Improving Net-to-Gross Reservoir Estimation with Small-Scale Geological Modeling
Author: subsurfaceAI

Geoscientists have often been frustrated by the arbitrary assignment of petrophysical log cut-offs to define reservoir intervals capable of hosting producible hydrocarbon. The traditional practice is to derive “pseudo-permeability” from well logs such as gamma ray, density, and sonic. However, this indirect approach can introduce large errors in estimates of net-to-gross reservoir and, hence, reserve volumes.

We introduce a method for improving the accuracy of net-to-gross reservoir estimation with a small-scale geological modeling and upscaling approach. The first step is to generate cm- to dm-scale geological models for representative flow units in a well interval. The approach combines stochastic and deterministic modeling methods to mimic the sedimentary processes behind siliciclastic deposition. The resulting 3D models accurately simulate bedding structures observed in core and outcrop, and capture the geological heterogeneities that impact fluid flow.

The second step is to populate the resulting “digital rock models” with porosity and permeability values derived from core. Finally, by applying flow-based upscaling algorithms, we upscale the models to the well-log scale and calibrate modeled permeabilities to core and log data. The upscaling output includes facies-dependent property values that honor both core measurements and small-scale heterogeneities observed at core scale. The resulting property models provide a scientifically sound basis for calculating net reservoir. The modeling and upscaling approach was applied to a reservoir characterization study to identify net reservoir below the resolution of conventional petrophysical logs. The results helped to resolve major discrepancies between the static and dynamic reservoir model.

Peter Phillips and Renjun Wen, Geomodeling.

Presented at AAPG Annual Convention, Long Beach, California, April 1-4, 2007

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