基于环雾流理论的气井临界流速预测模型

doi:10.11949/j.issn.0438-1157.20180851

化工学报 ›› 2019, Vol. 70 ›› Issue (4): 1318-1330.DOI: 10.11949/j.issn.0438-1157.20180851

基于环雾流理论的气井临界流速预测模型

沈伟伟¹(),邓道明¹(),刘乔平²,宫敬¹

1. 中国石油大学（北京）油气管道输送安全国家工程实验室，石油工程教育部重点实验室，城市油气输配技术北京市重点实验室，北京 102249
2. 中石化重庆涪陵页岩气勘探开发有限公司，重庆 408014

收稿日期:2018-07-25 修回日期:2019-01-17 出版日期:2019-04-05 发布日期:2019-04-05
通讯作者: 邓道明
作者简介:<named-content content-type="corresp-name">沈伟伟</named-content>（1994—），男，硕士研究生，<email>swwogst@163.com</email>|邓道明（1965—），男，博士，副教授，<email>ddmmpf@cup.edu.cn</email>
基金资助:
国家重大科技专项(2016ZX05066005-001，2016ZX05028-004-001)

Prediction model of critical gas velocities in gas wells based on annular mist flow theory

Weiwei SHEN¹(),Daoming DENG¹(),Qiaoping LIU²,Jing GONG¹

1. National Engineering Laboratory for Pipeline Safety, MOE Key Laboratory of Petroleum Engineering, Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum, Beijing 102249, China
2. Sinopec Chongqing Fuling Shale Gas Exploration and Production Corporation, Chongqing 408014, China

Received:2018-07-25 Revised:2019-01-17 Online:2019-04-05 Published:2019-04-05
Contact: Daoming DENG

摘要/Abstract

摘要：

井筒积液是伴随气井生产的常见现象，积液会导致气井产量降低，严重时甚至会使得气井停产。精确的积液预测有助于及时采取措施以减少积液带来的危害，而临界气体流速是气井积液预测的关键。回顾了气井积液预测的相关研究，指出了最小压降模型、液滴模型的局限性，基于现有实验观察认为液膜模型有较好的适用性。考虑到斜井中液膜周向不均匀分布及气相核心中液滴夹带，提出了更符合实际的环雾流模型用于不同管径、不同井斜角下的气井积液预测。基于以往室内实验数据和现场生产数据，将新模型与现有6种积液预测模型进行对比评价。综合考虑模型预测结果正确率及预测误差，认为新的环雾流模型较其他模型预测结果更优，可准确方便地对气井积液进行预测。

关键词: 斜井, 天然气, 积液, 临界气体流速, 预测, 环雾流, 模型

Abstract:

Liquid loading, which can reduce the gas rate and even kill the gas well, is a common phenomenon associated with gas well production. Accurate prediction of liquid loading will help operators take measures in time to reduce the hazards, and the critical gas velocity is the key parameter to predict the liquid loading in gas wells. The related researches on the prediction of liquid loading in gas wells are reviewed, and the limitations of the minimum pressure drop model and the liquid droplet model are pointed out. Based on the experimental observations, it is considered that the liquid film model manifests better applicability. Considering the droplet entrainment in gas core and the uneven film distribution along the circumference of the tubing in deviated gas wells, a more practical annular mist flow model is proposed to judge the liquid loading of gas wells with different tubing diameters and inclination angles. The previous indoor experimental and field operating data are used to compare the new model with the six existing prediction models for liquid loading. Considering correctness and prediction error of the model prediction results, it is considered that the new annular mist flow model is better than other models, and can be used to predict liquid loading in gas well accurately and conveniently.

Key words: deviated wells, natural gas, liquid loading, critical gas velocity, prediction, annular mist flow, model

中图分类号:

TE 372

沈伟伟, 邓道明, 刘乔平, 宫敬. 基于环雾流理论的气井临界流速预测模型[J]. 化工学报, 2019, 70(4): 1318-1330.

Weiwei SHEN, Daoming DENG, Qiaoping LIU, Jing GONG. Prediction model of critical gas velocities in gas wells based on annular mist flow theory[J]. CIESC Journal, 2019, 70(4): 1318-1330.

图/表 12

图1 垂直和倾斜管道液膜分布

Fig.1 Film distribution in vertical and inclined pipes

图2 液膜均匀和不均匀分布情况下倾斜管道周向截面图

Fig.2 Cross section of inclined pipes for uniform and non-uniform circumferential distribution of liquid film

图3 垂直管道界面剪切应力与无量纲液膜厚度的关系

Fig.3 Interfacial shear stress vs non-dimensional film thickness for vertical pipes

图4 环状流模型

Fig.4 Schematic diagram of annular flow model

图5 各模型预测结果与Guner和Alsaadi实验数据对比

Fig.5 Fig.5 Comparison between prediction results of each model with Guner and Alsaadi experimental data

图6 各模型预测结果与Guner和Alsaadi实验数据偏差

Fig.6 Deviation between prediction results of each model with Guner and Alsaadi experimental data

表1 各模型平均预测误差及预测误差的标准差

Table 1 Average prediction errors and standard deviation of prediction errors for each model

Model	Average prediction error/%	Standard deviation of prediction error/%
new model	3.23	9.24
Barnea model	-20.12	23.64
Luo model	26.40	19.16
Turner model	-0.68	56.43
Belfroid model	-30.0	9.81
MulFlow(flow regime transition)	2.95	40.87
MulFlow(minimum shear stress)	4.45	33.37

图7 Turner积液井观测气速与计算临界气速

Fig.7 Calculated critical gas velocities vs measured gas velocities for Turner loading wells

图8 Turner未积液井观测气速与计算临界气速

Fig.8 Calculated critical gas velocities vs measured gas velocities for Turner unloading wells

表2 各模型对Turner数据气井状态的正确预测数

Table 2 Correct prediction count of gas well states for Turner data with each model

Model	Correct prediction count of gas well state(loading)	Correct prediction count of gas well state(unloading)	Total correct prediction count of gas well state
new model	32/37	47/53	79/90
Barnea model	37/37	41/53	78/90
Luo model	37/37	38/53	75/90
Turner model	28/37	51/53	79/90
Belfroid model	15/37	52/53	67/90
MulFlow(flow regime transition)	30/37	47/53	77/90
MulFlow(minimum shear stress)	37/37	28/53	65/90

图9 Veeken积液井观测气速与计算临界气速

Fig.9 Calculated critical gas velocities vs observed gas velocities for Veeken loading wells

表3 Veeken数据各模型预测误差及气井状态正确预测数

Table 3 Prediction error and correct prediction count of gas well states for Veeken data with each model

Model	Average prediction error/%	Standard deviation of prediction error/%	Correct prediction count of gas well state
new model	8.70	26.63	40/67
Barnea model	-4.12	22.10	22/67
Luo model	51.29	37.11	62/67
Turner model	-42.57	14.16	1/67
Belfroid model	-38.69	15.54	1/67
MulFlow(flow regime transition)	-7.56	28.60	17/67
MulFlow(minimum shear stress)	12.21	39.52	38/67

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基于环雾流理论的气井临界流速预测模型

Prediction model of critical gas velocities in gas wells based on annular mist flow theory

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