Microporosity is commonly assumed to be non-connected porosity and not commonly studied in geoengineering industry. However, the presence of micropores plays a key role in connecting macropores and it can contribute significantly to the overall flow performance. In this study, targeted CO₂ storage carbonate fields in Southeast Asia have significant amounts of microporosity ranging from 10 to 60% of the total measured porosity. Microporosity can only be seen in high resolution images. To study the unresolved and the resolved microporosity, Middle Miocene carbonate samples from CO₂ storage candidate fields were scanned using lower resolution micro-computed micro-tomography (micro-CT) and higher resolution synchrotron light source to understand the pore scale structure of the carbonate sample at different length scales. This paper proposes a proof-of-concept upscaling method that integrates multiscale 3D imaging techniques and trendline analysis to establish porosity-permeability relationships with microporosity insight. After image acquisition and processing, the images were divided into smaller sub-volumes. Pore-scale modelling was conducted to predict the permeability using Darcy-Brinkman-Stokes (DBS) model. Then, a nano-scale porosity-permeability transform is generated using natural log trendline fitting based on simulation results. The porosity-permeability transform is further extended to three cases to cover the low case, mid case, and high case of datapoint fittings and is further validated with laboratory measured data. The established porosity-permeability transforms in this study have been applied to compare with petrophysical derived porosity-permeability transforms with better performance (higher R² value) for low permeability datapoint. The multiscale imaging upscaling workflow has integrated machine learning during image segmentation with pore-scale modelling and trendline fitting during the upscaling study. It emphasises the importance of seeing the unseen (unresolved microporous phase) to understand the internal texture and microstructure of a rock sample in order to understand the connectivity of the overall flow performance in a carbonate rock.