Open Collections

Abstract

Steel is widely employed in the petroleum sector for general engineering and structural applications; however, one of the most challenging application for steel is pipeline construction. Pipelines are the most effective mode of transportation for oil and gas, as well as for new energy sources like hydrogen. Pipeline properties must meet high standards to ensure public safety and operational reliability. Advanced low carbon steels are manufactured through a combination of thermo-mechanical processing (TMCP) and microalloying. The TMCP combines controlled hot rolling with accelerated cooling to obtain the required microstructure and properties. Niobium is a common microalloying element used in the production of linepipe steels to retard austenite recrystallization and decomposition. This study focused on the run-out table stage of TMCP. An X70 linepipe steel was selected, which contains 0.05 wt% carbon and 0.06 wt% niobium. Continuous cooling tests were conducted using a Gleeble 3500 thermomechanical simulator to study the phase transformation. Austenite grain size was measured in-situ with a laser ultrasonics for metallurgy (LUMet) system and further verified using electron backscattered diffraction (EBSD) reconstruction. Three reheat treatments were designed to investigate the effect of processing conditions on austenite decomposition. These include two prior austenite grain size (PAGS): 19 and 54 μm, three cooling rates: 3, 10, and 30°C/s, two retained strains: 0.2 and 0.4, and two states of niobium: in solution and precipitated. The resulting microstructures were characterized using optical microscopy and EBSD. It was observed that an increase in PAGS and cooling rate and decrease in retained strain resulted in lower transformation temperatures. Additionally, niobium in solution significantly decreases transformation temperatures. A wide range of microstructures were obtained in this study including ferrite, bainite, and mixed microstructure. EBSD data was analyzed to quantify microstructure features such as kernel average misorientation (KAM) and high angle grain boundaries density. These features were subsequently used to establish microstructure-processing correlations.