Multivariate Statistical Modeling of Crude Oil Viscosity and Mole Percent Components for Reservoir Fluids (original) (raw)
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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.
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...
Fluid Property Correlations for Malaysian Oils
Reservoir fluid property data are one of the fundamental input datasets for all reservoir engineering calculations; this can include calculations for estimating volumes (material balance) and interpreting data (WTA), to modeling dynamic reservoir performance (simulation). In addition to understanding the static and dynamic reservoir behavior, fluid properties are also essential for the design of surface and subsurface facilities. The reservoir fluid properties are most often obtained from laboratory experiments conducted on representative fluid samples. When laboratory PVT results are not available or the fluid sample’s validity is questionable, empirical correlations can be used to estimate the reservoir fluid properties. These fluid property correlations can also be used to quality check laboratory PVT results and for quick assessments requiring PVT input. Fluid property correlations have taken on two standards. The first is a generic world wide variety of correlation typically based on a widespread range of data, which often works well for certain types of oil. The second group of correlations are specific to a geographical region and/or oil type for which they were developed. None of the generic correlations were found to provide good estimates of the fluid properties for Malaysian oils. This paper describes the development of a new set of Malaysian specific fluid correlations, based on a dataset of 329 samples. Fluid property data and reservoir oil compositions were acquired from 111 offshore fields with a range in oil gravity from 18.9 oAPI to 55.5 oAPI and bubble point pressures ranging from 585 psia to 5408 psia. Three different methodologies were investigated for developing the fluid correlations. These included (1) non-linear regression of empirical models, (2) non-parametric regression and (3) artificial neural network modelling. This provided a thorough approach for establishing the best solution as well as comparatively highlighting the strengths and weaknesses of each technique. The accuracy of the correlations were examined through statistical techniques and tested on a separate validation dataset. The final results for each fluid property (Pb, Bob, co, ρob, µod, µob, and µoi) provided low absolute average relative errors and good correlation coefficients. In addition to establishing fluid property correlations for Malaysian oils, correlations were also developed for reservoir oil composition, which is new to industry. For each component (N2 through C7+) a non-parametric correlation was established based on primary independent parameters. The composition correlations were successfully tested for a number of non-database samples which gave results consistent with the laboratory data. The final correlation results for these samples (oil composition and PVT properties) were further used to characterize an EOS model. The model was tuned to synthetic PVT data based on the correlations. The tuned EOS model was then used to simulate laboratory tests which gave results that matched very well with the original PVT laboratory experiments. From very limited primary input data (ϒAPI, ϒg, Rsb, and Tf) a fully representative EOS model was able to be developed, demonstrating the strength of these newly developed correlations and the value of this process for initial fluid characterization.
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Four nonlinear regression techniques were explored to model gas oil viscosity on the base of Walther’s empirical equation. With the initial database of 41 primary and secondary vacuum gas oils, four models were developed with a comparable accuracy of viscosity calculation. The Akaike information criterion and Bayesian information criterion selected the least square relative errors (LSRE) model as the best one. The sensitivity analysis with respect to the given data also revealed that the LSRE model is the most stable one with the lowest values of standard deviations of derivatives. Verification of the gas oil viscosity prediction ability was carried out with another set of 43 gas oils showing remarkably better accuracy with the LSRE model. The LSRE was also found to predict better viscosity for the 43 test gas oils relative to the Aboul Seoud and Moharam model and the Kotzakoulakis and George.
MODIFICATION OF PRESSURE -VOLUME - TEMPERATURE CORRELATION FOR SUDANESE CRUDE OIL
2016
This research is to develop a correlation which can be applied by reservoir engineers for evaluating dead oil viscosity. Oil viscosity has been counted as one of the most fundamental properties of fluids in calculating the pressure difference of fluid flow through pipes in well or surface equipment or porous media inside the reservoir. It is also required in interpreting production prediction, analysis of hydrocarbon transport problems that may arise in the well life time. This paper introduces a tuning to Beggs and Robinson’s viscosity model and Beal’s viscosity model to accurately predict the viscosity of dead heavy oil of Sudanese fields. The tuning of the models stated for dead heavy oil has been developed using a database of PVT analysis of dead heavy oil to assess the accuracy of the models published in literature. Subsequently, by using statistical analysis and regression graph techniques “45 degree plot”, the models with the best approximation to the values of the PVT reports were tuned, thus resulting in two models with absolute average error rates of Beggs and Robinson’s viscosity model and Beal’s viscosity models 16% and 22% respectively, These rates are valid for oils with API gravities ranging from 31.4 to 34.37 and temperatures ranging from 120.3 to 249.3 in order to accurately predict/correlate the viscosity of under-saturated heavy oils. The modified correlations of Beal and Beggs & Robinson are test and validated using data from another field Y in Sudan in order to prove the performance of the modified/developed correlations in the fields of Sudan.