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Improving Net-to-Gross Reservoir Estimation with Small-Scale Geological Modeling*

By

Peter Phillips1 and Renjun Wen1

 

Search and Discovery Article #40252 (2007)

Posted August 15, 2007

 

*Adapted from oral presentation at AAPG Annual Convention, Long Beach, California, April 1-4, 2007

 

1Geomodeling Technology Corp, Calgary, Alberta ([email protected])

 

Abstract 

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.

uAbstract

uIntroduction

uDefinitions

uPetrophysical cut-offs

uPermeability modeling

uWorkflow

uOil sands model

  uCase study

  uWorkflow

  uResults

uConclusions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uIntroduction

uDefinitions

uPetrophysical cut-offs

uPermeability modeling

uWorkflow

uOil sands model

  uCase study

  uWorkflow

  uResults

uConclusions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uIntroduction

uDefinitions

uPetrophysical cut-offs

uPermeability modeling

uWorkflow

uOil sands model

  uCase study

  uWorkflow

  uResults

uConclusions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uIntroduction

uDefinitions

uPetrophysical cut-offs

uPermeability modeling

uWorkflow

uOil sands model

  uCase study

  uWorkflow

  uResults

uConclusions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uIntroduction

uDefinitions

uPetrophysical cut-offs

uPermeability modeling

uWorkflow

uOil sands model

  uCase study

  uWorkflow

  uResults

uConclusions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uIntroduction

uDefinitions

uPetrophysical cut-offs

uPermeability modeling

uWorkflow

uOil sands model

  uCase study

  uWorkflow

  uResults

uConclusions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uIntroduction

uDefinitions

uPetrophysical cut-offs

uPermeability modeling

uWorkflow

uOil sands model

  uCase study

  uWorkflow

  uResults

uConclusions

Introduction 

Introduce method for improving the accuracy of net-to-gross reservoir estimation:

1. Generate cm- to dm-scale digital geological models.

2. Populate the resulting ‘digital rock models’ with porosity and permeability, and sedimentological characteristics gathered from core, core plug, and profile permeameter.

3. Apply flow-based upscaling algorithms.

 

The output includes facies-dependent property values that honor core measurements and small-scale heterogeneities observed at core scale.

 

Definitions 

  • 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.

 

Petrophysical Cut-offs

(Figure 1) 

  • Interval thickness of net reservoir might be below conventional petrophysical log resolution

  • Φ, kv/kh, NTG interpretations suffer from averaging because of tool resolution

  • Potential to underestimate NTG reservoir

  • Might not recognize Φ, kv/kh in area of high shale content

 

 

Permeability Modeling 

Key Assumptions in Conventional Permeability Modeling 

  • Only absolute permeability grid is simulated at the geocellular grid scale (~50 x 50 x 1 m)

  • The underlying assumption is that permeability has an isotropic scale

  • This is not true for most reservoirs

 

Improving Permeability Modeling (Figure 2) 

  • Provide input to generate Kx, Ky, Kz grids at geocellular grid scale

  • Effect of sub-meter heterogeneity is reflected by the directional permeability at geo-cellular scale

 

 

Breaking Tradition (Figure 2)

     Workflow:

  • Generate small-scale (cm- to dm-scale) geological models for representative flow units in a well interval.

  • Populate the resulting digital rock models with porosity and permeability values derived from core.

  • Upscale the models to the well-log scale, and calibrate modeled permeabilities to core and log data.

     Deliverable:

  • Facies-dependent property values that honor both core measurements, and small-scale heterogeneity observed at core scale.

 

Workflow

(Figures 3, 4, 5, 6, 7, 8, 9, and 10) 

1. Specify input parameters for bedding and petrophysics.

2. Create flow unit models (sub-models).

3. Create stack model (i.e., many sub-models) ~ 6.5 m x 30 cm x 30 cm

  • GR configured sedimentary cycle

  • FMI reference for bedding interpretation

  • Core images for stack model

 

Figure 3. Workflow.

Figure 4. Geological investigation.

Figure 5. Digital representation of lithofacies.

Figure 6. Development of the sub-model. The sub-model represents a particular lithofacies within the cored interval. Realizations (geometry, effective permeability, and porosity) are beside the core description. The table has Ø, Kx, Ky, Kz, Kh, Kv/Kh, and an estimate of net-to-gross reservoir by depth.

Figure 7. Generate small-scale models.

  • Select a defined bedding structure template.

  • Assign a lithofacies interval to it.

  • Populate geometric parameters and rock type components to represent the observed bedding structures and sand:shale ratio for that lithofacies.

  • Populate sub-model’s petrophysical parameters, including variance.

  • Realize directional perm, porosity, and net to gross in the sub-model.

Figure 8. Modeling the effects of diagenesis in carbonates with SBED.

Figure 9. Carbonate model : Kx = 2214, Ky = 2235, Kz = 2005, Φ = 0.201.

Figure 10. Moving windows upscaling. Moving windows upscaling results of Kz (blue) and Kh (orange) show flow unit boundaries and the core description log for the entire McMurray section. Note the gamma ray signature and extremely low permeability (<200 mD) of the abandoned channel fill section from 302 to 312 m.

 

MacMurray Formation: Oil Sands Model

(Figure 11) 

 

Case Study 

     Objective:

  • To deliver effective porosity and directional permeability values to populate the static reservoir model

     Data:

  • • Core photos, FMI log

  • Core plug data (directional perm and porosity)

  • Mini-permeameter results (perm and porosity)

  • Dipmeter and stratigraphic fabric from core photo analysis

 

     Lithofacies Assessment:

  • Thin-bedded storm sand beds make accurate estimates of net sand pay thickness in offset wells very uncertain.

     Identify Representative Lithofacies:

  • Divided the cored interval into representative lithofacies (flow units). FMI logs and high-quality core photos were used to delineate 525 lithofacies intervals over 27 m of core.

     Core Assessment:

  • Overall reservoir quality is very poor, however, core plug measurements showed 20 to 28% porosity and 0.5 to 20 mD for air permeability.

 

Workflow (Figures 12, 13, 14, 15, and 16)

 

Results (Figure 17) 

  • The horizontal permeability (Kh) results plotted for the entire cored interval and compared to previous estimates derived using the traditional phi-k transform.

  • Modeling derived results indicate higher permeabilities than the log-derived data, with calculated values ranging from 0.56 to 11.52 mD.

  • The results are consistent with values observed in core plug analysis, and account for the original discrepancy in permeabilities between the client's static and dynamic models.

 

 

Conclusions 

Small-scale heterogeneity modeling can improve reservoir characterization studies:

  • Identifies net reservoir below logging tool resolution.

  • Recognizes Φ, kv/kh, and NTG in areas of high shale content, improving estimation of net reservoir.

Models can be transferred into flow properties, providingcritical reservoir properties for informed decisions.

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