Stochastic inversion of gravity, magnetic, tracer, lithology, and fault data for geologically realistic structural models: Patua Geothermal Field case study. (September 2021)

• Record Type: Journal Article
• Authors: Pollack, Ahinoam; Cladouhos, Trenton T.; Swyer, Michael W.; Siler, Drew; Mukerji, Tapan; Horne, Roland N.
• Is Part Of: Geothermics. Volume 95(2021)
• Publication Date: 2021-09
• Subjects: Stochastic inversion -- Uncertainty quantification -- Joint inversion -- Structural models -- MCMC -- Genetic algorithms; Hydrogeology -- Periodicals; Geothermal resources -- Periodicals; Énergie géothermique -- Périodiques; GEOTHERMAL ENGINEERING; GEOTHERMAL ENERGY; GEOTHERMAL EXPLORATION; Geothermal resources; Hydrogeology; Periodicals; Electronic journals; 621.44
• DOI: 10.1016/j.geothermics.2021.102129
• Languages: English
• ISSNs: 0375-6505

Abstract

Highlights: Open-source code to stochastically invert five typical geothermal datasets. Tests four algorithms: genetic algorithm, MCMC, simulated annealing and NSGA. Algorithms create complex geologically realistic structural models. Tested successfully on real data from Patua Geothermal Field in Nevada, USA. Method helps advance fault targeting capability in geothermal reservoirs. Abstract: Financial risk due to geological uncertainty is a major barrier for geothermal development. Production from a geothermal well depends on the unknown location of subsurface geological structures, such as faults that contain hydrothermal fluids. Traditionally, geoscientists collect many different datasets, interpret the datasets manually, and create a single model estimating faults' locations. This method, however, does not provide information about the uncertainty regarding the location of faults and often does not fully respect all observed datasets. Previous researchers investigated the use of stochastic inversion schemes for addressing geological uncertainty, but often at the expense of geologic realism. In this paper, we present algorithms and open-source code to stochastically invert five typical datasets for creating geologically realistic structural models. Using a case study with real data from the Patua Geothermal Field, we show that these inversion algorithms are successful in finding an ensemble of structural models that are geologically realistic and match the observed data sufficiently. Geoscientists can use this ensemble of models to optimize reservoir management decisions given structural uncertainty.