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October 14, 2005
Oil companies could soon harness the power of supercomputers to tackle problems such as where to place equipment and how to clean up oil spills. For decades, the industry already has used computers to maximize profit and minimize environmental impact, so this represents a logical extension of computing capabilities.
Typically, companies take seismic measurements of an oil reservoir and simulate drilling scenarios on a local computer. Now, according to Tahsin Kurc, assistant professor of biomedical informatics at Ohio State University, he and his colleagues are developing a software system and related techniques to let supercomputers at different locations share the workload. The system runs simulations faster and in much greater detail and enables analysis of large amounts of data.
The OSU scientists are employing the same tools and techniques used to connect computing resources in biomedical research. Whether they are working with images from digitized microscopes or MRI machines, their focus is on creating software systems that pull important information from the available data. From that perspective, a seismic map of an oilfield isn't that different than a brain scan, Kurc said. Both involve complex analyses of large amounts of data.
In an oilfield, rock, water, oil and gas mingle in fluid pools underground that are hard to discern from the surface, and seismic measurements don't tell the whole story. Yet oil companies must couple those measurements to a computer model of how they can utilize the reservoir, so that they can accurately predict its output for years to come. And they can't even be certain that they're using exactly the right model for a field's particular geology.
"You never know the exact properties of the reservoir, so you have to make some guesses," Kurc said. "You have a lot of choices of what to do, so you want to run a lot of simulations."
The same problems arise when a company wants to minimize its effects on the environment around the reservoir, or track the path of an oil spill. Each simulation can require hours or even days on a PC, and generate tens of gigabytes of data. Oil companies have to simplify their computer models to handle such large datasets.
Kurc and his Ohio State colleagues -- Joel Saltz, professor and chair of the Department of Biomedical Informatics; assistant professor Umit Catalyurek; research programmer Benjamin Rutt and graduate student Xi Zhang -- are enabling technologies to spread that data around supercomputers at different institutions.
Their project is part of a larger collaboration with researchers at the University of Texas at Austin, Oregon State University, University of Maryland and Rutgers University. The institutions joined to utilize the TeraGrid network, which links supercomputer centers around the country for large-scale studies. Programs like OSU's DataCutter are considered "middleware," because they link different software components. The goal, Kurc said, is to design middleware that works with a range of applications.
"We try to come up with commonalities between the applications in that class," he said. "Do they have a similar way of querying the data, for instance? Then we develop algorithms and tools that will support that commonality."
DataCutter coordinates how data is processed on the network, and filters the data for users. The researchers tested DataCutter with an oilfield simulation program developed at the University of Texas at Austin. They ran three simulations over the TeraGrid: one to assess the economic value of an oilfield, one to locate sites of bypassed oil and one to evaluate different production strategies, such as the placement of pumps and outlets in an oil field.
The source data came from simulation-based oilfield studies at the UT-Austin. That data and the output data from the simulations were spread around three sites: the San Diego Supercomputer Center, the University of Maryland and Ohio State.
Using distributed computers, they were able to reduce the execution time of one simulation from days to hours and another from hours to several minutes. But Kurc feels that speed isn't the only benefit that oil companies would get from doing their simulations on computing infrastructures such as TeraGrid. They would also have access to geological models and datasets at member institutions, which could boost the accuracy of their simulations.
May 23, 2013 |
he study of climate change is one of those scientific problems where it is almost essential to model the entire Earth to attain accurate results and make worthwhile predictions. In an attempt to make climate science more accessible to smaller research facilities, NASA introduced what they call ‘Climate in a Box,’ a system they note acts as a desktop supercomputer.
May 22, 2013 |
At some point in the not-too-distant future, building powerful, miniature computing systems will be considered a hobby for high schoolers, just as robotics or even Lego-building are today. That could be made possible through recent advancements made with the Raspberry Pi computers.
May 16, 2013 |
When it comes to cloud, long distances mean unacceptably high latencies. Researchers from the University of Bonn in Germany examined those latency issues of doing CFD modeling in the cloud by utilizing a common CFD and its utilization in HPC instance types including both CPU and GPU cores of Amazon EC2.
May 15, 2013 |
Supercomputers at the Department of Energy’s National Energy Research Scientific Computing Center (NERSC) have worked on important computational problems such as collapse of the atomic state, the optimization of chemical catalysts, and now modeling popping bubbles.
May 10, 2013 |
Program provides cash awards up to $10,000 for the best open-source end-user applications deployed on 100G network.
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