2003 Seattle Annual Meeting (November 2–5, 2003)

Paper No. 3
Presentation Time: 2:00 PM

VISUALIZATION TO UTILIZATION: INTEGRATION OF 3D DATA SETS FOR CONSTRUCTION OF A GEOLOGIC MODEL OF A DEEPWATER SILICICLASTIC CHANNEL SYSTEM, AINSA, SPAIN


THURMOND, John B.1, LØSETH, Tore M.2, MARTINSEN, Ole2, SOEGAARD, Kristian2, RIVENÆS, Jan C.2, AIKEN, Carlos L.V.1 and XU, Xueming3, (1)Department of Geosciences, Univ of Texas at Dallas, P.O. Box 830688, Richardson, TX 75083-0688, (2)Norsk Hydro Rsch Centre, Bergen, Norway, (3)Center for Lithosheric Studies, Univ. of Texas at Dallas, 2601 N. Floyd RD, Richardson, TX 75074, thurmond@student.utdallas.edu

Construction of reservoir analog models from outcrop data is an essential part of geoscience research in the petroleum industry because it enables prediction of fluid flow in subsurface reservoirs. Collection of 3D data is emphasized in the subsurface, with the goal being preservation of three-dimensionality from data collection through to final interpretation. Therefore, it is essential that quantitative 3D data be collected from outcrop analogs, in order to accurately compare the outcrop data with subsurface data. The outcrops of the Ainsa basin in north-central Spain are well-studied examples of a deepwater siliciclastic system analogous to a variety of subsurface petroleum reservoirs. We have collected photorealistic 3D outcrop data from the main outcrop belts in the area and constructed a single database of integrated data from a wide variety of sources, including remote sensing, structural data, cored wells, measured sections, and existing interpretations. These data can be viewed and interpreted simultaneously in an immersive visualization environment. This allows for collaborative interpretation of a wide variety of diverse data resulting in a very high quality and quantitative geologic model of the area. By taking a new approach to outcrop data collection, we have successfully constructed a three-dimensional geologic model, which can be used for a wide variety of purposes including training, parameter sensitivity studies, and subsurface prediction.