Performance evaluation of boosting machine learning algorithms for lithofacies classification in heterogeneous carbonate reservoirs
Gespeichert in:
| Veröffentlicht in: | Marine and petroleum geology Jg. 145; S. 105886 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
01.11.2022
|
| ISSN: | 0264-8172 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| ArticleNumber | 105886 |
|---|---|
| Author | Abbas, Mohammed A. Wood, David A. Al-Mudhafar, Watheq J. |
| Author_xml | – sequence: 1 givenname: Watheq J. orcidid: 0000-0002-5327-8300 surname: Al-Mudhafar fullname: Al-Mudhafar, Watheq J. – sequence: 2 givenname: Mohammed A. surname: Abbas fullname: Abbas, Mohammed A. – sequence: 3 givenname: David A. surname: Wood fullname: Wood, David A. |
| BookMark | eNqFkL9OwzAQhz0UibbwDPgFUmwncdyBAVX8qVQJhu7RxTmnrhK7st1KzLw4KUUMLEx3-t19J903IxPnHRJyx9mCMy7v94sBwgFTh34hmBBjWiolJ2TKhCwyxStxTWYx7hlj1ZLxKfl8x2B8GMBppHiC_gjJeke9oY33MVnX0QH0zjqkPUJw5wD6zgebdkOkI0v7sfUGtMVIdQ8xWmP15Yx1dIcJg-_QoT-OcwiNd5CQBowYTt6GeEOuDPQRb3_qnGyfn7ar12zz9rJePW4yEIqlbJkbLqRELJRpTctU2yAvoGENq1TRClHmXAopGOZlhTguqEZyKAuuy6UW-ZxUl7M6-BgDmvoQ7Ojro-asPuur9_Wvvvqsr77oG8mHP6S26fvBFMD2__Jf8lWEdg |
| CitedBy_id | crossref_primary_10_1016_j_jece_2024_112210 crossref_primary_10_1007_s12145_025_01745_9 crossref_primary_10_1038_s41598_025_95490_0 crossref_primary_10_3390_s24124013 crossref_primary_10_1016_j_cageo_2024_105735 crossref_primary_10_1109_ACCESS_2023_3349216 crossref_primary_10_1007_s12145_024_01515_z crossref_primary_10_1016_j_est_2022_106150 crossref_primary_10_1038_s41598_025_86088_7 crossref_primary_10_1007_s11600_025_01692_5 crossref_primary_10_1109_ACCESS_2024_3507569 crossref_primary_10_1007_s13369_025_10511_4 crossref_primary_10_1190_geo2024_0352_1 crossref_primary_10_1016_j_aiig_2025_100121 crossref_primary_10_1007_s12145_024_01581_3 crossref_primary_10_1144_petgeo2023_067 crossref_primary_10_1007_s13369_025_10270_2 crossref_primary_10_1016_j_marpetgeo_2023_106168 crossref_primary_10_1038_s41598_023_49080_7 crossref_primary_10_1152_ajpgi_00139_2024 crossref_primary_10_1007_s11770_025_1215_y crossref_primary_10_1002_ese3_2073 crossref_primary_10_1016_j_marpetgeo_2024_107263 crossref_primary_10_1038_s41598_023_40904_0 crossref_primary_10_1186_s40517_024_00323_4 crossref_primary_10_1007_s13146_024_01042_4 crossref_primary_10_1016_j_heliyon_2023_e20242 crossref_primary_10_1615_JPorMedia_2024052284 crossref_primary_10_1190_geo2022_0770_1 crossref_primary_10_1002_ese3_1579 crossref_primary_10_1007_s12145_023_01129_x crossref_primary_10_3389_feart_2023_1200913 crossref_primary_10_1007_s12145_023_01099_0 crossref_primary_10_1016_j_istruc_2024_107882 crossref_primary_10_1016_j_engeos_2024_100341 crossref_primary_10_1016_j_marpetgeo_2023_106419 crossref_primary_10_1038_s41598_025_98789_0 crossref_primary_10_1515_geo_2022_0465 crossref_primary_10_3389_fmars_2023_1055843 crossref_primary_10_1016_j_jafrearsci_2025_105631 crossref_primary_10_2118_217466_PA crossref_primary_10_1016_j_jappgeo_2023_104990 crossref_primary_10_3389_feart_2023_1095611 crossref_primary_10_3390_app132412978 crossref_primary_10_3390_pr12010125 |
| Cites_doi | 10.1190/INT-2018-0245.1 10.1190/INT-2015-0044.1 10.1007/s42979-021-00592-x 10.24996/ijs.2019.60.12.15 10.1016/j.jmva.2010.06.017 10.2113/geoarabia1801139 10.1007/s40808-017-0277-0 10.1007/s13146-020-00583-8 10.1007/s13202-017-0360-0 10.1016/j.sedgeo.2020.105790 10.1109/TGRS.2020.2981687 10.2136/sssaj2007.0241 10.1007/s11004-019-09838-0 10.1144/SP370.14 10.1111/j.1475-2743.2010.00305.x 10.1016/j.jafrearsci.2020.103826 10.1023/A:1022648800760 10.1016/j.marpetgeo.2018.12.022 10.1016/j.cageo.2020.104475 10.1111/j.1747-5457.1998.tb00646.x 10.1088/1742-2132/8/4/011 10.1016/S0167-9473(01)00065-2 10.1007/s13146-017-0388-8 10.1016/j.petrol.2021.109463 10.1007/s12182-020-00474-6 10.1016/j.marpetgeo.2020.104720 10.2113/geoarabia140491 10.1016/j.asej.2020.01.010 10.1007/s12145-022-00829-0 10.1016/j.marpetgeo.2020.104415 10.1016/j.marpetgeo.2020.104687 10.1016/j.marpetgeo.2019.07.026 10.1007/s10596-021-10033-6 10.1016/B978-0-444-63529-7.00016-X 10.1007/s13146-021-00746-1 10.1145/2990508 10.1144/GSL.SP.1985.018.01.03 10.1214/aos/1016218223 |
| ContentType | Journal Article |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.marpetgeo.2022.105886 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geology Engineering |
| ExternalDocumentID | 10_1016_j_marpetgeo_2022_105886 |
| GroupedDBID | --K --M -~X .~1 0R~ 1B1 1RT 1~. 1~5 29M 4.4 457 4G. 5GY 5VS 6OB 7-5 71M 8P~ 9DU 9JN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYWO AAYXX ABFNM ABJNI ABMAC ABQEM ABQYD ABWVN ABXDB ACDAQ ACGFS ACLOT ACLVX ACRLP ACRPL ACSBN ACVFH ADBBV ADCNI ADEZE ADMUD ADNMO AEBSH AEIPS AEKER AENEX AEUPX AFFNX AFJKZ AFPUW AFTJW AGHFR AGQPQ AGUBO AGYEJ AHHHB AIEXJ AIGII AIIUN AIKHN AITUG AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU APXCP ASPBG ATOGT AVWKF AXJTR AZFZN BKOJK BLXMC CITATION CS3 DU5 EBS EFJIC EFKBS EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HMA HVGLF HZ~ IHE IMUCA J1W KOM LY3 LY6 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- ROL RPZ SAC SDF SDG SEP SES SEW SPC SPCBC SSE SSZ T5K WH7 WUQ XPP ZMT ZY4 ~02 ~G- ~HD |
| ID | FETCH-LOGICAL-a280t-93f1266ee48fdfd08dbe14ab0b0784d2253162620e357eed088b61a541c59c23 |
| ISICitedReferencesCount | 51 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000862135100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0264-8172 |
| IngestDate | Sat Nov 29 07:19:31 EST 2025 Tue Nov 18 22:20:41 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-a280t-93f1266ee48fdfd08dbe14ab0b0784d2253162620e357eed088b61a541c59c23 |
| ORCID | 0000-0002-5327-8300 |
| ParticipantIDs | crossref_primary_10_1016_j_marpetgeo_2022_105886 crossref_citationtrail_10_1016_j_marpetgeo_2022_105886 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-11-00 |
| PublicationDateYYYYMMDD | 2022-11-01 |
| PublicationDate_xml | – month: 11 year: 2022 text: 2022-11-00 |
| PublicationDecade | 2020 |
| PublicationTitle | Marine and petroleum geology |
| PublicationYear | 2022 |
| References | Siddharth (10.1016/j.marpetgeo.2022.105886_bib72) 2020 Wood (10.1016/j.marpetgeo.2022.105886_bib81) 2019; 110 Anderton (10.1016/j.marpetgeo.2022.105886_bib16) 1985; 18 Miall (10.1016/j.marpetgeo.2022.105886_bib53) 1990 Middleton (10.1016/j.marpetgeo.2022.105886_bib54) 1973; 84 Long (10.1016/j.marpetgeo.2022.105886_bib47) 2006 Amaefule (10.1016/j.marpetgeo.2022.105886_bib14) 1993 Dunham (10.1016/j.marpetgeo.2022.105886_bib29) 1962 Mirkes (10.1016/j.marpetgeo.2022.105886_bib55) 2011 Koeshidayatullah (10.1016/j.marpetgeo.2022.105886_bib41) 2020; 122 Abbas (10.1016/j.marpetgeo.2022.105886_bib1) 2019 Gonulalan (10.1016/j.marpetgeo.2022.105886_bib34) 2010 Al-Ali (10.1016/j.marpetgeo.2022.105886_bib5) 2019 Tang (10.1016/j.marpetgeo.2022.105886_bib74) 2008 Walker (10.1016/j.marpetgeo.2022.105886_bib78) 1992 Nanjo (10.1016/j.marpetgeo.2022.105886_bib59) 2019 Tuszynski (10.1016/j.marpetgeo.2022.105886_bib76) Gressly (10.1016/j.marpetgeo.2022.105886_bib36) 1838; 2 He (10.1016/j.marpetgeo.2022.105886_bib39) 2019; 101 Pan (10.1016/j.marpetgeo.2022.105886_bib60) 2021; 208 Gu (10.1016/j.marpetgeo.2022.105886_bib37) 2021; 36 Al-Mudhafar (10.1016/j.marpetgeo.2022.105886_bib10) 2020 Al Moqbel (10.1016/j.marpetgeo.2022.105886_bib4) 2011; 8 Moreton (10.1016/j.marpetgeo.2022.105886_bib57) 2015; 68 Pires (10.1016/j.marpetgeo.2022.105886_bib61) 2010; 101 Halotel (10.1016/j.marpetgeo.2022.105886_bib38) 2020; 52 Al-Mudhafar (10.1016/j.marpetgeo.2022.105886_bib8) 2017; 7 dos Anjos (10.1016/j.marpetgeo.2022.105886_bib27) 2021; 25 Zhao (10.1016/j.marpetgeo.2022.105886_bib85) 2015; 3 Zong (10.1016/j.marpetgeo.2022.105886_bib88) 2017; 8 Lucia (10.1016/j.marpetgeo.2022.105886_bib48) 1999 Tharwat (10.1016/j.marpetgeo.2022.105886_bib75) 2018 Abbas (10.1016/j.marpetgeo.2022.105886_bib2) 2019; 60 Alsharhan (10.1016/j.marpetgeo.2022.105886_bib13) 1997 Leverett (10.1016/j.marpetgeo.2022.105886_bib43) 1941 Liu (10.1016/j.marpetgeo.2022.105886_bib45) 2020; 58 Chen (10.1016/j.marpetgeo.2022.105886_bib24) 2016 Liu (10.1016/j.marpetgeo.2022.105886_bib44) 2020; 410 Al-Mudhafar (10.1016/j.marpetgeo.2022.105886_bib7) 2016 De Ribet (10.1016/j.marpetgeo.2022.105886_bib26) 2018 Reading (10.1016/j.marpetgeo.2022.105886_bib66) 1996 Zhao (10.1016/j.marpetgeo.2022.105886_bib86) 2022 Abdulaziz (10.1016/j.marpetgeo.2022.105886_bib3) 2020; 11 Meyer (10.1016/j.marpetgeo.2022.105886_bib52) Friedman (10.1016/j.marpetgeo.2022.105886_bib33) 2002; 38 Cantrell (10.1016/j.marpetgeo.2022.105886_bib89) 2020; 118 Ya (10.1016/j.marpetgeo.2022.105886_bib84) 2016; 43 Zheng (10.1016/j.marpetgeo.2022.105886_bib87) 2021; 123 Jalabert (10.1016/j.marpetgeo.2022.105886_bib40) 2010; 26 Liu (10.1016/j.marpetgeo.2022.105886_bib46) 2020; 17 Breiman (10.1016/j.marpetgeo.2022.105886_bib20) 1984 Pires de Lima (10.1016/j.marpetgeo.2022.105886_bib62) 2019; 7 Bressan (10.1016/j.marpetgeo.2022.105886_bib21) 2020; 139 Sarker (10.1016/j.marpetgeo.2022.105886_bib69) 2021; 2 Lee (10.1016/j.marpetgeo.2022.105886_bib42) 1999 Ameur-Zaimeche (10.1016/j.marpetgeo.2022.105886_bib15) 2020; 166 Aqrawi (10.1016/j.marpetgeo.2022.105886_bib17) 1998; 21 R Development Core Team (10.1016/j.marpetgeo.2022.105886_bib65) 2006 Duan (10.1016/j.marpetgeo.2022.105886_bib28) 2020; 35 Friedman (10.1016/j.marpetgeo.2022.105886_bib32) 2000; 28 Al-Mudhafar (10.1016/j.marpetgeo.2022.105886_bib12) 2019 Embry (10.1016/j.marpetgeo.2022.105886_bib31) 1971; 19 Bergstra (10.1016/j.marpetgeo.2022.105886_bib18) 2012; 13 Bestagini (10.1016/j.marpetgeo.2022.105886_bib19) 2017; 2137 Rubing (10.1016/j.marpetgeo.2022.105886_bib68) 2019 Al-Ameri (10.1016/j.marpetgeo.2022.105886_bib6) 2009; 14 Woan (10.1016/j.marpetgeo.2022.105886_bib80) 2012 Greenwell (10.1016/j.marpetgeo.2022.105886_bib35) Pittman (10.1016/j.marpetgeo.2022.105886_bib63) 1992 Wang (10.1016/j.marpetgeo.2022.105886_bib79) 2012 Martin (10.1016/j.marpetgeo.2022.105886_bib51) 2009; 73 Moradi (10.1016/j.marpetgeo.2022.105886_bib56) 2019; 34 Chatterjee (10.1016/j.marpetgeo.2022.105886_bib23) El-Sebakhy (10.1016/j.marpetgeo.2022.105886_bib30) 2010 Mahdi (10.1016/j.marpetgeo.2022.105886_bib49) 2013; 18 Rostamian (10.1016/j.marpetgeo.2022.105886_bib67) 2022; 208 Murphy (10.1016/j.marpetgeo.2022.105886_bib58) 2006 Van Bellen (10.1016/j.marpetgeo.2022.105886_bib77) 1959 Shekar (10.1016/j.marpetgeo.2022.105886_bib71) 2019 Wood (10.1016/j.marpetgeo.2022.105886_bib82) 2022 Burchette (10.1016/j.marpetgeo.2022.105886_bib22) 2012; 370 Probst (10.1016/j.marpetgeo.2022.105886_bib64) 2019; 20 Sutton (10.1016/j.marpetgeo.2022.105886_bib73) 2012 Al-Mudhafar (10.1016/j.marpetgeo.2022.105886_bib9) 2017; 3 Chen (10.1016/j.marpetgeo.2022.105886_bib25) Schapire (10.1016/j.marpetgeo.2022.105886_bib70) 1990; 5 Marc (10.1016/j.marpetgeo.2022.105886_bib50) 2017; 112 Wu (10.1016/j.marpetgeo.2022.105886_bib83) 2019; 17 |
| References_xml | – volume: 7 start-page: SF27 issue: 3 year: 2019 ident: 10.1016/j.marpetgeo.2022.105886_bib62 article-title: Convolutional neural networks as aid in core lithofacies classification publication-title: Interpretation doi: 10.1190/INT-2018-0245.1 – volume: 3 start-page: SAE29 issue: 4 year: 2015 ident: 10.1016/j.marpetgeo.2022.105886_bib85 article-title: A comparison of classification techniques for seismic facies recognition publication-title: Interpretation doi: 10.1190/INT-2015-0044.1 – volume: 2 start-page: 160 year: 2021 ident: 10.1016/j.marpetgeo.2022.105886_bib69 article-title: Machine learning: algorithms, real-world applications and research directions publication-title: SN COMPUT. SCI. doi: 10.1007/s42979-021-00592-x – year: 1999 ident: 10.1016/j.marpetgeo.2022.105886_bib48 – volume: 17 start-page: 26 issue: 1 year: 2019 ident: 10.1016/j.marpetgeo.2022.105886_bib83 article-title: Hyperparameter optimization for machine learning models based on bayesian optimization publication-title: J. Electron. Sci. Technol. – ident: 10.1016/j.marpetgeo.2022.105886_bib76 – volume: 43 start-page: 136 issue: 1 year: 2016 ident: 10.1016/j.marpetgeo.2022.105886_bib84 article-title: Geologic features and genesis of the barriers and intercalations in carbonates: a case study of the Cretaceous Mishrif Formation, West Qurna oil field, Iraq publication-title: Petrol. Explor. Dev. – volume: 60 start-page: 2656 issue: 12 year: 2019 ident: 10.1016/j.marpetgeo.2022.105886_bib2 article-title: Reservoir units of Mishrif Formation in Majnoon oil field, southern Iraq publication-title: Iraqi J. Sci. doi: 10.24996/ijs.2019.60.12.15 – volume: 101 start-page: 2464 issue: 10 year: 2010 ident: 10.1016/j.marpetgeo.2022.105886_bib61 article-title: Projection-pursuit approach to robust linear discriminant analysis publication-title: J. Multivariate Anal. doi: 10.1016/j.jmva.2010.06.017 – year: 1993 ident: 10.1016/j.marpetgeo.2022.105886_bib14 article-title: Enhanced reservoir description: using core and log data to identify hydraulic flow units and predict permeability in uncored intervals/wells – volume: 18 start-page: 139 issue: 1 year: 2013 ident: 10.1016/j.marpetgeo.2022.105886_bib49 article-title: Sedimentological characterization of the mid cretaceous Mishrif reservoir in southern Mesopotamian Basin, Iraq publication-title: GeoArabia doi: 10.2113/geoarabia1801139 – volume: 3 start-page: 12 year: 2017 ident: 10.1016/j.marpetgeo.2022.105886_bib9 article-title: Integrating kernel support vector machines for efficient rock facies classification in the main pay of Zubair formation in South Rumaila oil field, Iraq. Model publication-title: Earth Syst. Environ. doi: 10.1007/s40808-017-0277-0 – ident: 10.1016/j.marpetgeo.2022.105886_bib25 – year: 2019 ident: 10.1016/j.marpetgeo.2022.105886_bib68 article-title: New workflow of facies modeling based on deposition study, seismic data and artificial modification: a case study for the Mishrif Formation of the H oilfield, Iraq – year: 1999 ident: 10.1016/j.marpetgeo.2022.105886_bib42 article-title: Electrofacies characterization and permeability predictions in carbonate reservoirs: role of multivariate analysis and nonparametric regression – volume: 35 start-page: 1 year: 2020 ident: 10.1016/j.marpetgeo.2022.105886_bib28 article-title: Lithology identification and reservoir characteristics of the mixed siliciclastic-carbonate rocks of the lower third member of the Shahejie formation in the south of the Laizhouwan Sag, Bohai Bay Basin, China publication-title: Carbonates Evaporites doi: 10.1007/s13146-020-00583-8 – year: 2012 ident: 10.1016/j.marpetgeo.2022.105886_bib79 article-title: AdaBoost for feature selection, classification and its relation with SVM*, A review – year: 2019 ident: 10.1016/j.marpetgeo.2022.105886_bib59 article-title: Carbonate lithology identification with machine learning – volume: 7 start-page: 1023 year: 2017 ident: 10.1016/j.marpetgeo.2022.105886_bib8 article-title: Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms publication-title: J. Pet. Explor. Prod. Technol. doi: 10.1007/s13202-017-0360-0 – volume: 410 year: 2020 ident: 10.1016/j.marpetgeo.2022.105886_bib44 article-title: Automatic identification of fossils and abiotic grains during carbonate microfacies analysis using deep convolutional neural networks publication-title: Sediment. Geol. doi: 10.1016/j.sedgeo.2020.105790 – year: 2006 ident: 10.1016/j.marpetgeo.2022.105886_bib47 – year: 2022 ident: 10.1016/j.marpetgeo.2022.105886_bib86 article-title: Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: a case study in Wenchang A Sag, Pearl River Mouth Basin publication-title: J. Petrol. Sci. Eng. – volume: 58 start-page: 7269 issue: 10 year: 2020 ident: 10.1016/j.marpetgeo.2022.105886_bib45 article-title: Facies identification based on multikernel relevance vector machine publication-title: IEEE Trans. Geosci. Rem. Sens. doi: 10.1109/TGRS.2020.2981687 – year: 2012 ident: 10.1016/j.marpetgeo.2022.105886_bib73 – volume: 20 start-page: 1 year: 2019 ident: 10.1016/j.marpetgeo.2022.105886_bib64 article-title: Tunability: importance of hyperparameters of machine learning algorithms publication-title: J. Mach. Learn. Res. – volume: 73 start-page: 485 year: 2009 ident: 10.1016/j.marpetgeo.2022.105886_bib51 article-title: Optimizing pedotransfer functions for estimating soil bulk density using boosted regression trees publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj2007.0241 – ident: 10.1016/j.marpetgeo.2022.105886_bib35 – year: 2019 ident: 10.1016/j.marpetgeo.2022.105886_bib5 article-title: Improved carbonate reservoir characterization: a case study from a supergiant field in southern of Iraq – year: 2019 ident: 10.1016/j.marpetgeo.2022.105886_bib71 article-title: Grid search-based hyperparameter tuning and classification of microarray cancer data – volume: 2137 year: 2017 ident: 10.1016/j.marpetgeo.2022.105886_bib19 article-title: A machine learning approach to facies classification using well logs publication-title: Proceedings of the SEG International Exposition and 87th Annual Meeting – volume: 52 start-page: 5 year: 2020 ident: 10.1016/j.marpetgeo.2022.105886_bib38 article-title: Value of geologically derived features in machine learning facies classification publication-title: Math. Geosci. doi: 10.1007/s11004-019-09838-0 – volume: 370 start-page: 17 issue: 1 year: 2012 ident: 10.1016/j.marpetgeo.2022.105886_bib22 article-title: Carbonate rocks and petroleum reservoirs: a geological perspective from the industry publication-title: Geol. Soc. Lond. Spec. Publ. doi: 10.1144/SP370.14 – year: 2020 ident: 10.1016/j.marpetgeo.2022.105886_bib72 – volume: 26 start-page: 516 year: 2010 ident: 10.1016/j.marpetgeo.2022.105886_bib40 article-title: Estimating forest soil bulk density using boosted regression modeling publication-title: Soil Use Manag. doi: 10.1111/j.1475-2743.2010.00305.x – year: 1959 ident: 10.1016/j.marpetgeo.2022.105886_bib77 – year: 2016 ident: 10.1016/j.marpetgeo.2022.105886_bib7 article-title: Applied geostatistical reservoir characterization in R: review and implementation of rock facies classification and prediction algorithms-Part I – volume: 166 year: 2020 ident: 10.1016/j.marpetgeo.2022.105886_bib15 article-title: Lithofacies prediction in non-cored wells from the Sif Fatima oil field (Berkine basin, southern Algeria): a comparative study of multilayer perceptron neural network and cluster analysis-based approaches publication-title: J. Afr. Earth Sci. doi: 10.1016/j.jafrearsci.2020.103826 – start-page: 159 year: 1941 ident: 10.1016/j.marpetgeo.2022.105886_bib43 article-title: Capillary behavior in porous solids publication-title: Transact. AIME – volume: 5 start-page: 197 year: 1990 ident: 10.1016/j.marpetgeo.2022.105886_bib70 article-title: The strength of weak learnability publication-title: Mach. Learn. doi: 10.1023/A:1022648800760 – year: 1997 ident: 10.1016/j.marpetgeo.2022.105886_bib13 – volume: 101 start-page: 410 year: 2019 ident: 10.1016/j.marpetgeo.2022.105886_bib39 article-title: Using neural networks and the Markov chain approach for facies analysis and prediction from well logs in the Precipice Sandstone and Evergreen Formation, Surat Basin, Australia publication-title: Mar. Petrol. Geol. doi: 10.1016/j.marpetgeo.2018.12.022 – volume: 2 start-page: 1 year: 1838 ident: 10.1016/j.marpetgeo.2022.105886_bib36 article-title: Observations géologiques sur le Jura Soleurois publication-title: Neue Denkschriften Der Allgemeinen Shweizerischen Gesellschaft fur die gesammten Naturwissenschaften – volume: 139 year: 2020 ident: 10.1016/j.marpetgeo.2022.105886_bib21 article-title: Evaluation of machine learning methods for lithology classification using geophysical data publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2020.104475 – year: 2018 ident: 10.1016/j.marpetgeo.2022.105886_bib26 article-title: Machine learning provides higher-quality insights into facies heterogeneities over complex carbonate reservoirs in a recently developed abu dhabi oilfield, Middle East – year: 2008 ident: 10.1016/j.marpetgeo.2022.105886_bib74 article-title: Improved carbonate reservoir facies classification using artificial neural network method – volume: 21 start-page: 57 issue: 1 year: 1998 ident: 10.1016/j.marpetgeo.2022.105886_bib17 article-title: Mid-cretaceous rudist-bearing carbonates of the Mishrif Formation: an important reservoir sequence in the Mesopotamian Basin, Iraq publication-title: J. Petrol. Geol. doi: 10.1111/j.1747-5457.1998.tb00646.x – volume: 8 start-page: 592 issue: 4 year: 2011 ident: 10.1016/j.marpetgeo.2022.105886_bib4 article-title: Carbonate reservoir characterization with lithofacies clustering and porosity prediction publication-title: J. Geophys. Eng. doi: 10.1088/1742-2132/8/4/011 – year: 2006 ident: 10.1016/j.marpetgeo.2022.105886_bib65 – volume: 38 start-page: 367 year: 2002 ident: 10.1016/j.marpetgeo.2022.105886_bib33 article-title: Stochastic gradient boosting publication-title: Comput. Stat. Data Anal. doi: 10.1016/S0167-9473(01)00065-2 – volume: 34 start-page: 335 issue: 2 year: 2019 ident: 10.1016/j.marpetgeo.2022.105886_bib56 article-title: Inversion of well logs into rock types, lithofacies and environmental facies, using pattern recognition, a case study of carbonate Sarvak Formation publication-title: Carbonates Evaporites doi: 10.1007/s13146-017-0388-8 – volume: 208 year: 2022 ident: 10.1016/j.marpetgeo.2022.105886_bib67 article-title: Evaluation of different machine learning frameworks to predict CNL-FDC-PEF logs via hyperparameters optimization and feature selection publication-title: J. Petrol. Sci. Eng. doi: 10.1016/j.petrol.2021.109463 – volume: 17 start-page: 954 issue: 4 year: 2020 ident: 10.1016/j.marpetgeo.2022.105886_bib46 article-title: Lithofacies identification using support vector machine based on local deep multi-kernel learning publication-title: Petrol. Sci. doi: 10.1007/s12182-020-00474-6 – volume: 123 year: 2021 ident: 10.1016/j.marpetgeo.2022.105886_bib87 article-title: Electrofacies classification of deeply buried carbonate strata using machine learning methods: a case study on ordovician paleokarst reservoirs in Tarim Basin publication-title: Mar. Petrol. Geol. doi: 10.1016/j.marpetgeo.2020.104720 – year: 1992 ident: 10.1016/j.marpetgeo.2022.105886_bib78 – volume: 208 year: 2021 ident: 10.1016/j.marpetgeo.2022.105886_bib60 article-title: An optimized XGBoost method for predicting reservoir porosity using petrophysical logs publication-title: J. Petrol. Sci. Eng. – year: 2012 ident: 10.1016/j.marpetgeo.2022.105886_bib80 article-title: Improved reservoir characterization using petrophysical classifiers within electrofacies – year: 2019 ident: 10.1016/j.marpetgeo.2022.105886_bib1 article-title: Clustering analysis and flow zone indicator for electrofacies characterization in the upper shale member in luhais oil field, SouthernIraq – volume: 14 start-page: 91 issue: 4 year: 2009 ident: 10.1016/j.marpetgeo.2022.105886_bib6 article-title: Petroleum system analysis of the Mishrif reservoir in the ratawi, Zubair, north and south Rumaila oil fields, southern Iraq publication-title: GeoArabia doi: 10.2113/geoarabia140491 – volume: 112 start-page: 88 issue: C year: 2017 ident: 10.1016/j.marpetgeo.2022.105886_bib50 article-title: LogitBoost autoregressive networks publication-title: Comput. Stat. Data Anal. – year: 2020 ident: 10.1016/j.marpetgeo.2022.105886_bib10 – volume: 11 start-page: 1387 issue: 4 year: 2020 ident: 10.1016/j.marpetgeo.2022.105886_bib3 article-title: Prediction of carbonate diagenesis from well logs using artificial neural network: an innovative technique to understand complex carbonate systems publication-title: Ain Shams Eng. J. doi: 10.1016/j.asej.2020.01.010 – year: 2022 ident: 10.1016/j.marpetgeo.2022.105886_bib82 article-title: Carbonate/siliciclastic lithofacies classification aided by well-log derivative, volatility and sequence boundary attributes combined with machine learning publication-title: Earth Science Informatics doi: 10.1007/s12145-022-00829-0 – start-page: 108 year: 1962 ident: 10.1016/j.marpetgeo.2022.105886_bib29 – volume: 19 start-page: 730 issue: 4 year: 1971 ident: 10.1016/j.marpetgeo.2022.105886_bib31 article-title: A late devonian reef tract on northeastern banks Island,Northwest territories publication-title: Bull. Can. Petrol. Geol. – volume: 13 start-page: 281 year: 2012 ident: 10.1016/j.marpetgeo.2022.105886_bib18 article-title: Random search for hyper-parameter optimization publication-title: J. Mach. Learn. Res. – volume: 118 start-page: 104415 year: 2020 ident: 10.1016/j.marpetgeo.2022.105886_bib89 article-title: Depositional and diagenetic controls on reservoir quality: Example from the upper Cretaceous Mishrif Formation of Iraq publication-title: Mar. Petrol. Geol. doi: 10.1016/j.marpetgeo.2020.104415 – volume: 122 year: 2020 ident: 10.1016/j.marpetgeo.2022.105886_bib41 article-title: Fully automated carbonate petrography using deep convolutional neural networks publication-title: Mar. Petrol. Geol. doi: 10.1016/j.marpetgeo.2020.104687 – year: 1990 ident: 10.1016/j.marpetgeo.2022.105886_bib53 – year: 2011 ident: 10.1016/j.marpetgeo.2022.105886_bib55 – year: 2018 ident: 10.1016/j.marpetgeo.2022.105886_bib75 – volume: 110 start-page: 347 year: 2019 ident: 10.1016/j.marpetgeo.2022.105886_bib81 article-title: Lithofacies and stratigraphy prediction methodology exploiting an optimized nearest-neighbour algorithm to mine well-log data publication-title: Mar. Petrol. Geol. doi: 10.1016/j.marpetgeo.2019.07.026 – year: 2010 ident: 10.1016/j.marpetgeo.2022.105886_bib30 article-title: Data mining in identifying carbonate litho-facies from well logs based from extreme learning and support vector machines – year: 1996 ident: 10.1016/j.marpetgeo.2022.105886_bib66 – year: 2019 ident: 10.1016/j.marpetgeo.2022.105886_bib12 article-title: Clustering analysis for improved characterization of carbonate reservoirs in a southern Iraqi oil field – volume: 25 start-page: 971 year: 2021 ident: 10.1016/j.marpetgeo.2022.105886_bib27 article-title: Deep learning for lithological classification of carbonate rock micro-CT images publication-title: Comput. Geosci. doi: 10.1007/s10596-021-10033-6 – volume: 68 start-page: 529 year: 2015 ident: 10.1016/j.marpetgeo.2022.105886_bib57 article-title: Characterizing alluvial architecture of point bars within the McMurray Formation, Alberta, Canada, for improved bitumen resource prediction and recovery publication-title: Dev. Sedimentol., Elsevier doi: 10.1016/B978-0-444-63529-7.00016-X – volume: 84 start-page: 979 year: 1973 ident: 10.1016/j.marpetgeo.2022.105886_bib54 – volume: 36 start-page: 79 issue: 4 year: 2021 ident: 10.1016/j.marpetgeo.2022.105886_bib37 article-title: Carbonate lithofacies identification using an improved light gradient boosting machine and conventional logs: a demonstration using pre-salt lacustrine reservoirs, Santos Basin publication-title: Carbonates Evaporites doi: 10.1007/s13146-021-00746-1 – ident: 10.1016/j.marpetgeo.2022.105886_bib23 – volume: 8 start-page: 1 issue: 3 year: 2017 ident: 10.1016/j.marpetgeo.2022.105886_bib88 article-title: Learning k for kNN Classification publication-title: ACM Transact. Intelligent Syst. Technol. doi: 10.1145/2990508 – year: 2006 ident: 10.1016/j.marpetgeo.2022.105886_bib58 – volume: 18 start-page: 31 year: 1985 ident: 10.1016/j.marpetgeo.2022.105886_bib16 article-title: Clastic facies models and facies analysis publication-title: Geol. Soc. Lond. Spec. Publ. doi: 10.1144/GSL.SP.1985.018.01.03 – year: 2010 ident: 10.1016/j.marpetgeo.2022.105886_bib34 – start-page: 191 year: 1992 ident: 10.1016/j.marpetgeo.2022.105886_bib63 article-title: Relationship of porosity and permeability to various parameters derived from mercury injection-capillary pressure curves for sandstone publication-title: AAPG (Am. Assoc. Pet. Geol.) Bull. – volume: 28 start-page: 337 issue: 2 year: 2000 ident: 10.1016/j.marpetgeo.2022.105886_bib32 article-title: Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors) publication-title: Ann. Stat. doi: 10.1214/aos/1016218223 – ident: 10.1016/j.marpetgeo.2022.105886_bib52 – year: 1984 ident: 10.1016/j.marpetgeo.2022.105886_bib20 – year: 2016 ident: 10.1016/j.marpetgeo.2022.105886_bib24 article-title: XGBoost,: a scalable tree boosting system |
| SSID | ssj0007901 |
| Score | 2.5460234 |
| SourceID | crossref |
| SourceType | Enrichment Source Index Database |
| StartPage | 105886 |
| Title | Performance evaluation of boosting machine learning algorithms for lithofacies classification in heterogeneous carbonate reservoirs |
| Volume | 145 |
| WOSCitedRecordID | wos000862135100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: ScienceDirect database issn: 0264-8172 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0007901 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3JbtswECXcpAWaQ9CmLZpu4KE3QYY2WtTRCNIlQIIcDMQ3gRJJy4EtpbYc5N5v6X92KIqUlBZIc-hFMGSQXuaJ8zh8M4PQZ0pkzMEtu8A9qAsMnLss4MIFVz0RHvFEyPOm2UR8cUHn8-RyNPplcmFuV3FZ0ru75Oa_mhrugbFV6uwjzG0nhRvwGowOVzA7XP_J8Je9VICulrcihUCot43Ked0oKIVpGbFw2GpRbZZ1oaszqLzkopIMHvutkyt6rfREzMgiC6WgqeDzhZLP5myTqQi8ar-iIryVOR8ybaKYyi_U9QiEUsWL3dpZiEE0f7pyz3e8YFKrva8UK_3hnI27s6lM552dV4WKtHNnat-7ajVDjTjf3G_DGLAD9m0YQ692wMxc6sfDpTkivcUVqCDVdbP_WPd1COJ6vFaHVvWiSesMgnE3Ylhp-54HtLpEI3m7Tu1EqZoo1RM9QftBTBJYPPen30_nZ9blx0nTZ9v-hoGQ8K_fqUeDenxm9gIdthsRPNUAeolGojxCB73ylEfo2VdtplfoZw9UuAMVriQ2oMItqLABFe5AhWEs7oEKD0GFlyUegApbUOEOVK_R7Mvp7OSb27bvgKederWbhNIH-idERCWX3KM8E37EMi8DWhpxcCShP1H9EERIYqBq4O-yic9I5OckyYPwDdorq1K8RZgBLYZtgC8kDIylbNYdEgpOwD9xJo7RxPyZad6WtlcdVlbpAwY9Rp4deKOruzw05N3jh7xHzzvEf0B79WYnPqKn-W293G4-tVj6DYcNrA0 |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Performance+evaluation+of+boosting+machine+learning+algorithms+for+lithofacies+classification+in+heterogeneous+carbonate+reservoirs&rft.jtitle=Marine+and+petroleum+geology&rft.au=Al-Mudhafar%2C+Watheq+J.&rft.au=Abbas%2C+Mohammed+A.&rft.au=Wood%2C+David+A.&rft.date=2022-11-01&rft.issn=0264-8172&rft.volume=145&rft.spage=105886&rft_id=info:doi/10.1016%2Fj.marpetgeo.2022.105886&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_marpetgeo_2022_105886 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0264-8172&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0264-8172&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0264-8172&client=summon |