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Dynamic liquid level prediction in oil wells during oil extraction based on WOA-AM-LSTM-ANN model using dynamic and static information

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  • Leng, Chunyang
  • Jia, Mingxing
  • Zheng, Haijin
  • Deng, Jibin
  • Niu, Dapeng

Abstract

Accurately predicting the dynamic liquid level is the key to energy efficient operation of oil wells, however, the dynamic liquid level of oil wells in the same area varies widely, and the existing methods cannot achieve uniform modeling of the dynamic liquid level of multiple wells with high accuracy. To this end, based on the parameters of the multi-wells production process, this paper proposes method for predicting the dynamic liquid level of oil wells based on a Long short-term memory (LSTM) with attention mechanism (AM) and artificial neural network (ANN) optimized by the whale optimization algorithm (WOA). First, the factors significantly related to the change in the dynamic liquid level are identified and divided into dynamic and static information. Dynamic features are extracted using AM-LSTM. AM can enhance the impact of important information when extracting dynamic features using LSTM; Static features are extracted through ANN; Finally, both dynamic and static features are used as inputs to ANN to predict the dynamic liquid level. Solve the prediction model parameter selection problem with WOA. Using historical oilfield data collected in the field for validation, the experiment proves that the proposed method in this paper is effective for predicting the dynamic liquid level of multi-wells. Therefore, this prediction model can be used as a tool to detect the dynamic liquid level, which can achieve the purpose of reducing energy consumption and improving efficiency during oil extraction.

Suggested Citation

  • Leng, Chunyang & Jia, Mingxing & Zheng, Haijin & Deng, Jibin & Niu, Dapeng, 2023. "Dynamic liquid level prediction in oil wells during oil extraction based on WOA-AM-LSTM-ANN model using dynamic and static information," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223023757
    DOI: 10.1016/j.energy.2023.128981
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    1. Biao Xiong & Ruiping Li & Dong Ren & Huigang Liu & Tao Xu & Yingping Huang, 2021. "Prediction of flooding in the downstream of the Three Gorges Reservoir based on a back propagation neural network optimized using the AdaBoost algorithm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(2), pages 1559-1575, June.
    2. Edwin van den Heuvel & Zhuozhao Zhan, 2022. "Myths About Linear and Monotonic Associations: Pearson’s r, Spearman’s ρ, and Kendall’s τ," The American Statistician, Taylor & Francis Journals, vol. 76(1), pages 44-52, January.
    3. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
    4. Niu, Zhewen & Yu, Zeyuan & Tang, Wenhu & Wu, Qinghua & Reformat, Marek, 2020. "Wind power forecasting using attention-based gated recurrent unit network," Energy, Elsevier, vol. 196(C).
    5. Massaoudi, Mohamed & Refaat, Shady S. & Chihi, Ines & Trabelsi, Mohamed & Oueslati, Fakhreddine S. & Abu-Rub, Haitham, 2021. "A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting," Energy, Elsevier, vol. 214(C).
    6. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    7. Hu, Gang & Xu, Zhaoqiang & Wang, Guorong & Zeng, Bin & Liu, Yubing & Lei, Ye, 2021. "Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression," Energy, Elsevier, vol. 224(C).
    8. Huang, Ruike & Peng, Yiqiang & Yang, Jibin & Xu, Xiaohui & Deng, Pengyi, 2022. "Correlation analysis and prediction of PEM fuel cell voltage during start-stop operation based on real-world driving data," Energy, Elsevier, vol. 260(C).
    9. Xu, Yuanjin & Li, Fei & Asgari, Armin, 2022. "Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms," Energy, Elsevier, vol. 240(C).
    10. Zeng, Chunlei & Wu, Changchun & Zuo, Lili & Zhang, Bin & Hu, Xingqiao, 2014. "Predicting energy consumption of multiproduct pipeline using artificial neural networks," Energy, Elsevier, vol. 66(C), pages 791-798.
    Full references (including those not matched with items on IDEAS)

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