基于瞬态多相流模拟器的柱塞排水采气模型及故障诊断  

Modeling and applications of plunger lift for gas well deliquification via a transient multiphase simulator

作　　者：朱建军 贾皓 王浩宇 曹光强[2] 朱海文 ZHU Jianjun;JIA Hao;WANG Haoyu;CAO Guangqiang;ZHU Haiwen(College of Mechanical and Transportation Engineering,China University of Petroleum-Beijing,Beijing 102249,China;PetroChina Research Institute of Petroleum Exploration&Development,Beijing 100083,China;McDougall School of Petroleum Engineering,The University of Tulsa,Tulsa 74104,USA)

机构地区：[1]中国石油大学(北京)机械与储运工程学院,北京102249 [2]中国石油勘探开发研究院,北京100083 [3]塔尔萨大学石油工程系,美国塔尔萨74104

出　　处：《石油科学通报》2021年第4期626-637,共12页Petroleum Science Bulletin

基　　金：国家自然科学基金青年基金(项目号:52004304);中央高校基本科研业务费专项资金(项目号:20190184,20200127)联合资助。

摘　　要：天然气是国民经济发展的重要能源。井底积液是其开采过程中面临的重要问题之一。目前柱塞气举工艺被广泛应用于采出井底积液。从现场监测数据中可以看出:故障工况一般很少重复发生,出现故障时的数据趋势特征也各不相同。因此需要大量异常工况的数据来提高模型精度,若从现场整理总结这部分数据则会大大损失天然气井的经济效益。因此在柱塞气举工艺中使用动态模拟来生成特定异常工况的数据集,有助于更好了解监测数据中反映的工况变化趋势。通过监测使用柱塞气举的气井实时数据,分析了同一时刻下气井不同位置的不同参数,并总结出相应的数据特征。使用瞬态多相流模拟器(OLGA),通过调整气藏储层动态、柱塞参数或管道边界条件,对柱塞气举工艺的动态模型进行优化,使模型与生产数据吻合良好,得到符合预测的正常与异常工况的模拟数据。在不同生产条件下分别与现场数据验证;最后使用OLGA生成了几种异常工况数据(如油管破裂、电动阀故障等),并对模拟结果作相应分析,也可将该结果添加到机器学习数据库中对模型进行训练,能够有效减少数据的相关性,提高模型预测精度。基于OLGA模拟柱塞举升系统在不同运行条件下的工艺参数,发现与现场数据拟合较好,尤其是在预测管压、套压以及产量等方面有很好的一致性。通过控制变量模拟,可以获得柱塞工艺在反常工况下的运行参数和特征,进而可得合成数据用于训练深度学习模型。与常规数据清洗方法相比,本文提出的基于瞬态柱塞举升工艺模型的合成数据方法被验证为一种有效且可靠的数据准备方法,可用于更好地训练基于机器学习算法的柱塞反常工况诊断模型。Plunger lift has been widely used in unconventional gas wells to remove liquid accumulated at the well bottom.Production surveillance provides large amount of data of production processes and abnormal operations,which can be used in machine learning(ML)to develop algorithms for anomaly diagnosis and operation optimization.However,in the surveillance data the majority is daily operation and the data of failure cases are rare.Also,the failure cases may not be repeatable,and many failure case signatures are not available until they happen.Large data of anomaly cases are needed to improve the ML model accuracy.Dynamic simulation of the plunger lift process offers an alternative way to generate synthetic data on the specified anomalies to be used to train the ML model.It also helps better understand the trends reflected in the surveillance data and their root causes.From the available surveillance data of gas wells with plunger lift,the simultaneous measurements of different parameters at different points in a production system with normal and abnormal occurrences can be analyzed and the corresponding trends/signatures can be identified.The typical signatures that conform to pre-determined anomalous patterns can be obtained.Using a commercial transient multiphase flow simulator,the actual field data of tubing/casing pressures can be matched through a tuning process.Trial-and-error is needed to improve the dynamic plunger lift model so that a good agreement with the production data can be achieved by adjusting the reservoir performance,plunger parameters or surface pipeline boundary conditions.After validation under different flow conditions,synthetic datasets for various operational and flow conditions can be generated by performing parametric studies.Unlike the field data,the synthetic data from the dynamic simulations mainly comprise anomaly signatures(e.g.tubing rupture,missed arrival of plunger,etc.),which can be added to the ML data pool to reduce the data covariance and increase independency.The dynamic multipha

关 键 词：气井积液 柱塞气举工艺 动态模拟 异常工况诊断 OLGA 

分 类 号：TE377[石油与天然气工程—油气田开发工程]