Seismic data and nuclear magnetic resonance (NMR) data are two of the highly trustable kinds of information in hydrocarbon reservoir engineering. Reservoir fluids influence the elastic wave velocity and also determine the NMR response of the reservoir. The current study investigates different pore types, i.e., micro, meso, and macropores’ contribution to the elastic wave velocity using the laboratory NMR and elastic experiments on coal core samples under different fluid saturations. Once a meaningful relationship was observed in the lab, the idea was applied in the field scale and the NMR transverse relaxation time (T₂) curves were synthesized artificially. This task was done by dividing the area under the T₂ curve into eight porosity bins and estimating each bin’s value from the seismic attributes using neural networks (NN). Moreover, the functionality of two statistical ensembles, i.e., Bag and LSBoost, was investigated as an alternative tool to conventional estimation techniques of the petrophysical characteristics; and the results were compared with those from a deep learning network. Herein, NMR permeability was used as the estimation target and porosity was used as a benchmark to assess the reliability of the models. The final results indicated that by using the incremental porosity under the T₂ curve, this curve could be synthesized using the seismic attributes. The results also proved the functionality of the selected statistical ensembles as reliable tools in the petrophysical characterization of the hydrocarbon reservoirs.

Clay minerals significantly alter the pore size distribution (PSD) of the gas hydrate-bearing sediments and sandstone reservoir rock by adding an intense amount of micropores to the existing intragranular pore space. Therefore, in the present study, the internal pore space of various clay groups is investigated by manually segmenting Scanning Electron Microscopy (SEM) images. We focused on kaolinite, smectite, chlorite, and dissolution holes and characterized their specific pore space using fractal geometry theory and parameters such as pore count, pore size distribution, area, perimeter, circularity, and density. Herein, the fractal properties of different clay groups and dissolution holes were extracted using the box counting technique and were introduced for each group. It was observed that the presence of clays complicates the original PSD of the reservoir by adding about 1.31-61.30 pores/100 μm² with sizes in the range of 0.003-87.69 μm². Meanwhile, dissolution holes complicate the pore space by adding 4.88-8.17 extra pores/100 μm² with sizes in the range of 0.06-119.75 μm². The fractal dimension () and lacunarity () values of the clays’ internal pore structure fell in the ranges of 1.51-1.85 and 0.18-0.99, respectively. Likewise, and of the dissolution holes were in the ranges of, respectively, 1.63-1.65 and 0.56-0.62. The obtained results of the present study lay the foundation for developing improved fractal models of the reservoir properties which would help to better understand the fluid flow, irreducible fluid saturation, and capillary pressure. These issues are of significant importance for reservoir quality and calculating the accurate amount of producible oil and gas.

Natural gas hydrate (NGH) deposits represent a vast and clean energy source. However, sustainable gas production from these resources remains an unsolved technical problem due to potential geohazards and climate challenges. A critical issue in this regard is the difficulty of obtaining in situ samples, which are essential for detailed laboratory studies of NGH’s geomechanical and chemical behavior for safe and green gas production after hydrate dissociation. Currently, the retrieval of representative samples from NGH reservoirs is hindered by significant technological limitations and high costs. Consequently, laboratory-synthesized gas hydrate-bearing sediment (HBS) samples are crucial for controlled research purposes and validating numerical simulation models and are used in the majority of research studies. With this in mind and considering the complexity of synthesizing HBS samples, this study comprehensively reviews different methods of synthesizing gas hydrates in porous media, including excess-gas, excess-water, dissolved-gas, spray, bubble injection, and hybrid techniques. Each method produces distinct hydrate morphologies (e.g., pore-filling, cementing, grain-coating, etc.) and saturation levels, with trade-offs in speed, uniformity, reproducibility, and ease of control. Furthermore, the current review details the synergic application of non-invasive characterization techniques, i.e., X-ray Computed Tomography (CT) and Nuclear Magnetic Resonance (NMR), in studying gas hydrates. CT provides high-resolution three-dimensional (3D) structural images of pore geometry and hydrate distribution, while NMR/MRI (Magnetic Resonance Imaging) quantifies fluid saturations and tracks hydrate formation/dissociation dynamics in real time. The synergistic use of CT and NMR offers a powerful multimodal approach, overcoming individual limitations such as CT’s poor hydrate–water contrast detection and NMR’s indirect hydrate inference, which could help in the sustainable synthesis of particular hydrate morphologies. Finally, the critical analysis of current technological challenges or gaps and also the emerging trends and future directions in the study of HBS, including advanced imaging techniques, AI-assisted analysis, and standardization efforts, etc., are discussed. It was found that the selection of the most appropriate method for natural gas hydrate synthesis is mostly task-specific, and the emerging technologies have facilitated the synthesis of HBS samples with more precise control of morphology, saturation, etc. This review provides the required insights for sustainable synthesis and characterization of hydrate-bearing sediments samples and serves sustainable gas production from natural gas hydrate reservoirs.