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