Inference of field‐scale fracture transmissivities in crystalline rock using flow log measurements

Abstract

[1] Characterization of transmissivity for crystalline rock is conducted through simulation by conditioning against borehole flow rates obtained from high-resolution, in situ field measurements during extraction pumping. Full three-dimensional discrete fracture network simulations are carried out according to specifications obtained from site characterization data in a stochastic Monte Carlo setting. A novel method of conditioning is thereby introduced and applied using nonparametric comparison tests, which provide quantifiable measures of accuracy enabling evaluation of simulated results against field measurements. The assumption of a constitutive relationship (perfect correlation) between fracture size and transmissivity is adopted. The method is evaluated against both single and multiple realizations, various domain size, and fracture length configurations and shown to be robust for the cases considered. When the introduced method of conditioning is applied, transmissivity parameterization can be inferred to a narrow range with a quantifiable accuracy in terms of a probability value. Results indicate that elementary interpretation of transmissivity based on homogenization of a porous medium will generally underestimate transmissivity. Further implications on advective transport for natural flow conditions are briefly evaluated, indicating advective breakthrough times can be overestimated up to a factor of about 10 in the median.Citation: Frampton, A., and V. Cvetkovic (2010), Inference of field-scale fracture transmissivities in crystalline rock using flow log measurements, Water Resour. Res., 46, W11502,

Linking Structural and Transport Properties in Three‐Dimensional Fracture Networks

“…Fracture apertures are determined by correlating their width to the fracture radii using a positively correlated power law relationship: where γ = 5.0 × 10 −4 and β = 0.5 are dimensionless parameters. This correlation between fracture size and aperture is a common assumption in DFN models (Bogdanov et al, ; de Dreuzy et al, ; Hyman et al, ; Frampton & Cvetkovic, ; Joyce et al, ; Wellman et al, ).…”

Flow‐dependence of matrix diffusion in highly heterogeneous rock fractures

“…Field data indicate that T is correlated with fracture length when considering a wide range of scales [Dershowitz et al, 2003;Hermanson et al, 2005;Hartley et al, 2006;Neuman, 2008]. Since fracture length is commonly assumed to follow a power-law distribution [Walmann et al, 1996;de Dreuzy et al, 2001], T in fracture networks then also follows a power-law distribution [Bonnet et al, 2001;Hartley et al, 2006;Frampton and Cvetkovic, 2010].…”

“…It is most readily measurable along boreholes either using pump tests or flow logs [e.g., Fransson, 2002;Gustafson and Fransson, 2005;Frampton and Cvetkovic, 2010;Follin, 2008]. Pump tests seal off sections of boreholes on a given scale using packers, ranging from say 0.1 m to tens of meters.…”

Inverse modeling of hydraulic tests in fractured crystalline rock based on a transition probability geostatistical approach

“…In particular, we compare calibration based only on hydraulic heads and calibration based on both hydraulic heads and flow rates and we quantify reduction in uncertainty of fractured rock properties by using flow rates as targets for calibration. This objective differs from that of Frampton and Cvetkovic [2010], who aimed at the characterization of transmissivity for crystalline rock through simulation by conditioning against PFL borehole flow rates. They did not focus on uncertainty of the fractured rock properties.…”