Use of a relational database for the classification of fluvial sedimentary systems and the interpretation and prediction of fluvial architecture Luca Colombera, Nigel P. Mountney, William D. McCaffrey Fluvial & Eolian Research Group – University of Leeds
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Use of a relational database for the classification of fluvial sedimentary systems and the interpretation and prediction of fluvial architecture Luca Colombera,
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Use of a relational database for the classification of fluvial sedimentary systems and the interpretation
and prediction of fluvial architecture
Luca Colombera, Nigel P. Mountney, William D. McCaffrey
Fluvial & Eolian Research Group – University of Leeds
Fluvial architecture
Orton & Reading (1993) Shanley & McCabe (1994)
Interpretations and subsurface predictions of fluvial architecture rely on classification schemes, facies models and depositional models:qualitative approaches based on limited number of examples
OverviewCreation of a relational database for the digitization of fluvial sedimentary architecture :
the Fluvial Architecture Knowledge Transfer System (FAKTS)
Quantitative characterization of fluvial architecture applicable to:
• determination of importance of controlling factors
DB designThe sedimentary and geomorphic architecture of preserved ancient successions and modern rivers is translated into the database schema by subdividing it into geological objects – common to the stratigraphic and geomorphic realms – which belong to different scales of observation nested in a hierarchical fashion.
FAKTSFAKTS conceptual and logical schemes
ImplementationEach object type is assigned to a table and each individual object is given a unique identifier to implement the nested containment relationships.
The same numerical indices are also used for re-creating neighbouring relationships between objects belonging to the same scale.
Implementation
2 classes:Channel-complex
Floodplain
GENETIC UNITS CLASSIFICATIONSDEPOSITIONAL ELEMENTS
North (1996): “at present, much is being published in the format of multiple vertical profiles, photomontages and line drawings because we still do not really know how to handle all the available facts.”
Cain (2009)Cain (2009)
Cain (2009)Cain (2009)
Amorosi et al. (2008)Amorosi et al. (2008)
Robinson & Robinson & McCabe(1997)McCabe(1997)
Database Output UNIT PROPORTIONS
North (1996): “at present, much is being published in the format of multiple vertical profiles, photomontages and line drawings because we still do not really know how to handle all the available facts.”
Database Output UNIT DIMENSIONS
Miall & Jones (2003): “the database on large-scale fluvial architecture, especially sandbody width and length, remains extremely small”
Aggradation rate (m/Kyr)
0
10
20
30
40
50
0.080.170.290.45
Chan
nel-c
ompl
ex T
(m)
Database Output UNIT TRANSITIONS
N = 1024
Facies transition within 4Facies transition within 4thth order channel-fills order channel-fills
Transition count matricesCOUNT (Z) Sh Sl Sm Sp Sr Ss St …
Possibility to filter on linked architectural properties: dimensions, type of genetic units, bounding surfaces, etc.
N = 515
Right lateral AE
Left
late
ral A
EDatabase Output
FILTERING ON ARCHITECTURAL PROPERTIESFacies overlying 4th order BS
G- S-
F- Gmm
Gcm Gh
Gt Gp
St Sp
Sr Sh
Sl Ss
Sm Sd
Fl Fsm
Fm C
P
Facies overlying 5th order BS
N = 432 N = 260
Right-hand strike lateral transitions from AE’s left-hand neighbouring CH elements
Spatial and temporal evolution
ORGAN ROCK FM. Permian – SE Utah ORGAN ROCK FM. Permian – SE Utah (data from Cain 2009) (data from Cain 2009)
KAYENTA FM. Jurassic – SE Utah KAYENTA FM. Jurassic – SE Utah Quantitative
investigation of spatial and temporal sedimentary trends
Synthetic depositional models
Brierley (1996): “By definition, individual models must synthesize information from a range of examples; otherwise, each case study could be considered a model itself.”
NO FILTERS
FILTERS MODEL
All systems
41 case studies28 basins19 Formations11 rivers1,408 Depositional El.’s
(1,192 classified )1,344 DE transitions2,591 Architectural El.’s
(2,274 classified) 4,885 AE transitions11,908 Facies units
(11,100 classified)13,581 FU transitions
N = 2274
Architectural Architectural element proportionselement proportions
Brierley (1996): “By definition, individual models must synthesize information from a range of examples; otherwise, each case study could be considered a model itself.”
River pattern:BRAIDED
NO FILTERS
FILTERS MODEL
All systems
Braided systems
N = 964
Architectural Architectural element proportionselement proportions
23 case studies11 Basins8Formations6 Rivers396Depositional El.’s1163 Architectural El.’s4,948 Facies units
Synthetic depositional models
Brierley (1996): “By definition, individual models must synthesize information from a range of examples; otherwise, each case study could be considered a model itself.”
River pattern:BRAIDED
Basin climate:SEMIARID
NO FILTERS
FILTERS MODEL
All systems
Braided systems
Braidedsemiarid systems
N = 438
Architectural Architectural element proportionselement proportions
Brierley (1996): “By definition, individual models must synthesize information from a range of examples; otherwise, each case study could be considered a model itself.”
River pattern:BRAIDED
Basin climate:SEMIARID
Discharge regime:
EPHEMERAL
NO FILTERS
FILTERS MODEL
All systems
Braided systems
Braidedsemiarid systems
Braidedsemiarid
ephemeralsystems N = 86
Architectural Architectural element proportionselement proportions
North & Prosser (1993): “Are the results from outcrop and modern environment studies being translated into predictive tools suitable for modelling subsurface geology?”
Subsurface applications
de Marsily et al. (2005): “future work should be focused on improving the facies models […] A world-wide catalog of facies geometry and properties, which could combine site genesis and description with methods used to assess the system, would be of great value for practical applications.”
QUANTITATIVE INFORMATION FROM:
• identified modern and ancient reservoir analogues
• synthetic depositional models used as synthetic analogues
TO BE USED FOR:
• guiding subsurface correlations
• deriving constraints for stochastic reservoir modelling:genetic/material unit: proportions, absolute and relative dimensional parameters, Indicator auto- and cross-variograms, transition probabilities/rates…