Catching quakes caused by energy exploration before they happen

Sandia scientists use 3D-printed rocks, machine learning to detect unexpected earthquakes

Sandia geoscientists used 3D-printed rocks and an advanced, large-scale computer model of past earthquakes to understand and prevent earthquakes triggered by energy exploration.

Injecting water underground after unconventional oil and gas extraction, or fracking, geothermal energy stimulation and carbon dioxide sequestration all can trigger earthquakes. Of course, energy companies do their due diligence to check for faults — breaks in the earth’s upper crust that are prone to earthquakes — but sometimes earthquakes, even swarms of earthquakes, strike unexpectedly.

Sandia geoscientists studied how pressure and stress from injecting water can transfer through pores in rocks down to fault lines, including previously hidden ones. They also crushed rocks with specially engineered weak points to hear the sound of different types of fault failures, which will aid in early detection of an induced earthquake.

3D printing reveals structural information

To study different types of fault failures and their warning signs, Sandia geoscientist Hongkyu Yoon needed a bunch of rocks that would fracture the same way each time he applied pressure — pressure not unlike the pressure caused by injecting water underground. Natural rocks collected from the same location can have vastly different mineral orientation and layering, causing different weak points and fracture types.

Several years ago, Hongkyu started using additive manufacturing, or 3D printing, to make rocks from a gypsum-based mineral under controlled conditions, believing that these rocks would be more uniform. To print the rocks, Hongkyu and his team sprayed gypsum in thin layers, forming 1-by-3-by-half inch rectangular blocks and cylinders.

However, as he studied the 3D-printed rocks, Hongkyu realized that the printing process also generated minute structural differences that affected how the rocks fractured. This piqued his interest, leading him to study how the mineral texture in 3D-printed rocks influences how they fracture.

Read the Lab News story and watch a video explaining how a Sandia team collected the data they needed to “train” a deep-learning algorithm to identify signals of seismic events faster and more accurately.