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Note that these parameters represent a simplified, homogenized analytical equivalent of a highly complex, heterogeneous subsurface fracture network.
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Further relevant detailed discussion can be found in the [Cape Station case study methodology section](Fervo_Project_Cape-5.html#calibration-with-fervo-implemented-field-design).
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See also the [effective number of fractures sensitivity analysis below](#sensitivity-analysis-effective-number-of-fractures-section).
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## Results
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Both models demonstrate high predictive fidelity, tracking steady-state flowing temperatures within 1.5°C of the empirical data.
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* **Overall Fit:** GEOPHIRES mathematically achieves a tighter overall fit, yielding a lower Root Mean Square Error (RMSE) and a higher coefficient of determination (R²).
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* **Systematic Bias:** The Fervo model exhibits slightly less systemic underestimation, with a cold bias of {{ fervo_bias_degc }}°C compared to the GEOPHIRES cold bias of {{ geophires_bias_degc }}°C.
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* **Systematic Bias:** The GEOPHIRES model exhibits slightly less systemic underestimation, with a cold bias of {{ geophires_bias_degc }}°C compared to the Fervo cold bias of {{ fervo_bias_degc }}°C.
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* **R² Context:** While the Fervo model yields a relatively low R² ({{ fervo_r2 }}), GEOPHIRES achieves a stronger R² of {{ geophires_r2 }}, indicating it more successfully captures the underlying physical trend of the data (the slight thermal drawdown) rather than simply averaging the noise. However, it is important to note that the absolute R² ceiling for both models is inherently suppressed by the dataset. Because the steady-state temperature profile is essentially a flat plateau, natural sensor variance and minor reservoir oscillations may account for a disproportionately large portion of the total variance, keeping the R² scores modest despite the favorable absolute error (RMSE).
To evaluate the model's predictive behavior over a longer timeframe, the GEOPHIRES simulation was extended to an 8-year horizon.
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This timeframe aligns with the redrilling interval modeled in the [Cape Station case study](Fervo_Project_Cape-5.html) and provides a
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realistic view of the anticipated thermal decline before major wellfield intervention would be required.
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To evaluate the model's predictive behavior over a longer timeframe, the GEOPHIRES simulation was extended to an {{ long_term_forecast_years }}-year horizon.
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This timeframe aligns with the redrilling interval modeled in the [Cape Station case study](Fervo_Project_Cape-5.html)
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and provides a plausible view of the anticipated thermal decline before major wellfield intervention would be required.
#### Sensitivity Analysis: Effective Number of Fractures
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As detailed in the methodology's [GEOPHIRES Reservoir Parameters](#geophires-reservoir-parameters) table, the baseline
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`Number of Fractures` ({{ input_params['Number of Fractures'] }}) represents a deliberately de-rated analytical
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equivalent designed to proxy a highly complex, heterogeneous subsurface fracture network. While this calibrated
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baseline provides the tightest statistical fit for the current two-year empirical window, assigning an exact number to
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the effective fracture surface area inherently relies on analytical interpretation.
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To evaluate the bounding envelope of the reservoir's thermal drawdown, a sensitivity analysis was performed on this
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effective `Number of Fractures`. By expanding the model to include other plausible values (ranging from {{ fracture_sensitivity_range_low }} to {{ fracture_sensitivity_range_high }}
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fractures), the analysis demonstrates the sensitivity of the long-term thermal decline to the idealized fracture surface area.
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