OGAI-R1: Oil & Gas AI Model for Engineering & Technical Knowledge

OGAI-R1 is a fine-tuned version of TinyR1-32B, designed specifically for oil and gas engineering applications. It is optimized for engineering calculations, wellbore stability analysis, reservoir management, and document-based retrieval-augmented generation (RAG).

The model has been trained using GainEnergy's GPT-4o Oil & Gas Training Set, incorporating expert knowledge, technical formulas, and structured query-response interactions.

🏗 Why Use OGAI-R1?

• 🚀 Fine-tuned for oil & gas engineering tasks (drilling, production, reservoir, and refining).
• 💡 Optimized for RAG – Enhanced document understanding and retrieval.
• 📚 Long-Context Retention – Handles up to 32K tokens for complex engineering workflows.
• ⚡ LoRA Fine-Tuning on TinyR1-32B – Enables efficient inference and quick knowledge retrieval.

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🛠 How to Use OGAI-R1

1️⃣ Install Required Dependencies

pip install torch transformers accelerate bitsandbytes

2️⃣ Load the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "GainEnergy/OGAI-R1"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Load model
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

# Run inference
prompt = "Explain the principles of reservoir simulation in petroleum engineering."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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📦 Model Variants

Model Name	Base Model	Precision	Context Window	Use Case
OGAI-R1	TinyR1-32B	FP16	32K tokens	Engineering Calculations & RAG
OGAI-8x7B	Mixtral-8x7B	4-bit	32K tokens	Oil & Gas AI Assistant
OGAI-Reasoner	DeepSeek-R1	FP16	128K tokens	Logical Reasoning & AI Simulation

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📌 Key Capabilities

✅ Engineering Calculations – Computes reservoir volumes, wellbore stability, mud weight, casing depth, and more.
✅ Technical Document Understanding – Trained on oil and gas technical literature, drilling reports, and engineering manuals.
✅ Retrieval-Augmented Generation (RAG) – Enhances AI-driven document retrieval for faster decision-making.
✅ High-Context Retention (32K tokens) – Supports long technical reports, operational workflows, and AI-driven engineering analysis.

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🚀 Use Cases

• Wellbore Stability & Drilling Optimization
• Hydraulics & Fluid Flow Simulations
• Reservoir Engineering & Petrophysics Analysis
• AI-Powered Document Retrieval & RAG Workflows
• Technical Compliance & Regulatory Document Processing

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📡 Deployment Options

Platform	Compatible?	Recommended Setup
Hugging Face Inference API	✅ Yes	Deploy via hf.co/GainEnergy/OGAI-R1
RunPod.io (Serverless GPU)	✅ Yes	A100-40GB or RTX 4090
AWS EC2 (G5 Instances)	✅ Yes	ml.g5.2xlarge (8 vCPUs, 32GB RAM)
Local GPU (Consumer Hardware)	✅ Yes	Requires ≥16GB VRAM (RTX 3090, 4090)

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⚠️ Limitations

🚧 Optimized for Oil & Gas Engineering – Not designed for general-purpose AI tasks.
🚧 Requires domain-specific expertise – Outputs should be validated by industry experts.
🚧 Computational requirements – Running the full TinyR1-32B model requires high-end GPUs.

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🔗 Resources

• GainEnergy AI Platform – Explore AI-powered drilling automation.
• Hugging Face Model Hub – Download & deploy the model.

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📚 Citing OGAI-R1

@article{ogai-r1-2025,
  title={OGAI-R1: An AI Model for Oil & Gas Engineering Optimization},
  author={GainEnergy AI Team},
  year={2025},
  publisher={Hugging Face Models}
}