Effective prediction of lost circulation from multiple drilling variables: a class imbalance problem for machine and deep learning algorithms
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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2190-0566
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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online resource.
- Bibliographic citation
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Effective prediction of lost circulation from multiple drilling variables: a class imbalance problem for machine and deep learning algorithms ; volume:12 ; number:1 ; day:8 ; month:12 ; year:2021 ; pages:83-98 ; date:1.2022
Journal of petroleum exploration and production technology ; 12, Heft 1 (8.12.2021), 83-98, 1.2022
- Creator
- Contributor
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SpringerLink (Online service)
- DOI
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10.1007/s13202-021-01411-y
- URN
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urn:nbn:de:101:1-2022042419215851932613
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:20 AM CEST
Data provider
This object is provided by:
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Wood, David
- Mardanirad, Sajjad
- Zakeri, Hassan
- SpringerLink (Online service)
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Lost Paradise Lost
The application of deep learning algorithms to classify subsurface drilling lost circulation severity in large oil field datasets
Investigation of different environmental-friendly waste materials as lost circulation additive in drilling fluids
High Performance Shape Memory Epoxy Syntactic Foam Composites as Lost Circulation Material in Deep Drilling
Design and experimental study of a new hydraulic tool for lost-circulation materials (LCM) plugging while drilling
Automated lost circulation severity classification and mitigation system using explainable Bayesian optimized ensemble learning algorithms
Smart lost circulation materials for productive zones
Ultra-Deepwater Riserless Mud Circulation with Dual Gradient Drilling
Experimental investigation of fracture width limitations of granular lost circulation treatments
Stability of Intensity Imbalance between Left‐ and Right‐Circulation Modes in GaN Hexagonal Microdisk
A self-breaking supramolecular plugging system as lost circulation material in oilfield
‘Level-off’ cement plugging method to cure lost circulation verified with case studies
Lost Paradise Lost
The application of deep learning algorithms to classify subsurface drilling lost circulation severity in large oil field datasets
Investigation of different environmental-friendly waste materials as lost circulation additive in drilling fluids
High Performance Shape Memory Epoxy Syntactic Foam Composites as Lost Circulation Material in Deep Drilling
Design and experimental study of a new hydraulic tool for lost-circulation materials (LCM) plugging while drilling
Automated lost circulation severity classification and mitigation system using explainable Bayesian optimized ensemble learning algorithms
Smart lost circulation materials for productive zones
Ultra-Deepwater Riserless Mud Circulation with Dual Gradient Drilling
Experimental investigation of fracture width limitations of granular lost circulation treatments
Stability of Intensity Imbalance between Left‐ and Right‐Circulation Modes in GaN Hexagonal Microdisk
A self-breaking supramolecular plugging system as lost circulation material in oilfield
‘Level-off’ cement plugging method to cure lost circulation verified with case studies