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Open-source computational pipelines implementing physics-informed machine learning, rock physics diagnostics, and automated multi-well log processing workflows (Python/Jupyter) for subsurface reservoir characterization, uncertainty quantification, and bypassed hydrocarbon identification in clastic and carbonate reservoirs.

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Subsurface-AI-Pipelines

Open-source computational pipelines implementing physics-informed machine learning, rock physics diagnostics, and automated multi-well log processing workflows (Python/Jupyter) for subsurface reservoir characterization, uncertainty quantification, and bypassed hydrocarbon identification in clastic and carbonate reservoirs.

About

Open-source computational pipelines implementing physics-informed machine learning, rock physics diagnostics, and automated multi-well log processing workflows (Python/Jupyter) for subsurface reservoir characterization, uncertainty quantification, and bypassed hydrocarbon identification in clastic and carbonate reservoirs.

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