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pywellfast calculation methods

introduction

pyfastwell is dependent on two python libraries developed by TNO: pythermonomics, and pywellgeo, which do the techno-economic calculations, and defined the well architectures respectively.

pyfastwell is an extension of these packages. It includes: - Analytical Element Method (AEM) (Fokker et al., 2005, Egberts et al., 2013) to calculate the well flow performance of advanced well architectures for homogeneous reservoir properties - coldfront tracking in the reservoir, based on finite difference solution - effects of brine properties (heat capacity, viscosity, density) on well flow performance - wellbore heat losses, frictional losses and thermosiphon effects - geothermal doublet techno-economic performance, including well flow results, coldfront evolution, and thermal performance - evaluation of Skin effects to be adopted in doubletCalc1D to obtain similar results

pywelllfast has been benchmarked against semi-analytical (and numerical) well models. It includes many tests and examples, including examples for Monte Carlo, sensitivity simulations to analyse uncertainties in reservoir properties, and well architectures

Approach

The geothermal power which can be produced takes into account the production flow rate Q, and the cooling of the produced brine in the heat conversion process:

E = 𝜂 Q C ΔT

where:

  • E is the converted power [W]
  • 𝜂 is the conversion efficiency [-], which depends on the heat conversion system, band here is assumed to be 1
  • Q is the flow rate of the produced brine [m³/s]
  • C is the volumetric heat capacity of the brine or circulation fluid [J m⁻³ K⁻¹]
  • ∆T is the temperature difference between the producer and injector at the topside at the heat exchanger [K]

Doublet well architectures

The well architecture can be defined in a flexible way, using the pywellresult package.

Doublet flow performance

Achievable flow rates are calculated with the AEM model in pyfastwell. The AEM model calculates the well inflow performance based on the well architecture, reservoir properties, and fluid properties. The coldfront evolution in the reservoir is calculated based on a finite difference solution, taking into account the flow rate, reservoir properties, and thermal properties of the fluid. The AEM model section provides more details on the AEM implementation in pyfastwell. and has been extensively benchmarked against semi-analytical and numerical models as described in the AEM benchmark section.

Doublet coldfront evolution

The coldfront evolution in the reservoir is calculated based on a finite difference solution, taking into account the flow rate, reservoir properties, and thermal properties of the fluid. the coldfront evolution is described in coldfront evolution.

Geothermal Resources

The geothermal resource is considered as a permeable subsurface layer, which can be corresponding a permeable sedimentary rock or fractured basement or magmatic rock. The geothermal resource parameters include reservoir depth, thickness, temperature, brine compositions, and reservoir flow properties such as permeability and porosity.

Economic Model

  • The economic model performs a NPV and LCOE calculation, incorporating a discounted cash flow approach:
  • CAPEX (Capital Expenditure) and OPEX (Operational Expenditure) are calculated based on the well design, reservoir properties
  • The LCOE is calculated as the ratio of the total discounted cost to the total discounted energy produced over the lifetime of the geothermal system.
  • The economic model allows for sensitivity analysis on key parameters such as reservoir properties, asscoiated flow rate, and conversion efficiency.

Key Performance indicators

  • key performance indicators: The key performance indicators (KPIs) are calculated based on the technical performance and economic model, providing insights into the feasibility and profitability of geothermal projects. These include:
  • net power (power in MWth)
  • Leveleized Cost of Energy (LCOE in €ct/kWh)
  • Net present value (NPV in million €)

References

  1. Egberts, P., Shatyrbayeva, I., Fokker, P.A., 2013. Well inflow modelling for wells not aligned to a numerical grid. SPE 165986.
  2. Fokker, P. A., Verga, F., & Egberts, P. J., 2005 New semianalytic technique to determine horizontal well productivity index in fractured reservoirs. SPE Reservoir Evaluation & Engineering, 8(02), 123-131.
  3. Van Wees et al., 2012. Geothermal aquifer performance assessment for direct heat production–Methodology and application to Rotliegend aquifers. Netherlands Journal of Geosciences, 91(4), 651-665. https://doi.org/10.1017/S0016774600000433
  4. Mijnlieff, 2020. Introduction to the geothermal play and reservoir geology of the Netherlands. Netherlands Journal of Geosciences.https://doi.org/10.1017/njg.2020.2