Improved lithology prediction in channelized reservoirs by integrating stratigraphic forward modelling: towards improved model calibration in a case study of the Holocene Rhine-Meuse fluvio-deltaic system.

Stratigraphic forward modelling (SFM) provide the means to produce geologically coherent and realistic models. In this paper, we demonstrate the possibility of matching lithological variability simulated with a basin-scale advection-diffusion SFM to a data-rich real-world setting, i.e. the Holocene Rhine-Meuse fluvio-deltaic system in the Netherlands. SFM model calibration to real-world data in general has proven non-trivial. This study focuses on a novel inversion process constrained by the top surface and the sand proportion observed at specific pseudo-wells in the study area. Goodness-of-fit expressed by a new fitness function, gives the error calculated as the average of two calibration constraints. Computational efficiency was increased significantly by implementing a new optimization process in two hierarchical steps: a) optimization in terms of sediment load and discharge, which are the most influential parameters having the largest uncertainty and b) optimization with respect to the remaining uncertain parameters, these being sediment transport parameters. The calibration process described allows for the most optimal combination of achieving acceptable levels of goodness-of-fit, feasible runtimes and multiple (non-unique) solutions to obtain synthetic stratigraphic output best matching real-world datasets. By removing model realizations which are geologically unrealistic, calibrated SFM models provide a multiscale stratigraphic framework for reconstructing static models of reservoirs which are consistent with the palaeogeographic layout, basin-fill history and external drivers (e.g. sea level, sediment supply). The static reservoir models that are matched with highest certainty therefore contain the highest geological realism and may be used to improve deep subsurface reservoir or aquifer property prediction. The new methodology was applied to the well-established Holocene Rhine-Meuse dataset which allows a rigorous testing of the optimization and the calibrated SFM allows investigation of controls of the Holocene development on the sedimentary system

Reducing the uncertainty of static reservoir models: implementation of basin-scale geological constraints

We propose a new workflow for building static reservoir models of siliciclastic fluvio-deltaic systems. The proposed strategy requires a process-based stratigraphic simulation model which incorporates a reservoir-scale alluvial architecture module nested within a low-resolution basin-scale (sequence-stratigraphic) model. The basin-scale model is run with the intent to approximate large-scale basin-fill properties (based on geological/geophysical background information about palaeotopography, sea level, sediment supply, subsidence, and so forth). Subsequently, the model may be stochastically optimised by dedicated post-processing software to mimic sub-grid (reservoir-scale) properties of selected parts of the basin fill. This approach allows us to narrow down the range of possible scenarios (realisations) from the outset, which results in more reliable uncertainty estimates associated with reservoir models. Pilot studies suggest that the improvement of geological credibility of stochastically simulated fluvial reservoir models may go hand-in-hand with a significant reduction of the computational effort of inverting basin-scale (process-based) stratigraphic forward models. The implementation of geological constraints on object-based models is expected to improve estimation of sand-body connectivity and dynamic reservoir behaviour, and will therefore contribute to reduction of the non-uniqueness in current static reservoir models. Furthermore, the uncertainty associated with each basin-scale parameter can be propagated all the way through to reserve estimation. The partitioning of the overall uncertainty into contributions at the basin and reservoir scales may be quantitatively assessed, and the information content of all available data may be quantified. Copyright 2013, Society of Petroleum Engineers