Petrophysical properties are fundamental to understanding fluid flow processes in hydrocarbon reservoirs. Special Core Analysis (SCAL) routinely used in industry are time-consuming, expensive, and often destructive. Alternatively, easily available thin section data is lacking the representation of pore space in 3D, which is a requisite for generating pore network models (PNM) and computing petrophysical properties. In this study, these challenges were addressed using a numerical SCAL workflow that employs pore volume reconstruction from thin section images obtained from confocal laser scanning microscopy (CLSM). A key objective is to investigate methods capable of 2D to 3D reconstruction, to obtain PNM used for the estimation of transport properties. Representative thin sections from a well-known Middle-Eastern carbonate formation were used to obtain CLSM images. The thin-sections were specially prepared by spiking the resin with UV dye, enabling high-resolution imaging. The grayscale images obtained from CLSM were preprocessed and segmented into binary images. Generative Adversarial Networks (GAN) and Two-Point Statistics (TPS) were applied, and PNM were extracted from these binary datasets. Porosity, Permeability, and Mercury Injection Porosimetry (MIP) on the corresponding core plugs were conducted and an assessment of the properties computed from the PNM obtained from the reconstructed 3D pore volume is presented. Moreover, the results from the artificial pore networks were corroborated using 3D confocal images of etched pore casts (PCE). The results showed that based on visual inspection only, GAN outperformed TPS in mimicking the 3D distribution of pore scale heterogeneity, additionally, GAN and PCE outperformed the results of MIP obtained by TPS on the Skeletal-Oolitic facies, without providing a major improvement on more heterogeneous samples. All methods captured successfully the porosity while absolute permeability was not captured. Formation resistivity factor and thermal conductivity showcased their strong correlation with porosity. The study thus provides valuable insights into the application of 2D to 3D reconstruction to obtain pore network models of heterogeneous carbonate rocks for petrophysical characterization for quick decision. The study addresses the following important questions: 1) how legacy thin sections can be leveraged to petrophysically characterize reservoir rocks 2) how reliable are 2D to 3D reconstruction methods when predicting petrophysical properties of carbonates.