A microseismic event loaded from the Frontier Observatory for Research in Geothermal Energy (FORGE) distributed acoustic sensing (DAS) data into a Jupyter notebook showing energy from a microseismic event arriving at about 7.5 seconds. These microseisms bring information about the process of stimulation. However, in the data set there are relatively few and they are hard to find without specialized processing. Connecting hard-to-get SEG-Y data to easy-to-develop Jupyter notebooks promises to drive innovation in new AI/ML methods for detecting more microseisms and therefore, increasing the value of the existing data.
Geothermal is seriously hot right now (pun intended). Already this year, the DOE has tendered over $50 million worth of grants (here, here and here) to develop enhanced geothermal power in the United States. A $1.4 million grant was recently awarded to Rice University to adapt its DAS technologies for carbon capture and storage applications.
In Utah FORGE there are 71,880 recordings with 110 known microseismic events now visible with expert developed signal processing. Scientists can pull all the data and relevant metadata into their Jupyter notebooks and start exploring. Read on for how…
Enhanced Geothermal Systems in Brief
Conventional geothermal systems, or hydrothermal systems, occur naturally throughout the world. However, capturing enough steam or energy from heated water to generate sustainable power from them requires highly specific conditions, including sufficient heat, fluid saturation and permeability. By fracturing and pumping water through high-temperature formations, enhanced geothermal systems technology can provide the fluid content and fracture conductivity needed—but not naturally present—to produce continuous steam and energy transfer.
EGS technology has the potential to power tens of millions of homes and businesses with clean, domestic energy. The United States Geothermal Technologies Office (GTO) pursues emerging, innovative R&D initiatives that support commercially viable EGS by 2030. At the forefront of EGS is the Utah Frontier Observatory for Research in Geothermal Energy (FORGE), a first-of-its-kind accelerator for EGS pioneers.
Utah FORGE Shows the Way Forward
The mission of the Utah FORGE observatory is to enable cutting-edge well construction technologies to make commercial enhanced geothermal power possible. It provides a treasure trove of drilling, logging, completion and other geophysical data through the Geothermal Data Repository (GDR).
The Utah FORGE observatory sits above a large volume of hot crystalline granite up to six miles deep. The site provides a controlled environment where multidisciplinary researchers from universities, national laboratories, and industry partners can develop, test and optimize EGS technologies.
In 2020, researchers drilled the industry’s first deep, highly deviated geothermal well through the formation. This innovative approach, coupled with unprecedented collaboration and data dissemination, will allow a series of tests, measurements and models to facilitate the development of large-scale, economically sustainable EGS resources.
In 2021, the FORGE project is focusing its R&D efforts on zonal isolation, field-scale characterization, stimulation and well configuration for geothermal conditions.
Empowering EGS Understanding with AI/Machine Learning
Applied artificial intelligence makes the necessary understanding possible. The GDR shares open source surface and subsurface data from FORGE, including well plans, survey data, drilling data, well logs, activity reports, an interactive geoscience map, and more. These detailed datasets have the potential to revolutionize the way experts approach EGS by enabling rapid, high-quality modeling—such as fracture simulation and earth modeling—like never before.
By applying advanced machine learning technology, domain experts will be able to interpret and leverage these complex datasets at scale, often in real-time for operational decision making. New-generation deep learning applications, such as Enthought’s SubsurfaceAI Seismic, will enable scientists to optimize drilling, reservoir stimulation, well connectivity and flow testing as researchers work on developing and accelerating breakthroughs in EGS operations.
Getting to Know FORGE Data
The 110 discovered microseismic events from within the 71,880 recordings (thanks to Ariel Lelouch for sharing) are very hard to see without specialized signal processing. We’re loading the data and putting all the relevant metadata with it so you can just pull it into your Jupyter notebook and start exploring. The above image is an example.
A microseismic event loaded from the Frontier Observatory for Research in Geothermal Energy (FORGE)
distributed acoustic sensing (DAS) data into a Jupyter notebook showing energy from a microseismic event arriving at about 7.5 seconds.
At the nerd layer, there’s an opportunity for data scientists, AI experts and seismologists to learn and apply their skills to this new frontier in energy and its associated geophysics.
For access to this collection of AI/ML ready FORGE DAS data, please get in touch with Enthought.
About the Author: Ben Lasscock, holds a Ph.D. and a B.Sc. in theoretical physics from the University of Adelaide. Before coming to geoscience, Ben worked as a portfolio manager at a large hedge fund in Australia. He has publications in the areas of high energy physics, Bayesian time series analysis and geophysics.
It doesn’t take ✨magic✨ to integrate ChatGPT into your Jupyter workflow. Integrating ChatGPT into your Jupyter workflow doesn’t have to be magic. New tools are…
Top 5 Takeaways from the American Chemical Society (ACS) 2023 Fall Meeting: R&D Data, Generative AI and More
By Mike Heiber, Ph.D., Materials Informatics Manager Enthought, Materials Science Solutions The American Chemical Society (ACS) is a premier scientific organization with members all over…
There’s a long history of scientists who built new tools to enable their discoveries. Tycho Brahe built a quadrant that allowed him to observe the…
With the increasing importance of AI and machine learning in science and engineering, it is critical that the leadership of R&D and IT groups at...
From Data to Discovery: Exploring the Potential of Generative Models in Materials Informatics Solutions
Generative models can be used in many more areas than just language generation, with one particularly promising area: molecule generation for chemical product development.
Scientists gain superpowers when they learn to program. Programming makes answering whole classes of questions easy and new classes of questions become possible to answer….
OpenAI's ChatGPT, Google's Bard, and other similar Large Language Models (LLMs) have made dramatic strides in their ability to interact with people using natural language....
You know it. We know it. NumPy is cool. Pandas is cool. We can bend them to our will, but sometimes they’re not the right tools…
Mammalian cell culture is a fundamental tool for many discoveries, innovations, and products in the life sciences.
For many traditional innovation-driven organizations, scientific data is generated to answer specific immediate research questions and then archived to protect IP, with little attention paid...