Merge pull request #146 from softwareengineerprogrammer/graph-data-comparison

Project Red 2026 Update: Graph data comparison

Author: softwareengineerprogrammer

.gitignore

@@ -114,6 +114,9 @@ docs/hip_ra_x_parameters.rst
 _site/
 /docs/geophires_x.rstx
 /docs/temperature.txt
+
+src/geophires_docs/fervo_project_red-2026_graph-data-extraction.xcf
+
 /geophires_x.rst
 /modules.rst
 /Useful sites for Sphinx docstrings.txt

docs/Fervo_Project_Red.md

@@ -0,0 +1,90 @@
+.. raw:: html
+
+    <style type="text/css">
+        a.image-reference {
+            max-width: 66%;
+        }
+
+        a.image-reference > img, a.image-reference > img:active {
+            max-width: 100%;
+        }
+
+        a.image-reference {
+          display: block;
+          margin: auto;
+          align-content: center;
+        }
+    </style>
+
+
+# Fervo Project Red: Evaluating the Gringarten Model against Empirical EGS Data
+
+ℹ️ The GEOPHIRES (Gringarten) model parameters used in this evaluation can be explored and executed via the [Fervo_Project_Red-2026 example in the web interface](https://gtp.scientificwebservices.com/geophires/?geophires-example-id=Fervo_Project_Red-2026).
+
+---
+
+This document[^author] evaluates the accuracy of the analytical GEOPHIRES (Gringarten) reservoir model by comparing it against real-world Enhanced Geothermal Systems (EGS) empirical data from the Fervo Project Red site.
+For comparative context, it also includes the predictive temperature curve generated by Fervo's proprietary modeling.
+
+[^author]: Author: Jonathan Pezzino, Scientific Web Services LLC. GitHub profile: [softwareengineerprogrammer](https://github.com/softwareengineerprogrammer).
+
+**Data Source & Methodology Notes:** The empirical production data evaluated here is derived from Figure 5 of Fervo Energy's report, [Enhanced Geothermal Has Been Proven at Scale: Here’s What Two Years of Production Data Show](https://fervoenergy.com/enhanced-geothermal-has-been-proven-at-scale-heres-what-two-years-of-production-data-show/).
+It should be noted that this analysis contains inherent limitations: the data points were semi-manually extracted from
+the published chart using image processing techniques, which introduces minor digitization artifacts.
+
+Additionally, aligning the data for statistical analysis requires establishing a threshold between the initial
+thermal conditioning phase and steady-state operations.
+This boundary is an analytical judgment call necessitated by structural differences in the models: the Fervo curve
+appears to assume an idealized steady-state flow from inception, omitting the early thermal ramp-up phase entirely.
+Conversely, while the GEOPHIRES (Gringarten) model does account for early transient heat transfer, its precision
+during this rapid ramp-up is inherently constrained by its temporal resolution (100 time steps per year).
+
+## 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 -0.70°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.
+
+## Modeling Assumptions and Power Production Discrepancies
+
+While the Gringarten model accurately predicts the reservoir's thermal drawdown, translating that thermal energy into net electrical power introduces additional variables.
+Users comparing GEOPHIRES power production estimates to Fervo's published net generation may notice discrepancies.
+These arise from fundamental differences between an idealized techno-economic model and a real-world, non-commercial operational plant:
+
+* **Commercial Optimization:** Fervo has explicitly noted that Project Red is a non-commercial, non-optimized learning facility designed to prove EGS viability at scale. Its empirical power generation figures reflect this experimental testing phase rather than the maximized output modeled by GEOPHIRES for mature commercial operations.
+* **Capacity Factor and Transients:** While the GEOPHIRES model utilizes a 90% capacity factor, this derating is applied continuously across the simulation. Real-world output is subject to discrete transient operational events (such as thermal conditioning, testing, and maintenance shut-ins) which inherently causes the time-averaged power production to diverge temporally from an uninterrupted analytical model.
+* **ORC Efficiency:** The power generation results rely on the GEOPHIRES built-in supercritical ORC efficiency correlation, which assumes the selection of an optimal working fluid for each specific geofluid temperature. For further details, please refer to the [Surface Plant section in the Theoretical Basis for GEOPHIRES](Theoretical-Basis-for-GEOPHIRES.html#surface-plant). Real-world net generation is constrained by the specific, fixed surface equipment installed at the site, which may operate below this theoretical optimum. This limitation may be addressed in the future by [FGEM integration](https://github.com/NatLabRockies/GEOPHIRES-X/issues/395).
+
+---
+
+## Previous Versions
+
+1. [Fervo_Norbeck_Latimer_2023](https://gtp.scientificwebservices.com/geophires/?geophires-example-id=Fervo_Norbeck_Latimer_2023)
+
+---
+
+## Disclaimer: Independent Analysis
+
+This case study is an independent techno-economic evaluation developed by the author and contributors to the GEOPHIRES
+open-source project. It is not affiliated with, sponsored by, or endorsed by Fervo Energy.
+The author and contributors are not employees or agents of Fervo Energy, and this work has not been reviewed or
+approved by the company. All modeling assumptions, including those derived from public data sources, represent the
+independent interpretation of the author and the GEOPHIRES open-source community and do not constitute proprietary
+information or official company projections.
+
+---
+
+## Footnotes

docs/GEOPHIRES-Examples.md

@@ -8,3 +8,7 @@ or in the [web interface](https://gtp.scientificwebservices.com/geophires) under
 ## Case Study: 500 MW EGS Project Modeled on Fervo Cape Station
 
 See documentation: [Case Study: 500 MWe EGS Project Modeled on Fervo Cape Station](Fervo_Project_Cape-5.html).
+
+## Fervo Project Red: Evaluating the Gringarten Model against Empirical EGS Data
+
+See documentation: [Fervo Project Red: Evaluating the Gringarten Model against Empirical EGS Data](Fervo_Project_Red.html).

docs/_images/fervo_project_red-2026_production-temperature-data-vs-modeling-1.png

@@ -24,6 +24,7 @@ Contents
 
    GEOPHIRES-Examples
    Fervo_Project_Cape-5
+   Fervo_Project_Red
 
 
 ..   reference/index

docs/_images/fervo_project_red-2026_production-temperature-data-vs-modeling-2.png

@@ -2,3 +2,4 @@ sphinx>=1.3
 sphinx-py3doc-enhanced-theme
 m2r2
 Jinja2
+opencv-python

docs/_images/singh_et_al_base_simulation-production-temperature.png

@@ -92,6 +92,10 @@ def read(*names, **kwargs):
         # eg:
         #   "rst": ["docutils>=0.11"],
         #   ":python_version=="2.6"": ["argparse"],
-        'development': ['bumpversion', 'sphinx_py3doc_enhanced_theme']
+        'development': [
+            'bumpversion',
+            'sphinx_py3doc_enhanced_theme',
+            'opencv-python',  # generate_fervo_project_red_2026_docs
+        ],
     },
 )

docs/index.rst

@@ -1,10 +1,15 @@
 from __future__ import annotations
 
+import json
 import os
 from pathlib import Path
 from typing import Any
 
+from pint.facets.plain import PlainQuantity
+
 from geophires_x_client import GeophiresInputParameters
+from geophires_x_client import GeophiresXClient
+from geophires_x_client import GeophiresXResult
 
 
 def _get_file_path(file_name) -> Path:
@@ -75,3 +80,28 @@ def _get_input_parameters_dict(  # TODO consolidate with FervoProjectCape5TestCa
         # TODO preserve newlines
 
     return ret
+
+
+def _get_full_profile(
+    input_and_result: tuple[GeophiresInputParameters, GeophiresXResult], profile_key: str
+) -> list[PlainQuantity]:
+    """
+    :return: List of data points with length Time steps per year * Plant lifetime
+    """
+
+    input_params: GeophiresInputParameters = input_and_result[0]
+    result = GeophiresXClient().get_geophires_result(input_params)
+
+    with open(result.json_output_file_path, encoding='utf-8') as f:
+        full_result_obj = json.load(f)
+
+    net_gen_obj = full_result_obj[profile_key]
+    net_gen_obj_unit = net_gen_obj['CurrentUnits'].replace('CELSIUS', 'degC')
+    profile = [PlainQuantity(it, net_gen_obj_unit) for it in net_gen_obj['value']]
+    return profile
+
+
+def _get_full_production_temperature_profile(
+    input_and_result: tuple[GeophiresInputParameters, GeophiresXResult],
+) -> list[PlainQuantity]:
+    return _get_full_profile(input_and_result, 'Produced Temperature')