Introduction to Outcrop Analogs
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Bootlegger Wash, Prairie Canyon Member, Mancos Shale |
There is a rich history of using outcrop data in subsurface analysis and interpretation. Analog data may be used to incorporate important geologic information that commonly falls below the resolution of well log and seismic data. This helps to fill information gaps in incomplete representations of geology derived from subsurface data.
Currently, the TOADAL knowledgebase contains 23 complete outcrop patterns from the Permian Brushy Canyon Formation in West Texas. These patterns reflect the entire depositional profile, from submarine canyon to basin floor. The TOADAL knowledgebase is continuously growing and will eventually feature outcrop analog patterns for 100 documented deepwater systems worldwide.
Outcrop Analog Patterns
Each of the 23 outcrop analog patterns contains information on specific geologic attributes, such as sedimentary body shape, lithology, facies, bed thickness, and continuity and connectivity. The pattern naming scheme contains four parts that describe the defining characteristics for each pattern: profile position, body shape, stratigraphic phase, and, where relevant, seascape.
- Profile position identifies where the pattern is located along the longitudinal slope-to-basin profile.(this will link to the profile illustration Mike has)
- Body shape identifies the dominant sedimentary architecture as one of five major types: channelform, lobeform, wedgeform, drape, or resedimented or mass transport deposit.
- The stratigraphic phase of deposition places the pattern within the AIGR (link will go to AIGR text in Strat module-add) model for deepwater systems.
- Seascape indicates whether the pattern is located within the fairway or interfairway region of the depositional system.
Pattern data and information are presented in one-, two-, and three-dimensions that follow the standard subsurface formats, including:
- 1D - Well log/core attributes
- 2D - Cross-section interpretation
- 3D - Map display
- Outcrop dimensional data
Search for outcrop patterns by location (GIS), a graphical depositional energy matrix, or a description of patterns.
Learn more about using outcrop analog patterns
Why Build a Database Around the Brushy Canyon Formation?
The problem of depositional profile position plagues many deepwater outcrop analog datasets. This obstacle introduces uncertainty that may be propagated in subsurface interpretations. The exceptional Brushy Canyon Formation is considered an ideal analog because it contains complete and continuous basin margin to basin floor exposures from the same deepwater depositional system. This comprehensive framework reduces ambiguity and creates consistency in the interpretation of depositional profile position, a key input that is not preserved in isolated and segmented outcrops. Because of its complete slope-to-basin exposures, the Brushy Canyon outcrop is considered a "type depositional profile" and can be used to place and locate analogous examples from other systems for comparison and pattern identification.
Brushy Canyon analog patterns are organized and classified by descriptive geomorphic terms like submarine canyon, upper and lower slope, base of slope, and upper and lower basin floor. These classifications represent different measured gradient segments that correspond to changes in sedimentary architecture. In the geomorphic scheme, gradient is considered a global attribute for comparison because it is not dependent on scalar measurements such as distance or body size. This addresses the limitations of a static scalar classification scheme and allows for pattern comparisons regardless of the size of a depositional system.
The 23 distinct Brushy Canyon patterns provide a slope-to-basin framework which enables calibration and linkage to other, less complete outcrop or subsurface examples. The Brushy Canyon suite also has the advantage of having been studied and documented for more than a decade by the authors of this knowledgebase. This provides unprecedented consistency in data classification from multiple disparate examples.
