Abstract: Methods and systems for a novel three-dimensional (“3D”) pore roughness quantification are disclosed. The methods include obtaining an image of a pore space. The methods further include, using an image analysis system: discretizing the 3D image to generate a meshed surface; constructing a reference surface and a constructed surface from the meshed surface; evaluating a plurality of surface distances; and determining a roughness coefficient. The methods further include obtaining an observed T2 time for at least one sample depth in a well; determining a corrected T2 time from the observed T2 time; and determining an average pore size in a rock formation.

Abstract: A method includes modeling a reservoir using a lab scale set of models and a field scale set of models. The reservoir is modeled using the lab scale set of models by scanning a sample of the reservoir into the lab scale set of models to create modeled fractures, estimating hydraulic properties of the modeled fractures, estimating multi-phase dynamic properties of the modeled fractures, and determining characteristics of a flow regime of a fluid flowing through the modeled fractures. The reservoir is modeled using the field scale set of models by modeling a discrete fracture network of the reservoir, upscaling the discrete fracture network, and calibrating the discrete fracture network. An enhanced oil recovery operation is designed and performed on the reservoir using the calibrated discrete fracture network.

Abstract: Systems and methods for correcting NMR T2 times are disclosed. The method may include creating a plurality of 3D rough surface pore models, where each 3D rough surface pore model includes a rough surface, for each of the plurality determining a pore roughness coefficient (PRC) and a volume; and simulating a first T2 relaxation time based on the 3D rough surface pore model. The method may further include determining a smooth surface pore model, with the same volume as the 3D rough surface pore model, and simulating a second T2 relaxation time based on the smooth surface pore model. The method may still further includes determining a T2 rough surface correction factor based on the first and the second T2 relaxation times, and forming a data pair comprising the pore roughness coefficient (PRC) and the T2 rough surface correction factor; and fitting a T2 correction curve to the data pairs.

Abstract: Techniques for determining reservoir formation properties include generating digital models of core samples taken from one or more reservoir formations based on corresponding images of the core samples; determining a pore throat size distribution of the core samples based on the generated digital models; determining corresponding capillary pressure curves and corresponding NMR value distributions of the core samples with one or more numerical simulations; generating one or more machine-learning (ML) models based on the pore throat size distribution, corresponding capillary pressure curves, and corresponding NMR value distributions of the core samples; adjusting the one or more ML models with one or more reservoir data of the one or more reservoir formations; generating adjusted capillary pressure curves and NMR value distributions from the one or more adjusted ML models; and determining a reservoir formation specific pore throat size distribution from at least one of the adjusted capillary pressure curves or

Abstract: A method for calculating a fluid flow in a given underground medium, the method including receiving initial molar densities for components of the fluid; introducing a scalar auxiliary variable r into an inhomogeneous Helmholtz free energy equation F with a Peng-Robinson equation of state; calculating a molar density ni of each component of the fluid based on a discretized scalar auxiliary variable rk; and determining the flow of the fluid based on the calculated molar densities ni.