Bridge Reservoir Static and Dynamic Modeling Using Static Connectivity Analysis

Hong Tang¹, Ning Liu¹, Jordan Heltz², and Jose Moros^2
1Chevron ETC, Houston, TX
²Chevron CIEP, Houston, TX

Compared with extensive research on building complex geological models, limited studies are reported on ranking stochastic realization, comparing reservoirs, and incorporating geological uncertainty into decision making. Usually, the geologists examine the uncertainty of OOIP using experimental design and rank stochastic models based on the OOIP. Then P10-50-90 OOIP models are selected for flow simulation. This method assumes the ranking of OOIP is a good approximation of the ranking of reserve or recoverable pore volume. Even though under most circumstance, this intuitive assumption holds, our study shows this convenient assumption may not be true under circumstances such as the reservoir connectivity is low, net-to-gross (NTG) is low or heterogeneity is high.

Reservoir static connectivity analysis provides a quick quantitative approximation of reserve, which can be used for ranking stochastic models for dynamic forecast. We use three methods to rank the static reservoir connectivity: 1) static connected volume 2) apparent tortuosity 3) connectivity function. Ideal streamline simulation is used to validate the static ranking methods. These methods are applied in an a shoreface reservoir, Lifua field, Angola Block 0. Compared with OOIP, the connectivity analysis provides a better ranking of reserve and recovery factor. The study indicates that connectivity analysis is particularly useful for highly heterogeneous reservoir with low NTG. At early stage of reservoir development, these quick ranking methods can be combined and cross validated with other analytical methods such as material balance analysis, well transient test or decline curve analysis to help a better oil development plan.

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