A workflow for modelling fluvial meander-belt successions: Combining forward stratigraphic modelling and multi-point geostatistics

Abstract

A new workflow has been developed for modelling reservoir successions that comprise fluvial meander-belt deposits, based on algorithms that employ multi-point statistics (MPS). A library of training images – from which MPS modelling algorithms can borrow geological patterns for modelling fluvial meandering systems – has been built. The training images incorporate sedimentary architectures relating to point-bar deposits accumulated by fluvial meander-bend expansion and translation, as observed in high-sinuosity river systems and their preserved deposits in the geological record. The training-image library has been developed using a forward stratigraphic modelling software (PB-SAND) that simulates fluvial meander-bend evolution and resulting point-bar facies organization, and which has been constrained using sedimentological data from geological analogues. The training images are applied to two widely employed MPS modelling algorithms: SNESIM and DEESSE. Solutions to common issues encountered in MPS modelling workflows have been established through optimization of modelling settings for SNESIM and DEESSE. Auxiliary variables are used to simulate common facies trends. Application of the training-image library through the developed workflows for SNESIM and DEESSE has been tested; the sensitivity of unconditional simulation results to input parameters has been analysed to define modelling recipes, consisting of sets of appropriate modelling parameters for use with each training image and modelling algorithm. The creation of fluvial reservoir models that are geologically realistic using MPS algorithms remains challenging, but the proposed approach holds promise.