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Los Alamos National Laboratory’s GTCloud, or GeoThermal Cloud, project was one of 10 projects selected by the Department of Energy Friday to receive funding to apply machine-learning techniques to geothermal exploration and production.
The DOE awarded $5.5 million to the 10 new projects. The projects focus on machine learning for geothermal exploration and advanced analytics for efficiency and automation in geothermal operations.
Machine learning – the use of advanced algorithms to identify patterns in and make inferences from data – could assist in finding and developing new geothermal resources.
If applied successfully, machine learning could lead to higher success rates in exploratory drilling, greater efficiency in plant operations, and ultimately lower costs for geothermal energy, according to the DOE.
The other projects are:
* Colorado School of Mines: Applying new machine learning techniques to analyze remote-sensing images to identify the presence of blind geothermal resources based on surface characteristics. Colorado School of Mines will develop a methodology to automatically label data from hyperspectral images of Brady’s Hot Springs, Desert Rock and the Salton Sea.
* Lawrence Livermore National Laboratory: Developing and applying new machine learning techniques to a multi-physics (magnetotelluric and seismic) dataset from the Raft River geothermal field to better identify and target fracture zones for drilling production wells.
* National Renewable Energy Laboratory: Improving geothermal reservoir management by using machine learning in conjunction with physics-based subsurface flow paths and interwell connectivity models.
* Pennsylvania State University: Applying machine learning methodologies toward the study of microearthquakes and their links to probable zones of permeability, as well as the risks associated with induced seismicity in geothermal development.
* University of Arizona: Building a single web-based platform to allow geothermal researchers and developers access to unique continuously growing scientific and exploration data.
* University of Houston: Developing a methodology to automatically detect subsurface fault/fracture zones from seismic images, and reliably characterize the fractures with the fault/fracture zones using the “double-beam” method with machine learning. The investigators have already shown some success using the proposed techniques in an oil and gas setting (Marcellus Shale) and will now adapt these techniques to the more difficult geothermal environment.
* University of Nevada, Reno: Building on a prior GTO-funded project that was focused on defining geothermal ‘play fairways’ in Nevada; the previous project utilized several machine learning techniques to identify regions having high geothermal favorability but it relied to some degree on expert opinion where training data was lacking.
* University of Southern California: Developing novel data-driven predictive models for integration into real-time fault detection and diagnosis, and integrate those models by using predictive control algorithms to improve the efficiency of energy production operations in a geothermal power plant.
* Upflow Limited in New Zealand: Making available multiple decades of closely-guarded production data from one of the world’s longest operating geothermal fields, and combining it with the archives from the largest geothermal company operating in the U.S. Models developed from this massive data store will enable the creation of a prediction/recommendation engine that will help operators improve plant availability.
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