Fluvial Research Group
    SCHOOL OF EARTH AND ENVIRONMENT



FAKTS

Introduction to the Fluvial Architecture Knowledge Transfer System (FAKTS): a database of fluvial-reservoir analogues

The Fluvial Architecture Knowledge Transfer System (FAKTS) is a research-led flagship initiative of the Fluvial Research Group (FRG) at the University of Leeds. FAKTS is a relational database storing hard and soft data about fluvial sedimentary architecture that has been populated with data derived from both original FRG fieldwork studies and peer-reviewed literature syntheses. The database incorporates information from both modern rivers and ancient successions that have been selected because they are considered to represent potential analogues to hydrocarbon reservoirs hosted in fluvial rocks.

FAKTS comprises a database system that is recognized as the most sophisticated repository yet developed for the storage and structured retrieval of quantitative information relating to fluvial sedimentary architecture. The FAKTS database is available in its full form exclusively to FRG group sponsors

Click here to learn more and find out how FAKTS can be applied to resolve issues related to subsurface reservoir prediction and characterization.

Click here to access to FAKTS (FRG sponsors-only).

International recognition for FAKTS

“An elaborate new database system from which to sample input parameters relating to depositional systems, architectural elements and lithofacies in order to construct reservoir models for development engineering purposes. This approach appears to be by far the most sophisticated in this category of model building.” Quote from Andrew Miall in his new book “Fluvial Depositional Systems”, Springer, p. 4-5, relating to Colombera et al. (2012).

FAKTS on Ava Clastics

To enhance sponsor impact, the FRG has collaborated with external partner Petrotechnical Data Systems (PDS) to develop a product that enables direct coupling of FAKTS with modelling workflows: Ava Clastics

How does FAKTS work?

The FAKTS database is employed as a system for the digital reproduction of all the essential features of fluvial sedimentary architecture; it accounts for the style of internal organization of fluvial bodies, their geometries, grain size, spatial distribution, and the hierarchical and spatial reciprocal relationships of genetic units that comprise these geological bodies. FAKTS additionally classifies depositional systems – or parts thereof – according to both controlling factors (e.g. climate type, tectonic setting), and context-descriptive characteristics (e.g. channel/river pattern, dominant transport mechanism).

The FAKTS database can be interrogated either through a menu-driven online front-end hosted on the FRG web site, or by performing SQL queries on a downloadable version of the database in such a way that highly customized results can be obtained. The database output consists of user-defined sets of quantitative information on particular characters of sedimentary architecture, as derived form a suite of analogues, whose analogy to a particular reservoir is considered in terms of architectural properties and/or depositional-system parameters.

FAKTS output can be applied to fluvial-reservoir characterization and prediction. The database serves as a tool with which to achieve the following primary goals:

  • guide well correlation of fluvial sandstones;
  • condition object- and pixel-based stochastic reservoir models;
  • predict the likely heterogeneity of geophysically-imaged geobodies;
  • inform interpretation of lithologies observed in core.

How can FAKTS be applied to subsurface characterization problems?

  • Build quantitative facies models that describe the distribution of architectural elements within channelized and floodplain settings; characterize the scale, orientation and stacking of these elements and their style of juxtaposition relative to one another.
  • Build models that describe the likely internal facies arrangements present in individual architectural elements; determine the relative proportions of facies that make up certain elements and predict their vertical, cross-stream and downstream transitions.
  • Predict the expected dimensions of architectural elements away from the borehole; predict the most likely arrangement of neighbouring elements.
  • Filter the output from the database such that only those data from fluvial systems that meet the specified search criteria are returned.
  • Compare differences in sedimentary architecture for different types of fluvial system and controlling conditions: for example, compare differences in scale and connectivity of sand bodies in braided versus single-thread (meandering) rivers, or rivers developed in semi-arid versus sub-humid climatic settings, or pre-vegetation (i.e. pre-Silurian) fluvial successions versus post-vegetation successions, or fluvial successions preserved in rift basin settings versus those preserved in foreland basin settings.
  • Compile exhaustive comparative statistics for different types of fluvial system: for example, calculate channel-complex proportion, channel-complex thickness and width and channel-complex connectivity for different fluvial types.
  • Observe how the proportions of facies or architectural elements (and their transition probabilities) change as progressively more filters are included in a query: for example, compare a generic fluvial system, to a braided system, to a braided system developed in a semi-arid climate, to an ephemeral braided system.
  • Plot width-thickness relationships for any element (not just channels) and include filters to observe how such relationships vary between different fluvial system types.
  • Undertake a full analysis of lithofacies composition for any architectural element type (and filter by fluvial system type, climate, basin setting, geological age, palaeolatitude etc).
  • Make statistical comparisons between published case studies and compare with well-data from your own reservoirs.
  • Make statistical comparisons between modern systems and their ancient preserved successions; check the validity (or otherwise!) of your preferred modern system as an analogue for your subsurface reservoir succession.

FAKTS key features

The genetic units included in FAKTS are equally recognizable in both the stratigraphic and geomorphic realms, and belong to three hierarchies of observation: depositional elements, architectural elements and facies units, in order of descending scale.

  • The geometries of the genetic units are characterized by dimensional parameters describing their extent in the vertical, strike-lateral and downstream directions, relative to the channel-belt-scale flow direction (thickness, width and length); geometrical parameters are classified on type of observation (i.e. real, apparent, partial, or unlimited).
  • The reciprocal relations among genetic units are stored by recording and tracking (i) the containment of each unit within its higher scale parent unit (e.g. facies units within architectural elements), and (ii) the spatial relations between genetic units at the same scale, recorded as transitions along the vertical, cross-gradient and downstream directions.
  • The hierarchy of surfaces bounding the genetic units is also considered, through specification of bounding-surface orders for the basal surface of depositional elements and for surfaces across which architectural-element or facies-unit transitions occur.
  • Additional attributes are defined and recorded to improve the description of specific units (e.g. braiding index for channel complexes, grain-size distribution for facies units), whereas accessory information (e.g. ichnological or pedological characters) can be stored for every unit within open fields.
  • The database also stores statistical parameters referring to genetic-unit types and this enables storage of literature-derived data presented in this form.
  • Within the database, each genetic unit or set of statistical parameters is assigned to a stratigraphic volume called a subset; each subset is a portion of a dataset classified on system controls (e.g. subsidence rate) and system-descriptive parameters (e.g. river pattern, distality relative to other subsets).
  • For each case study of fluvial architecture, FAKTS also stores metadata describing, the methods of data acquisition employed, the chronostratigraphy of the studied interval, the geographical location, etc. A three-fold data-quality ranking system is also implemented for rating the reliability of datasets and genetic-unit classifications.

FAKTS output

All data stored within FAKTS can be filtered on analogue depositional-system parameters or associated architectural properties to match with a given subsurface system of interest, and the data retrieved can then be graphed or analysed in any spreadsheet application.

In its most basic form, FAKTS output consists of quantitative information about:

  • proportions of genetic units within higher-scale units or volumes;
  • geometrical parameters of genetic units;
  • spatial relationships of genetic units in three dimensions.

This output can be employed to generate information directly applicable to subsurface problems, such as plots of genetic-unit width-to-thickness aspect ratios, tabulated genetic-unit transition statistics, statistical distributions of user-defined genetic-unit net-to-gross values.

FAKTS content

FAKTS currently includes data associated with:

  • 186 case studies, comprising 104 ancient succession, 27 modern rivers, and other composite databases;
  • 13,652 classified depositional elements;
  • 6,326 classified architectural elements;
  • 36,228 classified facies units;
  • statistical summaries relating to more than 7,400 additional genetic units.

Over 500 additional peer-reviewed articles have been identified as containing architectural data suitable for database input, which is on-going. Figures are correct as of February 2017.

References