Machine Learning Application Guidelines in Flow Assurance
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-24231-1_10
Reference7 articles.
1. Trinh C, Meimaroglou D, Hoppe S (2021) Machine learning in chemical product engineering: the state of the art and a guide for newcomers. Processes. https://doi.org/10.3390/pr9081456
2. Dobbelaere MR, Plehiers PP, Van de Vijver R, Stevens CV, Van Geem KM (2021) Machine learning in chemical engineering: strengths, weaknesses, opportunities, and threats. Engineering 7:1201–1211
3. Obanijesu EO, Omidiora EO (2008) Artificial neural network’s prediction of wax deposition potential of Nigerian crude oil for pipeline safety. Pet Sci Technol 26:1977–1991
4. Hosseinzadeh A, Mohammadi A, Soroush E (2020) Predicting semiclathrate hydrates dissociation pressure using a rigorous machine learning approach. J Dispers Sci Technol 41:863–872
5. Alhashem M (2019) Supervised machine learning in predicting multiphase flow regimes in horizontal pipes. Soc Pet Eng Abu Dhabi Int Pet Exhib Conf 2019, ADIP 2019. https://doi.org/10.2118/197545-ms
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