A system dynamics approach for egs scenario analysis

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. INTRODUCTION From the perspective of available resource, the prospect of the amount of energy generated from enhanced geothermal systems (EGS), which allows for the exploitation of the Earth’s heat, is positive, given that estimates of the EGS resource to a depth of 10 km in the U.S. alone are over 100,000 times the nation’s annual consumption (MIT, 2006). However, when tasked with harnessing an EGS resource in a commercially competitive manner, the prospects are not so optimistic. What distinguishes EGS from most other energy sources is the difficulty and expense associated with characterizing, accessing, and then harnessing the energy. Taken to its extreme, we know that if we drill deep enough, a thermal source capable of generating huge amounts of energy exists. However, the costs associated with accessing that resource are clearly uneconomical. In addition, depending on the actual depth and temperature, the technology might not exist for reliable operations. Thus, tradeoffs must be made between the costs in terms of time and money needed to access and harness a resource against the production of both energy and dollars. If enough energy can be produced, at a levelized cost of electricity (LCOE) that is reasonably competitive, then that resource is most likely exploitable. The difficulty comes in evaluating those tradeoffs, especially in the face of competing technologies that are currently market-competitive. To illustrate this, consider a 100 o C geothermal resource at 1 km depth with a 2.5 o C per 100 m geothermal gradient, which is a realistic but relatively low gradient (MIT, 2006). By drilling deeper, one could gain access to a 150 o C source at 3000 m, or a 250 o C source at 7000 m. Since the source temperature greatly impacts the power generation capabilities, a tradeoff exists between drilling deeper to gain access to the higher temperature resource and the drilling costs. If one drills too deep, the drilling costs will outweigh the benefit of obtaining the higher temperature source. If one drills too shallow, then the ability to generate enough energy to warrant development may be compromised. Given this simple example, it is easy to see an optimal depth exists where the drilling costs are minimal enough, and the resource temperature great enough, to warrant development. Unfortunately, real world conditions are not that simple. The two fixed variables from above, drilling costs and temperature gradient, are dynamic, composite variables, meaning that they are time varying functions of other, more fundamental variables, many of which are also dynamic, composite variables themselves. Thus, when looking at drilling costs, one might need to consider the rock type(s), the operating temperature, the cost of steel, the size of the borehole, and so on. And while the temperature gradient informs us of the temperature of the resource, it says nothing about our ability to stimulate the reservoir and extract the heat, which determines the size of the power plant that could be installed at that location. Additional complexity arises when one considers the connections between the fundamental variables, such as how the borehole diameter, which impacts drilling costs, also impacts the mass flow rate, which in turn impacts the heat extraction and ultimately power production. To address this issue, we have employed a system dynamics (SD) approach to create an EGS scenario analysis model that allows a user to perform tradeoff and scenario analyses in real time. This allows them to identify the optimal solution space for a given set of resource characteristics, and power plant and well configurations. This paper documents what has been our initial effort in this project by first describing the objectives of our efforts from both a scientific and programmatic point of view. Following that is a discussion of our approach, which includes a description of system dynamics and how it is applied to EGS. We end the paper by presenting some deterministic results that come from examining a single scenario, as well as some risk assessment results that come from a probabilistic analysis.