A comprehensive model is formulated to predict the flow behavior for upward two-phase flow. The comprehensive model is composed of a model for flow pattern prediction and a set of independent models for predicting the flow characteristics such as holdup and pressure drop in bubble, slug and annular flows.

The comprehensive model is evaluated by using a well databank that is composed of 1775 well cases covering a wide variety of field data. The performance of the model is also compared with the six commonly used empirical correlations.

The overall performance of the model is in good agreement with the data. In comparison with the empirical correlations, the comprehensive model performs the best, with the least average error and the smallest scattering of the results.


Two-phase flow is commonly encountered in petroleum, chemical and nuclear industries. The frequent occurrence of two-phase flow presents engineers with the challenge of understanding, analyzing and designing two phase systems.

Due to the complex nature of two-phase flow, the problem was first approached through empirical methods. Recently the trend has shifted towards the modeling approach. The fundamental postulate of the modeling approach is the existence of flow patterns or flow configurations. Various theories have been developed for the prediction of flow patterns. Separate models were developed for each flow pattern to predict the flow characteristics such as holdup and pressure drop. By considering flow mechanics, the resulting models can be applied to flow conditions other than used for their development with more confidence.

The only studies published on comprehensive mechanistic modeling of two-phase flow in vertical pipes are by Ozon et al. and Hasan and Kabir. Nevertheless, more work is needed in order to develop models which describe the physical phenomena more rigorously.

The purpose of this study is to formulate a detailed comprehensive mechanistic model for upward two-phase flow. The comprehensive model first predicts the existing flow pattern and then calculates the flow variables by taking into account the actual mechanisms of the predicted flow pattern. The model is evaluated against a wide range of experimental and field data available in the updated TUFFP well databank. The performance of the model is also compared with six empirical correlations used in the field.

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