It is no secret that deepwater depositional systems hold some of the largest petroleum reservoirs on Earth. To get those reserves out safely, effectively and economically from such depths – and the International Energy Agency defines “deepwater” as a water depth of more than 400 meters – requires technology, expertise, and a little bit of luck.
The challenges associated with doing that – determining the presence, distribution and quality of those reserves – is the work of the Quantitative Clastics Laboratory at the Bureau of Economic Geology at the University of Texas at Austin.
“The opportunities abound and understanding the subsurface is more important than ever,” said Jacob A. Covault, a senior research scientist at QCL.
He said the work of the lab is to get as broad a perspective of what’s in the arena of deepwater reservoir characterization from a geology and geophysical standpoint. QCL develops predictive models, training and software for stratigraphic characterization and correlation in the subsurface.
“In conventional reservoirs, especially deepwater major capital projects, reservoir quality and connectivity/compartmentalization are the key uncertainties. Our bread and butter, the science of sed-strat (sedimentology and stratigraphy) directly addresses these uncertainties,” he said.
Deepwater Applications Onshore
And here he wants to make the distinction about how, in fact, there may not be much of a distinction in where hydrocarbons are located.
“In unconventionals and tight reservoirs, like some of the basin-floor clastic deposits of the Permian Basin, overall subsurface heterogeneity is important, including reservoir rock characteristics (i.e., facies), their extent, and their control on mechanical stratigraphy and ‘frac-ability,’” he said.
He admits that it’s a perhaps a stretch to consider onshore unconventional and tight reservoirs in the context of “deepwater,” but insists it is not as far-fetched as it seems, for many of the sed-strat concepts apply to understanding depositional systems and reservoir architecture.
“We consider ‘deepwater’ from a sed-strat process standpoint,” he said, meaning, for example, that although the petroliferous Permian Basin is onshore, it has, “a lot of ‘deepwater’ basin-floor clastics. On the other hand, the dynamic of deepwater sedimentary systems presents a challenge, as the complex interactions of topography and (submarine mass movements, including slumps, slides, turbidity currents, and debris flows) result in erosion and deposition that govern the lateral continuity and vertical connectivity of sandy and muddy architectural elements of submarine fans.”
The Importance of Integrated Models
Understanding these similarities and differences helps define the specific characteristics of deepwater and exponentially increases the success for exploration. Covault wants to underscore, though, that it’s not just the G&G reservoir characterization that’s important, but also factors having to do with integration and modeling.
“Geo-statistical methods in reservoir modeling can use semi-variograms, geometric parameters and/or training images to reproduce spatial statistics from available conditioning data, analogs and use of insights from depositional processes and stratigraphic evolution,” he said.
He said that models that fail to account for these processes might not capture realistic heterogeneity of deposits.
“For example, compensational stacking of lobe elements in a deepwater fan juxtaposes sandy axial deposits over muddier fringe deposits; this juxtaposition can introduce a baffle or barrier to flow that is only captured with proper stratigraphic ordering and compensational stacking,” he said.
Often, even when all these factors are put to work in deepwater, there are still intangibles that make projects successful or not.
“In some cases, failures work out because of economics. So, it’s difficult to say what’s a success and what’s a failure. It depends on your perspective,” said Covault.
On a personal note, Covault, who is co-principal investigator of the lab, along with Zoltan Sylvester (featured in EXPLORER last year regarding his ChronoLog software), said he has been interested in the field for most of his professional and academic life.
“I actually cut my teeth on this topic as a graduate student at Stanford working with Steve Graham and the late, great deepwater expert Bill Normark and colleagues at the USGS Menlo Park,” he said.
QCL studies depositional systems from a process standpoint, he said, favoring those that honor the stratigraphic relationships, and probably a suite of deterministic, realistic models.
“We support our sponsors in legacy oil and gas E&P in, for example, the Gulf of Mexico and the Atlantic margins. We’re also helping with recent discoveries like those offshore South America.”
Even though the industry, deepwater included, has seen job loss – according to a study released by Deloitte in 2020 reporting that 70 percent of the more than 107,000 oil industry jobs lost may never return – there is good news on the horizon. According to a Mordor Intelligence study of deepwater and ultra-deepwater, exploration and production is expected to grow at a compound annual growth rate of approximately 10.7 percent during the period of 2020–25, due to factors such as rising deepwater oil and gas activity, especially in Gulf of Mexico, the North Sea, Brazil, Guyana and Nigeria, improved viability of deepwater and ultra-deepwater projects, and tightening supply-demand gap.
Applications Beyond Oil
Having said all that, for Covault, this is not just about exploration.
“There have been more opportunities than ever to apply deepwater subsurface characterization techniques to challenges beyond oil and gas, from hazard assessment to a variety of storage assessments,” he said.
He cited the work he and his lab have been doing with the U.S. Geological Survey to correlate turbidites in the shallow subsurface of the deepwater Cascadia Subduction Zone and better date big earthquakes, as well as its effort with the UT-Austin Gulf Coast Carbon Center.
“Whereas the GCCC has tended to focus onshore and in state waters, lately we have been working with them and colleagues at the (Department of Energy National Energy Technology Laboratory) … to repurpose the valuable, extensive network of deepwater infrastructure in the Gulf of Mexico for renewable energy generation, (enhanced oil recovery) and (carbon capture and storage), among other opportunities for repurposing,” said Covault. “Deepwater subsurface characterization is fundamentally important to this and, given the vast store of legacy geophysical data available in the Gulf of Mexico, machine learning/artificial intelligence techniques are sure to play a prominent role.”