{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T05:17:38Z","timestamp":1773033458254,"version":"3.50.1"},"reference-count":48,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"vor","delay-in-days":62,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23B2083"],"award-info":[{"award-number":["U23B2083"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    Sedimentary microfacies identification is fundamental for reservoir characterization, directly influencing hydrocarbon exploration and production strategies. However, traditional methods relying on core analysis, seismic interpretation, and manual well\u2010log analysis face significant challenges: (1) high costs and limited coverage of coring data, (2) subjectivity in seismic facies interpretation, and (3) poor generalization of conventional machine learning models when trained on small datasets. To overcome these limitations, this study proposes Hopular\u2014a novel deep learning architecture leveraging modern Hopfield networks. We validated the framework using 4000 normalized data points from 10 wells, covering eight logging parameters and five microfacies types. Evaluations across small (\u2264\u2009500 samples), medium (\u2264\u20092000), and large (\u2265\u20093000) datasets demonstrated robust performance, with\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    scores of 0.704 (\u00b10.021), 0.809 (\u00b10.059), and 0.925, respectively. The model excels in capturing data relationships, particularly in small data regimes (11.6%\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    improvement over ensemble methods). In summary, Hopular provides an accurate, data\u2010efficient solution for microfacies identification and supports exploration in data\u2010scarce settings. This work advances reservoir characterization by combining Hopfield networks\u2019 associative memory with deep learning, offering reliable technical support for subsurface interpretation.\n                  <\/jats:p>","DOI":"10.1155\/int\/6562891","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T04:28:57Z","timestamp":1773030537000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Intelligent Sedimentary Microfacies Identification Model Based on Limited Well\u2010Logging Data"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9423-6601","authenticated-orcid":false,"given":"Tianru","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0074-4934","authenticated-orcid":false,"given":"Weiyao","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6642-3773","authenticated-orcid":false,"given":"Hongqing","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2192-6032","authenticated-orcid":false,"given":"Ming","family":"Yue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,3,4]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engeos.2022.09.006"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2019.03.017"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.marpetgeo.2018.03.017"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1515\/geo-2019-0042"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/en16020804"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2023.05.014"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2019.106668"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2022.115960"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/jmse8090706"},{"key":"e_1_2_12_10_2","doi-asserted-by":"crossref","unstructured":"LuoZ. 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