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This paper evaluates several empirical correlations for estimating the bubblepoint oil formation volume factor (FVF) for worldwide application. A total of 674 experimentally obtained pressure-volume-temperature (PVT) data gathered from different published sources is used for analysis of correlated parameters of physical properties and for comparison of the accuracy of correlations. A literature survey of empirical correlations for predicting bubblepoint oil FVF is provided along with their limit of applicability. The statistical and graphical results for the data used in this study show that some of the correlations were violating the physical behavior of bubblepoint oil FVF as a function of the gas relative density. Also, all the available correlations do not adequately represent the contribution of solution gas-oil ratio and temperature at their higher values.

Content uploaded by Saud M. Al-Fattah

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All content in this area was uploaded by Saud M. Al-Fattah on May 16, 2018

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... Al-Marhoun [27] published another improved correlation for prediction of OFVF using experimental PVT data from 700 bottom hole samples mostly from Middle East and North America. Later, in 1994, Al-Fattah and Al-Marhoun [28] evaluated several empirical correlations for prediction of OFVF. They used 647 experimentally obtained PVT data from published open literature. ...

... One of the most important conclusions which can be drawn from this study is that those correlations derived from wide range of parameters and numerous data sets from variety of geological regions, show smaller error and can be applied for prediction of bubble point pressure. It is valuable to note here that normalization of input parameters can strongly improve the performance of Fig. 2. Cross plots of (a) Frashad et al. [88], (b) Kartoatmodjo-Schmidt [28], (c) Al-Shammasi [54], and (d) proposed model for OFVF at P b . a model. ...

... Al-Marhoun [27] published another improved correlation for prediction of OFVF using experimental PVT data from 700 bottom hole samples mostly from Middle East and North America. Later, in 1994, Al-Fattah and Al-Marhoun [28] evaluated several empirical correlations for prediction of OFVF. They used 647 experimentally obtained PVT data from published open literature. ...

... One of the most important conclusions which can be drawn from this study is that those correlations derived from wide range of parameters and numerous data sets from variety of geological regions, show smaller error and can be applied for prediction of bubble point pressure. It is valuable to note here that normalization of input parameters can strongly improve the performance of Fig. 2. Cross plots of (a) Frashad et al. [88], (b) Kartoatmodjo-Schmidt [28], (c) Al-Shammasi [54], and (d) proposed model for OFVF at P b . a model. ...

Accurate prediction of the PVT properties of reservoir oil is of primary importance for improved oilfield development strategies. Experimental determination of these properties is expensive and time-consuming. Therefore, new empirical models for universal reservoir oils have been developed as a function of commonly available field data. In this communication, more than 750 experimental data series were gathered from different geographical locations worldwide. Successive linear programming and generalized reduced gradient algorithm as two constrained multivariable search methods were incorporated for modeling and expediting the process of achieving a good feasible solution. Moreover, branch-and-bound method has been utilized to overcome the problem of stalling to local optimal points. In-depth comparative studies have been carried out between the developed models and other published correlations. Finally, a group error analysis was performed to study the behavior of the proposed models as well as existing correlations at different ranges of independent variables. It is shown that the developed models are accurate, reliable and superior to all other published correlations.

... There are many empirical correlations for predicting different PVT properties, and they were developed using linear or nonlinear multiple regression or graphical techniques. The following authors presented several correlations and evaluation studies for the empirical correlations: Katz (1942), Standing (1947Standing ( , 1977, Lasater (1958), Chew and Connally (1959), Beggs and Robinson (1975), Vazquez and Beggs (1980), Glaso (1980), Saleh et al. (1987), Khan et al. (1987), Abdul-Majeed and Salman (1988), Al-Marhoun (1988, 1992, Labedi (1990), Sutton and Farshad (1990), McCain (1991), Dokla and Osman (1992), Macary and El-Batanoney (1993), Omar and Todd (1993), Petrosky andFarshad (1993), De Ghetto et al. (1994), Elsharkawy et al. (1994), Al-Fattah and Al-Marhoun (1994), Mahmood and Al-Marhoun (1996), Al-Shammasi (1997), Hanafy et al. (1997), Almehaideb (1997), and Dindoruk and Christman (2004). ...

Reservoir fluid properties at bubble points play a vital role in reservoir and production engineering computations. Ideally, the bubble point physical properties of crude oils are obtained experimentally. On some occasions, these properties are neither available nor reliable; then, empirically derived correlations or artificial neural network models are used to predict the properties.
This study presents a new single multi-input multi-output artificial neural network model for predicting the six bubble point physical properties of crude oils, namely, oil pressure, oil formation volume factor, isobaric thermal expansion of oil, isothermal compressibility of oil, oil density, and oil viscosity. A large database comprising conventional PVT laboratory reports was collected from major producing reservoirs in the Middle East. The model input is constrained mathematically to be consistent with the limiting values of the physical properties. The new model is represented in mathematical format to be easily used as empirical correlations.
The new neural network model is compared with popular fluid property correlations. The results show that the developed model outperforms the fluid property correlations in terms of the average absolute percent relative error.

... Al-Fattah and Al-Marhoun [43] evaluated several empirical correlations for OFVF. They worked with 647 experimentally gathered PVT data from published literature. ...

Pressure-volume-temperature (PVT) properties of crude is necessary for reservoir engineering calculations and pipe line flow calculations. In order to improve oil field development strategies, an accurate prediction of PVT properties is one of the most important tasks. For several hydrocarbon systems, a large number of PVT correlations have been eshtablished. In most cases, overall accuracy of these correlations are often limited due to compositional variation, impurities, etc. PVT properties of crude oil can also be determined through experimental analyses. However, it is time-consuming and expensive. In this paper the classifying criteria of PVT properties of heavy oil is based on region. This paper reviews the existing PVT models around the world as per their region, addressing the shortcomings of these models, and explores new ways that can be added in this regards. And It will also show better way in future to get PVT properties more accurately with less error.

... He recommended Standing's (1947) correlations for formation volume factor at and below the bubble point pressure. Al-Fattah and Al-Marhoun (1994) published an evaluation of all available oil formation volume factor correlations. They used 674 data sets from published literature. ...

Reservoir fluid properties such as bubble point pressure, oil formation volume factor and viscosity are very important in reservoir and petroleum production engineering computations such as outflow–inflow well performance, material balance calculations, well test analysis, reserve estimates, and numerical reservoir simulations. Ideally, these properties should be obtained from actual measurements. Quite often, however, these measurements are either not available or very costly to obtain. In such cases, empirically derived correlations are used to predict the needed properties using the known properties such as temperature, specific gravity of oil and gas, and gas–oil ratio. Therefore, all computations depend on the accuracy of the correlations used for predicting the fluid properties. Almost all of these previous correlations were developed with linear or nonlinear multiple regression or graphical techniques. Artificial neural networks, once successfully trained, offer an alternative way to obtain reliable and more accurate results for the determination of crude oil PVT properties, because it can capture highly nonlinear behavior and relationship between the input and output data as compared to linear and nonlinear regression techniques. In this study, we present neural network-based models for the prediction of PVT properties of crude oils from Pakistan. The data on which the networks were trained and tested contain 166 data sets from 22 different crude oil samples and used in developing PVT models for Pakistan crude oils. The developed neural network models are able to predict the bubble point pressure, oil formation volume factor and viscosity as a function of the solution gas–oil ratio, gas specific gravity, oil specific gravity, and temperature. A detailed comparison between the results predicted by the neural network models and those predicted by other previously published correlations shows that the developed neural network models outperform most other existing correlations by giving significantly lower values of average absolute relative error for the bubble point, oil formation volume factor at bubble point, and gas-saturated oil viscosity.

... Several researchers performed studies in evaluating different empirical correlations (Ali, 1991;Al-Fattah and Al-Marhoun, 1994;Lide et al., 2007;Asadisaghandi and Tahmasebi, 2011);Lide et al. (2007) compared different correlations used for Venturi wet gas metering in oil and gas industry and showed that some correlations do not fit the experimental data very well. In the area of liquid desiccant dehumidification systems, literature reviews show a great drawback in evaluating the impact of different empirical correlations presented on the performance predictions of these systems. ...

Applying empiricalcorrelationstoestimatevariousparametersusedinamathematicalmodelis
inevitable. Inthisstudy,amathematicalmodelisdevelopedforapacked-bedairdehumidifier and
the impactofsomewell-knownempiricalcorrelationsavailableinliteratureisevaluatedonthemodel's
predictions andaccuracy.Theresultsrevealthatindesigninganairdehumidifier,usingdifferent
empirical correlationsmayleadtoverydifferentpredictionsfortherequiredbedheight.Theequationsof
Onda etal.(1968) and Rochaetal.(1996) to calculatetheeffectiveinterfacialarea,theequationof
Treybal(1981) to calculatetheheattransfercoefficient, andtheequationsof Chung etal.(1996) to
calculate themasstransfercoefficient showpreciseresultsandincreasethereliabilityofthe
mathematical models

... B ob is defined as the volume of reservoir oil that would be occupied at bubble point pressure and reservoir temperature by one stock tank barrel oil, plus any gas dissolved in the oil at this pressure and temperature. Its evaluation is an essential step in the reservoir performance calculations and the design of various stages of oil field operations (Al-Fattah and Al-Marhoun, 1994). ...

To determine the bubble point oil formation volume factor (Bob), which is one of the most important PVT properties, several correlations have been proposed for different regions. None of the correlations could be applied as a universal correlation due to regional changes in crude oil compositions and properties. In this paper, a new correlation is proposed to predict the Bob for Middle East crudes. Genetic Algorithm (GA) was used as the dominant tool for development of the new correlation. A total of 429 data sets of different crude oils from Middle East reservoirs were used. These Data include Bob and conventional PVT properties. Among those, 286 data sets as training data and 143 data sets as test data were randomly selected for constructing the correlation and for correlation validation, respectively. The measured mean squared errors (MSEs) of predicted Bob from the correlation in the test data were 0.0029 and the correlation coefficient (R2) between predicted values from the model and experimental values in the test data was 0.9646. These results show a very good agreement with experimental data and are more accurate for Middle East crudes than those of all existing empirical correlations.

Pressure-Volume-Temperature (PVT) crude oil properties play a significant role in reservoir evaluation and field planning. PVT properties are usually determined through laboratory experiments on representative fluid samples. However, during the preliminary stages of exploration and appraisal, such data might not be available; hence, it is frequent to use empirical correlations to predict PVT properties. Oil formation volume factor (FVF) is one of the PVT properties used to convert the measured oil flow rate from surface conditions to reservoir conditions. In this paper, the Group Method of Data Handling (GMDH) has been used to predict the oil FVF at the bubble point pressure as a function of gas solubility, reservoir temperature, oil API gravity, and gas specific gravity. A total of 625 data sets were collected from published literature. Then, the data were divided into four sets: training, validation, testing, and deployment, with the ratio of 2:1:1:1. The results of the proposed correlation are compared against seven other correlations used in the petroleum industry. Also, trend analysis has been performed to confirm that the proposed correlation is physically sound. From the results, the proposed correlation is proven to accurately predict the oil FVF at the bubble point pressure with an average absolute percentage error AAPE of 1.333% and correlation coefficient of 0.995 for the deployment set.

Applying empirical correlations to estimate various parameters used in a mathematical model is inevitable. In this study, a mathematical model is developed for a packed-bed air dehumidifier and the impact of some well-known empirical correlations available in literature is evaluated on the model's predictions and accuracy. The results reveal that in designing an air dehumidifier, using different empirical correlations may lead to very different predictions for the required bed height. The equations of Onda et al. (1968) and Rocha et al. (1996) to calculate the effective interfacial area, the equation of Treybal (1981) to calculate the heat transfer coefficient, and the equations of Chung et al. (1996) to calculate the mass transfer coefficient show precise results and increase the reliability of the mathematical models.

Pressure-volume-temperature (PVT) properties are very important in reservoir engineering computations. There are many approaches for predicting various PVT properties based on empirical correlations, statistical regression and artificial neural networks (ANNs). Unfortunately, the developed correlations are often limited and global correlations are usually less accurate compared to local correlations. In this paper, a genetic-neuro-fuzzy inference system (GANFIS) is proposed for crude oil PVT properties prediction. Simulation experiments show that the proposed technique outperforms up-to-date methods.

In **Correlation of PVT Properties for UAE Crudes **(SPE Formation Evaluation, March 1992, Pages 41-46), Dokla and Osman developed a correlation for bubble point pressure that used 51 data points. They recommended the use of this correlation for UAE crudes because it gave better approximation than the correlations of Al-Marhoun, Glassø and Standing. The sign of the temperature exponent is negative in Eq. 9. We thought that this might be a typing mistake because the bubble point pressure is expected to increase with increasing temperature.
The data reported in the paper were used to perform regression with an SAS (statistical analysis system) package. The regression constants obtained were the same as those reported in Eq.9. An analysis of the t-statistics obtained from SAS indicated that the oil gravity has a high probability of not being part of the model, as Table D-1 shows. The table also shows that the t-set value for oil gravity is 0.123 while the percentage of the t-distribution at a 95% confidence level is 2.013. Because |t| <tn-p,1 – a/2, the hypothesis the coefficient multiplying in 0 is zero should be accepted. Therefore, 0 was dropped from the model.

This paper presents new correlations for formation volume factors (FVF) at, above and below bubblepoint pressure for oil and gas mixtures as empirical functions of solution gas oil ratio, gas relative density, oil relative density pressure, and reservoir temperature. The correlations are developed from a total of 11 728 experimentally obtained FVF at above and below bubblepoint pressure on oil-gas mixtures collected from fields allover the world. The data encompassed a wide range of gas-oil ratios, oil and gas relative densities, pressure and reservoir temperature.
The newly developed correlations outperform the existing correlations for FVF of oil and gas mixtures based on high correlation coefficient and on low values of average per cent relative error, average absolute per cent relative error and standard deviation.
Introduction
The evaluation of FVF at, above and below bubblepoint pressure of oil-gas mixtures is an important tool in reservoir performance calculations and the design of various stages of oilfield operations. The following presents a review of the development of FVF correlations. The empirical equations that form these published correlations are provided in the Appendix.
Oil FVF at Bubblepoint Pressure
In 1947, Standing(1,2) proposed a correlation for determining the formation volume factor of a gas-saturated oil. A total of 105 experimentally determined data points on 22 different crude oil gas mixtures from California fields were utilized in arriving at the correlation.
In 1980, Vazquez and Beggs(3,4) presented relationships for determining the formation volume factor. A total of 6004 data points were obtained from fields all over the world.
In 1980, Glas Φ (5) presented a correlation for calculating the formation volume factor. A total of 41 data points, obtained mostly from the North Sea Region, were used in the correlation.
In 1988, Al-Marhoun(6) presented a correlation for calculating the formation volume factor. A total of 160 experimentally determined data points on 69 different crude oils from the Middle East were used in the correlation.
Oil FVF Above Bubblepoint Pressure
In 1947, Calhoun(7) presented a chart correlation for calculating the undersaturated oil compressibility. The following equation is proposed here instead of chart reading:
Equation 1 (available in full paper)
In 1980, Vazquez and Beggs(3,4) presented a correlation for determining the undersaturated oil compressibility. A total of 2000 data points obtained from fields all over the world were used in the correlation.
The undersaturated oil compressibility is utilized in the following equation to calculate oil FVF above bubblepoint pressure:
Equation 2 (available in full paper)
Total FVF Below Bubblepoint Pressure
In 1980, Glass Φ (5) presented a correlation for calculating the total formation volume factor below bubblepoint pressure, A total of 36 data points from 15 crude samples, obtained mostly from the North Sea Region, were used in the correlation.
In 1988, Al-Marhoun(6) presented a correlation for calculating the total formation volume factor. A total of 1556 experimentally determined data points on 69 different curde oils from the Middle East were used in the correlation.

Laboratory data on bubble point pressures and reservoir volume factors havebeen correlated as functions of solution gas-oil ratio, calculated gas gravityof the pentanes-and-lighter fraction of the entire fluid, differential residualoil gravity, and reservoir temperatures.
Introduction
Several correlations of crude oil properties have appeared in theliterature.
D. L. Katz in 1942 presented five methods of predicting oil shrinkage, thesebeing of decreasing accuracy for decreasing amounts of informationavailable.
M. B. Standing in 1947 published three correlations of laboratory flashvaporization data of California crudes. From values of GOR (gas-oil ratio), gasgravity, liquid gravity, and temperature, his correlations will predict bubblepoint pressure, formation volumes of bubble point liquids, and two-phaseformation volumes.
Curtis and Brinkley in 1949 presented several correlations. From the gas-oilratio, an approximation of reservoir volume factor and barrels of condensaterecoverable per barrel of reservoir space may be obtained; along with liquidgravity and reservoir temperature, the GOR will allow prediction of bubblepoint pressure. These last correlations seem to be more qualitative thanquantitative.
Generally, laboratory bottom hole sample tests furnish information on solutiongas-oil ratios, residual oil gravities, bubble point pressures, viscosities ofoils, liquid shrinkages, and occasionally gas gravities. Each of these data hasits own applications and use in reservoir engineering calculations. Theparticular uses of correlated bottom hole sample data are found inProviding a basis for obtaining estimates of formation crude propertiesin fields where bottom hole sampling is impractical or impossible.Greatly reducing the time in obtaining the desired information.Determining the applicability of the results from various bottom holesamples to particular field problems.Avoiding, in many cases, the uncertainties of sampling by replacing it withan element over which greater control can be exercised.Permitting use of preliminary field data in application of productionprocedures before a bottom hole sample can be obtained and analyzed in thelaboratory.Serving as a check on data which may appear out of line.Estimating for a particular type crude the appropriate equilibriumconstants by working backward from the bubble point pressure.Estimating original or other past history properties of reservoirs thatwere not sampled in the past.
T.P. 2931

Empirical equations for estimating saturation pressure, oil formation volume factor (FVF) at saturation pressure, and two-phase FVF were derived as a function of reservoir temperature, total surface-gas gravity, producing GOR, and stock-tank oil gravity. These equations should be producing GOR, and stock-tank oil gravity. These equations should be valid for all types of oil/gas mixtures after correcting for nonhydrocarbons in surface gases and paraffinicity of oil.
Introduction
Pressure-volume-temperature (PVT) correlations are Pressure-volume-temperature (PVT) correlations are important tools in reservoir technology. These measurements form the basis for estimating the amount of oil in the reservoir, production capacity, and variations in produced gas/oil ratios during the reservoir's production life. PVT relations also are a requirement for calculating the recovery efficiency of a reservoir. Especially during the prospection phase, when only produced fluid properties are available from flowing tests, one can resort to empirically derived PVT relations. It is, of course, of great importance PVT relations. It is, of course, of great importance that such estimations be as accurate as possible. From PVT correlations published until now, Standing's work is perhaps the most widely used. His correlations were developed for California oils and make no corrections for oil type or nonhydrocarbon content. Other PVT relations developed for oils from other parts of the world, based on Standing's work, yield "best-fit lines" ordinarily parallel-shifted. Their differences can be understood from two factors not included in Standing's original correlations: (1) crude oils from other regions have different paraffinicity - i.e., they contain varying amounts of paraffinic oil components (saturated hydrocarbons in open chains), and (2) the surface gases from some reservoirs contain relatively large amounts of nonhydrocarbons (CO2, N2, and H2S). By considering the variation in these parameters, this paper develops generalized PVT correlations:
(1)
(2)
(3) These equations were derived from laboratory data, exclusively sampling North Sea oils. However, they should be valid for all types of gas/oil mixtures after correcting for nonhydrocarbons (CO2, N2, and H2S) in the surface gases and paraffinicity of the oil as defined by the Kuop factor.
Procedure Procedure PVT Measurements PVT Measurements Six reservoirs fluid samples were made from two North Sea separator liquid and gas samples. These reservoir samples were labeled A1, B1, and C1 and A2, B2, and C2. Fig. 1 shows the experimental procedure used in the PVT analysis. procedure used in the PVT analysis. JPT
P. 785

This paper evaluates several empirical PVT correlations for application in the Gulf of Mexico. Ideally, fluid properties are determined experimentally in the laboratory; however, these data are not always available. Correlations are consequently used to determine values for bubblepoint pressure, solution GOR, FVF, and viscosity. These values are necessary to compute oil reserves or for calculations involving flow through pipes or porous media. A review of the correlations is provided, along with the results of calculations made on 31 individual crude oil samples.

PVT properties are important reservoir parameters. Correlations are used whenever experimentally derived PVT data are not available. No universal PVT correlation is available and data from local regions are expected to give better approximation to estimated PVT values.
Available PVT data from UAE reservoirs was used to obtain correlation equations for bubble point pressure and oil FVF as function of oil specific gravity, gas gravity, solution GOR, and reservoir temperature. Error bounds of the obtained correlations are calculated and compared to other correlations available in the literature. The correlations of this study result in lower errors and should work better for UAE crudes.