Hunting exploration energy caused earthquakes before they happened

Albuquerque, New Mexico – Geoscientists at Sandia National Laboratories have used 3D-printed rocks and an advanced large-scale computer model of past earthquakes to understand and prevent earthquakes from energy exploration.

Groundwater injection after unconventional oil and gas extraction, known as fracking, geothermal stimulation and carbon dioxide sequestration, can trigger earthquakes. Of course, energy companies do due diligence to check for faults – breaks in the Earth’s upper crust that are prone to earthquakes – but sometimes earthquakes, and even earthquake swarms, strike unexpectedly.

Sandia geologists have studied how pressure and stress from water injection can travel through pores in rocks all the way to fault lines, including those previously hidden. They also crushed rocks with specially designed weak points to hear the sounds of different types of fault failures, which will aid in early detection of induced earthquake.

Contrast in 3D printing provides basic structural information

To study the different types of fault failures and their warning signs, Sandhya earth scientist Hongkyu Yoon needed a group of rocks that could fracture in the same way every time they were compressed – a pressure not unlike the pressure created by injecting water underground.

Natural rocks collected from the same site can have completely different directions and layers of minerals, causing weaknesses and different types of fractures.

Several years ago, Yun began using additive manufacturing, known as 3D printing, to make rocks from gypsum-based minerals under controlled conditions, believing that these rocks would be more consistent. To print the rock, Yoon and his team sprayed gypsum in thin layers, forming blocks and cylinders of 1 x 3 x 0.5 inch.

However, while studying 3-D rocks, Yun realized that the printing process also generated subtle structural differences that affected how the rocks were fractured. This piqued his interest, leading him to study how the metallic texture in 3D-printed rocks influences how they are fractured.

“It turns out that we can use this contrast in the mechanical and seismic responses to a 3D printed fracture to our advantage to help us understand the basic processes of fracture and its effect on fluid flow in rocks,” said Yoon. Earthquakes can be caused by fluid flow and pore pressure.

For these experiments, Yoon and collaborators at Purdue University, a university that Sandia has a strong partnership with, created a mineral ink using calcium sulfate powder and water. The researchers, including Purdue University professors Antonio Bobbitt and Laura Perak-Nolte, printed a layer of moist calcium sulfate, roughly half a sheet thick, and then applied an aqueous binder to tape the next layer to the first. The binder recrystallized some calcium sulfate into gypsum, which is the same mineral used in building drywall.

Researchers printed the same rectangular and cylindrical rocks based on gypsum. Some rocks have plaster mineral layers that run horizontally, while others have vertical mineral layers. The researchers also changed the direction in which they sprayed the binder, to create more contrast in the metallic layers.

The research team pressed the samples until they broke. The team examined the surfaces of the fracture using a laser and an X-ray microscope. Note that the path of the fracture depends on the orientation of the mineral layers. Yoon and colleagues describe this basic study in a paper published in Scientific Reports.

Acoustic signals and machine learning for classifying seismic events

Also, by working with his assistants at Purdue University, Yoon observed the sound waves coming from the printed samples as they were being broken. These sound waves are signs of small, rapid cracks. The team then combined the audio data with machine learning techniques, a type of advanced data analysis that can identify patterns in the data that appear to be unrelated, to detect signals of subtle seismic events.

First, Yoon and colleagues used a machine-learning technique known as a random-forest algorithm to group minute seismic events into groups that resulted from the same types of microstructures and identify about 25 important features in micro-sound data. They ranked these features in order of importance.

Using important features as evidence, they created a multi-layered “deep” learning algorithm – like the ones that let digital assistants work – and applied it to archived data gathered from real-world events. A deep learning algorithm was able to identify seismic event signals faster and more accurately than conventional monitoring systems.

Yoon said that within five years they hope to apply many different machine learning algorithms, such as these and algorithms that incorporate principles of Earth science, to detect induced earthquakes related to fossil fuel activities in oil or gas fields. Algorithms can also be applied to discover hidden faults that may become unstable due to carbon sequestration or geothermal stimulation, he said.

“One of the cool things about machine learning is its scalability,” said Yoon. “We always try to apply certain concepts developed under laboratory conditions to large-scale problems – which is why we do laboratory work. Once the machine learning concepts developed on a laboratory scale are proven to the archived data, it is very easy to scale them up. It’s up to large-scale problems.” Compared to traditional methods. “

The stress is transmitted through the rocks into deep faults

The hidden fault was the cause of a sudden earthquake at a geothermal stimulus site in Pohang, South Korea. In 2017, two months after the last thermal stimulus experiment ended, a 5.5-magnitude earthquake shook the region, the second-strongest earthquake in South Korea’s recent history.

After the earthquake, geologists discovered a deep hidden crack between two injection wells. To understand how stresses traveled from injecting water to the fault and triggering the earthquake, Kyung Won Chang, a geologist at Sandia, realized that he needed to think about more than the pressure of water on rocks. In addition to this deformation stress, he also needed to calculate how this pressure was transmitted to the rock as water flowed through the pores in the rock itself in his complex large-scale arithmetic model.

Zhang and colleagues describe stress transfer in a research paper published in Scientific Reports.

However, understanding the deformation stress and stress transfer through the pores of the rock is not sufficient to understand some of the earthquakes induced by energy exploration and prediction activities. The architecture of various faults must also be considered.

Using his model, Zhang analyzed a cube 6 miles long, 6 miles wide and 6 miles deep where a swarm of more than 500 earthquakes struck in Azle, Texas, from November 2013 to May 2014. The earthquakes occurred along two intersecting faults, one less than two miles below the surface and another longer. And deeper. While the shallow fault was closer to the sites of wastewater injection, the first earthquakes occurred along the longer and deeper fault.

In his model, Chang found that the water injection increased pressure on the shallow fault. At the same time, the pressure from the injection is transmitted through the rock to the deep rift. Because the deep fault was under more pressure at first, the earthquake swarm started there. He and Yoon shared the advanced computational model and their description of Azle earthquakes in a research paper recently published in the Journal of Geophysical Research: Solid Earth.

“In general, we need multi-physics models that combine different forms of stress that go beyond just pore pressure and rock deformation, to understand induced earthquakes and link them to energy activities, such as hydraulic stimulation and wastewater injection,” Zhang said.

Zhang said he and Yoon are working together to apply and scale machine learning algorithms to discover previously hidden errors and identify geological stress markers that can predict the magnitude of the resulting earthquake.

In the future, Zhang hopes to use these pressure signs to create a map of potential risks from induced earthquakes around the United States.

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His research efforts, as well as Leon’s initial work, were funded by Sandia’s laboratory-directed research and development program. Yoon received funding from the Department of Energy’s Fossil Energy Bureau to continue his research.

Sandia National Laboratories is a multitasking laboratory operated by the National Technology and Engineering Solutions Corporation of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. For the US Department of Energy’s National Nuclear Security Administration. Sandia Labs has primary research and development responsibilities in the areas of nuclear deterrence, global security, defense, energy technologies, and economic competitiveness, with major facilities in Albuquerque, New Mexico and Lefermore, California.

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