9580, (1995) 93. Yudin, I.K.; Anisimov, M.A. Dynamic light scattering monitoring of asphaltene aggregation in crude oils and hydrocarbon solutions. In Asphaltenes, Heavy Oils and Petroleomics; Mullins, O.C., Sheu, E.Y., Hammami, A., Marshall, A.G., Eds.; Springer: New York, 2007; Chapter 17. 94. Goual, L.; Sedghi, M.; Zeng, H.; Mostowfi, F.; McFarlane, R.; Mullins, O.C.; On the Formation and Properties of Asphaltene Nanoaggregates and Cluster by DC-Conductivity and Centrifugation, Fuel, 90, 2480-2490, (2011)


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95. Goual, L.; Sedghi, M.; Mostowfi, F.; McFarlane, R.; Pomerantz, A.E.; Saraji, S.; Mullins, O.C.; Cluster size and critical clustering concentration by centrifugation and DC-conductivity, Energy & Fuels, 28, 8, 5002–5013 (2014) 96. Korb, J. P.; Louis-Joseph, A.; Benamsili, L. Probing Structure and Dynamics of Bulk and Confined Crude Oils by Multiscale NMR Spectroscopy, Diffusometry, and Relaxometry. J. Phys. Chem. B 7002−7014, (2013) 97. Dutta Majumdar R, Gerken M, Mikula R, Hazendonk P. Validation of the Yen-Mullins model of athabasca oil-sands asphaltenes using solution-state 1H NMR relaxation and 2D HSQC spectroscopy. Energy Fuels 27, 6528–37, (2013) 98. Dutta Majumdar R, Gerken M, Hazendonk P. Solid-state 1H and 13C nuclear magnetic resonance spectroscopy of athabasca oil sands asphaltenes: evidence for interlocking p-stacked nanoaggregates with intercalated alkyl side chains. Energy Fuels, 29, 2790–800, (2015) 99. Dutta Majumdar, R.; Montina, T.; Mullins, O.C.; Gerken; M.; Hazendonk, P.; Insights into Asphaltene Aggregate Structure Using Ultrafast Magic Angle Spinning Solid-state 1H NMR Spectroscopy, Fuel, 193, 359-368, (2017) 100. Mullins, O.C.; Sheu, E.Y.; Hammami, A.; Marshall, A.G.; (Editors) Asphaltenes, Heavy Oil and Petroleomics, Springer, New York, (2007) 101. Mullins, O.C.; The Asphaltenes, Annual Review of Analytical Chemistry, 4, 393–418, (2011) 102. Mullins, O.C.; Pomerantz.; A.E.; Zuo, J.Y.; Dong, C.; Downhole Fluid Analysis and Asphaltene Science for Petroleum Reservoir Evaluation, Ann. Rev. Chem. Biomolec. Eng., 5: 325–345, (2014) 103. Hansen, C.M., Hansen Solubility Parameters: A User’s Handbook, 2nd ed.; CRC Press, Taylor & Francis Group: New York, (2005) 104. Redelius, P.; Hansen solubility parameters of asphalt, bitumen, and crude oils, In Hansen, C.M.; Hansen Solubility Parameters: A User’s Handbook, 2nd ed.; CRC Press, Taylor & Francis Group: New York, (2005); Chapter 9. 105. Akbarzadeh, K.; Dhillon, A.; Svrcek, W.Y.; Yarranton, H.W.; Methodology for the Characterization and Modeling of Asphaltene Precipitation from Heavy Oils Diluted with n-Alkanes. Energy Fuels, 18, 1434–1441, (2004) 106. Krishna, R.; Wesselingh, J.A. The Maxwell-Stefan Approach to Mass Transfer. Chem. Eng. Sci., 52, 861–911, (1997) 107. Zuo, J.Y., Pan, S., Wang, K., Mullins, O.C., Elshahawi, H., Canas, J., Chen, L., Dumont, H., Mishra, V., Garcia, G., Jackson R., A Quantitative Study on the Evolution of the Asphaltene Distribution during Gas Charge Processes, SPE Error! Reference source not found., SPE ATCE, San Antonio, Texas, (2017) 108. Ellis, D.V.; Singer J.M.; Well Logging for Earth Scientists, Springer, The Netherlands, 2008 109. Hudec, M.; Jackson, M.P.A.; Peel, F.J.; Influence of deep Louann structure on the evolution of the northern Gulf of Mexico, AAPG Bulletin 97, 10, 1711-1735, (2013) 57 ACS Paragon Plus Environment

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110. Mishra, V.; Canas, J.; Dumont, H.; Chen, L.; De Santo, I.; Pfeiffer, T.; Achourov, V.; Zuo, J.Y.; Mullins, O.C.; DFA Connectivity Advisor, a new workflow to use measured and modeled fluid gradients for analysis of reservoir connectivity, SPE OTC 25173, (2014) 111. Andrews, R.J.; Beck, G.; Castelijns, K.; Chen, A.; Cribbs, M.E.; Fadness, F.H.; Irvine-Fortescue, J.; Williams, S.; Hashem, M.; Jamaluddin, A.; Kurkjian, A.; Sass, B.; Mullins, O.C.; Rylander, E.; Van Dusen, A.; Quantifying Contamination using Color of Crude and Condensate, Oilfield Review, Autumn, (2001) 112. Elshahawi, H.; Ramaswami, S.; Zuo, J. Y.; Dong, C.; Mullins, O.C.; Zhang, D.; Ruiz-Morales, Y. Advanced reservoir evaluation using downhole fluid analysis and asphaltene Flory−Huggins−Zuo equation of state. Proceedings of the North Africa Technical Conference and Exhibition; Cairo, Egypt, (2013) 113. Ralston, C.Y.; Wu, X.; Mullins, O.C.; Quantum yields of crude oils, Applied Spectrosc. 50, 1563 (1996) 114. Juyal, P.; McKenna, A.M.; Yen, A.; Rodgers, R.P.; Reddy, C.M.; Nelson, R.L.; Andrews, A.B.; Atolia, E.; Allenson, S.J.; Mullins, O.C.; Marshall, A.G.; Analysis and identification of biomarkers and origin of blue color in an unusually blue crude oil, Energy & Fuels, 25, 172-182, (2011) 115. Gisolf, A.; Dubost, F.X.; Zuo, J.Y.; Williams, S.; Kristoffersen, J.; Achourov, V.; Bisarah, A.; Mullins, O.C.; SPE 121275, SPE Europe/EAGE Ann. Conf. Ex., Amsterdam, The Netherlands, 8-11 June, (2009) 116. Dumont, H.; Mullins, O.C.; Zuo, J.; Pomerantz, A.E.; Forsythe, J.C.; Vinay K. Mishra, V.K.; Garcia, G.; Compartments, Connectivity & Baffling Analyzed by the Extent of Equilibration of Asphaltene Gradients Using DFA, OTC 27143, Houston, TX (2016) 117. Pomerantz, A.E.; Ventura, G.T.; McKenna, A.M.; Cañas, J.A.; Auman, J.; Koerner, K.; Curry, D.; Nelson, R.L.; Reddy, C.M.; Rodgers, R.P.; Marshall, A.G.; Peters, K.E.; Mullins, O.C.; Combining Biomarker and Bulk Compositional Gradient Analysis to Assess Reservoir Connectivity, Org. Geochem. 41 (8), pp. 812-821, (2010) 118. Pastor, W.; Garcia, G.; Zuo, J.Y.; Hulme, R.; Goddyn, X.; Mullins, O.C.; Measurement and EoS Modeling of Large Compositional Gradients in Heavy Oils, Cartagena, Colombia, SPWLA, Ann., Symp., (2012) 119. Mishra, V.; Hammou, N.; Skinner, C.; MacDonald, D.; Lehne, E.; Wu, J.L.; Zuo, J.Y.; Dong, C.; Mullins, O.C.; Downhole Fluid Analysis & Asphaltene Nanoscience coupled with VIT for Risk Reduction in Black Oil Production, SPE 159857 ATCE, (2012) 120. Chen, L.; Forsythe, J.C.; Wilkinson, T.W.; Winkelman, B.; Meyer, J.; A. Canas, J.A.; Xu, W.; Zuo, J.Y.; Betancourt, S.S.; Lake, P.; Mullins, O.C.; A Study of Connectivity and Baffles in a Deepwater Gulf of Mexico Reservoir Linking Downhole Fluid Analysis and Geophysics; SPE ATCE 187231, (2017) 121. Elshahawi, H.; Mullins, O.C.; Hows, M.; Colacelli, S.; Flannery, M.; Zuo, J.Y.; Dong, C.; Reservoir Fluid Analysis as a Proxy for Connectivity in Deepwater Reservoirs, SPWLA, The Woodlands, TX, (2009) 122. Stainforth, J.G.; New Insights into Reservoir Filling and Mixing Processes, in Understanding Petroleum Reservoirs: Towards an Integrated Reservoir Engineering and Geochemical Approach, J.M. 58 ACS Paragon Plus Environment

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Cubitt, W.A. England, and S.R. Larter (eds.), London, England, Geological Society of London SP 237, 115–132, (2004) 123. Uchytil, S.; Mishra, V.K.; Betancourt, S.S.; Guthrie, J.; Huang, J.; Teerman, S.; Nguyen, A.; Stan Evans, S.; Nagarajan, N.; Mullins, O.C.; Impact of a Secondary Condensate Charge into an Oil Reservoir Evaluated by Downhole Fluid Analysis, Core Analysis, and Production, OTC 27240, Houston, TX, (2016) 124. Pfeiffer, T.; DiPrimio, R.; Achourov, V.; Mullins, O.C.; Tar mat on Baffles in the Middle of an Oil Column, SPWLA Ann. Symp. Iceland, (2016) 125. Zuo, J.Y.; Mullins, O.C.; Jackson, R.; Agarwal, A.; Ayan, C.; Wang, K.; Chen, Y.; Pan, S.; Elshahawi, H.; Dong, C.; Herold, B.; Kumar, S.; Understanding Reservoir Fluid Dynamic Processes by Using Diffusive Models, OTC 26964, Houston, TX, (2016) 126. Seifert, D.J.; Zeybek, M.; Dong, C.; Zuo, J.Y.; Mullins, O.C.; Black oil, heavy oil and tar mats, SPE 161144 ADIPEC Abu Dhabi, UAE, (2012) 127. Seifert, D.J.; Qureshi, A.; Zeybek, M.; Pomerantz, A.E.; Zuo, J.Y.; Mullins, O.C.; Mobile Heavy Oil and Tar Mat Characterization Within a Single Oil Column Utilizing Novel Asphaltene Science, SPE 163291, KIPCE, Kuwait (2012) 128. Chen, L.; Meyer, J.; Watson, T.; Canas, J.; Forsythe, J.C.; Mehey, S.; Kimball, S.; Larsen, D.; Nighswander, J.; Zuo, J.Y.; Mullins, O.C.; Applicability of Simple Thermodynamics for Asphaltene Gradients in Oilfield, Submitted, Fuel

129. Dumont, H., Mishra, V., Zuo, J.Y.; Mullins, O.C.; Permeable tar mat formation within the context of novel asphaltene science, SPE 163292, KIPCE, Kuwait, (2012) 130. Pomerantz, A.E.; M.R. Hammond, A.L. Morrow, Mullins, O.C.; Zare, R.N.; Two step laser mass spectrometry of asphaltenes, J. Amer. Chem Soc., 130 (23), 7216–7217, (2008) 131. Wu, Q.; Pomerantz, A.E.; Mullins, O.C.; Zare, R.N.; Fragmentation and Aggregation in Laser Desorption Laser Ionization and Surface Assisted Laser Desorption Ionization Mass Spectrometry, J. Amer. Soc. Mass Spec. 24, 7, 1116-1122, (2013) 132. Achourov, V.; Pfeiffer, T.; Kollien, T.; Betancourt, S.S.; Zuo, J.Y.; di Primio, R.; Mullins, O.C.; Gas Diffusion into Oil, Reservoir Baffling and Tar Mats Analyzed by Downhole Fluid Analysis, Pressure Transients, Core Extracts and DSTs, Petrophysics, 56, 4, 346–357, (2015) 133. Mander, J.; d’Ablaing, J.; Howie, J.; Wells, K.; Ramazanova, R.; Shepherd, D.; Lee, C.; 21st Century Atlantis–Incremental Knowledge from a Staged-Approach to Development, Illustrated by a Complex Deep-Water Field, in New understanding of petroleum systems of continental margins of the world, 32nd annual GCSSEPM Foundation conference, (2012) 134. Naidu, B. N.; Kothari, V.; Whiteley, N. J.; Guttormsen, J.; Burley, S. D. Calibrated Basin Modelling to Understand Hydrocarbon Distribution in Barmer Basin, India. Paper No. 10448. Presented at AAPG International Convention and Exhibition, Singapore, (2012)


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135. Head, I. M.; Jones, D. M.; Larter, S. R. Biological Activity in the Deep Subsurface and the Origin of Heavy Oil. Nature, 426, 344-352, (2003) 136. Forsythe, J.C.; Martin, R.; Santo, I.; Tyndall, R.; Arman, K.; Pye, J.; De Nicolais, N.; Nelson, R.K.; Pomerantz, A.E.; Kenyon-Roberts, S.; Zuo, J.Y.; Reddy, C.; Peters, K.E.; Mullins, O.C.; Reservoir implications of a spill-fill sequence of reservoir charge coupled with viscosity and asphaltene gradients from a combination of water washing and biodegradation, SPE ATCE 187044, (2017) 137. Hall, D. L., and Dolson, J., Using fluid inclusion data in exploration. In: J. Dolson; Understanding oil and gas shows and seals in the search for hydrocarbons; Springer, pp 349-383, (2016)


Reservoir Fluid Geodynamics - American Chemical Society

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