GeoForce — CNN Geothermal Reservoir Surrogate

Status: Deployed v1.1 Platform: platform.forcex-ai.com Model: Published on HuggingFace (GeoForce-CNN-v1.1) Dataset: Published on HuggingFace (geothermal-reservoir-dataset)

Executive Summary

A CNN-based surrogate model that replaces hours of TOUGH2 geothermal reservoir simulation with sub-second inference — achieving R²=0.997 accuracy with only 57K parameters. GeoForce is the flagship deployed product of the ForceX AI platform, bridging the gap between physics simulation fidelity and real-time operational decision-making.

Key Results

Metric Value
Model Accuracy R² = 0.997
Parameters 57K (lightweight)
Inference Time Sub-second (vs hours for TOUGH2)
Training Data Synthetic TOUGH2 simulation outputs
Version v1.1 (improved from initial ForceX prototype)

Technical Architecture

Model Design

  • Architecture: Convolutional Neural Network optimized for spatial reservoir data
  • Input: Reservoir parameters (permeability, porosity, injection rates, boundary conditions)
  • Output: Temperature and pressure field predictions across the reservoir grid
  • Training: Supervised learning on TOUGH2 simulation outputs
  • Validation: Cross-validated against held-out simulation scenarios

Physics-Informed Approach

GeoForce maintains physical consistency by:

  • Training on physics-simulator output (TOUGH2) ensuring conservation laws are respected
  • Encoding spatial relationships through convolutional layers
  • Validating predictions against known geothermal field behavior
  • Monitoring physical plausibility metrics alongside ML accuracy

Technology Stack

Component Technology
ML Framework PyTorch
Simulation Engine TOUGH2 (data generation)
API FastAPI
Frontend React + Vite + TypeScript (dashboard)
Deployment Docker, VPS
Model Registry HuggingFace Hub
Dataset Hosting HuggingFace Datasets
Testing pytest

Repository Structure

GeoForce/
├── surrogate/          # CNN model architecture and training
├── simulation/         # TOUGH2 simulation interface
├── solver/             # Physics solver utilities
├── agent/              # LangGraph agent for interactive queries
├── app/                # FastAPI backend
├── dashboard/          # React visualization dashboard
├── data/               # Training and validation datasets
├── notebooks/          # Exploration and analysis notebooks
├── demo/               # Demo scripts
├── tests/              # Test suite
├── docs/               # Documentation
└── tools/              # Utility scripts

Evolution from ForceX AI

GeoForce started as one of 12 products inside the ForceX AI platform. The standalone repository represents the production-hardened version with:

  • Improved CNN architecture (v1.0 → v1.1)
  • Published model weights on HuggingFace for reproducibility
  • Published training dataset for community use
  • Dedicated LangGraph agent for interactive reservoir analysis
  • Production API with monitoring and dashboards

Use Cases

  • Geothermal Field Development: Rapid scenario analysis for well placement
  • Operational Optimization: Real-time reservoir monitoring and prediction
  • Exploration: Quick screening of potential geothermal sites
  • Education: Teaching reservoir physics with instant feedback

Skills Demonstrated

  • CNN Surrogate Modeling
  • Physics-Informed Machine Learning
  • Geothermal Reservoir Simulation (TOUGH2)
  • Model Publishing (HuggingFace Hub)
  • Dataset Curation and Publishing
  • Production API Deployment

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