التفاصيل البيبلوغرافية
| العنوان: |
Better use of experience from other reservoirs for accurate production forecasting by learn-to-learn method. |
| المؤلفون: |
Hao-Chen Wang, Kai Zhang, Chen, Nancy, Wen-Sheng Zhou, Chen Liu, Ji-Fu Wang, Li-Ming Zhang, Zhi-Gang Yu, Shi-Ti Cui, Mei-Chun Yang |
| المصدر: |
Petroleum Science (KeAi Communications Co.); Feb2024, Vol. 21 Issue 1, p716-728, 13p |
| مستخلص: |
To assess whether a development strategy will be profitable enough, production forecasting is a crucial and difficult step in the process. The development history of other reservoirs in the same class tends to be studied to make predictions accurate. However, the permeability field, well patterns, and development regime must all be similar for two reservoirs to be considered in the same class. This results in very few available experiences from other reservoirs even though there is a lot of historical information on numerous reservoirs because it is difficult to find such similar reservoirs. This paper proposes a learn-tolearn method, which can better utilize a vast amount of historical data from various reservoirs. Intuitively, the proposed method first learns how to learn samples before directly learning rules in samples. Technically, by utilizing gradients from networks with independent parameters and copied structure in each class of reservoirs, the proposed network obtains the optimal shared initial parameters which are regarded as transferable information across different classes. Based on that, the network is able to predict future production indices for the target reservoir by only training with very limited samples collected from reservoirs in the same class. Two cases further demonstrate its superiority in accuracy to other widely-used network methods. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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