Fuzzy Logic Based Multi-Dimensional Image Fusion for Gas–Oil-Water Flows With Dual-Modality Electrical Tomography

This paper proposes a novel approach, whereby fuzzy logic and decision tree are utilized to overcome the challenges in analyzing images of gas–oil–water pipeline flow obtained using electrical resistance and capacitance dual-modality tomography. The first approach generates two axially stacked concentration images from two stacks of the cross-sectional concentration tomograms reconstructed from different modalities, respectively, and then registers two generated images in temporal and spatial terms. Afterward, a fuzzy logic method is applied to perform a pixel-level fusion to integrate the registered images based on the characteristics of electrical tomograms for multiphase pipeline flow. Later, a decision tree is utilized to derive the local concentration of each individual phase according to the fusion results. Using the data from real industrial cases, both feasibility and robustness of the proposed approach are demonstrated. In addition, the proposed approach also overcomes the limitations of conventional threshold-based methods on the request of a priori knowledge for the qualitative and quantitative analyses of gas–oil–water pipeline flow.