Highlights:• Fluvio-deltaic stratigraphy was simulated with a 2DH process-based model • Goodness of fit functions were used to infer boundary conditions from subsurface data • Information content of seismic and well data was evaluated • Depth of reservoir top across basin is best predictor of reservoir lithology ABSTRACT Forward stratigraphic modelling aims at representing the spatial distribution of lithology as a function of physical processes and environmental conditions at the time of deposition so as to integrate geological knowledge into the reservoir modelling workflow, thus increasing predictive capabilities of reservoir models and efficient exploitation of hydrocarbons. Application of process-based models in inverse mode is not yet well-established due to our limited insight into the information content of common subsurface data and the computational overhead involved.In this paper we examine inverse modelling of stratigraphy by using a typical dataset acquired in the hydrocarbon industry, which consists of seismic data and standard logs from a limited number of wells. The approach is based on the use of a forward model called SimClast, developed at Delft University of Technology, to generate facies distribution and architecture at the regional scale. Three different goodness of fit functions were proposed for model inversion, following an inference approach. A synthetic reservoir unit was used to investigate the impact of the uncertainty affecting the input parameters and the information content of seismic and well data.The case study showed that the model was more sensitive to the initial topography and to the location of the sediment entry point than to sea level. The depth of the seismic reflector corresponding to the top-reservoir surface was the most informative data source; the initial and boundary conditions of the simulation were constrained by evaluating the depth of this reflector across the whole basin area. In the reservoir area, where the seismic-to-well tie was established, the depth of the reservoir top does not give enough information for constraining the model parameters. Our results thus indicate that evaluation of basinscale data permits reduction of uncertainty in (geostatistical) reservoir models relative to the current workflow, in which only local data are used. Effective use of well data to generate reservoir models conditioned to basin-scale scenarios requires post-processing methods to downscale the output of the forward model used in the experiments.

 Stratigraphic Forward Model (SFM) provides channel body volumetrics at basin scale  Basin-scale constraints from the SFM were integrated into reservoir models  Uncertainty of basin-fill parameters was propagated to reserve estimation  Inference of basin-fill parameters from reservoir data reduced their uncertainty

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This paper presents the evaluation of the subsidence potentially induced by underground storage of natural gas in a marginal depleted field located in Southern Italy. The critical aspect of the study was the lack of data because economic and logistic reasons had restricted data acquisition at the regional scale to perform a geomechanical study. This limitation was overcome by accurately gathering the available data from public sources so that the geometry of a largescale 3D model could be defined and the formations properly characterized for rock deformation analysis. Well logs, seismic data and subsidence surveys at the regional scale, available in open databases and in the technical literature, were integrated with the available geological and fluid-flow information at the reservoir scale. First of all, a 3D geological model, at the regional scale, incorporating the existing model of the reservoir was developed to describe the key features of a large subsurface volume while preserving the detail of the storage reservoir. Then, a regional geomechanical model was set up for coupled mechanic and fluid-flow analyses. The stress and strain evolution and the associated subsidence induced in the reservoir and surrounding formations by historical primary production as well as future gas storage activities were investigated. Eventually, the obtained results were validated against the measurements of ground surface movements available from the technical literature for the area of interest, thus corroborating the choice of the most critical geomechanical parameters and relevant deformation properties of the rocks affecting subsidence.

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.