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.

Multiscale energy and mass transport processes constitute the fundamental scientific foundation for sustainable geo-energy development and carbon neutrality. This perspective synthesizes cutting-edge advances in the field into three transformative thematic areas: thermodynamically consistent pore-scale modeling with robust numerical schemes that embed fundamental physical laws into mathematical formulations; molecular-scale insights and data-driven acceleration techniques bridging nanoscopic interfacial phenomena to reservoir-scale engineering; and coupled multiphysics-artificial intelligence frameworks for hydrogen infrastructure safety and supercritical CO₂ geothermal systems. Recent research reveals a paradigm shift toward living digital twins that integrate rigorous mathematical physics, multiscale computing, and artificial intelligence, charting a clear course toward carbon-neutral energy systems.

Pore-network models are widely used to describe pore-scale flow in porous media, and their reliability depends critically on accurate extraction of pore and throat structures. A new extraction framework, termed the topological pore-network finder, is proposed in this work, which combines topological data analysis, medial access path search, and flashlight search medial axis. The topological data analysis is used to identify pore connectivity and cluster the void space, thereby providing robust initial pore centers. The medial access path search method then traces strings between connected pore centers along the medial axis, while the flashlight search medial axis method is used to refine the resulting paths and improve computational efficiency. The method is validated using toy porous media, two- and three-dimensional digital rock samples. Sensitivity analyses show that the pore-network finder is stable with respect to image resolution and string discretization. Compared with the classical maximal-ball method, the pore-network finder achieves at least an order-of-magnitude acceleration while preserving the main geometric statistics and flow-response characteristics of the extracted networks. In addition, because the method operates in continuous space and can reuse information from previous states, it is well suited to quasi-dynamic updates during deformation. The pore-network finder therefore provides an efficient and accurate tool for pore-network extraction and subsequent pore-scale characterization in geo-energy systems.

As a sustainable and renewable energy source, geothermal energy holds significant potential for addressing global energy demands and mitigating climate change. However, the development of geothermal resources involves complex interactions among temperature, fluid flow, stress, and chemistry, collectively known as thermal-hydraulic-mechanical-chemical multiphysics coupling. This work aims to provide a comprehensive overview of such a coupling simulation in geothermal energy development, encompassing theoretical frameworks, numerical models, and practical applications. By integrating insights from various disciplines, this perspective contributes to advancing the understanding and optimization of geothermal energy extraction processes.