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First, Time-Aware Imputation LSTM (TI-LSTM) is designed for modeling irregular time intervals and incomplete measurements. It decays the long-term memory component as the producing well conditions may be varied during the water cut stage. Second, Attention-Embedding LSTM (ATEM) is designed to improve the effectiveness of anomaly detection. It focuses on the correlation between the last and historical measurements in a given sequence. Comparison experiments with several state-of-the-art methods, including mTAN, GRU-D, T-LSTM, ATTAIN, and BRITS are conducted. Results show that the proposed ATIN performs better in accuracy, F1-score, and area under curve (AUC).<\/jats:p>","DOI":"10.3233\/ida-230301","type":"journal-article","created":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T18:08:56Z","timestamp":1699380536000},"page":"1007-1027","source":"Crossref","is-referenced-by-count":0,"title":["ATIN: Attention-embedded time-aware imputation networks for production data anomaly detection"],"prefix":"10.1177","volume":"28","author":[{"given":"Xi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science, Southwest Petroleum University, Chengdu, Sichuan, China"},{"name":"Lab of Machine Learning, Southwest Petroleum University, Chengdu, Sichuan, China"}]},{"given":"Hu","family":"Chen","sequence":"additional","affiliation":[{"name":"Petroleum Engineering School, Southwest Petroleum University, Chengdu, Sichuan, China"}]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[{"name":"Oil and Gas Field Development Department, Geological Exploration and Development Research Institute, Chengdu, Sichuan, China"}]},{"given":"Zhaolei","family":"Fei","sequence":"additional","affiliation":[{"name":"Oil and Gas Field Development Department, Geological Exploration and Development Research Institute, Chengdu, Sichuan, China"}]},{"given":"Fan","family":"Min","sequence":"additional","affiliation":[{"name":"School of Computer Science, Southwest Petroleum University, Chengdu, Sichuan, China"},{"name":"Lab of Machine Learning, Southwest Petroleum University, Chengdu, Sichuan, China"},{"name":"Institute for Artificial Intelligence, Southwest Petroleum University, Chengdu, Sichuan, China"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-230301_ref1","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.petrol.2020.108182","article-title":"Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models","volume":"200","author":"Otchere","year":"2021","journal-title":"Journal of Petroleum Science and Engineering"},{"key":"10.3233\/IDA-230301_ref2","doi-asserted-by":"crossref","unstructured":"H.H. 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