Oil & Gas - Maximising Recovery Programme (formerly SHARP) IOR Views

Introduction

The 3-D permeability tensor, and hence the fluid flow behaviour of a fractured reservoir is strongly influenced by the fracture (or fault) anisotropy. This parameter measures the directional variation in fracture orientation, distribution, connectivity, surface area and aperture (or fault thickness & throw) within a reservoir volume. 1-D and 2-D fracture attributes (Figure 1) are determined routinely from cores or onshore reservoir analogues. However, it is usually not possible to predict reliably the equivalent 3-Dstructural attributes (Figure 1) – and hence the 3-D fracture anisotropy – from these datasets (Harris et al., 2003).

Figure 1: Strategies for sampling 1-, 2- and 3-D fault & fracture attributes. Upper row shows an example of a fault system mapped from 3-D seismic data. Lower row shows an example of sub-seismic scale fractures (deformation bands) within a porous sandstone host rock. Table lists the 1-, 2- & 3-D attributes relevant to characterising the fracture anisotropy of a reservoir.

We propose to address this problem by applying innovative digital data acquisition (including laser scanning), visualisation and analysis workflows to construct deterministic 3-D fracture models derived from outcrops, quarries and mines in a range of tectonic and sedimentological environments that are appropriate to our sponsors’ offshore reservoirs. These “virtual outcrop models” will provide a unique opportunity to quantify the geometric and geospatial attributes of sub-seismic fracture networks in three-dimensions. Results will be presented to sponsors in the form of an Interactive 3-D Digital Atlas, with desktop and immersive visualisation capabilities.

Methodology

Quantifying the 3-D geometric and geospatial attributes of the sub-seismic fracture network is a non-trivial problem. The solution requires: (1) exceptional 3-D exposure; and (2) an accurate, efficient method to capture the 3-D structural data. The Durham team has developed new GAVA (Geospatial Acquisition, Visualisation & Analysis) workflows to capture, visualise and analyse 3-D geological data (Figure 2; McCaffrey et al., 2005). The critical advantage is that GAVA workflows preserve geospatial information so that the faults and fractures are located in their correct absolute XYZ positions. Real Time Kinematic GPS and ground-based LiDAR (“Laser Scanning”) equipment (both of which have sub-centimetre precision) are used to collect surface topography and fault/fracture trace data from natural outcrops or active quarry/mine faces (Clegg et al., 2005; Trinks et al., 2005; Wilson et al., 2005). We then apply 3-D regression statistics and semi-automated surface fitting algorithms to construct the 3-D fracture model by interpolating the fracture planes into the outcrop, and between successive active quarry/mine faces (Figure 2). The application of 3-D regression methods is an important step that enables us to quantify the quality-of-fit between the modelled fracture planes and the fracture traces observed in outcrop (Jones et al., 2004). It is then a relatively straightforward process to extract the 1-D, 2-D and 3-D fracture attributes and to import the raw data (surface scans & fault/fracture maps) and 3-D fracture models into a desktop or immersive visualisation environment.

Figure 2: Key steps in the GAVA workflow. (a) Data acquisition. In this example, we are making a laser scan of deformation bands in sandstone. (b) Raw point cloud data produced by the laser scanner captures the surface topography & fracture trace geometry. (c) We then fit a surface to the fracture trace data using 3-D regression statistics. (d) Finally, we produce a deterministic 3-D virtual outcrop model of the sub-seismic scale fracture network.

Deliverables

1. An interactive 3-D Digital Atlas (with desktop & immersive visualisation capability) of sub-seismic faults and fractures in a range of tectonic and sedimentological environments.

The Atlas will incorporate:

1. A quantitative database of the 1-D, 2-D and 3-D structural attributes of sub-seismic faults and fractures;
2. An assessment of the sensitivity of fluid flow to variations in the 3-D sub-seismic fracture network.

References

1. Clegg, P., Trinks, I., McCaffrey, K., Holdsworth, B., Jones, R., Hobbs, R., Waggott, S., 2005, Towards the virtual outcrop, Geoscientist 15, 8-9.
2. Harris, S.D., McAllister, E., Knipe, R.J., Odling, N.E., 2003, Predicting the three-dimensional population characteristics of fault zones: a study using stochastic models. Journal of Structural Geology 25, 1281-1299.
3. Jones, R.R., McCaffrey, K.J.W., Wilson, R.W., Holdsworth, R.E., 2004, Digial field data acquisition: towards increased quantification of uncertainty during geological mapping. In: Curtis, A., Wood, R., (eds), Geological Prior Information: Informing Science and Engineering, Geological Society, London, Special Publications 239, 43-56.
4. McCaffrey, K.J.W., Jones, R.R., Holdsworth, R.E., Wilson, R.W., Clegg, P., Imber, J., Holliman, N., Trinks, I., 2005, Unlocking the spatial dimension: digital technologies and the future of geoscience fieldwork. Journal of the Geological Society, London 162, 927-938.
5. Trinks, I., Clegg, P., McCaffrey, K., Jones, R., Hobbs, R., Holdsworth, B., Holliman, N., Imber, J., Waggott, S., Wilson, R., 2005, Mapping and analysing virtual outcrops. Visual Geosciences, DOI 10.1007/s10069-005-0026-9
6. Wilson, R., McCaffrey, K., Jones, R., Imber, J., Clegg, P., Holdsworth, B., 2005, Lofoten has its faults!, Geoscientist 15, 5-9.

Acknowledgements

The authors would like to thank Dr David Healy (Liverpool University) for his considerable input into an earlier version of this proposal and Dr Duncan Anderson (ITF, Aberdeen) for his encouragement and logistical support.