eprintid: 19094
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/90/94
datestamp: 2024-06-04 14:11:32
lastmod: 2024-06-04 14:11:32
status_changed: 2024-06-04 14:04:51
type: book
metadata_visibility: show
creators_name: Qasim, A.
creators_name: Lal, B.
title: Machine Learning Application in Gas Hydrates
ispublished: pub
note: cited By 1
abstract: The issue of gas hydrates is one of the major concerns in flow assurance industry. In order to study the gas hydrates theroretically, reserachers have developed different thermodynamic and kinetic models to predict the hydrate formation parame­ters. The use of machine learning in gas hydrate inhibition prediction and analysis has become a well-established field of study as computational capability has increased in recent years. In the literature, both supervised and unsupervised learning methods have been applied to predict the gas hydrate parameters. This chapter has discussed the conventional modeling approaches in gas hydrate parameters prediction and the use of machine learning techniques in this field of study. Four different case studies involving the use of machine learning in gas hydrates prediction have also been presented. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
date: 2023
publisher: Springer Nature
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174775060&doi=10.1007%2f978-3-031-24231-1_9&partnerID=40&md5=c19804eb70890243ee386974b579db7b
id_number: 10.1007/978-3-031-24231-1₉
full_text_status: none
publication: Machine Learning and Flow Assurance in Oil and Gas Production
pagerange: 155-174
refereed: TRUE
isbn: 9783031242311; 9783031242304
citation:   Qasim, A. and Lal, B.  (2023) Machine Learning Application in Gas Hydrates.     Springer Nature, pp. 155-174.  ISBN 9783031242311; 9783031242304