Probabilistic MWe Estimation Using Experimental Design and Response Surface Methodology: Findings from Four Fields

Abstract

Stochastic power (MWe) capacity prediction from a calibrated reservoir model through building a polynomial model and implementing Monte Carlo simulation is the main focus of this work. The polynomial model is the fitted response of the numerical model to changes in the model’s uncertain parameters. Six key stochastic parameter sets were chosen to build several versions of the numerical model using the Experimental Design (ED) and Response Surface Methodology (RSM) framework. This approach was tested
and implemented in the calibrated reservoir model of the Rotorua, Ohaaki, Wairakei and Leyte Geothermal Fields using four Experimental Designs (ED): the three-level Full Factorial, two-level Full Factorial, three-level Box-Behnken and two-level Plackett-Burman design. Overall, the EDRSM framework using the Plackett-Burman fractional design proved to be a practical approach for estimating potential capacity from a calibrated natural-stated model using the chosen six uncertain parameters: permeability in the x, y and z-direction, porosity, reinjection enthalpy (RI Enthalpy), and the fraction of reinjection (%RI).