I am a seasoned geoscientist with over 15 years of experience in rock physics, quantitative interpretation (QI), and geomechanics, specializing in pore pressure prediction. My work integrates advanced machine learning techniques with geophysical modeling to address complex subsurface challenges.
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Basin-wide_rock_physics: A comprehensive project focused on basin-wide rock physics modeling using Jupyter Notebook.
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Basin-wide-rock-physics---3D: Extends rock physics template calculations from 1D to 3D for enhanced subsurface analysis.
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Sand-RPT-Compare-Different-Sand-Models: Compares various sand models within a rock physics template framework.
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Stress-concentration-and-wellbore-breakout: Analyzes stress concentration around wellbores and the potential for breakout scenarios.
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Stress-concentration---3D: Visualizes stress concentration around wellbores in a 3D context.
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Predict-Beta-Function-Using-Neural-Network: Utilizes deep neural networks to predict the diagenesis Beta function, crucial for pore pressure prediction.
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Using-ML-to-Discover-New-Physical-Model: Explores machine learning techniques to uncover new physical models for seafloor temperature estimation.
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Lithology-Classification-Using-Deep-Neural-Network: Implements machine learning to classify lithology using seismic attributes (Vp, Density, PR) and deep neural networks for geophysical interpretation.
- Email: [email protected]
- LinkedIn: https://www.linkedin.com/in/kevin-liu-geophysics
Feel free to explore my repositories and reach out if you have any questions or collaboration ideas.