Reservoir connectivity uncertainty from stochastic seismic inversion

Abstract

Static reservoir connectivity analysis is sometimes based on 3D facies or "geobody" models defined by combining well data and inverted seismic impedances. However, this is mostly performed from deterministic inversion results that provide limited information on uncertainty and may yield biased estimates of reservoir volume. Here, we present a workflow exploiting stochastic impedance realisations for facies characterisation and connectivity analysis with uncertainty, and illustrate the workflow using seismic and well data from a turbidite sand reservoir. After pre-stack stochastic inversion, we perform seismic facies classification based on cross-plots of inverted elastic attributes, calibrated to log data. Applying the classification to all impedance realisations, we generate multiple seismic sand facies models that can be averaged to produce a sand probability cube, reflecting the level of uncertainty in the inversion process. We then identify all connected sand "geobodies" in each realisation. We rank the facies realisations based on computed geobody statistics. Finally, we combine the geobodies from all realisations to calculate the probability of connection to given wells and the uncertainty in connected sand volume.

A Direct Estimation Method for the Russell Fluid Factor Based on Stochastic Seismic Inversion

“…Stochastic inversion is a kind of highresolution inversion method which is different from the conventional ones. Many scholars have carried out research on deterministic inversion and stochastic inversion (Francis et al, 2005(Francis et al, , 2006Moyen et al, 2009;Sams et al, 2008;Sancevero et al, 2005). Studies have shown that deterministic inversion gives only local smoothing estimation, and stochastic inversion method can provide a plurality of inversion results.…”

A Geomorphology-centric ranking scheme for stochastic seismic inversion realizations

“…Unlike other ranking criteria that could have be applied (Suleiman et al 2012;Moyen and Doyen 2009), our approach is to use an unbiased deterministic seismic inversion result as the target geometry for ranking. The method we have adopted is as follows:…”

Facies as the key to using seismic inversion for modelling reservoir properties

“…n Structural framework of the reservoir as interpreted from a geological and sedimentological analysis of the reservoir n Well log data in terms of facies, elastic and reservoir properties appropriately upscaled to the detail required of the model n Seismic data including variations in signal to noise and variations in frequency content that realistically capture spatial differences in data quality n Petrophysical relationships between reservoir properties, such as porosity, permeability, and volume of clay n Facies-dependent rock physics relationships between elastic and reservoir properties n Facies-dependent depth trends such as compaction, pressure or geological trends n Facies-dependent saturation height functions n Geological expectations for zonal facies distributions and facies ordering rules n Expected heterogeneity between facies as well as within facies assigned facies based on subsequent analysis of those properties (e.g., Moyen and Doyen, 2009;Grana and Della Rossa, 2010). Although such approaches overcome problems with the difference of scale mentioned previously, these clearly still do not use facies as a means to control the distribution of properties and as a consequence may also result in misleading characterization of a reservoir, as demonstrated by Sams (2001) and Rimstad and Omre (2009), amongst others.…”