Semester of Graduation

Fall 2020

Degree

Master of Science in Petroleum Engineering (MSPE)

Department

Petroleum Engineering

Document Type

Thesis

Abstract

Although leak incidents continue, a pipeline remains the most reliable mode of transportation within the oil and gas industry. It becomes even more important today because the projection for new pipelines is expected to increase by 1 billion BOE through 2035. In addition, increasing number and length of subsea tiebacks face new challenges in term of data acquisition, monitoring, analysis, and remedial actions. Passive leak-detection methods commonly used in the industry have been successful with some limitations in that they often cannot detect small leaks and seeps. In addition to a thorough review of related topics, this study investigates how to create a framework for a smart pigging technique for pipeline leak detection, as an active leak detection method.

Numerical modeling of smart pigging for leak detection requires two crucial components: detailed mathematical descriptions for fluid-solid and solid-solid interactions around pig, and network modeling for the calculation of pressure and rate along the pipeline using iterative algorithms. The first step of this study is to build a numerical model that shows the motion of a pig along the pipeline with no leak, i.e., at a given injection rate, a pig first accelerates until it reaches its terminal velocity, beyond which the pig moves at a constant velocity. The second step is to construct a network model that consists of two pipeline segments (one upstream and the other downstream of leak location) through which the pig travels and at the junction of which fluid leak occurs. By putting these multiple mechanisms together and using resulting pressure signatures, this study presents a new method to predict the location and size of a leak present in pipeline.

Committee Chair

Kam, Seung Ihl

DOI

10.31390/gradschool_theses.5222

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