Accurately monitoring subsurface carbon dioxide storage
Their novel monitoring system can rapidly monitor carbon dioxide
sequestered underground.

Date:
April 28, 2022
Source:
Texas A&M University
Summary:
Capturing and storing carbon dioxide (CO2) deep underground can
help combat climate change, but long-term monitoring of the stored
CO2 within a geological storage site is difficult using current
physics-based methods.



FULL STORY ========================================================================== Capturing and storing carbon dioxide (CO2) deep underground can help
combat climate change, but long-term monitoring of the stored CO2 within a geological storage site is difficult using current physics-based methods.


========================================================================== Texas A&M University researchers proved that unsupervised machine-learning methods could analyze the sensor-gathered data from a geological
carbon-storage site and rapidly depict the underground CO2 plume locations
and movements over time, lowering the risk of an unregistered CO2 escape.

Project lead Siddharth Misra, the Ted H. Smith, Jr. '75 and Max
R. Vordenbaum '73 DVG Associate Professor in the Harold Vance Department
of Petroleum Engineering, used seed money from the Texas A&M Energy
Institute to begin the research.

"The project was designed to facilitate long-term CO2 storage at low
risk," said Misra. "Current physics-driven models are time consuming to
produce and assume where the CO2 is in a storage site. We are letting
the data tell us where the CO2 actually is. We are also providing rapid visualization because if you cannot see the CO2, you cannot control it
deep underground." Increasing levels of CO2 in the atmosphere raise
global temperatures because the gas absorbs heat radiating from the
Earth, releases it back to the Earth over a long time and stays in the atmosphere far longer than other greenhouse gases.

Since more CO2 exists than can be easily filtered out by Earth's natural processes, it's essential to keep it out of the air by other means.

Sequestering the unwanted gas underground isn't new, but monitoring its presence within a geological site is challenging because CO2 is invisible, quickly moves through cracks and escapes without detection.



========================================================================== Current, physics-driven models rely on statistics or numerical
calculations that match known physical laws backed by research
results. However, the latest geological sensors yield an enormous amount
of data suggesting a lot of variety exists in subsurface compositions
than was previously thought. Physics-driven models don't include the information because such variations aren't fully understood, but Misra
knew that data contained knowledge useful to the situation.

Misra and Keyla Gonzalez, his graduate researcher, began by showing where
the CO2 was spatially. Since the entire subsurface data set had to be
mined for clues, they used unsupervised machine learning to locate the
CO2. Unlike supervised machine learning, where computer algorithms are
taught which data will answer a specific question, unsupervised learning
uses algorithms to sift through data to find patterns that relate to the parameters of a problem when no definite answers to a question exist yet.

First, the algorithms assessed the presence of CO2 in the data using
five broad or qualitative ranges, from very high concentrations down to
zero traces of it.

Colors identified each range for a 2D visual representation, with the
brightest color for the highest content and black for no CO2. These generalizations sped up pinpointing the plume's location, how much area
it covered and its approximate size, shape and density.

The algorithms learned several workflow methods to read data and model
the CO2.

Misra and Gonzalez couldn't rely on only one method to find the "right"
answer because using unsupervised learning meant no real solution to the problem existed yet. And any answer found would have to be confirmed rigorously, so each answer was compared against the others. Similar
results proved the solutions were unique to finding only the CO2, no
matter which methods were used.

More data was needed to track the movement of the CO2 through time,
so the algorithms were taught to sift through and evaluate data in
different formats, such as crosswell seismic tomography. Because the
algorithms were already geared to a purely data-driven approach and
visualized on a general level, the spatial-temporal maps were quickly
generated no matter what information was used. Again, similar results
proved the researchers were on the right track.

Misra and Gonzalez published a paper on the research in the journal Expert Systems with Applications. Gonzalez has graduated and took a position
with TGS, an international energy data and intelligence company that
was impressed with the work.

"The next step will be the combination of rapid prediction, rapid
visualization and real-time decision making, something the U.S. Department
of Energy is interested in," said Misra. "Even though the work was
hard and required a lot of confirmation to validate, I can see so much potential in research like this.

Many more applications and breakthroughs are possible. Unsupervised
learning takes more effort but gives so much insight."

========================================================================== Story Source: Materials provided by Texas_A&M_University. Original
written by Nancy Luedke.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Keyla Gonzalez, Siddharth Misra. Unsupervised learning monitors the
carbon-dioxide plume in the subsurface carbon storage
reservoir. Expert Systems with Applications, 2022; 201: 117216 DOI:
10.1016/ j.eswa.2022.117216 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/04/220428125350.htm

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