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CNN-LSTM Model for Casing Collar Detection by Bennedict Wat is a document available to read on EtoBox.
This research article presents a CNN-LSTM model for the identification and prediction of casing collar signals to improve well depth measurement accuracy. The model demonstrates high performance metrics, including an accuracy rate of 99.8127% and an F1 score of 0.9974, indicating its effectiveness in detecting casing collars under various conditions. The study highlights the potential of deep learning techniques in enhancing the reliability of petroleum engineering operations.
- Author
- Bennedict Wat
- Language
- EN