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Utah FORGE: Discrete Fracture Network (DFN) Data

The FORGE team is making these fracture models available to researchers wanting a set of natural fractures in the FORGE reservoir for use in their own modeling work. They have been used to predict stimulation distances during hydraulic stimulation at the open toe section of well 1...

Finnila, A. and Podgorney, R. Golder Associates Inc.

Jun 24, 2020
66 Resources
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Sedimentary Geothermal Feasibility in Nevada, Western Utah, Colorado, and the Gulf Coast Region of Texas Final Report

The objectives of this project were to (1) perform a literature review of sedimentary geothermal resources, (2) identify data sources and develop data-collection methodologies that characterize selected resources, (3) screen sedimentary basins and formations for sedimentary geothe...

Johnston, H. et al National Renewable Energy Laboratory

Jul 01, 2020
1 Resources
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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells increasing or decreasing the fluid flow rates across the wells and drilling new wells at appropriate locations. Th...

Beckers, K. et al National Renewable Energy Laboratory

Oct 20, 2021
6 Resources
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Using Fully Coupled Hydro-Geomechanical Numerical Test Bed to Study Reservoir Stimulation with Low Hydraulic Pressure

This paper documents our effort to use a fully coupled hydro-geomechanical numerical test bed to study using low hydraulic pressure to stimulate geothermal reservoirs with existing fracture network. In this low pressure stimulation strategy, fluid pressure is lower than the minimu...

Fu, P. et al Lawrence Livermore National Laboratory

Jan 31, 2012
2 Resources
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Utah FORGE: Well 16A(78)-32 Simplified Discrete Fracture Network Data

Finnila, A. Golder Associates Inc.

Jun 01, 2021
3 Resources
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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulat...