Molecule-Based Characterization Methodology for Correlation and Prediction of Properties for Crude Oil and Petroleum Fractions

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1 Molecule-Based Characterization Methodology for Correlation and Prediction of Properties for Crude Oil and Petroleum Fractions An Industry White Paper

2 Summary A molecule-based characterization methodology was developed for correlation and prediction of assays and properties for crude oil and petroleum fractions. From readily accessible crude oil, the assay data such as distillation curve, API gravity, and PNA contents, identifies an optimal set of chemical compositions of model hydrocarbon molecules that were designed to mimic measurable physical and chemical properties of crude oil. The model hydrocarbon molecules cover significant classes of hydrocarbon constituent molecules that are present in crude oil where each class is characterized by its molecular structural segments and segment distribution function parameters. The relative weights of the various classes of the hydrocarbon constituent molecules, the respective structural segment types, and the segment distribution function parameters together form a molecular profile of the crude oil. The chemical compositions of model hydrocarbon molecules, as derived from the molecule profile, are then used to interpolate, extrapolate, and predict crude oil assays and properties based on molecular thermodynamic models. The molecule-based methodology for crude oil assay characterization represents a superior alternative to the traditional statistics-based methodology and other empirical expressions used to interpolate between assay data points or extrapolate for heavy cuts. The molecule profile further offers a molecular insight into crude oil for planning, scheduling, and process simulation of oil production and petroleum refining operations. Assay Characterization Each crude oil has unique molecular characteristics. A crude oil assay is the physical and chemical evaluation of crude oil feedstock by petroleum testing laboratories. Assays vary considerably throughout the industry from just a handful of key properties for the whole crude, to a full set of physical, chemical, and chromatographic measurements on distilled or blended fractions and residues of the crude oil 2. Results of crude oil assay testing provide extensive hydrocarbon analysis data for refiners, oil traders, and producers. For example, assay data helps refineries determine if a specific crude oil feedstock is compatible for a particular petroleum refinery or if the crude oil could cause yield, quality, production, environmental, or other problems. The determination of crude oil assay is a lengthy, tedious, and costly process 3. The conventional approach to perform an assay consists of a set of measurements on the crude oil and its fractions. Depending on the time and cost involved, assay data can be very detailed or quite limited. Detailed assay data will generally include a full TBP analysis of the whole crude and various fractions, densities, and physical and chemical properties that are pertinent to the whole crude and fractions including gas chromatographic analysis of light ends and naphtha fractions. On the other hand, limited data may comprise only TBP curve, densities, and sulfur content of the whole crude 4. Regardless of the amount of data available, it is often necessary for crude oil assay experts to predict or estimate the missing properties to meet various business needs, such as refinery planning and scheduling and refinery process simulation. 1

3 Statistically derived predictive methods have been extensively used in the industry for the prediction or estimation of crude oil properties and assays 2. The issues with statistical methods are as follows: For cases where data is abundant but has high uncertainties, the quality of models or correlations is often questionable For cases where data is scarce or sporadic, it may not be possible to develop statistically meaningful models For cases where data is not available, models or correlations simply cannot be developed Additionally, lower order polynomial expressions are often used for interpolation and arithmetic probability functions for extrapolation of boiling point curves and other crude oil properties 5,6. Other analytical approaches include the prediction of the crude oil properties by correlating the data obtained with rapid surrogate measurements (usually spectroscopic measurements) to existing crude assays 3,7. Clearly, there is a critical business need for a fundamentally better crude oil assay methodology. Molecule-based characterization methodologies offer the strongest scientific basis for correlation and prediction of assays and properties for crude oil and petroleum fractions. Molecular Species in Crude Oil As the carbon number increases, the number of possible hydrocarbon constituent molecules in crude oil increases exponentially, quickly becoming totally unmanageable 8. Nevertheless, it is well recognized that hydrocarbon molecules in crude oil are composed of three major classes of hydrocarbon molecules: paraffinic (P), naphthenic (N), and aromatic (A) 9. The paraffins are saturated hydrocarbons with linear chains, i.e. n-paraffins or with branched chains, i.e. iso-paraffins. The naphthenes are saturated hydrocarbons with one or more naphthenic rings plus paraffinic side chains. The paraffins and the naphthenes together are called the saturates. The aromatics are hydrocarbons with aromatic rings, naphthenic rings, and paraffinic side chains. Certain high molecular weight and highly-branched aromatic hydrocarbons are known as asphaltene molecules. In addition, significant amounts of hydrocarbon molecules in crude oil may contain sulfur as mercaptans, paraffinic sulfides, naphthenic sulfides, thiophenes, etc. Nitrogen-containing hydrocarbon molecules, such as carbazoles and quinolines, and oxygen-containing hydrocarbon molecules, such as phenols, naphthenic acids, and aromatic acids are also present in crude oil. These subclasses of hydrocarbon molecules account for sulfur content, nitrogen content, and total acid number, respectively. Additional classes and subclasses of molecules may also be present. 2

4 Molecule-Based Assay Characterization The molecule-based characterization methodology includes the following key elements: Identifying the necessary classes and subclasses of model hydrocarbon constituent molecules for crude oil Generating these model hydrocarbon constituent molecules from a minimal set of molecular structural repeating units (or structural segments ) Determining the resulting chemical compositions of these model hydrocarbon constituent molecules from weights of various classes and subclasses of model molecules and probability distribution of their structural segments Calculating properties and assays for crude oil or petroleum fractions based on the chemical composition of these model hydrocarbon constituent molecules, properties of individual model molecules, and accurate and thermodynamically consistent molecular thermodynamic models for hydrocarbon mixtures A prerequisite to this process is a robust regression algorithm which facilitates determination of chemical compositions of crude oil in terms of the model hydrocarbon constituent molecules from readily available assay data. Figure 1 shows some of the molecular structural repeating units or segments that are selected to make up the model hydrocarbon constituent molecules in the three major classes of hydrocarbon constituent molecules: P, N, and A. Structure CH 3 CH 2 Formula CH 3 CH 2 CH Structure Formula C C 6 H 12 C 4 H 6 Structure Formula C 5 H 10 C 3 H 4 C 6 H 6 Structure Formula C 4 H 2 Figure 1: Segments for paraffinic, naphthenic, and aromatic molecules For example, here the CH 3 segment is the methyl end group, the CH 2 segment is the zero-branch methylene repeat group, the segment is the one-branch methylene group, and the segment is the two-branch methylene group. These four segments are chosen to make up the paraffinic (P) class of hydrocarbon constituent molecules. Figure 2 shows the paraffinic hydrocarbon constituent molecules with up to sixty zero-branch methylene groups, up to three one-branch methylene groups, and up to two, two-branch methylene groups, for a total (including the necessary end groups) of up to seventy-four carbons. 3

5 Segments CH 2 CH 3 Probable number in molelcular species 0~3 0~2 0~60 2, 3, 4, 5, 6, 7, 8, 9 Structure of molecular species Figure 2. Paraffinic (P) hydrocarbon constituent molecules Table 1 below summarizes the numbers of components considered for some of the hydrocarbon constituent molecules. Components Number Carbon number range n-paraffins 61 C2 C62 Iso-paraffins 723 C4 C74 Naphthenes 1230 C5 C80 Aromatics 2918 C6 C90 Total 4932 C2 C90 Table 1. Numbers of components considered for some classes of hydrocarbon constituent molecules These molecular structural segments are selected to make up the molecules in each class of molecules. With the segment approach, all the components that are associated with the segments for the classes of molecules can be generated a priori and their concentrations will be normalized based on the selected probability distribution functions and function parameters for the specific segments. While a variety of probability distribution functions can be considered, the gamma distribution function 10 is probably the most often applied 11. Figure 3 shows the gamma probability distribution function chosen for the CH 2 segment of P with a scale parameter of 11 and shape parameter of 1. 4

6 Figure 3. Gamma probability distribution function as a function of segment number for n-paraffinic molecules with a scale parameter of 11 and a shape parameter of 1 The relative contents of the various classes of hydrocarbon constituent molecules are then identified from regression against assay data such as distillation yield, API gravity, viscosity, paraffin content, naphthene content, and aromatic content of selected distilled fractions as described below. Molecular Thermodynamic Models for Hydrocarbon Molecules Molecular thermodynamic models, such as the segment-based PC-SAFT equation-of-state (EOS), offers thermodynamically consistent frameworks to accurately calculate physical properties for the hydrocarbon constituent molecules and their mixtures 12,13. The PC-SAFT EOS requires segment-based parameters for the segments that build up the model P, N, and A hydrocarbon constituent molecules. These segment-based parameters include segment ratio, segment size, and segment energy parameters 13. These parameters have been identified from regression of experimental data on vapor pressure, liquid density, and liquid heat capacity of hundreds of hydrocarbon compounds that are made up of these segments. Availability of thermodynamic framework for accurate and thermodynamically consistent property calculations provides a key foundation for the successful development of the molecule-based assay characterization methodology. Of particular significance is the ability of the PC-SAFT EOS to accurately correlate and predict vapor pressure and liquid density, simultaneously. Vapor pressure and liquid density are the two most important and prevalent assay properties for crude oil and petroleum fractions. Accurate and thermodynamically consistent property models for both vapor pressure and liquid density are prerequisites not only for accurate correlation and prediction of assay properties, but also for meaningful identification of the molecular profile for crude oil from measured assay data. 5

7 Given molecule profile and validated molecular thermodynamic models, wide varieties of crude oil assay properties can be predicted from a consistent set of chemical compositions of the model hydrocarbon constituent molecules. Here, the interrelationships between assay properties are soundly rooted in the consistent set of chemical compositions and the rigor of molecular thermodynamic models. Table 2 provides a partial list of such assay properties. The list will grow with the knowledge and future development of molecular thermodynamic models. Accentricity Dynamic Viscosity Nitrogen Content UOP K Aniline Point Gross Heating Value n-paraffin Content Wax Content Aromaticity Hydrogen Content Paraffin Content Aromatic Content Iso-Paraffin Content Rams Carbon Residue Asphaltene Content Kinematic Viscosity Refractive Index Basic Nitrogen Content Mercaptan Sulfur Content RVP C5 Content Micro Carbon Residue Saturates Content Carbon Content Molecular Weight Specific Volume Conradson Carbon Residue N+2A Standard Liquid Density Critical Pressure N+A Sulfide Content Critical Temperature Naphthalene Content Sulfur Content Critical Volume Naphthenic Acid Content Thiophene Content Carbon to Hydrogen Ratio nc7 Content Total Acid Number Distillation Cut Yields Net Heating Value TVP Table 2: A partial list of assay physical and chemical properties currently calculated with the molecular characterization methodology Molecular profiles have been determined for a large number of crude assays and are available for all the assays in the built-in library of Aspen Assay Management, as well as assays that can be downloaded from a number of public websites. These profiles can be used as is or as a starting point for updating assay data of the same or similar crudes. 6

8 A Molecular Characterization Example To illustrate, by example, the molecule-based assay characterization methodology, a North American Bryan Mound Sweet crude oil assay from the US Department of Energy s Strategic Petroleum Reserve is highlighted 14. This crude oil is light and sweet with an API gravity of 36.3, sulfur content of wt%, total acid number of 0.10 mg KOH/g, nitrogen content of wt%, micro carbon residue of 2.1 wt%, and reid vapor pressure of 4.80 psi for the whole crude. Figure 4 shows the assay data for the whole crude and for various distillation cuts. Figure 4. USA Bryan Mound Sweet crude oil assay downloaded from the Web 12 Depending on the types of assay property data available for regression, one can identify a subset or all of the molecular profile parameters for the crude oil, i.e. the relative P, N, A, sulfur, nitrogen, oxygen molecular contents, and the probability distribution functions and function parameters for the structural segments for each class or subclass of molecules. Figure 5 shows a specific selection of such molecular profile parameters. 7

9 Figure 5. Selected molecular profiling parameters used for the USA Bryan Mound Sweet crude oil assay Initial estimates for the molecule profile parameters can be entered. Figure 6 shows a specific set of assay data and quality of data as standard deviations that are used to identify the molecular profile parameters shown in Figure 5. Figure 6. Selected assay data and their corresponding standard deviations for the USA Bryan Mound Sweet crude oil assay The regression calculations minimize the Sum of Squared Residuals (SSR) which is a measure of the goodness of the fit between the experimental data selected for the fit and the calculated values. 8

10 Figure 7 shows typical regression results with SSR reported, along with the values of experimental data points and correlated values for these data points. Figure 7. Regression results showing SSR, the assay data, and the correlated values for the USA Bryan Mound Sweet crude oil assay The optimized molecular profile parameters including values of the P, N, and A contents and the associated segment probability distribution functions and function parameters for the crude oil assay are given in Figure 8. Figure 8. Optimized molecular profile parameters for the USA Bryan Mound Sweet crude oil assay 9

11 Figure 9 shows the full assay characterization results for the whole crude and all distillation cuts. Figure 9. Tabular assay characterization results for the USA Bryan Mound Sweet crude oil assay Here, all the assay properties listed in Table 2 are calculated from the identified molecular profile parameters. Furthermore, Figures 10 through 12 show the assay data and the correlation results for distillation cut yields, API gravity, and sulfur content, respectively. Figure 10. Distillation yields in volume measurement data and model correlation results as a function of boiling point for the USA Bryan Mound Sweet crude oil assay 10

12 Figure 11. API gravity measurement data and model correlation results as a function of boiling point for the USA Bryan Mound Sweet crude oil assay Figure 12. Sulfur content measurement data and model correlation results as a function of boiling point for the USA Bryan Mound Sweet crude oil assay 11

13 Figures 13 through 15 show the predicted values for molecular weight, Refractive Index, and gross heating value, respectively. Figure 13. Model-predicted molecular weight as a function of boiling point for the USA Bryan Mound Sweet crude oil assay Figure 14. Model-predicted refractive index at 20 F as a function of boiling point for the USA Bryan Mound Sweet crude oil assay 12

14 Figure 15. Model-predicted gross heating value as a function of boiling point for the USA Bryan Mound Sweet crude oil assay To provide a glimpse into the identified chemical compositions for the Bryan Mound Sweet crude oil assay, Figure 16 shows the assay data and the correlation results for the distributions of paraffins, naphthenes, and aromatics versus boiling point temperature. Figure 16. P, N, and A content measurement data and model correlation results as a function of boiling point for the USA Bryan Mound Sweet crude oil assay 13

15 Figure 17 shows the predicted distributions of subclasses of paraffins, naphthenes, and aromatics versus boiling point temperature. Figure 17. Model-predicted P, N, and A subclass distributions by volume as a function of boiling point for the USA Bryan Mound Sweet crude oil assay Figures 18 and Figure 19 show the predicted area distributions of subclasses of P, N, and A molecules versus boiling point temperature and carbon number, respectively. Figure 18. Model-predicted P, N, and A subclass area distributions by volume as a function of boiling point for the USA Bryan Mound Sweet crude oil assay 14

16 Figure 19. Model-predicted P, N, and A subclass area distributions by volume as a function of carbon number for the USA Bryan Mound Sweet crude oil assay Benefits The molecule-based crude oil assay characterization methodology is practical, comprehensive, and easy-to-use. It requires only readily available crude oil assay data and it represents a superior alternative to the traditional statisticsbased methodology and other empirical expressions used to interpolate between assay data points or extrapolate for heavy cuts. It offers a self-consistent molecular basis to correlate and predict wide ranges of assay properties, robustly and consistently. Such self-consistent molecular basis makes it possible to perform consistency, check on available assay data, and identify questionable or erroneous assay data points. Most importantly, the molecule-based crude oil assay characterization methodology extracts from crude oil assay data the molecular insight critical to business decisions in all petroleum refining operations. 15

17 References 2 M. Unavane, Statistical Tools for Managing and Manipulating Crude Oil Data, Exploration & Production, 2010, 8, M. Watt, S. Roussis, Crude Assay, Practical Advances in Petroleum Processing, Chapter 3, (2006) 4 Oil & Gas Journal Databook, 2006 Edition, PennWell Corporation, Tulsa, Oklahoma, Aspen Technology, Inc., Aspen HYSYS, V7.3, Burlington, MA. March S. Sánchez, J. Ancheyta, W.C. McCaffrey, Comparison of Probability Distribution Functions for Fitting Distillation Curves of Petroleum, Energy & Fuels, 2007, 21, J.M. Brown, Method for Analyzing An Unknown Material as A Blend of Known Materials Calculated So As to Match Certain Analytical Data and Predicting Properties of the Unknown Based on the Calculated Blend, US Patent 6,662,116 B2, December 9, M.M. Saine Aye, N. Zhang, A Novel Methodology in Transforming Bulk Properties of Refining Streams into Molecular Information, Chem. Eng. Sci. 2005, 60, J.G. Speight, The Chemistry and Technology of Petroleum, 4th Edition, CRC Press, Taylor & Francis Group, Boca Raton, Florida, S.P. Pyl, Z. Hou, K.M. Van Geem, M.F. Reyniers, G.B. Marin, M.T. Klein, Modeling the Composition of Crude Oil Fractions Using Constrained Homologous Series, Ind. Eng. Chem. Res. 2011, 50, J. Gross, G. Sadowski, Perturbed-Chain SAFT: An Equation-of-State Based on a Perturbation Theory for Chain Molecules, Ind. Eng. Chem. Res. 2001, 40, J. Gross, O. Spuhl, F. Tumakaka, G. Sadowski, Modeling Copolymer Systems Using the Perturbed-Chain SAFT Equation of State, Ind. Eng. Chem. Res. 2003, 42, accessed on April 17,

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