This paper was also presented as SPE 107195 at the EUROPEC/EAGE Conference and Exhibition, 11–14 June 2007, London, U.K.


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.


Geoscientists have often been frustrated by the arbitrary assignment of petrophysical log cut-offs to define reservoir intervals capable of hosting producible hydrocarbon (net pay). Because defining net pay is critical to estimating reserves, this process deserves an exacting level of scrutiny. Our Canadian software company introduced a method for improving net-to-gross estimation with a unique small-scale geological modeling and upscaling program. The deliverables include rigorous, flow-based property models which, when upscaled to the level of the reservoir grid, 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 of this analysis helped to resolve previous discrepancies between the static and dynamic reservoir model.

Although "net-to-gross" is a common industry term, asset team members might not agree on its definition. To complicate matters more, the process for defining cut-offs to estimate net-to-gross is not universal. In this paper, we use several terms to describe a rock's ability to hold and flow hydrocarbons, as defined by Worthington and Cosentino 1:

  • Net sand comprises those rocks that might have useful reservoir properties. Defined by a shale cut-off.

  • Net reservoir comprises those net sand intervals that do have useful reservoir properties. Defined by log-derived porosity cut-off.

  • Net pay comprises those hydrocarbon-bearing reservoir intervals that can be produced economically using a particular recovery method. Defined by log-derived water saturation cut-off.

  • Gross rock comprises all rocks within the evaluation interval.

  • Net-to-gross is a generic term encompassing three definitions. It can be based on net sand, net reservoir, or net pay, expressed as net-to-gross sand, net-to-gross reservoir, or net-to-gross pay.

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