Slug Flow Prediction And Slug Prevention Research In The Underwater Production System Of A Gas Field In The South China Sea

With the continuous increase in the exploration and development depth of offshore oil and gas resources,underwater production systems have become the mainstream development model for deepwater development projects due to their high efficiency and low cost.However,due to long-term operation in a complex underwater environment,the entire production process often faces accidental contradictions or unexpected uncontrollable factors,especially slugging caused by increased production,restarting,and pigging,and slugging flow generated in daily production.risks of.Slug flow causes violent fluctuations in the liquid holdup pressure of the fluid in the pipe,causing the pipeline to be subjected to intermittent stress shocks and vibrations,which can easily cause fatigue failure,increase power loss,and reduce transmission efficiency;slugs may cause overflow of the pipe outlet separator Flow or cut-off makes the slug trap difficult to work.Therefore,it is necessary to study the prediction and prevention of slug flow in the underwater production system.This article takes the underwater production system of a gas field in the South China Sea as the research object.The main research contents are as follows:(1)Numerical simulation study of slug flow in underwater production system based on OLGA software,exploring the influencing factors of slug flow in gas-liquid two-phase flow and the law of influence on the apparent flow velocity of gas and liquid phases;combined with a large number of simulation results,based on MATLAB software Perform multi-factor sensitivity analysis of slug flow.(2)Combined with a large number of simulation results,the BP neural network model was established,and the Adaboost algorithm was used to improve the BP neural network.For training samples with large errors,the classification weight was effectively changed,and the BP network of multiple weak classifiers was iteratively trained.This establishes a slug flow prediction model based on the Adaboost-BP algorithm,so as to obtain better predictions and expectations.The sluggish flow prediction accuracy of the BP neural network model improved by the Adaboos algorithm is relatively high.(3)Based on the OLGA software,the underwater production system jumper,manifold,and submarine pipeline models are established to study the changes in the amount of liquid in the pipeline and the amount of stagnant liquid at the end of the pipeline when the transportation volume is increased,stopped and restarted,so as to prevent slugs.The emergence of pigs plays a role in prediction and prevention;when studying pig pigging,the pig speed,pipeline inlet pressure and pipeline outlet cumulative liquid volume changes under different delivery rates,formulate a reasonable pigging plan to avoid pigging Larger slugs play a role in preventing slugs.