El-Sayed A. Osman

Prediction of Oil PVT Properties Using Neural Networks

Reservoir 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 Bob for any change in reservoir temperature, Gas Oil Ratio (GOR), gas gravity and oil gravity. The trends were found to obey the physical laws.

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

Knowledge 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. Introduction PVT properties of oil field brines are very important in several reservoir engineering computations. These properties include formation volume factor (FVF), isothermal compressibility, density and viscosity. These properties are used in material balance calculations, water flooding, enhanced oil recovery and numerical reservoir simulations. The compressibility of water is a component of the reservoir fluid effective compressibility which is used in material balance calculations. Water formation volume factor is used in both material balance calculations and en evaluating wateroil ratios. Density and viscosity are widely used in mobility ration determination in water flooding and in reservoir simulation. Ideally, brine PVT data should be obtained from laboratory studies on bottom-hole collected samples. However, in some instances, these data are either not available or reliable; then, empirically derived correlations are used to predict brine PVT properties. There are many empirical correlations for predicting different brine PVT properties, they were developed using linear or non-linear multiple regression or graphical techniques. 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. 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. The objective of this study is therefore, to develop new predictive models for brine density, formation volume factor (FVF), isothermal compressibility and viscosity based on Artificial Neural Networks (ANN). 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. Also, developing a comprehensive model to predict all properties in terms of pressure, temperature and salinity was attempted. However, except for viscosity, results were satisfactory for all other properties. This is because viscosity depends only on temperature and salinity.

ARTIFICIAL NEURAL NETWORK MODEL FOR ACCURATE PREDICTION OF PRESSURE DROP IN HORIZONTAL AND NEAR-HORIZONTAL-MULTIPHASE FLOW

ABSTRACT Accurate prediction of pressure drop for multiphase flow in horizontal and near horizontal pipes is needed for effective design of flow lines and piping networks. The increased application of horizontal wells further signified the need for accurate prediction of pressure drop. Several correlations and mechanistic models have been developed since 1950. In addition to the limitations on the applicability of all existing correlations, they all fails to provide the desired accuracy of pressure drop predictions. The recently developed mechanistic models provided some improvements in pressure drop prediction over the empirical correlations. However, there is still a need to further improve the accuracy of prediction for a more effective and economical design of wells and surface piping networks. This paper presents an Artificial Neural Network (ANN) model for prediction of pressure drop in horizontal and near-horizontal multiphase flow. The model was developed and tested using field data covering a wide range of variables. A total of 225 field data sets were used for training- and 113 sets data for cross-validation of the model. Another 112 sets of data were used to test the prediction accuracy of the model and compare its performance against existing correlations and mechanistic models. The results showed that the present model significantly outperforms all other methods and provides predictions with accuracy that has never been possible. A trend analysis was also conducted and showed that the present model provides the expected effects of the various physical parameters on pressure drop.