The Experimental Study And Reserch Of A Method For Forecast Inner Corrosion Life Of The Wet-Crude Pipeline

With the increasing consumption of oil and natural gas, the mileage of the pipeline is also increasing. Due to the relatively high viscosity of the oil, so the conveying process will be implemented in the high-temperature and high-pressure conditions; in the mining process, H2S and CO2as petroleum associated gas went into the transportation system. With the hydrocarbon exploration work developments getting deeper and deeper, the concentration of oil and gas associated gas is also increasing; with the flow rate and flow pattern of the medium inside the tubes are constantly changing, under the interaction of many factors, internal corrosion of the pipeline have become increasingly prominent. Modern pipeline in China started late, most of the service lines are in the aging status, there are some risks in the operation of pipelines, therefore, in order to ensure the safe operation of the pipeline and reduce unnecessary waste of resources, so we have to predict, analysis and forecast the inner corrosion positions and corrosion rate of the pipe wall. At the same time by the above prediction, we could predict remaining lifetime of pipelines. This not only provides a basis for the detection program formulation in the service pipeline system, but also protects the people’s property and personal safety and avoids environmental pollution.Taking Xinjiang oilfield pipeline as the research object, we could come to corrosion key factors by analysis experimental data. While taking advantage of the Fluent software to simulate fluid flow conditions in the pipeline, and combined with NACE SP0208-2008to determine the prone to the location of the internal corrosion, and the establish the internal corrosion position prediction model. According to the corrosion position, field data capture, AMSE/ANSI B31G criteria, the API579criteria and (SY/T10048-2003),and established the inner corrosion rate prediction model based on BP neural network, then began life prediction. Finally, develop a Win32-based platform based on the wet-crude pipeline corrosion life prediction software with C#.