Project Description

This doctoral research aims to advance the thermodynamic and physical characterization of sour (sulfur-containing) reservoir fluids, with a distinct focus on the complex phase behavior and measurement anomalies present at low sulfur concentrations under ambient and reservoir conditions. Real-world characterization of these systems is frequently compromised by experimental uncertainties, including chemical reactivity, sorption, and interactions between sulfur-containing species and experimental hardware.

To resolve these challenges, this project will pioneer a hybrid framework that tightly integrates high-pressure, high-temperature experimental measurements (Pressure-Volume-Temperature) and core-flooding displacement studies with relevant Equations of State (EOS) physics-based modeling. Artificial Intelligence (AI) and Machine Learning (ML) techniques may be implemented alongside traditional thermodynamic frameworks to automate data reconciliation, perform high-dimensional consistency checks, and accelerate the predictive modeling of complex phase envelopes. Ultimately, this research will minimize predictive uncertainty, providing robust data and models to support critical asset-management and engineering decisions in energy and sustainability operations.