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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
• develop quantitative synthetic depositional models
• derive constraints on subsurface predictions
• identify modern and ancient reservoir analogues
Approach to
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
ARCHITECTURAL ELEMENTS
FACIES UNITS
14 classes partly based on Miall’s (1996) scheme;
enhanced geomorphic expression
24 textural ± structural classes partly based on
Miall’s (1996) scheme
DATASET/SUBSET CLASSIFICATIONSMETADATA
INTERNAL PARAMETERS
EXTERNAL CONTROLS
• Authors/reference• Basin• Lithostratigraphic unit• River• Age• Methods/data type• Data Quality Index• etc…
• Basin gradient• Discharge regime• River pattern• Drainage pattern• Aggradation rates• Load-type dominance• Relative distality• etc…
• Subsidence rates/types• Basin/catchment climate• Basin/catchment vegetation• Relative eustatic change• Catchment lithologies• Catchment uplift rates• Catchment geomorphic processes• etc…
Data Entry
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 …
Sh 555 116 218 145 211 59 169 …Sl 122 283 151 89 25 33 121 …
Sm 215 142 561 119 51 25 103 …Sp 143 87 106 350 56 22 155 …Sr 152 19 50 37 121 4 76 …Ss 68 55 16 20 7 58 57 …St 208 145 124 137 103 42 698 …… … … … … … … … …
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
Sandy deposits:
Facies proportions:CH channel-fill CH channel-fill characterizationcharacterization
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
NO FILTERS
FILTERS MODEL
All systems
Braided systems
N = 964
Architectural Architectural element proportionselement proportions
CH channel-fill CH channel-fill characterizationcharacterization
Sandy deposits:
Facies 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
CH channel-fill CH channel-fill characterizationcharacterization
Sandy deposits:
Facies proportions:
8 case studies2,704 genetic 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
Discharge regime:
EPHEMERAL
NO FILTERS
FILTERS MODEL
All systems
Braided systems
Braidedsemiarid systems
Braidedsemiarid
ephemeralsystems N = 86
Architectural Architectural element proportionselement proportions
Sandy deposits:
Facies proportions:CH channel-fill CH channel-fill characterizationcharacterization
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…
INDICATOR VARIOGRAM COMPUTATION
RELATIVE DIMENSIONAL PARAMETERS COMPUTATIONFacies modelling applications
CHCHFFFF
CSCS
FLUVSIM (Deutsch & Tran 2002) simulation
paleoflow
Possibility to tailor the models filtering on genetic units…
…and on boundary conditions.
FLUVSIM (Deutsch & Tran 2002) simulations
SISIM (Deutsch & Journel 1998) simulations
Facies modelling applications
ConclusionsFAKTS database
Quantitative characterization of fluvial architecture applicable to:
•determine the importance of controlling factors
•develop quantitative depositional models
•derive constraints on borehole correlations
•derive constraints on stochastic simulations of fluvial architecture
•identify modern and ancient reservoir analogues
•compare the geomorphic organization of modern rivers with preserved stratigraphic architecture
•assess the influence of 1D data sampling density on observations and interpretations