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Luca Colombera, Nigel P. Mountney, William D. McCaffrey, Fabrizio Felletti

A database approach for constraining object- and pixel-based stochastic simulations of fluvial sedimentary architecture: example application to quantification of connectivity. Luca Colombera, Nigel P. Mountney, William D. McCaffrey, Fabrizio Felletti.

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Luca Colombera, Nigel P. Mountney, William D. McCaffrey, Fabrizio Felletti

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  1. A database approach for constraining object- and pixel-based stochastic simulations of fluvial sedimentary architecture: example application to quantification of connectivity Luca Colombera, Nigel P. Mountney, William D. McCaffrey, Fabrizio Felletti Fluvial & Eolian Research Group – University of Leeds

  2. 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

  3. 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. FAKTSconceptual and logical schemes after Colombera et al. (2012)

  4. Implementation Genetic units classifications DEPOSITIONAL ELEMENTS ARCHITECTURAL ELEMENTS FACIES UNITS 2 classes: Channel-complex Floodplain 14 classes of subenvironments: Genetic bodies/facies associations with geomorphic significance 25 textural ± structural classes largely 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 • Catchmentlithologies • Catchment uplift rates • Catchment geomorphic processes • etc…

  5. Data Entry Cain (2009) Amorosi et al. (2008) Cain (2009) Robinson & McCabe(1997) 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.”

  6. 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.”

  7. Database Output unit DIMENSIONS Miall & Jones (2003): “the database on large-scale fluvial architecture, especially sandbody width and length, remains extremely small”

  8. Database Output unit Transitions Transition count matrices Facies transition within 4th order channel-fills N = 1024

  9. Reservoir/aquifer analogue selection UP-TO-DATE FIGURES FAKTS contains now: 4,285 classified large-scale depositional elements, 3,446 classified architectural elements, 20,101 facies units; from 111 case studies, including : 25 modern rivers, 65 ancient successions, 2 other published composite databases. Synthetic analogues

  10. Subsurface applications North & Prosser (1993): “Are the results from outcrop and modern environment studies being translated into predictive tools suitable for modelling subsurface geology?” 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 static-connectivity models • obtaining constraints to stochastic facies modelling: • genetic/material unit: proportions, absolute and relative dimensional parameters, • Indicator auto- and cross-variograms, transition probabilities/rates…

  11. FAKTS facies-modelling applications MODEL-CONDITIONING PROBLEMS after Deutsch & Tran (2002) after Mariethoz et al. (2009) after Guo & Deutsch (2010)

  12. FAKTS facies-modelling applications OVERVIEW FAKTS provides a wealth of quantitative data – from several classified case studies – with which to fully constrain stochastic structure-imitating simulations of fluvial reservoir/aquifer architecture, overcoming the main problems encountered when relying on traditional databases.

  13. FAKTS facies-modeling applications OBJECT-BASED SIMULATION CONSTRAINTS Geometrical parameters FLUVSIM (Deutsch & Tran 2002) simulations after Colombera et al. (In press)

  14. FAKTS facies-modelling applications OBJECT-BASED SIMULATION CONSTRAINTS Relative dimensional parameters Relative dimensional parameters can be derived as FAKTS stores genetic-unit absolute sizes, transitions and hierarchical nesting. after Colombera et al. (In press) FF CH CS FLUVSIM (Deutsch & Tran 2002) simulations

  15. FAKTS facies-modelling applications PIXEL-BASED SIMULATION CONSTRAINTS Material units Material units defined on any categorical and/or continuous variable: flexibility in the choice of reservoir-quality categories. after Colombera et al. (In press)

  16. FAKTS facies-modelling applications PIXEL-BASED SIMULATION CONSTRAINTS Indicator auto-variograms It is possible to inform indicator auto-variogram model form and parameters on material-unit proportions and modality, mean and variance in size, for each FAKTS direction.

  17. FAKTS facies-modelling applications PIXEL-BASED SIMULATION CONSTRAINTS Indicator cross-variograms • Indicator cross-variograms can be informed on FAKTS-derived: • Proportions p • Transition rates r (from transition frequency and mean size) after Colombera et al. (In press)

  18. FAKTS facies-modelling applications PIXEL-BASED SIMULATION CONSTRAINTS Transition probabilities/rates and lithotype rules Possibility to derive parameters that enable the simulation of genetic- and material-unit spatial relationships and juxtapositional trends.

  19. FAKTS facies-modelling applications INCLUDING BOUNDING-SURFACE INFORMATION

  20. Static-connectivity studies MULTI-SCALE CONNECTIVITY ANALYSIS OF CLASSIFIED FLUVIAL SYSTEMS Connectivity function Downstream direction Possibility to investigate the impact of several scales of heterogeneity on reservoir static connectivity and its variability associated with types of fluvial depositional systems.

  21. Static-connectivity studies MULTI-SCALE CONNECTIVITY ANALYSIS OF CLASSIFIED FLUVIAL SYSTEMS Possibility to investigate the impact of several scales of heterogeneity on reservoir static connectivity and its variability associated with types of fluvial depositional systems. Vertical Horizontal Future work Dynamic-connectivity studies for assessing architectural controls and N:G threshold between connectivity-limited and permeability-heterogeneity-limited reservoirs for a range of different classified fluvial systems. Inclusion of porosity and permeability data for every order of genetic units. After Anderson et al. (1999)

  22. Conclusions • FAKTS major advantages for conditioning facies modelling: • possibility to choose different modelling categories corresponding to different scales of heterogeneity, and adopt a multi-scale approach; • possibility to define any type of material units (on any categorical and/or continuous variable) to be used as modelling categories; • possibility to derive absolute and relative dimensional parameters with which to condition object-based simulations; • possibility to generate models of indicator auto- and cross-variogramswith which to constrain variogram-based simulations; • possibility to obtain transition frequency/probability matrices with which to constrain Markov chain-based simulations or with which to establish lithotype rules or contact matrices for plurigaussian simulations; • possibility to employ database output to fully constrain unconditional simulations of fluvial architecture and to use the resulting realizations as 3D training images for multiple-point-statistics simulations.

  23. Thank you for you attention We thank our sponsors IAS is thanked for travel grant

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