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Preliminary report on applications of machine learning techniques to the Nevada play fairway analysis

Conference · · Stanford Geothermal Program Technical Report
OSTI ID:1999432
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  1. Nevada Bureau of Mines and Geology; University of Nevada, Reno
  2. Aprovechar Lab L3C; Earth Resources Laboratory, Massachusetts Institute of Technology
  3. Nevada Bureau of Mines and Geology; University of Nevada; Reno
  4. USGS
  5. Hi-Q Geophysical, Inc.
  6. Earth Resources Laboratory, Massachusetts Institute of Technology

We are applying machine learning (ML) techniques, including training set augmentation and artificial neural networks, to mitigate key challenges in the Nevada play fairway project. The study area includes ~85 active geothermal systems as potential training sites and >12 geologic, geophysical, and geochemical features. The main goal is to develop an algorithmic approach to identify new geothermal systems in the Great Basin region. Major objectives include: 1) integrate ML techniques into the geothermal community; 2) develop open community datasets, whereby all play fairway and ML datasets and algorithms are publicly released and available for modification by various user groups; 3) identify data acquisition targets with high value for future work; 4) identify new signatures to detect blind geothermal systems; and 5) foster new capabilities for characterizing subsurface temperature and permeability. Initially, ML techniques are being applied to the same play fairway datasets and workflow. ML will then be applied to both enhanced and additional datasets, with modification of the PFA workflow to incorporate the new datasets. Finally, ML will be applied to define new workflows using the enhanced and additional datasets. An algorithmic approach that empirically learns to estimate weights of influence for diverse parameters can potentially scale and perform better than the play fairway analysis. Initial work on this project has involved 1) evaluating potential positive and negative training sites, 2) transformation of datasets into formats suitable for ML, and 3) initial development and testing of ML techniques.

Research Organization:
Nevada Bureau of Mines and Geology; Univ. of Nevada, Reno, NV (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office
Contributing Organization:
Aprovechar Lab L3C; Hi-Q Geophysical, Inc.; Earth Resources Laboratory, Massachusetts Institute of Technology; United States Geological Survey
DOE Contract Number:
EE0008762
OSTI ID:
1999432
Report Number(s):
DOE-UNR-8762-1
Journal Information:
Stanford Geothermal Program Technical Report, Vol. 216; Conference: 45th Workshop on Geothermal Reservoir Engineering, Stanford, CA, , Stanford, CA (United States), 10-12 Feb. 2020; Related Information: https://pubs.usgs.gov/publication/70211874
Country of Publication:
United States
Language:
English