3‐D characterization of a clastic reservoir analog: From 3‐D GPR data to a 3‐D fluid permeability model

Abstract

A three‐dimensional (3‐D) 100 MHz ground‐penetrating radar (GPR) data volume is the basis of in‐situ characterization of a fluvial reservoir analog in the Ferron Sandstone of east‐central Utah. We use the GPR reflection times to image the bounding surfaces via 3‐D velocity estimation and depth migration, and we use the 3‐D amplitude distribution to generate a geostatistical model of the dimensions, orientations, and geometries of the internal structures from the surface down to ∼12 m depth. Each sedimentological element is assigned a realistic fluid permeability distribution by kriging with the 3‐D correlation structures derived from the GPR data and which are constrained by the permeabilities measured in cores and in plugs extracted from the adjacent cliff face. The 3‐D GPR image shows that GPR facies changes can be interpreted to locate sedimentological bounding surfaces, even when the surfaces do not correspond to strong GPR reflections. The site contains two main sedimentary regimes. The upper ∼5 m contain trough cross‐bedded sandstone with average permeability of ∼40 md and maximum correlation lengths [Formula: see text]. The lower ∼7 m contain scour and fill fluvial deposits with average permeability varying from ∼30 md to ∼15 md as clay content increases, and maximum correlation lengths [Formula: see text]. These representations are suitable for input to fluid flow modeling.

Quantitative integration of high-resolution hydrogeophysical data: A novel approach to Monte-Carlo-type conditional stochastic simulations and implications for hydrological predictions

“…There are close analogies between hydrocarbon reservoirs and aquifers in general, and their geophysical characterization in particular (e.g., Szerbiak et al, 2001). However, an important difference between the two disciplines is that high-resolution crosshole tomographic methods, which are widely used in hydrogeophysical studies, are generally unavailable for the geophysical characterization of hydrocarbon reservoirs.…”

Application of ground penetrating radar imaging to deepwater (turbidite) outcrops

“…For a fixed degree of water saturation, these changes are due mainly to differences in lithology between sediments, but they may also be due to facies change within units (Szerbiak, McMechan, Corbeanu, Forster, & Snelgrove, 2001). Coherent noise in GPR data, when present, can often be distinguished from reflections and can usually be attenuated Young & Sun, 1999).…”

Applying GPR-amplitude wave maps and Am-scans as a semi-quantitative approach to the internal structure of sediments

“…Radargrams show then the vertical propagation of the same reflector through apparently increasing TWT intervals. This kind of approaches have been used in other cases different to archaeological surveys to laterally correlate lateral net changes in the subsoil related with faults (Demanet et al, 2001;Green et al, 2003), to infer hydrogeological characteristics and permeability models (Asprion and Aigner, 1997;Corbeanu et al, 2002;McMechan et al, 1997;Szerbiak et al, 2001), internal characterization of infrastructures (Sudarmo et al, 1996), establish the drainage pipe patterns (Allred et al, 2008) cartography of clay content or clayish areas (De Benedetto et al, 2011;Weaver, 2006), internal characterization of karst processes (Kruse et al, 2006;PueyoAnchuela et al, 2009) or internal characterization of sediments by means isolated triggers (Nielsen et al, 2009), or to correlate sedimentary structures at glacio-fluvial deposits (Beres et al, 1999), gravel deltas (Asprion and Aigner, 2000), fluvial channels , aeolian river dunes (van Dam, 2002), coastal barriers (Jol et al, 2002) or multidisciplinary analysis for sedimentary structures, fractures and archaeological features (Grasmueck et al, 2004).…”