Enhancing Pipeline Simulations through Artificial Intelligence and Machine learning - A Smart Proxy Modelling Approach

Aug 5, 2026

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Enhancing pipeline simulations is essential for improving operational efficiencies and managing risks in the oil and gas industry. Traditional pipeline simulators rely on mathematical modeling assumptions and often incur high energy and computational costs, which can limit flexibility. This thesis proposes an AI and machine learning–driven smart proxy model as an efficient, cost-effective alternative to conventional full-physics approaches. The model predicts key parameters such as flow rates, pressure, and temperature using OLGA-generated scenarios and refinement with the IMPROVE AI platform, producing accurate, robust performance across varying conditions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages84

Reading Time3 h 32 min

AccessFree to download

Chapter One: Introduction and Problem Statement

Introduction

Problem Statement

Research Objectives

Chapter Two: Literature Review

Introduction

Multiphase Flow System

Introduction to Multiphase Flow System

Multiphase flow Models

Types of Multiphase Flow Models

Graduate Theses, Dissertations, and Problem Reports 2024 Enhancing Pipeline Simulations through Artificial Intelligence and Machine learning: A smart Proxy Modelling Approach AFEEZ SHITTU West Virginia University Follow this and additional works at: [https://researchrepository.wvu.edu/etd](https://researchrepository.wvu.edu/etd) Part of the Complex Fluids Commons, Computational Engineering Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Other Engineering Commons, and the Petroleum Engineering Commons Recommended Citation SHITTU, AFEEZ, "Enhancing Pipeline Simulations through Artificial Intelligence and Machine learning: A smart Proxy Modelling Approach" (2024) . Graduate Theses, Dissertations, and Problem Reports. 12501. [https://researchrepository.wvu.edu/etd/12501](https://researchrepository.wvu.edu/etd/12501) This Thesis is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s) . You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [researchrepository@mail.wvu.edu](researchrepository@mail.wvu.edu). Enhancing Pipeline Modelling and Simulations through Artificial Intelligence and Machine learning: A Smart Proxy Modelling Approach. Afeez Shittu Thesis Submitted to the Benjamin M. Startler College of Engineering and Mineral Resources at West Virginia University In Partial Fulfillment of the Requirement for the degree of Master of Science In Petroleum and Natural Gas Engineering Shahab D. Mohaghegh, Chair Samuel Ameri, Professor Muhammed El Sgher, Ph.D. Department of Petroleum and Natural Gas Engineering Morgantown, West Virginia 2024 Keywords: Artificial Intelligence, Multiphase Flow System, Pipeline Modelling and Simulations, Petroleum Engineering, Pipeline Network, Neural Network, Smart Proxy Models Copyright 2024 Afeez Shittu Abstract Enhancing Pipeline Modelling and Simulations through Artificial Intelligence and Machine learning: A Smart Proxy Modelling Approach. Shittu Afeez 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 fullphysics 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 ofthe multiphas

FAQs

What problem does the thesis address in pipeline simulations?

It addresses limitations of traditional pipeline simulators, which rely on mathematical modeling assumptions and often require high energy and computational effort.

How does the proposed approach improve simulation efficiency?

It integrates AI and machine learning through a smart proxy model that serves as a flexible alternative to conventional full-physics models.

Which tools and data sources are used to build and train the smart proxy model?

The pipeline network is modeled and simulated with OLGA, generating datasets that are then trained and refined on the IMPROVE AI platform.

Aug 5, 2026

Upload by Ezra