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渤海油田 CO2 腐蚀速率预测模型
彭龙,韩国庆 ,杨杰,LANDJOBO PAGOU ARNOLD
中国石油大学(北京)石油工程教育部重点实验室,北京 102249
CO2 corrosion rate prediction model of production systems in the Bohai Oilfield
PENG Long, HAN Guoqing, YANG Jie, LANDJOBO PAGOU ARNOLD
Key Laboratory of Petroleum Engineering, China University of Petroleum-Beijing, Beijing 102249, China

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摘要  国内外常用的CO2 腐蚀速率预测模型主要有 3 类,分别为经验型预测模型、半经验型预测模型和机理型 预测模型。经验型预测模型以Norsok M-506 模型为代表,此类模型对腐蚀机理考虑较少,主要根据实验数据和 现场数据拟合建立。著名的半经验型预测模型有DW系列模型,半经验模型是在考虑过程动力学及介质传输过 程的基础上,对实验数据和现场数据进行拟合而建立的预测模型。经典的机理型预测模型有Nesic模型,机理 模型采用动力学公式求解腐蚀速率,物理意义明确,与现场数据结合少。针对当前渤海油田没有适用的CO2 腐 蚀速率预测模型问题,本文总结了现有CO2 腐蚀速率预测模型,对比分析各个模型腐蚀机理、腐蚀形态和影响 因素。以半经验型研究模型为基础,结合渤海油田实际生产条件下的CO2 腐蚀实验,在充分考虑温度、CO2 分 压、流速、腐蚀产物膜的基础上,引入CO2 腐蚀速率修正因子,建立适用于渤海区域的CO2 腐蚀速率预测模型。 将本文建立模型与DWM、ECE、BP等半经验模型进行对比分析,结果发现,本文所建模型相对误差最小,更 接近渤海区域实际CO2 腐蚀速率,验证了本文所建模型可靠性。腐蚀实验结果和模型预测结果均表明,N80 管 材的抗CO2 腐蚀能力最差,13Cr管材的抗腐蚀能力最强。通过建立井筒腐蚀速率分布预测图版,对比不同管材 CO2 腐蚀速率,为渤海区域油气井油套管管材优选提供理论依据。
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关键词 : 渤海油田;CO2 腐蚀;腐蚀速率预测模型;可行性分析;管材优选
Abstract
The prediction models of CO     2     corrosion rate are summarized, and the corrosion mechanism, corrosion morphology,      and influence factors of each model are compared and analyzed in combination with CO     2     corrosion experiments under actual      production conditions in the Bohai oil field. There are three types of CO     2     corrosion prediction models around the world. They are      the empirical prediction model, semi-empirical prediction model and the mechanism prediction model. The empirical model is      built considering less corrosion mechanism but more experimental data and field data. And the Norsok M-506 model is one of the      most famous empirical models. The semi-empirical model is established by fitting experimental data and field data based on the      dynamics process and the medium transmission process. The DWM, DW91, DW93, and DW95 are well-known semi-empirical      prediction models for calculating the CO     2     corrosion rate. The mechanism model uses dynamic formula to calculate the CO         corrosion rate, which has a clear physical meaning and is less integrated with experimental data and field data. The Nesic model      is most classic mechanism model to predict the CO     2     corrosion rate. Based on a semi-empirical and semi-mechanical model, the      CO     2     corrosion rate prediction model is built in this paper fully considering the effects of temperature, CO     2     partial pressure, flow      rate, CO     2     corrosion product film, and the CO     2     corrosion rate correction factor. Besides, a CO     2     corrosion experiment under actual      production conditions in Bohai oilfield is conducted in order to let the CO     2     corrosion rate prediction model more suitable for the      Bohai oilfield. The comparative study of the model established in this paper with DWM, ECE, and BP semi-empirical models re     vealed that this paper’s developed model has the smallest average relative error and is similar to the actual rate of CO     2     corrosion      in the Bohai oilfield, confirming that the CO     2     corrosion rate prediction model built in this paper has great prediction effects and      is more accuracy. Both the results of the corrosion experiment and model predictions in this paper have revealed that N80 pipe      has the worst resistance to CO     2     corrosion and the 13Cr pipe has the best resistance to CO     2     corrosion. The ranking of four kinds of      pipes for CO     2     corrosion resistance is N80 < 1Cr < 3Cr < 13Cr. The wellbore corrosion rate distribution forecast contrasts the CO         corrosion rate of various pipe materials, which provides a theoretical framework for the optimization of oil production and casing    
pipes in the Bohai oilfield.  


Key words: Bohai oilfield; CO2 corrosion; CO2 corrosion rate prediction; feasibility analysis; pipe optimization
收稿日期: 2020-12-30     
PACS:    
基金资助:国家自然科学基金资助项目(51574256) 资助
通讯作者: hanguoqing@163.com
引用本文:   
PENG Long, HAN Guoqing, YANG Jie, LANDJOBO PAGOU ARNOLD. CO2 corrosion rate prediction model of production systems in the Bohai Oilfield. Petroleum Science Bulletin, 2020, 04: 531-540
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