3D Digital Solid Models of Petroleum Reservoir Outcrop Analogues

Jamie Pringle (Jamie.Pringle@pet.hw.ac.uk) and Andy Gardiner (Andy.Gardiner@pet.hw.ac.uk) of the Institute of Petroleum Engineering, at Heriot-Watt University outline their current outcrop analogue research

Over the past 10 years, the Genetic Units Project, within the (newly re-named) Institute of Petroleum Engineering has concentrated on providing quantitative data from petroleum reservoir outcrop analogues.  These data are used to produce static and dynamic models of the reservoir and, by the use of multiple scenarios and realisations, to quantify the degree of uncertainty.  On a sub-seismic scale, models require well-distributed, high-resolution, quantitative 3D data on the geometry, spatial distribution and petrophysical properties of the genetic sedimentary units within the reservoir.  Although core and log data are detailed, wells may be too sparsely distributed for accurate interpolation through inter-well volumes.

In a typical reservoir model, the missing information is simulated stochastically.  Current stochastic models rely on geostatistics obtained from 1D sedimentary logs or 2D outcrop analogues.  However, there are drawbacks to using 1D and 2D information to build a 3D reservoir model.  Statistical measures may be skewed by outcrop shape and orientation.  Parameters such as channel sinuosity and connectivity often remain undefined, although they may be of critical importance to sweep efficiency.

Our avenue of recent research, undertaken under the Genetic Units and Geotipe Projects, has therefore, concentrated on creating detailed 3D Digital Solid Models (DSMs) of petroleum reservoir outcrop analogues (see Pringle et al., 2003a).  DSMs are built from both surface and subsurface image products (see Pringle et al., 2001).  First a 3D surface Digital Terrain Model (DTM) is built from either conventional survey information or the digital photogrammetric products of aerial and terrestrial stereo-photography (Figure 1).

Figure 1:  Digital Outcrop Model (DOM) of the Bridges of Ross study site, County Clare, Western Ireland, produced from gridding differential GPS positional data points (dots) in Schlumberger’s Petrel software.  A Carboniferous, turbidite channel is exposed on 3 sides of a study site, with a 3D GPR dataset acquired (dense dots) to trace the intra-channel fill in three dimensions. (Click image for larger view)

Next, outcrop or aerial photographs, if available, are added to create a Digital Outcrop Model (DOM).  Finally, 3D Ground Penetrating Radar (GPR) dataset is added to build a Digital Solid Model (DSM) (Figure 2).  The GPR image volume is acquired behind the outcrop and scaled from time to depth by GPR velocity profiles of the cliff face (Pringle et al., 2003b). 

Figure 2:  Multiple, 2D GPR profiles, (50m grid spacing) obtained behind Alport Castles cliff face, in Derbyshire, UK, have been processed, and spatial information added, and then imported into Midland Valley's 3D Move software.  Correlation of sub-surface reflection events can be observed between the 2D GPR profiles.  Approximate locations of cliff section A and pseudo-wells 1-3 are marked. (Click image for larger view)

3D geometries are extracted from DSMs, and can then be used to build a suite of reservoir models (Figure 3).  These models can be increasingly detailed, incorporating varying amounts of 1D (sedimentary logs converted to pseudo-well logs), 2D (surveyed horizons on cliff faces) and 3D (GPR) information.  Analysis of these models will assess the reservoir connectivity and continuity variations that are a direct result of the input information.

Figure 3:  Reservoir model of Alport Castles, within a DEM produced from aerial photogrammetry.  Reservoir model is stochastically filled using 1D pseudo-wells (marked W1-5), produced from sedimentary logs.  Colour variations in the reservoir model reflect different bed elements.  Further models have been produced, integrating sub-surface (GPR) information. (Click image for larger view)

Current research is completing the workflow, by subjecting the reservoir models to repeated fluid flow simulations, with varying well locations to test the impact of well position on potential reservoir connectivity on each reservoir model.  Simulation results can then be used to evaluate the contribution of current outcrop analogue geostatistics in determining field scale predictions.

DSMs have the added bonus of providing digital data for virtual fieldtrips.  For example, in allied research, a digital model of Peak Cavern, in Derbyshire, UK, has been created by combining surveying, archaeological, sedimentary and sub-surface information (both resistivity and GPR) into a CAD model. A digital fly-through of the data has been constructed.  Results of the collaboration are being published in the new journal Speleology (Pringle et l 2003c).

References

Pringle, J.K., Clark, J.D., Westerman, A.R., Stanbrook, D.A., Gardiner, A.R. & Morgan, B.E.F. 2001. Virtual Outcrops: 3D reservoir analogues. In: Ailleres, L. & Rawling, T. 2001. Animations in Geology. Journal of the Virtual Explorer, 3.  http://virtualexplorer.earth.monash.edu.au/VEjournal/2001/Volume4/pringle.html

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

Pringle, J.K., Westerman, A.R., Clark, J.D., Guest, J.A., Ferguson, R.J., and Gardiner, A.R., (2003b), The use of Vertical Radar Profiles (VRPs) in GPR surveys of rock strata, In; Bristow, C.S. & Jol, H. (eds.), GPR in Sediments, Geological Society Special Publication 211, 221-242.

Pringle, J.K., Westerman, A.R., Schmidt, A., Harrison, J., Shandley, D., Beck, J., Donahue, R.E. & Gardiner, A.R. (2003c), Investigating Peak Cavern, Castleton, Derbyshire, UK: integrating cave survey, geophysics, geology and archaeology to create a 3D digital CAD model. Speleology, 1.

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