Fervo_Project_Red.md content & styling

Author: softwareengineerprogrammer

docs/Fervo_Project_Red.md

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+# Fervo Project Red
 
 [Fervo_Project_Red-2026 example web interface link](https://gtp.scientificwebservices.com/geophires/?geophires-example-id=Fervo_Project_Red-2026)
 
+This document evaluates the accuracy of geothermal production temperature modeling against empirical field data from the Fervo Project Red site. It compares measured flowing temperatures against two predictive models: Fervo's proprietary model and the analytical GEOPHIRES (Gringarten) model.
+
+## Production Temperature: Measured vs. Modeled
+
+The charts below plot the measured flowing temperature over a roughly two-year period. Data points captured during early thermal conditioning and transient operations (e.g., shut-ins, flow-rate testing) are rendered in gray and excluded from the steady-state statistical alignment.
 
 ![](_images/fervo_project_red-2026_production-temperature-data-vs-modeling-1.png)
+
+*Detail view of the steady-state temperature plateau (175°C–185°C):*
 ![](_images/fervo_project_red-2026_production-temperature-data-vs-modeling-2.png)
 
+## Statistical Alignment Analysis
+
+The variance analysis (results displayed in legend captions) evaluates the predictive accuracy of both models against the measured steady-state data (excluding the initial thermal conditioning/ramp-up period).
+
+Both models demonstrate high predictive fidelity, tracking steady-state flowing temperatures within 1.5°C of the empirical data.
+
+* **Overall Fit:** GEOPHIRES mathematically achieves a tighter overall fit, yielding a lower Root Mean Square Error (RMSE) and a higher coefficient of determination (R²).
+* **Systematic Bias:** The Fervo model exhibits slightly less systemic underestimation, with a cold bias of -0.50°C compared to the GEOPHIRES cold bias of -0.83°C.
+* **R² Context:** The relatively low R² values for both models are expected statistical artifacts. Because the steady-state temperature profile is essentially a flat plateau, natural sensor variance and minor reservoir oscillations account for a disproportionately large portion of the total sum of squares, suppressing the R² score despite the low absolute error.
 
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