---
id: xtgeo
version: "4.25.1"
license: LGPL-3.0
license_treatment: copyleft
maintenance: active
---

# xtgeo — XTGeo is a Python library for 3D grids, surfaces, wells, etc

License: copyleft · Maintenance: active · Downloads: 129.7K/mo

## What it is and what it does

XTGeo is a Python library for reading, manipulating, and writing subsurface reservoir modelling data used in oil and gas exploration and production. It provides high-performance handling of 3D corner-point grids, 2D regular surfaces, seismic cubes (via segyio), and well data, with a C backend and NumPy/Pandas integration for speed. The library is built by Equinor and targets geoscientists and reservoir engineers working with reservoir simulation software like RMS.

The package supports multiple industry-standard formats (RMS, Eclipse, SEGY, Irap binary) and allows cross-data operations—for example, slicing a surface with a seismic cube. It ships pre-built wheels for Python 3.11–3.14 on Linux, macOS (64-bit), and Windows, reducing installation friction. Well data, lines, and polygons are supported but noted as less mature than grid and surface handling.

Use it for:

- Load and modify 3D corner-point grids from RMS or Eclipse simulations, then export to a different format.
- Read seismic cubes in SEGY format and perform spatial operations (e.g., extract values at well locations).
- Manipulate regular 2D surfaces (maps) with rotation and resampling for reservoir analysis.
- Batch process well trajectory and log data alongside grid and surface data in a single workflow.
- Integrate subsurface data manipulation into a larger geoscience or reservoir engineering pipeline.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

XTGeo manipulates subsurface reservoir modelling data—3D grids, surfaces, seismic cubes, and wells—using NumPy and C backends for performance, with support for industry formats like RMS and Eclipse.

Yes, if you work with subsurface reservoir data in industry-standard formats. The package is actively maintained, has no known vulnerabilities, supports current Python versions, and offers wheels for all major platforms. The LGPL-3.0 license is permissive for most use cases but requires attention if you plan to modify and redistribute the library. Install friction is moderate due to C compilation, but pre-built wheels mitigate this for standard platforms.

## Install

pip install xtgeo
uv add xtgeo
poetry add xtgeo

## Installing xtgeo

Before you install: Medium install friction due to C backend compilation requirements. The package is actively maintained (released 2 days ago) with wheels for Python 3.11–3.14 across Linux, macOS, and Windows, reducing friction for most users. Supports current Python versions.

License in practice: Licensed under LGPL-3.0 (copyleft). Any derivative work or modification must be distributed under the same license; static linking or bundling may trigger copyleft obligations depending on your deployment model.

Quickstart:

pip install xtgeo

import xtgeo

mysurf = xtgeo.surface_from_file("myfile.gri")
mysurf.values[mysurf.values < 2000] = 2000
mysurf.to_file("newfile.gri")

Requires Python 3.11 or later; C backend compilation may be needed on platforms without pre-built wheels.

Verify before relying:

- Maturity level of well data, line, and polygon support (described as 'somewhat immature' in the excerpt).
- Performance characteristics and scalability limits for very large grids or seismic cubes.
- Scope and cost of optional ROXAR API integration for RMS workflows.

## Package facts

- License: LGPL-3.0 (copyleft)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 129.7K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

3D grid manipulation, seismic cube processing, reservoir surface modelling, well data handling, subsurface data formats, corner-point grid library, geoscience data tools, geoscience, reservoir-modelling, subsurface-data

[View on SkillFed](https://skillfed.io/packages/xtgeo) · [View on PyPI](https://pypi.org/project/xtgeo/)