K-value program for crude oil components at high pressures based on PVT laboratory data and genetic programming (original) (raw)
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Fuel, 2003
In this paper, 732 high-pressure K-values obtained from PVT analysis of 17 crude oil and gas samples from a number of petroleum reservoirs in the UAE are used. Material balance techniques are used to extract the K-values of crude oil and gas components from the constant volume depletion and differential liberation tests for the oil and gas samples, respectively. These K-values are then correlated and the resulting correlation compared with published correlations. Comparisons of results show that currently published correlations give poor estimates of K-values for non-hydrocarbon and hydrocarbon components, while the proposed new correlation improved significantly the average absolute deviation for non-hydrocarbon and hydrocarbon components. The average absolute error between experimental and predicted K-values for the new correlation was 20.5% compared with 76.1% for the Whitson and Torp correlation, 84.27% for the Wilson correlation, and 105.8 for the McWilliams correlation. Additionally, the bubble point and dew point pressures are calculated for these 17 samples and compared with experimental values. The average absolute error in the saturation pressures for the new correlation was 6.08% compared with 56.34% for the Wilson correlation, 57.84% for the Whitson and Torp correlation, and 9.28% for the Peng-Robinson equation of state with default parameters.
Journal of King Saud University - Engineering Sciences, 2016
In this paper, two correlations for oil formation volume factor (B o) for volatile oil reservoirs are developed using non-linear regression technique and genetic programming using commercial software. More than 1200 measured values obtained from PVT laboratory analyses of five representative volatile oil samples are selected under a wide range of reservoir conditions (temperature and pressure) and compositions. Matching of PVT experimental data with an equation of state (EOS) model using a commercial simulator (Eclipse Simulator), was achieved to generate the oil formation volume factor (B o). The obtained results of the B o as compared with the most common published correlations indicate that the new generated model has improved significantly the average absolute error for volatile oil fluids. The hit-rate (R 2) of the new non-linear regression correlation is 98.99% and the average absolute error (AAE) is 1.534% with standard deviation (SD) of 0.000372. Meanwhile, correlation generated by genetic programming gave R 2 of 99.96% and an AAE of 0.3252% with a SD of 0.00001584. The importance of the new correlation stems from the fact that it depends mainly on experimental field production data, besides having a wide range of applications especially when actual PVT laboratory data are scarce or incomplete.
Structural modeling of petroleum fractions based on mixture viscosity and Watson K factor
Two procedures have been developed for structural modeling of petroleum fractions based on mixture viscosity and Watson K factor. The representative molecules of paraffinic, naphthenic and aromatic hydrocarbons, based upon Ruzicka's structural model, have been determined for lube-oil cut SAE 10 from Tehran oil refinery. Unlike previous methods, the newly developed procedures do not require time-consuming and costly laboratory data such as true boiling point profile. Good agreement between predictions of the new models and experimental results has been observed. Moreover, the proposed methods take less run-time than previous models due to less experimental and computational complexities. The results indicate that Ruzicka's procedure, based on vapor pressure, is only applicable for light hydrocarbon mixtures, while the new methods can be applied for structural modeling of a wide range of petroleum fractions. Furthermore, as a result of this study, the application of a vapor pressure constraint leads to a higher degree of accuracy than the earlier suggested constraint, partial pressure, by Ruzicka.
Rapid method for the determination of solution gas-oil ratios of petroleum reservoir fluids
Journal of Natural Gas Science and Engineering, 2015
Accurate determination of pressure-volume-temperature (PVT) properties of petroleum reservoirs is essential in material balance calculations, inflow performance, well-test analysis, reservoir simulation, etc. Ideally, those data should be obtained experimentally; however, experimental measurements require accurate and enough sampling, and are time consuming, expensive and tedious. Therefore, seeking for a simple, reliable and accurate model for prediction of PVT properties of petroleum systems is of a vital importance. In this communication, a large PVT data bank, covering a wide range of thermodynamic conditions was collected from variety of geographical locations around the world. Afterward, gene expression programming (GEP) was employed to develop a universal model for solution gas: oil ratio. The proposed model is a function of bubble point pressure, gas specific gravity, and oil API gravity, and has a very simple format with only one tuning parameter. The proposed model was compared to both explicit and implicit models available in literature for prediction of solution gas: oil ratio, using statistical and graphical error analyses. The results of this study indicate that the proposed model is more accurate, reliable and efficient compared to all other published correlations.
Genetic Programming (GP)-Based Model for the Viscosity of Pure and Hydrocarbon Gas Mixtures
Energy & Fuels, 2009
Accurate determination of the viscosity and phase behavior of pure hydrocarbon gases and hydrocarbon gas mixtures is essential for reliable reservoir characterization and simulation and, hence, for optimum usage and exploitation. The variety of possible hydrocarbon gas mixtures at different conditions of interest preclude obtaining the relevant data by experimental means alone; thus, the development of prediction methods is required. Many pure hydrocarbon gas viscosity correlations are available. However, a wide-ranging and accurate viscosity correlation of gas mixtures associated with heavier hydrocarbon components and impurity components, such as carbon dioxide, nitrogen, helium, and hydrogen sulphide, is not available. Therefore, this paper presents a new pure hydrocarbon gas and gas mixture viscosity model over a wide range of temperatures and pressures as a function of gas density, pseudo-reduced temperature, pseudo-reduced pressure, and the molecular weight of pure and hydrocarbon gas mixtures. The new model is designed to be simpler and eliminate the numerous computations involved in any equation of state (EOS) calculation. The developed new model yields a more accurate prediction of the pure gas and gas mixture viscosity compared to the commonly used correlations.
Gas–oil ratio correlation (R s ) for gas condensate using genetic programming
Journal of Petroleum Exploration and Production Technology, 2014
A new correlation for solution gas-oil ratio (R s) for gas condensate reservoir was developed in this paper by using genetic programming algorithm of a commercial software (Discipulus) program. Matching PVT experimental data with an equation of state model, a commercial simulator (Eclipse simulator) was used to calculate the solution gas-oil ratio (R s) values used in this study. More than 1,800 solution gas-oil ratio (R s) values obtained from the analysis of eight gas condensate fluid PVT laboratory reports, selected under a wide range of reservoir temperature and pressure, composition and condensate yield, were used. Comparisons of the results showed that currently published correlations of gas-oil ratio (R s) for gas condensate gave poor estimates of its value (the average absolute error for Standing correlation was 63.48 with a standard deviation (SD) equal to 0.724, the average absolute error for Glaso correlation was 61.19 % with a SD equal to 0.688, the average absolute error for Vasques and Beggs correlation was 52.22 % with a SD equal to 0.512, the average absolute error for Marhoun correlation was 56.34 % with a SD equal to 0.519 and the average absolute error for Fattah et al. correlation was 18.6 % with a SD equal to 0.049). The proposed new correlation improved extensively the average absolute error for gas condensate fluids. The average absolute error for the new correlation was 10.54 % with a SD equal to 0.035. Also, the hit-rate (R 2) of the new correlation was 0.9799 and the fitness variance was 0.012. The importance of the new correlation comes from depending only on readily available production data in the field and can have wide applications when representative PVT lab reports are not available.
Development of an Expert System for Reservoir Fluid PVT Properties Correlations
The accuracy of determination of the crude oil PVT properties is essential for solving many reservoir engineering, production engineering, reserve accurate estimates and surface production and operational problems. A large number of PVT correlations for oil exist in the petroleum literature and numerous studies are also present for with data favoring one correlation over the other. In the absence of PVT representative data from laboratory experiments, it is often difficult to choose which correlation to use to calculate different PVT properties. We approached this problem in two ways. First, we developed an expert system that checks the input parameters (e.g. reservoir parameters) against the valid ranges of input data for different correlations as cited by the author of each correlation, and then recommends which correlations to use for specific input parameters. Second, we tested all available PVT correlations for black oil on a database of selected 3500 data points of crudes to develop criteria on which correlations to use for each PVT property for any specific range of input data. These specific crudes were selected to allow testing of those guidelines on a wide range of reservoir input data for black oils. Our database included oils with oAPI ranging from 17 to 51, gas-oil-ratios of 8 to 7,800 scf/STB, formation volume factor at bubble point of 1.04 to 4.47 bbl/STB, bubble point pressures of 60 to 4,739 psia, and reservoir temperatures of 40 to 270 ◦F. The present work included 14 bubble points, 6 solution-gas-oil ratio, 14 formation volume factors, 13 oil compressibilities, 14 dead oil viscosities, 9 saturated oil viscosities, 10 under saturated oil viscosities ,12 under-saturated densities, 2 total formation volume factors and 2 saturated oil density correlations. An amazing match was concluded due to combining both the developed PVT-Calculator and PVT-Expert System, which made the conclusion more applicable to be tested to different PVT data points in all future applications. Based on this study, guidelines for selecting an appropriate correlation for PVT oil properties and specific guiding ranges are introduced for black oil PVT properties correlations .These guidelines are recommended in programming of PVT correlations regardless of their geographic origin.
EXTENDING THE RESERVOIR OILS C+ COMPOSITIONAL ANALYSIS AND ITS EFFECT ON PHASE BEHAVIOR MODELING
Master Thesis, 2016
Predicting the phase behavior of naturally occurring petroleum fluids, involves the application of EOS based models. When using compositional reservoir phase behavior simulation models, problems and uncertainty arise with the lumped C+ fraction. The most common practice to overcome such problems is the “breaking-down” or “splitting” of the plus fraction. Extended compositional analysis enhances the accuracy of EOS models predictions, especially for light petroleum systems, such as gas condensates and volatile oils. However obtaining experimentally defined extended compositional analyses is often costly, time-consuming and requires expensive and sophisticated laboratory instruments while, even by the state-of-the-art, a full description of the petroleum fluids constituents cannot be obtained. On this concept, several splitting correlations have been developed in order to extend the composition of petroleum fluids. Their philosophy is based on the observation that for most petroleum fluids an exponential relationship between mole fraction and carbon number-molecular weight, exists. This Thesis, revises three of the most popular splitting methods. Extended compositional experimental analyses to C20+ for five different oils of world-wide origin are available and accompanied with PVT test analyses, namely, Constant Composition Expansion and Differential Liberation tests. These extended compositions are lumped to C7+ and C12+ using simple material balance equations and then re-extended to C20+ by applying three of the most popular splitting techniques. The extended compositional experimental data is used to evaluate the performance of these applied splitting techniques. The most accurate extended C20+ compositions are used to model and predict the phase behavior of each fluid through the application of EOS based models, while laboratory data from PVT tests is used to tune the EOS models. The predictions of the phase behavior of the petroleum fluids by the EOS based models demonstrate the effects of extending the reservoir oils C+ compositional analysis to C20+, on phase behavior modeling. The results show that for the oils tested, Pedersen’s method is the most reliable. For most oils, a very good match of PVT properties can be obtained, while small differences were observed between the lumped C12+ and the extended C20+ EOS models. Useful remarks for each method are underlined and correlated with each type of fluid. SUBJECT AREA: Hydrocarbons phase behavior simulation KEYWORDS: crude oil, EOS, simulation, fluid characterization, splitting
Improved Models for the Estimation of PVT Properties of Crude Oils
2019
Reservoir fluid properties, such as oil formation volume factor and bubble point pressure, are vital parameters in many computations associated with petroleum engineering. These computations include hydrocarbon reserve estimation, and consequently, economic efficiency evaluation, flu id flow in porous media, and improved and enhanced oil recovery. Prior to the computations, the pressure -volume-temperature (PVT) properties of reservoir oil must be determined. PVT properties, in turn, are ascertained either by empirical methods, laboratory measurements, or via equations of state. The latter two methods, however, are expensive and time-consuming and require complex calculations. Therefore, it is necessary to develop an accurate and reliable model for the determination of petroleum fluid’s physical properties. In this paper, a soft-computing approach is employed to develop efficient models for the calculation of bubble point pressure and oil formation volume factor properties. In pursu...
Journal of Petroleum Science and Engineering, 2019
Detailed understanding of the behavior of crude oils and their interactions with reservoir formations and other in-situ fluids can help the engineers to make better decisions about the future of oil reservoirs. As an important property, interfacial tension (IFT) between crude oil and brine has great impacts on the oil production efficiency in different recovery stages due to its effects on the capillary number and residual oil saturation. In the present work, a new mathematical model has been developed to estimate IFT between crude oil and brine on the basis of a number of physical properties of crude oil (i.e., specific gravity, and total acid number) and the brine (i.e., pH, NaCl equivalent salinity), temperature, and pressure. Genetic programming (GP) methodology has been implemented on a data set including 560 experimental data to develop the IFT correlation. The correlation coefficient (R 2 = 0.9745), root mean square deviation (RMSD = 1.8606 mN/m), and average absolute relative deviation (AARD = 3.3932%) confirm the acceptable accuracy of the developed correlation for the prediction of IFT.