Application of random forests classifier in water flooded layer identification

Jinzi Liu; Xinyu Liu; Lifeng Guo; Guannan Shi; Hui Du

doi:10.1117/12.2623536

1 December 2021 Application of random forests classifier in water flooded layer identification

Jinzi Liu, Xinyu Liu, Lifeng Guo, Guannan Shi, Hui Du

Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 1207924 (2021) https://doi.org/10.1117/12.2623536
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China

Abstract

The traditional identification method of watered-out layer is mainly based on logging interpretation, the evaluate model is established according to the change of logging curve of watered-out layer. but the error is usually very large. Based on ensemble learning method, this paper selects random forests algorithm to establish classifiers of different reservoir watered-out levels, and it verifies with actual oilfield data. The results show that the accuracy of this method reaches 96.2%, and it can be effectively used to identify watered-out layers.

Citation Download Citation

Jinzi Liu, Xinyu Liu, Lifeng Guo, Guannan Shi, and Hui Du "Application of random forests classifier in water flooded layer identification", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 1207924 (1 December 2021); https://doi.org/10.1117/12.2623536

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