Declining output from conventional reservoirs has led to an increased interest in unconventional reservoirs, some of which are characterized by low permeability, shale-dominated, thinly bedded clastics. Historically, these reservoirs have not attracted attention as hydrocarbon exploration targets because of their inherent low deliverability and because it has been difficult to recognize net pay with conventional technology.

This paper presents a geological modeling and upscaling method that can help to identify net reservoir below the level of conventional petrophysical log resolution by modeling small-scale sedimentary details that can impact reservoir performance. The approach combines deterministic and stochastic modeling methods to generate 3-D near-well-bore models that capture laminar-scale, bedding-scale, and lithofacies-scale sedimentary structures. Using these geologically realistic models as a basis, we generate petrophysical models that honor input data derived from core plugs, core observations, and well log data. We then apply flow-simulation-based upscaling methods to derive effective properties for input to full-field reservoir simulations. The output includes pseudo-log curves of net-to-gross, porosity, directional permeability (kx, ky, kz), and the ratio of vertical to horizontal permeability (kv/kh) for a modeled well interval.

The modeling and upscaling approach was applied to a thin-bedded reservoir study to produce net-to-gross reservoir curves based on a geologically realistic distribution of sand and shale. The study identified a well interval where model-derived permeabilities were substantially higher than permeability estimates derived with the conventional phi-k transform. These results were consistent with core observations and core plug measurements, and accounted for original discrepancies between the static and dynamic reservoir model. The geomodel-based upscaling method provided more accurate permeability results and, hence, more appropriate cut-offs for estimating net-to-gross sand.

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