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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: mit
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+ tags:
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+ - oil-and-gas
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+ - drilling
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+ - physics-informed
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+ - edge-ai
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+ - realtime
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+ - ml-agent
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+ - deepboreai
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+ library_name: deepboreai-sdk
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+ pipeline_tag: tabular-classification
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+ model-index:
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+ - name: DeepBoreAI Agent
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+ results: []
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+ ---
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+
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+ # DeepBoreAI Agent: Real-Time Predictive Drilling Model
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+
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+ **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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+
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+ ---
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+
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+ ## Model Purpose
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+
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+ This model is part of the **DeepBoreAI ML Agent Suite** and is specialized in:
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+
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+ - Predicting mechanical/differential sticking
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+ - Optimizing rate of penetration (ROP)
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+ - Identifying hole cleaning inefficiencies
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+ - Detecting washouts and mud losses
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+
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+ Each model is informed by a hybrid architecture that blends:
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+ - Physical laws of drilling dynamics (e.g., conservation of energy, pressure balance)
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+ - Online learning algorithms that adapt to new drilling conditions
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+
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+ ---
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+
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+ ## Use Cases
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+
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+ - **Real-time drilling optimization**
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+ - **Anomaly detection and alerting**
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+ - **Autonomous drilling guidance systems**
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+ - **Rig edge computing deployments**
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+
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+ ---
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+
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+ ## How to Use
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+
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+ Install the DeepBoreAI SDK:
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+
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+ ```bash
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+ pip install deepboreai-sdk
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+ ```
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+
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+ Use this model in Python:
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+
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+ ```python
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+ from deepboreai_sdk.sdk import DeepBoreAI
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+ client = DeepBoreAI()
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+
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+ data = {
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+ "bit_depth": 2000,
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+ "wobs": 15.2,
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+ "rpm": 130,
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+ "torque": 500,
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+ "flow_rate": 400,
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+ "mud_density": 1.1,
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+ "annular_pressure": 80
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+ }
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+
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+ result = client.post_telemetry(data)
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+ print(result)
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+ ```
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+
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+ ---
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+
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+ ## Model Details
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+
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+ - **Architecture**: Physics-informed neural network with online learning
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+ - **Precision**: Validated at 90%+ on historical and synthetic drilling datasets
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+ - **Latency**: Optimized for <1s inference on edge devices
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+
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+ ---
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+
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+ ## Citation
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+
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+ If you use this model or DeepBoreAI, please cite:
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+
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+ ```
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+ @software{deepboreai2025,
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+ author = {DeepBoreAI Team},
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+ title = {DeepBoreAI: Real-Time Predictive AI Agents for Drilling},
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+ year = 2025,
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+ url = {https://huggingface.co/deepboreai},
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+ license = {MIT}
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+ }
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+ ```
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+
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+ ---
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+
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+ ## License
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+
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+ MIT License. Free for academic and commercial use.