One of the key aims of Getech’s Lithofacies Prediction Project is to provide explorationists with the tools with which to predict lithofacies in areas of limited or no data, with a particular focus on source, reservoir and seal facies distribution and character.
The methodology is based on an understanding of the processes that dictate depositional systems. Predictions are generated using climate and tide modelling results from Getech’s Earth System Modelling and the application of algorithms based on published sediment data; Getech’s Global Palaeogeographies provide the boundary conditions for models. The model results are then tested and further constrained using Getech’s global data layers and databases.
Predicting global source rock distribution has been the main focus of the Lithofacies Prediction Project so far. This is discussed in further detail here: Organic matter prediction
Together with the focus on marine source rock, we are also in the early stages of examining the terrestrial sediment flux from source to sink. This includes modelling the approximate sediment fluxes for each drainage basin within all palaeogeographic reconstruction timeslices from the beginning of the Jurassic to the Present Day. The approach uses empirical equations from Present Day sediment flux databases (Hovius, 1998; Jansson, 1988; Meybeck, 1994; Syvitski et al., 2003), along with the Palaeodrainage, Palaeotopography reconstructions and Earth System Models results to assess the erosion, transportation and deposition from each fluvial source to the shelf and slope environments.
In addition, preliminary analysis of lacustrine environments has begun, in particular focusing on the size and area of lake systems and examining the surrounding associated climate and environment to gain an insight to the type of system that existed during that period.
Currently we provide modelled predictive results in the form of a digital atlas for every stage from 200 Ma to the Present Day, together with a toolbox that contains all parameter predictions and intermediate data used in the modelling. However, we are currently developing a client interface that allows the interactive creation of predictions, where assumptions and thresholds used can be altered to generate predictions based on more regional knowledge of the local area of interest.