Introduction 

Outcrop analogues can give additional geometric and kinematic constraints to help bridge the critical scale-gap needed to integrate seismic and borehole datasets. We use a range of digital survey methods (Figure 1) to capture detailed, spatially referenced outcrop data (Jones et al. 2004; McCaffrey et al. 2005). Of the various methods, Terrestrial Laser Scanning (also commonly called ground-based LiDAR) is usually the most efficient way to capture large amounts of spatially precise data from relevant areas of outcrop. With typical acquisition rates up to 12,000 points a second, laser-scanning makes it possible to rapidly acquire a detailed virtual copy of an outcrop, in which the topography of the outcrop is represented by a point cloud comprising tens or hundreds of millions of points (Figure 2b). With modern high-speed laser scanners (Figs. 1f and 2a), an experienced operator will usually be able to survey many hundred square metres of outcrop per day (depending on the nature of the topography and the level of detail required). In areas of good exposure and 3D topography, laser scanning is therefore an extremely efficient way to study large outcrop analogues of sedimentary and structural architectures on a scale directly comparable to seismically imaged structures, but with 100-1000 times better resolution. In this way, outcrop studies based on laser-scanning can effectively bridge the gap between seismic and borehole datasets.  

While laser-scanning has high potential to enhance outcrop studies, it is not a replacement for careful geological field work, and in our experience, the most useful approach to studying outcrop analogues is to integrate key geological observations, measurements, and interpretations into the detailed geospatial outcrop model derived from laser-scanning. To facilitate this integration in the field, the precise locations of geological observations and measurements are recorded using either differential Real-Time Kinematic (RTK) GPS (Figure 1a, b) or laser-ranging devices (Figure 1c), or are tied directly to specific points in the laser-scan dataset. RTK GPS is also ideal to enable laser-ranger and laser-scan data to be georeferenced to a global coordinate system.  

Outcrop to Basin Scale Models 

Additional digital methods are also useful to provide wider geographical and geological context to the virtual outcrop analogue (Figure 3). Integrating the laser-scan data with other more regional datasets, such as aerial images draped over a DEM, subsurface maps, ground-penetrating radar (Pringle et al., 2003), seismic sections and satellite data, make it easier for the geologist to visualise the spatial and scaling relationships between structures seen in outcrop and those of reservoir and basin scale (Jones et al., 2007, in press). Equally useful is to isolate the virtual outcrop dataset and to incorporate it directly into a reservoir model from a hydrocarbon field of current interest--to allow the asset team to study the analogue in full detail within the context of their own subsurface volume.  

Quantification of Structural Attributes 

The laser-scan data provide unprecedented detail and allow spatial variation in various fracture attributes to be quantified, including 3D curvature (Figure 4), fracture connectivity, branch-line geometry, relationship between corrugations and fault splays, detailed fault throw profiles, and the spatial correlation between fracture density and fold curvature. Measurement of such fracture parameters, collected from a range of outcrop analogues, provides direct quantitative input for calibration of geomechanical models and for validation of fracture networks derived by deterministic or stochastic methods. A case study using regular laser-scanning of an active opencast coal mine, provides additional constraint, with 3D fault geometries sequentially revealed throughout the rock volume, as the working coal face progressively migrates with time.  

References 

Jones, R.R., McCaffrey, K.J.W, Wilson, R.W., and Holdsworth, R.E. 2004. Digital field data acquisition: towards increased quantification of uncertainty during geological mapping, in Curtis, A., Wood, R., eds., Geological Prior Information: Geological Society Special Publication 239, p.43-56.

Jones, R.R., McCaffrey, K.J.W., Clegg, P., Wilson, R.W., Holliman, N.S., Holdsworth, R.E., Imber, J., and Waggott, S., 2007 (in press), Integration of regional to outcrop digital data: 3D visualisation of multi-scale geological models: Computers & Geosciences, v.33.

Kokkalas, S., Jones, R.R., McCaffrey, K.J.W., Clegg, P., 2007 (in press), Quantitative fault analysis at Arkitsa, Central Greece, using Terrestrial Laser Scanning (“LiDAR”): Bulletin of the Geological Society of Greece, vol. XXXVII, p.2007.

McCaffrey, K.J.W., Jones, R.R., Holdsworth, R.E., Wilson, R.W., Clegg, P., Imber, J., Holliman, N., and Trinks, I. 2005, Unlocking the spatial dimension: digital technologies and the future of geoscience fieldwork: Journal of the Geological Society, London, v. 162, p.927-938.

Pringle, J.K., Clark, J.D., Westerman, A.R., and Gardiner, A.R. 2003, Using GPR to image 3D turbidite channel architecture in the Carboniferous Ross Formation, County Clare, Western Ireland, in Bristow, C.S., Jol, H., Eds., GPR in Sediments: Geological Society Special Publication 211, p. 309-320.