Depositional conditioning of three dimensional training images: Improving the reproduction and representation of architectural elements in sand-dominated fluvial reservoir models

摘要

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 multipoint 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.