Lithofacies classification of a geothermal reservoir in Denmark and its facies-dependent porosity estimation from seismic inversion
•A novel system combining ANN and HMM is proposed to classify lithofacies of a potential geothermal reservoir in Denmark.•Depositional rules and complex data distributions are considered at the same time.•The classified lithofacies are then used as constraints for the prediction of porosity.•Differe...
Saved in:
| Published in: | Geothermics Vol. 87; p. 101854 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Oxford
Elsevier Ltd
01.09.2020
Elsevier Science Ltd |
| Subjects: | |
| ISSN: | 0375-6505, 1879-3576 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | •A novel system combining ANN and HMM is proposed to classify lithofacies of a potential geothermal reservoir in Denmark.•Depositional rules and complex data distributions are considered at the same time.•The classified lithofacies are then used as constraints for the prediction of porosity.•Different regression neural networks are trained and applied within each type of lithofacies.
Characterization of geothermal reservoirs is an important step for exploration and development of geothermal energy, which is reliable and sustainable for the future. Based on the inversion results of seismic reflection data, lithofacies and porosity are predicted beyond well locations on a potential geothermal reservoir in the north of Copenhagen, onshore Denmark. To classify the lithofacies, a new system of Artificial Neural Networks-Hidden Markov Models is proposed to consider the complex spatial distribution of rock properties and the intrinsic depositional rules. Artificial Neural Networks can overcome the common Gaussian assumption for the distribution of rock properties. At the same time, the transition matrix in Hidden Markov Models provides the conditional probability for the lithofacies transitions along the vertical direction. After classification, the resulting lithofacies are used to constrain the porosity prediction, in which the Artificial Neural Networks is trained and applied within each type of lithofacies, as a regression process. The novelty of this approach is in the integration of statistics and computer science algorithms that allows capturing hidden and complex relations in the data that cannot be explained by traditionally deterministic geophysical equations. This workflow could also improve the prediction accuracy and the uncertainty quantification of the porosity distribution given rock properties. |
|---|---|
| AbstractList | •A novel system combining ANN and HMM is proposed to classify lithofacies of a potential geothermal reservoir in Denmark.•Depositional rules and complex data distributions are considered at the same time.•The classified lithofacies are then used as constraints for the prediction of porosity.•Different regression neural networks are trained and applied within each type of lithofacies.
Characterization of geothermal reservoirs is an important step for exploration and development of geothermal energy, which is reliable and sustainable for the future. Based on the inversion results of seismic reflection data, lithofacies and porosity are predicted beyond well locations on a potential geothermal reservoir in the north of Copenhagen, onshore Denmark. To classify the lithofacies, a new system of Artificial Neural Networks-Hidden Markov Models is proposed to consider the complex spatial distribution of rock properties and the intrinsic depositional rules. Artificial Neural Networks can overcome the common Gaussian assumption for the distribution of rock properties. At the same time, the transition matrix in Hidden Markov Models provides the conditional probability for the lithofacies transitions along the vertical direction. After classification, the resulting lithofacies are used to constrain the porosity prediction, in which the Artificial Neural Networks is trained and applied within each type of lithofacies, as a regression process. The novelty of this approach is in the integration of statistics and computer science algorithms that allows capturing hidden and complex relations in the data that cannot be explained by traditionally deterministic geophysical equations. This workflow could also improve the prediction accuracy and the uncertainty quantification of the porosity distribution given rock properties. Characterization of geothermal reservoirs is an important step for exploration and development of geothermal energy, which is reliable and sustainable for the future. Based on the inversion results of seismic reflection data, lithofacies and porosity are predicted beyond well locations on a potential geothermal reservoir in the north of Copenhagen, onshore Denmark. To classify the lithofacies, a new system of Artificial Neural Networks-Hidden Markov Models is proposed to consider the complex spatial distribution of rock properties and the intrinsic depositional rules. Artificial Neural Networks can overcome the common Gaussian assumption for the distribution of rock properties. At the same time, the transition matrix in Hidden Markov Models provides the conditional probability for the lithofacies transitions along the vertical direction. After classification, the resulting lithofacies are used to constrain the porosity prediction, in which the Artificial Neural Networks is trained and applied within each type of lithofacies, as a regression process. The novelty of this approach is in the integration of statistics and computer science algorithms that allows capturing hidden and complex relations in the data that cannot be explained by traditionally deterministic geophysical equations. This workflow could also improve the prediction accuracy and the uncertainty quantification of the porosity distribution given rock properties. |
| ArticleNumber | 101854 |
| Author | Feng, Runhai Balling, Niels Grana, Dario |
| Author_xml | – sequence: 1 givenname: Runhai surname: Feng fullname: Feng, Runhai email: r.feng@geo.au.dk organization: Department of Geoscience, Aarhus University, Høegh-Guldbergs Gade 2, 8000 Aarhus C, Denmark – sequence: 2 givenname: Niels surname: Balling fullname: Balling, Niels organization: Department of Geoscience, Aarhus University, Høegh-Guldbergs Gade 2, 8000 Aarhus C, Denmark – sequence: 3 givenname: Dario surname: Grana fullname: Grana, Dario organization: Department of Geology and Geophysics, University of Wyoming, 1000 E. University Ave., Laramie, USA |
| BookMark | eNqNkE1LJDEQhoO44Kj7HyKee0zSH-k-iYyfMLCX9RzSSUVrnEnGJA549o9vtHdB9jSngqLqqbeeY3LogwdCzjibc8a7i9X8CUJ-hrhBk-aCia9-3zYHZMZ7OVR1K7tDMmO1bKuuZe0ROU5pxRiTrWQz8rHE_BycNgiJmrVOCR0anTF4GhzV9B9er2mEBHEXMFL09Br8RscXqr2lmBOdEJWFLXgLPtNtiCFhfqeQMm4moothQxNgKmkLZAcxlfYp-eH0OsHPv_WEPN7e_F7cV8tfdw-Lq2WlaylyZQY-St61vHP1yHQjxm6U_dC48qLVnOux7qGB0dqmt7IVpue9qN0wDp22w2jqE3I-cbcxvL6VWGoV3qIvJ5UoZqToeibK1DBNmZI_RXBqG0v--K44U5_O1Up9c64-navJedm9_G_XYP56PUeN670Ii4kARcQOIapUvHoDFiOYrGzAPSh_AHTfq5s |
| CitedBy_id | crossref_primary_10_1016_j_chaos_2022_112007 crossref_primary_10_1016_j_ijggc_2022_103583 crossref_primary_10_1016_j_geothermics_2025_103477 crossref_primary_10_1016_j_energy_2024_133947 crossref_primary_10_1016_j_cageo_2021_104763 crossref_primary_10_1016_j_geothermics_2023_102719 crossref_primary_10_1016_j_seta_2023_103532 crossref_primary_10_1007_s12517_022_09780_2 crossref_primary_10_1016_j_petsci_2025_09_008 crossref_primary_10_1007_s12665_023_10749_x crossref_primary_10_1016_j_jngse_2022_104470 crossref_primary_10_1016_j_geothermics_2023_102651 crossref_primary_10_1016_j_petrol_2020_107995 crossref_primary_10_1007_s11004_025_10198_1 crossref_primary_10_1016_j_gete_2021_100282 crossref_primary_10_1002_gj_4604 crossref_primary_10_1038_s41598_022_21444_5 crossref_primary_10_1080_10916466_2021_1983600 crossref_primary_10_1016_j_energy_2024_130320 crossref_primary_10_1016_j_petsci_2022_03_001 crossref_primary_10_1016_j_acags_2025_100273 crossref_primary_10_1016_j_petrol_2021_108816 crossref_primary_10_1190_geo2020_0609_1 crossref_primary_10_1190_geo2022_0363_1 crossref_primary_10_1016_j_geothermics_2023_102702 crossref_primary_10_1016_j_geothermics_2022_102406 crossref_primary_10_1016_j_geothermics_2022_102401 crossref_primary_10_1109_TGRS_2020_3049012 crossref_primary_10_1190_geo2020_0424_1 crossref_primary_10_1016_j_geothermics_2020_101974 crossref_primary_10_1007_s12145_023_01079_4 crossref_primary_10_1016_j_gsf_2021_101311 |
| Cites_doi | 10.1016/j.geothermics.2019.02.006 10.34194/geusb.v20.4890 10.1016/j.geothermics.2013.09.007 10.1109/TGRS.2018.2841059 10.1016/0005-2795(75)90109-9 10.1190/1.3587220 10.1190/1.6241045.1 10.1130/0016-7606(1973)84<979:JWLOTC>2.0.CO;2 10.1007/s11004-015-9604-z 10.1190/1.1444923 10.1016/j.marpetgeo.2018.03.004 10.1016/j.geothermics.2019.06.008 10.1016/j.geothermics.2009.11.003 10.1023/A:1011044812133 10.1190/geo2019-0405.1 10.1190/geo2014-0233.1 10.34194/geusb.v4.4771 10.1190/1.3483770 10.1016/j.geothermics.2019.101722 10.1190/1.1438908 10.1109/5.18626 10.34194/geusb.v35.4633 10.1016/j.geothermics.2019.07.005 10.1190/geo2017-0670.1 10.1109/TIT.1967.1054010 10.1007/s11004-016-9671-9 10.34194/geusb.v1.4681 10.1007/s10044-019-00807-1 10.1016/j.geothermics.2015.06.001 10.1016/j.geothermics.2019.04.003 10.2118/10011-PA 10.1190/geo2014-0502.1 10.1190/1.1487078 10.1016/j.geothermics.2016.09.003 10.1190/1.1635051 10.34194/geusb.v38.4393 10.1190/1.1468618 10.1190/INT-2019-0184.1 |
| ContentType | Journal Article |
| Copyright | 2020 Elsevier Ltd Copyright Elsevier Science Ltd. Sep 2020 |
| Copyright_xml | – notice: 2020 Elsevier Ltd – notice: Copyright Elsevier Science Ltd. Sep 2020 |
| DBID | AAYXX CITATION 7ST 8FD C1K FR3 KR7 SOI |
| DOI | 10.1016/j.geothermics.2020.101854 |
| DatabaseName | CrossRef Environment Abstracts Technology Research Database Environmental Sciences and Pollution Management Engineering Research Database Civil Engineering Abstracts Environment Abstracts |
| DatabaseTitle | CrossRef Civil Engineering Abstracts Engineering Research Database Technology Research Database Environment Abstracts Environmental Sciences and Pollution Management |
| DatabaseTitleList | Civil Engineering Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geography |
| EISSN | 1879-3576 |
| ExternalDocumentID | 10_1016_j_geothermics_2020_101854 S0375650519304961 |
| GeographicLocations | Denmark |
| GeographicLocations_xml | – name: Denmark |
| GroupedDBID | --K --M -~X .~1 0R~ 1B1 1RT 1~. 1~5 29H 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAHCO AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARJD AAXUO ABMAC ABQEM ABQYD ABXDB ABYKQ ACDAQ ACGFS ACIWK ACLVX ACRLP ACSBN ADBBV ADEZE ADMUD AEBSH AEKER AENEX AFKWA AFRAH AFTJW AGHFR AGUBO AGYEJ AHHHB AHIDL AI. AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG ATOGT AVWKF AXJTR AZFZN BELTK BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HMA HVGLF HZ~ IHE IMUCA J1W JARJE KOM LY3 LY6 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SAC SDF SDG SEP SES SET SEW SPC SPCBC SSE SSR SSZ T5K TN5 UHS VH1 WUQ XPP ZMT ~02 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7ST 8FD AGCQF C1K FR3 KR7 SOI |
| ID | FETCH-LOGICAL-a372t-c91b716516f3b0a42b6b7894f357da11ab38e4ebdd48d752c81823f9b96ad9bc3 |
| ISICitedReferencesCount | 35 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000551469800002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0375-6505 |
| IngestDate | Wed Aug 13 06:50:10 EDT 2025 Sat Nov 29 07:20:30 EST 2025 Tue Nov 18 20:49:28 EST 2025 Fri Feb 23 02:47:25 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Geothermal reservoir characterization Markov priors Lithofacies classification Artificial Neural Networks Porosity prediction Seismic inversion results |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-a372t-c91b716516f3b0a42b6b7894f357da11ab38e4ebdd48d752c81823f9b96ad9bc3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2505726802 |
| PQPubID | 2047465 |
| ParticipantIDs | proquest_journals_2505726802 crossref_primary_10_1016_j_geothermics_2020_101854 crossref_citationtrail_10_1016_j_geothermics_2020_101854 elsevier_sciencedirect_doi_10_1016_j_geothermics_2020_101854 |
| PublicationCentury | 2000 |
| PublicationDate | September 2020 2020-09-00 20200901 |
| PublicationDateYYYYMMDD | 2020-09-01 |
| PublicationDate_xml | – month: 09 year: 2020 text: September 2020 |
| PublicationDecade | 2020 |
| PublicationPlace | Oxford |
| PublicationPlace_xml | – name: Oxford |
| PublicationTitle | Geothermics |
| PublicationYear | 2020 |
| Publisher | Elsevier Ltd Elsevier Science Ltd |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier Science Ltd |
| References | Elfeki, Dekking (bib0060) 2001; 33 Grana (bib0095) 2018; 83 Nielsen, Mathiesen, Bidstrup (bib0180) 2004; 4 Edgar, van der Baan (bib0050) 2011; 76 Mallick, Adhikari (bib0150) 2015; 80 Kumar, Sugianto, Li, Patel, Land (bib0125) 2014 Fang, Yang (bib0065) 2015; 80 Lindberg, Grana (bib0130) 2015; 47 Scales, Snieder (bib0205) 1997; 62 Balling, Hvid, Mahler, Møller, Mathiesen, Bidstrup, Nielsen (bib0025) 2002 Eidesgaard, Schovsbo, Boldreel, Ólavsdóttir (bib0055) 2019; 82 Rosenkjaer, Gasperikova, Newman, Arnason, Lindsey (bib0195) 2015; 57 Bredesen, Dalgaard, Mathiesen, Rasmussen, Balling (bib0035) 2020; 8 Vosgerau, Mathiesen, Andersen, Boldreel, Leth Hjuler, Kamla, Kristensen, Brogaard Pedersen, Pjetursson, Nielsen (bib0220) 2016; 35 Calvo-Zaragoza, Toselli, Vidal (bib0040) 2019; 22 Vosgerau, Gregersen, Kristensen (bib0225) 2017; 38 Feng, Luthi, Gisolf, Angerer (bib0080) 2018; 56 Goodfellow, Bengio, Courville (bib0090) 2016 Kristensen, Hjuler, Frykman, Olivarius, Weibel, Nielsen, Mathiesen (bib0120) 2016; 4 Krawczyk, Stiller, Bauer, Norden, Henninges, Ivanova, Huenges (bib0115) 2019; 7 Mathiesen, Nielsen, Bidstrup (bib0155) 2010; 20 Ars, Tarits, Hautot, Bellanger, Coutant, Maia (bib0010) 2019; 80 Maithya, Fujimitsu (bib0145) 2019; 81 Feng (bib0070) 2020 Mukerji, Jørstad, Avseth, Mavko, Granli (bib0170) 2001; 66 Viterbi (bib0215) 1967; 13 Avseth, Mukerji, Mavko (bib0015) 2005 Avseth, Mukerji, Mavko, Dvorkin (bib0020) 2010; 75 Weibel, Olivarius, Kristensen, Friis, Hjuler, Kjøller, Mathiesen, Nielsen (bib0230) 2017; 65 Grana, Azevedo, Liu (bib0105) 2020 Fuchs, Balling, Mathiesen (bib0085) 2020; 83 Keelan (bib0110) 1982; 34 Matthews (bib0160) 1975; 405 Feng, Luthi, Gisolf, Angerer (bib0075) 2018; 93 Aki, Richards (bib0005) 2002 Rabiner (bib0185) 1989; 77 Zwaan, Longa (bib0235) 2019; 82 Lindsay, Koughnet (bib0135) 2001; 20 Lüschen, Wolfgramm, Fritzer, Dussel, Thomas, Schulz (bib0140) 2014; 50 Grana, Fjeldstad, Omre (bib0100) 2017; 49 Casini, Ciuffi, Fiordelisi, Mazzotti, Stucchi (bib0045) 2010; 39 Saggaf, Toksöz, Mustafa (bib0200) 2003; 68 Nielsen (bib0175) 2003; 1 Ulrych, Sacchi, Woodbury (bib0210) 2001; 66 Middleton (bib0165) 1973; 84 Røgen, Ditlefsen, Vangkilde-Pedersen, Nielsen, Mahler (bib0190) 2015 Bosch, Zamora, Utama (bib0030) 2002; 67 Calvo-Zaragoza (10.1016/j.geothermics.2020.101854_bib0040) 2019; 22 Krawczyk (10.1016/j.geothermics.2020.101854_bib0115) 2019; 7 Edgar (10.1016/j.geothermics.2020.101854_bib0050) 2011; 76 Feng (10.1016/j.geothermics.2020.101854_bib0080) 2018; 56 Balling (10.1016/j.geothermics.2020.101854_bib0025) 2002 Kristensen (10.1016/j.geothermics.2020.101854_bib0120) 2016; 4 Vosgerau (10.1016/j.geothermics.2020.101854_bib0225) 2017; 38 Saggaf (10.1016/j.geothermics.2020.101854_bib0200) 2003; 68 Røgen (10.1016/j.geothermics.2020.101854_bib0190) 2015 Maithya (10.1016/j.geothermics.2020.101854_bib0145) 2019; 81 Feng (10.1016/j.geothermics.2020.101854_bib0070) 2020 Nielsen (10.1016/j.geothermics.2020.101854_bib0175) 2003; 1 Lindsay (10.1016/j.geothermics.2020.101854_bib0135) 2001; 20 Mukerji (10.1016/j.geothermics.2020.101854_bib0170) 2001; 66 Rosenkjaer (10.1016/j.geothermics.2020.101854_bib0195) 2015; 57 Avseth (10.1016/j.geothermics.2020.101854_bib0015) 2005 Casini (10.1016/j.geothermics.2020.101854_bib0045) 2010; 39 Fang (10.1016/j.geothermics.2020.101854_bib0065) 2015; 80 Grana (10.1016/j.geothermics.2020.101854_bib0095) 2018; 83 Rabiner (10.1016/j.geothermics.2020.101854_bib0185) 1989; 77 Fuchs (10.1016/j.geothermics.2020.101854_bib0085) 2020; 83 Grana (10.1016/j.geothermics.2020.101854_bib0105) 2020 Vosgerau (10.1016/j.geothermics.2020.101854_bib0220) 2016; 35 Lüschen (10.1016/j.geothermics.2020.101854_bib0140) 2014; 50 Weibel (10.1016/j.geothermics.2020.101854_bib0230) 2017; 65 Lindberg (10.1016/j.geothermics.2020.101854_bib0130) 2015; 47 Ulrych (10.1016/j.geothermics.2020.101854_bib0210) 2001; 66 Feng (10.1016/j.geothermics.2020.101854_bib0075) 2018; 93 Kumar (10.1016/j.geothermics.2020.101854_bib0125) 2014 Grana (10.1016/j.geothermics.2020.101854_bib0100) 2017; 49 Matthews (10.1016/j.geothermics.2020.101854_bib0160) 1975; 405 Nielsen (10.1016/j.geothermics.2020.101854_bib0180) 2004; 4 Middleton (10.1016/j.geothermics.2020.101854_bib0165) 1973; 84 Goodfellow (10.1016/j.geothermics.2020.101854_bib0090) 2016 Avseth (10.1016/j.geothermics.2020.101854_bib0020) 2010; 75 Keelan (10.1016/j.geothermics.2020.101854_bib0110) 1982; 34 Viterbi (10.1016/j.geothermics.2020.101854_bib0215) 1967; 13 Scales (10.1016/j.geothermics.2020.101854_bib0205) 1997; 62 Mallick (10.1016/j.geothermics.2020.101854_bib0150) 2015; 80 Ars (10.1016/j.geothermics.2020.101854_bib0010) 2019; 80 Bosch (10.1016/j.geothermics.2020.101854_bib0030) 2002; 67 Bredesen (10.1016/j.geothermics.2020.101854_bib0035) 2020; 8 Elfeki (10.1016/j.geothermics.2020.101854_bib0060) 2001; 33 Eidesgaard (10.1016/j.geothermics.2020.101854_bib0055) 2019; 82 Aki (10.1016/j.geothermics.2020.101854_bib0005) 2002 Zwaan (10.1016/j.geothermics.2020.101854_bib0235) 2019; 82 Mathiesen (10.1016/j.geothermics.2020.101854_bib0155) 2010; 20 |
| References_xml | – volume: 84 start-page: 979 year: 1973 end-page: 988 ident: bib0165 article-title: Johannes Walther’s law of the correlation of facies publication-title: GSA Bull. – volume: 66 start-page: 55 year: 2001 end-page: 69 ident: bib0210 article-title: A Bayes tour of inversion: a tutorial publication-title: Geophysics – volume: 34 start-page: 2483 year: 1982 end-page: 2491 ident: bib0110 article-title: Core analysis for aid in reservoir description publication-title: J. Pet. Technol. – start-page: 15 year: 2002 end-page: 16 ident: bib0025 article-title: Denmark publication-title: Atlas of Geothermal Resources in Europe. Publication No. EUR 17811 – volume: 20 start-page: 188 year: 2001 end-page: 191 ident: bib0135 article-title: Sequential Backus Averaging: upscaling well logs to seismic wavelengths publication-title: Lead. Edge – volume: 4 start-page: 2 year: 2016 end-page: 27 ident: bib0120 article-title: Pre-drilling assessments of average porosity and permeability in the geothermal reservoirs of the Danish area publication-title: Geotherm. Energy – year: 2005 ident: bib0015 article-title: Quantitative Seismic Interpretation: Applying Rock Physics Tools to Reduce Interpretation Risk – volume: 77 start-page: 257 year: 1989 end-page: 286 ident: bib0185 article-title: A tutorial on hidden Markov models and selected applications in speech recognition publication-title: Proc. IEEE – volume: 57 start-page: 258 year: 2015 end-page: 274 ident: bib0195 article-title: Comparison of 3D MT inversions for geothermal exploration: casestudies for Krafla and Hengill geothermal systems in Iceland publication-title: Geothermics – year: 2014 ident: bib0125 article-title: Using relative seismic impedance to predict porosity in the Eagle Ford shale publication-title: SEG Annual Meeting – volume: 80 start-page: 45 year: 2015 end-page: 59 ident: bib0150 article-title: Amplitude-variation-with-offset and prestackwaveform inversion: a direct comparison using a real data example from the Rock Springs Uplift, Wyoming, USA publication-title: Geophysics – volume: 75 start-page: A31 year: 2010 end-page: A47 ident: bib0020 article-title: Rock-physics diagnostics of depositional texture, diagenetic alterations, and reservoir heterogeneity in high-porosity siliciclastic sediments and rocks — a review of selected models and suggested work flows publication-title: Geophysics – volume: 76 start-page: V59 year: 2011 end-page: V68 ident: bib0050 article-title: How reliable is statistical wavelet estimation? publication-title: Geophysics – volume: 82 start-page: 203 year: 2019 end-page: 211 ident: bib0235 article-title: Integrated assessment projections for global geothermal energy use publication-title: Geothermics – volume: 67 start-page: P573 year: 2002 end-page: P581 ident: bib0030 article-title: Lithology discrimination from physical rock properties publication-title: Geophysics – volume: 22 start-page: 1573 year: 2019 end-page: 1584 ident: bib0040 article-title: Hybrid hidden Markov models and artificial neural networks for handwritten music recognition in mensural notation publication-title: Pattern Anal. Appl. – year: 2015 ident: bib0190 article-title: Geothermal energy use, 2015 country update for Denmark publication-title: Proceedings, World Geothermal Congress 2015 – year: 2020 ident: bib0070 article-title: A Bayesian approach in machine learning for lithofacies classification and its uncertainty analysis publication-title: IEEE Geosci. Remote Sens. Lett. – year: 2002 ident: bib0005 article-title: Quantitative Seismology – volume: 83 start-page: M15 year: 2018 end-page: M24 ident: bib0095 article-title: Joint facies and reservoir properties inversion publication-title: Geophysics – year: 2016 ident: bib0090 article-title: Deep Learning – volume: 80 start-page: 56 year: 2019 end-page: 68 ident: bib0010 article-title: Joint inversion of gravity and surface wave data constrained by magnetotelluric: application to deep geothermal exploration of crustal fault zone in felsic basement publication-title: Geothermics – volume: 82 start-page: 296 year: 2019 end-page: 314 ident: bib0055 article-title: Shallow geothermal energy system in fractured basalt: a case study from Kollafjørður, Faroe Islands, NE-Atlantic Ocean publication-title: Geothermics – volume: 68 start-page: 1969 year: 2003 end-page: 1983 ident: bib0200 article-title: Estimation of reservoir properties from seismic data by smooth neural networks publication-title: Geophysics – volume: 62 start-page: 1045 year: 1997 end-page: 1046 ident: bib0205 article-title: To Bayes or not Bayes? publication-title: Geophysics – volume: 83 start-page: 1 year: 2020 end-page: 18 ident: bib0085 article-title: Deep basin temperature and heat-flow field in Denmark – new insights from borehole analysis and 3D geothermal modelling publication-title: Geothermics – volume: 50 start-page: 167 year: 2014 end-page: 179 ident: bib0140 article-title: 3D seismic survey explores geothermal targets for reservoir characterization at Unterhaching, Munich, Germany publication-title: Geothermics – volume: 47 start-page: 719 year: 2015 end-page: 752 ident: bib0130 article-title: Petro-elastic log-facies classification using the expectation-maximization algorithm and hidden Markov models publication-title: Math. Geosci. – volume: 20 start-page: 19 year: 2010 end-page: 22 ident: bib0155 article-title: Identifying potential geothermal reservoirs in Denmark publication-title: Geol. Survey Denmark Greenl. Bull. – volume: 81 start-page: 12 year: 2019 end-page: 31 ident: bib0145 article-title: Analysis and interpretation of magnetotelluric data in characterization ofgeothermal resource in Eburru geothermalfield, Kenya publication-title: Geothermics – volume: 35 start-page: 23 year: 2016 end-page: 26 ident: bib0220 article-title: A WebGIS portal for exploration of deep geothermal energy based on geological and geophysical data publication-title: Geol. Survey Denmark Greenl. Bull. – volume: 4 start-page: 17 year: 2004 end-page: 20 ident: bib0180 article-title: Geothermal energy in Denmark publication-title: Geol. Survey Denmark Greenl. Bull. – volume: 80 start-page: R265 year: 2015 end-page: R280 ident: bib0065 article-title: Inversion of reservoir porosity, saturation, and permeability based on a robust hybrid genetic algorithm publication-title: Geophysics – volume: 7 start-page: 1 year: 2019 end-page: 18 ident: bib0115 article-title: 3-D seismic exploration across the deep geothermal research platform Groß Schönebeck north of Berlin/Germany publication-title: Geotherm. Energy – volume: 66 start-page: 988 year: 2001 end-page: 1001 ident: bib0170 article-title: Mapping lithofacies and pore-fluid probabilities in a North Sea reservoir: seismic inversions and statistical rock physics publication-title: Geophysics – volume: 1 start-page: 459 year: 2003 end-page: 526 ident: bib0175 article-title: Late Triassic – Jurassic development of the Danish basin and the Fennoscandian border zone, southern Scandinavia publication-title: Geol. Survey Denmark Greenl. Bull. – volume: 8 start-page: T275 year: 2020 end-page: T291 ident: bib0035 article-title: Seismic characterization of geothermal sedimentary reservoirs: a field example from the Copenhagen area, Denmark publication-title: Interpretation – volume: 49 start-page: 493 year: 2017 end-page: 515 ident: bib0100 article-title: Bayesian Gaussian mixture linear inversion for geophysical inverse problems publication-title: Math. Geosci. – volume: 38 start-page: 29 year: 2017 end-page: 32 ident: bib0225 article-title: Towards a geothermal exploration well in the Gassum Formation in Copenhagen publication-title: Geol. Survey Denmark Greenl. Bull. – volume: 405 start-page: 442 year: 1975 end-page: 451 ident: bib0160 article-title: Comparison of the predicted and observed secondary structure of T4 phage lysozyme publication-title: Biochim. Biophys. Acta (BBA) - Protein Struct. – volume: 93 start-page: 218 year: 2018 end-page: 229 ident: bib0075 article-title: Reservoir lithology classification based on seismic inversion results by Hidden Markov Models: applying prior geological information publication-title: Mar. Pet. Geol. – year: 2020 ident: bib0105 article-title: A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data publication-title: Geophysics – volume: 65 start-page: 135 year: 2017 end-page: 157 ident: bib0230 article-title: Predicting permeability of low enthalpy geothermal reservoirs: a case study from the Upper Triassic−Lower Jurassic Gassum Formation, Norwegian–Danish Basin publication-title: Geothermics – volume: 56 start-page: 6663 year: 2018 end-page: 6673 ident: bib0080 article-title: Reservoir lithology determination by hidden Markov random fields based on a Gaussian mixture model publication-title: IEEE Trans. Geosci. Remot. Sens. – volume: 39 start-page: 4 year: 2010 end-page: 12 ident: bib0045 article-title: Results of a 3D seismic survey at the Travale (Italy) test site publication-title: Geothermics – volume: 13 start-page: 260 year: 1967 end-page: 269 ident: bib0215 article-title: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm publication-title: IEEE Trans. Inf. Theory – volume: 33 start-page: 569 year: 2001 end-page: 589 ident: bib0060 article-title: A markov chain model for subsurface characterization: theory and applications publication-title: Math. Geol. – volume: 80 start-page: 56 year: 2019 ident: 10.1016/j.geothermics.2020.101854_bib0010 article-title: Joint inversion of gravity and surface wave data constrained by magnetotelluric: application to deep geothermal exploration of crustal fault zone in felsic basement publication-title: Geothermics doi: 10.1016/j.geothermics.2019.02.006 – volume: 20 start-page: 19 year: 2010 ident: 10.1016/j.geothermics.2020.101854_bib0155 article-title: Identifying potential geothermal reservoirs in Denmark publication-title: Geol. Survey Denmark Greenl. Bull. doi: 10.34194/geusb.v20.4890 – volume: 50 start-page: 167 year: 2014 ident: 10.1016/j.geothermics.2020.101854_bib0140 article-title: 3D seismic survey explores geothermal targets for reservoir characterization at Unterhaching, Munich, Germany publication-title: Geothermics doi: 10.1016/j.geothermics.2013.09.007 – volume: 56 start-page: 6663 issue: 11 year: 2018 ident: 10.1016/j.geothermics.2020.101854_bib0080 article-title: Reservoir lithology determination by hidden Markov random fields based on a Gaussian mixture model publication-title: IEEE Trans. Geosci. Remot. Sens. doi: 10.1109/TGRS.2018.2841059 – volume: 405 start-page: 442 issue: 2 year: 1975 ident: 10.1016/j.geothermics.2020.101854_bib0160 article-title: Comparison of the predicted and observed secondary structure of T4 phage lysozyme publication-title: Biochim. Biophys. Acta (BBA) - Protein Struct. doi: 10.1016/0005-2795(75)90109-9 – volume: 76 start-page: V59 issue: 4 year: 2011 ident: 10.1016/j.geothermics.2020.101854_bib0050 article-title: How reliable is statistical wavelet estimation? publication-title: Geophysics doi: 10.1190/1.3587220 – volume: 62 start-page: 1045 issue: 4 year: 1997 ident: 10.1016/j.geothermics.2020.101854_bib0205 article-title: To Bayes or not Bayes? publication-title: Geophysics doi: 10.1190/1.6241045.1 – volume: 84 start-page: 979 issue: 3 year: 1973 ident: 10.1016/j.geothermics.2020.101854_bib0165 article-title: Johannes Walther’s law of the correlation of facies publication-title: GSA Bull. doi: 10.1130/0016-7606(1973)84<979:JWLOTC>2.0.CO;2 – volume: 47 start-page: 719 issue: 6 year: 2015 ident: 10.1016/j.geothermics.2020.101854_bib0130 article-title: Petro-elastic log-facies classification using the expectation-maximization algorithm and hidden Markov models publication-title: Math. Geosci. doi: 10.1007/s11004-015-9604-z – volume: 66 start-page: 55 issue: 1 year: 2001 ident: 10.1016/j.geothermics.2020.101854_bib0210 article-title: A Bayes tour of inversion: a tutorial publication-title: Geophysics doi: 10.1190/1.1444923 – volume: 93 start-page: 218 year: 2018 ident: 10.1016/j.geothermics.2020.101854_bib0075 article-title: Reservoir lithology classification based on seismic inversion results by Hidden Markov Models: applying prior geological information publication-title: Mar. Pet. Geol. doi: 10.1016/j.marpetgeo.2018.03.004 – start-page: 15 year: 2002 ident: 10.1016/j.geothermics.2020.101854_bib0025 article-title: Denmark – volume: 82 start-page: 203 year: 2019 ident: 10.1016/j.geothermics.2020.101854_bib0235 article-title: Integrated assessment projections for global geothermal energy use publication-title: Geothermics doi: 10.1016/j.geothermics.2019.06.008 – volume: 39 start-page: 4 issue: 1 year: 2010 ident: 10.1016/j.geothermics.2020.101854_bib0045 article-title: Results of a 3D seismic survey at the Travale (Italy) test site publication-title: Geothermics doi: 10.1016/j.geothermics.2009.11.003 – volume: 33 start-page: 569 issue: 5 year: 2001 ident: 10.1016/j.geothermics.2020.101854_bib0060 article-title: A markov chain model for subsurface characterization: theory and applications publication-title: Math. Geol. doi: 10.1023/A:1011044812133 – year: 2020 ident: 10.1016/j.geothermics.2020.101854_bib0105 article-title: A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data publication-title: Geophysics doi: 10.1190/geo2019-0405.1 – volume: 7 start-page: 1 issue: 15 year: 2019 ident: 10.1016/j.geothermics.2020.101854_bib0115 article-title: 3-D seismic exploration across the deep geothermal research platform Groß Schönebeck north of Berlin/Germany publication-title: Geotherm. Energy – volume: 80 start-page: 45 issue: 2 year: 2015 ident: 10.1016/j.geothermics.2020.101854_bib0150 article-title: Amplitude-variation-with-offset and prestackwaveform inversion: a direct comparison using a real data example from the Rock Springs Uplift, Wyoming, USA publication-title: Geophysics doi: 10.1190/geo2014-0233.1 – volume: 4 start-page: 17 year: 2004 ident: 10.1016/j.geothermics.2020.101854_bib0180 article-title: Geothermal energy in Denmark publication-title: Geol. Survey Denmark Greenl. Bull. doi: 10.34194/geusb.v4.4771 – volume: 75 start-page: A31 issue: 5 year: 2010 ident: 10.1016/j.geothermics.2020.101854_bib0020 article-title: Rock-physics diagnostics of depositional texture, diagenetic alterations, and reservoir heterogeneity in high-porosity siliciclastic sediments and rocks — a review of selected models and suggested work flows publication-title: Geophysics doi: 10.1190/1.3483770 – year: 2015 ident: 10.1016/j.geothermics.2020.101854_bib0190 article-title: Geothermal energy use, 2015 country update for Denmark – volume: 83 start-page: 1 year: 2020 ident: 10.1016/j.geothermics.2020.101854_bib0085 article-title: Deep basin temperature and heat-flow field in Denmark – new insights from borehole analysis and 3D geothermal modelling publication-title: Geothermics doi: 10.1016/j.geothermics.2019.101722 – volume: 20 start-page: 188 issue: 2 year: 2001 ident: 10.1016/j.geothermics.2020.101854_bib0135 article-title: Sequential Backus Averaging: upscaling well logs to seismic wavelengths publication-title: Lead. Edge doi: 10.1190/1.1438908 – volume: 77 start-page: 257 issue: 2 year: 1989 ident: 10.1016/j.geothermics.2020.101854_bib0185 article-title: A tutorial on hidden Markov models and selected applications in speech recognition publication-title: Proc. IEEE doi: 10.1109/5.18626 – volume: 35 start-page: 23 year: 2016 ident: 10.1016/j.geothermics.2020.101854_bib0220 article-title: A WebGIS portal for exploration of deep geothermal energy based on geological and geophysical data publication-title: Geol. Survey Denmark Greenl. Bull. doi: 10.34194/geusb.v35.4633 – volume: 82 start-page: 296 year: 2019 ident: 10.1016/j.geothermics.2020.101854_bib0055 article-title: Shallow geothermal energy system in fractured basalt: a case study from Kollafjørður, Faroe Islands, NE-Atlantic Ocean publication-title: Geothermics doi: 10.1016/j.geothermics.2019.07.005 – year: 2016 ident: 10.1016/j.geothermics.2020.101854_bib0090 – volume: 83 start-page: M15 issue: 3 year: 2018 ident: 10.1016/j.geothermics.2020.101854_bib0095 article-title: Joint facies and reservoir properties inversion publication-title: Geophysics doi: 10.1190/geo2017-0670.1 – year: 2002 ident: 10.1016/j.geothermics.2020.101854_bib0005 – volume: 13 start-page: 260 issue: 2 year: 1967 ident: 10.1016/j.geothermics.2020.101854_bib0215 article-title: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.1967.1054010 – volume: 49 start-page: 493 issue: 4 year: 2017 ident: 10.1016/j.geothermics.2020.101854_bib0100 article-title: Bayesian Gaussian mixture linear inversion for geophysical inverse problems publication-title: Math. Geosci. doi: 10.1007/s11004-016-9671-9 – volume: 1 start-page: 459 year: 2003 ident: 10.1016/j.geothermics.2020.101854_bib0175 article-title: Late Triassic – Jurassic development of the Danish basin and the Fennoscandian border zone, southern Scandinavia publication-title: Geol. Survey Denmark Greenl. Bull. doi: 10.34194/geusb.v1.4681 – volume: 22 start-page: 1573 year: 2019 ident: 10.1016/j.geothermics.2020.101854_bib0040 article-title: Hybrid hidden Markov models and artificial neural networks for handwritten music recognition in mensural notation publication-title: Pattern Anal. Appl. doi: 10.1007/s10044-019-00807-1 – volume: 57 start-page: 258 year: 2015 ident: 10.1016/j.geothermics.2020.101854_bib0195 article-title: Comparison of 3D MT inversions for geothermal exploration: casestudies for Krafla and Hengill geothermal systems in Iceland publication-title: Geothermics doi: 10.1016/j.geothermics.2015.06.001 – volume: 4 start-page: 2 issue: 6 year: 2016 ident: 10.1016/j.geothermics.2020.101854_bib0120 article-title: Pre-drilling assessments of average porosity and permeability in the geothermal reservoirs of the Danish area publication-title: Geotherm. Energy – year: 2014 ident: 10.1016/j.geothermics.2020.101854_bib0125 article-title: Using relative seismic impedance to predict porosity in the Eagle Ford shale publication-title: SEG Annual Meeting – volume: 81 start-page: 12 year: 2019 ident: 10.1016/j.geothermics.2020.101854_bib0145 article-title: Analysis and interpretation of magnetotelluric data in characterization ofgeothermal resource in Eburru geothermalfield, Kenya publication-title: Geothermics doi: 10.1016/j.geothermics.2019.04.003 – volume: 34 start-page: 2483 issue: 11 year: 1982 ident: 10.1016/j.geothermics.2020.101854_bib0110 article-title: Core analysis for aid in reservoir description publication-title: J. Pet. Technol. doi: 10.2118/10011-PA – volume: 80 start-page: R265 issue: 5 year: 2015 ident: 10.1016/j.geothermics.2020.101854_bib0065 article-title: Inversion of reservoir porosity, saturation, and permeability based on a robust hybrid genetic algorithm publication-title: Geophysics doi: 10.1190/geo2014-0502.1 – volume: 66 start-page: 988 issue: 4 year: 2001 ident: 10.1016/j.geothermics.2020.101854_bib0170 article-title: Mapping lithofacies and pore-fluid probabilities in a North Sea reservoir: seismic inversions and statistical rock physics publication-title: Geophysics doi: 10.1190/1.1487078 – volume: 65 start-page: 135 year: 2017 ident: 10.1016/j.geothermics.2020.101854_bib0230 article-title: Predicting permeability of low enthalpy geothermal reservoirs: a case study from the Upper Triassic−Lower Jurassic Gassum Formation, Norwegian–Danish Basin publication-title: Geothermics doi: 10.1016/j.geothermics.2016.09.003 – volume: 68 start-page: 1969 issue: 6 year: 2003 ident: 10.1016/j.geothermics.2020.101854_bib0200 article-title: Estimation of reservoir properties from seismic data by smooth neural networks publication-title: Geophysics doi: 10.1190/1.1635051 – year: 2020 ident: 10.1016/j.geothermics.2020.101854_bib0070 article-title: A Bayesian approach in machine learning for lithofacies classification and its uncertainty analysis publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 38 start-page: 29 year: 2017 ident: 10.1016/j.geothermics.2020.101854_bib0225 article-title: Towards a geothermal exploration well in the Gassum Formation in Copenhagen publication-title: Geol. Survey Denmark Greenl. Bull. doi: 10.34194/geusb.v38.4393 – year: 2005 ident: 10.1016/j.geothermics.2020.101854_bib0015 – volume: 67 start-page: P573 issue: 2 year: 2002 ident: 10.1016/j.geothermics.2020.101854_bib0030 article-title: Lithology discrimination from physical rock properties publication-title: Geophysics doi: 10.1190/1.1468618 – volume: 8 start-page: T275 issue: 2 year: 2020 ident: 10.1016/j.geothermics.2020.101854_bib0035 article-title: Seismic characterization of geothermal sedimentary reservoirs: a field example from the Copenhagen area, Denmark publication-title: Interpretation doi: 10.1190/INT-2019-0184.1 |
| SSID | ssj0007570 |
| Score | 2.4266691 |
| Snippet | •A novel system combining ANN and HMM is proposed to classify lithofacies of a potential geothermal reservoir in Denmark.•Depositional rules and complex data... Characterization of geothermal reservoirs is an important step for exploration and development of geothermal energy, which is reliable and sustainable for the... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 101854 |
| SubjectTerms | Algorithms Artificial Neural Networks Classification Conditional probability Geothermal energy Geothermal power Geothermal reservoir characterization Lithofacies classification Markov chains Markov priors Neural networks Porosity Porosity prediction Properties (attributes) Reservoirs Rock properties Rocks Seismic inversion results Seismic surveys Spatial distribution Statistical analysis Workflow |
| Title | Lithofacies classification of a geothermal reservoir in Denmark and its facies-dependent porosity estimation from seismic inversion |
| URI | https://dx.doi.org/10.1016/j.geothermics.2020.101854 https://www.proquest.com/docview/2505726802 |
| Volume | 87 |
| WOSCitedRecordID | wos000551469800002&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: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-3576 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0007570 issn: 0375-6505 databaseCode: AIEXJ dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1ba9swFBYhHdteyq60azc02Ftw8V0S7KVs3Y1RxtZB3oxuad01dknckPf98R1Zku1lFDLGXpxgIyvx9-nonONzQehVkmYKrK48EFIy47qJApEqHsDaknmsQ65l2DabIKendDplX0ajtc-FWV2RqqLrNbv-r1DDOQDbpM7-BdzdTeEEfAfQ4Qiww3Er4D-XzUU947BmlxNpdGMTDNQphnxyrtukq3lb0d_4ZOvShKSD5KnmfPGje5tgbxH4LrnNBDT1uo3gMIU55i5I0WSnLHW5NCH2ZbWy3rehxvveTTeIqofH1QqYrzfVBS97X2pbHtzS0xZ59rncNm_tLZj19dBLASapD8NyrjOfPtPHKrUpWyQLQEW0r7W1lcCUsCDJbFMYL6LtnvyHtLeOh8uj8_6fHJnJzRVqa1NvFNP-ZqY0M4LeCraRsZt3YpIxOkY7xx9Ppp-6XZxkbaPB7ifeRS_72MBbJrxNt9nY5VvV5ewB2nU2Bz62XHmIRrp6hO4BMLZa-WP0c8AZ_DtncD3DHPecwR1ncFlhxxkMnMHAGbzJGew5g3vOYMMZ7DiDO848Qd_fnZy9-RC47hwBT0jcBJJFAoztLMpniQh5GotcEMrSGYCneBRxkVCdaqFUShXJYgmqYZzMmGA5V0zI5CkaV3Wl9xAmIjS97yTseBoMdiUUfJJUCskoJ0ruI-qfayFd6XrTQeWq8DGKl8UAksJAUlhI9lHcDb229Vu2GfTag1c4RdQqmAUwb5vhhx7wwgkGuG48AXFOw_jZv939AN3vF9ghGjeLG_0c3ZGrplwuXjga_wIZmcXd |
| 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=Lithofacies+classification+of+a+geothermal+reservoir+in+Denmark+and+its+facies-dependent+porosity+estimation+from+seismic+inversion&rft.jtitle=Geothermics&rft.au=Feng%2C+Runhai&rft.au=Balling%2C+Niels&rft.au=Grana%2C+Dario&rft.date=2020-09-01&rft.pub=Elsevier+Ltd&rft.issn=0375-6505&rft.eissn=1879-3576&rft.volume=87&rft_id=info:doi/10.1016%2Fj.geothermics.2020.101854&rft.externalDocID=S0375650519304961 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0375-6505&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0375-6505&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0375-6505&client=summon |