## 4 Conclusions  
The imaging, accurate identification and modelling of pores and fractures have become flagship programmes in the leading oil and gas industries because of their immense applications in contaminant transport and CO\({}_{2}\) storage, and often, porosity is considered a basic parameter for reservoir characterisation. However, quite frequently, the identification procedure of pores and fractures from the image samples is manual or inaccurate if automated. Above all, micro-pores and fractures in a grayscale rock image (CT scan samples) are mostly unidentifiable or indistinguishable using the contemporary techniques of automatic identification. Prompted by this fact, in the current work, we developed a novel pixel-wise multi-objective image analysis strategy to automatically identify the pores and fractures from the input CT scan images of rocks. We have tested the applicability and efficiency of the proposed technique using CT scan images of carbonate rocks, and we firmly believe that the proposed methodology will retain its success in the presence of the CT scan images of various other types of rock samples. This belief is held because of its adopted pixel-wise intensity analysis mechanism, which enables the algorithm to understand the largest intensity variations among different pixels along with its neighbouring pixels and accordingly, classify them as bright or dark, thus helping to identify pores and fractures in the CT scan images. However, an accurate understanding of the intensity variation among the pixels in an input image sample depends on a perfect selection of \(\alpha\), \(\beta\), \(\gamma\), \(\delta\) and I \(\in\) R+, which requires expert judgement ability of the model developer. Next, to juxtapose the accuracy of the proposed methodology, we compared its performance with that of deep CNN and the method developed by [15] using the same image samples. The results clearly show a better identification accuracy of pores and fractures by the proposed approach using the input CT scan images. * We identified a possible reason for the poor identification accuracy of supervised deep CNNs as the unidentified necessary shape-related features of any \(\mathcal{H}_{i}\), which are highly required during the training of supervised deep CNNs to accurately classify any specific \(\mathcal{H}_{i}\) as a pore or fracture. Particularly, the irregular shapes of all \(\mathcal{H}\) make it difficult for model developers to identify the suitable supervised deep CNN features for their accurate classifications. * However, this problem might be handled well using capable unsupervised deep CNN counterparts, which can automatically and intelligently identify the features required to recognise irregular objects with higher accuracy. This possibility will be investigated in our subsequent research works. The poor identification accuracy of deep CNN might be due to its present implementation, although we firmly believe that the accuracy can barely be improved in the absence of large and high-quality training samples. * Possible Implementation of the Proposed System: The proposed system can be prepared as a module, attached to the imaging device and linked with its guiding software to simultaneously identify the pores and fractures (even at the micro-level) during the imaging of rock samples. Gathering many meaningful real data samples has always been a major problem in any real-world rock imaging scenario, which often has a major negative impact on the identification accuracy of deep learning contemporaries. Therefore, a deep CNN module, if attached to a subsequent imaging device and its guiding software as an add-on, should be pre-trained because of the lack of relevant training samples. However, the biggest challenge for the pre-trained models for understanding objects without fixed geometric shapes (such as pores and fractures) is perfectly defining and learning the necessary shape-related features that distinguish the pores from the fractures.