| ---
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| title: ODIN — Operational Drilling Intelligence Network
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| emoji: 🛢️
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| colorFrom: gray
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| colorTo: green
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| sdk: gradio
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| sdk_version: 6.9.0
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| app_file: app.py
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| pinned: true
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| license: mit
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| ---
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|
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| # ODIN — Operational Drilling Intelligence Network
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|
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| > Multi-agent AI system for subsurface and drilling engineering analysis
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| > Built on the public Equinor Volve Field dataset · SPE GCS 2026 ML Challenge
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|
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| ---
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|
|
| ## Overview
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|
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| ODIN is a CrewAI-powered multi-agent system that answers complex drilling engineering questions by reasoning over structured data (WITSML, EDM) and unstructured reports (Daily Drilling Reports). It combines real-time data retrieval, RAG over domain knowledge, and a Gradio chat interface with inline Plotly visualizations.
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|
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| **Key capabilities:**
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| - Drill phase distribution & NPT breakdown analysis
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| - ROP / WOB / RPM performance profiling
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| - Cross-well KPI comparison
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| - BHA configuration review and handover summaries
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| - Stuck-pipe and wellbore stability root-cause analysis
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| - Evidence-cited answers with confidence levels
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|
|
| ---
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|
|
| ## Architecture
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|
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| ```
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| User Query
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| │
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| ▼
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| Orchestrator (orchestrator.py)
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| │ Classifies query → lean or full crew
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| │
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| ├── LEAN (chart / compare queries, ~40s)
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| │ Analyst ──► Lead (Odin)
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| │
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| └── FULL (deep analysis, ~80s)
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| Lead ──► Analyst ──► Historian ──► Lead (Odin)
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| ```
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|
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| **Agents:**
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| | Agent | Role |
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| |---|---|
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| | **Odin (Lead)** | Synthesizes findings, grounds in Volve KB |
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| | **Data Analyst** | Runs DDR / WITSML / EDM queries & Python charts |
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| | **Historian** | Searches operational history, validates stats |
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|
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| **Tools available to agents:**
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| - `DDR_Query` — Daily Drilling Report search
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| - `WITSML_Analyst` — Realtime drilling log analysis
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| - `EDM_Technical_Query` — Casing, BHA, formation data
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| - `CrossWell_Comparison` — Multi-well KPI comparison
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| - `VolveHistory_SearchTool` — RAG over Volve campaign history
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| - `python_interpreter` — Pandas + Plotly for custom charts
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|
|
| ---
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|
|
| ## Tech Stack
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| | Layer | Technology |
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| |---|---|
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| | LLM | Google Gemini 2.5 Flash (via `google-generativeai`) |
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| | Agent framework | CrewAI 1.10 |
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| | RAG / Vector store | ChromaDB + `sentence-transformers` |
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| | Data processing | Pandas, NumPy, PDFPlumber |
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| | Visualisation | Plotly (HTML) + Kaleido (PNG) |
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| | UI | Gradio 6 |
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|
|
| ---
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|
|
| ## Data
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| This project uses the **Equinor Volve Field open dataset** (released under the Volve Data Sharing Agreement).
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| > Download from: [https://www.equinor.com/energy/volve-data-sharing](https://www.equinor.com/energy/volve-data-sharing)
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| After downloading, extract to `data/raw/` and run the ETL pipeline:
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|
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| ```bash
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| python src/data_pipeline/run_pipeline.py
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| ```
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|
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| Then build the knowledge base:
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| ```bash
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| python src/rag/build_volve_db.py
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| python src/rag/build_openviking_db.py
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| ```
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|
|
| ---
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|
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| ## Quickstart (judges)
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|
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| ```bash
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| # 1. Clone & install
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| git clone <repo-url>
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| cd odin
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| python -m venv venv
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| source venv/bin/activate # Windows: venv\Scripts\activate
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| pip install -r requirements.txt
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| # 2. Download runtime data (~400 MB knowledge bases + processed CSVs)
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| python scripts/download_data.py
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|
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| # 3. Add your Gemini API key
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| cp .env.example .env
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| # Edit .env: set GOOGLE_API_KEY=<your key>
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| # Free key at: https://aistudio.google.com/app/apikey
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|
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| # 4. Run
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| python src/agents/app.py
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| ```
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| Open `http://localhost:7860` in your browser.
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|
|
| ---
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|
|
| ## Project Structure
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|
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| ```
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| odin/
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| ├── src/
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| │ ├── agents/ # Main application
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| │ │ ├── app.py # Gradio UI (entry point)
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| │ │ ├── orchestrator.py # Query routing & streaming
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| │ │ ├── crew.py # CrewAI agent definitions & tasks
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| │ │ ├── tools.py # DDR / WITSML / EDM / RAG tools
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| │ │ └── data_tools.py # Python interpreter tool + data helpers
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| │ │
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| │ ├── data_pipeline/ # ETL: raw Volve data → processed CSV
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| │ │ ├── run_pipeline.py
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| │ │ ├── parse_witsml_logs.py
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| │ │ ├── parse_ddr_xml.py
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| │ │ └── parse_edm.py
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| │ │
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| │ └── rag/ # Knowledge base builders
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| │ ├── build_volve_db.py
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| │ └── build_openviking_db.py
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| │
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| ├── tests/
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| │ └── prompts/ # Agent prompt test cases
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| │
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| ├── data/ # ← NOT in git (download separately)
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| │ ├── raw/ # Original Volve dataset
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| │ ├── processed/ # ETL output (CSV / Parquet)
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| │ └── knowledge_base/ # ChromaDB vector stores
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| │
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| ├── outputs/ # ← NOT in git (generated at runtime)
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| │ └── figures/ # Plotly charts (HTML + PNG)
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| │
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| ├── requirements.txt
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| ├── .env.example
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| └── promptfooconfig.yaml # Evaluation harness (PromptFoo)
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| ```
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|
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| ---
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|
|
| ## Rate Limits
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| The system is tuned for the Gemini free tier (15 RPM):
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| | Crew mode | LLM calls | Target time |
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| |---|---|---|
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| | Lean (chart / compare) | ~6 calls | ~40s |
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| | Full (deep analysis) | ~10 calls | ~80s |
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|
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| Automatic 429 retry with exponential back-off (10 → 20 → 40 → 60s) is built in.
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|
|
| ---
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|
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| ## License
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| Source code: MIT
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| Volve dataset: [Volve Data Sharing Agreement](https://www.equinor.com/energy/volve-data-sharing) (not included in this repo)
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|
