XMeshGraphNet for Reservoir Simulation#

An example for surrogate modeling using X-MeshGraphNet on reservoir simulation datasets.

Overview#

Reservoir simulation predicts reservoir performance using physical and mathematical models. It plays critical roles in production forecasting, reservoir management, field development planning, and optimization. Despite advances in parallel computing and GPU acceleration, routine reservoir simulation workflows requiring thousands of simulations remain computationally expensive, creating a need for faster surrogate models.

This example provides a reference implementation of XMeshGraphNet (X-MGN) for building reservoir simulation surrogates. X-MGN is naturally compatible with the finite volume framework commonly used in reservoir simulation. It is particularly effective for systems with irregular connections such as faults, pinch-outs, dual-porosity, and discrete fractures, etc. Furthermore, X-MGN scales efficiently to industry-scale reservoir models with millions of cells.

Quick Start#

Prerequisites#

Python Version: Python 3.10 or higher (tested with Python 3.10 and 3.11)

Install Dependencies:

pip install -r requirements.txt

0. Dataset Preparation#

You need to provide reservoir simulation data with ECLIPSE/IX style output format to use this example.

⚠️ Dataset License Disclaimer

Users are responsible for verifying and complying with the license terms of any dataset they use with this example. This includes datasets referenced in this documentation (such as the Norne Field dataset) or any proprietary data. Please ensure you have the appropriate rights and permissions before using any dataset for your research or commercial applications.

Option 1: Use Your Own Simulation Data#

If you have your own reservoir simulation dataset, ensure all simulation cases are stored in a single directory with ECLIPSE/IX style output files:

<your-dataset>/
├── CASE_1.DATA
├── CASE_1.INIT
├── CASE_1.EGRID
├── CASE_1.UNRST
├── CASE_2.DATA
├── CASE_2.INIT
└── ... (multiple cases)

Option 2: Sample Data#

Note: A downloadable sample dataset will be made available soon.

  • Example 1: Waterflood in a 2D quarter five-spot model with varying permeability distributions generated using a geostatistical method (1000 samples).

  • Example 2: Based on the publicly available Norne Field dataset. A Design of Experiment and sensitivity study identified fault transmissibility and KVKH multipliers as key variables, which were then varied using Latin Hypercube Sampling to generate 500 samples. This well-known model contains numerous faults represented by Non-Neighbor Connections (NNCs), which X-MGN naturally handles through its graph structure.

An open-source reservoir simulator, OPM, was used to generate both datasets.

Expected Data Format#

  • Format: ECLIPSE/IX compatible binary files

  • Required files per case: .INIT, .EGRID, .UNRST (or .X00xx), .UNSMRY (or .S00xx)

  • Storage: All cases in a single directory

Example Visualization: Norne Field#

Static reservoir property and domain partitions:

Permeability (PERMX) distribution

X-MeshGraphNet partitioning (0=halo region)

1. Data Preprocessing#

Configure your dataset path in conf/<your-config>.yaml by setting dataset.sim_dir to point to your simulation data directory, then run:

python src/preprocessor.py --config-name=<your-config>

Note: Replace <your-config> with your configuration file name from the conf/ directory (without the .yaml extension). For example, use config for conf/config.yaml. Use the same config name for training and inference steps below.

What it does:

  • Reads simulation binary files (.INIT, .EGRID, .UNRST) in the dataset directory.

  • Extracts variables specified in the configuration file

  • Builds graph structures with nodes (grid cells) and edges (connections)

  • Creates autoregressive training sequences for next-timestep prediction

  • Saves processed graphs

2. Training#

Multi-GPU training is supported:

torchrun --nproc_per_node=4 --nnodes=1 src/train.py --config-name=<your-config>

3. Inference and Visualization#

Run autoregressive inference to predict future timesteps:

python src/inference.py --config-name=<your-config>

Output Location: Results are saved to outputs/<your-experiment-name>/inference/

Output Files:

  • HDF5 files: Contain predictions and targets for each simulation case, organized by timestep and variable

  • GRDECL files: Eclipse-compatible ASCII format that can be imported into popular software such as Petrel and ResInsight for visualization

Example Results: Autoregressive Inference#

The following shows water saturation and pressure predictions for the Norne field across 64 timesteps spanning 10 years of operation with varying well controls. X-MGN demonstrates good predictability, especially for near-term predictions. As expected for autoregressive prediction, errors accumulate over time, but the model maintains reasonable accuracy throughout:

Pressure

30 Jul 2001(Timestep 21, Day 1362)

16 Sep 2003(Timestep 42, Day 2140)

Ground Truth

X-MGN Prediction

Prediction Error

Water Saturation

30 Jul 2001(Timestep 21, Day 1362)

16 Sep 2003(Timestep 42, Day 2140)

Ground Truth

X-MGN Prediction

Prediction Error

Experiment Tracking#

Launch MLflow UI to monitor training progress (replace <your-experiment-name> with your experiment name from the config):

cd outputs/<your-experiment-name>
mlflow ui --host 0.0.0.0 --port 5000

Access the dashboard at: http://localhost:5000

References#