Imposing Multiple Seismic Inversion Constraints on Reservoir Simulation Models

Abstract

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractWell data reveal reservoir layering with relatively high vertical resolution but are areally sparse, whereas seismic data have low vertical resolution but are areally dense. Improved reservoir models can be constructed by integrating these data. The proposed method combines stochastic seismic inversion, finerscale well data, and geologic continuity models to build ensembles of geomodels.Stochastic seismic inversions operating at the mesoscale (≈10 m) generate rock property estimates that are consistent with regional rock physics and true-amplitude imaged seismic data. These can be used in a cascading workflow to generate ensembles of fine-scale reservoir models, wherein each realization from the Bayesian seismic inversion is treated as an exact constraint for an associated finer scale stochastic model. We use two-point statistical models for the fine-scale model, modeling thickness and porosity of multiple facies directly. The update of these fine-scale models by the seismic constraints yields highly correlated truncated Gaussian distributions. These generate potentially rich pinchout behavior and flexible spatial connectivities in the fine scale model.The seismic constraints confine the fine-scale models to a posterior subspace corresponding to the constraint hypersurface. A Markov Chain Monte Carlo samples the posterior distribution in this subspace using projection methods that exploit the reduced dimensionality that comes with the exact constraints.These methods are demonstrated in three-dimensional flow simulations on a cornerpoint grid, illustrating the effects of stratigraphic variability on flow behavior.

3D Sedimentological and geophysical studies of clastic reservoir analogs: Facies architecture, reservoir properties, and flow behavior within delta front facies elements of the Cretaceous Wall Creek Member, Frontier Formation, Wyoming

“…In this case, the models including GPR have fewer and more dispersed concretions, which improves sweep efficiency. The importance of radar data is assessed using an experimental design (nearly orthogonal array; Kalla et al 2007) of 18 runs that varied the concretion proportion trend T , overall proportion F , flow direction O, and -most importantly -use of radar data G. Briefly, analysis of the resulting response surface indicates that Using radar data G affects average permeability and sweep efficiency significantly (95 percent level), but no significant effect on breakthrough time.…”

“…The good match of mean response is meaningless if the geostatistical parameters do not affect flow significantly. A 16-run orthogonal design (Kalla et al 2007) with 6 factors -variogram range,R, dip angle,D, cement fraction F , trend T , conditioning wells C, and flow direction O -was used to assess sensitivity (Table 6.2). %&'()*+',-./0%"1-2'-3'45&'#&-1404"14"607'%&07"804"-21'' $5&!?!6,):*5*2:*2357!+,572@5*2)-!A5:!*A,!:59,!*+,-.B!+5-6,!5-.!4+)4)+*2)-!)=!)>:,+C,.…”

“…However, this project demonstrated that even weakly informative data can enhance a geomodel, and this might be true for deep seismic data as well. Alternatively, a downscaling approach (Kalla et al 2008) or seismic inversion (Gunning and Glinsky 2004). However, it is difficult to include trace-to-trace correlation during inversion, and significant, difficult post-processing may be required .…”

“…This will require some permeability upscaling from the fine, GPR-trace grid to a manageable flow simulation model. All responses will then be tested for significant sensitivities, and upscaling procedures outlined using designed experiments and statistical analysis (White and Royer 2003;Kalla et al 2007). …”