常规油气

基于交替条件期望变换的凝析气藏露点压力预测模型

  • 孙博文 ,
  • 郭平 ,
  • 伍轶鸣 ,
  • 汪周华 ,
  • 周代余 ,
  • 刘志良
展开
  • 1.西南石油大学油气藏地质及开发工程国家重点实验室,四川 成都 610500
    2.中国石油塔里木油田分公司勘探开发研究院,新疆 库尔勒 841000
孙博文(1990 —),男,在读博士研究生,主要从事油气相态、气田开发理论与方法研究工作。通讯地址:四川省成都市新都区新都大道8号油气藏地质及开发工程国家重点实验室,邮政编码:610500。E-mail: 178558944@qq.com

收稿日期: 2019-05-13

  网络出版日期: 2020-08-07

基金资助

“十三五”国家科技重大专项“塔里木盆地碳酸盐岩油气田提高采收率关键技术示范工程”(2016ZX05053);中国石油天然气股份有限公司重大科技专项“塔里木盆地大油气田增储上产关键技术研究与应用”(2018E-1804)

Dew point pressure prediction model of condensate gas reservoir based on alternating conditional expectation transform

  • Bowen SUN ,
  • Ping GUO ,
  • Yiming WU ,
  • Zhouhua WANG ,
  • Daiyu ZHOU ,
  • Zhiliang LIU
Expand
  • 1.State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, Sichuan 610500, China
    2. Research Institute of Exploration and Development, PetroChina Tarim Oilfield Company, Korla, Xinjiang 841000, China

Received date: 2019-05-13

  Online published: 2020-08-07

摘要

凝析气藏的高效开发需要准确的流体相态性质数据,其中准确预测露点压力是凝析气藏开发过程中的重要问题。针对凝析气藏露点压力传统预测方法精度较低的问题,基于最优化理论和应用统计分析,通过实测数据资料拟合,提出了一种利用交替条件期望变换方法(ACE)确定的凝析气藏露点压力非参数回归模型,获取了具有统计意义的凝析气藏露点压力显式关联式。在皮尔逊(Pearson)关联性分析基础上,该模型自变量选取气藏温度、(C1、C2-C6、C7+)摩尔分数、C7+相对分子质量、C7+相对密度。采用公开发表的27组露点压力数据探索自变量和因变量之间的潜在函数关系,并对9组TLM油田实测露点压力数据进行预测。结果表明:该模型精度较高,具有良好的泛化能力,模型回归的平均绝对相对误差(AARD)为2.16 %,模型预测AARD仅为4.8 %,其中最大绝对相对误差(ARD)为9.21 %,最小ARD为0.34 %,本研究为凝析气藏露点压力预测提供了一种参考方法。

本文引用格式

孙博文 , 郭平 , 伍轶鸣 , 汪周华 , 周代余 , 刘志良 . 基于交替条件期望变换的凝析气藏露点压力预测模型[J]. 油气藏评价与开发, 2020 , 10(4) : 107 -112 . DOI: 10.13809/j.cnki.cn32-1825/te.2020.04.017

Abstract

The efficient development of condensate gas reservoirs requires accurate fluid phase properties data, among which accurate prediction of dew point pressure is an important issue in the development of condensate gas reservoirs. In order to solve the problem of low accuracy of traditional prediction methods for dew point pressure of condensate gas reservoirs, based on optimization theory and applied statistical analysis, and by fitting measured data, a non-parametric regression model determined by alternating conditional expectation transformation(ACE) is proposed, and an explicit correlation of dew point pressure with statistical significance is obtained. Based on Pearson correlation analysis, the independent variables of the model are gas reservoir temperature, mole fraction of (C1, C2-C6, C7+), and molecular weight and relative density of C7+. The potential function relation between independent and dependent variables is analyzed by 27 sets of experimental data for published dew point pressure, and 9 groups of measured dew point pressure data of TLM oilfields are predicted. The results show that the model has high precision and good generalization ability. The average absolute relative deviation(AARD) of model regression is 2.16 %, and the predicted AARD is only 4.8 %. The maximum absolute relative deviation(ARD) is 9.21 % and the minimum is 0.34 %. This study provides a reference method for dew point pressure prediction of condensate gas reservoirs.

参考文献

[1] 崔书姮, 汤勇, 赵曜 , 等. 高温高压凝析气藏石蜡沉积条件预测研究[J]. 油气藏评价与开发, 2017,7(3):55-58.
[1] CUI S H, TANG Y, ZHAO Y , et al. Study on prediction of wax deposition conditions in high temperature and high pressure condensate gas reservoir[J]. Reservoir Evaluation and Development, 2017,7(3):55-58.
[2] 李建奇, 杨志伦, 张春雨 , 等. 反凝析作用对苏里格气田上古生界气藏开发的影响[J]. 天然气工业, 2015,35(4):45-51.
[2] LI J Q, YANG Z L, ZHANG C Y , et al. Impacts of retrograde condensation on the development of Upper Paleozoic gas reservoirs in the Sulige Gasfield, Ordos Basin[J]. Natural Gas Industry, 2015,35(4):45-51.
[3] 彭松 . 致密砂岩凝析气藏反凝析伤害机理及合理开发方式研究[D]. 成都:西南石油大学, 2015.
[3] PENG S . Research on mechanism of condensate damage and reasonable development method in tight sand gas-condensate reservoirs[D]. Chengdu: Southwest Petroleum University, 2015.
[4] 中国石油勘探开发研究院. 油气藏流体物性分析方法: GB/T 26981-2011[S]. 北京: 中国标准出版社, 2012.
[4] Research Institute of Petroleum Exploration and Development. Test method for reservoir fluid physical properties: GB/T 26981-2011[S]. Beijing: Standards Press of China, 2012.
[5] VALIOLLAHI S, KAVIANPOUR B, RAEISSI S , et al. A new Peng-Robinson modification to enhance dew point estimations of natural gases[J]. Journal of Natural Gas Science and Engineering, 2016,34:1137-1147.
[6] HAJI-SAVAMERI M, MENAD N A, NOROUZI-APOURVARI S , et al. Modeling dew point pressure of gas condensate reservoirs: Comparison of hybrid soft computing approaches, correlations, and thermodynamic models[J]. Journal of Petroleum Science and Engineering, 2020,184:1-18.
[7] ZHONG ZHI, LIU SIYAN, KAZEMI M , et al. Dew point pressure prediction based on mixed-kernels-function support vector machine in gas-condensate reservoir[J]. Fuel, 2018,232:600-609.
[8] 张可, 李实, 廉黎明 , 等. 交替条件期望变换确定油气最小混相压力新方法[J]. 油气藏评价与开发, 2012,2(1):23-28.
[8] ZHANG K, LI S, LIAN L M , et al. The new method to determine the minimum miscibility pressure of oil and gas by using alternating conditional expectation transform[J]. Reservoir Evaluation and Development, 2012,2(1):23-28.
[9] 江安, 雷少飞 . 基于交换条件数学期望的CO2最小混相压力预测模型[J]. 中国科技论文, 2016,11(17):2029-2034.
[9] JIANG A, LEI S F . The use of alternating conditional expectation to predict CO2 minimum miscibility pressure[J]. China Sciencepaper, 2016,11(17):2029-2034.
[10] FENG Q H, ZHANG J Y, ZHANG X M , et al. The use of alternating conditional expectation to predict methane sorption capacity on coal[J]. International Journal of Coal Geology, 2014,121:137-147.
[11] 陶德硕, 侯健, 魏翠华 . 基于交替条件期望的油藏井间连通性定量表征[J]. 科学技术与工程, 2014,14(31):55-60.
[11] TAO D S, HOU J, WEI C H . Quantitative characterization of dynamic connectivity of reservoir well based on alternating conditional expectation[J]. Science Technology and Engineering, 2014,14(31):55-60.
[12] FENG Q H, ZHANG J Y, ZHANG X M , et al. Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network[J]. Fuel Processing Technology, 2015,129:120-129.
[13] 蒙园, 张建华, 龙日尚 . 基于交替条件期望的短期负荷概率密度预测[J]. 华北电力大学学报(自然科学版), 2018,45(1):58-65.
[13] MENG Y, ZHANG J H, LONG R S . Short-term load probability density forecasting based on alternating conditional expectation. Journal of North China Electric Power University(Natural Science Edition), 2018,45(1):58-65.
[14] 孙志道, 胡永乐, 李云娟 , 等. 凝析气藏早期开发气藏工程研究[M]. 北京: 石油工业出版社, 2003.
[14] SUN Z D, HU Y L, LI Y J , et al. Gas reservoir engineering study of prophase condensate reservoir development[M]. Beijing: Petroleum Industry Press, 2003.
[15] 杨帆, 冯翔, 阮羚 , 等. 基于皮尔逊相关系数法的水树枝与超低频介损的相关性研究[J]. 高压电器, 2014,50(6):21-25.
[15] YANG F, FENG X, RUAN L , et al. Correlation study of water tree and VLF tanδ based on pearson correlation coefficient[J]. High Voltage Apparatus, 2014,50(6):21-25.
[16] 陈丽群, 刘敏, 张建业 , 等. 新露点压力预测模型的建立与对比分析[C]// 2017年全国天然气学术年会论文集.杭州:中国石油学会天然气专业委员会, 2017: 912-921.
[16] CHEN L Q, LIU M, ZHANG J Y , et al. Establishment and comparison of new dew point pressure prediction model[C]// 2017 National Natural Gas Academic Annual Conference. Gas Committee of CPS, Hangzhou, 2017: 912-921.
文章导航

/