{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T10:01:02Z","timestamp":1756893662172,"version":"3.37.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030365912"},{"type":"electronic","value":"9783030365929"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-36592-9_17","type":"book-chapter","created":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T01:02:43Z","timestamp":1575853363000},"page":"203-213","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Digital Rock Modeling of a Terrigenous Oil and Gas Reservoirs for Predicting Rock Permeability with Its Fitting Using Machine Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1694-5896","authenticated-orcid":false,"given":"Vladimir","family":"Berezovsky","sequence":"first","affiliation":[]},{"given":"Ivan","family":"Belozerov","sequence":"additional","affiliation":[]},{"given":"Yungfeng","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Marsel","family":"Gubaydullin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,10]]},"reference":[{"key":"17_CR1","first-page":"443","volume":"106","author":"P Renard","year":"2001","unstructured":"Renard, P., Genty, A., Stauffer, F.: Laboratory of the tensor. J. Geophys. Res. 106, 443\u2013452 (2001)","journal-title":"J. Geophys. Res."},{"issue":"1","key":"17_CR2","doi-asserted-by":"publisher","first-page":"97","DOI":"10.2118\/0115-0097-JPT","volume":"67","author":"C Carpenter","year":"2015","unstructured":"Carpenter, C.: Digital core analysis and pore-network modeling in mature-field project. J. Petrol. Technol. 67(1), 97\u201399 (2015)","journal-title":"J. Petrol. Technol."},{"issue":"4","key":"17_CR3","doi-asserted-by":"publisher","first-page":"141","DOI":"10.3897\/issn2541-8416.2018.18.4.141","volume":"18","author":"IP Belozerov","year":"2018","unstructured":"Belozerov, I.P.: Experimental determination of the digital core model. Arct. Environ. Res. 18(4), 141\u2013147 (2018)","journal-title":"Arct. Environ. Res."},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Berezovsky, V., Belozerov, I., Yur\u2019ev, A., Gubaydullin, M.: Examination of permeability clastic oil and gas reservoir\u2019s rock by molecular dynamics simulation using high-performance computing. In: Supercomputer Days in Russia. Proceedings of the International Conference. Supercomputer Consortium of Universities of Russia, Russian Academy of Sciences, pp. 195\u2013205 (2018)","DOI":"10.1007\/978-3-030-05807-4_18"},{"issue":"10","key":"17_CR5","first-page":"25","volume":"86","author":"AI Tupitsyna","year":"2016","unstructured":"Tupitsyna, A.I., Fadin, Y.: Study of permeability and percolation properties of systems of solid rectangular particles by computer simulation. J. Tech. Phys. 86(10), 25\u201331 (2016)","journal-title":"J. Tech. Phys."},{"key":"17_CR6","unstructured":"Galechan, A.M.: Percolation analysis of hysteresis of phase permeabilities in two-phase flow in oil reservoirs: dissertation for the degree of Candidate of Physical and Mathematical Sciences, Moscow (2018)"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Neary, P.: Automatic hyperparameter tuning in deep convolutional neural networks using asynchronous reinforcement learning. In: 2018 IEEE International Conference on Cognitive Computing (ICCC), pp. 73\u201377 (2018)","DOI":"10.1109\/ICCC.2018.00017"},{"key":"17_CR8","doi-asserted-by":"publisher","first-page":"22084","DOI":"10.1109\/ACCESS.2018.2812809","volume":"6","author":"S Park","year":"2018","unstructured":"Park, S., Yu, S., Kim, M., Park, K., Paik, J.: Dual autoencoder network for retinex-based low-light image enhancement. IEEE Access 6, 22084\u201322093 (2018)","journal-title":"IEEE Access"},{"key":"17_CR9","unstructured":"Li, Y., Pu, T., Cheng, J.A.: Biologically inspired neural network for image enhancement. In: 2010 International Symposium on Intelligent Signal Processing and Communication Systems, pp. 1\u20134 (2010)"},{"key":"17_CR10","unstructured":"Zhao, Y., Zan, Y., Wang, X., Li, G.: Fuzzy C-means clustering-based multilayer perceptron neural network for liver CT images automatic segmentation. In: 2010 Chinese Control and Decision Conference, pp. 3423\u20133427 (2010)"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Kinattukara, T., Verma, B.: Clustering based neural network approach for classification of road images. In: 2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR), pp. 172\u2013177 (2013)","DOI":"10.1109\/SOCPAR.2013.7054121"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Joseph, S., Ujir, H., Hipin, I.: Unsupervised classification of Intrusive igneous rock thin section images using edge detection and colour analysis. In: 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 530\u2013534 (2017)","DOI":"10.1109\/ICSIPA.2017.8120669"},{"key":"17_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/978-3-642-21735-7_7","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2011","author":"J Masci","year":"2011","unstructured":"Masci, J., Meier, U., Cire\u015fan, D., Schmidhuber, J.: Stacked convolutional auto-encoders for hierarchical feature extraction. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011. LNCS, vol. 6791, pp. 52\u201359. Springer, Heidelberg (2011). \nhttps:\/\/doi.org\/10.1007\/978-3-642-21735-7_7"},{"issue":"11","key":"17_CR14","doi-asserted-by":"publisher","first-page":"2351","DOI":"10.1109\/LGRS.2015.2478256","volume":"12","author":"J Gong","year":"2015","unstructured":"Gong, J., Fan, J., Wang, H., Ma, X., Li, B., Chen, F.: High-resolution SAR image classification via deep convolutional autoencoders. IEEE Geosci. Remote Sens. Lett. 12(11), 2351\u20132355 (2015)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Ke, M., Lin, C., Huang, Q.: Anomaly detection of Logo images in the mobile phone using convolutional autoencoder. In: 2017 4th International Conference on Systems and Informatics (ICSAI), pp. 1163\u20131168 (2017)","DOI":"10.1109\/ICSAI.2017.8248461"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Ji, J., Mei, S., Hou, J., Li, X., Du, Q.: Learning sensor-specific features for hyperspectral images via 3-dimensional convolutional autoencoder. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1820\u20131823 (2017)","DOI":"10.1109\/IGARSS.2017.8127329"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Steinkraus, D., Buck, I., Simard, P.Y.: Using GPUs for machine learning algorithms. In: Eighth International Conference on Document Analysis and Recognition, ICDAR 2005, pp. 1115\u20131120 (2005)","DOI":"10.1109\/ICDAR.2005.251"},{"key":"17_CR18","unstructured":"Chellapilla, K., Puri, S., Simard, P.: High performance convolutional neural networks for document processing. In: Tenth International Workshop on Frontiers in Handwriting Recognition. Suvisoft (2006)"},{"key":"17_CR19","unstructured":"Ciresan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: Twenty-Second International Joint Conference on Artificial Intelligence (2011)"},{"issue":"11","key":"17_CR20","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"issue":"4","key":"17_CR21","doi-asserted-by":"publisher","first-page":"61","DOI":"10.3390\/info7040061","volume":"7","author":"M Peng","year":"2016","unstructured":"Peng, M., Wang, C., Chen, T., Liu, G.: NIRFaceNet: a convolutional neural network for near-infrared face identification. Information 7(4), 61 (2016)","journal-title":"Information"},{"key":"17_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/978-3-642-15825-4_10","volume-title":"Artificial Neural Networks \u2013 ICANN 2010","author":"D Scherer","year":"2010","unstructured":"Scherer, D., M\u00fcller, A., Behnke, S.: Evaluation of pooling operations in convolutional architectures for object recognition. In: Diamantaras, K., Duch, W., Iliadis, Lazaros S. (eds.) ICANN 2010. LNCS, vol. 6354, pp. 92\u2013101. Springer, Heidelberg (2010). \nhttps:\/\/doi.org\/10.1007\/978-3-642-15825-4_10"},{"key":"17_CR23","unstructured":"Feng, C.: The basement of CNN: fully connected layer (2017). (in Chinese)"},{"issue":"3","key":"17_CR24","first-page":"1","volume":"5","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Cogn. Model. 5(3), 1 (1986)","journal-title":"Cogn. Model."},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"Jarrett, K., Kavukcuoglu, K., Ranzato, M.A., Lecun, Y.: What is the best multi-stage architecture for object recognition? In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2146\u20132153 (2010)","DOI":"10.1109\/ICCV.2009.5459469"},{"key":"17_CR26","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.patrec.2017.07.016","volume":"105","author":"M Ribeiro","year":"2018","unstructured":"Ribeiro, M., Lazzaretti, A.E., Lopes, H.S.: A study of deep convolutional auto-encoders for anomaly detection in videos. Pattern Recognit. Lett. 105, 13\u201322 (2018)","journal-title":"Pattern Recognit. Lett."},{"key":"17_CR27","unstructured":"Krizhevsky, A., Hinton, G.E.: Using very deep autoencoders for content-based image retrieval. In: ESANN (2011)"},{"key":"17_CR28","volume-title":"TensorFlow Actual Combat","author":"W Huang","year":"2017","unstructured":"Huang, W., Tang, Y.: TensorFlow Actual Combat. Publishing House of Electronics Industry, Beijing (2017). (in Chinese)"},{"key":"17_CR29","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Kudlur, M.: Tensorflow: a system for large-scale machine learning. In: 12th {USENIX} Symposium on Operating Systems Design and Implementation, OSDI 2016, pp. 265\u2013283 (2016)"},{"key":"17_CR30","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1023\/B:PMMC.0000048126.87171.f9","volume":"43","author":"M Petrov","year":"2004","unstructured":"Petrov, M., Gaidukov, V., Kadushnikov, R., Antonov, I., Nurkanov, E.: Numerical method for modelling the microstructure of granular materials. Powder Metall. Met. Ceram. 43, 330\u2013335 (2004)","journal-title":"Powder Metall. Met. Ceram."}],"container-title":["Communications in Computer and Information Science","Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-36592-9_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T01:10:13Z","timestamp":1575853813000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-36592-9_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030365912","9783030365929"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-36592-9_17","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 December 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RuSCDays","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russian Supercomputing Days","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Moscow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ruscdays2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/russianscdays.org\/en\/node\/1","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"127","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"60","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"47% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}