Petres is a lightweight, open-source Python library for corner-point reservoir grid generation, property modeling, and visualization. It provides a fully code-driven workflow for static reservoir modeling.
For complete documentation, see the Petres documentation.
Stability Notice
Petres is currently in early development. The API is not yet stable and may change without notice.
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Open Access: Free alternative for engineers and students without access to expensive commercial softwares.
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Scriptable Modeling: Avoid UI complexity and work with code-driven workflows.
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Fully Customizable: Integrate your own code alongside built-in methods.
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AI Integration: Use the Python ecosystem to apply AI and Machine Learning techniques.
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Grid Generation: Construct Corner-Point, Rectilinear, and Regular grids.
Apply boundary polygons to deactivate cells outside the target region. -
Structural Modeling: Generate horizon and zone surfaces from well tops to support grid construction.
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Property Modeling: Assign petrophysical properties to grid cells using stochastic or deterministic methods, derived attributes, or interpolation from well data.
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Import & Export Grids: Handle Eclipse grids (SLB reservoir simulator) using the
.GRDECLfile format. Visualize and export modeled Corner-Point grids. -
Visualization: Interactive 2D and 3D rendering of Corner-Point grids, structural zones, horizons, and spatial property distributions.
Full installation instructions are available in the documentation.
Import and visualize a corner-point grid from a .GRDECL file:
from petres.grids import CornerPointGrid
# Define the path to the ".GRDECL" file containing the grid data
path = r"https://raw.githubusercontent.com/jamalbaylit/petres/v0.1.0/data/opm/norne/grdecl/norne_with_props.grdecl"
# Import corner-point grid from a ".GRDECL" file, including specified properties
grid = CornerPointGrid.from_grdecl(
path,
properties=["PORO", "PERMX"]
)
# Visualize grid
grid.show(scalars="depth", z_scale=5)
# Visualize property
grid.show(scalars="PORO", z_scale=5)| Component | Implementation |
|---|---|
| Grid Operations | High-performance, vectorized array computations using NumPy |
| 2D Plotting | Visualization via Matplotlib |
| 3D Visualization | Interactive rendering and mesh handling via PyVista |
| Kriging Interpolation | Ordinary and Universal Kriging via PyKrige |
| RBF Interpolation | Multi-dimensional Radial Basis Function interpolation via SciPy |
| IDW Interpolation | In-house implementation of Inverse Distance Weighting |
Contributions of all sizes are welcome, whether it's fixing bugs, improving documentation, adding new features, or suggesting ideas for future development.
If you'd like to contribute, please read the developer guide to learn about the project structure, development workflow, coding standards, testing, and how to submit a pull request.
For questions, bug reports, or collaboration opportunities contact via jamalbaylit@gmail.com or connect via LinkedIn.