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Characterization Indicators and Prediction Methods of Steam Breakthrough Time in Heavy Oil Steam Flooding
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Xiu-tian YAO1, Ping-yuan GAI2, 3, Zhao-xiang ZHANG2, 3, Ting-ting HAO2, 3, Tong TONG2, 3, Zhong-ping ZHANG2, 3
Science Technology and Engineering | 2025, 25(8) : 3181 - 3189
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Science Technology and Engineering | 2025, 25(8): 3181-3189
Petroleum and Natural Gas Industry
Characterization Indicators and Prediction Methods of Steam Breakthrough Time in Heavy Oil Steam Flooding
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Xiu-tian YAO1, Ping-yuan GAI2, 3, Zhao-xiang ZHANG2, 3, Ting-ting HAO2, 3, Tong TONG2, 3, Zhong-ping ZHANG2, 3
Affiliations
  • 1 Gudao Oil Production Plant of Shengli Oilfield Dongying 257000 China
  • 2 Petroleum Engineering Technology Research Institute of Shengli Oilfield Dongying 257000 China
  • 3 Key Laboratory of Heavy Oil Recovery Technology of Shandong Province Dongying 257000 China
Published: 2025-03-18 doi: 10.12404/j.issn.1671-1815.2402520
Outline
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As a critical unconventional oil and gas resource within the global energy framework, heavy oil has garnered significant attention for its development efficiency. Although steam flooding technology has improved the efficiency of heavy oil production, the phenomenon of steam breakthrough negatively impacts thermal efficiency and reservoir development. Traditional prediction methods have shown inadequate precision and delayed response when dealing with long-term oilfield time series data. Data from 13 steam flooding well groups in the Shengli oilfield heavy oil block were utilized. An innovative approach was adopted, using the instantaneous temperature ratio between production and injection wells as an indicator of steam breakthrough time. Pearson correlation coefficient analysis was employed to select key factors related to steam breakthrough time. Based on these factors, a deep learning model built on the Transformer architecture was developed, achieving accurate predictions of the instantaneous temperature ratio. The predictions closely aligned with oilfield observation data, demonstrating higher prediction accuracy and stability compared to traditional long short-term memory (LSTM) models. The research results not only provide a new perspective for the precise prediction of steam breakthrough time in heavy oil reservoirs but also further validate the extensive potential of deep learning technology in oilfield development applications, supporting the construction of intelligent oilfield management and decision support systems.

heavy oil reservoir  /  steam drive  /  steam emergence time prediction  /  self-attention mechanism  /  Transformer  /  deep learning
Xiu-tian YAO, Ping-yuan GAI, Zhao-xiang ZHANG, Ting-ting HAO, Tong TONG, Zhong-ping ZHANG. Characterization Indicators and Prediction Methods of Steam Breakthrough Time in Heavy Oil Steam Flooding[J]. Science Technology and Engineering, 2025 , 25 (8) : 3181 -3189 . DOI: 10.12404/j.issn.1671-1815.2402520
Year 2025 volume 25 Issue 8
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Article Info
doi: 10.12404/j.issn.1671-1815.2402520
  • Receive Date:2024-04-08
  • Online Date:2025-07-29
  • Published:2025-03-18
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  • Received:2024-04-08
  • Revised:2024-12-17
Funding
Affiliations
    1 Gudao Oil Production Plant of Shengli Oilfield Dongying 257000 China
    2 Petroleum Engineering Technology Research Institute of Shengli Oilfield Dongying 257000 China
    3 Key Laboratory of Heavy Oil Recovery Technology of Shandong Province Dongying 257000 China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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