Print this pageAAPG ANNUAL CONFERENCE AND EXHIBITION
Making the Next Giant Leap in Geosciences
April 10-13, 2011, Houston, Texas, USA
Three-Dimensional Numerical Modelling of Clinoforms Within Deltaic and Shoreface Reservoirs
(1) Earth Science and Engineering, Imperial College London, London, United Kingdom.
(2) BG-Group, Stavanger, Norway.
(3) Chevron Energy Technology Company, Perth, WA, Australia.
Key factors influencing fluid flow and reservoir behaviour include facies architecture and heterogeneity distribution conditioned to stratal surfaces. Within shallow-marine reservoirs, clinoforms are one common type of stratal surface. Clinoforms are paleoseaward-dipping surfaces whose geometry preserves the depositional morphology of the delta-front or shoreface slope and whose position reflects shoreline progradation history. Clinoform surfaces control aspects of facies architecture within parasequences and can also act as barriers or baffles to flow when there are permeability contrasts (e.g. carbonate cementation or mudstone deposition) along them. Under certain displacement conditions, it is therefore important to include clinoforms in reservoir models. However, standard reservoir modelling techniques are not well suited to capturing clinoform surfaces, particularly if they are numerous, below seismic resolution and/or difficult to correlate between wells. We present a numerical algorithm that generates multiple clinoform surfaces within a volume defined by two bounding surfaces, for example a delta-lobe deposit or shoreface parasequence.
We use a geometrical approach to describe the shape of a clinoform surface, by combining the relative height between any top and base bounding surfaces with a function such as a power law. The method is flexible and allows the user to define the progradation direction of the clinoform surfaces as well as the parameters which control the geometry, spacing and permeability of individual clinoform surfaces. The distribution and geometry of the clinoform surfaces are then matched with data-rich outcrop analog or data-poor subsurface observations, which enables a greater deterministic or stochastic component to be incorporated into the resulting model, as appropriate. Additional geologic surfaces can be added (for example, to represent boundaries between facies associations or individual facies) before a corner-point grid is constructed, which conforms to all surfaces and allows rock-property distribution prior to flow simulation. The resulting surface-based models accurately capture and preserve clinoform surfaces within fluid-flow simulations and render upscaling unnecessary. The method is illustrated using clinoforms documented at outcrop from fluvial-dominated delta-lobe deposits in the Cretaceous Ferron Sandstone, Utah, USA.