Nuclear Magnetic Resonance T1–T2 Spectra in Heavy Oil Reservoirs

Abstract

Low-field nuclear magnetic resonance (NMR) has been widely used in the petroleum industry for reservoir evaluation. Fluid properties and petrophysical parameters can be determined from NMR spectra, obtained from processing echo data measured from the NMR tool. The more accurate NMR spectra are, the higher the reliability of reservoir evaluation based on NMR logging is. The purpose of this paper is to obtain more precise T1–T2 spectra in heavy oil reservoirs, with focus on the T1–T2 data acquisition and inversion. To this end, four inversion algorithms were tested on synthetic T1–T2 data, their precision was evaluated and the optimal inversion algorithm was selected. Then, the sensitivity to various acquisition parameters (wait time and echo spacing) was evaluated with T1–T2 experiments using a disordered accumulation of glass beads with a diameter of 45 μm saturated with heavy oil and distilled water. Finally, the sensitivity to various inversion parameters (convergence tolerance, maximum number of iterations and regularization parameter) was evaluated using the optimal inversion algorithm. The results showed that the inverted T1–T2 spectra loss some relaxation information when the number of echo train is less than 7. The peak of the heavy oil signal gradually moves along the direction of increase in the T2 and the intensity of the heavy oil signal gradually decreases with increasing echo spacing. The echo spacing should be as small as possible for T1–T2 measurements in heavy oil reservoirs on the premise that the NMR instrument operates normally. A convergence tolerance that is too large or a maximum number of iterations that is too small may result in exiting the iteration prematurely during the inversion. A convergence tolerance of 1 × 10−7 and a maximum number of iterations of 30,000 are recommended for the inversion of the T1–T2 spectra. An appropriate regularization parameter is an important factor for obtaining accurate T1–T2 spectra from the optimal inversion algorithm.

High-precision calculation of gas saturation in organic shale pores using an intelligent fusion algorithm and a multi-mineral model

“…In response, some scholars have proposed saturation models based on non-resistivity characteristics, such as the density calculation model based on density characteristics proposed by Alfred et al (2013) and Zhu et al (2019a), and the uranium content model proposed by Liu et al (2017). Another technique is the saturation calculation model; however, few studies have applied the saturation calculation model to shale gas reservoirs, and the accuracy of the saturation calculations cannot be guaranteed (Guo et al, 2019). Moreover, since the saturation parameter is more microscopic than the parameters, such as porosity, an algorithm-based version of the saturation evaluation model has not been reported.…”