---
tags:
  - ollama
  - oil-and-gas
  - engineering
  - deepseek
  - qwen
  - real-time-optimization
  - petroleum
  - reservoir
  - drilling
  - production
  - AI
  - machine-learning
license: mit
library_name: ollama
model_name: OGAI Reasoner
base_model: deepseek-r1
quantization: q4_k_m
pipeline_tag: text-generation
language: en
---

# OGAI Reasoner

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.

## Model Details
- **Base Architecture**: DeepSeek (Qwen2)
- **Parameters**: 7.62B
- **Quantization**: Q4_K_M
- **Size**: 4.7GB
- **License**: MIT

## Key Features
- Advanced petroleum engineering calculations
- Real-time optimization capabilities
- Comprehensive uncertainty quantification
- Industry-standard compliance
- Multi-domain expertise:
  - Reservoir Engineering
  - Well Engineering & Drilling
  - Production Engineering

## Capabilities
- **Reservoir Analysis**
  - PVT calculations
  - Material balance
  - Pressure transient analysis
  - Decline curve interpretation

- **Well Engineering**
  - Trajectory optimization
  - Drilling parameter optimization
  - Wellbore stability analysis
  - Completion design

- **Production Engineering**
  - Nodal analysis
  - Artificial lift optimization
  - Network optimization
  - Production forecasting

## Technical Specifications
- Temperature: 0.7 (Balanced precision)
- Top-p: 0.95 (High coherence)
- Top-k: 50 (Diverse solutions)
- Presence/Frequency Penalties: 0.1

## Input/Output Format
- Structured JSON inputs
- Standardized calculation outputs
- Comprehensive metadata
- Industry-standard units support

## Usage Examples
```python
# Basic calculation request
{
    "calculation_type": "pvt_analysis",
    "inputs": {
        "parameters": {
            "pressure": 3000,
            "temperature": 180,
            "oil_gravity": 35
        },
        "units": "field"
    }
}
```

## Installation
```bash
ollama pull gainenergy/ogai-reasoner:latest
```

## Deployment Requirements
- Minimum 8GB RAM
- 10GB storage
- CUDA-compatible GPU recommended

## Best Practices
1. Provide complete input parameters
2. Specify units explicitly
3. Include data quality metrics
4. Document assumptions
5. Validate results against standards

## Support
For technical support and questions:
- GitHub Issues
- Documentation: [docs/](docs/)
- Community Forum: [discuss.gainenergy.ai](https://discuss.gainenergy.ai)

## License
MIT License - See LICENSE file for details

## Acknowledgments
- DeepSeek team for the base model architecture
- Our partners, Merlin ERD
- SPE for industry standards and best practices
- Open-source contributors

---

**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.