Data Fusion for Hydrothermal Reservoir Characterization Through Use of Bayesian Statistical Inference and MCMC Maximum Likelihood Models

Abstract

Geothermal operators model reservoirs using several sets of data that come from various sources and are often related. Many different models of the reservoir can fit some subset of the available data. The challenge at hand is to find those models that best fit all or most of the available data. A new data fusion and inversion methodology is introduced for implementation into a proposed modelling tool for hydrothermal systems. Algorithms are expected to be developed for joint inversion Bayesian inference of data from geothermal exploration, and for model likelihoods using parallel tempering (PT) Markov Chain Monte Carlo (MCMC) to characterize Icelandic hydrothermal systems. The project builds on a methodology from the National Information and Communications Technology of Australia (NICTA, current name Data61) where a similar approach was developed for Enhanced Geothermal Systems (EGS). Major differences in analyses of hydrothermal systems from EGS systems include permeability response and fluid flow through a naturally made fracture network. The aim for using Bayesian inference and MCMC likelihood methods is to interpret probabilistic models for quantification of uncertainty in low and high temperature hydrothermal systems. The purpose of this research is to develop a tool that can improve the accuracy of reservoir models that are vital to reducing risk of geothermal projects, and to support better decision making for exploration and development of the hydrothermal reservoirs.