Correlation of Bubblepoint Pressures for Reservoir Oils--A Comparative Study

McCain, William D.; González, Rodolfo Soto; Valkó, Peter; Blasingame, Thomas Alwin

doi:10.2118/51086-ms

Cited by 24 publications

(6 citation statements)

References 10 publications

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“…This error is found to be within the error bound of 13% suggested by McCain et al 35 . The common thing between the present model and that of Al-Marhoun (1988) 10 correlation, is that they both were developed for Saudi Crude oils; this supports the idea of developing local/regional correlations for predicting PVT properties rather than universal ones which recommended by other researchers 23 .…”

Section: Bubble-point Pressure (Pb) Modelsupporting

confidence: 84%

“…In their comparative study, McCain et al 35 considered three independent means for developing P b correlations, namely, non-linear regression of a model, neural network models, and non-parametric regression. Using 728 global data sets; they found that the best possible correlations of P b are accurate to an average absolute error of about 13 percent.…”

Section: Pvt Neural Network Modelsmentioning

confidence: 99%

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Using Artificial Neural Networks to Develop New PVT Correlations for Saudi Crude Oils

Al-Marhoun

Osman

2002

All Days

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractReservoir fluid properties data are very important in reservoir engineering computations such as material balance calculations, well testing, reserve estimates, and numerical reservoir simulations. Ideally, those data should be obtained experimentally. On some occasions, these data are not either available or reliable; then, empirically derived correlations are used to predict PVT properties. However, the success of such correlations in prediction depends mainly on the range of data at which they were originally developed. These correlations were developed using linear, non-linear, multiple regression or graphical techniques.Recently, researchers utilized artificial neural networks (ANN) to develop more accurate PVT correlations. ANNs are biologically inspired non-algorithmic, non-digital, massively parallel distributive and adaptive information processing systems. They resemble the brain in acquiring knowledge through learning process, and storing knowledge in interneuron connection strengths.The present study presents new models developed to predict the bubble point pressure and, the formation volume factor at the bubble point pressure.The models were developed using 283 data sets collected from Saudi reservoirs. These data were divided into three groups: the first was used to train the ANN models, the second was used to crossvalidate the relationships established during the training process and, the last was used to test the models to evaluate their accuracy and trend stability. Trend tests were performed to ensure that the developed model would follow the physical laws. Results show that the developed models outperform the published correlations in terms of absolute average percent relative error, and standard deviation.

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Section: Bubble-point Pressure (Pb) Modelsupporting

confidence: 84%

Section: Pvt Neural Network Modelsmentioning

confidence: 99%

Using Artificial Neural Networks to Develop New PVT Correlations for Saudi Crude Oils

Al-Marhoun

Osman

2002

All Days

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“…However, the method that was used here is based on the Alternating Conditional Expectation (ACE) algorithm [26,27]. A comparison between Neural Network and ACE algorithm was used to build bubble point pressure correlation for oil reservoirs [28] and it was found that the predictive strength of ACE is much higher compared to Neural Network for the studied samples. The ACE algorithm is based on the concept of developing non-parametric transformations of the dependent and independent variables.…”

Section: Non-parametric Regression Analysismentioning

confidence: 99%

Inflow Performance Relationship Correlation for Solution Gas-Drive Reservoirs Using Non-Parametric Regression Technique

Daoud¹,

Salam²,

Hashem³

et al. 2017

TOPEJ

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Background:The Inflow Performance Relationship (IPR) describes the behavior of flow rate with flowing pressure, which is an important tool in understanding the well productivity. Different correlations to model this behavior can be classified into empirically-derived and analytically-derived correlations. The empirically-derived are those derived from field or simulation data. The analytically-derived are those derived from basic principle of mass balance that describes multiphase flow within the reservoir. The empirical correlations suffer from the limited ranges of data used in its generation and they are not function of reservoir rock and fluid data that vary per each reservoir. The analytical correlations suffer from the difficulty of obtaining their input data for its application. Objectives:In this work, the effects of wide range of rock and fluid properties on IPR for solution gas-drive reservoirs were studied using 3D radial single well simulation models to generate a general IPR correlation that functions of the highly sensitive rock and fluid data. Methodology:More than 500 combinations of rock and fluid properties were used to generate different IPRs. Non-linear regression was used to get one distinct parameter representing each IPR. Then a non-parametric regression was used to generate the general IPR correlation. The generated IPR correlation was tested on nine synthetic and three field cases. Results & Conclusion:The results showed the high application range of the proposed correlation compared to others that failed to predict the IPR. Moreover, the proposed correlation has an advantage that it is explicitly function of rock and fluid properties that vary per each reservoir.

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“…However, parametric regressions may yield erroneous and even misleading results when the relationship between the dependent and the independent variables is unknown (Jensen and Lake, 1985;Wendt et al, 1986;Xue et al, 1997). More recently, nonparametric methods based on successive refinements have gained wide popularity in a variety of disciplines ranging from engineering to air and soil pollution control (Steele, 1990;Gordon et al, 1993;Smith, 1994;McCain et al, 1998;Al-Ajmi and Holditch, 2001;Nashawi and Malallah, 2006;2009). The main advantage of these techniques is that they define the regression surface in an iterative fashion while remaining completely data driven as opposed to other model driven correlations.…”

Section: Nonparametric Optimal Transformationsmentioning

confidence: 99%

Prediction of Water Influx of Edge-Water Drive Reservoirs Using Nonparametric Optimal Transformations

2012

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The accurate estimation of water influx into a petroleum reservoir is very important in many reservoir engineering applications, such as material balance calculation, design of pressure maintenance programs, and advanced reservoir simulation studies. These applications have heavily relied on the classical work of van Everdingen and Hurst for finite and infinite edge-water drive reservoirs. However, for both types of water drive reservoirs, the calculation of water influx is not a straight forward task. Table lookup and interpolation between time entries are needed, and furthermore for finite aquifers, interpolation between tables may be also required.The paper presents nonparametric optimal transformations models for the prediction of dimensionless water-influx and dimensionless pressure drop for finite and infinite edge-water drive reservoirs using Graphical Alternating Conditional Expectation (GRACE). In order to achieve maximum efficiency, all the terms involved in the models are used in dimensionless form. GRACE transformations are totally data driven and do not assume any a priori functional form. The results of the various cases are in excellent agreement with the original tables of van Everdingen and Hurst. IntroductionPetroleum reservoirs are often surrounded from the edge or the bottom by water aquifers that support the reservoir pressure through water influx. In response to a pressure drop in the petroleum reservoir, the water aquifer reacts to offset, or retard, pressure decline by providing a source of water influx or encroachment. To determine the effect that an aquifer has on the oil and gas production, it is important to estimate the amount of water that has entered into the reservoir from the aquifer. Such calculation is not a simple and risk-free task due to the involvement of many unknown parameters. For instance, aquifer pressure, thickness, permeability, porosity, shape, and areal extent are usually all unknown variables. Furthermore, water aquifer models are classified according to the flow geometry as either edge-water or bottom-water drive (Figures 1 and 2). These models have completely different flow behavior. The type of the water aquifer, its size, properties, and the amount of water that it can deliver into the reservoir for a certain pressure drop during a specific period of time affect the entire production life of the reservoir. A good knowledge of the aquifer properties, specifically the amount of water that it can provide into the reservoir, dictates the production schedule and the development strategies that need to be implemented in order to optimize oil recovery. Many authors have presented different models for estimating the water influx. These models apply to different flow regimes, including steady-state, modified steady-state (Schilthius, 1963), pseudo-steady-state (Hurst, 19431958;Leung, 1986), and unsteady-state (Fetkovitch, 1971;Leung, 1988). van Everdingen and Hurst (1949) presented the most commonly used water-influx model. This model is basically a solution of the radi...

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Correlation of Bubblepoint Pressures for Reservoir Oils--A Comparative Study

Cited by 24 publications

References 10 publications

Using Artificial Neural Networks to Develop New PVT Correlations for Saudi Crude Oils

Using Artificial Neural Networks to Develop New PVT Correlations for Saudi Crude Oils

Inflow Performance Relationship Correlation for Solution Gas-Drive Reservoirs Using Non-Parametric Regression Technique

Prediction of Water Influx of Edge-Water Drive Reservoirs Using Nonparametric Optimal Transformations

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