Emergency response and thinking for foreboding clogging-up in Lu-Ning Oil Pipeline

The transportation media of Lu-Ning Oil Pipeline is the mixture of Shengli crude oil and the imported crude oil, which is characterized with high viscosity and high pour point. In February 2022, the pipeline was shut down for nearly 30 hours due to the leakage caused by weld cracking. However, after the restart-up, a trend of foreboding clogging-up appeared in Ningyang?Tengzhou Section with a distance of 147.1 km due to the continuous decrease of flow rate and outlet temperature under the maximum allowable outlet pressure. Finally, this foreboding clogging-up event was successfully handled by injecting hot diesel, and the flow rate returned to normal about 6 days later. Generally, valuable experience has been accumulated from the foreboding clogging-up event and the emergency response for the safety of heating pipelines in large diameter and great station spacing: (1) It is critical to increase the flow rate if the tendency of foreboding clogging-up appears in the pipeline and the outlet pressure approaches to the allowable upper limit. (2) The hot thin oil should be firstly injected into the second half section and the "bottleneck" section of pipeline to clear the key section and the node. (3) The injection ratio of hot thin oil in the initial stage should be slightly higher than the theoretical value and decreases with the stabilization of operation conditions. (4) The heating facilities should be kept in normal operation through hot oil circulation within the station, so as to increase the outlet temperature of crude oil in pipeline. Finally, the following suggestions were proposed: (1) it is very necessary to set in-station circulating section of hot crude oil pipeline. (2) The properties of oil transported by Lu-Ning Pipeline, the transportation law and technologies for flow assurance should be studied in depth. (3) The contingency plan for foreboding clogging-up of pipeline should be clarified and detailed based on operation data mining.