ANNs Trained on Synthetic and Lab Data for Modeling Steady-State Multiphase Pipe Flow

The present work considers the development of a machine learning model trained on synthetic and lab data for the steady-state multiphase pipe flow. We propose a new method for calculating flow characteristics such as liquid holdup, flow regime, and pressure gradient in the pipe segment based on ANNs and transfer learning technique. Besides, the created tool is implemented within the marching algorithm for calculating flow parameters along the whole pipe. The segment module consists of three sub-models, namely, for calculating liquid holdup, defining flow regime, and estimating pressure gradient. For sub-models creation, we use transfer learning methodology: on the first stage, the ANNs are trained on synthetic data, which we generate by using OLGAS mechanistic model; on the second stage, we train meta-models additionally on the real data, which in our case presented by lab measurements. As a result, we create the new multiphase flow correlation, which includes the basics of the physics-based OLGAS model and is tuned for real data that can be in the general case field measurements. At the final stage, we apply marching algorithms with the suggested segment model to the field dataset for testing purposes.