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Jones et al. “High resolution quantitative outcrop analogues” of 13 Revised version 24/08/2007

Calibration and validation of reservoir models: the importance of high resolution, quantitative outcrop analogues

RICHARD R. JONES1,2, KENNETH J.W. MCCAFFREY3, JONATHAN IMBER3, RUTH WIGHTMAN3,

STEVEN A.F. SMITH3, ROBERT E. HOLDSWORTH3, PHILLIP CLEGG3,4,

NICOLA DE PAOLA3, DAVID HEALY3,5, ROBERT W. WILSON3,6.

1. Geospatial Research Ltd., Dept. of Earth Sciences, University of Durham, DH1 3LE, UK 2. e-Science Research Institute, University of Durham, DH1 3LE, UK 3. Reactivation Research Group, Dept. of Earth Sciences, University of Durham, DH1 3LE, UK 4. Current address: GeoPressure Technology Ltd., Mountjoy Research Centre, Stockton Road, Durham, DH1 3UZ, UK 5. Current address: Institute of Geoscience Research, Curtin University of Technology, GPO Box U1987, Perth

WA6845, Australia 6. Current address: BP, Chertsey Road, Sunbury-on-Thames, Middlesex, TW16 7BP, UK

Corresponding author: [email protected]

Abbreviated title: “High resolution quantitative outcrop analogues”

Abstract – Rapidly developing methods of digital acquisition, visualisation and analysis allow highly detailed outcrop models to be constructed, and used as analogues to provide quantitative information about sedimentological and structural architectures from reservoir to sub-seismic scales of observation. Terrestrial laser-scanning (lidar) and high precision Real-Time Kinematic GPS are key survey technologies for data acquisition. 3D visualisation facilities are used when analysing the outcrop data. Analysis of laser-scan data involves picking of the point-cloud to derive interpolated stratigraphic and structural surfaces. The resultant data can be used as input for object-based models, or can be cellularised and upscaled for use in grid-based reservoir modelling. Outcrop data can also be used to calibrate numerical models of geological processes such as the development and growth of folds, and the initiation and propagation of fractures. Reservoir geologists have utilised a wide variety of different kinds of geological modelling over many years. These have helped to increase our understanding of basin dynamics, hydrocarbon systems and petroleum-related processes. Collectively, the various different approaches to modelling have spanned many orders of magnitude, from characterisations of overall lithospheric-scale properties, down to modelling of grain-scale processes (Fig. 1a). Types of modelling include analogue and numerical methods. Analogue models (e.g. sand-box models, flume experiments etc.) have a long track-record of providing useful insights into structural and sedimentological processes. Numerical-based approaches to modelling are ubiquitous in hydrocarbon exploration and production, aided by ever increasing improvements in the price-performance ratio of computers.

Common to all modelling strategies is the need to calibrate the model with realistic values of geological properties, and to test the validity of the model in relation to real-world petroleum systems. Input for models often relies most heavily on indirect geophysical data (Fig. 1b), particularly regional gravity and magnetic survey, 3D seismic and well-log data, together with direct analysis of well core, when available. Further input can be gained from studying suitable reservoir analogues (Fig. 1c). Direct geological observations made on well exposed outcrops can help to reduce some of the uncertainty normally associated with remotely imaged geophysical data. Another significant advantage is that outcrop analogues at reservoir scale also span scales of observation that extend down to sub-seismic levels. Thus, outcrop studies help to fill the gap in the


scale range (below ca. 2.5x101m) not normally captured using geophysical methods, and provide greater dimensionality than well-logs and core, which are essentially 1D in nature.

Although reservoir analogues have been used by petroleum geologists for many years, they have traditionally been based on predominantly qualitative outcrop studies. Within an overall study area, any quantitative study is typically restricted to only small regions of outcrop. Furthermore, most studies use 1D analysis methods, such as logging sedimentary sections, or measuring fractures along a line transect. Such limitations can be overcome using a number of modern digital survey technologies (Fig. 2), including methods based on high-precision GPS, laser-based distance measurement, and calibrated digital photography. This paper discusses ways in which survey methodologies based on digital technologies such as these can be combined to capture detailed geospatial outcrop data, and how the data can be interpreted to produce quantitative reservoir analogues that are 3D (or semi-3D) in character, which can help to calibrate and validate geological models used by reservoir geologists.

Quantitative Reservoir Analogues Where possible, when developing new procedures to capture and process quantitative outcrop data, we have based our approach on basic principles adopted from standard hydrocarbon exploration strategies. In this way, our workflows utilise elements of digital data acquisition, data processing, 3D computer graphics, and geological interpretation. Our workflows group together a range of complimentary methods, collectively termed “GAVA” (Geospatial Acquisition, Visualisation & Analysis).

Fig. 1. Schematic diagram showing the approximate range of scales typically covered by a variety of types of numerical and analogue modelling; (a) Representative examples of modelling methods; (b) Examples of indirect geophysical observations typically used to calibrate models of petroleum systems; (c) Types of direct observations that are essential in validating the results of modelling. Outcrop analogues are particularly important in covering the lack of indirect geophysical data for scales of observation below seismic resolution. From McCaffrey et al. (2005)


Fig. 2. Examples of methods of digital data acquisition; (a) Real-Time Kinematic (RTK) dGPS, stationary base-station. In the field, differential GPS locates the base station with an accuracy of ca. 0.5m; this is improved by post-processing to ca. 10 mm; (b) Two RTK dGPS rover-units. A positional fix relative to the base-station can be made instantaneously, typically with a precision of ca. 10mm; (c) MDL LaserAce 300 laser-ranging device, with hand-held PDA data-logger. The laser-ranger is used to measure the precise location of individual observations and structural measurements relative to the instrument. RTK GPS is then used to measure the accurate location of the instrument, and thus the absolute position of all its relative measurements; (d) terrestrial laser-scanning using MDL Quarryman. The data captured includes x,y,z position and intensity information for each point scanned, and the resultant laser-scan point-cloud can be imported into most 3D visualisation tools; (e) false-colour laser-scan point-cloud from MDL scanner, imported into Gocad; (f) Riegl LMS-Z360i laser-scanner, with top-mounted high resolution digital camera (to give true-colour point cloud data) and RTK dGPS unit to record precise scanner location; (g) true-colour point cloud data from Riegl LMS-Z360i scanner. Locations: (a) analysis of fault-related folding, Howick, NE England (see Pearce et al. 2006a); (b) segmented faults, Lamberton, SE Scotland; (c) study of onshore analogues for Devonian clastics of West Orkney Basin, Kirtomy, N Scotland; (d-e) faulting in Carboniferous sandstone/shale sequence, NE England; (f-g) study of fractured carbonates, Flamborough, E England.


Acquisition

Our primary survey methods (Fig. 2) are based on a combination of terrestrial laser-scanning (e.g. Ahlgren & Holmlund 2002; Jennette & Bellian 2003; Jones et al., 2004; Løseth et al., 2004a, 2004b; Bellian et al., 2005; Clegg et al., 2005), laser-ranging (Xu et al., 2001, Løseth et al., 2003; Jones et al., 2004), high-precision GPS (e.g. Xu et al., 2000; Maerten et al., 2001; McCaffrey et al., 2005a; Pearce et al., 2006a,b), and digital photogrammetry (Pringle et al., 2001, 2004; Hodgetts et al., 2004). The most suitable technology to capture a given outcrop depends on a number of factors:

o the purpose of the study; o the nature of the outcrop, including amount of exposure and accessibility; o the level of detail required; o the spatial precision needed; o time and cost constraints.

In most situations optimum results are obtained by combining more than one method. Terrestrial laser-scanning is our preferred core technology for acquisition of highly detailed, photo-realistic outcrop models. This is usually supported by high-precision “Real Time Kinematic” (RTK) GPS to provide geospatial control, as well as laser-ranging to allow additional geological observations and measurements to be referenced to the laser-scan data with centimetre precision. The resultant outcrop models are sufficiently detailed to be used for virtual fieldtrips, and provide enhanced ability to study parts of the exposure that are not easily (or safely) accessible on the real-world outcrop. Virtual outcrop models can be further analysed to provide quantitative information about structural and sedimentological architectures (see below). Where possible, valuable additional 3D constraint on outcrop models can be gained by acquiring data from the shallow sub-surface, using methods that include ground penetrating radar (GPR), ultra-shallow seismics, and boreholes sited behind the outcrop face (e.g. Pringle et al., 2003, 2006; Young et al., 2003; Stepler et al., 2004).

To construct a complete reservoir-scale model at the resolution of a highly detailed virtual outcrop analogue would place unrealistic demands on available hardware performance. An effective strategy to overcome this issue is to build multi-scale models, in which local areas of detailed outcrop are placed within a wider geological and topographical context by including nested levels of coverage (Jones et al., in press). Multi-scale models can provide reasonably seamless coverage from outcrop to basin scales, with larger areas showing progressively less detail. Whilst data capture at the outcrop scale typically uses survey-grade equipment (laser-scanners and RTK dGPS), coverage for larger areas uses digital geological mapping (McCaffrey et al., 2005a; Clegg et al., 2006; Wilson 2006) and regional-scale remotely imaged data (e.g. satellite and aerial images, geophysical datasets).

Visualisation

Visualisation is closely linked to analysis and interpretation of the acquired digital outcrop data, and is of central importance in maximising the usefulness of the virtual outcrop model as a reservoir analogue. In our work we routinely use a range of visualisation equipment that varies in terms of processor power, memory (RAM), and graphics capability, as well as cost. Most modern desktop PC’s have impressive 3D visualisation capability, and are powerful enough for routine visualisation and analysis. For example, we typically use high-end PC’s running Windows or Linux to visualise and analyse laser-scan datasets comprising coloured point-clouds containing up to 20 million points (typically covering an outcrop area of 1-3 km2), or to visualise a multi-scale model consisting of 1000 km2 of regional satellite data, as well as 100 km2 of high resolution imagery and digital geological map data, plus local areas of detailed virtual outcrop data embedded into the model. For more demanding visualisation tasks involving larger datasets, we use a dedicated Silicon Graphics workstation.

3D visualisation is enhanced by using auto-stereoscopic displays (Fig. 3a), which provide a stereoscopic image without the need for stereo-glasses (Holliman, 2005), and which significantly improve the users’ ability to work with 3D data. For fully immersive real-time interactive graphics sessions we use a purpose built HIVE (High Impact Visualisation Environment) equipped with


stereoscopic back-projection and 3D wireless tracking system (Fig. 3b). The HIVE can be driven from Windows, Linux and Silicon Graphics environments. Because desktop PC’s have a lower overhead in terms of maintenance and technical support, we generally use low-end machines for much of the preliminary processing, basic visualisation tasks, and construction of first versions of a virtual model, reserving the high-performance, immersive graphics environment for detailed re-interpretation and collaborative sessions involving multiple users (Fig. 3b).

The various different formats of geospatial data used in virtual outcrop models are compatible with many of the visualisation software tools in common use in exploration and production environments. For smooth visualisation of laser-scan data, the choice of software can be critical, since not all visualisation tools are optimised to display very large point-cloud datasets. Trial and error, and a degree of experience, are often needed to optimise the performance of a particular combination of hardware and software. One advantage of some of the software tools that are specifically designed to render point-cloud data, is that it is possible to use different visualisation modes for display of the outcrop model (Fig. 4). Showing all the data as individual points (“glyphs”) is very efficient in terms of computing power (so very large datasets can be manipulated in real-time), though there is a lack of detail when close to the outcrop. By converting the point-cloud into a meshed surface, and draping a digital image onto the mesh, it is possible to show very high levels of detail in the model. However, this carries a much higher overhead in terms of graphics performance, so that it is best suited for showing close-up areas of detail, or for viewing larger models that have been decimated to reduce the number of panels in the mesh (at the expense of detail in the surface topography).

Fig. 3. Data analysis in 3D visualisation environments: (a) dual-head graphics display with both auto-stereoscopic (left) and normal screens (right), on Windows PC using Schlumberger’s Petrel software. The auto-stereoscopic screen is similar in appearance to a standard LCD display, but generates a 3D image without the need for stereo glasses. (b) Collaborative 3D interpretation session in fully immersive, interactive HIVE, using custom point-cloud visualisation software on a high-end PC running Linux.


Analysis – interpretation of geospatial data to produce 3D models

Virtual outcrop models are no substitute for real field study on the outcrop, and geological observations in the field are always the most important element in maximising the analysis of digital data to full effect. However, using a virtual model in addition to field study can certainly enhance interpretation significantly. Visualising the model allows the geologist to view parts of the outcrop that are not normally accessible (e.g. areas of outcrop above head height, cliff sections flanked by water), and the ability to zoom rapidly in and out from the outcrop provides perspective at different scales of observation. Elevated vantage points can often give a clearer view of stratigraphy, and it is useful to be able to navigate quickly to the exact viewpoint that shows how structures of other geological features are aligned in 3D. Often it is useful to try viewing positions inside an outcrop looking out, as it can sometimes be easier to recognise geological surfaces from a vantage point inside the rock. The virtual model can also be rendered using false colour to emphasise different aspects of the outcrop.

Most importantly, the digital data provide the basis for geospatial analysis of the outcrop to derive quantitative interpretation of structural geometries (e.g. Maerten et al. 2001; Ahlgren et al., 2002; Jones et al., 2004; Trinks et al., 2005; Pearce et al., 2006a,b; Kokkalas et al., 2007) and sedimentological architectures (e.g. Pringle et al., 2001, 2004, 2006; Løseth et al., 2003, 2004a, 2004b; Jennette & Bellian, 2003; Hodgetts et al., 2004; Bellian et al., 2005; Labourdette & Jones, in press). Examples of quantitative information derived from virtual outcrop models and used in reservoir modelling include:

o overall facies distribution; o lateral variations in stratigraphy; o morphology of fluvial and submarine channels; o turbidite architectures;

Fig. 4. Different visualisation modes for laser-scan point-cloud data: (a) rendering data as individual points can be efficient for the graphics hardware, and is particularly useful when picking precise geological surfaces in the raw data; (b) meshing the point-cloud and draping a detailed image over the meshed surface needs to be optimised carefully to avoid placing much higher demands on hardware, but when done well can give photo-realistic results even close-up to the model (height of arch: ca. 2 m).


o fold geometries, including non-cylindrical anticlinal closures and whale-back fold culminations;

o 3D fracture distributions, giving a characterisation of bulk structural heterogeneity; o fracture orientation, density and connectivity, and their relation to fold closures; o effects of sub-seismic scale damage zones adjacent to basin-scale faults.

In most cases, the virtual outcrop data are interpreted by “picking” stratigraphic and structural surfaces within the point cloud (Fig. 5), in a process comparable with horizon picking in 3D seismic (Jones et al., 2004; Clegg et al., 2005; Trinks et al., 2005). The picks are combined with other data measured directly in the field (e.g. surface traces of structures surveyed with RTK dGPS equipment), and the point sets are interpolated to produce continuous surfaces that can be extrapolated into (and out of) the sub-surface. Clearly, interpolation is best constrained when good 3D exposures are used, and/or when additional data are available from the shallow sub-surface close to the outcrop.

Calibration of Geological Modelling using Digital Outcrop Data Once the virtual outcrop data has been fully picked, the resultant stratigraphic-structural interpretation can be used to calibrate reservoir models. Interpolated surfaces are imported into object-based modelling software for further analysis (e.g. Gocad, Petrel, 3DMove, TrapTester etc.), or the model can be cellularised and upscaled for use in cell-based modelling and fluid-flow simulation. A seminal study by Løseth and co-workers (Løseth, 2004; Løseth et al., 2004), using virtual outcrop models as a framework for simulating hydrocarbon production, has emphasised the critical influence of fine-scale heterogeneity, both on STOIIP (“stock tank oil initially in place”) and sweep rates. Using a fine-scale model incorporating channel geometries derived from digital outcrop data, the study showed that standard upscaling and modelling methods in this case significantly overestimated reserves by as much as twice the actual magnitude. Studies such as these highlight the importance of calibrating reservoir models with real-world outcrop data recorded by direct observation from reservoir scale down to sub-seismic resolution (c.f. Fig. 1c).

Fig. 5. (a) Fault picking in laser-scanner point-cloud data; (b) interpolated plane through the picked points. (Study of fractured carbonates, Flamborough, E England. See also Waggott et al., 2005).


In addition to the calibration of reservoir models, digital outcrop data can also be used as a quantitative basis for calibration of other kinds of modelling, including detailed studies that examine structural geometries in relation to a wide range of other variables such as petrophysical properties and geological processes. Numerical modelling methods have the advantage that individual variables can be isolated and their effects studied, so that large amounts of a model’s parameter-space can be fully examined. The disadvantage is that since all modelling requires simplifying assumptions to be made, care is needed to ensure that complex numerical models retain a reasonable approximation to geological reality. In contrast, digital outcrop data provide an excellent quantitative measure of real-world geology, but there are still far too few available outcrop studies to be able to populate multi-parameter models adequately. By combining both approaches, however, we can use the sparse but validated outcrop data as key reference points with which to calibrate the results of multi-parameter numerical modelling (Fig. 6).

The following case studies illustrate the way in which detailed digital outcrop data can be used as a basis for improved reservoir characterisation and increased understanding of structural processes. The case studies are part of ongoing work to analyse seismic to sub-seismic scale faulting, and to quantify relationships between fracturing and folding. The study areas lie in the northernmost onshore outcrops of the Carboniferous Northumberland Basin, and are related to a phase of Late Carboniferous oblique extension (De Paola et al. 2005a).

Case study: displacement patterns in relay faults

The long-term aims of this study are to analyse 3D geometry and displacement patterns of small-scale segmented fault and fracture systems seen in outcrop, to compare these quantitatively with larger-scale structures imaged in 3D seismic, and to use the 3D outcrop models to test the effect of the fracture network on fluid flow. Figs. 7a & 7b show a set of segmented normal faults in a sandstone/shale sequence from coastal exposures at Lamberton, SE Scotland (Wightman et al., 2007; Imber et al., 2007).

Fig. 6. Symbolic depiction of the use of quantitative outcrop data to calibrate multi-parameter numerical models. The results of numerical modelling can populate the entire parameter-space (here limited to three variables for clarity), but need to be validated with real-world data: the outcrop data (represented by individual spheres) are sparse, but represent points of geological reality within the numerical model.


The faults exposed at Lamberton have small displacements, typically of the order of 10-1m, and

therefore in order to ensure that the precision of the digital equipment was adequate for such a small-scale study, measurements of fault tips and hangingwall and footwall cut-offs were repeated using three different methods. Fault geometry and displacement were directly measured in the field

Fig. 7. Segmented faulting in sandstone/shale sequence at Lamberton, SE Scotland: (a) outcrop photo; (b) structural interpretation showing exposed fault planes and fault tips; (c) lidar laser scan data from the outcrop, with fault panels picked from three sandstone horizons (note: the sandstone pedestal on the right of the photo in (a) was removed by erosion prior to acquisition of the lidar data); (d) meshed fault panels imported into Badley’s TrapTester for further analysis; (e) example of fault attribute data derived from the outcrop model: plot of throw population showing exponential distribution (Imber et al., 2007); (f) numerical analysis of relay fault development using elastic dislocation modelling.


using both RTK dGPS equipment (see Fig. 2b), and also by manual measurements of fault plane dip and offset using compass-clinometer, steel rule and measuring tape. In addition, the outcrop was scanned in detail using lidar equipment, and the position of the footwall and hangingwall cut-offs (and hence fault displacements) were derived from the virtual laser scan point cloud (Fig. 7c). Data were then exported to TrapTester, GoCad and ArcGIS software for further analysis (Fig. 7d). Many different spatial and geometrical attributes can be rapidly derived directly from the 3D network model; the cumulative fault throw plot shown in Fig. 7e is just one of many examples (Imber et al. 2007). The virtual outcrop dataset and derived 3D fracture network model are now being used by the project sponsors (BG and Shell) to extract additional quantitative fracture characteristics as part of their ongoing exploration activities. We are also using the Lamberton data to condition numerical models of relay fault development based on elastic dislocation methods (Fig. 7f; Healy et al., 2004), and to compare the real-world data with the predictions of distinct element modelling (c.f. Imber et al., 2004).

Case study: 3D fold geometry and models of fold development

Meso-scale folds in the Northumberland basin are intimately associated with domains of wrench-faulting during regional transtension (De Paola et al. 2005a, 2005b). Folds are typically highly non-cylindrical, with fold amplitude often decreasing rapidly along strike from local culminations and antiformal closures. In many localities, the strong mechanical anisotropy of the limestone/sandstone/shale sequence is also likely to have had an important influence on fold development. Folded bedding surfaces in coastal exposures in NE England were surveyed using RTK dGPS equipment at Howick (Fig. 8a,c; Pearce et al., 2006a), and RTK and laser scanning at Scremerston (Fig. 8b,d; Pearce et al., 2006b). The raw field data from the folded surfaces were meshed using Matlab and Gocad software to form a detailed model of the fold geometry (Fig. 8c). This was the basis for comparison with models of fault-propagation folding (McCaffrey et al. 2005b), based on the trishear models of Cristallini & Allmendinger (2001). The data have also been used to compare spatial variations in fracture density with position on the fold and curvature of the

Fig. 8. Non-cylindrical fold development at Howick (a,c), and Scremerston (b,d), NE England: (a) RTK survey of a single folded bedding surface; (b) laser scanning of a tight fold pair. A, B, & C are reference points to help comparison between upper and lower images; (c) meshed RTK data (fold surface contoured according to curvature); (d) lidar data with picked fracture traces across the fold hinge.


fold surface, to test the supposition that the highest concentration of fracturing will coincide with greatest curvature (Pearce et al., 2006b).

Conclusions There is currently renewed interest in outcrop geology within the hydrocarbon sector, driven by a need to improve reservoir characterisation during exploration, as well as ongoing efforts to tackle production related issues. New methods of digital field survey, in particular terrestrial laser-scanning supported by high-precision Real Time Kinematic dGPS equipment, allow quantitative outcrop geology to be captured with unprecedented levels of detail and geospatial accuracy. Digital data can be analysed using standard 3D visualisation facilities, and interpreted and collated into virtual outcrop models, which encapsulate the geospatial distribution of sedimentological and structural architectures. Multi-scale virtual models combine data from several orders of magnitude, and help to place detailed outcrop data in the wider context of reservoir or regional scale geology.

Data from outcrop models can be exported in standard industry formats for further analysis in many modelling software packages. These include object-based tools (e.g. TrapTester, 3DMove, Petrel, Gocad), and cell-based reservoir modelling software (e.g. Eclipse, IRAP RMS etc.). Digital outcrop data can also be used to validate the predictions made by other kinds of modelling, including numerical models of geological processes such as the formation of folds and the development of 3D fracture arrays.

Thus, outcrop data can provide a quantitative 3D framework for the calibration and validation of deterministic and stochastic geological models used in hydrocarbon exploration and production. Virtual outcrop models fill an important gap in the range of scales of observable geological data (Fig. 1). Quantitative outcrop data have demonstrated the critical importance of small-scale anisotropy imaged at a finer resolution than typical grid sizes used in reservoir modelling, and future improvements in the ability of modelling software to match geological reality will significantly enhance reservoir characterisation.

Acknowledgements Editorial assistance from Paul Griffiths and constructive reviews from Jamie Pringle and an anonymous referee have helped to improve this paper. Parts of this work were developed with assistance of the following sponsors: Ocean Margins Link project (NER/T/S/2000/01018), funded by Statoil UK Ltd, BP and NERC; FR3DA consortium members Shell, BG and DTI; as well as NERC Follow-on Funding (NE/C506964/1). RRG gratefully thank Schlumberger and Badleys for non-commercial software licences and ongoing assistance. Mark Pearce gave valuable assistance with data collection and interpretation. Nick Holliman and Immo Trinks at the e-Science Research Institute at Durham, and Dave Stevenson, Gary Wilkinson and members of RRG have given essential assistance.

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Pearce, M.A., Smith, S.A.F., Jones, R.R., McCaffrey, K.J.., Clegg, P., Holdsworth, R.E, 2006b. Quantifying Fold and Fracture Attributes Using Real Time Kinematic (RTK) GPS and Laser- Scanning. American Geophysical Union, Annual Meeting, 11-15 December. Programs with Abstracts.

Pringle, J.K., Clark, J.D., Westerman, A.R., Stanbrook, D.A., Gardiner, A.R., Morgan, B.E.F. 2001. Virtual Outcrops: 3-D reservoir analogues. In: Ailleres, L., Rawling, T. (Eds.), Animations in Geology. Journal of the Virtual Explorer, 3.

Pringle, J.K., Clark, J.D., Westerman, A.R., 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, 309-320.


Pringle, J.K., Westerman, A.R., Clark, J.D., Drinkwater, N.J., Gardiner, A.R. 2004. 3D high-resolution digital models of outcrop analogue study sites to constrain reservoir model uncertainty: an example from Alport Castles, Derbyshire, UK. Petroleum Geoscience 10, 343-352.

Pringle, J.K., Howell, J.A., Hodgetts, D., Westerman, A.R., Hodgson, D.M. 2006. Virtual outcrop models of petroleum reservoir analogues: a review of the current state-of-the-art. First Break, 24, 33-42.

Stepler, R.P., Witten, A.J., Slatt, R.M. 2004. The Meter Reader – Three-dimensional imaging of a deep marine channel-levee/overbank sandstone behind outcrop with EMI and GPR. The Leading Edge, 23, 964-1082.

Trinks, I., Clegg, P., McCaffrey, K.J.W., Jones, R.R., Hobbs, R., Holdsworth, R.E., Holliman, N., Imber, J., Waggott, S., Wilson, R. 2005. Mapping and analysing virtual outcrops. Visual Geosciences, doi:10.1007/s10069-005-0026-9.

Waggott, S., Clegg, P. & Jones, R. 2005. Combining Terrestrial Laser Scanning, RTK GPS and 3D Visualisation: application of optical 3D measurement in geological exploration. Proceedings of the 7th Conference on Optical 3D Measurement Techniques, Vienna, 3rd-5th October 2005. Paper available for download from: http://www.geospatial-research.com/RnD/research_news.htm

Wightman, R., Imber, J., Jones, R.R., Healy, D., Holdsworth, R.E., McCaffrey, K.J.W. 2007. Damage Zone Evolution in Coal Measures Strata From the Northumberland Basin, NE England. American Association of Petroleum Geologists, International Meeting, Programs with Abstracts, Long Beach, USA, April 1-4.

Wilson, R.W. 2006. Digital fault mapping and spatial attribute analysis of basement-influenced oblique extension in passive margin settings. Unpublished PhD thesis, University of Durham.

Xu, X., Aitken, C.L.V, Bhattacharya, J.B., Davies, R.K., Corbeanu, R.M., Nielsen, K.C., McMechan, G.A., Abdelsalam, M.G. 2000. Creating virtual 3-D outcrop. The Leading Edge 19, 197-202.

Xu, X., Bhattacharya, J.B., Davies, R.K., Aitken, C.L.V. 2001. Digital Geologic Mapping of the Ferron Sandstone, Muddy Creek, Utah, with GPS and Reflectorless Laser Rangefinders. GPS Solutions 5, 15-23.

Young, R.A., Slatt, R.M., & Staggs, J.G. 2003. Application of ground penetrating radar imaging to deepwater (turbidite) outcrops. Marine & Petroleum Geology, 20, 809-821.

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