The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning

• Published in: Geofluids 2021-04, Vol.2021, p.1-13
• Main Authors: Tan, Chaodong, Wang, Song, Deng, Hanwen, Han, Guoqing, Du, Guanghao, Song, Wenrong, Zhang, Xiongying
• Format: Article
• Language: English
• Subjects: Coalbed methane; Deep learning; Eigenvalues; Failure; Failure analysis; Health; Long short-term memory; Machine learning; Methane; Optimization; Prediction models; Principal components analysis; Statistical analysis; Statistical methods; Time series; Wells
• ISSN: 1468-8115; 1468-8123
• DOI: 10.1155/2021/6641395

Aiming at the problems of the current production and operation status of the progressive cavity pump (PCP) in coalbed methane (CBM) wells which cannot be timely monitored, quantitatively evaluated, and accurately predicted, a five-step method for evaluating and predicting the health status of PCP wells is proposed: data preprocessing, principal parameter optimization, health index construction, health degree division, and health index prediction. Therein, a health index (HI) formulation was made based on deep learning, and a statistical method was used to define the health status of PCP wells as being healthy, subhealthy, or faulty. This allowed further research on the HI prediction model of PCP wells based on the long short-term memory (LSTM) network. As demonstrated in the study, they can reflect both the change trend and the contextual relevance of the health status of PCP wells with high accuracy to achieve real-time, quantitative, and accurate assessment and prediction. At the same time, the conclusion gives good guidance on the production performance analysis and failure warning of the PCP wells and suggests a new direction for the health status assessment and warning of other artificial lift equipment.