{"id":76901,"date":"2025-09-21T11:35:08","date_gmt":"2025-09-21T11:35:08","guid":{"rendered":"https:\/\/www.europesays.com\/ie\/76901\/"},"modified":"2025-09-21T11:35:08","modified_gmt":"2025-09-21T11:35:08","slug":"scientists-find-the-invisible-culprit-behind-dry-oil-wells","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ie\/76901\/","title":{"rendered":"Scientists Find the Invisible Culprit Behind Dry Oil Wells"},"content":{"rendered":"

\t\t\"Oil<\/a>Oil wells often run dry long before they should, leaving behind untapped reserves hidden by complex underground geology. Researchers at Penn State are using one of the nation\u2019s most powerful supercomputers to add a \u201ctime dimension\u201d to seismic imaging, uncovering rock structures that standard 3D scans miss. Credit: Shutterstock<\/p>\n

Penn State team uncovers hidden structures missed by traditional seismic scans that prevent oil extraction.<\/strong><\/p>\n

A frequent challenge in oil drilling is that wells can stop producing even when seismic scans suggest oil remains underground.<\/p>\n

To address this, researchers at Penn State University used PSC\u2019s 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.<\/p>\n

Why it\u2019s important<\/p>\n

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.<\/p>\n

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.<\/p>\n

\u201cWe actually tested \u2026 data from the North Sea. You know, they started drilling in 2008 and based on their estimation \u2026 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,\u201d notes Tieyuan Zhu, Penn State.<\/p>\n

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\u2019s NSF-supported Bridges-2 system provided the necessary resources, made possible through an allocation from ACCESS, the NSF\u2019s network of advanced computing facilities.<\/p>\n

How PSC helped<\/p>\n

Oil doesn\u2019t sit in pools underground. When it\u2019s present, it\u2019s 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.<\/p>\n

Despite those sophisticated maps, though, wells drilled based on those images often come up short. Zhu\u2019s team reasoned that there were literally parts of the picture that the 3D imaging wasn\u2019t capturing. They suspected that obtaining images of the same reserves on different dates \u2014 adding time to create a kind of 4D animation \u2014 would help build a more accurate picture.<\/p>\n

Adding dimensions to the data<\/p>\n

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 \u2014 how oil damped out its loudness.<\/p>\n

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 \u2014 like RAM in a laptop \u2014 so that it didn\u2019t 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 \u2014 eight to 16 times as much as a high-end gaming laptop.<\/p>\n

\u201cWe have two postdocs and also one graduate student using Bridges-2 \u2026 the first phase of using Bridges-2 was to parallelize our research code \u2026 and make it more practical \u2026 The second phase is really to implement the code to the field data \u2026 PSC guaranteed me a hundred thousand computing hours, and also the memory to store my data, my field data \u2026 That just cannot be achieved with our local [resources],\u201d explains Tieyuan Zhu, Penn State.<\/p>\n

The team\u2019s 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\u2019t 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.<\/p>\n

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\u2019s group may explore in scaling up their work is using Bridges-2\u2019s extreme memory nodes, which have 4,000 gigabytes of RAM apiece.<\/p>\n

References: \u201cAdvancing attenuation estimation through integration of the Hessian in multiparameter viscoacoustic full-waveform inversion\u201d by Guangchi Xing and Tieyuan Zhu, 29 July 2024, Geophysics.
DOI: 10.1190\/geo2023-0634.1<\/a><\/p>\n

\u201cWhy do seismic attenuation models enhance time-lapse imaging? A 2D viscoacoustic full-waveform inversion case study from the Volve field\u201d by Donggeon Kim and Tieyuan Zhu, 19 June 2025, Geophysics.
DOI: 10.1190\/geo2024-0793.1<\/a><\/p>\n

Never miss a breakthrough: Join the SciTechDaily newsletter.<\/a><\/b><\/p>\n","protected":false},"excerpt":{"rendered":"Oil wells often run dry long before they should, leaving behind untapped reserves hidden by complex underground geology.…\n","protected":false},"author":2,"featured_media":76902,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[77],"tags":[18,997,19,17,9951,133,6235,7905],"class_list":{"0":"post-76901","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-science","8":"tag-eire","9":"tag-geology","10":"tag-ie","11":"tag-ireland","12":"tag-oil","13":"tag-science","14":"tag-seismology","15":"tag-supercomputing"},"share_on_mastodon":{"url":"","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/76901","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/comments?post=76901"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/76901\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media\/76902"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media?parent=76901"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/categories?post=76901"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/tags?post=76901"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}