Data Clustering for Uncertainty Reduction – A Case Study of Solids Transport in Oil/Gas Pipelines

Shin, Jisup

doi:10.1016/b978-0-444-63433-7.50107-3

Cited by 1 publication

(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%

“…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%

See 1 more Smart Citation

A Review on “Management by Exception” Surveillance for Well Management: To maximize Oil Production

Malik¹,

Hasan²,

Jasamai³

2018

IJET

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The business processes and decisions of oil and gas operations generate large amounts of data, which causes surveillance engineers to spend more time gathering, and analyzing them. To do this manually is inefficient. Hence, this study is proposed to leverage on data driven surveillance by adopting the principle of management by exception (MBE). The study aims to minimize the manual interaction between data and engineers; hence will focus on monitoring well production performance through pre-determined parameters with set of rules. The outcome of this study is a model that can identify any deviations from the pre-set rules and the model will alert user for deviations that occur. The model will also be able to predict on when the well be offline if the problem keeps on persisting without immediate action from user. The objective of this paper is to present a literature review on the prediction and management by exception for the above mentioned well management. The results presented in this paper will help in the development of the proposed prediction and management model. The literature review was conducted based on structured literature review methodology, and a comparative study among the collected works is analyzed and presented in this paper.

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Section: Literature Reviewmentioning

confidence: 99%