README.md

metadata

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

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

Installation

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/
• Community Forum: 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

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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.