Contents:
The database serves as a tool with which to achieve the following primary goals: 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: Compile exhaustive comparative statistics for different types of fluvial system: Observe how the proportions of facies or architectural elements and their transition probabilities change as progressively more filters are included in a query: 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!
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. 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.
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.
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. 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.
A quantitative approach to fluvial facies models: Tidal Sedimentation of the Sunderban's Thakuran Basin. Dynamics and Geomorphology of Mountain Rivers.
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