Penn State team uncovers hidden structures missed by traditional seismic scans that prevent oil extraction.
A frequent challenge in oil drilling is that wells can stop producing even when seismic scans suggest oil remains underground.
To address this, researchers at Penn State University used PSC’s Bridges-2 supercomputer to incorporate a time dimension into seismic imaging and to examine how oil reduces the strength of sound waves passing through it. Their early results indicate that hidden rock formations inside reservoirs may block access to portions of the oil. The team is now expanding its work to study full-scale oil fields.
Why it’s important
Extracting oil from increasingly remote and deeper sites requires smarter methods. While waste has always been costly, today efficiency and environmental responsibility are more critical than ever.
Geologists typically rely on the way sound waves move through the Earth to identify oil deposits and estimate the size of reserves. However, wells often dry up after producing only part of their predicted output. Tieyuan Zhu of Penn State, along with his students and postdoctoral researchers, set out to investigate this problem and to improve predictions of how much oil a reservoir can realistically yield.
“We actually tested … data from the North Sea. You know, they started drilling in 2008 and based on their estimation … they could produce oil for 20 years, 30 years. But unfortunately, after two years, there was nothing. Their well is dry. They just got confused. Where is the oil? Gone? The big issue actually is the complexity of the geology in the reservoir,” notes Tieyuan Zhu, Penn State.
To examine additional details from seismic sound data beyond what earlier studies considered, the team needed significantly greater computing capacity. They also required substantial memory so the processors could hold large portions of the problem without repeatedly retrieving information from storage, which would slow the work. PSC’s NSF-supported Bridges-2 system provided the necessary resources, made possible through an allocation from ACCESS, the NSF’s network of advanced computing facilities.
How PSC helped
Oil doesn’t sit in pools underground. When it’s present, it’s soaked into porous rock. Solid rock transmits sound more readily than oil-drenched rock. So experts can spot oil reserves by the way they slow down sound traveling through them. Much like a medical ultrasound, these seismic methods produce 3D images of where that oil-sodden rock sits.
Despite those sophisticated maps, though, wells drilled based on those images often come up short. Zhu’s team reasoned that there were literally parts of the picture that the 3D imaging wasn’t capturing. They suspected that obtaining images of the same reserves on different dates — adding time to create a kind of 4D animation — would help build a more accurate picture.
Adding dimensions to the data
Another piece of the puzzle would be to include more features of the seismic data in the analysis. Previously, oil reserves were spotted by the longer amount of time it takes sound to move through them. To this time data, the Penn State scientists added the amplitude of the signal — how oil damped out its loudness.
This all posed computational problems. The computer would need lots of fast processors to crunch the calculations in a reasonable amount of time. But it would also need to temporarily store parts of the problem in its memory — like RAM in a laptop — so that it didn’t need to keep going back to read the stored data, which slows everything down. Bridges-2, with over a thousand powerful central processing units (CPUs) in its regular memory nodes, could provide the speed. It could also provide the memory, as its CPU nodes each feature between 256 and 512 gigabytes of RAM — eight to 16 times as much as a high-end gaming laptop.
“We have two postdocs and also one graduate student using Bridges-2 … the first phase of using Bridges-2 was to parallelize our research code … and make it more practical … The second phase is really to implement the code to the field data … PSC guaranteed me a hundred thousand computing hours, and also the memory to store my data, my field data … That just cannot be achieved with our local [resources],” explains Tieyuan Zhu, Penn State.
The team’s repeated measurements and expanded analysis yielded paydirt. They found that the images mapped out by time alone, in a single measurement, missed structures within the oil reserve. Some of these structures, such as a layer of more solid rock within the reserve, wouldn’t affect the speed of the sound enough to be detected. But it would prevent a well from sucking up the oil below it. The solution, in some cases, was simple. Drill a little deeper, and the rest of the oil would be accessible.
The current report was just a proof of concept for their approach in a limited geological area, about 9 square miles. Currently, the team is expanding their computations to more nodes, so that the method can produce accurate maps for much larger areas, dozens of square miles. Another option Zhu’s group may explore in scaling up their work is using Bridges-2’s extreme memory nodes, which have 4,000 gigabytes of RAM apiece.
References: “Advancing attenuation estimation through integration of the Hessian in multiparameter viscoacoustic full-waveform inversion” by Guangchi Xing and Tieyuan Zhu, 29 July 2024, Geophysics.
DOI: 10.1190/geo2023-0634.1
“Why do seismic attenuation models enhance time-lapse imaging? A 2D viscoacoustic full-waveform inversion case study from the Volve field” by Donggeon Kim and Tieyuan Zhu, 19 June 2025, Geophysics.
DOI: 10.1190/geo2024-0793.1
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6 Comments
This has all been known for 120 years. The key is formation pressure maintenance. Flooding the formation with water or carbon dioxide gas restores pressure and allows additional oil to be recovered. Up to 70 % of the oil will always be permanently trapped in the formation. Physical mining can be done but is usually way to expensive. It all depends on the price of crude.
Actually, pointing out that they discovered thin impermeable strata blocking petroleum flow is NOT a matter of low reservoir pressure. It is a matter of previously unknown barriers.
Fracking under the ocean also just seems like a monumentally bad idea.
Well written. Thanks for the clarity.
AI doesn’t make oil… therefore it has probably already reached the limits of it’s usefulness in the oilfield. It’s actually value has been greatly exaggerated.
They discovered 4D seismic… something the industry has already been using for decades to track reservoir sweep.