1. 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

2. 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

3. Overview Creation 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

4. Approach to DB design The 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. FAKTS conceptual and logical schemes

5. Implementation Each 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.

6. Implementation 2 classes: Channel-complex Floodplain GENETIC UNITS CLASSIFICATIONS DEPOSITIONAL 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 CLASSIFICATIONS METADATA 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…

7. 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) Amorosi et al. (2008) Robinson & McCabe(1997)

8. 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.”

9. 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 Channel-complex T (m)

10. Database Output UNIT TRANSITIONS N = 1024 Facies transition within 4 th order channel-fills Transition count matrices COUNT (Z) ShSlSmSpSrSsSt… Sh55511621814521159169… Sl122283151892533121… Sm2151425611195125103… Sp143871063505622155… Sr152195037121476… Ss6855162075857… St20814512413710342698… ………………………

11. Possibility to filter on linked architectural properties: dimensions, type of genetic units, bounding surfaces, etc. N = 515 Right lateral AE Left lateral AE Database Output FILTERING ON ARCHITECTURAL PROPERTIES Facies overlying 4 th order BS G-S- F-Gmm GcmGh GtGp StSp SrSh SlSs SmSd FlFsm FmC P Facies overlying 5 th order BS N = 432N = 260 Right-hand strike lateral transitions from AE’s left-hand neighbouring CH elements

12. Spatial and temporal evolution ORGAN ROCK FM. Permian – SE Utah (data from Cain 2009) KAYENTA FM. Jurassic – SE Utah Quantitative investigation of spatial and temporal sedimentary trends

13. 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 FILTERSMODEL All systems 41 case studies 28 basins 19 Formations 11 rivers 1,408 Depositional El.’s (1,192 classified ) 1,344 DE transitions 2,591 Architectural El.’s (2,274 classified) 4,885 AE transitions 11,908Facies units (11,100 classified) 13,581 FU transitions N = 2274 Architectural element proportions Sandy deposits: Facies proportions: CH channel-fill characterization

14. 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 FILTERSMODEL All systems Braided systems N = 964 Architectural element proportions CH channel-fill characterization Sandy deposits: Facies proportions: 23 case studies 11Basins 8Formations 6Rivers 396Depositional El.’s 1163Architectural El.’s 4,948Facies units

15. 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 FILTERSMODEL All systems Braided systems Braided semiarid systems N = 438 Architectural element proportions CH channel-fill characterization Sandy deposits: Facies proportions: 8 case studies 2,704genetic units

16. 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 FILTERSMODEL All systems Braided systems Braided semiarid systems Braided semiarid ephemeral systems N = 86 Architectural element proportions Sandy deposits: Facies proportions: CH channel-fill characterization

17. 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…

18. INDICATOR VARIOGRAM COMPUTATION RELATIVE DIMENSIONAL PARAMETERS COMPUTATION Facies modelling applications CH FF CS FLUVSIM (Deutsch & Tran 2002) simulation

19. 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

20. Conclusions FAKTS 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