OneGeo - Well Log Analysis Platform

OneGeo is an engineering-grade subsurface data platform designed to ingest, visualize, and interpret LAS (Log ASCII Standard) data. Built for high performance and data-grounded AI insights.

🌐 Live Platform: well-log-analyzer.vercel.app

🚀 Submission Checklist & How to Run

Prerequisites

• Python 3.10+ (Backend: FastAPI)
• Node.js 18+ (Frontend: React)
• PostgreSQL (Database)
• Groq API Key (For ultra-fast Llama-3.3-70b interpretations)

1. Backend Setup

cd Backend
pip install -r requirements.txt
# Create .env based on .env.example
python main.py

The server will start at http://localhost:8000.

2. Frontend Setup

cd frontend
npm install
npm run dev

The platform will be accessible at http://localhost:5173.

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🛠 Technical Decisions (Simply Explained)

1. Why PostgreSQL?

We chose PostgreSQL because it is reliable and handles scientific data perfectly:

• Organized: It keeps wells, charts, and history neatly linked together.
• Flexible: Every well has different curve names (like Gamma Ray or Gas). We use a "JSON" feature to store these without breaking the database.
• Fast: It can save over 10,000 lines of data in just a few seconds.

2. Smart File Storage (S3 + Local)

• Amazon S3: Files are saved safely in the cloud so they are never lost.
• Local Fallback: If you don't have AWS setup, the app automatically saves files to a local folder so it still works perfectly for you.

3. Smooth Charts (No Lag)

Reading 50,000+ data points makes most websites slow. We solved this by:

• Accurate Math: Python calculates the "peaks" and "averages" on the full data so nothing is missed.
• Fast Viewing: We only send a smaller "sample" of the points to your screen. This makes the charts feel super smooth while still showing the correct trends.

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🌐 Cloud Deployment (Optional)

If you choose to deploy your One-Geo platform to the cloud, here is your roadmap:

1. Database (PostgreSQL)

• Local: You are currently using localhost.
• Cloud: Use AWS RDS, Supabase, or Railway. Update your DATABASE_URL in the environment settings.

2. Backend (FastAPI)

• Hosting: Use Render, Railway, or AWS App Runner.
• Requirement: Set all .env variables in your provider's "Environment Variables" dashboard.
• Command: Use gunicorn -w 4 -k uvicorn.workers.UvicornWorker main:app.

3. Frontend (React)

• Hosting: Use Vercel, Netlify, or AWS Amplify.
• Config: Ensure VITE_API_URL points to your deployed Backend URL.

4. Storage (S3)

• The Best Part: Since you already configured S3, your files will stay in the SAME cloud bucket whether you run the app locally or in production!

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🔬 AI Analysis (Data-Grounded)

Our AI doesn't just "guess"—it uses actual math to stay accurate:

1. Fact-Checking: Before the AI speaks, our system calculates real engineering ratios (like Gas Wetness).
2. Hard Evidence: We give these exact numbers to the AI. This stops it from making things up ("hallucinating") and forces it to use the real spikes in your data.
3. Instant Reports: Powered by Groq, your geological reports are ready in under 2 seconds.

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✦ Key Features (Requirements Saturation)

• ✅ LAS 2.0 Ingestion: Full parsing of ~WELL, ~CURVE, and ~ASCII blocks.
• ✅ Interactive Charting: Zoom and depth-windowing via the Depth Brush.
• ✅ Deep AI Interpretation: Hydrocarbon potential and formation analysis.
• ✅ GeoBot (Bonus): Technical assistant grounded in real-time well statistics.
• ✅ Secure Infrastructure: All API Keys are processed strictly server-side.

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