3D Electrical Impedance Tomography System for Annular Multiphase Flow Metering

Abstract

With the rapid progress of tomography systems, a need has emerged for innovation that can provide high accuracy measurements, fast acquisition rate, and low-cost robust systems for multiphase flow and medical instruments [3]. The main objective of this work is to design and implement a 3D Electrical Impedance Tomography (EIT) system to effectively monitor a multiphase flow which consists of an annular flow comprising of an air-core of unknown diameter, surrounded by a liquid phase [1], such as oil and water mixture of high water-cut s

The system consists of an array of electrode sensors evenly distributed around the pipe, confining the multiphase flow. Subsequently, a dedicated hardware module (GPU) is used to reconstruct these signals, captured from the sensors, into a 3D image of the flow by solving the forward-inverse problem [12]. Finally, the annular flow is generated using a flow conditioner consisting of a swirl cage followed by a bluff body that generates vortices that push the high dense liquid phase to the outer side of the pipeline [5]. The 3D EIT system is designed to accurately measure the gas liquid fraction and the liquid-liquid fraction and provides the 3D volume image of the flow. Hence, it can be utilized for pipeline and reservoirs characterization to achieve high efficiency, cost reduction, and safer production.

The approach is to design a dedicated, highly parallel hardware architecture that can sustain the high computation complexity of the 3D EIT algorithm. Coincidentally, advanced software development tools are available to help effectively develop these parallel hardware algorithms. The main novelty is utilizing GPUs to outperform the existing real-time tomography hardware platforms, mainly based on Field Programmable Gate Array (FPGAs), which have the disadvantage of being less dense and feature lower precise arithmetic operations [29]. Matrix multiplication and matrix inversion are considered the most intensive computation in Electric Impedance Tomography (EIT). Therefore, GPU threads will be utilized for faster matrix multiplication/inversion in the proposed design, which is the pipeline for real-time reconstruction. Preliminary results were obtained by running matrix multiplication simulation for different sizes of matrices and comparing their computation time to validate that running matrix multiplication on the GPU threads is faster than sequentially executed on the CPU. For example, the multiplication done in parallel on the NVIDIA 2080 RTX GPU for a matrix of size 10000x10000 was 54.6% faster than sequentially on the Intel i7-9700k CPU.

In conclusion, the innovation in this thesis is in terms of hardware design and software development to speed up the process of acquisition and processing of EIT reconstruction. As well as in terms of applying this method to easily detect a multiphase annular core with the integration of an Artificial Neural Network.

Date of Award	Apr 2023
Original language	American English
Supervisor	Mahmoud Meribout (Supervisor)