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Research on Oil Well Production Prediction Based on GRU-KAN Model Optimized by PSO

Author

Listed:
  • Bo Qiu

    (School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Jian Zhang

    (School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Yun Yang

    (School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Guangyuan Qin

    (School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Zhongyi Zhou

    (School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Cunrui Ying

    (School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, China)

Abstract

Accurately predicting oil well production volume is of great significance in oilfield production. To overcome the shortcomings in the current study of oil well production prediction, we propose a hybrid model (GRU-KAN) with the gated recurrent unit (GRU) and Kolmogorov–Arnold network (KAN). The GRU-KAN model utilizes GRU to extract temporal features and KAN to capture complex nonlinear relationships. First, the MissForest algorithm is employed to handle anomalous data, improving data quality. The Pearson correlation coefficient is used to select the most significant features. These selected features are used as input to the GRU-KAN model to establish the oil well production prediction model. Then, the Particle Swarm Optimization (PSO) algorithm is used to enhance the predictive performance. Finally, the model is evaluated on the test set. The validity of the model was verified on two oil wells and the results on well F14 show that the proposed GRU-KAN model achieves a Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination ( R 2 ) values of 11.90, 9.18, 6.0% and 0.95, respectively. Compared to popular single and hybrid models, the GRU-KAN model achieves higher production-prediction accuracy and higher computational efficiency. The model can be applied to the formulation of oilfield-development plans, which is of great theoretical and practical significance to the advancement of oilfield technology levels.

Suggested Citation

  • Bo Qiu & Jian Zhang & Yun Yang & Guangyuan Qin & Zhongyi Zhou & Cunrui Ying, 2024. "Research on Oil Well Production Prediction Based on GRU-KAN Model Optimized by PSO," Energies, MDPI, vol. 17(21), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5502-:d:1513275
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    References listed on IDEAS

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    1. Pan, Shaowei & Yang, Bo & Wang, Shukai & Guo, Zhi & Wang, Lin & Liu, Jinhua & Wu, Siyu, 2023. "Oil well production prediction based on CNN-LSTM model with self-attention mechanism," Energy, Elsevier, vol. 284(C).
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