There are two significant challenges in building a reservoir model integrating all available information. One challenge is that wells and seismic data measure the reservoir at different scales of resolution. The other challenge lies in how to account for conceptual geological knowledge with resolution at multiple scales.
In this paper, we present a case study of integrating well data, seismic data, and conceptual geologic models. The well and seismic data are of good quality, but conventional well-seismic data calibration indicates that the seismic data are unable to fully differentiate sand from shale. The reason for this poor well-seismic data calibration is that well log and seismic data measure the reservoir at different scales. Well logs are able to differentiate sand from shale, whereas seismic data are better at detecting larger scale depositional geometries.
A new workflow is presented to deal with this problem. First, principal component analysis clustering is used to identify characteristic patterns of certain depositional facies, from which sandy and shaly channels are interpreted. Next, multiple-point geostatistical simulation is performed to build a depositional-facies model, which integrates both hard and soft data but also incorporates realistic depositional-facies geometries provided by our geological knowledge of this reservoir. Finally, different lithofacies (sand and shale) indicators and corresponding petrophysical properties are simulated honoring the limited well data.
The results show that not only are the geological features better reproduced, but also is the uncertainty about the reservoir significantly reduced because of a better integration of corresponding three-dimensional seismic data.