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

Abstract

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.

Integrated geologic, geophysical, and petrophysical data to construct full field geologic model of Cambrian-Ordovician and Upper Cretaceous reservoir formations, Central Western Sirte Basin, Libya

“…Integrating geologic, geophysical, and petrophysical data into one complete model of the whole field is an important process to help better understand the complexity of reservoir formations, thereby facilitating significant gains in productivity in terms of identifying and managing reservoirs (Zou et al, 2006;Denney, 2013;Adeoti et al, 2014). It provides reservoir engineers with a reliable and comprehensive basis upon which they can simulate and manage reservoirs (Al-Harbi et al, 2013;Karamitopoulos et al, 2013). In general, however, combining these various forms of data represents a significant challenge, mostly because of the fact that reliable integration requires a comprehensive understanding of the inherent uncertainties of the numerous components in a given geologic system (Hosseini et al, 2012;Kamali et al, 2013;Musa et al, 2013;Rodríguez Torrado et al, 2015).…”