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Hybrid Model for Annular Pressure Prediction Based on Time Series Data
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Yang-jie ZHANG1, 2, Zhi ZHANG1, 2, *, Yang WANG2, Hao-yun DENG1
Science Technology and Engineering | 2025, 25(5) : 1870 - 1877
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Science Technology and Engineering | 2025, 25(5): 1870-1877
• Papers·Mining and Metallurgical Engineering •
Hybrid Model for Annular Pressure Prediction Based on Time Series Data
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Yang-jie ZHANG1, 2, Zhi ZHANG1, 2, *, Yang WANG2, Hao-yun DENG1
Affiliations
  • 1 State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
  • 2 School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, China
Published: 2025-02-18 doi: 10.12404/j.issn.1671-1815.2402208
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In order to avoid wellbore failure caused by abnormal annulus band pressure and resulting safety accidents, the annulus band pressure value is accurately predicted, and preventive measures are taken in advance when it exceeds the control value. An autoregressive integrated moving average-long short term memory (ARMI-LSTM) model was proposed. The model was trained to predict the annular band pressure of example wells based on actual annular band pressure time series data and feature capture data sets, and compared with a single model and recurrent neural network (RNN) model. The results show that the model has a good performance in error, fitting accuracy and overall performance after training with actual data, which can provide a reference for improving the prediction accuracy and efficiency of annular band pressure value, and is helpful to well integrity design.

annular pressure prediction  /  time series data  /  neural network  /  hybrid model
Yang-jie ZHANG, Zhi ZHANG, Yang WANG, Hao-yun DENG. Hybrid Model for Annular Pressure Prediction Based on Time Series Data[J]. Science Technology and Engineering, 2025 , 25 (5) : 1870 -1877 . DOI: 10.12404/j.issn.1671-1815.2402208
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Year 2025 volume 25 Issue 5
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Article Info
doi: 10.12404/j.issn.1671-1815.2402208
  • Receive Date：2024-03-28
  • Online Date：2025-07-29
  • Published：2025-02-18
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History
  • Received：2024-03-28
  • Revised：2024-11-18
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Affiliations
    1 State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
    2 School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, China
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