Quantitative Clastics Laboratory
Areas of Focus
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The project team acquires both small (hundreds of sq km) and large (thousands of sq km) 3-D seismic data sets to allow harvesting of morphometric data on various elements of the depositional systems. The seismic is investigated using techniques that employ conventional seismic sequence framework development, quantitative seismic geomorphologic analysis, various attribute extractions, export and harvesting of morphologic information in ArcGIS and ErMapper, and export of these data into various programs for statistical analyses of temporal and spatial distribution and relationships.
Quantitative Seismic Geomorphology
Quantitative seismic geomorphology (QSG) is a new direction in the application of seismic geomorphology that will create a step change in our knowledge and characterization of older clastic environments. QSG is a technique that uses 3-D seismic data integrated with core and logs to investigate the nature and architecture of reservoirs through quantitative data collection of the system's morphometrics through analyses of spatial and temporal variability of reservoirs. Techniques integrate seismic investigation, visualization, GIS and ERMapper tools and statistical analysis.
Data Mining of Modern and Ancient Systems
The QCL team also works closely with the LASR research group to mine the significant historical archive of outcrop data provided from previous clastic systems research at the Bureau. These outcrop results, collected mostly on shallow marine and fluvial systems of the U.S. Western Cretaceous outcrops, consist of thousands of detailed facies and lithology measurements, integrated with over 100 kilometers of detailed architectural drawings from photopanoramic images. Datasets include significant subsurface logs integrated with outcrop data, as well as facies porosity, permeability and velocity measurements. These data are being harvested for the morphologic information that they document.
Both the seismic-derived and outcrop-derived data form the basis for probabilistic models of reservoir occurrence, character and evolution. 3-D Visualizations of these research results are conveyed to members as are statistical data sets, vrml-based training modules and are combined with results from published works and previous studies to form a larger HTML-based Sedimentary Analogs Database (SAND).