Facies classification using semi-supervised deep learning with pseudo-labeling strategy

Abstract

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Facies classification using semi-supervised deep learning with pseudo-labeling strategyAuthors: Asghar SaleemJunhwan ChoiDaeung YoonJoongmoo ByunAsghar SaleemRISE.ML Lab., Hanyang UniversitySearch for more papers by this author, Junhwan ChoiRISE.ML Lab., Hanyang UniversitySearch for more papers by this author, Daeung YoonRISE.ML Lab., Hanyang UniversitySearch for more papers by this author, and Joongmoo ByunRISE.ML Lab., Hanyang UniversitySearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3216086.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractQuantitative facies classification is the key to linking seismic data to lithology to evaluate important reservoir properties. During the past several years, the size of seismic volumes has piled up to the extent that it is challenging for experts to examine every seismic volume to classify the facies. This has motivated machine learning approach for predicting seismic facies in an efficient way. However, labeled data (well data) is limited by various constraints and is very expensive to obtain, whereas, there is a plethora of unlabeled data (seismic data). Geophysicists are tasked to interpret enormous amount of unclassified data on the basis of sparse amount of labeled data. In this study, we have adopted Semi-Supervised Learning using pseudo-labeling to facies analysis in order to overcome the scarcity of labeled data by leveraging unlabeled data. With each step, a small amount of data is classified starting from the vicinity of the well and gradually moving away from the well. The classified data (called ‘pseudo label data’) are added to the label data used in retraining the classifier, adding the diversity to the classifier that accounts for lateral change in lithology while moving away from well. Following our proposed workflow, we have shown that the accuracy of a trained classifier on limited amount of labeled data can be enhanced considerably by combining a small number of labeled well data with a large pool of inverted seismic data using pseudo-labeling technique. Furthermore, with results of applying the proposed workflow to field data, outperforming conventional methods, we have achieved an accuracy of 99.69% and loss as low as 0.001 for both training and validation, moreover, classification task is carried out with error as low as 0.004.Presentation Date: Monday, September 16, 2019Session Start Time: 1:50 PMPresentation Start Time: 2:40 PMLocation: 217APresentation Type: OralKeywords: reservoir characterization, neural networks, facies, machine learningPermalink: https://doi.org/10.1190/segam2019-3216086.1FiguresReferencesRelatedDetailsCited byDeep learning for automated seismic facies classificationEkaterina Tolstaya and Anton Egorov14 March 2022 | Interpretation, Vol. 10, No. 2Incremental semi-supervised learning for intelligent seismic facies identification9 July 2022 | Applied Geophysics, Vol. 19, No. 1Automatic Neural Network-Based Seismic Facies Classification Using Pseudo-Labels21 February 2022Reservoir Prediction Based on Closed-Loop CNN and Virtual Well-Logging LabelsIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Semisupervised facies classification with reconstruction cooperationSaleem Asghar and Joongmoo Byun1 September 2021Interpretable Semisupervised Classification Method Under Multiple Smoothness Assumptions With Application to Lithology IdentificationIEEE Geoscience and Remote Sensing Letters, Vol. 18, No. 3Segmentation of Seismic Images1 January 2021Data augmentation using CycleGAN for overcoming the imbalance problem in petrophysical facies classificationDowan Kim and Joongmoo Byun30 September 2020 SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Asghar Saleem, Junhwan Choi, Daeung Yoon, and Joongmoo Byun, (2019), "Facies classification using semi-supervised deep learning with pseudo-labeling strategy," SEG Technical Program Expanded Abstracts : 3171-3175. https://doi.org/10.1190/segam2019-3216086.1 Plain-Language Summary Keywordsreservoir characterizationneural networksfaciesmachine learningPDF DownloadLoading ...