Semester
Summer
Date of Graduation
2026
Document Type
Dissertation
Degree Type
PhD
College
Statler College of Engineering and Mineral Resources
Department
Petroleum and Natural Gas Engineering
Committee Chair
Shahab D. Mohaghegh
Committee Co-Chair
Samuel Ameri
Committee Member
Kashy Aminian
Committee Member
Qingqing Huang
Committee Member
Roger Chen
Abstract
Oil and gas pipeline networks operate under complex multiphase flow conditions and dynamic transient regimes that present significant challenges for traditional monitoring, anomaly detection, and operational decision-making methodologies. Many existing monitoring approaches rely on high-fidelity physics-based transient flow simulations to accurately represent system behavior. However, these models are computationally intensive and therefore difficult to implement in real-time operational environments.
This dissertation presents a physics-guided artificial intelligence framework for intelligent pipeline monitoring and integrity management based on the concept of Smart Proxy Modeling. By integrating first principles engineering knowledge with machine learning techniques, the proposed framework develops computationally efficient surrogate models that preserve and emulate the underlying physical dynamics governing complex pipeline systems.
Smart Proxy models learn the response behavior of transient flow simulators, enabling rapid prediction of critical operational variables such as oil production rates and flow dynamics. Deviations between predicted system responses and field measurements are analyzed to detect abnormal flow conditions and identify potential pipeline leaks. Once anomalies are detected, AI-driven leak estimation models are employed to quantify leak magnitude and severity. In addition, deep learning algorithms are applied to acoustic signal data to classify multiphase flow regimes and improve flow characterization.
Collectively, these methodologies establish an integrated intelligent monitoring framework capable of real-time system analysis, early detection of pipeline integrity threats, and data-driven operational optimization for complex oil and gas pipeline systems.
Recommended Citation
Sajedian, Ali, "AI-Based Smart Proxy Models for Flow Assurance, Integrating Oil Rate Prediction, Pipeline Monitoring, and Leak Detection" (2026). Graduate Theses, Dissertations, and Problem Reports (ETD). 13434.
https://researchrepository.wvu.edu/etd/13434