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.

Despite holding a significant proportion of the known hydrocarbon reserves by the carbonates, enough studies have not been conducted to understand their geomechanical parameters and their predictability, especially in Iran. The present paper introduces a novel approach for obtaining geomechanical parameters of carbonate rocks through investigating the relationship between geomechanical parameters and Archie’s coefficients in one of the Iranian fractured carbonate reservoirs. The geomechanical parameters here included Young’s modulus, bulk modulus, and shear modulus, while Archie’s coefficients were the cementation factor and formation resistivity factor. For this purpose, 14 representative limestone core plugs were chosen and Archie’s coefficients were determined and the effect of increasing confining pressure on these parameters was investigated. Then, through numerical simulations using Particle Flow Code (PFC3D), the geomechanical parameters of the reservoir, including Young’s modulus (E), (dynamic and static), bulk and shear modulus, and Poisson’s ratio were obtained. The numerical results were verified by the laboratory tests and wireline logs’ data and showed a satisfying matching. Finally, the empirical relations between geomechanical parameters and Archie’s coefficients were determined for the target reservoir. According to the obtained results, the average correlation between Young’s modulus and both the cementation factor and formation resistivity factor was about 95%, which is a satisfying accuracy. As expected, shear modulus and bulk modulus had a stronger relationship with E being respectively about 99% and 98%. Moreover, lab-measured porosity and Young’s modulus had a correlation coefficient of about 95%. The obtained results of the present study provide new insights into the simultaneous evolution of the reservoir’s mechanical and petrophysical characteristics.