2014
DOI: 10.1016/b978-0-444-63433-7.50107-3
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Data Clustering for Uncertainty Reduction – A Case Study of Solids Transport in Oil/Gas Pipelines

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(2 citation statements)
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“…The results showed that the arrangement of data generated by SOM were consistent with that of generated by reservoir-engineering principles. Cremaschi et al and Shin and Cremaschi argued that estimating flow velocity in oil and gas pipelines is complex in nature (22,23). This could be seen from the various outcomes from different prediction models though they were supplied with the same inputs.…”
Section: Literature Reviewmentioning
confidence: 99%
See 1 more Smart Citation
“…The results showed that the arrangement of data generated by SOM were consistent with that of generated by reservoir-engineering principles. Cremaschi et al and Shin and Cremaschi argued that estimating flow velocity in oil and gas pipelines is complex in nature (22,23). This could be seen from the various outcomes from different prediction models though they were supplied with the same inputs.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Use of machine learning method (Bao & Guan, 2016) No Oil production prediction K-Meansdata discretion (Foroud, et al, 2016) No Optimize production by looking at geological models K-Meansdata discretion (Awoleke & Lane, 2011) No Well water production prediction SOMsee how data are clustered K-Meansdetermine number of clusters Neural Networkprediction (Hu, et al, 2015) No Oil production prediction K-Meansdata discretion (G. (Popa, et al, 2015) No Perforation strategy optimization C-Meanscluster log data (Grieser, et al, 2008) No Overall well investigation SOMdata clustering (Cremaschi, et al, 2015) No Flow velocity estimation in pipelines K-Meansdata clustering (Shin & Cremaschi, 2014) No Flow velocity estimation in pipelines K-Meansdata clustering (Ding, et al, 2015) No Investigate high-permeability zone C-Meansdata clustering (Cui, et al, 2016) No Oil recovery improvement for high water-cut reservoirs K-Meanscluster/group the subjects (Liu, et al, 2009) No Measurement of water content in crude oil K-Meansdata preprocessing for prediction (Singh, et al, 2014) No Measurement for forecasted oil recovery K-Meansdata discretion…”
Section: Purposementioning
confidence: 99%