| Autor: |
CUI Chuanzhi, LU Shuiqingshan, WU Zhongwei, GAI Pingyuan, LIU Tingfeng |
| Zdroj: |
Journal of Shenzhen University Science & Engineering; Sep2023, Vol. 40 Issue 5, p622-630, 9p |
| Abstrakt: |
A real-time and reliable prediction method for the onset of a steam breakthrough in heavy oil wells is proposed by integrating deep learning algorithms with dynamic data from the wells. To address the issues such as frequent fluctuations and high noise level in single indicator data, and inability to accurately characterize the steam breakthrough time, a set of indicator parameters is constructed based on the actual injection and production data of the oilfield, effectively representing the formation time of the steam channel. These parameters are combined with the variation coefficient-G1 hybrid cross-weighting method to fuse into a comprehensive breakthrough identification curve of steam channeling. On this basis, suitable time series data are selected as input features using the similarity measurement method based on standard mutual information, with the corresponding breakthrough identification curve as the output time series for constructing the learning samples. A sequence-to-sequence deep learning framework is used to establish a prediction model for steam breakthrough time, and the effectiveness and superiority of the model are verified by the actual predictions and comparison with the traditional machine learning methods. This method simulates the mapping relationship between the characteristics of injection production time series and the identification curve of steam channeling in a data-driven way, which can effectively improve the efficiency and accuracy of steam channeling time prediction, and has certain guiding significance for the intelligent early warning of steam channeling. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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