@article{ZHANG2026, 
author = {Tao ZHANG and Shengpeng HE and Jianguo LI and Liang GONG and Shuyu SUN},
title = {Study on intelligent recognition of phase change flow patterns in geothermal production wells},
year = {2026},
journal = {Petroleum Science Bulletin},
volume = {11},
number = {2},
pages = {581-591},
keywords = {flow pattern identification, geothermal production well, flash evaporation, least Squares SVM},
url = {https://www.sciopen.com/article/10.3969/j.issn.2096-1693.2026.03.008},
doi = {10.3969/j.issn.2096-1693.2026.03.008},
abstract = {This study addresses the fluid flash evaporation phase change in geothermal production wells. A forced circulation visual experimental platform was designed to investigate flow pattern evolution and differential pressure fluctuation characteristics during flash evaporation, and high-precision flow pattern recognition was achieved via signal decomposition and machine learning. Key steps include: constructing an experimental system with fluid dynamic control, temperature regulation, data acquisition, and a visual pipe section; recording flow patterns (bubble, slug, churn, annular flow) via high-speed photography and analyzing their triggering conditions/morphological features; collecting differential pressure signals (2~3 meters height) and identifying distinct amplitude-frequency-morphology characteristics among flow patterns; applying CEEMD to decompose signals and extract IMF energy spectra; and developing a PSO-LSSVM model using multi-parameters (inlet temperature, velocity, IMF spectra) for high-accuracy recognition. Results provide theoretical support for flash evaporation localization and severity assessment, aiding wellbore optimization and geothermal extraction efficiency improvement.}
}