Preliminary Report on Applications of Machine Learning Techniques to the Nevada Geothermal Play Fairway Analysis

TitlePreliminary Report on Applications of Machine Learning Techniques to the Nevada Geothermal Play Fairway Analysis
Publication TypeConference Proceedings
Year of Conference2020
AuthorsFaulds, J, Brown, S, Coolbaugh, M, DeAngelo, J, Queen, JH, Treitel, S, Fehler, M, Mlawsky, E, Glen, JM, Lindsey, C, Burns, E, Smith, C, Gu, C, Ayling, B
Conference Name45th Workshop on Geothermal Reservoir Engineering
PublisherStanford University
Abstract

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.

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