Fluvial & Eolian Research Group – University of Leeds 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

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

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 after Colombera et al. (2012)

Genetic units classifications 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 Catchment lithologies Catchment uplift rates Catchment geomorphic processes etc…

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.”

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”

Database Output unit Transitions Transition count matrices COUNT (Z) Sh Sl Sm Sp Sr Ss St … 555 116 218 145 211 59 169 122 283 151 89 25 33 121 215 142 561 119 51 103 143 87 106 350 56 22 155 152 19 50 37 4 76 68 55 16 20 7 58 57 208 124 137 42 698 Facies transition within 4th order channel-fills N = 1024

Reservoir/aquifer analogue selection 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. UP-TO-DATE FIGURES Synthetic analogues

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…

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

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.

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

Relative dimensional parameters 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) CH FF CS FLUVSIM (Deutsch & Tran 2002) simulations

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)

Indicator auto-variograms 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.

Indicator cross-variograms 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)

Transition probabilities/rates and lithotype rules 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.

FAKTS facies-modelling applications INCLUDING BOUNDING-SURFACE INFORMATION

Connectivity function 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.

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)

FAKTS major advantages for conditioning facies modelling: 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-variograms with 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.

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