A comparative study on the performance of multiphase flow models against offshore field production data

Abstract In the oil industry, multiphase flow models are commonly used to simulate the flow from reservoir to the production unit. Flow simulations help in the production monitoring and optimization to support the decision-making process. Despite the need for accurate simulations to support production operations, multiphase flow models are mostly developed using laboratory data and rarely validated under field-scale conditions. To improve the understanding of multiphase flow models in field conditions, this work evaluates the performance of a large set of models for a dataset composed of two production units with 20 producing wells and 865 measured production points. The wells are divided and analyzed in two segments: the surface flowline and the wellbore. The evaluation compares the models' performances using various statistical parameters and trending charts. The evaluation analyzes the impacts of production parameters, well geometry, predicted flow pattern, and pressure drop components on the model performances. Overall, Ansari's model presented the best performance with 13.8% absolute average percent error for the flowline segment and 14.1% error for the wellbore segment. In addition, Gray can also generate good results for most but not all of the tested wellbores. From the production parameters evaluation, higher flow rate tests resulted in more stable outputs. Ansari and Beggs models showed a trend of increasing errors for wells with larger horizontal wellbore lengths. Also, Beggs model showed larger errors when the transition flow pattern was predicted. Finally, both Ansari and Beggs models showed larger overprediction errors for tests with higher pressure losses in the flowline segment, while Gray showed smaller errors for these cases. The conclusions can be used to select the best models given the production system's flow parameters, or be incorporated in systems to increase the accuracy.