Rock Grain Size Prediction from NMR Measurement and Digital Rock Modeling- Experimental Validation

Abstract

Abstract This paper presents an experimental study to test and validate a technique of predicting rock grain size distribution (GSD) from nuclear magnetic resonance (NMR) measurements and digital rock modeling. GSD is one of the basic parameters in geo-science and petroleum engineering. Such information is commonly obtained from sieve or laser particle size analysis in the laboratory on a limited number of core samples. The measured GSD is then approximated to be representative of the targeted reservoir rock. A technique that estimates a continuous GSD profile along a wellbore embodies a significant technical and economic advantage. In this paper, GSD was predicted from NMR responses of fully water saturated rock samples using digital rock modeling techniques, which generates a rock model and simulates NMR responses by considering mineralogy, surface relaxivity and the surface roughness of rock grains. For validation, NMR measurement, GSD, mineralogy, and permeability measurements were performed on the rock samples used for digital rock modeling. Three outcrop Berea sandstone samples were used in this study; one for calibration to tune the modeling parameters and the other two for validation. The sieve analysis method was used to measure the GSD, in which petrographic observation was applied to confirm that grains were properly disaggregated. Results from this study indicated that sample composition played a key role in the prediction of GSD from NMR measurements, because grain surfaces change with mineralogy and are affected by diagenetic processes. A parameter, surface roughness factor, was used to account for these effects. For Berea sandstone samples, if its composition is assumed to be pure quartz, the predicted GSD could not match with the measured one. Once the measured mineralogy was used in the modeling, it agreed well with the measured GSD. The same parameters were further used to predict GSD for all remaining Berea sandstone samples, with satisfactory results. The presented methodology may be applied downhole to predict a continuous GSD profile of a reservoir interval, providing that mineralogical composition and an NMR log are available. Drill cuttings or prior knowledge based on cores can also provide such information. The predicted GSD provides useful information for geo-science and petroleum engineering applications such as permeability prediction.