Oak Ridge computational tool available to oil, gas industry

By The Bakken Magazine Staff | January 16, 2015

Oak Ridge National Laboratory has developed a computational tool that anyone involved in oil and gas exploration can use to reduce costs and improve accuracy.

Clayton Webster, Oak Ridge senior scientist and leader of the computational and applied mathematics group, said the new approach uses a multilevel Monte Carlo method involving computational algorithms. Repeated random sampling with the tool provides a more accurate picture of the earth’s subsurface. 

“What we wanted to do is reduce the overall uncertainty or the confidence in where drills are placed and where we get the biggest bang for our buck,” Webster said.

According to Webster, the computational tool developed by the Oak Ridge research team is a perfect fit for oil and gas exploration.

“We develop a lot of techniques like this, but it’s rare that we will go out and directly apply them to an application,” he explained. “Typically, we’ll develop a method independent of an application and let others apply it. But in this case, we decided to apply it directly because it was a very good sales pitch for what we are doing.”

The Oak Ridge computational tool can result in a dramatic reduction in computational costs, Webster noted.

“It’s somewhat problem-dependent, but I can tell you from the kind of problems we looked at—simple exploration type problems—that we were able to see cost reductions on the order of up to 50 to 60 percent,” he said.

“It means you can go further and you can do more exploration,” he continued. “If you’re able to afford some amount of cost in the exploration process, then you can go a lot further and get more confidence in your results.”

Perhaps the best part is that anyone involved in oil and gas exploration can apply the tool to their computational method.

“You can take the approach that we’ve done―and any exploration company or individual looking at these applications can essentially do exactly what we’re doing by using their existing technology,―but then adding on this ability to balance errors as you go through some type of hierarchal sampling or optimization procedure,” Webster said.

Webster and his colleagues will soon be publishing a paper on their research, which could be used by those interested in applying their approach. In six to eight months, Oak Ridge should have a software application available.