Deep learning-assisted dual-modal tomography for phase flow rate estimation in two-phase oil-water flow systems

Abstract

Accurately estimating phase flow rates in multiphase systems is crucial for many industries, where precise measurements are essential for operational efficiency and safety. Addressing this issue, this paper introduces an approach that employs deep learning-assisted dual-modal electromagnetic flow tomography (EMFT) and electrical tomography (ET) to predict both oil and water flow rates in two-phase oil-water flows. To facilitate the generation of the data, we first simulate diverse flow conditions using COMSOL Multiphysics software and the convection–diffusion equation, aiming to create a realistic representation of two-phase oil-water flows. The dual-modal system measurement data, generated from these simulations and simulated by using a dense finite element mesh, provide reliable inputs for the deep learning model. Moreover, this study also integrates experimental data into both the training and testing phases, improving the ability of the proposed approach to estimate flow rates accurately in practical investigations. The results from laboratory experiments demonstrate the potential of the deep learning-assisted dual-modal ET and EMFT approach in effectively resolving the challenges of estimating flow rates in two-phase oil-water flow systems. By combining the deep learning capabilities with dual-modal tomography, this study offers valuable insights for future applications and represents a significant step forward in the field of multiphase flow rate estimation.