opm-simulators/python/test/ml/hybrid_newton

Hybrid Newton Test Suite

This test suite validates the integration of Hybrid Newton (HyNE) with machine learning models inside the flow reservoir simulator. It ensures that adding ML-based approximations does not degrade solver performance and, in some cases, improves convergence.

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Dependencies

In a similar way as for opm-common ML module, these tests require TensorFlow to be installed. The CPU version is enough for this test and allows faster installation.

pip install tensorflow-cpu

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Test Framework

• Implemented with Python’s built-in unittest.

• Each test case defines a specific Hybrid Newton configuration via JSON.

• Simulations are run using flow(therefore assuming it is installed):

  • Baseline → Standard Newton solver (no ML).
  • Hybrid Newton → Newton solver with ML-based updates injected.

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Baseline Simulation

Before running hybrid cases:

• Runs a standard flow simulation with the given deck.

• Extracts:

  • Baseline Newton iterations (baseline_iters) from simulation logs.
  • Reservoir state variables (PRESSURE, SGAS, SWAT, etc.) from UNRST files.

• Stores n_cells and times for consistency across tests.

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Hybrid Newton Simulation

For each test case:

1. A minimal Keras model is built that maps input → output features.

   • Weights are initialized to copy output variables directly.
   • Models are exported using kerasify.export_model.

2. A JSON config is generated with:

   • Input/output features.
   • Scaling and feature engineering options.
   • Apply time and active cells.

3. flow is run with:

   --use-hy-ne=true --hy-ne-config-file=config.json
   

4. Hybrid Newton iteration counts are extracted and compared to baseline.

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Assertions

Each test validates:

• Convergence parity Hybrid and baseline must produce the same number of timesteps.
• Iteration bounds hybrid_iters[t] <= baseline_iters[t] for all timesteps.
• Zero Newton cases Some cases require the first two timesteps to have 0 Newton iterations.
• Config integrity Config JSONs must exist and be valid.

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Test Categories

Category	Description
ABSOLUTE_CASES	Tests absolute features.
RELATIVE_CASES	Tests relative features.
FEATURE_ENGINEERING_CASES	Tests features engineered inputs/outputs (e.g., log).
SCALING_CASES	Applies scaling strategies (e.g., min-max, standard).
MULTI_MODEL_CASES	Combines multiple models into one simulation.
ZERO_NEWTON_CASES	Use all features, resulting in 0 Newton iterations.
ALL_CASES	Runs all cases together as a stress test.

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Utilities

The suite uses helper functions from utils.py:

• collect_input_features → Prepares input arrays from UNRST data.
• compute_output_vars → Builds expected outputs from reservoir state.
• write_config → Writes the HyNE JSON configuration.
• export_model → Saves Keras models in a format usable by flow.
• extract_newtit_from_file → Extracts iteration counts from solver logs.
• extract_unrst_variables → Reads simulation outputs from UNRST files.

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