Modeling the Properties of Crude Oil and Gas Systems Using RBF Network

Elsharkawy, Adel M.

doi:10.2118/49961-ms

Cited by 47 publications

(9 citation statements)

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“…Elsharkawy 15 presented a new technique to model the behavior of crude oil and natural gas systems using a radial basis function neural network model (RBFNM). The model can predict B ob , solution gas-oil ratio, R s , oil viscosity, saturated oil density, undersaturated oil compressibility, and evolved gas gravity.…”

Section: Oil Pvt Neural Network Modelsmentioning

confidence: 99%

Artificial Neural Networks Models for Predicting PVT Properties of Oil Field Brines

Osman

Al-Marhoun

2005

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractKnowledge of chemical and physical properties of formation water is very important in various reservoir engineering computations especially in water flooding and production. 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 brine PVT properties. These correlations offer a handy and an acceptable approximation of formation water 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 oil PVT correlations. The developed models outperformed the existing correlations. However, there is no similar research done so far to utilize the power of ANN in developing similar models for formation waters. In the present study, two new models were developed to predict different brine properties. The first model predicts brine density, formation volume factor (FVF), and isothermal compressibility as a function of pressure, temperature and salinity. The second model is developed to predict brine viscosity as a function of temperature and salinity only. An attempt was made to develop a comprehensive model to predict all properties in terms of pressure, temperature and salinity. The results were satisfactory for all other properties except for viscosity. This was attributed to the fact that viscosity depends only on temperature and salinity. The models were developed using 1040 published data sets. These data were divided into three groups: training, cross-validation and testing. Radial Basis Functions (RBF) and Multi-layer Preceptor (MLP) neural networks were utilized in this study. 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, correlation coefficient and standard deviation.

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Section: Oil Pvt Neural Network Modelsmentioning

confidence: 99%

Artificial Neural Networks Models for Predicting PVT Properties of Oil Field Brines

Osman

Al-Marhoun

2005

All Days

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“…Elsharkawy 33 presented a new technique to model the behavior of crude oil and natural gas systems using a radial basis function neural network model (RBFNM). The model can predict B ob , solution gas-oil ratio, R s , oil viscosity, saturated oil density, undersaturated oil compressibility, and evolved gas gravity.…”

Section: Pvt Neural Network Modelsmentioning

confidence: 99%

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

Al-Marhoun

Osman

2002

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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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“…Elsharkawy 35 presented a new technique to model the behavior of crude oil and natural gas systems using a radial basis function neural network model (RBFNM). The model can predict oil formation volume factor, solution gas-oil ratio, oil viscosity, saturated oil density, undersaturated oil compressibility, and evolved gas gravity.…”

Section: Neural Network Modelsmentioning

confidence: 99%

Prediction of Oil PVT Properties Using Neural Networks

2001

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractReservoir fluid properties are very important in reservoir engineering computations such as 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. All computations, therefore, will depend on the accuracy of the correlations used for predicting the fluid properties.This study presents Artificial Neural Networks (ANN) model for predicting the formation volume factor at the bubble point pressure. The model is developed using 803 published data from the Middle East, Malaysia, Colombia, and Gulf of Mexico fields. One-half of the data was used to train the ANN models, one quarter to cross-validate the relationships established during the training process and the remaining one quarter to test the models to evaluate their accuracy and trend stability. The results show that the developed model provides better predictions and higher accuracy than the published empirical correlations. The present model provides predictions of the formation volume factor at the bubble point pressure with an absolute average percent error of 1.789%, a standard deviation of 2.2053% and correlation coefficient of 0.988. Trend tests were performed to check the behavior of the predicted values of B ob for any change in reservoir temperature, Gas Oil Ratio (GOR), gas gravity and oil gravity. The trends were found to obey the physical laws.

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Modeling the Properties of Crude Oil and Gas Systems Using RBF Network

Cited by 47 publications

References 0 publications

Artificial Neural Networks Models for Predicting PVT Properties of Oil Field Brines

Artificial Neural Networks Models for Predicting PVT Properties of Oil Field Brines

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

Prediction of Oil PVT Properties Using Neural Networks

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