Event-Based Models as a Quantitative Laboratory for Testing Quantitative Rules Associated with Deep-Water Distributary Lobes
Published:December 01, 2006
Michael J. Pyrcz, Morgan Sullivan, Nicholas J. Drinkwater, Julian Clark, Andrea Fildani, Tim McHargue, 2006. "Event-Based Models as a Quantitative Laboratory for Testing Quantitative Rules Associated with Deep-Water Distributary Lobes", Reservoir Characterization: Integrating Technology and Business Practices, Roger M. Slatt, Norman c. Rosen, Michael Bowman, John Castagna, Timothy Good, Robert Loucks, Rebecca Latimer, Mark Scheihing, Hu Smith
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Analysis of modern depositional systems, high-resolution seismic and outcrop data reveal a significant degree of complexity in the heterogeneity associated with deep water distributary lobes. These heterogeneities commonly have a significant influence on reservoir performance. This complexity is the result of variations in sand body architectures related to varying depositional and erosional processes. The translation of these processes into quantitative rules is a powerful exercise for the purpose of testing our understanding of deposi-tional processes and hence forming predictive geologic models. Yet, the influence of coupled rules is difficult to assess a priori due to feedbacks and interference. A computationally efficient and intuitive quantitative framework is, therefore, a valuable means to explore potential rules and their associated interactions.
In the event-based framework, architectural elements are assigned to forward-simulated flow-event paths that obey simple geologic rules. The geologic rules qualitatively relate to sedimentary processes and constrain the geometry and location of architectural elements given the current state of the model for any time step. Rules may be coded to model allogenic and autogenic sedimentary processes such as avulsion, aggradation, progradation, retrogradation, and meander migration, along with the evolving influence of gradient and accommodation. Thus, event-based models can aid in the empirical testing of quantitative rules. This exercise leads to an improved quantitative understanding of process and the construction of more accurate geologic models that may better predict reservoir performance of these often economically challenging developments.