Fit for Purpose Compositional Input for Allocation Using Equations of State Thomas Hurstell, Letton Hall Group

Transcription

1 UPM Fit for Purpose al Input for Allocation Using Equations of State Thomas Hurstell, Letton Hall Group Abstract Methods are presented to develop compositional input for use in allocation systems using equation of state simulation models. These methods address fluid characterization and compositional conditioning in a manner that accounts for the limitations and requirements posed by allocation processes and are intended for the purpose of allocation specifically. The fluid characterization method outlines a process that uses the laboratory analyses typically available and used in allocation processes in conjunction with the component libraries included with commercial simulators to develop a component set that generates results that meet the allocation system needs. This simplified method was compared to a more rigorous method to give a sense of the degree of difference between results of the two methods. The results of that evaluation are presented. By applying some basic thermodynamic principles to the component set, fluid characterization is simplified into an automated process. An example of that process is presented here. It is shown that, by shifting simulation model boundaries, physical properties and compositional data normally provided through laboratory analysis can be generated using the simulator. A method using the same thermodynamic principles is described to condition compositional input to represent the production streams at allocation period conditions. The application of these methods will generate compositional input fit for allocation purposes. Introduction In allocation systems containing dissimilar fluids, a process simulation model (PSM) using an equation of state (EOS) may be used to determine the liquid/vapor volumetric properties of the production streams as they are comingled and processed. These liquid/vapor volumetric properties, referred to as phase behavior, are a function of process temperatures and pressures, fluid volumes, and fluid characterization. While fluid volumes, temperatures, and pressures are output from measurement devices, fluid characterization involves many factors and data sources, including: basic and extended compositional analyses; empirical fluid and process data; correlations; assumptions; and the PSM. Fluid characterization is the most complex and critical element in the effective use of an EOS in a hydrocarbon allocation system. Challenges include limited data due to constant change in production rates and conditions and the required quick turnaround from receipt of raw data to final allocation. With the PSM, fluid characterization is a function of the compositional input. al input includes a component set, component mole fractions, pseudo- components (PsC) physical properties, gas/oil ratios, sampling conditions, and allocation conditions. There is an abundance of literature available for guidance in fluid characterization and phase behavior modeling of reservoir fluids. In general, this guidance is in the form of a rigorous approach to reservoir fluid modeling over the entire temperature/pressure spectrum from reservoir to atmospheric conditions. It usually involves the characterization of a single reservoir fluid for modeling. In an allocation process, multiple fluid streams must be characterized for simultaneous evaluation in a PSM through a temperature and pressure regime which is but a small segment of the total reservoir spectrum. To apply the rigor involved in reservoir fluid characterization to a multiple stream allocation system within the inherent data and time constraints may be overwhelming and unnecessary. The purpose of this paper is to present a fluid characterization methodology that will produce PSM compositional input which is fit for the purpose of allocation. The methodology addresses the particular needs of the allocation process, data and time constraints, and the need for accurate results. Basic Assumptions The measured hydrocarbon production streams used as initial volume inputs to the model are unstable single phase fluids and the product of a two- phase, gas/liquid separation process. At the end of the separation process, there is no net exchange of mass or energy between the two phases. The resulting liquid and gas streams are in equilibrium. This assumption is applied in compositional validation and when deriving gas compositions from liquid compositions. Fig. 1 illustrates this basic concept. Copyright 2015, Letton Hall Group. This paper was developed for the UPM Forum, February 2015, Houston, Texas, U.S.A., and is subject to correction by the author(s). The contents of the paper may not necessarily reflect the views of the UPM Forum sponsors or administrator. Reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Letton Hall Group is prohibited. Non-commercial reproduction or distribution may be permitted, provided conspicuous acknowledgment of the UPM Forum and the author(s) is made. For more information, see

2 MEASURED GAS VOLUME GAS COMPOSITION S V SEPARATOR PRESSURE and TEMPERATURE P T EQUILIBRIUM SATURATED GAS (DEWPOINT) TWO PHASE SEPARATION SATURATED LIQUID (BUBBLEPOINT) UNSTABLE LIQUID LIQUID COMPOSITION S MEASURED LIQUID VOLUME Fig 1- Basic Assumptions and terminology V Because the two phases are in equilibrium, the individual volumes are saturated. The measured liquid volume is at bubble point pressure at separation temperature. For a given composition there is only one pressure for that temperature (See Fig. 2). The measured gas volume is at dew point temperature at separation pressure. For a given composition there is only one temperature for that pressure (See Fig. 3). This assumption is used in compositional validation and model tuning. Laboratory Analyses Laboratory analysis of production fluids are the base resource used in the development of the compositional input. The analyses typical contain mole and mass percent for a basic list of components Nitrogen, CO2, Methane (C1) through Heptanes- plus (C7+) with some measured physical properties and factors such as shrinkage and flash. Table 1 contains a list of components include in a C7+ analysis. There is a typical extended analysis which includes a breakout of the C7 through C9 components into paraffins, naphthenes, and aromatics (PNA) and a C10+ component (Table 2). For liquids, an analysis could extend out to C30 or greater with groups of components reported as single carbon number (SCN) components, C11, C12, C13 C30+. Other data will include density, and Molecular Weight (MW) of the plus component and the full sample. Samples used in complex allocation systems are collected as often as once an allocation period. This frequency is required to capture the change in separator fluids caused by changing well rates and alignments. The frequency could be less when there is little change in well production or alignment from one allocation period to the next. The sample analyses bear little resemblance to the full PVT reports used in reservoir simulation. Pressure Fig. 2- Typical bubble point curve Pressure Bubble Point Separa\on Fig. 3- Typical dew point curve Separa\on Pressure Dew Point Curve Dew Point Upstream Production Measurement Forum

3 Nitrogen Methane Carbon Dioxide Ethane Propane Isobutane n- Butane Isopentane n- Pentane Hexanes Heptanes Plus Table 1- Component set from C7+ analysis. Isopentane n- Pentane Neohexane 2- Methylpentane 3- Methylpentane n- Hexane Methylcyclopentane Benzene Cyclohexane 2- Methylhexane 3- Methylhexane Dimethylcyclopentanes n- Heptane Methylcyclohexane Trimethylcyclopentanes Toluene 2- Methylheptane 3- Methylheptane Dimethylcyclohexanes n- Octane C8 Aromatics C9 Naphthenes C9 Paraffins n- Nonane Decanes Plus Table 2- Extended analysis component set Typical Component Set Development A typical component set used in phase behavior modeling includes the non- hydrocarbon components Nitrogen and CO2, light end components methane (C1) through hexane (C6), and a group of two to six pseudo- components (PsC) representing the heavier components usually heptanes- plus (C7+). The PsC are developed following methods similar to the ones described by Whitson (1980) 1. These methods characterize mole fraction/molecular weight relationship and physical properties of the C+ fraction with a minimal amount of information for the C+ fraction. A typical component set used in phase behavior modeling is found in Table 3. The number of PsC is a function of the first and last carbon number of the SCN making up the C+ fraction. The distribution of the SCN into PsC is a function of the number of PsC and the lowest and highest SCN MW. Critical properties are a function of molar weighting or boiling points. Mole fractions, MW, and density for each SCN making up a PsC is either provided by a laboratory are estimated using correlations. Each contributing production stream has a unique set of PsC. Because of comingling, flashing, condensing, and recycling, the component set for the PSM must include each set of PsC. The creation, validation, and tuning of this component set may not be manageable. Gas compositional outputs will contain one or two PsC from each liquid stream. Many allocation processes use output gas compositional data to determine gas energy content. For this case, a heating value for each PsC in the gas would have to be established. Nitrogen CO2 Methane Ethane Propane i- Butane n- Butane i- Pentane n- Pentane Hexanes PsC1 (C7- C10) PsC2 (C11- C14) PsC3 (C15- C18) PsC4 (C19- C25) PsC5 (C26- C35) Table 3- Typical component set used in a phase behavior model Simplified Method Component Set Development To simplify fluid characterization for use in an allocation process, the characterization of C7 through C9 is done using multiple components. This approach takes advantage of the standard extended analysis provided by laboratories, coupled with the component libraries included with commercially available simulators. C10+ is grouped into a single PsC that will serve as the base for tuning parameters. Multiple PsC for individual streams are not used. With a basic C7+ and extended C10+ fluid analysis, a component set with all but one element common to all fluids is developed. One PsC unique to each stream is created, minimizing the total number of PsC required. Upstream Production Measurement Forum

4 The component set has the same basic components as those shown in Table 2 minus the multiple PsC. A PNA breakdown of the C7 through C9 components derived from the extended analysis is added. The PNA distribution in a SCN is not normally defined in a typical phase behavior model; however, it is used in this method to provide better definition of gas compositions. This is helpful in determining energy content of gas volumes. This PNA distribution is created with surrogate components from the library to represent each type of compound from each SCN. One PsC representing C10+ for each measured volume is created. This component list applies to both liquid and gas volumes. An example of what a component set would look like is shown in Table 4. Component / Component Group Nitrogen Carbon Dioxide Methane Ethane Propane Iso-butane n-butane Iso-pentane n-pentane C6 Paraffins C6 Naphthenes C6 Aromatics C7 Paraffins C7 Naphthenes C7 Aromatics C8 Paraffins C8 Naphthenes C8 Aromatics C9 Paraffins Decanes and heavier n = number of contributing liquid streams Surrogate Nitrogen Carbon Dioxide Methane Ethane Propane Iso-butane n-butane Iso-pentane n-pentane n-hexane Cyclohexane Benzene n-heptane Methylcyclohexane Toluene n-octane 1,1-Dimethylcyclohexane Ethyl benzene n-nonane PsC n (C10+) Table 4- Component set with surrogate components Commercial simulators contain extensive libraries of components complete with physical properties and interaction coefficients. The properties of the surrogates selected are assumed to be representative of the group of compounds it has been selected to represent. For PsC the commercial simulators will estimate the physical properties, including critical properties, acentricity, and interaction coefficients, given the MW and density of the component. Recognizing that it is widely accepted that this approach will produce less accurate results then the more rigorous methods described above, it is presented here as a means of improving results when little or no fluid characterization is performed or when the more rigorous methods are not feasible for a particular allocation process. Unless an allocation methodology already incorporates the more rigorous approach, there is no loss in accuracy by implementing this method. The difference in accuracy between the methods falls well within the uncertainties associated with lab analysis, measurement, water cut, and pressure/temperature instrumentation. Each allocation system should be checked for sensitivity to differences in fluid characterization methods. A basic comparison of a multiple PsC system with a single PsC system, given some minimal data, is presented here. For this exercise the available data was an extended analysis for a liquid under pressure and an extended analysis of the flash gas and residual liquid resulting from a flash to ambient conditions. Two component systems were established. One being a multiple PsC system built following the general methods outlined above and a second single PsC system assembled in accordance with the new methodology. The residual liquid and the flash gas were the total product of the full pressurized liquid. Because the gas to oil ratio was given, a mass balance was performed to validate the three analyses. In addition, an equilibrium check was performed. A flash simulation was performed for each of the component systems. The PsC were entered as calculated with no further tuning or manipulation of critical properties or interaction coefficients. The MW and density of the C10+ provided in the lab analysis was used for the single PsC system. Both systems were accurate at predicting flash gas (Table 5) and residual liquid (Table 6) compositions. The results for physical properties are found in Table 7. Ideally this type of evaluation would be performed for each type of fluid modeled, and at the expected allocation conditions. Another evaluation was performed comparing the performance of the systems at higher bubble points. Base liquids were created with bubble points in the 450- psig range and in the 1,500- psig range. The MW and density of the C10+ component in the single PsC system were adjusted such that the fluid density and bubble points would match in the two systems. The base liquids were than flashed to 70 F and 0 psig. Table 8 contains a comparison of the results. The single PsC was then re- tuned to match the density and MW of the multiple PsC fluid. The bubble point curves were plotted for comparison. (See Fig. 4). Upstream Production Measurement Forum

5 Multiple PsC Single PsC Component Lab Analysis Simulation Lab Analysis Simulation Component Nitrogen Nitrogen Carbon Dioxide Carbon Dioxide Methane Methane Ethane Ethane Propane Propane Isobutane i- Butane n- Butane n- Butane Isopentane i- Pentane n- Pentane n- Pentane n- Hexane C6 Paraffin PsC C6 Naphthene PsC Benzene PsC C PsC Toluene PsC C PsC E- Benzene C C10+* MW MW Heat Value, Btu/scf 2, , Heat Value, Btu/scf 2, , Table 5- Comparison of Flash Gas results for Multiple PsC and Single PsC systems Multiple PsC Single PsC Component Lab Analysis Simulation Lab Analysis Simulation Component Nitrogen Nitrogen Carbon Dioxide Carbon Dioxide Methane Methane Ethane Ethane Propane Propane Isobutane i- Butane n- Butane n- Butane Isopentane i- Pentane n- Pentane n- Pentane n- Hexane C6 Paraffin PsC C6 Naphthene PsC Benzene PsC C PsC Toluene PsC C PsC E- Benzene C C10+* MW MW Density, lb/ft Density, lb/ft Table 6- Comparison of Residual Liquid results for Multiple PsC and Single PsC systems Upstream Production Measurement Forum

6 Lab Analysis Multiple PsC Single PsC Base Liquid MW Density, lb/ft Bubble point, psig Shrink Factor Flash Factor Flash Gas MW Heat Value, Btu/scf 2, , ,417.3 Residual Liquid MW Density, lb/ft Table 7- Comparison of flash simulation results Multiple PsC Single PsC % Diff. Multiple PsC Single PsC % Diff Bubble point, psig , ,533.2 Density, lb/ft MW Shrinkage Factor % % Flash Factor % % Flash Gas MW % % Heat Value, Btu/scf 2, , % 1, , % Table 8- Comparison of multiple PsC and Single PsC flash calculation results for bubble points of 465 psig and 1533 psig Pressure (psig) ( F) Single PsC Tuned Density and Bubblepoint Mul\ple PsC Single PsC Tuned Density and MW Fig. 4- Bubble point curves for multiple and single PsC component sets. Upstream Production Measurement Forum

7 The differences in results from the two systems would likely meet an acceptance criteria for most allocation systems. If the results are not satisfactory, the fluid characterization method applied can be modified to meet a criteria. Tuning and target parameters are selectable, acceptance tolerances are adjustable, and the component set can be modified. This subject is further addressed later in this paper. Modifications to the component set can include additional PsC without adding complexity to the method if all but one PsC has fixed properties, using a single PsC as a base for tuning parameters in fluid characterization. Fluid Characterization Using the assumption that a measured liquid volume is at bubble point, the fluid sample gathered at this point is at bubble point at the sample temperature. The fluid must be at bubble point when used in EOS calculations to establish the fluid as single phase and to properly determine volumes. The density of the measured liquid volume is critical to the calculation of volume changes caused by flashing, pressure and temperature changes, and interaction with other streams. It is also easily quantified by a laboratory. Because both of these parameters are critical to the EOS calculations and easily quantified, they are deemed target parameters. Other fluid parameters will be adjusted to match the value of these parameters to their target values. Other parameters can serve as target parameters, either in conjunction with or in place of these parameters. Shrinkage, flash factors, and MW, are examples. In general, the selection criteria is the parameter s criticality to the process and the confidence level of the parameter s target value. Target parameters can also be used indirectly to establish boundaries for parameters not targeted. The total fluid density minus the standard density of the known components is not equal to the plus component standard density, but it is a good approximation. The relation of the PsC density to the total density is a function of their MW, mole fractions, and non- ideality of the fluid. The MW of the total fluid, if supplied, is also a good approximation and can be used to determine the initial value of the PsC MW. If the MW is not supplied, correlations based on empirical data can be used to estimate it. The MW and density of the single PsC become the tuning parameters. They are chosen because their values are less reliable than parameters of other components, initial values are easily determined, degree of change can be used to gauge validity, and they are used in the estimation of other parameters. Thse other parameters include PsC boiling point, critical properties, acentricity, and interaction coefficients. These parameters can also act as tuning parameters. With component mole fractions and the initial values of the single PsC MW and density, fluid characterization is initialized. If the mole fractions assigned to the components are valid, the PsC MW and density can be reasonably adjusted to match the target parameter values to their targeted values. Tuning In the basis environment of the simulator, the EOS is identified, the component list is created, and the initial values for PsC MW and density are entered. The simulator will fully define the PsC from the data entered. In the simulation environment, the component mole fractions and sample temperature are entered into a material stream. The vapor fraction of the material stream is set to zero, indicating that the fluid is at bubble point. A mass or liquid volume is entered to start the calculations. This value is not material to the process. The simulator will calculate fluid properties including the total fluid density and bubble point pressure, both a function of PsC MW and density. These values are retrieved from the simulator and compared with the target values. Error values are calculated and compared with a preset adjustable tolerance. If the error values fall within the tolerance, no further calculations are performed and the fluid composition and PsC definition is deemed ready for the allocation process. If the calculated values do not meet the tolerance threshold, new PsC MW and density values are generated. The new values are entered into the simulator for another calculation iteration. The process is repeated until the targets are reached or some preset number of iterations has occurred. Table 9 contains an example of the iterative process used to in fluid characterization. It shows a fluid which starts with values of 270 and 55 psig for the PsC MW and density respectively. The initial calculation resulted in a density of psig for the fluid density with a target of psig and a bubble point of 293 with a target of 300. Neither value met the target criteria. The error in density was , well outside the tolerance of ± The bubble point had a error with a tolerance of ± New values were calculated for both using the degree of error to adjust the current values of the PsC MW and density. This process was repeated seven times until the error for both fell within tolerance. Derived Gas s In a typical allocation process, fluid samples are gathered for both the liquid and gas measured volumes. The samples are analyzed and in the case of allocations using EOS, the resulting compositions are entered into the PSM. A common sample validation here is an equilibrium check between the liquid and gas compositions. The PSM simulation starts at the outlets of the initial separation process and do not include the initial separation in the simulation. With this configuration, the measured and analytical inputs to the PSM are the liquid volume, Upstream Production Measurement Forum

8 Iteration PsC Input Fluid Density (lb/ft 3 ) Bubble point (psig) New PsC MW Density Calculated Target Error Calculated Target Error MW Density Initial (0.0168) (0.0232) (0.0070) (0.0006) (0.0015) (0.0012) (0.0002) (0.0008) Density Tolerance.0010 Bubble point Tolerance.0010 Table 9- Iterative process for tuning fluid characterization Variable Separation Not Modeled Separation Modeled Liquid Volume Measured Input Measured Input Liquid Analytical Input Analytical Input Liquid Measured Input Measured Input Liquid Pressure Measured Input PSM Calculated, liquid at bubblepoint Gas Volume Measured Input Measured Input Gas Analytical Input PSM Calculated, liquid/gas equilibrium Gas Measured Input PSM Calculated, liquid/gas equilibrium Gas Pressure Measured Input PSM Calculated, liquid/gas equilibrium Table 10- PSM inputs and their sources composition, pressure, and temperature along with the gas volume, composition, pressure, and temperature. There is no mathematical relationship established between the two fluids. These inputs are tabulated in Table 10. If the initial separation process is included in the PSM, the measured and analytical inputs are reduced to liquid volume, composition, temperature, and gas volume (See Fig. 5). The rest of the data is calculated by the PSM. Once a liquid composition has been defined, a gas composition is calculated. Because of equilibrium, there is one gas composition for that liquid composition at temperature and pressure. It is not necessary to specify the gas composition in the PSM. Liquid and gas composition, pressure, and temperature, can be calculated when the liquid composition and temperature are known. Additional data is simply an over- specification of the mathematical problem. The non- ideal nature of the separation process will produce only a minimal impact to the actual compositions. This impact can be addressed as part of an overall validation process for the PSM. Functional problems with the separator will create fluid characterization problems whether compositions are determined by sampling or PSM. Throughout the PSM, vapor/liquid equilibrium at separation points is ideal. Enabling the PSM to calculate compositions for the measured gas streams is consistent with the base simulation process. DERIVED GAS COMPOSITION MEASURED SEPARATOR TEMPERATURE CALCULATED SEPARATOR PRESSURE Fig. 5- PSM inputs P LIQUID COMPOSITION FROM ANALYSIS MEASURED GAS VOLUME T S S MEASURED LIQUID VOLUME V V Upstream Production Measurement Forum

9 Characterized Liquid Equilibrium Calculation Gas Pressure Combine in sample ratio Full Stream Separate at allocation conditions Allocation Gas Liquid Fig. 6- al input development flow diagram Sample Gas/Oil Ratio Allocation Pressure Allocation Liquid Reconditioned s Until now, the focus has been on characterizing fluids to meet physical properties found under sampled conditions. Before a composition is ready to be applied to a measured volume, it must be made representative of the volume at the conditions (pressure and temperature) in which the volume was measured. There are two types of sampling procedures, spot sampling and composite sampling. Spot samples gather fluid composition data at conditions of the time of sampling. When conditions vary over an allocation period, the composition data gathered with the spot sample may not be representative of the measured volume for the period. Composite samples gather flow weighted composition data over a period of time. The samples are flow weighted to address the variations in fluid composition resulting from changes in well rates and alignments and changes to separation conditions. Volume measurement is gathered for a fixed period which may not directly overlap the sampling period. Because of time constraints, composition data available for the allocation may not directly represent the allocation volumes. In order to properly define the composition data for the measured volume at allocation conditions, the liquid and gas volumes are combined at sampled conditions to form a single volume representative of the full inlet stream to the separation process. This volume is then separated at allocation conditions with the result being the fit for purpose allocation composition data to be applied to the measured volumes. See Fig. 6. There are limitations to this process. If significant changes occur in well alignment or production, the reconditioned sample may no longer be representative. Conclusions The use of EOS in allocation systems drives a need for fluid characterization methods which are practical for everyday use in allocation systems. Practical fluid conditioning methods are also required to prepare the characterized fluids for entry into the simulation model. 1. A fluid characterization method is presented that simplifies the generation of an input component set using standard laboratory analyses in conjunction with the software component libraries and pseudo- component capabilities. This approach compares well to a more rigous method over a limited range of pressures and temperatures. 2. The characterization is automated using an iterative process adjusting tuning parameters until target fluid parameters are met. This automation simplifies the fluid characterization process and facilitates repeatability and auditablility. An example of that process is given. 3. It is shown that physical properties and compositional data normally provided through laboratory analysis can be generated using the simulation model by shifting the model boundaries. 4. Before compositional input is applied in a simulation, it must be conditioned to represent the production streams at allocation period conditions. A method to condition compositional input is presented. References 1. Whitson, Curtis H Characterizing Hydrocarbon Plus Fractions. Paper (SPE 12233) European offshore Petroleum Conference and Exhibition, London, Oct d Upstream Production Measurement Forum