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Validation results of feature level and joint level CGR based on feature matching and signal matching. These results enable pipeline operators to establish defect repair schedules and re-inspection intervals with increased confidence.
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Traditional Corrosion Growth Rate (CGR) models used in the integrity assessment of corroded pipelines are deterministic. A common Magnetic Flux Leakage (MFL) inline inspection (ILI) tool performance specification on general corrosion anomaly depth is +/- 10% Wall Thickeness (WT) at 80% confidence which corresponds to a standard deviation of 7.81% WT. Probabilistic Corrosion Growth Rate (PCGR) models incorporate these large measurement uncertainties and provide more realistic reliability assessments
The company operates several thousands of kilometers of pipelines that transport oil and gas from the offshore and onshore fields. A Risk Based Inspection (RBI) approach is adopted to ensure the safe operation of the pipeline system in accordance with the design, company, and legal requirements.
During one of the many planned In-Line Inspection (ILI) programs undertaken to determine the integrity status of their pipelines, a 48" crude pipeline was reported to have significant external corrosion on one of its onshore sections with reported metal loss of up to 93% of its nominal wall thickness.
This paper reviews the corrosion management for a critical sour gas pipeline operated by Saudi Aramco. The 38-inch diameter pipeline transports untreated sour gas from Crude Processing Facility (CPF) to downstream Gas Plant and spans a total distance of 145 km. To prevent internal corrosion inside the pipeline, the wet sour gas is dehydrated using Tri-Ethylene Glycol (TEG) unit.
External corrosion in uninsulated pipelines is normally able to be prevented by cathodic protection (CP). Generally, external corrosion on buried pipelines cannot occur if CP current is getting onto the pipe. CP is an electrochemical means of corrosion control in which the oxidation reaction in a galvanic cell is concentrated at the anode and suppresses corrosion of the cathode (pipe) in the same cell. For instance, to make a pipeline a cathode, an anode is attached to it.
The intention of this work is to pose epistemic questions about corrosion measurement, statistical inference, and the role of machine learning in predicting corrosion growth. The audience of this work is practitioners implementing inferential algorithms or tools for corrosion prediction. In this work, an algorithm consists of a process for estimating the presence and severity of corrosion.
Corrosion is defined as the degradation of a material or its properties due to a reaction with the environment and is one of the most common pipeline integrity threats for operators. External corrosion may be visually inspected during excavation; however, due to accessibility, additional non-destructive examination (NDE) methods must be utilized to identify the presence and severity of internal corrosion.
Understanding the chemistry and electrical properties of how corrosion occurs aids in mitigating the presence of corrosion, specifically internal corrosion.
This paper will discuss the effectivity of ranking the crude pipelines due to their product corrosivity based on certain parameters such as corrosion coupons, cleaning pig deposit sampling analyses, microbial activity and previous ILI records.
This white paper analyzes the electromagnetic acoustic transducer (EMAT) tool performance and when combined with a multiple dataset platform, investigates the operator’s dig results from EMAT, and compares multiple inline inspection technologies used for a comprehensive seam assessment.