Penn State crew uncovers hidden buildings missed by conventional seismic scans that forestall oil extraction.
A frequent problem in oil drilling is that wells can cease producing even when seismic scans counsel oil stays underground.
To handle this, researchers at Penn State College used PSC’s Bridges-2 supercomputer to include a time dimension into seismic imaging and to look at how oil reduces the power of sound waves passing by it. Their early outcomes point out that hidden rock formations inside reservoirs might block entry to parts of the oil. The crew is now increasing its work to check full-scale oil fields.
Why it’s essential
Extracting oil from more and more distant and deeper websites requires smarter strategies. Whereas waste has all the time been pricey, right this moment effectivity and environmental accountability are extra vital than ever.
Geologists usually depend on the way in which sound waves transfer by the Earth to determine oil deposits and estimate the scale of reserves. Nonetheless, wells typically dry up after producing solely a part of their predicted output. Tieyuan Zhu of Penn State, alongside together with his college students and postdoctoral researchers, got down to examine this downside and to enhance predictions of how a lot oil a reservoir can realistically yield.
“We really examined … knowledge from the North Sea. , they began drilling in 2008 and primarily based on their estimation … they might produce oil for 20 years, 30 years. However sadly, after two years, there was nothing. Their effectively is dry. They only bought confused. The place is the oil? Gone? The large concern really is the complexity of the geology within the reservoir,” notes Tieyuan Zhu, Penn State.
To look at extra particulars from seismic sound knowledge past what earlier research thought of, the crew wanted considerably better computing capability. In addition they required substantial reminiscence so the processors might maintain massive parts of the issue with out repeatedly retrieving data from storage, which might sluggish the work. PSC’s NSF-supported Bridges-2 system supplied the mandatory sources, made doable by an allocation from ACCESS, the NSF’s community of superior computing amenities.
How PSC helped
Oil doesn’t sit in swimming pools underground. When it’s current, it’s soaked into porous rock. Strong rock transmits sound extra readily than oil-drenched rock. So consultants can spot oil reserves by the way in which they decelerate sound touring by them. Very similar to a medical ultrasound, these seismic strategies produce 3D pictures of the place that oil-sodden rock sits.
Regardless of these subtle maps, although, wells drilled primarily based on these pictures typically come up quick. Zhu’s crew reasoned that there have been actually elements of the image that the 3D imaging wasn’t capturing. They suspected that getting pictures of the identical reserves on completely different dates — including time to create a form of 4D animation — would assist construct a extra correct image.
Including dimensions to the info
One other piece of the puzzle can be to incorporate extra options of the seismic knowledge within the evaluation. Beforehand, oil reserves had been noticed by the longer period of time it takes sound to maneuver by them. To this time knowledge, the Penn State scientists added the amplitude of the sign — how oil damped out its loudness.
This all posed computational issues. The pc would wish numerous quick processors to crunch the calculations in an affordable period of time. However it will additionally must quickly retailer elements of the issue in its reminiscence — like RAM in a laptop computer — in order that it didn’t must preserve going again to learn the saved knowledge, which slows every part down. Bridges-2, with over a thousand highly effective central processing items (CPUs) in its common reminiscence nodes, might present the velocity. It might additionally present the reminiscence, as its CPU nodes every characteristic between 256 and 512 gigabytes of RAM — eight to 16 instances as a lot as a high-end gaming laptop computer.
“We’ve two postdocs and in addition one graduate pupil utilizing Bridges-2 … the primary section of utilizing Bridges-2 was to parallelize our analysis code … and make it extra sensible … The second section is admittedly to implement the code to the sector knowledge … PSC assured me 100 thousand computing hours, and in addition the reminiscence to retailer my knowledge, my discipline knowledge … That simply can’t be achieved with our native [resources],” explains Tieyuan Zhu, Penn State.
The crew’s repeated measurements and expanded evaluation yielded paydirt. They discovered that the pictures mapped out by time alone, in a single measurement, missed buildings inside the oil reserve. A few of these buildings, corresponding to a layer of extra stable rock inside the reserve, wouldn’t have an effect on the velocity of the sound sufficient to be detected. However it will forestall a effectively from sucking up the oil under it. The answer, in some instances, was easy. Drill a bit of deeper, and the remainder of the oil can be accessible.
The present report was only a proof of idea for his or her strategy in a restricted geological space, about 9 sq. miles. At present, the crew is increasing their computations to extra nodes, in order that the strategy can produce correct maps for a lot bigger areas, dozens of sq. miles. An alternative choice Zhu’s group might discover in scaling up their work is utilizing Bridges-2’s excessive reminiscence nodes, which have 4,000 gigabytes of RAM apiece.
References: “Advancing attenuation estimation by 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 fashions improve time-lapse imaging? A 2D viscoacoustic full-waveform inversion case research from the Volve discipline” by Donggeon Kim and Tieyuan Zhu, 19 June 2025, Geophysics.
DOI: 10.1190/geo2024-0793.1
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