COMPONENT PARTITIONING IN CARBON DIOXIDE/CRUDE OIL MIXTURES

A DISSERTATION

SUBMITTED TO THE DEPARTMENT OF PETROLEUM ENGINEERING

AND THE COMMITTEE ON GRADUATE STUDIES

OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

By Karen Denise Hagedorn October 1992 © Copyright 1992 by Karen Denise Hagedom

11 I certify that I have read this thesis and that in my opin ion it is fully adequate, in scope and in quality, as a dissertation for the degree of Doctor of Philosophy.

F.M. Orr, Jr. (Principal Advisor)

I certify that I have reeid this thesis and that in my opin ion it is fully adequate, in scope and in quality, as a dissertation for the degree of Doctor of Philosophy.

K. Aziz

I certify that I have read this thesis and that in my opin ion it is folly adequate, in scope and in quality, as a dissertation for the degree of Doctor of Philosophy.

S.C. Brassell Indiana University

Approved for the University Committee on Graduate Studies:

lU Acknowledgements

This work would not have been possible without the support and assistance of nu merous persons and organizations. First, I would like to thank Professor LjTin Orr for guidance, patience, a sense of humor, and lots of free flights. I would also like to thank Dr. Simon Brassell for his assistance with the GC/MS instrimaent and for providing valuable insight into petroleum geochemistry. Thanks to Dr. Brassell, Dr. Khalid Aziz, and Dr. BiU Brigham for their helpful suggestions. Special thanks to all of the people who helped out in the lab (especially when my patience was wearing very thin): Milind Deo, Aaron Stessman, Maurice Stadler, David Schechter, Dengen Zhou, and Rod Batycky. Thanks to Eric Papp for taming the mass spectrometer. Thanks to Maurice Stadler and Aaron Stessman for the SIMDIS and Means PVT data and to Mustafa Yihnaz for the Oil D data. Thanks to Birol Dindoruk for taking the time to modify his simulator for me and teaching me how to use it. Thanks are also due to the many people who provided moral support, or just listened to me when I was ranting and raving over some experimental aggravation. Special thanks to David Shallcross, Monica and Michael Cation, Deanna and Mark Kanady. I must also thank my parents for their support and for pretending to understand why I had to be in school for so long. The oil samples and slim tube displacement results presented in this report were furnished by Exxon, Mobil, Unocal, Amoco, and the Teikoku Oil Co. Their assistance is gratefully acknowledged. This work was supported in part by the Department of Energy, the SUPRI-C Industrial Affihates Program, the National Science Foundation, and the Stanford Center for Materials Research. That support is greatly appreciated. Finally, I would like to dedicate this thesis with deepest gratitude and affectionto Dr. Susie Evers, my high school science teacher, for believing in me and teaching me to believe in myself. She is proof of how much of a difference one person can make in your lifetime.

VI Abstract

The development of miscibility in a CO2 displacement process is dependent upon the extraction of light to intermediate hydrocarbons from the oil into the C02-rich displacing phase. Therefore, it is important to understand how the size and struc ture of individual components in a crude oil affect the way these components are extracted by dense CO2. Knowledge of how individual components behave can then be extended to understand how the overall oil composition affects the performance of a CO2 displacement process. The analytical technique of gas chromatography/mass spectrometry (GC/MS) has been used to analyze equilibrium phase samples from C02/crude oil mixtures. K-values, or partition coefficients, have been measured for over 250 individual com ponents in several crude oils. The K-value data clearly show that both molecular size and structure affect the way a component is extracted by dense CO2. Specifically, multi-ring aromatic compoimds are extracted much less efficiently by CO2 than other components with similar size or similar GO elution times. The conventional simulated distillation (SIMDIS) compositional analysis assimaes that components that elute together from a GC also partition similarly. This as sumption is clearly not adequate to characterize oils that have significant multi-ring aromatic fractions. A new technique is presented to characterize the multi-ring aromatic fraction of a crude oil, using a GC/MS analysis of the whole oil. This new characteriza tion scheme was successful in simulating the slim tube displacement performances of two oils with markedly similar SIMDIS distributions, but with significantly different

vn molecular type compositions. Inclusion of the multi-ring aromatic properties in the oil characterization scheme reduces slim tube recoveries, and consequently raises the niinimum misdbility pressure (MMP). These effects axe identical to those observed experimentally for a highly aromatic crude oil.

fS>,

vm Contents

Acknowledgements iv

Abstract vii

1 Introduction 1 1.1 Literature Survey 2 1.2 Scope of This Work 15

2 Experimental Apparatus and Data Analysis 17 2.1 Experimental Apparatus 17 2.1.1 Gas Chromatograph 17 2.1.2 Mass Spectrometer 18 2.1.3 Tuning 20 2.1.4 Operating Conditions 21 2.2 Phase Sample Analysis 22 2.3 Data Analysis 24 2.3.1 Peak Quantitation 26 2.3.2 Spectrum Identification 26 2.3.3 Phase Sample Analysis 28

3 Experimental Results 31 3.1 Phase Equilibrium Samples 31 3.1.1 Means Oil 35 3.1.2 OilW 49

IX 3.1.3 Kubiki Oil 54 3.2 Whole Oil Analyses and Phase Behavior 57 3.3 Summary 69

4 Numerical Modelling 73 4.1 Multi-ring Aromatic Critical Properties 74 4.2 Characterizing the Oil 77 4.3 Equation of State Calculations 78 4.4 Development of Miscibility in 1-D Systems 86 4.5 Slim Tube Simulations 96

5 Discussion 107

6 Conclusions 111

A Data Quantitation and Spectrum Analysis 121 A.l Quantitation 121 A.2 Spectrum Identification 123 A.3 Coeluting Components 126

B Oil Characterization 131

C Critical Property Equations 137

D Raw Data Output 141 List of Tables

3.1 Sampling conditions for PVT experiments 32 3.2 K-values for C13 molecules in Sample 1 40 3.3 Chemical type distributions for Means, Oil W, and Kubiki 60 3.4 Chemical type distributions for Bell Creek, Oil D, and Ganado .... 67

4.1 Measured or estimated critical properties for multi-ring aromatics . . 74 4.2 Measured boiling points and specific gravities for multi-ring aromatics 75 4.3 Critical pressures for multi-ring aromatics 75 4.4 Critical temperatures for multi-ring aromatics 76 4.5 Acentric factors for multi-ring aromatics 76 4.6 Synthetic oil compositions 81 4.7 Relative permeability function parameters 97 4.8 Nine pseudocomponent oil description - non-MRA oil 97 4.9 Nine pseudocomponent oil description - first aromatic case 99 4.10 Nine pseudocomponent oil description - second aromatic case .... 100

B.l Kubiki SCN mole fractions and molecular weights 132 B.2 Extended SCN distribution for Kubiki 134 B.3 Kubiki oil EOS characterization 135

D.l BeU Creek oil analysis 143 D.2 Ganado oil analysis 151 D.3 Kubiki oil analysis 161 D.4 Means oil analysis 172 D.5 Oil D oil analysis 184

xi D.6 Oil W oil analysis 198 D.7 Means upper phase analysis at 3000 psia and 105®F 207 D.8 Meains lower phase analysis at 3000 psia and 105®F 215 D.9 Means upper phase analysis at 1500 psia and 105®F 223 D.IO Means lower phase analysis at 1500 psia and 105®F 231 D.ll Means upper phase analysis at 3950 psia and 125®F 239 D.12 Means lower phase analysis at 3950 psia and 125®F 249 D.13 Oil W upper phase analysis at 2000 psia and 125®F 259 D.14 Oil W lower phase analysis at 2000 psia and 125®F 269 D.15 Oil W upper phase analysis at 3100 psia and ITCF 278 D.16 Oil W lower phase analysis at 3100 psia and 170®F 288 D.17 Kubiki upper phase analysis at 2000 psia and 125®F 299 D.18 Kubiki lower phase analysis at 2000 psia and 125®F 305 D.19 Kubiki upper phase analysis at 3950 psia and 125®F 313 D.20 Kubiki lower phase analysis at 3950 psia and 125®F 323

xn List of Figures

2.1 Schematic of ion volume 19 2.2 Diagram of PVT cell and sampling system (from Stessman [48]) ... 23 2.3 Schematic diagram of phase sample analysis 25

3.1 Simulated distillation analyses for Means, Oil W, and Kubiki 33 3.2 Chemical structures found in crude oils 34 3.3 K-values as a function carbon niunber for Sample 1, Means at 3000 psia and 105®F 36 3.4 K-values as a function of elution time for Sample 1, Means at 3000 psia and 105^ 37 3.5 Alkane K-values for Sample 1, Means at 3000 psia and 105''F 38 3.6 Cyclic K-values for Sample 1, Means at 3000 psia and 105^F 39 3.7 Multi-ring aromatic K-values for Sample 1, Means at 3000 psia and 105''F 41 3.8 K-values versus elution time for Sample 2, Means at 1500 psia and 105®F 43 3.9 n-Alkane K-values versus elution time for Samples 1 and 2 44 3.10 Pressure-composition diagram for Means crude oil and CO2 at 105^F (data from Stessman [48]) 45 3.11 Slim tube displacement data for Means at 105®F (data provided by Exxon) 46 3.12 Pressure-composition diagram for Means crude oil and CO2 at 120®F (data from Stessman [48]) 47 3.13 K-values versus elution time for Sample 3, Means at 3950 psia and 125®F 48 3.14 n-Alkane K-values versus elution time for Samples 1 and 3 50

xm 3.15 Slim tube displacement data for Oil W at 170®F (data provided by donor of Oil W) 51 3.16 K-values versus elution time for Sample 4, Oil W at 3100 psia and 170°F 52 3.17 K-values versus elution time for Sample 5, Oil W at 2000 psia and 125®F 53 3.18 Slim tube displacement data for Kubiki at 125®F (Data provided by the Teikoku Oil Co.) 55 3.19 K-values versus elution time for Sample 6, Kubiki at 2000 psia and 125°F 56 3.20 K-values versus elution time for Sample 7, Kubiki at 3950 psia and 125®F 58 3.21 Multi-ring aromatic K-values for Sample 7, Kubiki at 3950 psia and 125®F 59 3.22 Oil W carbon number distribution by chemical t3^e 62 3.23 Kubiki carbon number distribution by chemical type 64 3.24 Bell Creek carbon nimiber distribution by chemical type 66 3.25 Oil D carbon nimaber distribution by chemical type 68 3.26 Ganado carbon number distribution by chemical type 70

4.1 Boiling point versus elution time for pure components in crude oil . . 79 4.2 K-values for paraffinic and aromatic synthetic oils 82 4.3 Effect of multi-ring axomatics on slim tube displacement 84 4.4 K-values for MRA and non-MRA components in Kubiki oil description 85 4.5 Three component theory of multicontact miscibility 87 4.6 Four component theory of multicontact miscibility, Orr et al [36] . . 89 4.7 Pseudoternary diagram For Kubiki oil and methane at 125®F and 4000 psia 91 4.8 Pseudoternary diagram For Kubiki oil at 125®F and 2500 psia .... 92 4.9 Pseudoternary diagram for Kubiki oil at 125®F and 4000 psia 94 4.10 Pseudoternary diagram for Kubiki oil at 125®F and 2500 psia, BIP's=0.08 95 4.11 Slim tube simulations, Kubiki 9-component non-MRA and aromatic lumped descriptions 98 4.12 Slim tube simulations, Kubiki 9-component aromatic lumped descriptions101

XIV 4.13 Slim tube simulations, effect of multi-ring aromatic binary interaction parameters 103 4.14 Slim tube simulations, effect of aU binary interaction parameters . . . 104 4.15 Comparison of experimental and calculated slim tube recoveries at 125®F.106

A.l Total ion chromatogram for Means upper phase sample 122 A.2 Example normal alkane mass spectrum 124 A.3 Example branched alkane ma^s spectrum 125 A.4 Example cycloalkane mass spectrimi 127 A.5 Example alkylbenzene mass spectrum 128 A.6 flxample spectrum of coeluting components 129

XV Chapter 1

Introduction

Miscible gas enhanced oil recovery (EOR) using CO2 as the injected solventis one of the most common and most productive EOR techniques currently in use in the United States. Accurate prediction of the performance of these processes depends on an imderstanding of the complex phase behavior of the reservoir oil with the injected solvent. This phase behavior of a C02/crude oil system is a strong function of the oil composition. The extraction of light to intermediate hydrocarbons from the oil into a C02-rich upper phase governs the development of miscibility in a CO2 displacement process. Therefore, it is important to understand howdifferent types of hydrocarbon compounds present in a crude oil are extracted into this upper phase. Individual component behavior can then be extended to show how the distribution of components in aji oil aspects the phase behavior of a miscible process. Miscible processes are usually modelled using compositional simulation. Because of computing time constraints, compositional descriptions for crudeoils are generally limited to a small number of pseudocomponents. These pseudocomponents, or cuts, are usuallybasedon chromatographic retentiontime. Thistechnique, called simulated distillation (SIMDIS), assumes that compounds that elutetogether from a GC behave similarly with respect to the process ofinterest,in this case extractionby dense CO2. Knowledge of howdifferent tjrpes of components are extracted with respect to elution time is important in determining how the hundreds of components present in a crude oil should be assigned to a small number of pseudocomponents in order to represent 2 CHAPTER 1. INTRODUCTION the total oil composition accurately.

1.1 Literature Survey

A considerable effort has been devoted to the study of the effectof oil composition on the behavior of C02/crude oil systems. The speed and relative ease of use of gas chromatography (GC) to provide quantitative analysis of the molecular size distri bution of components in a crude oil has resulted in a considerable body of evidence on variations in crude oil composition and how they affect the properties of the oil and the behavior of the oil in processes such as CO2 flooding. The addition of such analytical techniques as PNA (paraflSn, naphthene, aromatic) analyses and NMR has resulted in limited supplementary information as to the chemical type compositionof crude oils. The relationship between the extraction of components from a crude oil by CO2 and the development of misdbility in a CO2 displacementprocessis wellestablished. Hutchinson and Braun [19] presented the theory of multicontact miscibiUty devel opment. They described a process in which the injected solvent extracts light to intermediate components from the oil into a solvent-rich phase. Because the mobil ity of this solvent-rich phase is higher than that of the oil, the solvent moves ahead and contacts fresh oil. Additional light to intermediate material is extracted by the solvent-rich phase, which continues to move ahead in the displacement until enough hydrocarbon material is present in the solvent-rich phase to render it miscible with the oil. Thus, the multicontact miscible process results in a "transition zone," which is miscible with the oil at the leading edge, miscible with the injected solvent at the trailing edge, and has compositions within that are continuously miscible along the displacement length. Hutchinson and Braim described this process primarily for the high pressure gas drive, using lean gas (methane) as the injected solvent. Rathmell et al. [43] showed that using CO2 in place of the high pressure lean gas resulted in development of miscibility at significantly lower pressures. They con cluded that the mechanism for miscibility development was the same for the two processes, with CO2 extracting light to intermediate (C2 - Ce) components into the 1.1. LITERATURE SURVEY 3 solvent-rich phase. The decrease in sizeof the two-phase region and the changein tie line slopes within the two-phase region were suggested as the reasons for the lower pressure requirements when CO2 was used as the solvent. Because the development of miscibihty relies so heavilyon the extraction of light to intermediate hydrocarbons from the oil, variations in the composition of the oil should affect the displacement performance. Yellig and Metcalf [59] investigated the effects of both oil composition and tem perature on the minimimi pressure required for misdbiUty in one dimensional (1-D) slim tube displacements using CO2 as the solvent. They described the experimen tal slim tube apparatus and recommended a criterion for determining the minimum miscibility pressure (MMP) from the experimental data. A slim tube is a length of smedl diameter tubing packed with a porous medium. The tube is initially saturated with crude oil, which is displaced at constant pressure by the injected solvent (CO2). Recovery of the oil is recorded as a function of the amoxmt of solvent injected. Results of a series of slim tube displacements at different pressures are usually presented as a plot of oil recovery corresponding to 1.2 pore volumesof injection versus displacement pressure. The miTiiTrmTn pressure required for miscibility can be interpreted from this plot using criteria such as the point where the curve breaks over or some arbitrary high recovery value. Yellig and Metcalf suggested the use of the brezikover point as the MMP criterion. The compositional variations investigated by Yellig and MetcaJf included only variations in the "light" (CO2, Ci, ^^2) ^ind "intermediate" (C2 - Ce) fractions. The same 0?+ fraction from a West Texas crude oil was used for all experiments. Compo sition was not found to have a significant effect on MMP. Yellig and Metcalf claimed that variations in the C7-I- composition would have negligible effect on MMP, but did not investigate the effect of variations in C7+ composition. Temperature, however, was foimd to have a significant effect on MMP. A correlation was proposed that related CO2 MMP to temperature alone and did not include oil composition. One of the first works to axidress the effects of oil composition on development of miscibility was that of Holmand Josendal [18]. Slimtube experiments were performed on oils with a wide variety of compositions. Holm and Josendal presented a correlation 4 CHAPTER 1. INTRODUCTION for predicting the slim tube minimum miscibility pressure (MMP) that included a more detailed compositional description of the oil than had been included in previous MMP correlations [59, 22]. Holm and Josendal's data showed that MMP could be correlated with CO2 density and with the amount of "extractable" hydrocarbons in the oil. Extractable hydrocarbons were defined as the Cs to C30 fraction of the oil. They also argued that the amount of C5 to C12 compoimds in the C5 to C30 fraction of the oil would affect the MMP. They reasoned that an abundance of these light compounds would further reduce the MMP because the C5 to C12 hydrocarbons would be extracted more efficiently than C13 to C30 hydrocarbons. Thus, Holm and Josendal concluded that the mechanism of miscibility development in a CO2 process is the extraction of these C5 to C30 hydrocarbons by dense CO2. The density of CO2 required for this extraction was found to be a function of the carbon number distribution of the C5 to C30 fraction. In one experiment. Holm and Josendal replaced the 400 - 800 ®F boihng fraction of a paraffinic crude with the same boiling fraction of an aromatic crude. A lower MMP resulted, leading to the conclusion that aromaticity decreased the MMP. They suggested that the increased solvency of the aromatics contributed to the more fa vorable phase behavior. A detailed breakdown of the carbon number distribution of hydrocarbons within these fractions was not reported, however. Goricnik and Sarapa [13] conducted slim tube displacements on several Croat ian oils. The slim tube MMP was found to be a function of the composition of the oils, specifically of the aromatic content. Two heavy oils, having similar C5 to C30 fractions but different aromatic contents, were compared. Neither of these oils devel oped miscibility in a sHm tube with CO2. However, miscibiUty was achieved when equal quantities of C5 to C12 hydrocarbons were added to the two oils. This addition resulted in one of the oils (KR) having a higher C5 to C12 content than the other. According to Holm and Josendal [18], this should have resulted in the KR oil having a lower MMP. In fact, the KR oil (with the light components added) had a higher slim tube MMP. Goricnik and Saxapa explained this discrepancy as the result of the difference in aromatic content between the two oils. The KR oil had an aromatic content of 43 wt. % of the C5 to C40 fraction, which was approximately twice that of LI. LITERATURE SURVEY 5 the other oil. Therefore, they concluded that, contrary to the findings of Holm and Josendal [18], aromaticity was detrimental to the development of miscibility. Monger [32] used synthetic oils (hydrocarbon mixtures) to study the effects of aromaticity on CO2 flooding. Analog components for paxaffinic and aromatic oils were based on a match of physical properties, although the exax:t methodology was not clearly defined. Aromaticity was found to be beneficial to the development of miscibility. However, the aromatic oil had biphenyl (caxbon number 12) as its heaviest component, whereas the paraffinic oil contained squalane (carbon number 30) as the heavy component. It seems likely that the "benefit" of the aromatic oil was the decrease in size of the heaviest molecule as opposed to the structure of that molecule. Wilbum et al. [54] extended the work of Monger [32], using analog components in paraffinic, naphthenic, aromatic, and mixed synthetic oils. It was reported that parafl&ns and aromatics were beneficial to miscibility development, and that naph- thenes were detrimental. The oils were compared on the basis of equivalent molar averaged boiling point. However, the size distributions of components differed for the different oils. Most of the results observed could be attributed to the differences in molecular size, instead of t3rpe. The effect of oil composition on the phase behavior of two Japanese crude oils was reported by Morii et al. [34]. The two oils studied had similar quantities of the C5 to C30 fraction, but had different carbon number distributions and different aromatic contents. The lighter, less aromatic oil showed much higher slim tube recoveries and a lower MMP than the heavier, more aromatic oil. The heavier oil, Kubiki, produced a much flatter slim tube recovery versus pressure curve with no real breakover point as seen in most slim tube displacements. Recoveries were low (less than 85%), even when a sight glass indicated that the flow was single phase. Morii et al. attributed these differences in phase behavior to the carbon number distributions alone and not to the aromatic contents of the oils. The effect of carbon number distribution on MMP was examined by Orr and Silva [37], who reported a modification to the Holm and Josendal [18] MMP correlation. Orr and Silva proposed that various carbon nimiber cuts be weighted by a partition- coefficient factor that was a function of molecular size alone and was independent of 6 CHAPTER 1. INTRODUCTION temperature and pressure. Molecular size and the individual cuts themselves were defined based on elution time from a gas chromatograph. Orr and Silva argued that small aromatic molecules present in many crude oilsnormally contain side chains that improvetheir solubilityin dense CO2. They alsosuggestedthat aromatic components in heavy fractions of the crude oil would be extracted so poorly that they would have minimal impact on development of miscibihty. Therefore, they argued that the differences in partitioning behavior related to chemical type could be neglected. The effect of chemical size and structure on the partitioning behavior of various hydrocarbons in binary systems with CO2 has been fairly well documented. Francis [11] reported solubility data for 261 compounds with CO2. Although many differ ent types of compounds were studied, the effects of structure on solubility were not discussed extensively. Dandge et al. [6] reported the effects of structure on the solubilities of pure hydro carbons in dense CO2. It was suggested that CO2 density alone controlled solubiUty. n-AIkanes with 12 carbon atoms or less were found to be completely miscible with CO2. Solubility then decreased rapidly with increasing carbon number. Branched alkanes up to C19 were miscible with CO2 (depending on the degree of branching). They suggested that the increase in solubility with branching wasthe result of the ef fect of bond type on intermolecularforces. The lower boilingpoints of these branched alkanes axe indicative of the same effect. Dicyclic compounds were found to be only partially soluble in CO2, with increasing hydrogenation improving solubility. Dandge et al. also pointed out from Francis' [11] data that dicyclic naphthenic and aro matic molecules are only partially soluble in CO2, whereas aliphatic and monocyclic molecules in the same boiling range are miscible. They concluded that chemical structure, rather than physical constants such as boiling point, affects solubiHty. Wang [50] simidated the extraction process and development of miscibility in a high pressure visual cell. Phase samples from C02/crude oil mixtures were analyzed by GC. The amount of hydrocarbons extracted was found to be a function of the CO2 density. Temperature was also reported to have an effect, with the more efficient extraction occurring when CO2 was in the liquid state. However, the changes in temperature were accompanied by a corresponding change in the density of CO2. 1.1. LITERATURE SURVEY 7

Thus, the observed effects of temperature on extraction could be attributed to changes in CO2 density. Partitioning behavior of individual molecules in hydrocarbon mixtures was ad dressed by Silva and Orr [46]. By analyzing phase samples from PVT experiments with gas chromatography, K-values, or partition coefficients, were evaluated for in dividual components of S3aithetic oils. These K-values are a measure of how well a component is extracted by dense CO2, with the highest K-values representing the most efficient extraction. A component's K-value was reported to decrease with varia tions in structure m the following order: branched alkanes, normal alkanes, aromatics, and naphthenes. It was also shown that increasing the aromaticity of the synthetic oil did not improvethe solubilityof the heaviest component (squalane). This result is contradictory to the effect of aromaticity that was suggested by Holm and Josendal [18]. Phase samples from C02/crude oil PVT experiments were also analyzed by GC. The aromatic contents of the crude oils were determined by NMR. K-values for single carbon number cuts, based on GC retention time, did not show an effect of aro maticity. However, when pure aromatic compounds, including multi-ring aromatics, were added to the crude oil, the K-values of these multi-ring aromatics were consid erably lowerthan the averagevaluefor the cut with whichthey eluted. Silvaand Orr postulated that these compounds were present only in small quantities in the actual crude oil and that significant branching on the aromatics that were present offset the effect of the aromatic ring. Silva and Orr also discussed the effects of branching on GC elution time and suggested the possibility of an impact on simulated distillation (SIMDIS) interpretation. However, they concluded that the effect was small due to the relationship between extraction behavior and elution time. The significant differ ence in extraction behavior between the multi-ring aromatics and other components with similar elution times was not discussed. Wilson et al. [56] attempted to relate retention time on a nonpolar chromato- graphic column to boiling point for various hydrocarbons. Aromatics, especially multi-ringaromatics, were foundto haveshorter retention times than similarly boiling aliphatic molecules. The appropriateness of using gas chromatographic elution time (simulated distillation) in place of true boiling point (TBP) distillation for crude oil 8 CHAPTER L INTRODUCTION compositionalanalysis wasstudied by Green et al. [14], Jackson et al. [20], and Chorn [4]. All found good agreement between TBP distillation and SIMDIS results. How ever, no attempt was made to study retention time versus boiling point for individual compounds or classes of compounds. From the discussion given above, it is evident that the GC elution time, chem ical structure, and boiling point of a molecule are all related, and are also related to the partitioning behavior of that molecule in dense CO2. However, the actual relationships between these variables, especially for components in a complex mix ture such as a crude oil, axe not entirely clear. The confusion is largely due to the limitation that most anal3^ical techniques are unable to provide a detailed, molecule- by-molecule compositional description of the oil, including GC retention times. By combining a gas chromatograph (GC) with a mass spectrometer (MS), however, a detailed molecular description can be obtained for each component as it elutes from the GC. The use ofgas chromatography/mass spectrometry (GC/MS) to analyzehydrocar bon mixtures is not new. Kuras and Hala [28] describedthe use ofa massspectrometer for structural group type analysis of petroleum fractions and products. Sequential molecule-by-molecule type analysesby GC/MS for very light petroleum fractions were also presented. Coal-derived fluids were analyzed using GC/MS by Bertsch et al. [3]. A nonpolar stationary phase was used to separate components in roughly boiling point order. Bertsch et al. only analyzed up to about C20, but, even with no sample preparation, were able to identify most compounds. Sincethe late 60's, GC/MS has been increasingly usedin petroleum exploration to analyze specific classes of compounds caUed biomarkers. Generally, significant sample preparation is required, and only a small number of molecules axe identified. Philp and Gilbert [42] described the use of computerized GC/MS to analyze Australian crudes for oil-oil and oil-source rock correlations. Various biomarkers were discussed. Philp and Gilbert also showed that quantitative data from GC/MS were analogous to GC results usmg a flame ionization detector (FID). The first comprehensive published use of GC/MS to analyze whole crude oils is 1.1. LITERATURE SURVEY 9 that of Zadro et al. [61]. Four Australian crudeoils were analyzed quantitatively by GC/MS. Zadro et al. identified 293 peaks in the range of Cs to C31, representing about 370 components. The problems of quantitatively separating coeluting compo nents that have similar structures were discussed. Zadro et al. also pointed out the inabilityto detect many components that are present in small quantities, especially if those components coelute with compounds of much higher concentrations. However, for those peaks that weredetected, the highest unidentifiable fraction for the four oils was 2.4%. Yilmaz [60] analyzed four Permian Basin crude oils using GC/MS. Operating conditions for those analyses were similar to those used by Zadro et al. [61]. Nearly 400 components were identified in each of the oils. Yilmaz also attempted to relate elution time from a nonpolar column to boiling point and to molecular structure. Components from C4 to Cio were found to elute in boiling point order, regardless of structure. No investigation was made, however, of the higher boiling fractions. Durand et al. [9] identified about 550 peaks up to C20 in a crude oil. The use of GC/MS to calculate a molecular weight distribution for the various boiling ranges of the oil was described. Molecularweightis just one ofthe properties that must be defined for a component in a mixture in order to use an equation of state (EOS) to predict phase behavior. Critical properties, such as critical pressure, critical temperature, and acentric factor must also be defined for each component. Because the precise composition is rarely known from TBP distillations or SIMDIS data, nimierous correlations have been developed in order to assign critical properties to the various cuts that are obtained experimentally. Each of these cuts actually consists of many components. Single carbon number fractions, based on normal alkane boiling points or elution times, have usually been the most detailed compositional descriptions attainable. Critical property correlations for undefined fractions of crude oils require varying degrees of experimental data; from actual measurements of boiling points, specific gravities and molecular weights of distillation cuts to simple weight fraction distributions from SIMDIS. The earliest critical property correlations were generally based on boihng points 10 CHAPTER L INTRODUCTION and specific gravities of distillation cuts, as these were routinely measured. Watson [51] defined a characterization factor, or K-factor, based on the boiling point and specific gravity of a cut: Tb^ = (1-1) where is the normal boiling point in degrees Rankine and SGis the specific gravity of a cut. This K-factor, also known as the UOP K-factor, was shownto be a function of the aromaticity of the cut, rangingfrom 12.5for purely paraffinic fractions to 10 for highly aromatic fractions. Critical temperatures and pressures and molecular weights for various boiling cuts were presented as fimctions of the K-factor. Many subsequent correlations have made use of the Watson K-factor to accoimt for variations in the molecular compositions of the various cuts. Hariu and Sage [16] presented a method of estimating the molecular weight of a fraction from its boiling point and K-factor. Correlationsfor molecular weight, criticaltemperature, critical pressure,and acen tric fax:tor were reported by Kesler and Lee [24]. These rather complicated empirical functions related critical properties to boiling point, specific gravity, and K-factor. Riazi and Daubert [45] proposedempiricalcorrelations for molecular weight, liquid density, liquid molar volimie, critical temperature, critical pressure, critical volimie, refractive index, heat of vaporization, and ideal gas heat capacity. All correlations were of the simple form: 0 = (1.2) where 0 is the property of interest, a,b, and c are correlation constants, and A and B are any two parameters that characterize the molecular forces. Boiling point and specific gravity were chosen as these parameters. Twu [49] related critical properties of real firactions of petroleum and coal-tar liquids to those of normal alkanes using a perturbation expansion. Correlations for critical temperature, critical pressure, critical volume and molecular weightwereeval uated for accuracy using many different types of hydrocarbons from C5 to €20- The suggested range of applicability of the Twu correlation is much broader than that of most correlations, to a boiling point of 1778®R and a specific gravity of 1.436. LL LITERATURE SURVEY 11

While gas chromatographic analyses axe much faster and easier to perform than distillation analyses, they do not yield the individual cut properties such as boiling point, specific gravity, and molecular weight that are necessary for most correlations. Often, the only data available for the heavy (C7+) fraction axe the weight fraction distribution from SIMDIS and the measured specific gravity and molecular weight of the combined heavy fraction (the "plus" fraction). Therefore, methods axe required for estimating the boiling points, specificgravities, and molecular weights of individual cuts before any of the critical property correlations may be applied. Katz and Firoozabadi [23] presented in tabular form the average boiling point, specific gravity, and molecular weight for single carbon nxmaber (SCN) cuts obtained from gas chromatography. These values are completely independent of any measured oil properties, but are reasonable estimates if no information other than the SlMDlS analysis is known. Whitson [53] proposed a method for estimating singlecarbon number (SCN) spe cific gravities using SCN molecular weights and Watson K-factors. An average K- factor for the entire plus fraction is calculated from the measiured specific gravity and molecular weight for that fraction. K-factors for individual cuts axe estimated from their carbon nimiber and the average K-factor of the plus fraction. The SCN specific gravities are calculated from SCN molecular weights and K-factors using the Hariu and Sage [16] equations. The plus fraction averagedspecific gravity is calculated from the SCN gravities and is compared to the measured value. The plus fraction average K-factor is adjusted imtil the calculated SG agrees with the measured value. SCN boiling points are then calculated from SCN gravities and K-factors. At this point, any of the boiling point-specific gravity type correlations may be used to calculate SCN critical properties. Yaxborough [58] used an "aromaticity factor" in much the same way as Whitson [53] used the plus fraction average K-factor. SCN gravities are estimated from the aromaticity factor and the cut carbon number. Iteration on the eiromaticity factor is continued until the calculated average specific gravity agrees with the measured value. The advantage of the Whitson scheme over the Yaxborough method is that Whitson allows different cuts to have different K-factors. Also, Whitson's use of the 12 CHAPTER L INTRODUCTION

K-factor provides a more physical meaning to the cut properties than the "aromaticity factor", which is really just an indication of the degree of deviation from paxaflBnicity. The Yarborough method uses its own correlations for critical properties based on the aromaticity factor, whereas the Whitson method allowsthe calculation of boiling point, and therefore the use of any appropriate critical property correlation. Some correlations go a step further than using a general aromaticity type factor. These correlations make an attempt to account for the actual PNA (paraffins, naph- thenes, aromatics) distribution of the oil. Bergman [2] proposed a PNA method for estimating the critical properties of lean gas condensates. The PNA composition of a fraction is estimated using boiling point and specific gravity (both measured). No direct measurement is made of the actual PNA content. This method is limited to fractions up to about Cis- Peng and Robinson [40] alsopresented a method for estimating PNA content. Pro cedures for both distillation and chromatographic experimental data were reported. The relationships between boiling point and either specificgravity or molecular weight provide the basis for the PNA distribution. Again, no direct measurements of PNA content are included. The Peng-Robinson correlations are based on the assumption that the heptanes plus fraction consists of three families of compoimds: n-alkanes, n-alkylcyclohexanes, and n-alkylbenzenes. For one very aromatic (96%) absorber oil, the correlations performed poorly. When the n-alkylbenzene properties were replaced by those of the simileirly boiling methyl-naphthalene, predictions using the correla tions improved considerably. Thus, it was indicated that including all aromatics in one aromatic category might be too general. The actual PNA content of various distillation cuts was included in a characteriza tion scheme proposed by Pedersen et al. [38]. The specific gravities of the paraffinic, naphthenic, and aromatic fractions of the cuts are calculated from the 50 % boiling temperature of the cut. Critical properties for each of the P, N, and A fractions of the cut are calculated individually using an appropriate correlation and then are recombined for the average cut property using a mole fraction average miyiTig rule. Grigg and Lingane [15] reported the most extensive use of experimental composi tion data in characterizing a crude oil. GC/MS was used to analyze true distillation 1.1. LITERATURE SURVEY 13 cuts up to about C23. Actual PNA fractions and average molecular weights were calculated directly from the GC/MS data. Four crude oils with varying PNA distri butions were studied. The Peng-Robinson correlations for Pc, Tc, and acentric factor were used for a wide variety of different compounds present. Grigg and Lingane re ported that these correlations accurately reflected the effect of molecular structure on critical properties (specifically critical pressure), as long as there was only one ring in the molecule. (This idea was also suggested by Peng and Robinson [40].) Because it was recognized that multi-ring structures may be present in significant quantities, a new correlation for the critical properties of aromatic molecules with j rings was proposed: CjA = Cp+ j(CA - Cp) (1.3) where Cp is the critical property for the paraffin with the same mmiber of carbon atoms and Ca is the property of the single rmg aromatic with the same number of carbon atoms. Mixture critical properties are calculated using an "effective" mole fraction average mixing rule. The aromatic effective mole fraction also takes into account the number of aromatic rings present. This characterization scheme was successful in reproducing phase diagrams for four crude oils, although none of them had a significant multi-ring aromatic content. The correlations failed, however, to reproduce the phase diagram of a well-characterized synthetic oil system. Grigg and Lingane cautioned that the success in predicting the crude oil phase behavior may have resulted from a compensation of effects. Wilson et al. [56] also commented on the inaccuracies of most critical property correlations in predicting the properties of multi-ring aromatics. New correlations were proposed for those compoimds; however, only two-ring structures were used to develop the correlation, so extension to 3 or more rings is questionable. The availability of a GC/MS analysis for am oil, combined with improved critical property correlations applicable to various types of compounds, shouldresult in much greater accuracy in predicting the phase behavior of a C02/crude oil mixture. How ever, even with the most accurate critical property correlations and the most detailed compositional descriptions available, it is unlikely that equation of state calculations will produce accurate a priori predictions of the phase behavior. For this reason, it 14 CHAPTER 1. INTRODUCTION is necessary to have some experimental data with which to test the characterization. Various parameters, such as component properties and binary interaction parameters, can be adjusted, or "timed," to force the equation of state calculations to match the experimental results. The justification of this tuning is the fact that the property correlations are not completely accurate in the first place. Binary interaction param eters in particular are not known for many components, and equation of state results are highly sensitive to the values used. The characterization and timing process, including the effects of binary interaction parameters, is discussed fully by Stadler [47], Once the initial tuning process is complete, it is usually necessary to group the components further if large scale calculations, such as compositional simulations, are to be performed. Often, this lumping process is quite simple. Components are assigned to pseudocomponents with roughly equal mole or weight fractions. Pseudo- component properties are mole or weight fraction weighted averages of the original component properties. The optimal nimiber of pseudocomponents is obtained by trial and error as the minimum number of components that will still result in an accurate match of the experimental data. Recently, more systematic approaches have been taken to determine the optimal lumping scheme. Mehra et al. [31] reported a statistical approach to lumping that is based on minimization of the errors in predicting experimental phase saturations. K-values, or partitioning data, for individual components are not only important in understanding how the miscible process works, but are also useful in the practical aspects of modelling process performance. Li et al. [29] based the lumping procedure on the original component K-values from a flash calculation performed at the lowest operating pressure of the process being studied. Thus, Li et aVs lumping schemedepends on the operating conditions. K-values also provided the basis of a scheme presented by Newley and Merrill [35] to limip a tuned multicomponent description into a smaller number of pseudo- components. This scheme uses the K-values calculated at the saturation pressure of the original mixture. An iterative procedure assigns components to a predetermined number of pseudocomponents by minimizing the differences between the K-values 1.2. SCOPE OF THIS WORK 15 of the original components and the apparent K-values of the pseudocomponents to which they are assigned. Pseudocomponent properties are calculated from the orig inal properties using mixing rules that also incorporate the K-values. Newley and Merrill suggested that heavy components with small K-values have negligible effect on phase behavior and could be lumped together into a single pseudocomponent. Many additional lumping schemes have been reported that concentrate on optimal limiping of the light components (less than Cio), but combine everything else into a "plus" component and use the plus componentproperties as tuning parameters. Most of these procedures, as well as those that actually characterize the plus fraction, are designed to match saturation pressures from single contact PVT experiments. These experiments are, in fact, more sensitive to the light components than the plus fraction. However, the multicontact miscibility of a CO2 process depends quite strongly on the partitioning behavior of the heavier components [18]. Therefore, characterization schemesthat rely solelyon matching single contact PVT experiments may be missing important information in terms of displacement process performance.

1.2 Scope of This Work

The discussion given above clearly shows the necessity of understanding the be havior of individual molecules in a C02/crude oil system. Gas chromatography/mass spectrometry presents an opportimity to study how different types of molecules affect the phase behavior of a CO2 miscible process, and how this information can be used to predict the performance of these processes. The objectives of this work are:

1. to determine how molecular size and structure affect the partitioning behavior of individual molecules in a CO2/crude oil mixture, and how the distribution of these molecules in terms of the overall oil composition affects the CO2 dis placement performance, and

2. to evaluate how the detailed compositional data can be used to provide better predictions of process performance. 16 CHAPTER 1, INTRODUCTION

Gas chromatography/mass spectrometry was used to analyze equilibrium phase samples from C02/crude oil mixtures. Partition coefficients, or K-values, were mea sured for individual molecules present in the crude oils. Several whole oils were also ^ analyzed by GC/MS. The partitioning information, combined with the detailed oil compositions, provides an understanding of the differences in behavior observed in slim tube displacements for those oils. Slim tube displacements and single contact flashes were simulated niunerically ^ using the compositional information obtained from GC/MS analysis of an aromatic crude oil. The effect of including multi-ring aromatic properties in the oil character ization was studied.

HA Chapter 2

Experimented Apparatus and Data Analysis

The experimental equipment used in this study and procedures for analyzing the raw data resulting from the experiments are described in this chapter.

2.1 Experimental Apparatus

The equipment employed in the experimental portion of this study includes the computerized GC/MS system and the PVT system used to generate the equilibrium phase samples. The experimental GC/MS system consists of a Varian 3400 gas chro- matograph in tandem with a Finnigan MAT TSQ 70 mass spectrometer.

2.1.1 Gas Chromatograph

The gas chromatograph serves as an inlet separation device for the mass spec trometer. It is equipped with both on-column and split/splitless injectors. Cajrier gas flow is controlled by an inlet pressure controller. Cryogenic cooling of the GC oven is achieved by flash expansion of liquid CO2 into the oven. Flow of the coolant

17 18 CHAPTER 2. EXPERIMENTAL APPARATUS AND DATA ANALYSIS is controlled by a solenoid valve, which is actuated by the oven temperature sensor. The GC is connected to the mass spectrometer by a one inch diameter transfer line. The GC colimm runs through the transfer line and directly into the ion source of the mass spectrometer. The transfer line is maintained at a constant temperature by an auxiliary heater connected to the GC. Control of the GC and transfer luie operating conditions can be achieved either through the instrimient control capability of the GC/MS operating system or directly from the front of the GC.

2.1.2 Mass Spectrometer

The TSQ 70 instrument used in this study is a triple quadrupole instrument. The instrument is capable of running in single stage (MS) or two-stage (MS/MS) modes. Only the single stage mode was used in the analyses performed. The basic design of the mass spectrometer includes an ion source, a mass analyzer, and an electron multiplier. The entire system is maintained imder high vacuum. As molecules elute from the GC colunm, they enter the ion source, which includes the ion volume, the filament, two magnets, the collector, and the lenses. The source is maintained at 150^C to minimize formation of deposits. The ion volume is a cylinder with three holes in the sides and one end closed off (Fig. 2.1). Molecules from the GC colimm enter through the side hole in the ion volume and are bombarded with electrons from the filament, which enter the ion volume through the top. Thefilament is a thin rhenium wire which emits electrons when heated. The magnets focus the electron beam and force it to spiral within the ion volume, allowing for maximimi ionization of the sample. A potential difference between the filament and the ion volume accelerates the electrons into the ion volume. The collector is located below the ion volume and attracts excess electrons and unionized sample through the bottom hole in the ion volume. Ion fragments formed in the source are transmitted through the open end of the ion volume to the mass analyzer by the lenses. For positive ions, such as are analyzed in this work, the lenses are held at negative potentials. Ions from the ion source are separated by mass-to-charge ratio (m/z) in the mass analyzer. The TSQ 70 mass analyzer assembly consists of three sets of quadrupole rods separated by additional lenses. The four rods that make up a set are arranged in 2.1. EXPERIMENTAL APPARATUS

electrons In

sample in

excess electrons and unionized sample out

Figure 2.1: Schematic of ion volume a square pattern. Equal voltages are applied to a pair of opposing rods, and voltages equal in magnitude, but opposite in sign are applied to the other pair. Both ac and dc voltages can be applied to the rod pairs, and these voltages can be ramped during a scan. The ac voltage frequency is in the radio frequency range and is commonly termed the RF voltage. The first and third quadrupole sets can be employed either as mass analyzers or as ion transmission devices. The second (middle) quadrupole can only serve for ion transmission. For a quadrupole set to perform mass analysis, both dc and RF voltages must be applied. Ion tretnsmission requires only RF voltage. For single stage analysis (MS), only the first quadrupole set receives RF and dc voltage and the remaining two sets serve as ion transmission devices. The ratio of RF to dc voltage governs the ability of the quadrupole to separate ions based on mass-to-charge ratio (m/z). A 20 CHAPTER 2. EXPERIMENTAL APPARATUS AND DATA ANALYSIS specific RF/dc ratio produces an electrostatic field that results in stable oscillations for ions of a specific m/z value and unstable oscillations for all other m/z's. Those ions with stable, bounded oscillations are carried through the mass analyzer. All other ions either strike the rod assembly or are neutralized and pumped awayby the vacuum system. An instant later, the RF/dc ratio is changed and another m/z value becomes stable, while the previous one becomes unstable. The entire mass range (m/z range) from 10 to 2000 can be scanned in 0.2 seconds, so the potential change occurs very rapidly. At the end of a scan, the RF and dc potentials are zeroed and the whole process begins again. The quadrupole lenses serve to focus the ion beam, to shield the quadrupole sets from the voltages applied to the neighboring sets, and to shield the electron multiplier. The electron multiplier consists of two conversion dynodes and a continuous dyn- ode electron multiplier. Oneofthe conversion dynodes is positive, the other negative. Positiveions strike the negativedynode and secondary particles are produced, includ ing positive and negative ions, electrons, and neutrals. The negative ions and elec trons are accelerated into the cathode of the continuous dynode electron multiplier. Within the cathode, these secondary particles strike the walls with sufficient energy to dislodge electrons. Th^e electrons in turn collide with the wall of the cathode to dislodge moreelectrons. This process continues through the cathodeas the electrons move toward the ground potential. Eventually, enough electrons are generated to produce a measurable current at the end of the multiplier, where the electrons are collected by the anode. The current is then processed by the data system to jdeld the intensity of a given m/z valuefor a particular scan. The plot of ion intensity vs all m/z values for a particular scan is known as the mass spectrum. The smnmation of the intensities of all the ions in a particular scan is related to the amount of the component present.

2.1.3 Tuning

In order to obtain the most accurate data, various parameters must be tuned for optimal machine operation. The tuning process for the TSQ 70 is extremely complex. 2,1. EXPERIMENTAL APPARATUS 21 due to the numberofparametersthat can be tuned independently. However, the basic goal of the tuning process is simple: to obtain the maximum ion intensity possible for the entire m/z range being scanned. A calibration gas with a known mass spectrum is introduced into the source for tuning. The various time "knobs" are systematically adjusted until the proper peak ratios in the spectrum are obtained and the total ion intensity is maximized. Some ofthe tune parametersinclude the lens potentials,the transmission quadrupole RF potentials, the offset voltage (the ratio of RF to dc potential in the analyzing quadrupole), the calibration, and the resolution. The lens potentials are adjusted to maximize the extraction of ions from the source. The transmission RF potentials axe adjusted to ensure that ions are transmitted freely. The calibration ensures that the m/z peaks have the proper mass values. Offset voltage and resolution affect the shape of the peaks. Each of these parameters can be tuned independently for each specific mass imit, resulting in a "time table" of the appropriate parameter values versus m/z value. This tune table must be updated regularly, especicdly if any changesare made in the instrument configuration. While this complex tuning process has the advantage of allowing the user to optimize the machine performance, it has a disadvantage in that quantitative data are very sensitive to the tune table. If the instrument has not been tuned correctly, or if the tune has drifted and has not been updated, certain masses may be relatively enhanced or diminished and the quantitative data may not be entirely accurate.

2.1.4 Operating Conditions

The GC column used in all of the analyses was a fused silica capillary column 25m long with an external diameter of 0.32mm. The stationary phase was crosslinked dimethyl polysiloxane with a phase thickness of 0.52 microns. Helium was used as the carrier gas with an inlet pressure of 6 psig. Samples of 0.35/^1 were injected using the split injection technique. The injector temperature was 300®C. The GC column was temperature programmed with an initial isothermal period at -10®C for 5 minutes, a temperature ramp from -10® to 300®C at 3®C per minute, and a final isothermal 22 CHAPTER 2, EXPERIMENTAL APPARATUS AND DATA ANALYSIS period at 300®C for 10 minutes. Thus, the total time for one run was approximately 2 hours. Transfer line temperature was 300®C for the entire nm. lonization of the sample was achieved by electron impact (El) ionization with an ionization potential of 70 eV. The mass analyzer was scanned over an m/z range from 50 to 500 mass units in 1 second, representing a carbon number range of about C5 to C30. The split injection technique represents a possible source of quantitation error, tending to discriminate against heavier components. This technique was used, how ever, because it allows the use of a larger sample than other techniques. This technique also does not require the use of a solvent that could obscure the light fraction of the sample.

2.2 Phase Sample Analysis

The phase samples used in this analysis were generated in a conventional PVT system centered around a windowed RUSKA cell enclosed in a constant temperature oven. The volume, and hence the pressure, in the cell was controlled by movement of a mercury interface. The entire system (Fig. 2.2) is fully described by Stessman [48]. Crude oil was charged into the top port of the cell at atmospheric conditions. This transfer was accomplished by withdrawing mercury from the cell with the servo pump, while allowing oil to fill by gravity drainage &om the oil reservoir. All of the fill lines were thoroughly evacuated prior to filling to prevent contamination of the sample. Using the known oil molecular weight and measured density at atmospheric conditions, the volume of oil charged to the ceU was converted to moles of oil in the cell. The moles of CO2 required to achieve the desired overall molar composition were then converted to volumes using the CO2 molecular weight and the density of CO2 at the transfer pressure and temperature. CO2 was transferred from one of the supply vessels at room temperature and 1500 psia. The transfer was performed at these conditions because the reduced compressibility of CO2 at higher pressure allowed for more accurate volimie transfer. The constant pressure transfer was accomplished by 3

1. PVT Coll 10. Liquid Sample Collection 2. Supply Vessels 11. Gas Sampling Vessel to 3. Circulation Pump 12. Vacuum Pump 4. Hand Pump 13. Balance 5. Servo Pump 14. Gas Chromatograph 6. Hg Supply Vessel 1 e- pa 7. Densltometer 8. Viscosity neasorement 9. Back Pressure Regulator

^ © LJ g 53

MIS-fVTMf

to Figure 2.2: Diagram of PVT cell and sampling system (from Stessman [48]) CO 24 CHAPTER 2. EXPERIMENTAL APPARATUS AND DATA ANALYSIS simultaneously withdrawing mercury from the cell with the servo pump and pushing CO2 into the cell using the hand pump. Once the fluids were charged to the cell, and the pressure and temperature were set, the circulation pump wa^ turned on and allowed to circulate for at least 18 hours to establish equilibrium between the phases. Phases were sampled individually through the uppermost sampling port. The sampled phase passed through a densitometer and then was flashed to atmospheric conditions through a back-pressure regulator (BPR). The resultant vapor phase was collected in an evacuated sampling vessel. The liquid phase was collected in a cen trifuge tube. Liquid phase amounts were determined by weight, and vapor phase amounts were determined by applying the ideal gas law to the pressure change in the sampling vessel. The vapor phase was inmiediately passed to a gas chromatograph equipped with a thermal conductivity (TCD) detector. The liquid phase was ana lyzed by GC/MS. Thus, a two phase system in the PVT cell actually resulted in 4 sample analyses: upper phase vapor, upper phase liquid, lower phase vapor, and lower phase liquid. The sampling system is shown schematically in Fig. 2.3. To pre vent flashing the sampled phase through the evacuated dead volume between the cell and the BPR, this dead volume (about 4 cc.) was initially flUed with helium at the sampling pressure. Helium was used because it would not be detected by the TCD if any got into the sampled phases.

2.3 Data Analysis

Raw output data from a GC/MS analysis consist of ion intensities for the entire range of m/z values recorded at each scan. By summing up the intensities of all ions present in a particular scan, a Total Ion Chromatogram (TIC) can be constructed. The TIC is a plot of total ion intensity versus scan time. Philp cmd Gilbert [42] showed for four Australian oils that the TIC is virtually identical to an FID (flame ionization detector) trace from a GC analysis. Therefore, area percent from the TIC, if the instrument is tuned properly, can be directly related to weight percent for aW.

26 CHAPTER 2. EXPERIMENTAL APPARATUS AND DATA ANALYSIS compounds present. In actuality, a response factor unique to each compoimd should be used to convert area percent to weight percent; however, with the large number of components present in crude oil, it is not possible to estimate response factors for each of them. Therefore, in this analysis, all response factors were assumed to be equal to one. For the partition coefficient calculations, the response factor is irrelevant, as long as it is independent of composition, because it is present for both phases and divides out of the K-value equation.

2.3.1 Peak Quantitation

Peak axeas from the TIC are calculated automatically by the MS data analysis programs based on user supplied parameters. These parameters include:

1. A baseline parameter which allows the user to input a "width" of scans around a given peak from which the baseline and noise level are defined,

2. A labelling parameter allowing the user to define a multiple of the average noise level (calculated from the baseline command) below which peaks are excluded from area calculations, and

3. An area calculation parameter which defines the first and last scans to be in cluded in a particular peak's area calculation.

The areas for labelled peaks are calculated automatically by the program and are stored in a quantitation file along with their respective scan numbers. The scan number for a given peak (which actually covers several scans) is that scan which corresponds to the highest intensity point of the peak. The mass spectrum for the peak is then the mass spectrum corresponding to the peak scan niunber.

2.3.2 Spectrum Identification

The mass spectrum of a particular component is a function of its molecular struc ture. When bombarded with electrons in the source, a molecule fragments based on the stability of the fragment ions formed. For saturated compounds, each carbon 2,3. DATA ANALYSIS 27 atom has four bond sites. In a pure hydrocarbon, each of these sites will be occupied by either another caxbon atom or a hydrogen atom. A carbon atom that is bonded to four other carbon atoms is called a quatemaxy carbon, one that is bonded to three carbons and one hydrogen is a tertiary carbon, etc. When one of the carbon-carbon bonds fragments, the resulting positive ion has an order of one less than the original carbon. For example, fragmentation of quaternary carbon-carbon bond results in a tertiary ion. The order of decreasing stability for these fragment ions is: tertiary, sec ondary, primary. Therefore, a molecule is most likely to fragment at quaternary and tertiary carbons, which represent positions of branching on straight chain compoimds and positions of alkylation on cycloalkanes. Aromatic molecules are extremely stable, as are aromatics with only methyl groups attached. Fragmentation in aromatic molecules will generally occur only in alkyl groups of two carbon atoms or more. Fragmentation of alkyl groups follows the same rules as for aliphatic molecules. A second type of ion that forms in the source in addition to the fragment ions is the molecular ion, which results from the removal of an electron. The molecular ion is easily identified because it is the highest m/z peak (for spectra of only one component) and is generally the only even valued m/z peak. The intensity of the molecular ion depends on the degree of fragmentation. For highly branched alkanes, the molecular ion is sometimes absent. Identification of components is based on molecular weight (from the molecular ion), elution order, library spectnmi identification, and "user experience." While it is not always possible to identify individual isomers, especially for high carbon nimibers, the molecular weight, carbon number, and chemical type can be identified for most compounds. For carbon numbers greater than 10, many of the peaks consist of multiple com ponents coeluting from the GC. The library search routines are unable to resolve spectra of multiple components, so it is up to the user to identify which m/z peaks are associated with which components. This identification is largely based on user expertise. In order to separate quantitatively the coeluting components, the intensi ties of the ions associated with each component are summed and then the total area 28 CHAPTER 2. EXPERIMENTAL APPARATUS AND DATA ANALYSIS of the peak is divided between the components based on the ratios of the ion summa tions. Details on spectrum identification and quantitation of coeluting compounds are included in Appendix A. Once the quantitation and identification process is completed, the GC/MS anal ysis of the sample has resulted in a complete list of elution times, weight fractions, molecular weights, carbon nimibers, and chemical types for approximately 400 com ponents in the Cs to C30 fraction. The GC/MS procedure can be run on any Hquid sample, including whole crude oils or the liquid portions of the equilibriima phase samples.

2.3.3 Phase Sample Analysis

The separate vapor and liquid analyses of a sampled phase, by GC and GC/MS, respectively, are recombined mathematically using the known quantities of the two portions. Because Ce to Cs are present in both phases and are analyzed with different degrees of resolution, an assumption must be made about how the components in the less-resolved vapor phase should be divided to match the more detailed component analysis of the liquid phase. The assumption employed here was that components of the same carbon number (for these low carbon numbers) partition similarly into the two phases. Therefore, the Ce fraction (for example) of the vapor phase was divided up to match the more detailed compositional description of the liquid phase based on the distribution of Cq components in the liquid phase. Once the upper and lower phases have been mathematically recombined, K-values can be calculated for selected components based on the amount of that component in the two phases. Therefore, in order to be selected for K-value calculation, a compo nent must be detectable in both phases and may not be part of a peak that contains more than two components. Generally, peaks with three or more components axe small peaks and are very difficult to separate quantitatively. Calculated K-values for these components would probably be more a function of quantitation errors than of partitioning behavior. Peaks that were not selected for K-value calculations were not always identified, and only coeluting components for which K-values were calculated were separated 2.3, DATA ANALYSIS 29 quantitatively. This procedure did not affect the K-value results, since identity infor mation about non-selected components was not used. Whole crude oil samples were analyzed under operating conditions identical to those used for the phase samples. However, for the oils, all peaks were identified, if possible, and all coeluting components were separated quantitatively. No internal standard run was performed on any GC/MS analysis; therefore, no account was taken of the uneluted fraction. Standard SIMDIS analyses, including an internal standard run, were performed on the whole oil samples for quantitative purposes. These SIMDIS analyses requiredthree runs; a baselinerun (no sample), the sample itself, and the sample with a known quantity of internalstandard added. Single carbon number cuts, based upon normal alkane elution times, were quantified by calculating the area under the GC trace between the two normal alkanes, after the baseline had been subtracted. The purpose of the internal standard nm was to quantify the uneluted fraction. The SIMDIS procedure for crude oils is described by Worman and Green [57]. 30 CHAPTER 2. EXPERIMENTAL APPARATUS AND DATA ANALYSIS Chapter 3

Experimental Results

This chapter presents the experimental resultsof the GC/MS analyses of both the phase equilibrium samples and of several whole crude oils. Slim tube displacement data, provided by the companies that furnished the oil samples, are also presented for the crude oils. The raw GC/MS data for the crudeoilsand the phase samples are included in Appendix D.

3.1 Phase Equilibrium Seimples

This section presents the results of the component partitioning experiments de scribed in Chapter 2. Partitioning data are presented in terms of K-values of in dividual molecules. Carbon dioxide is normalized out of the compositions of both phases to isolate only the hydrocarbon behavior. Therefore, K-values presented here axe defined as: Vfj.t Ki = (3.1) l-ti/COj,! where and Wi^i are the weight fractions of component i in the upper and lower phases, respectively, and wco2,u and wcOij are the weight fractions of CO2 in the upper and lower phases. Weight fractions were used in place of mole fractions because they weremeasured directly in the GC/MS analysis. Conversionof the weightfraction based K-values to the traditional mole fraction based value would require simply

31 32 CHAPTERS. EXPERIMENTAL RESULTS multiplying the K-value by the ratio of the individual phase molecular weights. The sampling conditions for the partitioning experiments are summarized in Table 3.1. CO2 density at sampling conditions is also included.

Table 3.1: Sampling conditions for PVT experiments Pressure Temp. CO2 density Sample Oil (psia) (OF) (gm/cc) 1 Means 3000 105 .844 2 Means 1500 105 .618 3 Means 3950 125 .844 4 on W 3100 170 .647 5 Oil W 2000 125 .647 6 Kubiki 2000 125 .647 7 Kubiki 3950 125 .844

Three crude oils were selected for the partitioning experiments: Means (Permian Basin), Oil W (Gulf Coast), and Kubiki (Japan). These oils were selected on the basis of their carbon number distributions and on the types of molecules known to be present. The carbon ntunber distributions for these three oils as obtained from sim ulated distillation (SIMDIS) are presented in Fig. 3.1. More detailed compositional analyses of these oils will be presented later in this chapter. Figure 3.2 summarizes the types of chemical structures found in these crude oils. These drawings show the base structure of the molecules, which can alsoincludevajying degrees of alkylation. Of course, there are many more different structures present in crude oils than are represented in Fig. 3.2. In some cases, compounds with similar mass spectra (due to similar structures), were grouped together. For example, alkyloctahydroindenes (one 5-carbonand one 6-carbon saturated, fused rings) and alkyldecalins havealmost indistinguishable mass spectra. However, the structures of these two are quite sim ilar (two fused, saturated rings), so they are both included in the decalin category. Some saturated ring structures with more than two rings have also been labelled de- calins. However, these three- or more-ring, saturated compounds represent an almost negligible fraction of the decalin category. 3.1. PHASE EQUILIBRIUM SAMPLES 33

14 _ Means OilW Kubiki 12 _

10 _

(D 8 - Q.

o ® 6

4 _

2 _

45 CARBON NUMBER

Figure 3.1: Simulated distillation analyses for Means, Oil W, and Kubiki 34 CHAPTER 3. EXPERIMENTAL RESULTS

vA/y ^

n-alkane br alkane cycloalkane

^ CM®

1-ring arom* 2-ring arom S-ring arom (naphthalene)

OD CO (oQ

tetralin decaiin indene

Figure 3.2: Chemical structures found in crude oils 3.1. PHASE EQUILIBRIUM SAMPLES 35

Heteroatoms, or NSO (nitrogen, sulfur, oxygen) compounds are present in small quantities in someof the oils,but are not drawn in Fig. 3.2. In the analysesthat follow, sulfurcontainingmolecules havebeenexcluded fromthe NSO category. Therefore, the category labelled NSO actually contains almost exclusively oxygenated compounds.

3.1.1 Means Oil

The first oil studied was Means crude oil. PVT Sample 1 was generated at the reservoir temperature of 105®F and a pressure of 3000 psia. Figure 3.3 shows K-values for Sample 1 plotted versus carbon number as determined directly from the mass spec trum. While there is clearly a trend of decreasing K-value with an increase in the size of the molecule, there is also significant variation in K-value with chemical structure at a given carbon number. K-values for the molecules with a carbon number of 13 are presented in Table 3.2. The K-values for all components are included in Appendix D. The range of K-values for a particular carbon number is quite large, as e>ddenced in Table 3.2. Apparently, the K-value is a strong function of molecular structure for Ci3 molecules. Thus, carbon number is a rather poor correlating parameter for partitioning behavior. These data confirm previous binary results [6, 11]. In typical hydrocarbon analyses, however, compositions are determined based on elution time from a chromatographic column (SIMDIS). This technique is based on the assimip- tion that compounds that elute together from a GC partition similarly. Therefore, it makes sense to plot the partitioning data versus GC elution time. The same K-value data from Sample 1 is plotted versus elution time in Fig. 3.4. While the correlation is certainly improved, different classes of compounds show distinctive behavior. Fig ure 3.5 shows the K-values for branched alkanes versus a reference curve of normal alkanes for Sample 1. The K-values for branched alkanes tend to be slightly higher than those of n-aJkanes with similar elution times. In contrast. Fig. 3.6 shows the K-values for saturated cyclic compounds versus those of normal alkanes. Both one- and two-ring cyclics are included in this figure. Clearly, the K-values for the cyclic compounds are lower than the normal alkane K-values. However, the differences in K-values between both cyclic and branched molecules and the normal alkanes are 36 CHAPTERS. EXPERIMENTAL RESULTS

2.5

n-alkane * branched ♦ cycio V 2.0 one-ring-arom two-ring-arom • * three+-ring-arom 0 tetralin o A decalin 2 V indene 1.5 0 biphenyl a sulfur P NSO .•g X i;x» * - unknown S 1.0 4m** I ♦tJf** ***,

0.5 -^♦4 a A a •• ♦ « ••• ♦ d «

I I 0.0 !••••! ± ± « •• • ' I 10 15 20 25 30 35 40 CARBON NUMBER

Figure 3.3: K-values as a function carbon number for Sample 1, Means at 3000 psia and 105®F 3,1. PHASE EQUILIBRIUM SAMPLES 37

2.5

n-alkane branched ♦ cycio one-ring-arom 2.0 « two-ring-arom ® ♦ three+-ring-arom 0 tetralin * % A decalin I V indene 1.5 0 biphenyl a sulfur P NSO .•s ♦ ♦ *» X unknown ♦ ♦ (D 1.0 3 A '*»* § * * * ♦ WVAk^ ' A . # 4»» 0 ♦ 0 * » 0.5 0 0X * *

a •• * 0 ••••

0.0 J IILJIIL JIIL JL J L 1000 2000 3000 4000 5000 6000 SCAN NUMBER (ELUTIONTIME - SEC)

Figure 3.4: K-values as a function of elution time for Sample 1, Means at 3000 psia and lOS^'F 38 CHAPTERS. EXPERIMENTAL RESULTS

2.5

n-alkane branched

2.0

p ♦

1.5

>s ® 1.0 I

0.5

0.0 b j I I I I I I I I I I I i__j I I • I • ' ' ... I . I I I I 0 1000 2000 3000 4000 5000 6000 SCAN NUMBER (ELUTION TIME - SEC)

Figure 3.5: Alkane K-values for Sample 1, Means at 3000 psia and lOS'^F 3.1. PHASE EQUILIBRIUM SAMPLES 39

n-aikane

decalin

§ t

2

0> I

1000 2000 3000 4000 5000 6000 SCAN NUMBER (ELUTION TIME - SEC)

Figure 3.6: Cyclic K-values for Sample 1, Means at 3000 psia and 105®F 40 CHAPTERS. EXPERIMENTAL RESULTS

Table 3.2: K-values for C13 molecules in Sample 1

K-value type 0.9273030 br alkane 0.9256641 cycloalkane 0.9191930 n-alkane 0.9147595 br alkane 0.8966523 br alkane 0.8221080 tetralin 0.8216525 cycloalkane 0.7732788 cycloalkane 0.7562973 decalin 0.7404297 cycloalkane 0.7381052 cycloalkane 0.6120179 cycloalkane 0.5942336 cycloalkane 0.5217079 cycloalkane 0.4432040 tetralin 0.4153269 sulfur 0.3994214 3-ring arom 0.3581294 2-ring arom 0.2831350 sulfur 0.2106793 2-ring arom less pronounced than the difference in K-values between midti-ring aromatics and the other types of molecules. The K-values for these multi-ring aromatics in Sample 1 are plotted with the normal alkane data for comparison in Fig. 3.7. Substituted naphthalenes represent most of the multi-ring aromatic compounds in the Means crude oil. The K-values for these molecides are considerably lower than the K-values for other, coeluting molecules. These data imply that the SIMDIS assumption, that components that elute together from a GC partition similarly, is probably not valid for the extraction of multi-ring aromatics by CO2. Part of the scatter in the Ce to C20 range in Fig. 3.4 is imdoubtedly due to in evitable quantitation errors that arise from the combination of four phase composition measurements and &om the separation of coeluting components. However, while some 3.1. PHASE EQUILIBRIUM SAMPLES 41

2.5

— n-alkane ♦ 2-rlng arom ® 3+-ring arom 2.0 L

1.5 L

5 § 1.0 1

0.5 L

0.0 j I I I I I I I I I I I I i—I I I I I I I \ I—I—I—I—I—I 1000 2000 3000 4000 5000 6000 SCAN NUMBER (ELUTION TIME - SEC)

Figure 3.7: Multi-ring aromatic K-values for Sample 1, Means at 3000psia and 105®F 42 CHAPTER 3. EXPERIMENTAL RESULTS of the outlier points can be attributed to quantitation errors, the trends observed for groups of compounds still give a clear indication of relative extraction efficiency. Hy drocarbons larger than about C20 show less scatter, but only because compounds other than n-alkanes were present in concentrations so low that they could not be identified reliably. A second experiment (Sample 2) was run using Means at a lower pressmre (1500 psia) and the same temperature as the first sample to investigate the effect of pres sure on partitioning behavior. Figure 3.8 shows the individual compound K-values versus elution time for the second experiment. The same effects of chemical type on partitioning behavior are observed at this lower pressure. The K-values for multi-ring aromatics are considerably lower than those of other molecules with similar elution times. K-values for only normal alkanes from Samples 1 and 2 axe compared in Fig. 3.9. While the K-values for the larger molecules (latest elution times) decrease some what more rapidly for the low pressure case. Samples 1 and 2 show that a 1500 psi drop in pressure did not have a significant effect on partitioning behavior. The only observable trend is a slight steepening in the slope of the data. It should be noted that both of these pressures were above the three-phase region at this temperature (Fig. 3.10). Therefore, both ofthese samples represented liquid-liquid behavior. The phase behavior of Means stock tank oil and CO2 mixtures was fully described by Stessman [48]. The minimal change in extractionbehavior with pressure for Means at these con ditions agrees with the results ofa slim tubedisplacement experiment also performed at reservoir temperature. Figure 3.11 shows slim tube recovery as a percent of the original oil in place (%OOIP) versus pressure at 1.2 pore volumes of injection. The relatively flat recovery curve above about 1200 psi confirms that there is miniTnal effect ofpressure on phase behavior at the pressures investigated in Samples 1 and 2. To investigate the effect of temperature and of the upper phase state, Sample 3 was run at a higher temperature (125®F) than Samples 1 and 2. At this temperature, a three phase region does not occiu: for Means/C02 mixtures (Fig. 3.12). Sample 3 was run at a pressure that resulted in the same CO2 density as Sample 1. K-values for this experiment, shown in Fig. 3.13, are very similar to those of Sample 1 (Fig. 3.1. PHASE EQUILIBRIUM SAMPLES 43

2.5 - ♦ n-alkane branched cyclo 2.0 one-ring-arom * two-ring-arom three+-ring-arom 0 tetralin t A decalin ♦ ♦ V 2 ♦ indene 1.5 ♦ ♦♦ 0 biphenyl V V a sulfur * P NSO .♦ % unknown >» V »*• * ♦ 2 1.0 3 '4t 5 I * *,«4 »*i! V* 0 0.5

4 „ a ♦ V

••

0.0 JIIL JII L JIIL 1 JIIL 1000 2000 3000 4000 5000 6000 SCAN NUMBER (ELUTIONTIME - SEC)

Figure 3.8: K-values versus elution time for Sample 2, Means at 1500 psia and lOS^F 44 CHAPTERS. EXPERIMENTAL RESULTS

Sample 1 Sample 2

2 1.0

1000 2000 3000 4000 5000 6000 SCAN NUMBER (ELUTION TIME - SEC)

Figure 3.9: n-Alkane K-values versus elution tirae for Samples. 1 and 2 3.1. PHASE EQUILIBRIUM SAMPLES 45

2400

2000 L1-L2

1600 LLB i

a

1200 it

fa £

800

Li-V

400 .•r* LVB

•••** 65% ,40% , 0 JIL JL 0 10 20 30 40 50 60 70 80 90 100 Mole percent CO2

Figure 3.10: Pressure-composition diagram for Means crude oil and CO2 at 105®F (data from Stessman [48]) 46 CHAPTERS, EXPERIMENTAL RESULTS

100 SLIM TUBE RECOVERY -1.2 PV

8

1 o QC

500 1000 1500 2000 2500 3000 PRESSURE (psi)

Figure 3.11: Slim tube displacement data for Means at 105°F (data provided by Exxon) 3.1. PHASE EQUILIBRIUM SAMPLES 47

2400 _

2000 _

1600 _

ed

1200 _ CO C/3 £

LVBx*'

65% .40%

10 20 30 40 50 60 70 80 90 100 Mole percent CO2

Figure 3.12: Pressure-composition diagram for Means crude oil and CO2 at 120®F (data from Stessman [48]) 48 CHAPTERS. EXPERIMENTAL RESULTS

2.5

- ♦ • n-alkane branched ♦ cycio V 2.0 L one-ring-arom * * * two-ring-arom three-h-rlng-arom 0 * tetraiin o A decalin £5 V indene 1.5 L 0 biphenyl * a sulfur P NSO X ♦ \ « * % unknown

♦ 0 1.0 1

1 ♦ * ♦ *♦*<»» *. ' ♦ 0.5 L **w *

% * •• * a a 0.0 t JIL JL _i—I—I L J IILJIIL JL 1000 2000 3000 4000 5000 6000 SCAN NUMBER (ELUTION TIME - SEC)

Figure 3.13: K-values versus elution time for Sample 3, Means at 3950 psia and 125®F 3.1. PHASE EQUILIBRIUM SAMPLES 49

3.4). Again, the effects of chemical type on partitioning behavior, specifically the behaviorof the multi-ringaromatics, are similar to those observed for Sample 1. The normal alkane K-values for Sample 3 are compared to those of Sample 1 (same CO2 density) in Fig. 3.14. The extraction behavior for these normal alkanes is verysimilar for these two samples, despite the differences in temperature and upper phase state. Thus, Samples 1 and 3 confirm that temperature and upper phase state do not have a significant effect on extraction behavior, as long as CO2 density remains constant.

3.1.2 Oil W

The second set of samples was run using a Gulf Coast oil (Oil W), which has a carbon number distribution somewhat heavier than that of Means (Fig. 3.1). Oil W is relatively depleted in the smaller, more extractable molecules. Consequently, the intermediate fraction of Oil W appears enhanced in Fig. 3.1. The slim tube displeicement data for Oil W at the reservoir temperature of 170®F are presented in Fig. 3.15. Sample 4 was run at 170®F and the slim tube minimum misdbility pressure for this oil of 3100 psi. Partitioning data for Sample 4 are shown in Fig. 3.16. Comparison of Figs. 3.4 and 3.16 indicates that the relative extraction behavior for various t3rpes of compounds is similar to that observed in the Means experiments. The substituted naphthalenes still show poorer extraction behavior than other similarly eluting molecules, although the differences seem less pronounced. For comparison purposes. Sample 5 was run at the seime CO2 density as Sample 4, but at a temperature of 125®F, which required a pressure of 2000psia. A problem with the back pressure regulator during sampling led to difficulties in the analysis of the upper phase vapor sample, and consequently, in recombining the upper phase samples. As a result, the magnitude of the K-values&omthis sample may be somewhat suspect. However, the relative partitioning data for the various compounds is acceptable. The liquid volume recovered from the upper phase in Sample 5 indicated that a significant amount of material was being extracted at these conditions. This result agrees with the Sample3 observation that temperatiure does not affect extraction as long as CO2 density is constant. The Sample 5 partitioning data, shownin Fig. 3.17, confirm the results of Sample 4, which demonstrated that chemical type has a smaller effect on 50 CHAPTERS. EXPERIMENTAL RESULTS

Sample 1 Sample 3

o

? 1.0 L §

1000 2000 3000 4000 5000 6000 SCAN NUMBER (ELUTION TIME - SEC)

Figure 3.14: n-Alkane K-values versus elution time for Samples 1 and 3 3.1. PHASE EQUILIBRIUM SAMPLES 51

SUM TUBE RECOVERY -1.2 PV

O 60

2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 4000 PRESSURE (psi)

Figure 3.15: Slimtube displacement data for OilW at ITA'P (data provided by donor of oa W) 52 CHAPTERS. EXPERIMENTAL RESULTS

2.5

n-alkane * branched ♦ cycio ¥ 2.0 U one-rlng-arom 4 *♦ two-ring-arom t ® three+-rlng-arom * 0 tetralin o A > ♦ decalin s •n • ^ V indene 1.5 ♦♦V 0 biphenyi a sulfur P NSO X unknown A4 ♦ 1.0 A A § - AA

0.5 4 A

••

0.0 J L 1 _l L J 1 IL 1 J 1 IL 1 JIIL 1 JL 1000 2000 3000 4000 5000 6000 SCAN NUMBER (ELUTION TIME - SEC)

Figure 3.16: K-values versus elution time for Sample 4, Oil W at 3100 psia and 170®F 3.1. PHASE EQUILIBRIUM SAMPLES 53

2.5 * V n-alkane branched cyclo one-ring-arom 2.0 two-ring-arom three+-ring-arom 0 tetralin o * A decalin 2 * V indene 1.5 0 biphenyi • * ♦ <♦♦♦* ^ . a sulfur P NSO * • . X unknown >» ♦ /i* ♦»«!,* S 1.0 ♦ r » ♦ ♦ A aA.A .♦♦ 4P* 0 ^ A* A AA I

0.5 r4-% A ♦

0.0 JL JIIL JIL JIIL JL 1 1000 2000 3000 4000 5000 6000 SCAN NUMBER (ELUTION TIME - SEC)

Figure 3.17: K-values versus elution time for Sample 5, Oil W at 2000 psia and 125®F 54 CHAPTER 3. EXPERIMENTAL RESULTS extraction behavior for Oil W than was observed in the Means experiments. The apparent increase in K-values for the heaviest normal alkanes in Sample 5 (Fig. 3.17) is undoubtedly the result of quantitation errors. These molecules are present in very small amounts in Oil W, and consequently may not yield reliable quantitative data.

3.1.3 Kubiki Oil

The final oil studied in the partitioning experiments was Kubiki. Kubiki was selected because it displays unusual slim tube behavior, shown in Fig. 3.18. Because Kubikiand OilW havesimilar carbonnumberdistributions (Fig. 3.1),the first Kubiki sample (Sample 6) was nm at the same conditions as Sample 5. These conditions represent Kubiki's reservoir temperature and the minimum miscibility density for Oil W. This pressure on the Kubiki slim tube displacement curve corresponds to a very low recovery, an indication of aji immiscible displacement. Therefore, one would expect rather poor extraction behavior. The upper phase sample for these conditions was so volatile that the technique used here of separating gas and liquid samples was inadequate to maintain a proper material balance. As a result, many of the light ends (less than Cn) were lost to evaporation, and the magnitudes of the remaining K-values were affected. The loss of the light ends from the upper phase sample resulted in a relative increase in the weight fractions of the heavier components in the upper phase. This increase, in turn, resulted in higher K-values for the heavier components than would have occurred had the light ends been present. Correspondingly, the light component K-values were very low. However, the relative K-values of the different compounds at a given elution time are correct, and can provide some useful information. Figure 3.19 shows a portion of the data for Sample 6. The most useful information obtained from this sample is the data for an abundance of substituted naphthalenes and for aromatics with three or more rings. As Wcis seen in the Means samples, these multi-ring aromatics are extracted much less efficiently than other compounds with similar elution times. The anomalous behavior of the heaviest normal alkanes in Sample 6 results from the same quantitation errors that were observed for these molecules in Sample 5. 3.L PHASE EQUILIBRIUM SAMPLES 55

SLIM TUBE RECOVERY -1.2 PV 100

80 L

8 60

i 40 L QC

20 L single phase

J 1 IIIIIIIIII L—i I l__J IIII L •I.• JL 1500 2000 2500 3000 3500 4000 4500 PRESSURE (psi)

Figure 3.18: Slim tube displacement data for Kubiki at 125®F (Data provided by the Teikoku Oil Co.) 56 CHAPTERS. EXPERIMENTAL RESULTS

2.5

n-alkane branched ^ A cycio 2.0 one-ring-arom ♦ two-ring-arom ♦ three+-ring-arom 0 tetralin o A decalin £5 4 ♦ V indene 1.5 A 0 A biphenyl a sulfur ♦a P NSO .•g X unknown 4^

S> 1.0 ♦ 4* 4^ ♦ 0 A I • ^ • A A • •

0.5 ♦ # ♦ 4^*, ffi® ♦4 ♦

0.0 J 1 L J IIIIIIIII 1 IIIIIIII 1 1 I 1000 2000 3000 4000 5000 6000 SCAN NUMBER (ELUTION TIME - SEC)

Figure 3.19: K-values versus elution time for Sample 6, Kubiki at 2000 psia and 125®F 3.2. WHOLE OIL ANALYSES AND PHASE BEHAVIOR 57

The final PVT sample was run at the Kubiki MMP of 3950 psia at 125®F. K- values for this experiment are presented in Fig. 3.20. Multi-ring aromatics, compared to the normal alkanes in Fig. 3.21, again show much poorer extraction than other compounds with similar elution times. However, a larger liquid upper phase recovery indicates that overall extraction is much better at this pressure (3950 psia) than at 2000 psia. This result also corresponds to the higher recovery observed in the slim tube displacement at 3950 psia. The increased pressure also seems to have improved the extraction of the two-ringaromatics. Substituted naphthalene K-values increased with pressure to become closer to the values of other components that elute at the same time. However,extraction of compounds with three or more rings did not show the same improvement.

3.2 Whole Oil Analyses and Phase Behavior

The partitioning data of Section 3.1 clearly show the effects of chemical structure on extraction of individual hydrocarbons by dense CO2. However, the phase behavior of an oil with CO2 is also a function of the quantities and distribution of these compounds within the oil. A detailed GC/MS analysis of the oil itself, combined with the partitioning information of Section 3.1, can then be used to explain or predict the phase behavior of the oil with CO2. Slim tube displacements are a commonly performed experiment used to evaluate phase behavior. These experiments have an advantage over single contact PVT ex periments in evaluating phase behavior in that they more closely mimic the actual multicontact displacement process that occurs in a reservoir flow situation. The re sults of several slim tube experiments werepresented in Section 3.1 to illustrate the similaritybetween the effects of CO2 density on slimtube phase behavior and on par titioning behavior. Theknowledge ofhow different molecules behave in a CO2/crude oil mixture, as obtained from the partitioning behaviordata, can be used to illustrate the effect of oil composition on slim tube phase behavior. Figure 3.11 presented the slim tube results for Means oil. Means is a medium 58 CHAPTERS. EXPERIMENTAL RESULTS

2.5

n-alkane branched I ♦ cycio 2.0 one-ring-arom two-rlng-arom three+-ring-arom % ♦ 0 tetralin Q decalin ♦AA ♦ ♦ ♦ indene 1.5 / ♦♦ ♦v^ biphenyl sulfur NSO 5 A 0A unknown ^ aa aU 0^ a> 1.0 ^ A ♦ 5 A A • * * ^ • •©• 0.5 0*

©

0.0 j I I.•.• J 1 L 1 -I IIL J_ JL JIILJL 1000 2000 3000 4000 5000 6000 SCAN NUMBER (ELUTION TIME - SEC)

Figure 3.20: K-veduesversus elution time for Sample 7, Kubiki at 3950 psia and 125®F 3.2. WHOLE OIL ANALYSES AND PHASE BEHAVIOR 59

2.5

-•— n-alkane ♦ 2-rlng arom ® 3+-rlng arom 2.0 L

o s 1.5 L

.•2

® 1.0 L I

0.5 L

0.0 JIIIIIL JIIL I I I I I I I I I I 1000 2000 3000 4000 5000 6000 SCAN NUMBER (ELUTION TIME - SEC)

Figure 3.21: Multi-ring aromatic K-values for Sample 7, Kubiki at 3950 psia and 125''F 60 CHAPTERS, EXPERIMENTAL RESULTS

Table 3.3: Chemical type distributions for Means, Oil W, and Kubiki Type Means (%) Oil W (%) Kubiki (%) n-alkane 30.27 5.01 5.55 br alkane 23.26 34.19 19.67 cycloalkane 24.70 27.02 32.77 1-ring arom 12.37 11.52 3.28 2-ring arom 2.71 6.83 9.03 3-{-ring arom 0.95 0.11 8.98 tetralin 1.33 2.79 4.46 decalin 2.37 11.79 12.11 indene 0.00 0.00 0.56 biphenyl 0.39 0.45 1.26 sulfur 1.33 0.00 0.00 NSO 0.00 0.00 1.33 unknown 0.33 0.29 1.00 Temp (®F) 105 170 125 MMP (psi) 1200 3100 3950 gravity (30®API) Permian Basin oil [30]. The data for Means represent fairly typical slim tube behavior. Recovery increases sharply with pressure until a "breakover" point, after which additional incre2ises in pressure have a smaller impact on recovery. This breakover point is one commonly used criterion for evaluating the minimimi miscibility pressure (MMP) ofan oil. It hasbeenshown [37] that, forlow temperatures (less than 120®F), the extrapolated vapor pressure (EVP) of CO2 is a reasonable estimate of MMP. The EVP of CO2 at 105°F is approximately 1200 psia, which agrees well with the experimentally determined breakover point from Fig. 3.11. This relatively lowMMP for Meansis due to the presence of a signiiicant quantity of easily extractable material. Figure 3.1 shows that Means contains a larger percentage of the lighter hydrocarbons (less than C12) than either of the other oils studied in the partitioning experiments. Table 3.3 showsthe chemicaltype breakdown by weightfor the C5 to C35 fractions of the oils studied in Section 3.1. This fraction in Means is largely dominated by nor mal and branched alkanes, 1-ring cycloalkanes, and 1-ring substituted benzenes. The 3,2. WHOLE OIL ANALYSES AND PHASE BEHAVIOR 61 partitioning data of Section 3.1 indicate that these compounds are extracted with roughly equaJ efficiency, depending primarily on molecular size. The much less ex- tractable compounds, the multi-ring axomatics, are not present in sufficient quantities to be detrimental to the development of miscibility. It was mentioned previously, and in other works [18, 6], that CO2 density governs extraction behavior. The CO2 density at miscibility conditions of 1250 psia and 105°F for Means is 0.388 gm/cc. The second oil discussed in Section 3.1, Oil W, has a much smaller fraction of the light hydrocarbons than Means. Therefore, one would expect a higher CO2 den sity to be required for development of miscibility in Oil W, even if chemical type did not have an effect. Figure 3.15 shows an MMP of about 3100 psi at reservoir temperature of 170®F. This condition corresponds to a CO2 density of 0.647 gm/cc, which is significcLntly higher than the miscibility density for Means. Partitioning data showed that temperature did not have a significant effect on extrax:tion behavior, as long as CO2 density remained constant (Fig. 3.14). Therefore, the increase in CO2 density required for miscibility in Oil W must be attributed to the oil compo sition. The chemical type distribution of Oil W, shown in Table 3.3, is somewhat different from that of Means. Specifically, Oil W has a much lower concentration of normal alkanes, and much higher concentrations of branched alkanes and substituted decalins. However, Oil W is still composed predominantly of saturated compounds, which were not shown to differ substantially in extraction behavior. Therefore, the higher CO2 density required for miscibility is attributable primarily to the carbon number distribution of Oil W. It was pointed out in Section 3.1 that substituted naphthalenes in Oil W did not show the significantly poorer extraction behavior that was observed for those com pounds in Means. A potential explanation for this phenomenon may be found by looking at how the different molecular structures are distributed in terms of carbon nimiber. Figure 3.22 shows the carbon number distribution for Oil W, broken down into chemicaltypes. Quau[ititatively, these data may appear to be different from the SIMDIS results. This discrepancy is due to the exclusion of the unresolved com plex mixture (UCM, or the baseline "hump") in the MS quantitative analysis. The o> to

0.16 0 unknown

0.14 n NSO S sulfur 0.12 • biphenyl ^ Indene 0.1 ^ decalin ^ tetralin 0.08 § 3+-rlngarom 1 0.06 DU 2-ringarom 1-ringarom 0.04 B cycloalkane Q bralkane 0.02 B n-alkane 1 0 lU lotB, 11 i-i°A- I I I rrrn I'TTI IIIIIII I 1 11 16 21 26 31 36

CARBON NUMBER Co Figure 3.22: Oil W carbon number distribution by chemical type I 3.2, WHOLE OIL ANALYSES AND PHASE BEHAVIOR 63

SIMDIS data is quantitatively more correct; however, the semi-quantitative informa tion provided in the MS analysis is useful in imderstanding how the extraction process works. The singlecarbon number "bars" in Fig. 3.22 were calculated by summingthe amounts of all molecules eluting between two normal alkanes, including the second normal alkane. This last normal alkane is the "carbon number" on the x-axis. These data are analogous to a single caxbon number "slice" from SIMDIS, except that they also include the identification of the individual molecules present. Figure 3.22 shows the relatively small amount of carbon number 5, 6, and 7 compounds present in Oil W. The depletion of the C5 - Cu fraction means that significant extraction of the in termediate (C12 - C20) hydrocarbons will be required for miscibility. The substituted naphthalenes fall in this range. The Japanese oil, Kubiki, was chosen for the partitioning experiments because of its unusual slim tube behavior (Fig. 3.18). The partitioning data showed that there were a significant number of multi-ring aromatics present in Kubiki, and that these molecules were not extracted as wellas the other compounds present. Table 3.3 showsthat Kubiki does have a much higher concentration of multi-ring aromatics (2- ringj 3H-ring, biphenyl, NSO) than either of the other two oils. However, the carbon number distribution (SIMDIS analysis) for Kubiki is similar to that of Oil W. Given the similar "compositions" (based on SIMDIS alone), one would expect the higher temperature oil (Oil W) to have a higher MMP, to achieve the same CO2 density. Instead, the MMP for Kubiki, as determined by an outlet sight glass, is considerably higher than that of Oil W. In other words, the niiscibility density for Kubiki (.844 gm/cc) is much higher than for Oil W (.647 gm/cc). This increase in required density cannot be attributed to molecular size distribution, but must be associated with the types of compounds present. The partitioning data for Kubiki at the miscibility density of Oil W (.647 gm/cc) showed that these multi-ring aromatics were not extracted well (Fig. 3.19). When the pressure (density) was increased to the MMP, however, the naphthalene extraction improved (Fig. 3.20). Figure 3.23 showsthe carbon number distribution with chemical t3rpe breakdown for Kubiki. The data are similar to Oil W in carbon number distribution, but not in tjrpe. As mentioned previously, when the C5 to C12 range is depleted (as in Oil W and £

0.16 0 unknown

0.14 - • NSO sulfur 0.12 - m biphenyl indene 0.1 m decalin tetralln 0.08 m s 3+-ring arom I 0.06 DO 2-ring arom I Co BS 1-ring arom 0.04 H cycloalkane !?3 bralkane 0.02 I- I • n-alkane I I rri'T'r'rr I I i IIII rrn i i i i i I 6 11 16 21 26 31 36 CARBON NUMBER Co Figure 3.23: Kubiki carbon number distribution by chemical type i 3.2. WHOLE OIL ANALYSES AND PHASE BEHAVIOR 65

Kubiki), the intermediate hydrocarbons must be extracted in sufficient quantities to develop miscibiUty. In Oil W, this range contains primarily saturated compounds. In Kubiki, however, significant quantities of aromatics are present. Therefore, increased CO2 density is required to extract enough material into the upper phase to develop miscibility. The imusual, flat shape of the slim tube curve for Kubiki, then, results from the fact that significant increases in density are required to extract more material into the upper phase. This type of behavior has also been observed for heavier oils [12]. Kubiki behaves like a heavy oil because the multi-ring aromatics behave in a manner similar to that of higher carbon number aliphatics. The comparisons between the three oils reported so far indicate that, while size distribution is a governing factor in determining the phase behavior of an oil with CO2, the chemical type distribution can also be an important factor. The Kubiki results showedthat the presence of multi-ring aromatics is detrimental. To understand further the effects of chemical t3rpe on development of miscibility, three additional oils were ^alyzed by GC/MS. These oils have different chemical type distributions from the three oils already reported. These distributions are included in Table 3.4. The slim tube recovery curves are not available for these oils, but the reservoir temperature and MMP as determined from a slim tube are also reported in Table 3.4. The Bell Creek oil field is located in Wyoming. This oil is the lightest of the six oils studied, with an API gravity of about 35 degrees. The reservoir temperature is 110°F. The EVP of CO2 at this temperature is about 1300 psia. Table 3.4 shows the MMP of Bell Creek to be about 1500 psi, which is somewhat higher than the EVP. However, the slim tube displacement was conducted using a natural CO2 source that was not pure CO2. The contamination of the CO2 with up to 10 % methane probably resulted in an increase in the MMP over the pure CO2 value. The C5 to C35 fraction of Bell Creek is largely dominated by 1-ring cyclic com pounds and branched alkanes. Figure 3.24 shows that the cyclic compounds are primarily concentrated in the Ce to Cio range, which dominates the carbon number distribution. The intermediate fraction (Cio to C20) consists primarily of branched alkanes. The partitioning data of Section 3.1 would indicate that efficient extraction would occur in Bell Creek at relatively low CO2 density, due to the presence of small o> o>

0.2 unknown

0.18 • NSO

0.16 m sulfur B biphenyl 0.14 m Indene ^ 0.12 m decalin I 0.1 tetralin •s m 3+-ring arom ^ 0.08 I (ID 2-ring arom I Co 0.08 ES 1-ring arom

0.04 H cycloalkane !?3 br alkane 0.02 • n-alkane g II mrnrnUmm 0 TT I I Im;iiI I I I I I iI iI I I nn"rrrrrrrrrrrrrri I I IIIII 11 16 21 26 31 36 CARBON NUMBER Co

Figure 3.24: Bell Creek carbon number distribution by chemical type 3.2. WHOLE OIL ANALYSES AND PHASE BEHAVIOR 67

Table 3.4: Chemical type distributions for Bell Creek, Oil D, and Ganado Type BeU Creek (%) Oil D (%) Ganado (%) n-alkane 11.52 20.47 0.12 br alkane 25.39 13.68 1.35 cycloalkane 45.48 18.53 17.03 1-ring arom 7.20 32.86 9.88 2-ring arom 3.36 5.90 14.98 3+-ring axom 0.45 0.73 1.22 tetralin 0.90 1.96 10.24 decalin 5.52 1.02 44.48 indene 0.00 0.00 0.43 biphenyl 0.03 2.11 0.27 sulfur 0.00 1.09 0.00 NSO 0.15 0.00 0.00 unknown 0.00 1.64 0.00 Temp (®F) 110 127 165 MMP (psi) -1500 ^^1700 ^^4100

®Provided by Exxon ^ Provided by donor of Oil D ®Provided by Unocal

and mostly aliphatic molecules. This efficient extraction is confirmed by the fact that the MMP is very close to the EVP at this temperature. Because Bell Creek does not contain significant quantities of multi-ring aromatics, it appears that size distribution alone governs the pheise behavior. Much has been said so far about the effect of multi-ring aromatics on the devel opment of miscibility, but very little has been mentioned on the behavior of 1-ring aromatics (substituted benzenes). Oil D contains a much higher concentration of these compounds than the other oils. Figure 3.25 shows the distribution of these compounds in Oil D. Again, the carbon number distribution is dominated by the lighter components (Ce to Cio). Although the applicability of the EVP correlation is limited to temperatures less than 120®F, the fact that the MMP for Oil D is very close to the EVP at 125®F indicates that 1-ring aromatics are not detrimental to the o> 00

0.16 unknown

0.14 • NSO B3 sulfur 0.12 B biphenyl ^ indene 0.1 H decalin

0.08 ^ tetralin 3+-rlngarom I 0.06 on 2-ring arom I Co 1-ring arom 0.04 cycloalkane

0.02 br alkane

n-alkane

I I •Jiii.-.rrn IIIIIMIII i 1 6 16 21 26 31 36

CARBON NUMBER Co

Figure 3.25: Oil D carbon number distribution by chemical type i 3.3, SUMMARY 69 development of miscibility, and that size distribution alone governs the behavior of this oil. This result also implies that aromaticity alone is not sufficient to indicate how an oil will perform. The aromatic fraction must be further broken down by number of rings. The final oil presented is Ganado. This oil has been discussed in several MMP correlation papers [18, 37] because all correlationsfail to predict the MMP of 4100 psi at 165®F. These conditions correspond to a CO2density of .756gm/cc, which is higher than for all of the oils except Kubiki. Ganado has an imusual composition. Table 3.4 shows that it has almost no alkanes, normal or branched. Two-ring saturated compounds (decalins) are by far the most abundant components. Two-ring aromatics are present in substantial quantities, but 3-rings are not. Figure 3.26 shows that almost the entire composition is concentratedin the intermediate (Cio to C20) range. The light range components (Cq to Cio) are almost completely absent in Ganado. Based on size distribution alone, a high MMP is indicated for this oil. The size distribution of Ganado would seem to indicate a higher miscibility density required than for Kubiki, which is in fact not the case. Ganado also has a higher percentage of 2-ring aromatics than Kubiki. The lower CO2 density required for Ganado would indicate that, while 2-ring aromatics are not extracted as efficiently as aliphatic molecules, it is the additional presence of three- or more-ring aromatics that is responsible for the poorer slim tube behavior of Kubiki.

3.3 Summary

The experimental results demonstrate that both molecular size and structure af fect the way a component is extracted by dense CO2. While, in most cases, elution time is a reasonable way of correlating extraction behavior, certain classes of com pounds, especially the multi-ring aromatics, show anomalous behavior that cannot be accounted for with simple GC compositional analyses. While the qualitative arguments presented in this chapter are useful in under standing the compositionalfactors that governthe development of miscibilityin CO2 -4 o

0.2 0 unknown 0.18 • NSO

sulfur 0.16 s biphenyl 0.14 m indene 0.12 m decalin tetralin 0.1 m 3-i-ring arom 0.08 I dh 2-rlng arom I Co 0.06 Q 1-ring arom • cycloalkane 0.04 s br alkane 0.02 • n-alkane

0 IIIi-i'i^rrr rrrrr'rrrrrrr n ri"ft°ri"i^ri i i i i i 1 1 6 16 21 26 31 36 CARBON NUMBER CO

Figure 3.26: Ganado carbon number distribution by chemical type i 3.3, SUMMARY 71

processes, the effects of these factors must be quantified in order to be useful for predicting process performance. The next chapter will address the use of a detailed ^ compositional analysis, including multi-ring aromatic compounds, in phase behavior calculations and compositionalsimulations. 72 CHAPTER 3. EXPERIMENTAL RESULTS

(n Chapter 4

Numerical Modelling

The availability of a full GC/MS analysis for an oil presents both opportunity and challenge in terms of characterizing the oil for equation of state (EOS) calculations. Obviously, the more information that is available on an oil's composition, the more accurately it can be described. However, while GC/MS provides detailed component by component identification, it does not yield any information on the properties of these components. In order for the data to be useful for numerical modelling, some estimation must be made of either the critical properties or, at least, of the boiling point and specific gravity. Meastired critical properties are rarely available for any but the most simple structures present in crude oil. Critical properties are especially difficult to find for multi-ring aromatics. Boiling points and specific gravities are available for a few aromatic components. Experimental property data are scarce for many compounds other than the multi- ring aromatics. However, the residts of Chapter 3 suggest that most of these com pounds partition in a manner similar to the components with which they elute. There fore, the usual cut properties [23] are probably adequate. It is the components that have anomalous extraction behavior that must be dealt with specifically. The multi- ring aromatics are particularly anomalous.

73 74 CHAPTER 4. NUMERICAL MODELLING

4.1 Multi-ring Aromatic Critical Properties

The problem of assigning critical properties of multi-ring aromatic compounds is two-fold. The first problem is the lack of experimentally measured data. Mea sured properties are generally available only for the simplest, unsubstituted structures. Properties for some of the heavier compounds such as phenanthrene and pyrene have been estimated from thermal property measurements [26, 27]. These measured and estimated values are not always consistent. Table 4.1 lists some of the measured or estimated properties reported for multi-ring aromatics. The two reported property values of phenanthrene differslightly in Tg and Pgand substantially in acentric factor.

Table 4.1: Measured or estimated critical properties for multi-ring aromatics

Compoimd Tc (K) Pc (atm) UJ ref naphthalene 748.4 39.97 .3020 [44] phenanthrene 873.2 32.57 .5040 (1) phenanthrene 882.55 31.30 .3299 [25] anthracene 869 30.83 .3531 [25] pjarene 938.2 25.65 .3440 [1]

Measured boiling points and specific gravities are availablefor these compounds and for a few other multi-ring aromatics. These data can be used to compare critical property correlation values to the measured data and to predict properties when no measured critical properties axe available. Table 4.2 lists the boiling points, specific gravities, and molecular weights of some midti-ring aromatics, including those from Table 4.1. The second problem that arises in assigning properties to these compoundsis the choice of critical property correlation. Most correlations were developed for lighter, paxaffinic fractions. The critical property correlations that appear to be most ap plicable to these higher boiling point, multi-ring structures axe those of Twu [49] and Wilson et al. [56]. However, neither of these authors presents a correlation for 4.1. MULTI-RING AROMATIC CRITICAL PROPERTIES 75

Table 4.2: Measured boiling points and specific gravities for multi-ring aromatics Compound % rc) SG MW ref naphthalene 218 1.015 128 [56] Me naphthalene 244.6 1.02 142 [5] di-Me naphthalene 265 1.015 156 [5] tri-Me naphthalene 263 1.010 170 [5] fluorene 294 1.203 166 [5] phenanthrene 340 1.179 178 [41] pyrene 393 1.271 202 [5]

Table 4.3: Critical pressures for multi-ring aromatics Compound Measured (atm) Twu [49] Wilson et al. [56] naphthalene 39.97 36.6 38.8 Me naphthalene N/A 33.8 35.7 di-Me naphthalene N/A 31.4 32.5 tri-Me naphthalene N/A 31.3 32.2 fluorene N/A 33.7 48.8 phenanthrene 32.6 30.3 39.5 pyrene 25.65 27.2 41.9 acentric factor. The correlation of Kesler and Lee [24] is commonly used. The Twu, Wilson et d,^ and Kesler and Lee equations are all presented in Appendix C. Table 4.3 compares the measured critical pressures with those calculated from boil ing point and specific gravity using the Twu and Wilson et al. correlations. Clearly, the Twu correlation performs better, especially on the larger molecules, phenanthrene and p3rrene. It was mentioned previously that extension of the Wilson et al. correla tion to compoimds with three or more rings was questionable due to the absence of these molecules in the database from which the correlation was derived. The Wilson et al, Pc correlation overestimates the values of the properties for the three- and four-ring structures, even getting the trend in Pg with molecular size wrong. The Wilson et al. correlation also overestimates the critical temperatures of the 76 CHAPTER 4. NUMERICAL MODELLING

Table 4.4: Critical temperatures for multi-ring aromatics Compound Measured (K) Twu [49] Wilson et al. [56] naphthalene 748.4 733.9 740.7 Me naphthalene N/A 761.1 768.6 di-Me naphthalene N/A 779.4 787.2 tri-Me naphthalene N/A 776.3 783.8 fluorene N/A 839.5 871.0 phenanthrene 873.2 881.5 910.3 pyrene 938.2 944.3 990.6

Table 4.5: Acentric factors for multi-ring aromatics

Compoimd Measured Kesler & Lee [24] Edmister [10] naphthalene .302 .3557 .3546 Me naphthalene N/A .3946 .3932 di-Me naphthalene N/A .4313 .4298 tri-Me naphthalene N/A .4307 .4297 fluorene N/A .3625 .3617 phenanthrene .504,.3299 .4504 .4494 pyrene .344 .4723 .4713 three- and four-ring molecules, although the deviation is not as severe. Table 4.4 compares the measured Tg values with those from the two correlations. Again, the Twu correlation performs much better. The Kesler and Lee acentric factor correlation overestimates this value. Whitson 52] claimed that the Edmister [10] correlation yields lower acentric factors for frac tions over C20' Table 4.5 shows the measured versus calculated acentric factors using both the Kesler and Lee and Edmister correlations. The Edmister correlation does result in slightly lower acentric factors, although the difference is negligible. From Tables 4.3, 4.4, and 4.5, it appears that the Twu correlations for critical temperature and critical pressure and either the Kesler and Lee or Edmister corre lations for acentric factor should be used to estimate the critical properties of the 4.2. CHARACTERIZING THE OIL 77 multi-ring aromatic fractions. The Twu correlation does require the specific gravity of a cut, which may not be known from GC/MS. However, the Twu correlation for molecular weight may be rearranged to solve for specific gravity, given the molecu lar weight determined directly from the GC/MS data. This rearrangement resulted in calculated specific gravities that were in very good agreement with the measured values for the multi-ring aromatics in Table 4.2.

4.2 Characterizing the Oil

Full characterization of a crude oil for equation of state calculations requires the quantity of each component or pseudocomponent and the critical properties of that component. Whereas the GC/MS analysis cotild be used as part of a SIMDIS quan titative analysis, the imeluted fraction was not accounted for in these analyses. Also, the possible quantitation errors associated vath the GC/MS system that were dis cussed in Chapter 2 make this method undesirable. For a purely quantitative weight fraction distribution, it is faster and more accurate to perform a standard SlMDlS analysis, including an internal standard run to quantify the uneluted fraction, using conventional gas chromatography (GC)[57]. This section presents a new method developed for characterizing a crude oil that has a significant multi-ring aromatic fraction. The only data required are the weight fraction distribution from SIMDIS and the GC/MS quantitative data that was pre sented for the whole oils in Chapter 3 and Appendix D. This method requires that the single carbon number (SCN) cuts from SIMDIS be divided into two parts; multi- ring aromatics (MRA), and everything else (non-MRA). The fraction of a SCN cut that is multi-ring aromatic is easily determined from the GC/MS data. The non- MRA properties can be determined using the Katz and Firoozabadi [23] SCN boiling points and specific gravities, and any appropriate correlation. Becausethe Twu [49] correlation requires the boiHng point of a cut, someestimate must be made of the boiling point of the MRA fraction. Wilson et al. [56] showed that a plot of boiling point versus elution time residted in two straight lines; one 78 CHAPTER 4. NUMERICAL MODELLING for normal alkanes and one for multi-ring aromatics. This plot must be constructed for the particular column and conditions used; however, the GC/MS analysis of the oil itself can be used to to generate such a plot. No additional experiments are required. Fig. 4.1 was constructed from the Kubiki oil analysis, and demonstrates the discrepancy between normal alkane boiling points and those of similarly eluting multi-ring aromatics. Therefore, the standard correlations for estimating SON boiling points [23], which are basedon normalalkanes, are not accuratefor MRA components. The boiling point of a SON MRA fraction is obtained from Fig. 4.1 by locating the elution time corresponding to the SON n-cdkane on the lower curve, and then reading the boiling point of the coeluting MRA fraction from the upper curve. The molecularweightfor the MRAfraction is calculated directly from the GC/MS analysis. Boiling point and molecular weight are sufficient for the Twu correlation to determine the critical temperature and pressure for the MRA fraction. Acentric factor can be calculated using either the Kesler and Lee or Edmister correlations. Binary interaction parameters (BIP's) for multi-ring aromatic compounds with CO2 have been reported in the hterature [7]. Appropriate BIP's for the MRA cuts can be estimated from the pure component values of similarly eluting molecules. A detailed step by step characterization of the aromatic Kubiki oil, including CO2/hydrocarbon interaction parameters is presented as Appendix B.

4.3 Equation of State Calculations

Once the initial characterization process is complete, the fluid description can be used in equation of state (EOS) PVT calculations or in a compositional simulator. The Peng-Robinson EOS [39] is commonly used for petroleum applications. This cubic equation is of the form:

P = (4.1) where P is pressure, R is the universal gas constant, T is temperature, and V is molar volimie. For a system with only one component, the constants a and b are represented 4.3. EQUATION OF STATE CALCULATIONS 79

500

450

400

O 350

•o c 300 o Q. O) •E 250 'o m •• 200

^ n-alkane 150 • multi-ring aromatic

100 •. I 1000 2000 3000 4000 5000 6000 ELUTION TIME (sec)

Figure 4.1: Boiling point versus elution time for pure components in crude oil 80 CHAPTER 4. NUMERICAL MODELLING by the following pure component expressions:

02712 a = (4.2) a = [l +/c(l-V^)]^ (4.3) K = 0.37464 + 1.54226W - 0.26992w^ (4.4) 6 = (4.5) where Pc is critical pressure, Tc is critical temperature, is reduced temperature, a; is the acentric factor, = 0.457235529, and = 0.077796074. For systems with multiple components, such as the crude oils studied here, the constants a and b are calculated using the following miying rules:

o = (4.6) « i b = 'Exik (4.7) i where Xi is the mole fraction of component i, a,* and bi are calculated for each compo nent using the pure component expressions, and Sij is the binaryinteraction parameter between components i and j. It was mentioned previously that the initial fluid descriptions, based on composi tion only, rardy match experimental PVT data without some tuning. The point of tuning to experimental data theoretically is to correct for inaccuracies in the com positional analysis, or in the assignment of properties, particularly those for heavy pseudocomponents. Often, however, various EOS parameters are adjusted to get a match to the data without any regard for physical reality. For an oil that contains significant quantities of multi-ring aromatics, it is importajit to understand how the properties of these components will affect the results of EOS calculations. The effects of the various component properties on EOS calculations are rather well established. For example, an increase in the critical pressure of a component corresponds to higher volatility, and an increase in critical temperature decreases volatility. However, increasing the "ajomatidty" of a cut affects all of the proper ties. An increase in the multi-ring aromatic content will increase both the criticed 4.3. EQUATION OF STATE CALCULATIONS 81

temperature and critical pressure of a cut. Acentric factor decreases with multi-ring aromaticity, which again indicates increasing volatility. These adjustments, applied simultaneously, have opposing effects on the volatility of a component. Therefore, it is important to imderstand the effects of changingaromaticity on EOS computations. One of the simplest EOS calculations is the two-phase flash calculation. K-values for the individual components reveal the relative preferences of these components for the upper or lower phase, as seen in the experimental data of Chapter 3. To test the effects of multi-ring aromatics on numerical K-values, flash calculations were performed at 2000 psia and 125®F using the Peng-Robinson equation of state [39] on a paraflSn sjmthetic oil and a multi-ring aromatic sjoithetic oil. Table 4.6 lists the compositions of these two mixtures. Analog components were chosen based on

Table 4.6: Synthetic oil compositions Component no. mole % paraffinic oil MRA oil 1 80 CO2 CO2 2 2 methane methane 3 8 dodec£ine (C12) naphthalene 4 6 pentadecane (C15) phenanthrene 5 4 eicosane (C20) P3rrene similarity in elution time. The elution times for the MRA and alkane components wereobtained from the Kubiki oil analysis (Fig. 3.23). Figure 4.2 shows the K-values calculated for the three heaviest components plotted versus the carbon number of the normal alkanes (this corresponds to plotting versus elution time since analog components were chosen based on similar elution time). These K-values are defined as the mole fraction of a component in the upper phase divided by its mole fraction in the lower phase (as opposed to the experimental K-values of Chapter 3, Equation 3.1, which were based on weight fractions and had the CO2 normalized out). Clearly, the paraffinic compounds partition more favorably into the upper phase than do the MRA components. This result agrees with the results of Chapter 3, which show that multi-ring aromatics are extracted less efficiently by CO2 than coeluting paraffins. 82 CHAPTER 4. NUMERICAL MODELLING

0.14 _

0.12 n-alkane multi-ring aromatic

0.10

J 0.08 CO 5 0.06

0.04 _

0.02

0.00 I ' ' • I ' ' ' I ••• I •• ' I ' ' ' I • I •I ' • "t* ' I • 1 I ''IL 8 10 12 14 16 18 22 24 CARBON NUMBER

Figure 4.2: K-values for paraffinir and aromatic synthetic oils 4,3. EQUATION OF STATE CALCULATIONS 83

It was suggested in Chapter 3 that the presence of multi-ring aromatics in crude oils results in lower recoveries and a correspondingly higher slim tube MMP than would be expected for an oil with a low MRA content. These slim tube experiments can be modelled numerically using a compositional simulator. Using the synthetic oil descriptions from Table 4.6, shm tube displacements were simulated at a series of pressures. Recovery at 1.2 pore volumes of injection is plotted versus pressure in Fig. 4.3 for both oils. The aromatic oil shows a much less favorable displacement, with considerably lower recoveries. Therefore, the effect of multi-ring ajomaticity on sUm tube displacements is to decrease the recovery at a given pressure. The MRA curve from Fig. 4.3 also exhibits a more gradual increase in recovery with pressure than the alkane curve. Of course, real crude oils do not consist solely of n-alkanes or solely of multi- ring aromatics. It was shown in Chapter 3 that just the C5 to C30 fraction of the oil consists of himdreds of components with many different structures. The phase behavior of a crude oil with CO2 is a function of all of these components and their interactions. Using the Kubiki oil description from Appendix B, flash calculations were per formed using the Peng-Robinson equation of state at 2000 and 5000 psia and 125®F on a crude oil mixture with 85 mole % CO2. The resulting K-values are plotted versus carbon nimiber in Fig. 4.4. The K-valuesfor the multi-ring aromatics are lowerthan the K-values for the remainder of the SCN cut, as seen in the experiments. These numeric K-values were calculated from Equation 3.1. The experimental partitioning data from Chapter 3 demonstrated that increasing pressure resulted in K-values for substituted naphthalenes in Kubiki that were closer to those of other similarly eluting components than they were at lowerpressure. The results of the simulations agree with the experiments. At the higher pressure, the K-values of the smaller multi-ring aromatics, while still lower than those of the non- MRA's, are closer to the non-MRA values. Thus, the higher CO2 density results in more efficient extraction of the smaller multi-ring structures. The "bumps" in the K-value curves for the non-MRA components result from discontinuities in the BIP's for these components. Binary interaction parameters 84 CHAPTER 4. NUMERICAL MODELLING

SLIM TUBE RECOVERY -1.2 PV 100

O 60 i 8 40 GC

— n-alkanes — multi-ring aromatics 20 L

I I I •••I 'I ' I I I I I I I I I I I I I 1000 1500 2000 2500 3000 3500 4000 4500 5000 PRESSURE (psi)

Figure 4.3: Effect of multi-ring axomatics on slim tube displacement 4,3. EQUATION OF STATE CALCULATIONS 85

^— non-MRA, 2000 psia A MRA-2000 psia non-MRA, 5000 psia ° MRA- 5000 psia

o 5

15 20 25 CARBON NUMBER

Figure 4.4: K-values for MRA and non-MRA components in Kubiki oil description 86 CHAPTER 4. NUMERICAL MODELLING were taken from the literature [7], and were not necessarily a smooth function of carbon number. The BIP's, as well as the critical properties of the components, are reported in the characterization in Appendix B.

4.4 Development of Miscibility in 1-D Systems

It has been shown [36] that, in 1-D, vaporizing gas drives, components sort them selves out in a chromatographic s^aration based on their K-values. Therefore, the individual component K-values are important in the development of miscibility in these systems. The theory behind the development of multicontact miscibility in 1-D systems has been studied extensively [19, 43,17, 33]. For systems with threecomponents, the theory states that the minimum miscibility pressure for a vaporizing gas drive, such as a CO2 process, is the pressure at which the tie line extending through the initial oil composition becomes a critical tie line. The three component theory is presented graphically in Fig. 4.5. The injected gas, in this case, pure CO2, is component 1. Component 2 is some intermediate component, and component 3 is the heavy com ponent. The critical tie line is the line that is tangent to the bimodal curve at the plait point, P. The region inside the ternary diagram that is on the same side of the critical tie line as the two-phase region is called the region of tie line extensions. Multicontact miscibility is achieved if either the injected gas or the initial oil lies outside the region oftie lineextensions. For a pure CO2 displa-cement process, it will be the oil composition that determines the development of miscibility. An increase in pressure will cause the two-phase region to shrink, thereby driving the critical tie line towards heavier oil compositions. In other words, increasing pressure is required for the development of multicontact miscibility in heavier oil systems. The three component theory has been extended to systems with four or more components [33, 36, 21, 8]. The four component theory states that the addition of a component also adds a new key tie line, called the crossover tie line. Miscibility can also be achieved in these systems when the crossover tie lineapproaches a plait point, 4.4. DEVELOPMENT OF MISCIBILITY IN 1-D SYSTEMS 87

injection gas

critical region of tie line tie line extensions

initial oil

Figure 4.5: Three component theory of multicontact misdbility 88 CHAPTER 4. NUMERICAL MODELLING in addition to when the initial tie Une or injection tie line approaches criticality. The four component theory is shown schematically in the quaternary diagram of Fig. 4.6. The fourth component in this case is methane. The initial oil composition consists of methane, the intermediate component (C4), and the heavy component (Cio), and is represented by point a. This point lies on the bottom face of the quaternary diagram. The region of tie hne extensions extends clear across the bottom face. Therefore, the pressure required for the initial tie line to become critical will be considerably higher than the pressure represented by this diagram. The injection tie line (the tie line extending through pure CO2 on the CO2/intermediate/heavy face) is also well within the region of tie line extensions and would require a significant pressure increcise to reach criticality. Therefore, in this system, it is likely that the crossover tie line will govern the development of miscibility. The crossovertie line can be found quite simply [21]. The initial tie line is extended to the axis connecting the intermediate and heavy components. This intersection point is labeled point A in Fig. 4.6. The crossover tie line is that tie line on the CO2/intermediate/heavy face that extends through this intersection point. This tie line is closer to a plait point than the injection or initial tie lines, and therefore will govern the development of miscibility for this system. For systems with more than three or four components, such as crude oils, the phase behavior can be represented by a pseudoquaternary diagram, where true components are grouped into pseudocomponents for presentation purposes. The phase envelope is constructed from flash calculations using the full oil description. The liquid and vapor compositions from the flash are then combined into the pseudocomponents for plotting in the quaternary representation. The pseudoquaternary diagram can itseH be represented by a series of pseu- doternary "faces." From Fig. 4.6, it is clear that the CH4/intermediate/heavy and the C02/intermediate/heavy pseudotemary faces contain the important information in terms of miscibility development. The CH4/intermediate/heavy pseudotemary diagram for the Kubiki oil descrip tion from Appendix B at 4000 psia and 125®F is presented in Fig. 4.7. The phase envelope was constructed from the results of flash calculations that were performed 4.4. DEVELOPMENT OF MISCIBILITY IN 1-D SYSTEMS 89

Crogpver.Tie-Line ension

CH4

C10

Figure 4.6: Fourcomponent theory ofmulticontact miscibility, Orr et al. [36] 90 CHAPTER 4. NUMERICAL MODELLING using the full oil description of Appendix B. The resulting phase compositions were then grouped into the pseudocomponents. The intermediate component is defined as C5 - Ci5 and the heavy component is defined as C16+. The live Kubiki oil contains approximately 10 mole % methane, which was not included in the composition given in Appendix B. This methane is included in the initial oil composition in Fig. 4.7. The tie lines in this diagram are nearly parallel to the dilution line. In other words, the tie line extension that passes through the initial oil composition intersects the intermediate/heavy axis at almost exactly the composition of the dead oil from Ap pendix B. This means that the methane present has Httle effect on the development of miscibility in this system. In addition, the near coincidence of the initial oil tie line extension and the dead oil composition on the CH4/intermediate/heavy pseu- dotemary diagram means that the crossovertie line can be represented by the initial oil tie line on the pseudotemary diagram for the dead oil with CO2. The second set of tie lines on Fig. 4.7 results from flashes using the Kubiki oil composition from SIMDIS with only the non-MRA properties. In other words, the MRA and non-MRA fractions of a single carbon number cut were recombined and assigned the properties of the non-MRA fraction. While the phase envelope is smaller for the non-MRA case than for the true oil description, the tie line slopes axe similar. Therefore, the pseudotemary representation of the dead oil with CO2 is appropri ate for the non-MRA case as weU as for the oil description that includes the MRA components. Fig. 4.8 presents the C02/intermediate/heavy pseudotemary diagram for the Ku biki oil description from Appendix B at 125®F and 2500 psia. The original dead oil composition is approximately 53 mole % intermediates and 47 mole % heavy ends. The solid lines represent tie lines for the full Kubiki description, including the MRA components. The dashed lines represent the tie Hnes for the non-MRA case. The size of the two-phase envelope is much smaller for the non-MRA case than for the case that includes the multi-ring aromatics. Although the plait points axe diffictilt to iden tify for this pressure, clearly the critical tie line for each case intersects the bottom axis far to the right of the cmde oil composition. In other words, the oil composition lies well within the region of tie line extensions and multicontact miscibility caimot 4.4. DEVELOPMENT OF MISCIBILITY IN 1-D SYSTEMS 91

• MRA oil CH4 • non-MRA oil /• Dilution Line ^ /• \ 'V 11

V® 'b/ ill 1 \60 Vo ^ / V \30 \20

CrudeOa V°

V _ w v - __v —A C5-C,5 •9 <§>(?/?> <§>

Figuie 4.7: Pseudotemary diagram For Kubiki oil iuid methane at 125°F and 4000 psia 92 CHAPTER 4. NUMERICAL MODELLING

MRAoil non-MRA oil

Crude Oil C5-C15

Figure 4.8: Pseudotemaxy diagram For Kubiki oil at 125®F and 2500 psia 4.4. DEVELOPMENT OF MISCWILITY IN 1-D SYSTEMS 93

be achieved at this pressure for either "oil." When the pressure is increased to 4000 psia, the two-phase envelope shrinks for both cases, as is shown in Fig. 4.9. Estimation of the plait points and critical tie lines shows that the critical tie line is approaching the initial oil composition faster for the oil description that does not contain the MRA components. Therefore, the pressure required to drive the critical tie line to the initial oil composition will be lower for the non-MRA case. This situation corresponds to a lower MMP for the non-MRA oil with the same carbon number distribution as Kubiki. A similar result was shown experimentally in Chapter 3 for Oil W and Kubiki. Thus, equation of state computations for oils with ajid without mtilti-ring aromatics support the conclusion that it is the MRA fraction of the Kubiki oil that explains its tmusually high MMP. While an MMP of greater than 4000 psia agrees qualitatively with the experimen tal slim tube results for Kubiki that were presented in Chapter 3 (Fig. 3.18), this pressure and temperature represent a CO2 density that is much higher than the mis- cibility density of Oil W. However, Fig. 4.9 indicates that even the non-MRA oil is immiscible imder these conditions. To test the sensitivity of the phase envelope to the least certain of the equation of state parameters, the binary interaction parameters, flash calculations were performed using the same oil descriptions from the previous calculations, but with all C02/hydrocarbon interaction parameters set to 0.08. (See Table B.3 for the original values). This value of 0.08 is certainly within the realm of physically reasonable interaction parameters [7]. The phase envelopes at 2500 psia and 125®F for both the MRA inclusive and non-MRA oil descriptions with the new binary interaction parameters are presented in Fig. 4.10. Clearly, the size of the phase envelope for both oil descriptions is highly sensitive to the binary interaction parameters. The two-phase regions in Fig. 4.10 are considerably smaller than those of Fig. 4.8, and are also smaller than those of Fig. 4.9, which was for a higher pressure. However, the size of the two-phase envelope is also a function of the MRA properties. The interaction parameters are the same in both cases represented in Fig. 4.10, but the two-phase region is definitely smaller for the non-MRA description. The oil composition lies closer to the plait point tie line extension for the non-MRA oil; consequently, the MMP for the non-MRA oil will be