Geologic and structural models of the subsurface (geomodels) are crucial for making development decisions in geothermal fields, such as where to drill a well or where to enhance well permeability. This paper describes a Bayesian inversion strategy for finding a set of geomodels that reflect the subsurface uncertainty and match collected geophysical, geological and well-testing datasets. Specifically, this paper presents a case study of the second step of the Bayesian process, defining the prior uncertainty regarding the subsurface. We give an example of several possible conceptual models of the subsurface at the Patua Geothermal Field in west, central Nevada. In addition, we show an example of using the software PyNoddy to parameterize the subsurface structure and generate realizations of prior structural models.

Press the Back button in your browser, or search again.

Copyright 2020, Stanford Geothermal Program: Readers who download papers from this site should honor the copyright of the original authors and may not copy or distribute the work further without the permission of the original publisher.