🛢️ LAS Analyzer: Engineering-Grade Well Log Intelligence

Transforming raw subsurface data into actionable geological insights. LAS Analyzer is a full-stack platform designed to bridge the gap between complex petrophysical data and modern AI reasoning.

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🌎 Live Environment

• 🚀 Production App: las-analyzer.vercel.app
• 🔌 System API: https://13-126-13-42.sslip.io/api
• 💓 Health Status: https://13-126-13-42.sslip.io/health

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🏁 Getting Started

👉 Local Setup Guide: Run Literally Anywhere

If you are an evaluator or developer looking to run this project locally, please jump to our dedicated guide above.

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🔥 Project Highlights & Innovations

🔍 1. Smart LAS Ingestion Engine

Unlike generic data parsers, this system is engineered specifically for the Log ASCII Standard (LAS) format.

• Header Intelligence: Automatically mines critical metadata like WELL, FIELD, API Number, and Company from the LAS header.
• Metadata Indexing: Pre-calculates statistical envelopes (min, max, mean) for every curve during ingestion. This allows the AI to have instant "Contextual Awareness" without re-processing entire datasets for every query.
• Hybrid Storage Architecture: Combines the durability of Amazon S3 (for raw file preservation) with the agility of PostgreSQL (for structured data access and performance).

📊 2. High-Performance Petrophysical Visualizer

Visualization is the fundamental tool for geological reasoning.

• Synchronized Multi-Track Views: Render multiple curves side-by-side on a shared, unified depth axis.
• Precision Interaction: Built-in support for zooming, panning, and interval isolation to focus on specific reservoirs or zones of interest.
• Modern UI/UX: A clean, "Glassmorphism" dashboard built with Tailwind CSS and Plotly.js, optimized for high-density data interpretation.

🧠 3. Context-Aware AI Petrophysicist

We've integrated the world's most sophisticated reasoning model (Claude 3.5 Sonnet) directly into the workflow.

• Automated Lithology Insights: Identifies rock types, fluid contacts, and potential anomalies based on numerical curve signatures.
• Persistent AI Assistant: A specialized chatbot that understands the current well you are viewing. It doesn't just guess; it checks the actual statistics and metadata of the active well to answer your questions.

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🏗️ Technical Architecture

📐 System Design Overview

┌─────────────────────────────────────────────────────────────────────┐
│                         PRESENTATION LAYER                          │
│                                                                     │
│  ┌──────────────────┐  ┌──────────────────┐  ┌──────────────────┐   │
│  │  File Upload     │  │  Visualization   │  │  AI Insights     │   │
│  │  Component       │  │  Dashboard       │  │  Panel           │   │
│  │                  │  │                  │  │                  │   │
│  │  - Drag & Drop   │  │  - Plotly Charts │  │  - Analysis      │   │
│  │  - Validation    │  │  - Curve Select  │  │  - Statistics    │   │
│  │  - Progress      │  │  - Depth Range   │  │  - Insights      │   │
│  └──────────────────┘  └──────────────────┘  └──────────────────┘   │
│                                                                     │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │              Chatbot Interface (Bonus)                       │   │
│  │  - Message Input/Display                                     │   │
│  │  - Context-Aware Responses                                   │   │
│  └──────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────┘
                                    │
                              HTTP/REST API
                                    │
┌─────────────────────────────────────────────────────────────────────┐
│                         APPLICATION LAYER                           │
│                                                                     │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │                      API Gateway / Router                    │   │
│  │  - CORS Middleware                                           │   │
│  │  - Request Logging                                           │   │
│  │  - Global Error Handling                                     │   │
│  │  - Input Validation (Custom)                                 │   │
│  └──────────────────────────────────────────────────────────────┘   │
│                                    │                                │
│       ┌────────────────────────────┼──────────────────────────┐     │
│       │                            │                          │     │
│  ┌────▼─────────┐  ┌──────────────▼────────┐  ┌───────────────▼ ┐   │
│  │ File Service │  │  Data Service         │  │  AI Service     │   │ 
│  │              │  │                       │  │                 │   │
│  │ - Parse LAS  │  │ - Query Measurements  │  │ - Interpret     │   │
│  │ - Upload S3  │  │ - Filter by Depth     │  │ - Analyze       │   │
│  │ - Validate   │  │ - Aggregate Stats     │  │ - Chat          │   │
│  └──────────────┘  └───────────────────────┘  └─────────────────┘   │
│         │                      │                         │          │
└─────────┼──────────────────────┼─────────────────────────┼──────────┘
          │                      │                         │
          │         ┌────────────▼──────────────┐          │
          │         │                           │          │
          │         │    Direct Data Access     │          │
          │         │    (via pg-pool)          │          │
          │         │                           │          │
          │         └────────────┬──────────────┘          │
          │                      │                         │
┌─────────▼──────────────────────▼─────────────────────────▼──────────┐
│                         PERSISTENCE LAYER                           │
│                                                                     │
│  ┌──────────────────┐       ┌─────────────────────────────────┐     │
│  │   Amazon S3      │       │      PostgreSQL Database        │     │
│  │                  │       │                                 │     │
│  │  Bucket:         │       │  ┌───────────────────────────┐  │     │
│  │  las-files/      │       │  │  wells                    │  │     │
│  │                  │       │  │  - metadata & header      │  │     │
│  │  Object Key:     │       │  └───────────────────────────┘  │     │
│  │  {wellId}/       │       │                                 │     │
│  │  original.las    │       │  ┌───────────────────────────┐  │     │
│  │                  │       │  │  curves                   │  │     │
│  │  Access:         │       │  │  - curve definitions      │  │     │
│  │  - IAM Role      │       │  └───────────────────────────┘  │     │
│  │  - Pre-signed    │       │                                 │     │
│  │    URLs          │       │  ┌───────────────────────────┐  │     │
│  └──────────────────┘       │  │  measurements             │  │     │
│                             │  │  - depth-indexed values   │  │     │
│  ┌──────────────────┐       │  │  - indexed on (curve,     │  │     │
│  │  Claude API      │       │  │    depth)                 │  │     │
│  │                  │       │  └───────────────────────────┘  │     │
│  │  - API Key in    │       │                                 │     │
│  │    Backend ENV   │       │  ┌───────────────────────────┐  │     │
│  │  - Rate Limits   │       │  │  interpretations          │  │     │
│  │  - Error Retry   │       │  │  - AI-generated insights  │  │     │
│  └──────────────────┘       │  └───────────────────────────┘  │     │
│                             └─────────────────────────────────┘     │
└─────────────────────────────────────────────────────────────────────┘

Technology Stack

Layer	Tech Choice	Why?
Frontend	React 19 + Vite	Minimal bundle size, blazing fast HMR, and modern hooks.
Styling	Tailwind CSS	Utility-first approach for consistent, premium aesthetics.
Backend	Node.js (Express)	High concurrency for I/O bound parsing tasks.
Database	PostgreSQL 16	Relational power for complex well-to-curve modeling.
AI Intelligence	Claude 3.5 Sonnet	Superior technical reasoning compared to other LLMs.
Infrastructure	Docker + AWS	Industry-standard reliability and container portability.

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📂 Project Structure

• /frontend: React application, UI components, and visualization logic.
• /backend: Express API, LAS parsing engine, and database logic.
• /docker-compose.yml: Local and production container orchestration.
• /.github/workflows: Fully automated Build -> Push -> Deploy pipelines.
• run_locally.md: Comprehensive developer onboarding guide.

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🚢 Continuous Deployment (CI/CD)

This project follows professional DevOps practices:

• Build: Every push to main triggers a GitHub Action to build a production Docker image.
• Storage: Images are securely stored in Amazon ECR.
• Deployment: The pipeline SSHes into an AWS EC2 instance and orchestrates a fresh rollout using Docker Compose.
• Frontend: Automatically synchronized and served via Vercel with global edge CDN.

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📽️ Demo Presentation

[Placeholder: Google Drive Link(will add after making the video)]

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✅ Deliverables Checklist

• Core Architecture: Decoupled Full-Stack design via REST API.
• Data Ingestion: Multi-stage parsing and S3/Postgres storage.
• Visualization: Interactive Track-based Plotly.js charts.
• AI Interpretation: Deep petrophysical analysis via Anthropic AI.
• Bonus Feature: Specialized context-aware Well Chatbot.
• Cloud Deployment: Live on AWS and Vercel.
• Documentation: Standard README and Local Setup guides.

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👤 Project Information

Author: adldi07 (Adesh Kumar | IIT ISM Dhanbad) Project Status: 🟢 Production Ready / Full Feature Complete

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Designed and engineered for One-Geo as a demonstration of high-performance full-stack data applications.