Author ORCID Identifier

https://orcid.org/0009-0007-2817-5186

Semester

Summer

Date of Graduation

2024

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Petroleum and Natural Gas Engineering

Committee Chair

Dr. Shahab Mohaghegh

Committee Co-Chair

Prof. Samuel Ameri

Committee Member

Prof. Samuel Ameri

Committee Member

Dr. Mohammed El, Sgher

Abstract

ABSTRACT

Enhancing pipeline simulations is essential for improving operational efficiencies and effectively managing risks in the oil and gas industry. Traditional pipeline simulators, relying heavily on mathematical modeling assumptions, often face limitations due to their high energy and computational demands. This thesis addresses these challenges by introducing an innovative approach that integrates artificial intelligence (AI) and machine learning (ML) through a smart proxy model, offering a more efficient, cost-effective, and flexible alternative to conventional full-physics models used in pipeline simulation software.

The primary aim of this research is to develop and implement a smart proxy model capable of accurately predicting key pipeline parameters, such as flow rates, pressure, and temperature, thus enhancing its performance under varying operational conditions. Utilizing OLGA, a leading simulation software, the oil and gas pipeline network at the Lam and Zhdanov oil facilities in the Caspian Sea was modeled and simulated across various input scenarios. This process produced a comprehensive dataset that reflects a broad spectrum of operational conditions, which was subsequently trained and refined on the IMPROVE software—a cutting-edge AI platform from Intelligent Solution Inc. This step ensured the smart proxy model accurately captures the complex dynamics of the multiphase pipeline system.

Evaluations of the model’s performance against traditional simulation methods have shown significant reductions in energy usage and computational run time, alongside improvements in prediction accuracy and robustness under fluctuating conditions. This research highlights the potential of AI integration into pipeline simulation and management, proposing a scalable solution that could be adapted across different industry segments. Future efforts will focus on integrating real-time data and further optimizing the model to manage increasingly complex scenarios.

Keywords: Pipeline Simulations, Artificial Intelligence, Machine Learning, Smart Proxy Model, OLGA, IMPROVE software

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