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--- |
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tags: |
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- ollama |
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- oil-and-gas |
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- engineering |
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- deepseek |
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- qwen |
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- real-time-optimization |
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- petroleum |
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- reservoir |
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- drilling |
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- production |
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- AI |
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- machine-learning |
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license: mit |
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library_name: ollama |
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model_name: OGAI Reasoner |
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base_model: deepseek-r1 |
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quantization: q4_k_m |
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pipeline_tag: text-generation |
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language: en |
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--- |
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# OGAI Reasoner |
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OGAI Reasoner is an advanced engineering system for oil and gas operations, built on the DeepSeek architecture. It specializes in petroleum engineering calculations, real-time optimization, and technical analysis. |
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## Model Details |
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- **Base Architecture**: DeepSeek (Qwen2) |
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- **Parameters**: 7.62B |
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- **Quantization**: Q4_K_M |
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- **Size**: 4.7GB |
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- **License**: MIT |
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## Key Features |
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- Advanced petroleum engineering calculations |
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- Real-time optimization capabilities |
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- Comprehensive uncertainty quantification |
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- Industry-standard compliance |
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- Multi-domain expertise: |
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- Reservoir Engineering |
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- Well Engineering & Drilling |
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- Production Engineering |
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## Capabilities |
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- **Reservoir Analysis** |
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- PVT calculations |
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- Material balance |
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- Pressure transient analysis |
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- Decline curve interpretation |
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- **Well Engineering** |
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- Trajectory optimization |
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- Drilling parameter optimization |
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- Wellbore stability analysis |
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- Completion design |
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- **Production Engineering** |
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- Nodal analysis |
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- Artificial lift optimization |
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- Network optimization |
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- Production forecasting |
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## Technical Specifications |
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- Temperature: 0.7 (Balanced precision) |
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- Top-p: 0.95 (High coherence) |
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- Top-k: 50 (Diverse solutions) |
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- Presence/Frequency Penalties: 0.1 |
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## Input/Output Format |
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- Structured JSON inputs |
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- Standardized calculation outputs |
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- Comprehensive metadata |
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- Industry-standard units support |
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## Usage Examples |
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```python |
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# Basic calculation request |
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{ |
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"calculation_type": "pvt_analysis", |
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"inputs": { |
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"parameters": { |
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"pressure": 3000, |
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"temperature": 180, |
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"oil_gravity": 35 |
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}, |
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"units": "field" |
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} |
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} |
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``` |
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## Installation |
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```bash |
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ollama pull gainenergy/ogai-reasoner:latest |
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``` |
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## Deployment Requirements |
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- Minimum 8GB RAM |
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- 10GB storage |
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- CUDA-compatible GPU recommended |
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## Best Practices |
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1. Provide complete input parameters |
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2. Specify units explicitly |
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3. Include data quality metrics |
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4. Document assumptions |
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5. Validate results against standards |
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## Support |
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For technical support and questions: |
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- GitHub Issues |
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- Documentation: [docs/](docs/) |
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- Community Forum: [discuss.gainenergy.ai](https://discuss.gainenergy.ai) |
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## License |
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MIT License - See LICENSE file for details |
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## Acknowledgments |
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- DeepSeek team for the base model architecture |
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- Our partners, Merlin ERD |
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- SPE for industry standards and best practices |
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- Open-source contributors |
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--- |
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**Note**: This model is optimized for engineering calculations and technical analysis. While it provides recommendations, all results should be validated by qualified engineers before implementation. |
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