Forecasting of oil production driven by reservoir spatial–temporal data based on normalized mutual information and Seq2Seq-LSTM
Traditional machine learning methods are difficult to accurately forecast oil production when development measures change. A method of oil reservoir production prediction based on normalized mutual information and a long short-term memory-based sequence-to-sequence model (Seq2Seq-LSTM) was proposed...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2024-03-01
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| Series: | Energy Exploration & Exploitation |
| Online Access: | https://doi.org/10.1177/01445987231188161 |