Abstract

The discipline of geomorphology has a long and illustrious history. More recently tangential application of geomorphic principles to the study of stratigraphic sequences, reservoir heterogeneity and geobody formation has begun to recognize the additional insight that can be developed when we apply knowledge gained through modern geomorphic study to interpreting older strata and processes (Carter, 2003; Posamentier and Kolla, 2003; Posamentier, 2003). Evolving image technologies (3D seismic, multicomponent seismic, visualization, attribute analyses, laser outcrop imaging, etc.) now enable geoscientists to see in greater detail than ever before how seascapes and landscapes have evolved through time. There is little doubt that seismic geomorphology, when integrated with seismic and sequence stratigraphy, is a powerful tool for understanding basin evolution. However, applying geomorphic principles and laws to simply understanding the gross history of basin evolution is to only scratch the surface of what this new approach can bring to the table.

Quantitative seismic geomorphology (QSG) is a new direction in the interpretation of seismic morphologies that will create a step change in our knowledge, characterization, and understanding of older clastic environments. QSG is defined as “Quantitative analysis of the landforms, imaged in 3-D seismic data, for the purposes of understanding the history, processes and fill architecture of a basin.” (Wood, 2003). QSG uses 3-D seismic data integrated with core and logs to investigate the nature and architecture of reservoirs through (1) quantitative data collection of the system’s morphometrics and (2) analyses of the spatial and temporal variability of reservoirs. Such quantitative approaches have application in development planning, geohazards study, reservoir modeling, and assessment of exploration uncertainty and reservoir rock volumes (Carter, 2003; Mohammed, 2003; Posamentier, 2003; Wood, 2003).

Quantitative measurements of variables such as channel center line, sinuosity, number of bends, radius of curvature, meander length and width, channel depth and width, splay frequency and locations, levee heights and widths and taper rates, and debris-flow width, height, and runout distances can be measured from seismic data and used to predict the character and behavior (distribution) of these deposits. These types of seismically derived, quantitative data provide a rich data set for development of probabilistic approaches to reservoir uncertainty. Cumulative probability curves (see Capen, 1994) can be used to illustrate probability distribution of reservoir and seal morphologies. For example, data collected from Pliocene-age, northern Gulf of Mexico incised channels and valleys show the channel width at P50 = 840 meters, P10 = 330 meters, and P90 = 1250 meters (Wood, 2003). These data provide a foundation for choosing morphometrics of reservoir bodies for reservoir rock volume calculations, development well design, and correlation distances and drainage radii, as well as for building reservoir models and designing field floods.

QSG research in deep-water systems of eastern Venezuela and Trinidad shows the intimate relationship between mass-transport processes and leveed channel/fan systems. There, under-filled accommodation formed by passing debris flows allows later occupation by leveed channel systems. Leveed channels show significant variability in depth and sinuosity owing to local slope changes, which is contrary to the classic proximal-to-distal decrease in levee heights and sinuosity seen in many systems. Measurements show a steady decrease in sinuosity as the systems evolve, with regions of highest sinuosity migrating landward. There is a strong positive correlation between meanderbelt width —the reservoir “container”, and radius of curvature—a measure of channel reservoir extent within the “container.” In addition, zones of high radius of curvature show a propensity toward having more crevassing and overbank flow. Such quantitative relationships derived from seismic study can decrease our uncertainty significantly in predicting reservoir location and nature in deep-water settings and lead to cause-and-effect relationships that may be applied in a variety of deep-water settings.