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Optimisation of geothermal energy systems

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- 1. www.preene.com OPTIMISING GEOTHERMAL SYSTEMS Dr Martin Preene Preene Groundwater Consulting June 2014
- 2. www.preene.com SYNOPSIS • Introduction • Why optimise? • Key factors for geothermal systems • A dynamic systems modelling approach • Conclusion
- 3. www.preene.com PRACTICE PROFILE Preene Groundwater Consulting is the Professional Practice of Dr Martin Preene and provides specialist advice and design services in the fields of dewatering, groundwater engineering and hydrogeology to clients worldwide Dr Martin Preene has more than 25 years’ experience on projects worldwide in the investigation, design, installation and operation of groundwater control and dewatering systems. He is widely published on dewatering and groundwater control and is the author of the UK industry guidance on dewatering (CIRIA Report C515 Groundwater Control Design and Practice) as well as a dewatering text book (Groundwater Lowering in Construction: A Practical Guide to Dewatering)
- 4. www.preene.com INTRODUCTION • Many technical factors affect the development of geothermal systems • These are important but may only be indirectly related to the project objective of maximising power (electricity and heat) generation while minimising cost per unit power • Quantity of power that can be generated over the project lifetime is also important • Parasitic losses can be important • The whole system must be assessed and, if possible, optimised
- 5. www.preene.com WHY OPTIMISE? • An understanding of optimisation is important at project development stage to aid the development of a scheme that maximises net power output for a given level of investment • System optimisation is also important when looking at forward predictions of net saleable power during funding transactions or when agreeing power purchase agreements • Several cost optimisation models exist • There are some drawbacks and limitations with cost optimisation models. Our approach is to focus on optimising power outputs, to provide information to be used in financial models
- 6. www.preene.com COST TRENDS Source: EGEC Base year 2007
- 7. www.preene.com KEY FACTORS • Once a geological prospect has been identified, various key factors must be determined before potential power outputs can be assessed: • Location (where to drill), and distance between extraction and re-injection wells • Depth of drilling • Power conversion technology • Mass flow rate (pumping and re-injection rate) • Parasitic losses • Reservoir pressure drawdown • Reservoir temperature drawdown
- 8. www.preene.com KEY FACTORS • Parasitic losses - Generating and cooling system parasitic loads - Artificial lift parasitic loads - Others • Pressure drawdown - Significant reduction in geofluid pressure will occur at extraction wells; this strongly influences pumping parasitic losses - Impact of pressure drawdown can be expressed as well productivity index = production rate/drawdown - Productivity index will be lower at higher mass flow rates, and may reduce with time • Temperature drawdown - Geofluid circulation through the reservoir may reduce reservoir temperature in the long term - High mass flow rates may cause more rapid temperature drawdown and reduce cumulative power production over defined periods
- 9. www.preene.com DYNAMIC SYSTEMS MODELLING • Most simple, and many relatively complex, systems can be handled by spreadsheet based analysis, but it can be difficult to capture options, uncertainty and interactions • Tools like GoldSim are modelling environments for probabilistic (Monte Carlo) simulation of complex dynamic systems. These models are able to interact with other modelling environments to produce coupled models • In ‘Player’ mode, GoldSim can act as an interface for ‘non technical’ end users to investigate change in key system parameters
- 10. www.preene.com DYNAMIC SYSTEMS MODELLING
- 11. www.preene.com DYNAMIC SYSTEMS MODELLING
- 12. www.preene.com EXAMPLE MODELLING • Model outputs for a system based on binary power conversion, with a single doublet of extraction and re- injection well • Key external parameters are geothermal gradient and reservoir hydraulic properties (can be assigned a probability density function) • Key ‘optimisable’ parameters are depth of drilling and volumetric flow rate (can be varied within a defined range) • Model can be used to look at time series relationships and parameter relationships
- 13. www.preene.com MODELLING • Permeable sandstone aquifer (lower end of hydrofractured systems in terms of permeability). • Well depth of 4.5 km. • Geothermal Gradient of c. 0.047 C/m. • Well spacing of 200 m. • Mass flow rates between 10 kg/s and 50 kg/s. • Thermal ‘cut off’ at 120 C (not reached). • Run for 450 iterations. • Rest water level 1380 m below ground level. • Binary plant rejection temperature 330 K (57C). • District heating circuit (final) rejection temperature 290 K (17C). • Cooling load taken as 5% of gross electrical power output
- 14. www.preene.com TIME SERIES MODELLING Median (50%ile) 25th to 75th %ile 5th to 25th and 75th to 95th %ile <5 %ile, >95 %ile Median time of initial reservoir cooling
- 15. www.preene.com TIME SERIES MODELLING ailable at plant (MWth) Median (50%ile) 25th to 75th %ile 5th to 25th and 75th to 95th %ile <5 %ile, >95 %ile Power decreases as reservoir cooling occurs
- 16. www.preene.com TIME SERIES MODELLING June 22, 2014 16 Median (50%ile) 25th to 75th %ile 5th to 25th and 75th to 95th %ile <5 %ile, >95 %ile
- 17. www.preene.com TIME SERIES MODELLING
- 18. www.preene.com TIME SERIES MODELLING Median (50%ile) 25th to 75th %ile 5th to 25th and 75th to 95th %ile <5 %ile, >95 %ile
- 19. www.preene.com OPTIMISATION OF PARAMETERS • Simulation realisations At later times, temperature drawdown has reduced geofluid temperature (and therefore power production) at high flow rates Net Electrical Power versus Abstraction after 300 Days 0 1000 2000 3000 4000 5000 6000 0 10 20 30 40 50 60 Mass Flow Rate (kg/s) NetPower(kW) Net Electrical Power versus Abstraction after 3000 Days 0 500 1000 1500 2000 2500 3000 3500 0 10 20 30 40 50 60 Mass Flow Rate (kg/s) NetPower(kW)
- 20. www.preene.com OPTIMISATION OF PARAMETERS • Simulation realisations At later times, temperature drawdown has reduced geofluid temperature (and therefore power production) at high flow rates Net Electrical Power versus Abstraction after 3000 Days 0 500 1000 1500 2000 2500 3000 3500 0 10 20 30 40 50 60 Mass Flow Rate (kg/s) NetPower(kW)
- 21. www.preene.com OPTIMISATION OF PARAMETERS Simulation realisations At higher flow rates, temperature drawdown of geofluid occurs earlier. The temperature drawdown reduces gross thermal power and reduces conversion efficiencies
- 22. www.preene.com CONCLUSION • Prediction of saleable power from geothermal systems involves a complex series of interactions • Involves uncertainty in external factors (e.g. geothermal gradient and reservoir properties) • Involves selection of controllable parameters (e.g. well depth, mass flow rate) to optimise desired targets • A dynamic systems approach allows predictive modelling of potential resource and utilisation • Can be used for scenario assessment during feasibility, funding or project development stages. Can feed directly into financial models
- 23. www.preene.com OPTIMISING GEOTHERMAL SYSTEMS Dr Martin Preene Preene Groundwater Consulting June 2014

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