Visualization and Imaging
Technological innovation in extracting and refining petroleum has given us a set of sophisticated tools that work under a wide range of challenging reservoir conditions. But even the best extraction technology depends on a more certain assessment and analysis of the hydrocarbon in formation.
The days of drilling wells based on a hunch and intuition are long gone. Oil exploration and production today rely on sophisticated seismic and computational techniques to identify and characterize underground reservoirs.
A seismic survey is like a stereo camera, with one very important difference. Software plays the role of the camera’s lens, combining and focusing the huge volumes of data collected by seismic instruments into images of the earth’s subsurface. While automated focusing is essential, it remained a long-standing technical challenge. A solution came through advanced applied mathematics.
Developed at Rice, differential semblance optimization (DSO) focuses seismic data in a way similar to split-image focusing in an ordinary camera. Various parts of the data produce independent images, which are aligned to reveal the full picture -- the overall structure of the hydrocarbon reservoir.
The theoretical breakthrough for DSO came years ahead of the necessary computational power. Since 1992, The Rice Inversion Project (TRIP) has focused on accelerating DSO's performance and documenting its potential for accurate subsurface imaging. TRIP provides the computational and applied mathematics to make DSO feasible and Rice's Ken Kennedy Institute for Information Technology, Center for Computational Geophysics, Research Computing Support Group, and Chevron Visualization Laboratory provide the expertise and hardware to bring it to life.
From Abstract Equations to Crystal-Clear Imagery:
Rice’s Center for Computational Geophysics (CCG) provides the cross-disciplinary framework to turn theoretical advances like DSO into practical solutions. Working together, researchers from the Earth Sciences and Computational and Applied Mathematics departments apply high-performance computing and visualization methods to actual field measurements. CCG makes extensive use of the Chevron Visualization Laboratory at Rice for the technology needed to evaluate new theory-based solutions via high-resolution graphics. Advanced modeling and imaging make exploration more predictable and less costly — good news for producers and consumers alike.
For more information, please contact The Rice Inversion Project (TRIP), the Ken Kennedy Institute for Information Technology (K2I), the Chevron Visualization Laboratory and our experts Jan Odegard, Moshe Vardi, Maarten de Hoop, Bill Symes, Richard Gordon, Matthias Heinkenschloss, Fenglin Niu, and Alan Levander.