Title:

A System Dynamics Approach for EGS Scenario Analysis

Authors:

Thomas S. Lowry, Vincent C. Tidwell, Peter H. Kobos, Mark Antkowiak, Charles E. Hickox

Key Words:

EGS, System Dynamics, thermal drawdown, reservoir engineering

Conference:

Stanford Geothermal Workshop

Year:

2010

Session:

HDR/EGS

Language:

English

File Size:

263KB

View File:

Abstract:

In order for enhanced (or engineered) geothermal systems (EGS) to become commercially viable, many technical and economic hurdles must be overcome. Prioritizing which hurdles to address first is extremely important since the distance gained towards understanding EGS varies based on which hurdle is cleared. Complicating this situation is the fact that most of these technical and economic hurdles, as well as the gains that their understanding contributes towards the goal of commercializing EGS, are dependent variables, meaning that they exist in dynamic relationship with other processes. This complexity creates feedback and non-linear behavior that makes assessment and evaluation extremely difficult. To address this issue, a system dynamics (SD) approach has been employed to create an EGS scenario analysis model. SD models are unique in their ability to focus on the temporal dynamics while accounting for the various feedback loops and delays that are inherent in complex integrated systems such as EGS. A systems approach also has the general advantage of reduced computational burden and thus the opportunity to develop interactive models that operate in real time on a PC or across the web, allowing broad stakeholder engagement.

The model provides a basis for defining the technical and economic solution space for EGS across a variety of well, reservoir, and power plant configurations. Utilizing the Geothermal System Scoping Model developed by NREL as one of its key sub-models, the SD model has the ability to communicate with the Geothermal Electricity Technology Evaluation Model (GETEM) to provide baseline economic evaluations of the different scenarios. In this way, the SD model is able to identify the integrated technical and economic bottle necks and uncertainties associated with developing EGS.


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