International Conference on Gas Hydrates (ICGH) (6th : 2008)

CRITICAL DESCRIPTORS FOR HYDRATE PROPERTIES OF OILS: COMPOSITIONAL FEATURES Borgund, Anna E.; Høiland, Sylvi; Barth, Tanja; Fotland, Per; Kini, Ramesh A.; Larsen, Roar 2008-07

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Proceedings of the 6th International Conference on Gas Hydrates (ICGH 2008), Vancouver, British Columbia, CANADA, July 6-10, 2008 CRITICAL DESCRIPTORS FOR HYDRATE PROPERTIES OF OILS: COMPOSITIONAL FEATURES Anna E. Borgund ∗,1,2, Sylvi Høiland 2, Tanja Barth 1, Per Fotland 3, Ramesh A. Kini 4 and Roar Larsen 2 1 University of Bergen, Department of Chemistry, Bergen, Norway 2 SINTEF Petroleum Research, Norway 3 StatoilHydro R&D, Bergen, Norway 4 Chevron Energy Technology Company, Houston, TX, USA ABSTRACT In petroleum production systems, hydrate morphology is observed to be influenced by the crude oil composition. This work is aimed at identifying which crude oil compositional parameters that need to be determined in order to evaluate natural anti-agglomerating properties of crude oils, i.e. the critical compositional descriptors. The compositional features of 22 crude oils have been studied, and multivariate data analysis has been used to investigate the possibility for correlations between several crude oil properties. The results show that biodegradation together with a relatively large amount of acids are characteristic for non-plugging crude oils, while excess of basic compounds is characteristic for plugging crude oils. The multivariate data analysis shows a division of the non- biodegraded oils, which are all plugging, and the biodegraded oils. In addition, the biodegraded oils seem to be divided into two groups, one with plugging oils and one with mostly non-plugging oils. The results show that the wettability can be predicted from the variables biodegradation level, density, asphaltene content and TAN. Keywords: crude oil compositional features, multivariate data analysis, hydrate plugging tendency NOMENCLATURE GPC Gel permeation chromatography PCA Principal component analysis PLS Partial least square TAN Total acid number TBN Total base number INTRODUCTION Hydrates can cause problems in petroleum produc- tion lines, e.g. plugging of pipelines. Reliable phys- ical models for prediction of hydrate formation are available [1]. However, these models do not de- scribe the morphology of the hydrate particles, i.e. whether the particles agglomerate and grow into a ∗Corresponding author: Phone:+47 55 54 39 12 Fax: +47 55 54 39 05 Email: plug or remain as a dispersion of small hydrate par- ticles in oils. Some crude oils have been observed to have lower tendencies to form hydrate plugs in petroleum production lines than others when oper- ated within the thermodynamic conditions for hy- drate formation. Hence, the morphology of the hy- drates is influenced by crude oil composition. The oils with low tendencies to form hydrate plugs prob- ably contain natural inhibiting components that pre- vent hydrate plugging [2–6]. A possible mechanism for formation of a dispersion is adsorption of special compound types onto the hydrate surface, prevent- ing the small hydrate particles from agglomerating into large plugs. In a previous work [5], standard compositional pa- rameters alone, like asphaltene content, and total acid- and base numbers, were found insufficient in describing the black oil hydrate behavior. Although some properties are found to be more important than others, e.g. the acid profile, the complex chemical interplay between interfacial active components and bulk oil most likely excludes the possibility of iden- tifying one single compositional parameter that ex- plains the hydrate behavior for all crude oil systems. Acid fractions extracted from certain non-plugging crude oils were previously found to be able to al- ter hydrate behavior from plugging to non-plugging at operationally relevant PT conditions [7]. Other acid fractions, however, fail to do so, even though they too have been extracted from non-plugging oils. In order to develop a fundamental understanding of natural anti-agglomerating behavior of crude oils in terms of inhibiting mechanisms and critical compo- sitional features, more knowledge of the interaction between the acid fraction and the bulk oil is crucial. Understanding these aspects is important for assess- ment of hydrate plug risk as well as for searching for ways to obtain non-plugging hydrate behavior through modification of the chemical composition of the system. In this work, the wettability of freon hydrates in crude oil/brine emulsions is used to evaluate the plugging tendencies of crude oils [5]. Chemical characterization with special focus on the petroleum acids is performed. Multivariate techniques are uti- lized to find interaction effects among the parame- ters. The evaluation procedure for limiting the num- ber of compositional parameters necessary for pre- dicting the black oil hydrate behavior is described, and the critical descriptors are presented. MATERIALS AND METHODS Materials The data set consists of 22 crude oils, spanning from heavy biodegraded oils enriched in asphaltenes to light non-biodegraded oils and condensates. Two of of the oils were supplied by Chevron ETC and the rest by StatoilHydro ASA. The oils supplied by Sta- toilHydro have been investigated for several years, and published data have been assembled [5–11]. In this data set, the crude oils are marked with a letter, B - biodegraded oils and S - sweet, non-biodegraded oils, followed by a number indicating production field and a letter denoting different wells or differ- ent batches within one field. Experimental methods The crude oils are characterized with respect to the following properties and compositional features: Plugging tendencies in crude oil/water/gas sys- tems In order to characterize whether a crude oil will have a tendency to form hydrate plugs or not, both field experience and laboratory tests performed at field conditions in a stirred, high pressure sapphire cell are considered [2, 5, 6]. In this paper, oils that form dispersed hydrates are termed non-plugging crude oils, and oils with high tendencies to form hydrate plugs are termed plugging oils. Wettability The wettability of freon hydrates in crude oil/brine emulsions is directly correlated to the plugging ten- dency of the crude oil, see Høiland et al. [5] for details of the emulsion method. In the emulsion method, phase inversion in oil/brine/hydrate emul- sion systems is used for evaluating the wetting state of the system [12]. The wetting state, termed as ”wettability” in this paper, is a direct measure of the expected morphology in terms of whether the hydrates generated in the system tend to agglom- erate or not. The wettability is thus related to the anti-agglomerating behavior. The wettability pa- rameter (∆ϕ∗) is given as a number between - 1 and 1, where large positive values are correlated to non-agglomerating systems, and non-plugging crude oils. Negative values and values close to zero are correlated to agglomerating systems, and plug- ging crude oils. Biodegradation level, density and asphaltene content The biodegradation level of the crude oils are deter- mined using the Peters and Moldowan scale [13]. The densities of the crude oils are determined at 20 ◦C using an Anton Paar DMS60 densitometer connected to an Anton Paar DMA602HT measuring cell. The amount of asphaltenes is found from reflux- ing a portion of the oil with 40 times excess of hexane for 6 hours, filtering through a Whatman GF/F glass fibre filter, and solving the residue in dichloromethane:methanol 93:7. Titration: TAN and TBN A Metrohm autotitrator (model 798 MPT Titrino) connected to a Metohm Solvotrode combined LL PH glass electrode (model 6.0229.100) is used for the titrations. The amount of titratable acids (TAN, total acid number) is determined by the standard method ASTM664-89 [14], and is defined as the amount (mg) of potassium hydroxide (KOH) necessary to titrate 1 g of sample to a well-defined inflection point. The amount of titratable bases (TBN, total base number) is determined by the standard method ASTM2896-88 [15], with modifications according to Dubey and Doe [16], and is defined as the amount (mg) KOH necessary to titrate 1 g sample to a well- defined inflection point. Extraction of acids and analysis by GPC The acids have been extracted by the use of ion ex- change material, as described by Mediaas et al. [17]. The molecular weights of the acid fractions have been determined by gel permeation chromatography (GPC). In this technique molecules are separated ac- cording to their size and shape, and standards with known molecular weights are used to make a cal- ibration curve for determination of the molecular weight of the samples. More information can be found in Borgund et al. [9]. Multivariate data analysis Characterization of crude oil is performed by many different analytical procedures, and correlations are sometimes found between two compositional pa- rameters. In some cases a combination of several variables can correlate to other variables, and multi- variate data analysis can be used to investigate how several variables affect each other at the same time. A systematization of a large data set in terms of in- ternal correlations can in some cases reveal that ap- proximately the same information can be obtained from fewer analyses. Principal component analysis (PCA) Principal component analysis is used to extract sys- tematic information from large data sets [18]. The systematic variation in the data set can be described by principal components that describe common in- formation in several variables. The first principal component (PC1) is a linear combination of the orig- inal variables that explains as much as possible of the variance in the data set. PC2 is a vector that ex- plains most of the variance that is not explained by PC1, and that is orthogonal to PC1. PC3 can be ex- tracted in the same way, and explains the variance that is not described by PC1 and PC2. Principal components can be extracted until all the variance in the data set is explained. However, the purpose of PCA is a reduction of variables, and two or three principal components are the optimal to extract for a data set [19]. The samples and the variables in a data set are pro- jected in a plane defined by the principal compo- nents [20], see Figure 1. Plane Projection of A A Plane Projection of A A PC1 PC2 x2 x1 x3 Figure 1: The samples in a data set are projected in a plane defined by the principal components, adapted from Wold [20]. A plot with both samples and variables is called a bi- plot. The distance between the points is a measure of the similarities between the samples. Two samples that are close to each other in the plot are similar to each other. Variables that are close to each other are positively correlated, and variables that are situated in opposite direction (through origo) are negatively correlated. Variables that are located in an orthog- onal position to each other, when centered through origo, are independent of each other. Partial least squares (PLS) In the PLS technique one set of variables (X) is used to describe another set of variables (Y), e.g. only the the information in X that is relevant for Y is used to explain Y [19]. From the calculations, a model that predicts results can be obtained. Details of the statistical techniques can be found in the cited literature [18–21]. RESULTS AND DISCUSSION Review of previously reported data As mentioned above, acids extracted from crude oils have previously shown to hold inhibiting proper- ties [5–7]. From the collected data set, a moderately high TAN value seems to be characteristic for non- plugging crude oils. However, not all the oils with high TAN values are non-plugging. In addition to the TAN value, the amount of extractable acids is important. The biodegradation level has previously been shown to be important, in the sense that all the non- biodegraded oils are plugging and all the non- plugging oils are biodegraded. However, not all the biodegraded oils are non-plugging, so other fac- tors must be considered (biodegradation seems to be a necessary, but not sufficient criterion for non- plugging crude oils) [5, 6]. Multivariate data analysis was also previously used on these oils, and the TBN value and the amount of asphaltene were shown to be characteristic param- eters for the biodegraded, plugging crude oils [22]. Most of the biodegraded, plugging oils in this data set contain significantly more asphaltenes compared to the non-plugging oils. The amount of bases relative to the acids was pre- viously indicated as important [8]. From inspecting the collected data we find that most of the plugging oils have excess base (TBN is higher than TAN), and for the plugging, biodegraded oils the TBN number is significantly higher than the TAN value. In fact, for most of the non-plugging oils, the TAN value is higher than the TBN value (excess acid), see Figure 2. Evaluation of the plugging tendency of new oils In this present work, two new oils, B5a and B6a, have been characterized in the same way as the other oils. Both oils are found to be biodegraded, and thus from the current know-how, they might be non- plugging. Quite large amount of titratable acids are found in the oils, i.e. more than 2 mg KOH/g oil for both oils. Both oils also contain a significant amount of ex- tractable acids, but B5a contains considerable more extractable acids compared to B6a. From combin- ing GPC analysis of the acids with TAN results, B5a is found to mostly contain compounds with one acid group, while B6a contains a high level of com- pounds with more than one acid group. The TBN values of the oils are quite high, i.e. more than 2 mg KOH/g oil for both oils. A comparison with the TAN value shows that B5a has a small ex- cess of acids, while B6a has an excess of bases. These results indicate that B5a is a non-plugging oil, and that B6a is a plugging oil, see Figure 2. How- ever, it should be emphasized that the estimation of plugging tendency from this criteria alone is far from certain. The amount of asphaltenes is clearly lower in B5a compared to B6a. This is another indication that B5a is a non-plugging oil and B6a is a plugging oil, see Figure 3. Results from multivariate data analysis The different crude oil properties are correlated with the plugging tendency by the use of multivariate data analysis in order to define critical descriptors for hy- drate plugging. A PCA (principal component analysis) plot of the data comprising both new and collected data from literature, is shown in Figure 4. A similar analysis was previously presented by Høiland et al. [22], al- though without the new oils. The different oil sam- ples are shown in red (coded: B - biodegraded oils and S - sweet, non-biodegraded), and the variables in blue. The first principal component (x-axis) explains much of the variance in this data set (56.4 %). On this axis the biodegraded oils are separated from the non-biodegraded oils. All the biodegraded oils can be found to the right in the plot (inside dark-green square), and the non-biodegraded oils are found to -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 B2a B2b B2c B4b B4c S6a B5a B4a S4 S2b S2a B6a S4b S5a B1a S7a S3a S7b S1a S3b B1b/c B3a T A N - T B N Figure 2: TBN subtracted from TAN for the crude oils. The black bars represent non-plugging crude oils and the grey bars represent plugging crude oils. The new oils, B5a and B6a, are marked with white bars. the left in the plot (inside green square). The second principal component (y-axis) explains 26.4 % of the variance in the data set. The biodegraded oils are separated into two groups on this axis. In the lower right corner of the plot plugging oils are found, and higher in the plot a group of B2 and B4 oils are found in addition to the B5a oil. Most of these oils are non-plugging. However the B4b oil have tendencies to form hydrate plugs. The wettability seems to be an important parameter for the non-plugging crude oils, while the amount of asphaltene (Asph.cont) and the base number (TBN) seem to be important for the plugging, biodegraded crude oils, corresponding to results previously presented by Høiland et al. [22]. The oil B5a is situated amongst the non-plugging crude oils, and the B6a oil is situated with the plug- ging, biodegraded crude oils. The variable TAN- TBN (the TBN value is subtracted from the TAN value) is found in the same direction as the wetta- bility variable, but does not seem to have a direct correlation with any of the other variables. The results from the multivariate analysis show that a high total base number and large amount of as- phaltenes are indicative of plugging systems. How- ever, low amount of bases and asphaltenes do not directly correspond to non-plugging systems. In or- der to separate the non-plugging oils from plugging biodegraded oils with low amounts of asphaltenes (B2 and B4 oils, in top circle) more information is needed. 0 5 10 15 20 25 30 B3a B6a B1b/c B1a B2b B5a B4a B2a B2c B4b B4c a s p h a l t e n e  c o n t e n t ,  m g / g o i l Figure 3: The amount of asphaltenes in the biode- graded crude oils. The black bars are non-plugging oils and the grey bars are plugging oils. The new oils, B5a and B6a, are marked with white bars. Selected variables are used to make a regression model (PLS analysis), in which the wettability is predicted from other variables. The variables biodegradation level, density, asphaltene content and the TAN value, are used for predicting the wet- tability, see Figure 5. Only 13 samples are used for this regression model, because the other samples lack data for the wettability or a reliable value for biodegradation. The R value for the regression equation (R = 0.808) is not very good, but a clear trend can be seen from the plot in Figure 5. The analysis indicates that biodegradation level, density, asphaltene con- tent and the TAN value are important variables for the wettability of a system, and hence the plugging Comp. 1 (56.4%) C o m p .  2  ( 2 6 . 4 % ) -2.6 -0.6 1.4 3.4 -3.0 -1.0 1.0 3.0 B1a B1b/c B2aB2b B2c B3a B4aB4b B4c S1a S2aS2b S3a S3b S4 S4b S5a S6a S7aS7b B5a B6a Wettability Biodegr.level Density Asph.cont. TAN Acids_ion TBN TAN-TBN Biodegr.level TAN B2b Figure 4: A PCA plot of all the samples (red) and the variables (blue). The non-biodegraded oils (plugging) are found in the small green square to the left, and the biodegraded oils are found in the large dark green square to the right. The biodegraded oils are separated into two groups: circle in the lower right corner - plugging oils and higher circle - mostly non-plugging oils. tendency. It is unexpected that the density is impor- tant for the wettability of the system. The density values for the different oils alone do not reveal any correlation with plugging tendency. Thus, the vari- ables must be combined in order to obtain a predic- tion of the plugging tendency of crude oils. For a validity check, the oil B4a was removed from the data set, and a new model for prediction was made. The wettability of the oil was predicted from the new model, and a value of 0.32 was obtained compared to 0.35 which is the measured value. The same was performed with the oil Sb2, and the pre- dicted value was -0.06 compared to the measured value at -0.12. The model did not give a good pre- diction for the B4b oil. Thus, the model might not be very adequate for biodegraded oils with relatively Predicted vs Measured,(3 Comp), SEV = 0.170 Measured (Wettability) -4.0 -2.0 0.0 2.0 4.0 *10-4.0 -2.0 0.0 2.0 4.0 6.0 *10-1 B1a B2b B3a B4a B4c S2bS3aS3b S4bS5aS6a S7a S7b *10-1 Measured (Wettability) P r e d i c t e d ( W e t t a b i l i t y ) R = 0.808 R2 = 0.653 Figure 5: Predicted versus measured for the wetta- bility, based on 13 samples (PLS analysis). The wet- tability is predicted from using biodegradation level, density, asphaltene content and the TAN value. The regression model equation is omitted due to restric- tion from industry partners. low amounts of asphaltenes. By using the model for prediction of the wettability, the B5a oil is predicted to 0.30, which indicate an oil-wet system and non-plugging tendencies. B6a is predicted to -0.19, which indicate a water-wet sys- tem and plugging tendencies. The accuracy of the prediction is yet to be verified. To summarize, the results from the multivariate analysis give a group with plugging, biodegraded oils. The oil B6a can be found in this group. This oil also obtain a negative value of wettability from the prediction model. Thus, this oil is most likely a plugging oil. The B5a oil is situated in a group in the PCA-plot that mostly contain non-plugging, biode- graded oils. The prediction model also gives a pos- itive value, indicating a non-plugging oil. However, this group in the PCA-plot also contains a plugging oil, which was predicted wrongly by the prediction model. CONCLUSIONS The plugging tendency of crude oils can be pre- dicted from information of the composition of the oil. Biodegradation and moderately high amount of acids seem to be necessary for non-plugging crude oils. An excess of acids relative to the bases in the crude oil (TAN - TBN), as well as low amount of as- phaltenes for biodegraded oils, also seem to be im- portant for having non-plugging systems. The PCA analysis confirmed that large amounts of basic compounds and asphaltenes are connected with plugging, biodegraded oils, and that the non- plugging oils are connected with the wettability pa- rameter. From regression analysis it is shown that in most cases it is possible to predict the wettability of crude oils, based on the variables biodegradation level, density, asphaltene content and the TAN value. ACKNOWLEDGMENTS StatoilHydro and Chevron ETC are acknowledged for funding and permission to publish data. The Norwegian Research Council, the Petromaks pro- gram, is thanked for funding. The HYADES project group consists of experienced research personnel from both university, research in- stitution and industry, that actively participates in planning of activities and discussion of results. The in-kind contributions from both university and in- dustry partners are of essential value for the quality of the project, and are thus highly appreciated. The project group consists of the following persons: • SINTEF Petroleum Research: Roar Larsen, David Arla, Sylvi Høiland, and Jon Harald Kaspersen. • University of Bergen: Tanja Barth, Alex Hoff- mann, Pawel Kosinski, Anna E. Borgund, Guro Aspenes (PhD student), Boris Balakin (PhD student), Ziya Kilinc (MSc student), and Håkon Pedersen (MSc student). • Chevron ETC (Houston): Ramesh Kini, Lee Rhyne, and Hari Subramani. • StatoilHydro: Per Fotland and Kjell M. Askvik. REFERENCES [1] Sloan ED. Clathrate hydrates of natural gases. New York: Marcel Dekker, Inc., Second edi- tion, 1998. [2] Fadnes FH. Natural hydrate inhibiting com- ponents in crude oils. Fluid Phase Equilibria 1996;117:186-192. [3] Gaillard C, Monfort JP and Peytavy JL Inves- tigation of methane hydrate formaion in a re- circulating flow loop: Modeling of the kinet- ics and tests of efficiency of chemical additives on hydrate inhibition. Oil and Gas Technology 1999;54:365-374. [4] Bergflødt L. 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