Fluvial deposits create significant hydrocarbon reservoirs, although their characterisation can be difficult due to their differing scales of heterogeneity. Whilst numerical modelling methods have advanced to statistically honour fluvial input datasets, geologically realistic features are often lost, impacting hydrocarbon recovery predictions. Two dimensional training images are often used to dictate what heterogeneity is inputted into multi-point statistics based reservoir models. In this study, a three dimensional training image is built, based upon depositional conditions derived from outcrop and modern satellite imagery data of a fluvial system. The aims of this study are to: identify the heterogeneity within the modern and outcrop data and to replicate it in a three dimensional training image; to model such heterogeneity using object-based, sequential indicator simulation and multi-point statistics; and to qualitatively and quantitatively (through static net-connectivity testing) analyse the reproducibility and geological realism of the generated reservoir models. Digital photogrammetric data from Tuscher Canyon, Utah, of the Lower Castlegate Sandstone and satellite imagery from the Jamuna River, northern India, are used to depositionally condition a three dimensional training image. This training image was then used to generate the multi-point statistics models, which were then tested against more traditional object-based and sequential indicator simulation reservoir models. Results indicated that object-based models realistically reproduced heterogeneous architectural elements, however, the connectivity of net-reservoir elements were unrealistically shaped and over-connected. The sequential indicator simulated models produced unrealistic heterogeneous architectural elements and overestimated the connectivity of net-reservoir elements. The multi-point statistical models realistically reproduced heterogeneous architectural elements geometries and the connectivity of net-reservoir elements. Study implications suggest that, based upon limited data, depositional conditioning can generate three dimensional training images to produce reservoir models that are both geologically realistic and reproducible.
Keele University Faculty of Natural Science Funding for Training and Transferable Skills are acknowledged for part-funding this study. The Basin Dynamics Research Group at Keele University are thanked for stimulating discussion and comments on the manuscript. The second author would like to thank members of the FORCE consortium (AkerBP, BP, ConocoPhillips, DEA, ENGIE, ENI Norge, INEOS, Lundin Norway, Point Resources, Repsol, Statoil, Spirit Energy, Suncor Energy, Total and VNG Norge), the Norwegian Petroleum Directorate (NPD) and the Norwegian Research Council Petromaks 2 programme (project number 234111/E30) for funding through the SAFARI Phase 3 consortium. Schlumberger are acknowledged for providing academic software licenses. David Hodgetts (Manchester Uni.) and Schlumberger Ltd. are thanked for providing academic licensing of VRGS and Petrel software respectively.
- Architectural elements
- Depositional conditioning
- Multi-point statistic
- Training images