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Updated: Dec 6, 2025

Encapsulation and Permeability Characteristics of Plasma Polymerized Hollow Particles
Published on: August 16, 2012
Permeation Damage of Polymer Liner in Oil and Gas Pipelines: A Review
Hafiz Usman Khalid1, Mokhtar Che Ismail1, Norlin Nosbi1
1Department of Mechanical Engineering, Universiti Teknologi PETRONAS, Perak 32610, Malaysia.
Abstract:
Non-metallic pipe (NMP) materials are used as an internal lining and standalone pipes in the oil and gas industry, constituting an emerging corrosion strategy. The NMP materials are inherently susceptible to gradual damage due to creep, fatigue, permeation, processing defects, and installation blunder. In the presence of acid gases (CO2, H2S), and hydrocarbons under high pressure and temperature, the main damage is due to permeation. The monitoring of possible damage due to permeation is not well defined, which leads to uncertainty in asset integrity management. Assessment of permeation damage is currently performed through mechanical, thermal, chemical, and structural properties, employing Tensile Test, Differential Scanning Calorimetry (DSC), Fourier-transform Infrared Spectroscopy (FTIR), and Scanning Electron Microscopy (SEM)/Transmission Electron Microscopy (TEM), to evaluate the change in tensile strength, elongation, weight loss or gain, crystallinity, chemical properties, and molecular structure. Coupons are commonly used to analyze the degradation of polymers. They are point sensors and did not give real-time information. Polymers are dielectric materials, and this dielectric property can be studied using Impedance Analyzer and Dielectric Spectroscopy. This review presents a brief status report on the failure of polymer liners in pipelines due to the exposure of acid gases, hydrocarbons, and other contaminants. Permeation, liner failures, the importance of monitoring, and new exclusive (dielectric) property are briefly discussed. An inclusive perspective is provided, showing the challenges associated with the monitoring of the polymer liner material in the pipeline as it relates to the life-time prediction requirement.
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