---
license: apache-2.0
language:
- en
tags:
- geothermal
- reservoir-simulation
- physics-informed
- surrogate-model
- energy
- cnn
- pytorch
- geoscience
- indonesia
library_name: pytorch
pipeline_tag: other
metrics:
- r_squared
- mse
datasets:
- ForceX-AI/geothermal-reservoir-dataset
model-index:
- name: GeoForce-CNN-v1.1
  results:
  - task:
      type: other
      name: Geothermal Reservoir Prediction
    metrics:
    - name: Temperature RMSE
      type: rmse
      value: 3.31
    - name: Temperature R²
      type: r_squared
      value: 0.994
    - name: Pressure RMSE (MPa)
      type: rmse
      value: 0.354
    - name: Pressure R²
      type: r_squared
      value: 0.997
    - name: Inference Time (ms)
      type: latency
      value: 3.19
    - name: Speedup vs Simulator
      type: speedup
      value: 282350
---

# GeoForce-CNN v1.1: Physics-Informed CNN Surrogate for Geothermal Reservoir Prediction

**GeoForce** is a physics-informed convolutional neural network (CNN) that predicts temperature and pressure field evolution in geothermal reservoirs. It replaces computationally expensive numerical simulators with a compact 57,802-parameter model that runs in **3.2ms on CPU** — a **282,350x speedup** over finite-difference simulation.

<div align="center">

| Metric | Value |
|--------|-------|
| Temperature RMSE | **3.31°C** |
| Temperature R² | **0.994** |
| Pressure RMSE | **0.354 MPa** |
| Pressure R² | **0.997** |
| Physics Violations | **0.0%** |
| Inference Time | **3.19 ms** (CPU) |
| Speedup vs Simulator | **282,350x** |
| Parameters | **57,802** |

</div>

## Model Description

GeoForce maps static reservoir properties (temperature, permeability, porosity, depth, well locations, pressure) directly to spatiotemporal temperature and pressure fields over a 20-year production horizon. It was designed for rapid uncertainty quantification and well placement optimization in Indonesian geothermal systems.

### Architecture

Encoder-Residual-Decoder CNN:

```
Input (batch, 6, 32, 32)
    ↓
[Encoder]
    ConvBlock: Conv2d(6→32, 3×3) + BatchNorm + ReLU
    ConvBlock: Conv2d(32→32, 3×3) + BatchNorm + ReLU
    ↓
[Middle]
    ResidualBlock: Conv→BN→ReLU→Conv→BN + skip → ReLU
    ResidualBlock: Conv→BN→ReLU→Conv→BN + skip → ReLU
    ↓
[Decoder]
    ConvBlock: Conv2d(32→32, 3×3) + BatchNorm + ReLU
    Conv2d(32→10, 1×1) + Sigmoid
    ↓
Output (batch, 10, 32, 32)
  → Channels 0-4: Temperature at years 4, 8, 12, 16, 20
  → Channels 5-9: Pressure at years 4, 8, 12, 16, 20
```

### Input Channels (6)

| Channel | Content | Normalization |
|---------|---------|---------------|
| 0 | Initial temperature field (°C) | (T - 25) / 325 |
| 1 | Log₁₀ permeability (m²) | (log_k + 16) / 4 |
| 2 | Well mask (Gaussian decay) | [0, 1] |
| 3 | Base pressure (Pa) | (P - 5e6) / 20e6 |
| 4 | Porosity | (φ - 0.01) / 0.14 |
| 5 | Depth (m) | (z - 800) / 1700 |

### Physics-Informed Loss

The training loss combines data-driven MSE with three physics constraints:

```
L_total = L_data + 0.1 × L_physics
L_physics = 0.1 × L_temporal + 0.01 × L_spatial + 0.001 × L_darcy
```

- **Temporal smoothness**: Penalizes non-physical jumps between timesteps (energy conservation proxy)
- **Spatial smoothness**: Discrete Laplacian penalty enforcing diffusion behavior
- **Darcy coupling**: Constrains pressure gradients in high-permeability zones

## Training

- **Data**: 1,000 synthetic reservoir simulations via implicit finite-difference solver (coupled heat + Darcy flow)
- **Parameter ranges**: Calibrated to Indonesian geothermal fields (Kamojang, Wayang Windu, Darajat)
- **Split**: 800 train / 100 val / 100 test (seed=42)
- **Optimizer**: Adam (lr=1e-3, ReduceLROnPlateau)
- **Training time**: 44.9 minutes on CPU (no GPU required)
- **Best epoch**: 352 / 402 (early stopped, patience=50)
- **Best validation loss**: 0.000382

## Quick Start

```python
import torch
import numpy as np
from reservoir_cnn import ReservoirCNN

# Load model
model = ReservoirCNN(in_channels=6, n_time_steps=5, base_filters=32)
checkpoint = torch.load("geoforce_cnn_v1.1.pt", map_location="cpu", weights_only=False)
model.load_state_dict(checkpoint["model_state_dict"])
model.eval()

# Prepare input: 6-channel tensor on 32×32 grid
# Example: Kamojang-like reservoir
base_temp = 245.0     # °C
log_perm = -14.0      # log10(m²)
base_pressure = 15e6  # Pa (15 MPa)
porosity = 0.08
depth = 1500.0        # m
n_wells = 3

# Normalize inputs
inp = torch.zeros(1, 6, 32, 32)
inp[0, 0] = (base_temp - 25) / 325          # Temperature
inp[0, 1] = (log_perm + 16) / 4             # Permeability
# Channel 2: well mask (place wells with Gaussian decay)
for _ in range(n_wells):
    wx, wy = np.random.randint(4, 28, size=2)
    for i in range(32):
        for j in range(32):
            d = ((i - wy)**2 + (j - wx)**2) / 50.0
            inp[0, 2, i, j] = max(inp[0, 2, i, j].item(), np.exp(-d))
inp[0, 3] = (base_pressure - 5e6) / 20e6    # Pressure
inp[0, 4] = (porosity - 0.01) / 0.14        # Porosity
inp[0, 5] = (depth - 800) / 1700            # Depth

# Run inference
with torch.no_grad():
    output = model(inp)  # (1, 10, 32, 32) normalized [0,1]

# Denormalize to physical units
temperature = output[0, :5] * 350.0          # °C, shape (5, 32, 32)
pressure = output[0, 5:] * 50e6              # Pa, shape (5, 32, 32)

print(f"Year 20 avg temperature: {temperature[4].mean():.1f}°C")
print(f"Year 20 avg pressure: {pressure[4].mean()/1e6:.2f} MPa")
```

## Validation Results

### Per-Timestep Accuracy

| Year | Temp RMSE (°C) | Pressure RMSE (MPa) |
|------|---------------|---------------------|
| 4 | 4.025 | 0.356 |
| 8 | 3.262 | 0.364 |
| 12 | 3.333 | 0.326 |
| 16 | 3.005 | 0.332 |
| 20 | 2.811 | 0.390 |

### Benchmark Comparison

| Method | Temp Accuracy | Inference | Hardware |
|--------|--------------|-----------|----------|
| TOUGH2 (full physics) | Exact | 15-30 min | CPU |
| USGS Brady Hot Springs ML | < 3.68% relative | 0.9 s | GPU |
| Fourier Neural Operator | ~1-3% error | ~10 ms | GPU required |
| **GeoForce v1.1** | **3.31°C RMSE (1.3%)** | **3.2 ms** | **CPU only** |

## Version History

| Version | Params | Temp R² | Pressure R² | Status |
|---------|--------|---------|-------------|--------|
| v1.0 | 56,938 | 0.975 | -0.003 | Retired (pressure prediction failed) |
| **v1.1** | **57,802** | **0.994** | **0.997** | **Current — all thresholds pass** |

The v1.0 failure was caused by compressing three physical parameters (pressure, porosity, depth) into a single input channel. Each parameter now has its own dedicated channel in v1.1. Full failure analysis is documented in the [technical report](technical-report.md).

## Limitations

1. **2D grid only** (32×32 horizontal slice) — no vertical flow or gravity convection
2. **Single-phase** liquid water — no steam-water phase transitions
3. **Homogeneous permeability** per scenario — no fracture networks
4. **Synthetic training data** — not yet validated against real field measurements
5. **Max error of 47.3°C** in extreme edge cases near training distribution boundaries

## Use Cases

- **Uncertainty quantification**: Run 10,000+ Monte Carlo samples in < 1 minute
- **Well placement optimization**: Evaluate hundreds of configurations in seconds
- **Screening studies**: Rapidly assess reservoir viability before committing to full simulation
- **Education**: Demonstrate reservoir physics concepts with instant feedback

## Files

| File | Description |
|------|-------------|
| `geoforce_cnn_v1.1.pt` | PyTorch checkpoint (244 KB) |
| `reservoir_cnn.py` | Model architecture (ReservoirCNN class) |
| `validation_metrics_v1.1.json` | Full validation results on 100 test samples |
| `hyperparams.yaml` | Training configuration |
| `training_log.json` | Complete training history (402 epochs) |
| `technical-report.md` | Full technical report with methodology and failure analysis |

## Citation

```bibtex
@software{geoforce2026,
  title={GeoForce: Physics-Informed CNN Surrogate for Geothermal Reservoir Prediction},
  author={Riupassa, Robi Dany},
  year={2026},
  publisher={ForceX AI},
  url={https://huggingface.co/ForceX-AI/GeoForce-CNN-v1.1},
  version={1.1.0}
}
```

## About ForceX AI

ForceX AI builds AI-powered tools for the energy industry — geothermal, renewable, oil & gas, and nuclear. Our models are trained on real simulation data and validated against industry benchmarks.

- Platform: [platform.forcex-ai.com](https://platform.forcex-ai.com)
- Website: [forcex-ai.com](https://forcex-ai.com)