Modified analytical model for prediction of steam flood performance

被引:5
|
作者
Dutt, Ankit [1 ]
Mandal, Ajay [1 ]
机构
[1] Indian Sch Mines, Dept Petr Engn, Dhanbad 826004, Bihar, India
关键词
Enhanced oil recovery; Steam flooding; Analytical model; Oil recovery; Production performance;
D O I
10.1007/s13202-012-0027-9
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Steam flooding as a tertiary recovery method for recovery of oil from heavy oil reservoir has been of interest in recent years. Analytical models are very useful to predict oil recovery by steam flooding for preliminary forecasting purposes and sensitivity studies. Though different models are available, the predicted values did not satisfy the field value because of presumptions. In the present study, an attempt has been made to modify the existing Jeff Jones model and Chandra and Mamora model by considering the true profile of steam zone size in reservoir and vertical sweep efficiency for calculation of capture efficiency. The reservoir characteristics and production data of three oil fields, viz., Schoonebeek in the eastern part of Netherlands, San Ardo in Monterey County, California, USA and Hamaca in Venezuela's Orinoco heavy oil belt were analyzed for performance prediction of oil production. The modified model gave very satisfactory results for production performance, compared to the original Jeff Jones and Chandra and Mamora model.
引用
收藏
页码:117 / 123
页数:7
相关论文
共 50 条
  • [1] AN ANALYTICAL MEMORY HIERARCHY MODEL FOR PERFORMANCE PREDICTION
    Chennupati, Gopinath
    Santhi, Nandakishore
    Eidenbenz, Stephan
    Thulasidasan, Sunil
    [J]. 2017 WINTER SIMULATION CONFERENCE (WSC), 2017, : 908 - 919
  • [2] Analytical model for performance prediction of linear resolver
    Saneie, Hamid
    Nasiri-Gheidari, Zahra
    Tootoonchian, Farid
    [J]. IET ELECTRIC POWER APPLICATIONS, 2017, 11 (08) : 1457 - 1465
  • [3] Performance Evaluation of Different Machine Learning Based Algorithms for Flood Prediction and Model for Real Time Flood Prediction
    Kinage, Chinmayee
    Kalgutkar, Abhishek
    Parab, Amruta
    Mandora, Sejal
    Sahu, Sunita
    [J]. 2019 5TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION, CONTROL AND AUTOMATION (ICCUBEA), 2019,
  • [4] Analytical model for the prediction of the piezoresistive behavior of CNT modified polymers
    Panozzo, Francesco
    Zappalorto, Michele
    Quaresimin, Marino
    [J]. COMPOSITES PART B-ENGINEERING, 2017, 109 : 53 - 63
  • [5] Development of an analytical model for performance prediction of chemical FOR methods
    El-Tayeb, M.
    Abu El Ela, M.
    El-Banbi, A.
    Sayyouh, M. H.
    [J]. OIL GAS-EUROPEAN MAGAZINE, 2019, 45 (04): : 201 - 207
  • [6] A Scalable Analytical Memory Model for CPU Performance Prediction
    Chennupati, Gopinath
    Santhi, Nandakishore
    Bird, Robert
    Thulasidasan, Sunil
    Badawy, Abdel-Hameed A.
    Misra, Satyajayant
    Eidenbenz, Stephan
    [J]. HIGH PERFORMANCE COMPUTING SYSTEMS: PERFORMANCE MODELING, BENCHMARKING, AND SIMULATION (PMBS 2017), 2018, 10724 : 114 - 135
  • [7] PREDICTION OF POLYMER FLOOD PERFORMANCE
    PATTON, JT
    COATS, KH
    COLEGROV.GT
    [J]. SOCIETY OF PETROLEUM ENGINEERS JOURNAL, 1971, 11 (01): : 72 - &
  • [8] Semi-analytical model for the prediction of the Wilson point for homogeneously condensing steam flows
    Azzini, L.
    Pini, M.
    Colonna, P.
    [J]. INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW, 2018, 70 : 1 - 14
  • [9] Performance evaluation and operation optimization of the steam ejector based on modified model
    Zhang, Guojie
    Zhang, Xinzhe
    Wang, Dingbiao
    Jin, Zunlong
    Qin, Xiang
    [J]. APPLIED THERMAL ENGINEERING, 2019, 163
  • [10] A comprehensive analytical model for embedded parallel microprocessors performance prediction
    Olivieri, M
    Scarana, M
    [J]. 2004 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT), VOLS. 1- 3, 2004, : 1442 - 1447