Optimized Random Vector Functional Link network to predict oil production from Tahe oil field in China

被引:12
作者
Alalimi, Ahmed [1 ]
Pan, Lin [1 ]
Al-qaness, Mohammed A. A. [2 ]
Ewees, Ahmed A. [3 ]
Wang, Xiao [1 ]
Abd Elaziz, Mohamed [4 ]
机构
[1] China Univ Geosci, Fac Earth Resources, Wuhan 430074, Peoples R China
[2] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
[3] Damietta Univ, Dept Comp, Dumyat 34517, Egypt
[4] Tomsk Polytech Univ, Sch Comp Sci & Robot, Tomsk, Russia
来源
OIL & GAS SCIENCE AND TECHNOLOGY-REVUE D IFP ENERGIES NOUVELLES | 2020年 / 76卷
关键词
Accurate prediction - Functional links - Functional-link network - Historical dataset - Local search method - Optimization method - Reservoir sand-bodies - Search optimizer;
D O I
10.2516/ogst/2020081
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
In China, Tahe Triassic oil field block 9 reservoir was discovered in 2002 by drilling wells S95 and S100. The distribution of the reservoir sand body is not clear. Therefore, it is necessary to study and to predict oil production from this oil field. In this study, we propose an improved Random Vector Functional Link (RVFL) network to predict oil production from Tahe oil field in China. The Spherical Search Optimizer (SSO) is applied to optimize the RVFL and to enhance its performance, where SSO works as a local search method that improved the parameters of the RVFL. We used a historical dataset of this oil field from 2002 to 2014 collected by a local partner. Our proposed model, called SSO-RVFL, has been evaluated with extensive comparisons to several optimization methods. The outcomes showed that, SSO-RVFL achieved accurate predictions and the SSO outperformed several optimization methods.
引用
收藏
页数:10
相关论文
共 38 条
  • [1] Short-Term Solar Power Forecasting Using Random Vector Functional Link (RVFL) Network
    Aggarwal, Arpit
    Tripathi, M. M.
    [J]. AMBIENT COMMUNICATIONS AND COMPUTER SYSTEMS, RACCCS 2017, 2018, 696 : 29 - 39
  • [2] Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy
    Alwerfali, Husein S. Naji
    Al-qaness, Mohammed A. A.
    Abd Elaziz, Mohamed
    Ewees, Ahmed A.
    Oliva, Diego
    Lu, Songfeng
    [J]. ENTROPY, 2020, 22 (03)
  • [3] Received Signal Strength Based Indoor Positioning Using a Random Vector Functional Link Network
    Cui, Wei
    Zhang, Le
    Li, Bing
    Guo, Jing
    Meng, Wei
    Wang, Haixia
    Xie, Lihua
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 14 (05) : 1846 - 1855
  • [4] A complete workflow applied on an oil reservoir analogue to evaluate the ability of 4D seismics to anticipate the success of a chemical enhanced oil recovery process
    Dubos-Sallee, Noalwenn
    Fourno, Andre
    Zarate-Rada, Jeanneth
    Gervais, Veronique
    Rasolofosaon, Patrick N. J.
    Lerat, Olivier
    [J]. OIL & GAS SCIENCE AND TECHNOLOGY-REVUE D IFP ENERGIES NOUVELLES, 2020, 75 (03):
  • [5] Image Steganalysis via Random Subspace Fisher Linear Discriminant Vector Functional Link Network and Feature Mapping
    Fan, Lingyan
    Sun, Wuyi
    Feng, Guorui
    [J]. MOBILE NETWORKS & APPLICATIONS, 2019, 24 (04) : 1269 - 1278
  • [6] Predicting liquid flow-rate performance through wellhead chokes with genetic and solver optimizers: an oil field case study
    Ghorbani, Hamzeh
    Wood, David A.
    Moghadasi, Jamshid
    Choubineh, Abouzar
    Abdizadeh, Peyman
    Mohamadian, Nima
    [J]. JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY, 2019, 9 (02) : 1355 - 1373
  • [7] A Machine-Learning Methodology Using Domain-Knowledge Constraints for Well-Data Integration and Well-Production Prediction
    Guevara, Jorge
    Zadrozny, Bianca
    Buoro, Alvaro
    Lu, Ligang
    Tolle, John
    Limbeck, Jan W.
    Hohl, Detlef
    [J]. SPE RESERVOIR EVALUATION & ENGINEERING, 2019, 22 (04) : 1185 - 1200
  • [8] Prediction of Shale-Gas Production at Duvernay Formation Using Deep-Learning Algorithm
    Lee, Kyungbook
    Lim, Jungtek
    Yoon, Daeung
    Jung, Hyungsik
    [J]. SPE JOURNAL, 2019, 24 (06): : 2423 - 2437
  • [9] Li XG, 2015, INT CONF ACOUST SPEE, P4520, DOI 10.1109/ICASSP.2015.7178826
  • [10] Theories and practices of carbonate reservoirs development in China
    Li Yang
    Kang Zhijiang
    Xue Zhaojie
    Zheng Songqing
    [J]. PETROLEUM EXPLORATION AND DEVELOPMENT, 2018, 45 (04) : 712 - 722