Data Mining in Search of Hidden Oil

September 5, 1997

By Paul Pruess,

With oil companies presently recovering, on average, less than a third of the oil in proven reservoirs, any means of improving yield effectively increases the world's energy reserves. There are many techniques for coaxing reluctant oil from the ground; none of them work without knowing where the oil is hiding.

To find it, the DeepLook collaboration of oil-producing and service companies, led by BP Exploration, has awarded four grants--out of more than 100 proposals submitted--to develop new fluid-imaging techniques. One project, "Data Fusion for the Prediction of Reservoir Fluids," is being jointly undertaken by researchers from Berkeley Lab's Earth Sciences Division (ESD), Oak Ridge National Laboratory, and NASA's Jet Propulsion Laboratory.

"Our goal is to produce an efficient and robust characterization of the reservoir using soft computing techniques," says Masoud Nikravesh, an ESD researcher who is also a member of the Berkeley Initiative in Soft Computing (BISC) in UC Berkeley's Electrical Engineering and Computer Sciences Department. Nikravesh and colleague Larry Myer represent Berkeley Lab in the cooperative effort.

The data will come from huge corporate databases already in oil industry archives. Well logs and core samples can reveal the mineral composition, geologic structure, porosity, and fluid content of the rock underlying individual wells. But the number of wells in a field is limited; boreholes produce very narrow samples and may drill right past substantial deposits of oil. Data from seismic studies is cheaper and covers a much bigger volume of the subsurface, but can be notoriously difficult to resolve into interpretable pictures.

When many kinds of information are compared and combined--"fused"--the result can be so much data "that you can't find its structure just by looking at it," Nikravesh says. "It must also be mined. If you're mining gold you have to sift through a lot of sand to get a little gold; we have to sift a lot of numbers to get the real data."

The sifting tools of this "data mine" are techniques developed by Lotfi Zadeh of BISC: fuzzy logic, neural networks, and other computational methods that, as Nikravesh puts it, "exploit tolerance for imprecision, uncertainty, and partial truth."

An important objective is to uncover rules for interpreting disparate but complementary information from many different sources. Given dependable data from the resulting "intelligent" software, well-understood principles of physics can be used with confidence to construct a simulation of an oil reservoir more accurate than any now in existence.

In return for their investment in this kind of research, the DeepLook collaborators want practical answers: subsurface maps that pinpoint bypassed oil in a reservoir with known uncertainty, plus accurate predictions of the future performance of the field. They want programs that produce these maps and predictions cheaply and quickly. Wells are expensive--they can cost half a million dollars on dry land and a hundred times that in hostile environments or in complex geologic settings.

While Masoud Nikravesh is confident that the Berkeley Lab/Oak Ridge/JPL joint project will do the immediate job for the oil companies, the implications for this kind of intelligent software go much farther. As an example, he cites JPL's expertise in remote sensing, demonstrated by missions like the Mars Pathfinder.

During the DeepLook project, Berkeley Lab scientists have worked closely with NASA researchers Sandeep Gulati and Amir Fijani from the Ultracomputing Technologies Group at JPL, and together they are preparing fundamentally different techniques for characterizing geological structures. Says Nikravesh, "If we can learn to work seriously with each other, applying our knowledge of the Earth--and of soft computing--and their pioneering methods of imaging, whole new worlds will open."

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