This paper presents an innovative method for the identification of multiphase flow patterns and the estimation of pressure drop. This method uses a data-driven classification model based on a fuzzy inference system (FIS). The paper discusses the specific problem of two-phase, gas-liquid regime identification and pressure drop prediction for pipe flow and demonstrates the ability of the fuzzy-mechanistic (fuzzynistic) model to accurately identify the flow regime. In addition, the fuzzynistic model is computationally efficient, and can help to provide real-time monitoring and control of production and pipeline equipment. The fuzzynistic model is demonstrated for two-phase, gas-liquid pipe flow over a wide range of superficial velocities and pipe inclination angles. Seven flow regimes are identified.

Historically, regime predictions for multiphase flows have been based on either empirical maps created from experimental observations or on mechanistic models, which consider the physical mechanisms that cause variations of fluid phase distribution. Both have disadvantages in that they are either tied to limited experimental data or are prone to discontinuities at the regime transitions, or both. An innovative contribution of the fuzzynistic method is that it classifies flow patterns with associated weights. These flow patterns allow the aggregation of pressure drop functions of classified flow patterns to help estimate the corresponding pressure drop automatically across flow regime transitions. Flow regime maps generated using the fuzzy logic approach accurately mimic those generated by mechanistic models, but have “fuzzy” transitions between regimes because of the partial degree of membership to adjacent and neighboring regimes. Thus, along the flow path, partial degrees of membership to adjacent and neighboring flow regimes are assigned to account for the pressure drop prediction at the regime transitions. Furthermore, because the fuzzynistic method is computationally efficient, it could be used for real-time monitoring and control of equipment in wells, pipelines, and downstream processing.

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