petro-mcp 0.1.0

MCP server exposing petroleum engineering data and tools to LLMs

petro-mcp

MCP server that gives LLMs access to petroleum engineering data and tools.

Parse well logs, query production data, fit decline curves, calculate EUR, and run nodal analysis -- all through natural language with any MCP-compatible AI assistant.

Built by Groundwork Analytics

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What is this?

petro-mcp is a Model Context Protocol server that exposes petroleum engineering workflows to LLMs like Claude and other MCP-compatible assistants. Instead of writing scripts to parse LAS files or fit decline curves, just ask your AI assistant in plain English.

Why MCP? MCP is an open standard that lets AI assistants interact with external data and tools. While other energy MCP servers provide commodity prices (OilpriceAPI), operational data via RAG (Oil & Gas RAG), or EIA statistics (mcp-energy), petro-mcp is purpose-built for petroleum engineering workflows -- LAS well log parsing, physics-constrained decline curve analysis, nodal analysis, and production diagnostics.

Prerequisites

• Python 3.10 or later (download)
• Claude Desktop (download), Cursor (download), or any MCP-compatible client

Installation

pip install petro-mcp

Or install from source:

git clone https://github.com/petropt/petro-mcp.git
cd petro-mcp
pip install -e .

Quick Start

Configure with Claude Desktop

Add to your claude_desktop_config.json:

Config file location:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "petro-mcp": {
      "command": "petro-mcp",
      "args": []
    }
  }
}

If installed from source:

{
  "mcpServers": {
    "petro-mcp": {
      "command": "python",
      "args": ["-m", "petro_mcp.server"]
    }
  }
}

Configure with Cursor / VS Code

Add to your MCP settings (.cursor/mcp.json or equivalent):

{
  "mcpServers": {
    "petro-mcp": {
      "command": "petro-mcp"
    }
  }
}

Use it

Once configured, ask your AI assistant:

• "Read the LAS file at /data/wells/wolfcamp.las and summarize the curves"
• "Load production data from /data/prod.csv and fit a decline curve for Wolfcamp A-1H"
• "Calculate EUR with qi=800, Di=0.06, b=1.1 and a 5 bbl/day economic limit"
• "Run a nodal analysis: 3500 psi reservoir, PI=4.2, 2.875-inch tubing, 150 psi wellhead"

Tools

Tool	Description
read_las	Parse a LAS 2.0 file -- returns well header and curve statistics
get_header	Extract well header metadata (name, UWI, location, KB, TD)
get_curves	List all curves with units and descriptions
get_curve_values	Get curve data with optional depth range filtering
query_production	Query production CSV data by well name and date range
fit_decline	Fit Arps decline curves (exponential, hyperbolic, harmonic)
calculate_eur	Calculate Estimated Ultimate Recovery from decline parameters
calculate_ratios	Compute GOR, WOR, water cut; classify well type
run_nodal_analysis	Simplified IPR/VLP intersection for operating point
analyze_trends	Detect production anomalies: shut-ins, rate jumps, water breakthrough
calculate_pvt_properties	Black-oil PVT: Pb, Bo, Rs, viscosity, Z-factor (Standing, Beggs-Robinson)
calculate_bubble_point	Bubble point pressure from Standing's correlation
convert_oilfield_units	Convert between oilfield units (pressure, volume, density, API/SG, BOE)
list_oilfield_units	List all supported unit categories and conversions

Decline Curve Analysis

The decline curve tools are physics-constrained:

• b-factor bounded to [0, 2] (physical range for Arps models)
• Non-negative rates enforced throughout
• Three models: exponential (b=0), harmonic (b=1), hyperbolic (0 < b < 2)
• Returns R-squared, parameter uncertainties, and residual statistics

Production Data

Accepts CSV files with flexible column naming:

• Date column: date, Date, DATE
• Oil: oil, oil_rate, BOPD
• Gas: gas, gas_rate, MCFD
• Water: water, water_rate, BWPD
• Well name: well_name, well, Well Name

Prompt Templates

Built-in prompt templates for common workflows:

Prompt	Description
analyze_decline	Full decline analysis with model comparison and EUR
compare_completions	Compare completion effectiveness across wells
summarize_logs	Summarize log suite with pay zone identification
production_anomalies	Detect rate changes, shut-ins, water breakthrough
calculate_well_eur	Step-by-step EUR calculation with confidence intervals

Resources

Resource URI	Description
wells://list/{directory}	Browse wells in a directory of LAS/CSV files
wells://production/{file_path}	Per-well production summary from a CSV file

Example Data

The examples/ directory includes:

• sample_well.las -- Synthetic Wolfcamp well log (GR, RHOB, NPHI, ILD, SP)
• sample_production.csv -- 36 months of decline data for 3 Permian Basin wells
• usage_examples.md -- Detailed usage examples

Example Output

Prompt: "Read the LAS file at examples/sample_well.las and summarize the curves"

Well: Wolfcamp A-1H (Spraberry Trend, Midland County, TX)
Depth range: 5000–5020 ft, 0.5 ft step (41 samples)

┌───────┬──────┬────────────────────────────┬───────────────┐
│ Curve │ Unit │        Description         │     Range     │
├───────┼──────┼────────────────────────────┼───────────────┤
│ GR    │ GAPI │ Gamma Ray                  │ 22–126        │
│ RHOB  │ G/CC │ Bulk Density               │ 2.53–2.67     │
│ NPHI  │ V/V  │ Neutron Porosity           │ 0.09–0.23     │
│ ILD   │ OHMM │ Deep Induction Resistivity │ 3.9–175       │
│ SP    │ MV   │ Spontaneous Potential      │ -30.5 to -2.0 │
└───────┴──────┴────────────────────────────┴───────────────┘

Quick interpretation:
- Shale zones (~5002–5005 ft): High GR (>100), low resistivity,
  high NPHI — classic shale signature.
- Clean/tight zones (~5008–5010 ft): Low GR (<30), high resistivity
  (>100 ohm-m), low NPHI — likely tight carbonate, potentially
  hydrocarbon-bearing.

Limitations

• LAS 2.0 only -- LAS 3.0 and DLIS formats are not yet supported
• Arps models only -- Modified hyperbolic, Duong, and SEPD decline models are not yet implemented
• Simplified nodal analysis -- Uses Vogel IPR and a simplified friction model, not Beggs-Brill or Hagedorn-Brown. Results are directional/screening-level, not suitable for detailed well design
• CSV production data only -- No database, WITSML, or API connectors yet
• No visualization -- Returns JSON data only, no plots

Development

git clone https://github.com/petropt/petro-mcp.git
cd petro-mcp
pip install -e .
pip install pytest
pytest tests/ -v

Contributing

Contributions are welcome. Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Submit a pull request

Areas where contributions are especially valuable:

• WITSML real-time data support
• Reservoir simulation output readers (Eclipse, OPM Flow)
• OSDU API connector
• Additional decline curve models (modified hyperbolic, Duong, SEPD)
• Well spacing and interference analysis

License

MIT License. See LICENSE for details.

Need More?

petro-mcp covers single-well analysis workflows. For advanced capabilities, Groundwork Analytics offers:

• Multi-well batch analysis and ranking
• Probabilistic EUR (P10/P50/P90)
• Database integration (Enverus, IHS, WITSML)
• Custom AI workflows for your engineering team

Visit petropt.com or reach out at [e-mail]

About

Built by Groundwork Analytics

• Website: petropt.com
• LinkedIn: Groundwork Analytics
• X/Twitter: @petroptai