Keele Research Repository

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. 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 produced 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.