---
license: mit
tags:
  - oil-and-gas
  - drilling
  - physics-informed
  - edge-ai
  - realtime
  - ml-agent
  - deepboreai
library_name: deepboreai-sdk
pipeline_tag: tabular-classification
model-index:
  - name: DeepBoreAI Agent
    results: []
---

# DeepBoreAI Agent: Real-Time Predictive Drilling Model

**DeepBoreAI** delivers vendor-agnostic, physics-informed ML agents designed to predict and mitigate drilling hazards in real time. These agents are optimized for edge deployment, with live updates driven by telemetry from WITSML-compliant sources.

---

## Model Purpose

This model is part of the **DeepBoreAI ML Agent Suite** and is specialized in:

- Predicting mechanical/differential sticking
- Optimizing rate of penetration (ROP)
- Identifying hole cleaning inefficiencies
- Detecting washouts and mud losses

Each model is informed by a hybrid architecture that blends:
- Physical laws of drilling dynamics (e.g., conservation of energy, pressure balance)
- Online learning algorithms that adapt to new drilling conditions

---

## Use Cases

- **Real-time drilling optimization**
- **Anomaly detection and alerting**
- **Autonomous drilling guidance systems**
- **Rig edge computing deployments**

---

## How to Use

Install the DeepBoreAI SDK:

```bash
pip install deepboreai-sdk
```

Use this model in Python:

```python
from deepboreai_sdk.sdk import DeepBoreAI
client = DeepBoreAI()

data = {
    "bit_depth": 2000,
    "wobs": 15.2,
    "rpm": 130,
    "torque": 500,
    "flow_rate": 400,
    "mud_density": 1.1,
    "annular_pressure": 80
}

result = client.post_telemetry(data)
print(result)
```

---

## Model Details

- **Architecture**: Physics-informed neural network with online learning
- **Precision**: Validated at 90%+ on historical and synthetic drilling datasets
- **Latency**: Optimized for <1s inference on edge devices

---

## Citation

If you use this model or DeepBoreAI, please cite:

```
@software{deepboreai2025,
  author = {DeepBoreAI Team},
  title = {DeepBoreAI: Real-Time Predictive AI Agents for Drilling},
  year = 2025,
  url = {https://huggingface.co./tommytracx/DeepBoreAI},
  license = {MIT}
}
```

---

## License

MIT License. Free for academic and commercial use.