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.

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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

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Use Cases

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

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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)

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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

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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}
}

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License

MIT License. Free for academic and commercial use.