Date of Award

January 2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Petroleum Engineering

First Advisor

Kegang Ling

Abstract

The increasing reliance on shale plays for hydrocarbon production necessitates accurate 3D petrophysical and geomechanical models to optimize resource recovery. However, conventional methods struggle with shale reservoir complexities such as low permeability, anisotropy, and data heterogeneity, leading to uncertainties in property predictions. This dissertation integrates seismic attributes with machine learning (ML) techniques, including Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCNs), and Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP), to enhance predictive accuracy.LSTMs and TCNs effectively capture temporal dependencies, while WGAN-GP generates high-quality synthetic data to mitigate data scarcity. The study critically evaluates existing 3D modeling methodologies, identifying key limitations in data integration and resolution enhancement. Essential seismic attributes are selected based on strong correlations with reservoir properties. A robust data processing workflow, including seismic-to-well calibration and Principal Component Analysis (PCA), ensures optimal feature selection without information loss. Advanced ML models predict petrophysical and geomechanical properties, improving shale formation characterization. Shapley values enhance explainability, increasing trust in ML-driven predictions. Results demonstrate that integrating seismic attributes with ML improves model accuracy, reduces uncertainties, and supports better hydraulic fracturing and well placement strategies. This novel framework leveraging ML bridges critical gaps in traditional modeling, leading to reducing operational risks and supporting sustainable shale development in the oil and gas industry.

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