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Automated feature classification and knowledge extraction from wireline geophysical observations: big data potential for offshore resources assessment

Automated feature classification and knowledge extraction from wireline geophysical observations: big data potential for offshore resources assessment
Automated feature classification and knowledge extraction from wireline geophysical observations: big data potential for offshore resources assessment
Scientific drilling of the volcanic ocean crust recovers cores and undertakes downholewireline logging. However, because core recovery rates are typically low (<30%), interpreting thewireline data is essential to gain a complete understanding of the stratigraphy. Ocean Drilling ProgramHole 1256D samples 1500 m of in situ upper oceanic crust and has both core-derived lithostratigraphyand electrofacies classification based on geological interpretations of continuous downhole FormationMicroScanner imagery. We propose an automatic quantitative identification of electrofacies usingDecision Trees. The cores and existing electrofacies classification provide training and verification ofthe automated classification. The identification of various classes is a challenging problem due tomissing data, vertical shifts, horizontal misalignments, and multiclass unbalanced problem with 2classes representing 50% of the data. Additionally, the structure of the same class changes with depthleading to large intra-class variations. Distinctive features for each class were identified by observationof images based on texture/shapes, and Decision Tree classifier was trained. Classification accuracyabove 90% was achieved for the 3-classes for electrofacies with high recovery rates. In case of 9-classes, accuracy above 60% was achieved for some classes, though some challenges are remaineddue to strongly overlapped classes. A detailed analysis of the big data used for training the classifierand its performance is described. Combined analysis of drill cores and wireline geophysical data fromscientific boreholes into volcanic rocks provides excellent training opportunities to develop automatedrock classification methods for complex geological terranes that are of increasing interest to thehydrocarbons industry.
Veres, Galina
3c2a37d2-3904-43ce-b0cf-006f62b87337
Harris, Michelle
2ea5985e-614c-4d8a-9cb0-82d9590d4ebc
Teagle, Damon
396539c5-acbe-4dfa-bb9b-94af878fe286
Sabeur, Zoheir
74b55ff0-94cc-4624-84d5-bb816a7c9be6
Veres, Galina
3c2a37d2-3904-43ce-b0cf-006f62b87337
Harris, Michelle
2ea5985e-614c-4d8a-9cb0-82d9590d4ebc
Teagle, Damon
396539c5-acbe-4dfa-bb9b-94af878fe286
Sabeur, Zoheir
74b55ff0-94cc-4624-84d5-bb816a7c9be6

Veres, Galina, Harris, Michelle, Teagle, Damon and Sabeur, Zoheir (2018) Automated feature classification and knowledge extraction from wireline geophysical observations: big data potential for offshore resources assessment. International Congress on Environmental Modelling and Software: Modelling for Sustainable Food-Energy-Water Systems, Fort Collins, USA, United States. 24 - 28 Jun 2018.

Record type: Conference or Workshop Item (Paper)

Abstract

Scientific drilling of the volcanic ocean crust recovers cores and undertakes downholewireline logging. However, because core recovery rates are typically low (<30%), interpreting thewireline data is essential to gain a complete understanding of the stratigraphy. Ocean Drilling ProgramHole 1256D samples 1500 m of in situ upper oceanic crust and has both core-derived lithostratigraphyand electrofacies classification based on geological interpretations of continuous downhole FormationMicroScanner imagery. We propose an automatic quantitative identification of electrofacies usingDecision Trees. The cores and existing electrofacies classification provide training and verification ofthe automated classification. The identification of various classes is a challenging problem due tomissing data, vertical shifts, horizontal misalignments, and multiclass unbalanced problem with 2classes representing 50% of the data. Additionally, the structure of the same class changes with depthleading to large intra-class variations. Distinctive features for each class were identified by observationof images based on texture/shapes, and Decision Tree classifier was trained. Classification accuracyabove 90% was achieved for the 3-classes for electrofacies with high recovery rates. In case of 9-classes, accuracy above 60% was achieved for some classes, though some challenges are remaineddue to strongly overlapped classes. A detailed analysis of the big data used for training the classifierand its performance is described. Combined analysis of drill cores and wireline geophysical data fromscientific boreholes into volcanic rocks provides excellent training opportunities to develop automatedrock classification methods for complex geological terranes that are of increasing interest to thehydrocarbons industry.

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Automated Feature Classification and Knowledge Extraction from Wi - Accepted Manuscript
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Accepted/In Press date: 15 May 2018
Published date: 26 June 2018
Venue - Dates: International Congress on Environmental Modelling and Software: Modelling for Sustainable Food-Energy-Water Systems, Fort Collins, USA, United States, 2018-06-24 - 2018-06-28

Identifiers

Local EPrints ID: 421908
URI: http://eprints.soton.ac.uk/id/eprint/421908
PURE UUID: e16add97-a0e4-464d-9281-a152fde40bc3
ORCID for Damon Teagle: ORCID iD orcid.org/0000-0002-4416-8409
ORCID for Zoheir Sabeur: ORCID iD orcid.org/0000-0003-4325-4871

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Date deposited: 09 Jul 2018 16:30
Last modified: 18 Feb 2021 16:54

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Contributors

Author: Galina Veres
Author: Michelle Harris
Author: Damon Teagle ORCID iD
Author: Zoheir Sabeur ORCID iD

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