Print this pageAAPG ANNUAL CONFERENCE AND EXHIBITION
Making the Next Giant Leap in Geosciences
April 10-13, 2011, Houston, Texas, USA
Statistical Tools for Characterizing Autogenic Organization in Sedimentary Deposits
(1) Geosciences, Penn State University, University Park, PA.
(2) Earth and Environmental Sciences, Tulane University, New Orleans, LA.
Internally generated (autogenic) sedimentation patterns have been observed over a range of temporal and spatial scales in modern, ancient, and experimental depositional systems. Within a basin, sedimentary deposits may be randomly distributed (meaning there is an equal probability of deposition occurring anywhere across the basin), evenly distributed (i.e. compensational), or clustered. These styles of organization can occur with different intensities across different spatial and temporal scales within a basin. We employ two statistical measures to field and experimental datasets in order to quantify patterns of basin filling. The K Function is used to compare the spatial/temporal distribution of sedimentary bodies within a basin, while the Compensation Index evaluates variability in deposit thickness relative to available accommodation through time. Both measures enable randomness, evenness, and clustering to be identified and compared across a range of scales in sedimentary deposits and can be used on a variety of datasets including outcrop panels, well logs, and seismic data.
The Compensation Index and K Function for experimental data and the Upper Creataceous/Paleocene Ferris Formation (Hanna Basin, Wyoming) show clustering, compensation, and randomness over different spatial scales within each basin. In these successions, the temporal and spatial scales over which basin filling shifts from clustered to compensational may reflect characteristic avulsion behavior in the formative depositional system. This information is useful for identifying the temporal and spatial scales below which climate and tectonic signals may not be preserved in stratigraphy. Additionally, both statistics can be used to help validate and populate reservoir models.