Combining Machine Learning and a Multiphase Flow Model for Hybrid Virtual Flow Metering

Abstract

Abstract Virtual flow metering is a technology for indirect estimation of multiphase production rates. The most straightforward approach to obtain virtual measurements is to apply machine learning models, which enable fast but not necessarily accurate prediction of flow rates. In order to overcome any related limitations machine learning methods can be combined with physical conservation laws of mass, momentum and energy. The resulting hybrid virtual flow metering approach strongly relies on the capabilities of underlying physical simulator. In this work various approaches of combining measurements with multiphase flow models are investigated. It is demonstrated that a wellbore flow model has a central role in any type of hybridization scheme which connects the available measurements to the target flow rates. Due to specific requirements of the virtual flow metering applications, this simulator has to be fast and accurate enough to qualitatively describe flow in wellbores and pipelines. In this work, such a simulator is presented, which is based on a one-dimensional formulation for mass and momentum conservation, but contrary to other commercially available options, the current development is based on a drift-flux approach, which is less demanding on the interfacial closure terms and can be more easily tuned with field data. The performance of the simulator has been evaluated using a set of transient benchmark scenarios, where the reasonable match against the reference data has been demonstrated. The simulator is applicable to a wide range of multiphase flow problems of practical interest and uses advanced numerical algorithms to ensure robustness and performance. The closure relationships can be adjusted to improve the performance for a particular well. In addition, a dedicated graphical user interphase enables user-friendly interaction for setting up the computational case and processing the output production data.