@ARTICLE{Ornowski_Remigiusz_Application_2024,
 author={Ornowski, Remigiusz and Lackowski, Marcin and Kwidzinski, Roman},
 volume={vol. 45},
 number={No 4 (in progress)},
 journal={Archives of Thermodynamics},
 pages={5-12},
 howpublished={online},
 year={2024},
 publisher={The Committee of Thermodynamics and Combustion of the Polish Academy of Sciences and The Institute of Fluid-Flow Machinery Polish Academy of Sciences},
 abstract={Non-invasive real-time measurements of phase content in the reservoir fluid are highly advantageous in the oil and gas industry and remain a current research topic. The paper presents an innovative, self-designed multi-electrode capacitance meter intended for detecting multiphase flow patterns in a low-permittivity medium, such as the reservoir fluid. The ca-pacitance sensor is built with delta-sigma charge modulators capacitance-to-digital converters. Machine learning is applied to convert the capacitance measurements into a tomographic image of the flow pattern. At present, the meter is built with eight electrodes. It is shown that the measurements are repeatable and have a good signal-to-noise ratio. The implemented neural network is capable of correctly reconstructing the tomographic images for a test tube filled with reservoir fluid and placed in various locations inside the test section.},
 type={Article},
 title={Application of machine learning for reconstruction of multiphase fluid structure measured by a capacitance multi-electrode sensor},
 URL={http://journals.pan.pl/Content/133106/1_AoT_4-2024_Ornowski_757.pdf},
 doi={10.24425/ather.2024.151993},
 keywords={Electrical capacitance tomography, Multiphase reservoir flow, Neural networks, Machine learning, Deep learning},
}