Modelagem físico-computacional da RMN em meios porosos - aplicações na indústria do petróleo

Abstract

Extracting and understanding morphological properties of porous media is the focus of many areas of knowledge. In the case of oil industry, extracting the tridimensional morphological features of rock reservoirs allows the evaluation of the well productivity. Nuclear Magnetic Resonance (NMR) is one of the most important techniques applied in the study of reservoir rocks, capable of providing information about both their morphologies and the confined fluids. The most used parameters measured by NMR to study fluids in reservoir rocks are the magnetic relaxation and diffusion coefficients. Fluid molecules diffuse through the pore space and relax due to the fluid-surface interaction, macroscopically defined by the magnetic surface relaxivity ρ. The magnetic surface relaxation time TS is given in terms of three parameters: ρ, pore size distribution (PSD), and pore connectivity, being ρ extremely important and difficult to measure. The magnetic surface relaxivity can be determined by correlating TS to the PSD obtained from other techniques. However, in general, pore length scale defined by the molecular diffusion during the NMR magnetic relaxation is not equivalent to pore sizes measured, for example, by thin section and mercury intrusion. While the thin section offers essentially two-dimensional information about the pores, the mercury intrusion technique measures the bulk pore-throat size distribution. Due to this reason, a new NMR computational physics approach was proposed to correlate magnetic relaxation experimental data with those simulated using Digital Porous Media obtained by X-ray microcomputed tomography, which offers an alternative method to measure of ρ. This computational physics method, unlike the others, preserves the porous media morphology, molecular dynamics and NMR properties. Additionally, it is straightforward to implement advanced NMR pulse sequences using the proposed computational model, including those carried out under magnetic field gradients. Thus, it is possible to reproduce data resulting from NMR techniques applied in either laboratory or well-logging conditions. Therefore, the proposed computational physics model is capable of reproducing the NMR parameters of interest to the oil industry, most notably the longitudinal and transverse relaxation times T1 and T2, as well as the diffusion coefficient D and their correlations T1xT2, DxT2, and T2xT2.