Mechanistic and Data-Driven Modelling of Operational Parameters Prediction on Oil and Gas Transportation Pipeline Network
Author:
Affiliation:
1. College of Computer Science and Technology, China University of Petroleum (East China),Qingdao,China
2. Offshore Oil Production Plant of Shengli Oilfield Branch of Sinopec,Dongying,China
Funder
Qingdao Natural Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10403657/10403658/10403749.pdf?arnumber=10403749
Reference24 articles.
1. The Influence of Axial Compressive Stress and Internal Pressure on a Pipeline Network: A Review
2. Fast and accurate prediction of failure pressure of oil and gas defective pipelines using the deep learning model
3. Deeppipe: Theory-guided neural network method for predicting burst pressure of corroded pipelines
4. Deeppipe: Theory-guided LSTM method for monitoring pressure after multi-product pipeline shutdown
5. Modeling of wax disappearance temperature (WDT) using soft computing approaches: Tree-based models and hybrid models
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