1. Williams Fork Formation Reservoir Characterization at
An Overview of the
Williams Fork Formation
Reservoir Characterization at
Mamm Creek Field,
Piceance Basin, Colorado
Matthew J. Pranter, Alicia Hewlett,
Sait Baytok, Rachel Shaak
University of Colorado at Boulder

2. Outline Research Objectives Study Area
3-D Reservoir Characterization and Modeling of Matrix Properties
Seismic Interpretation, Fracture Analysis, and Fracture Modeling
Conclusions

3. Research Objectives
Within the southeastern Piceance Basin and Mamm Creek Field, how does the lower Williams Fork Formation vary in terms of stratigraphic architecture, shoreline stacking patterns, and lithology?
For the Williams Fork Formation at Mamm Creek Field, what is the stratigraphic variability of sandstone-body type and distribution, matrix reservoir quality, and static connectivity?
For the Williams Fork Formation at Mamm Creek Field, what are the main fault types and their distribution and how does fracture distribution vary with faulting, lithology, architectural elements and other parameters?

4. Study Area and Focus N Fracture intensity and seismic attributes
Regional distribution of coastal plain and marine deposits
Fracture intensity and seismic attributes
3-D reservoir modeling of tight-gas sandstones N
Discrete fracture
network modeling
3-D seismic analysis of fault and fracture distribution

5. Study Area and Focus data from 1,400 wells 3-D seismic survey 8 cores
Measured
Section
Reservoir
Model
Area
Seismic
Survey
data from 1,400 wells
3-D seismic survey
8 cores
12 borehole image logs
outcrop sandstone-body statistics
measured section
Image Logs
Cores

6. Reservoir Characterization and Modeling
2 mi2
91 wells
irregular 10-acre density

7. Stratigraphic Interval for Reservoir Modeling
(~2200 ft)
Coastal /Alluvial Plain
Shoreface/Deltaic
Marsh, Mire, Swamp, and Estuarine
Marine Shelf/Ramp
Modified from Carroll and others (2004); Cole and Pranter (2008)

8. Model Framework 91 wells 15 zones 40’ x 40’ x 1’ 89.4 million cells
Upper Williams
Fork Formation
Middle Williams
Fork Formation
V.E. = 1X
Lower Williams
Fork Formation
Paonia
Sh. Mbr.
91 wells
15 zones
40’ x 40’ x 1’
89.4 million cells
Upper Ss.
Middle Ss.
Cameo
Rollins
V.E. = 4X

9. Model Inputs and Constraints
Calculated Lithology logs
Interpreted Architectural Element Logs
crevasse
splay
sandstone/
clean sandstone
shaley
sandstone
point
bar
Variography
Outcrop dimensional statistics
and object shapes
Experimental Variogram
Azimuth: 39˚
Variogram Map

10. Architectural Element Object Shapes
Channel Bar / Point Bar
Crevasse Splay

11. Architectural elements
Vertical Constraint: Vertical Proportion Curves
Basic Lithology
Refined Lithology
Architectural elements
Upper Williams Fork
Middle Williams Fork
Lower Williams Fork
(Paonia)
Upper sandstone
Middle sandstone
Cameo-Wheeler
Top Rollins

12. 3-D Spatial Constraint: Seismic Probability Volume
Middle Williams Fork
Middle Sandstone
Provided by iReservoir.com

13. 3-D Lithology Modeling and 3-D Architectural-Element Modeling
Sequential-indicator simulation (SIS) of basic lithology
Sandstone, mudstone, coal modeled
With 3-D seismic-based spatial probability constraint
Sequential-indicator simulation (SIS) of refined lithology
Clean sandstone, shaley sandstone, mudstone, and coal modeled
Object-based simulation constrained to lithology
Constrained to outcrop dimensional statistics for fluvial architectural elements (Pranter et al., 2009)
Object-based simulation constrained to architectural elements

14. Modeling Examples Basic Lithology w/ 3-D seismic constraint
Refined Lithology
Basic Lithology
Refined Lithology
Architectural Elements

15. Future Modeling work
Petrophysical modeling
Porosity
Permeability (conventional core data)
Channel cluster analysis
Static sandstone-body connectivity

16. 3-D Seismic Interpretation
Uninterpreted
Interpreted
vertical
slices
through
seismic data
(amplitude)
Depth slice
through
ant tracking volume
without
interpretation
Depth slice
through
ant tracking volume
with
interpreted faults
Noise on survey edge

17. 3-D Seismic Interpretation
Fault interpretation based on seismic amplitude, ant-tracking results, and curvature attributes
Two near-vertical fault sets (shallow faults strike N60W; deep faults strike N45E) N
Uninterpreted
Interpreted
Picture on the left hand side shows uninterpreted seismic horizon surface map of middle sandstone with extracted ant-tracking attributes on it, red outline is the model area, dominant nne-ssw strike orientation can be seen
Picture on the right hand side shows interpreted faults cutting this surface N
Deep Faults
Middle Sandstone

18. 3-D Seismic Interpretation
Fault interpretation based on seismic amplitude, ant-tracking results, and curvature attributes
Two near-vertical fault sets (shallow faults strike N60W; deep faults strike N45E) N N
Uninterpreted
Interpreted
Picture on the left hand side shows uninterpreted seismic horizon surface map of Top mesaverde formation with extracted ant-tracking attributes, red outline is the model area, dominant wnw-ese strike orientation can be seen
Picture on the right hand side shows interpreted faults cutting this surface
Shallow Faults
Top Mesaverde Group

19. Fracture / Seismic Attribute Analysis
Relationship between ant tracked seismic attribute and fracture intensity in fractured zones
Fractured Zone
Upscaled Fracture Intensity
Ant-tracking Attribute
Correlation coefficient: 0.73
Relationship b/w ant tracked attributes and fracture intensity exists within fractured zones, therefore ant tracked attributes can be used to constrain distribution of fractures
Cross plots, which has correlation coefficient, show good relationship b/w ant tracked attributes and fracture intensity logs within 5 fractured zones
Only intensely fractured areas has seismic response, fracture swamps disturb seismic waves
Fracture properties (intensity) vs. seismic attribute relationship is still being investigated, more attributes to look at curvature(most negative, most positive, maximum, minimum, gaussian)instantaneous frequency, t attenuation etc.

20. Fracture Analysis and DFN Modeling
Fracture Intensity
and Lithology +
DFN (discrete fracture network) model Ki Kj Kk
Fracture Dip Angle and Dip Azimuth
Seismic attributes can be re-sampled into a 3-d grid and be used as fracture driver, in this case ant tracked attributes will be used to constrain fracture distribution; therefore, uncertainty can be reduced, distribution constraint to lithology and intensity property from fmi logs and seismic attributes
Given the availability of 3-d seismic data and 10 fmi logs, more accurate reservoir models that predict fracture density can be created.
Secondary properties can be used in modeling of intensity.
Distribution constraints are: distance to faults, seismic attributes, lithology
Orientation constraints are: dip azimuth, dip angle, and concentration properties
Geometry will be constrained based on outcrop data
Scale up fracture properties