Optimization of geothermal energy reservoir modeling using advanced numerical tools for stochastic parameter estimation and quantifying uncertainties

摘要

Geothermal energy is an option for low carbon production of heat or electric energy. For further developments of this resource, a major obstacle is the risk of project failure due to uncertain estimates of flow rate and temperature (and, hence, produced power) of geothermal installations. In this work, I develop and apply stochastic methods and modeling strategies for predicting the variation of pressure, temperature, and their uncertainty with time within geothermal reservoirs based on observed thermal and hydraulic rock property distributions. This comprises stochastic forward and inverse modeling approaches for simulating heat and tracer transport as well as fluid flow numerically. The approaches reduce the corresponding a priori uncertainties of perturbed parameters and states drastically by 50%-67% in case of temperature at a depth of 2000 m, depending on the target location. Furthermore, I estimate the spatial distribution of permeability as well as its uncertainty by applying the stochastic assimilation technique of Ensemble Kalman Filtering on production data for sedimentary rocks and fractured hard rocks. This addresses structure and parameter heterogeneity within the reservoir. I study different geothermal reservoirs, such as (i) numerous synthetic reservoirs to test the tools of Sequential Gaussian Simulation combined with geostatistical post-processing and Ensemble Kalman Filter. (ii) Further, I quantify temperature uncertainties of a doublet system in a sedimentary reservoir in The Hague, The Netherlands. (iii) In addition to temperature uncertainties, I study pressure uncertainties at a reservoir in the north-eastern German basin. Here, also a single-well design for exploitation of geothermal energy along a fault zone proofs to represent an alternative to doublet layouts. By gradient-based deterministic Bayesian inversion, basal specific heat flow is revealed. (iv) Finally, I investigate the hard rock reservoir of the Enhanced Geothermal System at Soultz-sous-Forêts, France, using Sequential Gaussian Simulation and Ensemble Kalman Filtering in an equivalent porous medium approach. A tracer circulation test performed in 2005 provides data for the inversion. Applying the two different stochastic methods allows for identifying best estimates for the heterogeneously distributes hydraulic parameters, studying their non-uniqueness, and comparing the results from stochastic (massive Monte Carlo, Ensemble Kalman Filter) and deterministic (gradient-based Bayesian inversion) estimation techniques. Based on the Ensemble Kalman Filter estimation results, I perform a long-term performance prediction with regard to transient temperature variation including corresponding uncertainties. The presented work flows constitute a method for creating calibrated reservoir models based on data which will allow the operators of a geothermal installation to compute production scenarios optimized with respect to profit or sustainability.