Exploring Critical Assumptions of Petrophysical Models in Fractured Aquifers by Comparing Estimated Porosity Values Obtained from Surface Nuclear Magnetic Resonance and Shallow Seismic Refraction Surveys.

Monday, 14 December 2015
Poster Hall (Moscone South)
Brady A Flinchum, Steve Holbrook, Dario Grana and Andrew Parsekian, University of Wyoming, Laramie, WY, United States
Estimating subsurface porosity from most near-surface geophysical techniques relies on petrophysical relationships. Using petrophysical relationships are challenging because they require many assumptions and oftentimes require site-specific constants. Despite complexities and challenges, the petrophysical relationships are critical to convert the measurable physical properties into hydrologic properties such as porosity, water content and ultimately hydraulic conductivity. In this study we compare porosities derived from shallow seismic refraction (SSR) and surface nuclear magnetic resonance (SNMR) in a fractured granite aquifer in the Laramie Range, Wyoming. To estimate porosity from the SSR data we use a Bayesian inversion based on Hertz-Mindlin contact theory and Hashin- Strickman boundaries. This type of petrophysical model requires us to make assumptions about the grain structure, mineralogy and water content. Using water table measurements from a borehole we assume that all pores are fully saturated below 10 meters, thus the SNMR measurement provides an estimate of porosity. If the petrophysical model and the assumptions that are required to use it were correct and the SNMR measurements were perfect, the estimates of porosities derived from two distinct physical measurements should provide the same porosity. Interestingly, we observe a large discrepancy in the porosities derived from this unique combination of measurements. At depths of 10 to 20 meters, the area that we interpret as fractured bedrock and where the assumption of fully saturated pores holds, the SSR predicted porosities are 15 to 20 % higher than those predicted by SNMR. Previous comparisons of the Bayesian inversion have shown it does well to predict porosity within the saprolite. The large discrepancy illustrates the need to use separate petrophysical models in the weathered and fractured zones of granite aquifers. More research is needed to figure out how to combine different petrophysical models that fulfill assumptions that fit conditions observed in the subsurface.