Uncertainty Analysis of Analytical and Numerical Geothermal Reservoir Simulations for Direct-Use Heating Applied to Cornell University, Ithaca, NY
Abstract
Geothermal reservoir simulations use analytical or numerical deterministic models with specified reservoir geologic properties and temperature to determine reservoir performance. Results are compared across utilization scenarios that reflect engineering design choices. Explicitly considering uncertainties in reservoir geologic properties and temperature, and propagating uncertainties through reservoir simulations, allows for probabilistic interpretations of the reservoir's ability to meet heating demands for each scenario. This can inform decision making with economic evaluations and project risk assessments. We evaluate the potential to meet the direct-use heating needs of Cornell University's main campus by simulating the performance of an injection-production well pair in sedimentary reservoir targets at about 2.3 km depth. Well logs within 30 km of Cornell are used to estimate probability distributions for porosity and density of the reservoir and caprocks. Core data are used to derive a permeability-porosity relationship for the reservoir. Probabilistic ranges of temperatures at depth are estimated using nearby well logs, a 1-D vertical heat conduction model, and kriging geostatistical stochastic simulation. Uncertainties in each variable are propagated through analytical reservoir simulations using a Monte Carlo analysis to estimate the produced heat for a base case and several utilization scenarios. The reservoir flow geometry is unknown, so we evaluate several analytical models and analogous numerical models with specified geometry. Analytical models include 1) calculated stored thermal energy to estimate an upper bound of the total available energy in the reservoir, 2) linear heat sweep, which treats the reservoir as a porous medium, and 3) parallel fractures to approximate a fracture-dominated system. Numerical simulations of thermal hydraulic behavior of the system are obtained by using the TOUGH2 software model. For computation time considerations, uncertainty analysis for numerical simulations uses simple scenarios for favorable and unfavorable geologic properties and temperature, defined by the most extreme 5th percentiles of their distributions. Heat production results for numerical simulations are compared to Monte Carlo results from analytical simulations.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.H31C..08S
- Keywords:
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- 1822 Geomechanics;
- HYDROLOGYDE: 1847 Modeling;
- HYDROLOGYDE: 1869 Stochastic hydrology;
- HYDROLOGYDE: 1873 Uncertainty assessment;
- HYDROLOGY