Simulating nuclear magnetic resonance can help identify water and oil within porous rock

Rocks are not solid masses but are composed of grains and crystals. The gaps between the small segments are quantified as porosity, and understanding the underlying pore structure of rock can be useful for measuring groundwater, carbon storage potential, and to aid in oil recovery.

Nuclear magnetic resonance (NMR) measurement is an efficient and non-destructive technique to provide information about a rock’s porosity, pore structure, and fluid retention, however, conducting such experiments is not always feasible. Though previous simulations failed to represent the complexity of rock, Gao et al. took advantage of new, high-resolution numerical models to investigate the feasibility of simulating NMR responses of digital rock cores.

Based on NMR theory, the magnitude of magnetization is approximate to a function of surface relaxation for different pores of water-saturated rock, and surface relaxation is closely related to the surface-to-volume ratio of each pore. The researchers examined a 3D digital core from Berea sandstone, identifying 12,529 pores and quantifying their structure and surface-to-volume ratios. They simulated NMR responses of rock containing water and oil from the perspective of pores and visually demonstrated the NMR responses in different pores in the rock.

“We were surprised that there is a strong correlation between pore volume and surface area, especially for smaller pores,” said author Feiming Gao.

This correlation means NMR responses can characterize the pore size distribution of rock. Their simulations can build a physical foundation for interpreting NMR results of pore material on a larger scale.

In the future, the researchers plan to simulate NMR responses in varying digital rock core pore structures by adding features such as micro-cracks into their model.

Source: “Simulation of nuclear magnetic resonance response based on the high-resolution three-dimensional digital core,” by Feiming Gao, Liang Xiao, Yuan Jin, and Jiaqi Li, Physics of Fluids (2024). The article can be accessed at https://doi.org/10.1063/5.0209056 .