Quantifying Uncertainty in Geothermal Reservoir Modeling

Abstract

An increased use of geothermal energy requires reliable estimates of the risk of failure and the project cost. These estimates can be provided by quantifying the uncertainty of subsurface rock properties and state variables, such as temperature or pressure, in a geothermal reservoir.
This quantification can be obtained by using a stochastic approach called Monte Carlo simulation. To this end, we integrated the stochastic algorithm “Sequential Gaussian Simulation” Sgsim into our in-house mass and heat flow simulator Shemat_suite. Sgsim generates an ensemble of parameter realizations for the same geometrical reservoir model, where each realization corresponds equally likely to the real situation defined by data. By providing this ensemble of realizations, the stochastic approach allows us not only to obtain average values and error estimates of a target rock property or state variable at any location in the geothermal reservoir but also their local probability distribution.
As a demonstration of this method, an exploration scenario is simulated for a projected geothermal district heat use in The Hague, Netherlands.