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Pipelines have been the main transportation pattern of oil and gas because of their safety and economy, which are considered as the lifeline of offshore oil and gas transportation. With the booming development of offshore oil industry, the frequency of pipeline leakage is also increasing. Corrosion is one of the important factors due to some characteristics such as operating environment, service life and transportation medium, etc., which damages the integrity of the pipeline and damage the normal operation of pipelines. Furthermore, leakage accidents caused by pipeline corrosion have occurred all over the world, accounting for 70~90% of total accidents, which has caused huge economy losses and catastrophic environmental damage.
Pipelines are an important part of offshore oil and gas field development facilities and the main means of gathering and transporting offshore oil and gas resources. However, pipelines are subject to deterioration and degradation in the corrosion media. Corrosion risk assessment and prediction is an effective way to avoid leakage of oil and gas field pipelines and facilities, ensure safe operation and save cost. Hence, in this study, a machine learning model with excellent predictive performance was constructed for corrosion rate, to provide an effective mean for processing complex corrosion data and to provide a useful tool for further exploration of submarine pipeline corrosion problems. Meanwhile, a method that can effectively and quickly evaluate the accuracy of corrosion rate prediction model was explored, which can be used as a reference to select the most appropriate and accurate Machine Learning (ML) model based on existing data.
This paper shares experiences and challenges of corrosion risk assessment in the down-stream petroleum industries and simplify ways of managing corrosion through effective corrosion assessment regime.
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Austenitic-ferritic stainless steels, commonly known as duplex stainless steels (DSSs), are a group of materials typically consisting of equal amounts austenite and ferrite. DSSs are well-known materials in chemical industry and are often a cost-effective alternative as they combine high mechanical strength and fatigue resistance with good corrosion properties. Contributing to the cost-effectiveness is the low nickel content compared to austenitic stainless steels. Advantages with DSSs are high chloride stress corrosion cracking resistance (SCC), where austenitic steels with moderate nickel content are inherently more sensitive. In combinations with carbon steel it can be a benefit to use DSSs since carbon steel and DSSs have matching thermal expansion.
In 1950s, as an important measure to improve the corrosion resistance of base metal, internal coating pipes was first applied to sour crude oil and natural gas pipelines [1]. Among the coating systems, FBE coating has good impact resistance, bending resistance, high bonding strength, good resistance for acid, alkali, salt, oil and water fluid. The coating can reduce the internal surface roughness friction resistance of piping & pipeline to reduce project investment.