A quantitative approach to fluvial facies models: Methods and example results

Traditional facies models lack quantitative information concerning sedimentological features: this significantly limits their value as references for comparison and guides to interpretation and subsurface prediction. This paper aims to demonstrate how a database methodology can be used to generate quantitative facies models for fluvial depositional systems. This approach is employed to generate a range of models, comprising sets of quantitative information on proportions, geometries, spatial relations and grain sizes of genetic units belonging to three different scales of observation (depositional elements, architectural elements and facies units). The method involves a sequential application of filters to the knowledge base that allows only database case studies that developed under appropriate boundary conditions to contribute to any particular model. Specific example facies models are presented for fluvial environmental types categorized on channel pattern, basin climatic regime and water‐discharge regime; the common adoption of these environmental types allows a straightforward comparison with existing qualitative models. The models presented here relate to: (i) the large‐scale architecture of single‐thread and braided river systems; (ii) meandering sub‐humid perennial systems; (iii) the intermediate‐scale and small‐scale architecture of dryland, braided ephemeral systems; (iv) the small‐scale architecture of sandy meandering systems; and (v) individual architectural features of a specific sedimentary environment (a terminal fluvial system) and its sub‐environments (architectural elements). Although the quantification of architectural properties represents the main advantage over qualitative facies models, other improvements include the capacity: (i) to model on different scales of interest; (ii) to categorize the model on a variety of environmental classes; (iii) to perform an objective synthesis of many real‐world case studies; (iv) to include variability‐related and knowledge‐related uncertainty in the model; and (v) to assess the role of preservation potential by comparing ancient‐system and modern‐system data input to the model.