LINKING DEEPWATER FAN DEPOSITIONAL PROCESSES AND STRATIGRAPHIC ARCHITECTURE USING COMPUTATIONAL STRATIGRAPHY

Reservoir characterization of deepwater deposits relies on the prediction of stratigraphic architecture away from sparse observation control points. Such a prediction requires using an understanding of deepwater depositional processes to predict the stratigraphic patterns that should form. Our current understanding of these processes comes largely from detailed interpretations and analysis of outcrops, well logs, cores, and seismic data (i.e., the stratigraphic record itself). Direct observations of deepwater depositional processes are rare, due to the challenges of data collection in extreme environments and the long timescales on which these processes act. Furthermore, reproducing these systems through physical experimentation has proven difficult, especially on large spatial scales. Thus, significant gaps remain in our understanding of deepwater depositional processes and the stratigraphic patterns they form. We present a physics-based, numerical model that simulates turbidity currents and the associated erosion, transport, and deposition of sediment. This new model allows us to observe the emergence of dynamic deepwater system processes and directly link them to the stratigraphic patterns they create. A detailed analysis of modeled deepwater fan systems shows depositional processes acting on hierarchical scales to produce stratigraphic patterns. These patterns themselves exhibit hierarchical trends in geobody organization, bedding surface trends, and internal spatial grain-size distributions. Further analysis of the relationships between hierarchical depositional processes and the resulting stratigraphy can lead to better subsurface prediction and improved reservoir characterization practices.