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:

pip install deepboreai-sdk

Use this model in 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.

tommytracx
/

DeepBoreAI