| --- |
| license: apache-2.0 |
| datasets: |
| - GainEnergy/quantum-oil-gas-dataset |
| base_model: |
| - GainEnergy/OGAI-R1 |
| library_name: transformers |
| tags: |
| - oil-gas |
| - quantum-computing |
| - hybrid-ai |
| - reservoir-engineering |
| - well-optimization |
| - retrieval-augmented-generation |
| - fine-tuned |
| - quantum-llm |
| - upstrima |
| model-index: |
| - name: OGAI-Quantum |
| results: |
| - task: |
| type: text-generation |
| name: Quantum AI for Oil & Gas Engineering |
| dataset: |
| name: GainEnergy Quantum Oil & Gas Dataset |
| type: custom |
| metrics: |
| - name: Quantum Reservoir Simulation Speedup |
| type: benchmark |
| value: Coming Soon |
| - name: Hybrid AI Computational Efficiency |
| type: benchmark |
| value: Coming Soon |
| - name: Quantum-RAG Retrieval Score |
| type: accuracy |
| value: Coming Soon |
| --- |
| |
| # **OGAI-Quantum: The Future of Oil & Gas AI (Coming Soon)** |
|
|
|  |
| [](LICENSE) |
|
|
| 🚀 **OGAI-Quantum** is a **next-generation hybrid AI model** that fuses **quantum computing principles with classical deep learning** to deliver breakthrough performance in **reservoir modeling, drilling optimization, seismic analysis, and energy AI workflows**. |
|
|
| 🌍 **COMING SOON**: Currently in **final development and quantum validation testing**. |
|
|
| --- |
| ## **🫠 Capabilities** |
| - **⚡ Quantum-Accelerated Simulations** – Faster reservoir modeling and seismic analysis. |
| - **🧠 Hybrid AI-Quantum Workflows** – Integrates quantum variational circuits with deep learning. |
| - **📚 Quantum-RAG for Technical Knowledge Retrieval** – Advanced AI-driven document retrieval for energy data. |
|
|
| ### **📌 Core Quantum Use Cases** |
| | **Use Case** | **Quantum Advantage** | |
| |--------------------------------|-----------------------| |
| | **Reservoir Simulation** | Multi-state quantum superposition for faster modeling | |
| | **Seismic Data Processing** | Quantum-based feature recognition in seismic datasets | |
| | **Well Placement Optimization** | Quantum annealing for high-dimensional search spaces | |
| | **Production Optimization** | Quantum variational circuits for real-time gas lift & production tuning | |
|
|
| --- |
|
|
| ## 🏢 **Quantum-Classical Hybrid Framework** |
| OGAI-Quantum is powered by **Upstrima's Quantum AI Engine**, combining **quantum-enhanced decision-making** with traditional deep learning. |
|
|
| ```yaml |
| System Architecture: |
| ├── Quantum Simulation Layer |
| │ ├── Quantum Gate Operations |
| │ ├── Qiskit & PennyLane Integration |
| │ ├── Variational Quantum Circuits (VQC) |
| │ ├── Quantum Annealing for Optimization |
| │ ├── Quantum Reservoir Simulation Models |
| │ ├── Seismic Data Quantum Processing |
| ├── Classical AI Model |
| │ ├── Fine-Tuned TinyR1-32B Model |
| │ ├── Hybrid Engineering Knowledge Base |
| │ ├── Neural Retrieval-Augmented Generation (RAG) |
| │ ├── Classical Physics-Based Simulations |
| │ ├── AI-Powered Technical Document Understanding |
| │ ├── Adaptive Learning & Model Refinement |
| └── Hybrid Orchestration Layer |
| ├── Quantum-Classical Task Partitioning |
| ├── Quantum State Virtualization Engine |
| ├── Quantum Pipeline API for High-Performance Computing |
| ├── Real-Time Quantum State Synchronization |
| ├── Cloud & Edge Deployment Support |
| ├── API Integration with Upstrima AI Suite |
| ``` |
|
|
| --- |
|
|
| ## 📦 **Model Variants** |
| | **Model Name** | **Base Model** | **Quantum Features** | **Context Window** | **Use Case** | |
| |-------------------|----------------|----------------|----------------|-------------| |
| | **OGAI-Quantum** | OGAI-R1 + Quantum | Yes | TDB tokens | **Hybrid AI for Energy & Engineering** | |
| | **OGAI-R1** | TinyR1-32B | No | 128k tokens | **Reservoir AI & RAG** | |
| | **OGMOE** | Mixtral-8x7B + MoE | No | 32K tokens | **Drilling Optimization & Decision Support** | |
|
|
| --- |
|
|
| ## 🚀 **Deployment & Integration** |
| OGAI-Quantum will be available on: |
| - **Hugging Face Inference API** |
| - **AWS Braket for Hybrid Quantum-Classical Workflows** |
| - **On-Premise Quantum-Classical HPC Deployment** |
|
|
| ### **🔧 Technical Stack** |
| - **Quantum Libraries:** `Qiskit`, `PennyLane`, `Cirq` |
| - **AI Frameworks:** `Transformers`, `AutoGPTQ`, `PEFT` |
| - **Data Pipelines:** `FAISS`, `Pinecone`, `LangChain` |
|
|
| --- |
|
|
| ## ⚠️ **Limitations** |
| 🚧 **Quantum Hardware Dependency** – While designed for hybrid execution, full quantum acceleration requires cloud-based quantum backends. |
| 🚧 **Experimental Hybrid AI** – Model performance is still undergoing validation for real-world **engineering applications**. |
| 🚧 **Not General-Purpose** – Optimized specifically for **oil & gas industry workflows**. |
|
|
| --- |
|
|
| ## 🔗 **Resources** |
| - **[Quantum Applications in Oil & Gas](https://huggingface.co/docs/quantum-oil-gas-applications)** – Technical whitepaper on **hybrid AI for energy**. |
| - **[GainEnergy AI Platform](https://gain.energy)** – Explore AI-powered **quantum-enhanced energy solutions**. |
| - **[Upstrima Quantum Computing Extension](https://huggingface.co/docs/upstrima-quantum-extension)** – WebAssembly-powered quantum simulation. |
|
|
| --- |
|
|
| ## 📚 **Citing OGAI-Quantum** |
| ```bibtex |
| @article{ogai-quantum-2025, |
| title={OGAI-Quantum: Hybrid Quantum-Classical AI for Oil & Gas Engineering}, |
| author={GainEnergy AI Team}, |
| year={2025}, |
| publisher={Hugging Face Models} |
| } |
| ``` |
