Hybrid wavelet transform with artificial neural network for forecasting of shear wave velocity from wireline log data: a case study
Shear wave velocity ( V s ) is an important variable for performing geomechanical and geophysical modeling and reservoir studies. Field tests to measure this variable directly are high costs and time consuming. Due to the operational difficulties mentioned above, it is more convenient estimating V s...
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| Veröffentlicht in: | Environmental earth sciences 2021, Vol.80 (1), Article 5 |
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| creator | Fattahi, Hadi Zandy Ilghani, Nastaran |
| description | Shear wave velocity (
V
s
) is an important variable for performing geomechanical and geophysical modeling and reservoir studies. Field tests to measure this variable directly are high costs and time consuming. Due to the operational difficulties mentioned above, it is more convenient estimating
V
s
without direct measurements from conventional log data. In this research, the hybrid of wavelet transform with artificial neural network is utilized to estimate the
V
s
. To input variables (log gamma, log compressional wave velocity, and log bulk density), preprocessing is done using wavelet transform and then variables are entered to artificial neural network model. The estimation abilities of the hybrid artificial neural network with wavelet transform were substantiated using field data achieved from Marun reservoir, Iran. The results obtained in this study show a positive effect of input parameters’ preprocessing using wavelet transform in the estimation of
V
s
, and it has led to noticeable increase in the accuracy of model calculations. |
| doi_str_mv | 10.1007/s12665-020-09320-9 |
| format | Article |
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V
s
) is an important variable for performing geomechanical and geophysical modeling and reservoir studies. Field tests to measure this variable directly are high costs and time consuming. Due to the operational difficulties mentioned above, it is more convenient estimating
V
s
without direct measurements from conventional log data. In this research, the hybrid of wavelet transform with artificial neural network is utilized to estimate the
V
s
. To input variables (log gamma, log compressional wave velocity, and log bulk density), preprocessing is done using wavelet transform and then variables are entered to artificial neural network model. The estimation abilities of the hybrid artificial neural network with wavelet transform were substantiated using field data achieved from Marun reservoir, Iran. The results obtained in this study show a positive effect of input parameters’ preprocessing using wavelet transform in the estimation of
V
s
, and it has led to noticeable increase in the accuracy of model calculations.</description><identifier>ISSN: 1866-6280</identifier><identifier>EISSN: 1866-6299</identifier><identifier>DOI: 10.1007/s12665-020-09320-9</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Artificial neural networks ; Biogeosciences ; Bulk density ; Compressional wave velocities ; Earth and Environmental Science ; Earth Sciences ; Environmental Science and Engineering ; Field tests ; Geochemistry ; Geology ; Geomechanics ; Hydrology/Water Resources ; Model accuracy ; Neural networks ; Original Article ; Preprocessing ; Reservoirs ; S waves ; Shear wave velocities ; Terrestrial Pollution ; Velocity ; Wave velocity ; Wavelet transforms</subject><ispartof>Environmental earth sciences, 2021, Vol.80 (1), Article 5</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a342t-c0e7eefd31192b6c03eeef4b34c92dc21e13feca087b2f32b77a8218fe1090913</citedby><cites>FETCH-LOGICAL-a342t-c0e7eefd31192b6c03eeef4b34c92dc21e13feca087b2f32b77a8218fe1090913</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12665-020-09320-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12665-020-09320-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Fattahi, Hadi</creatorcontrib><creatorcontrib>Zandy Ilghani, Nastaran</creatorcontrib><title>Hybrid wavelet transform with artificial neural network for forecasting of shear wave velocity from wireline log data: a case study</title><title>Environmental earth sciences</title><addtitle>Environ Earth Sci</addtitle><description>Shear wave velocity (
V
s
) is an important variable for performing geomechanical and geophysical modeling and reservoir studies. Field tests to measure this variable directly are high costs and time consuming. Due to the operational difficulties mentioned above, it is more convenient estimating
V
s
without direct measurements from conventional log data. In this research, the hybrid of wavelet transform with artificial neural network is utilized to estimate the
V
s
. To input variables (log gamma, log compressional wave velocity, and log bulk density), preprocessing is done using wavelet transform and then variables are entered to artificial neural network model. The estimation abilities of the hybrid artificial neural network with wavelet transform were substantiated using field data achieved from Marun reservoir, Iran. The results obtained in this study show a positive effect of input parameters’ preprocessing using wavelet transform in the estimation of
V
s
, and it has led to noticeable increase in the accuracy of model calculations.</description><subject>Artificial neural networks</subject><subject>Biogeosciences</subject><subject>Bulk density</subject><subject>Compressional wave velocities</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Environmental Science and Engineering</subject><subject>Field tests</subject><subject>Geochemistry</subject><subject>Geology</subject><subject>Geomechanics</subject><subject>Hydrology/Water Resources</subject><subject>Model accuracy</subject><subject>Neural networks</subject><subject>Original Article</subject><subject>Preprocessing</subject><subject>Reservoirs</subject><subject>S waves</subject><subject>Shear wave velocities</subject><subject>Terrestrial Pollution</subject><subject>Velocity</subject><subject>Wave velocity</subject><subject>Wavelet transforms</subject><issn>1866-6280</issn><issn>1866-6299</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kE9LAzEQxYMoWGq_gKeA59X82WY33qSoFQQveg7Z3Umbut3UJGvZs1_ctCt6MzCZCbz3m_AQuqTkmhJS3ATKhJhnhJGMSJ5ueYImtBQiE0zK09-5JOdoFsKGpMMpl0RM0NdyqLxt8F5_QgsRR6-7YJzf4r2Na6x9tMbWVre4g94fW9w7_46T5lBQ6xBtt8LO4LAG7Y8knGCutnHAxrsDykNrO8CtW-FGR32LNU5GwCH2zXCBzoxuA8x--hS9Pdy_LpbZ88vj0-LuOdM8ZzGrCRQApuGUSlaJmnBIz7zieS1ZUzMKlJv0H1IWFTOcVUWhS0ZLA5RIIimfoquRu_Puo4cQ1cb1vksrFcuLOeG5yEVSsVFVexeCB6N23m61HxQl6pC3GvNWKW91zFvJZOKjKSRxtwL_h_7H9Q0814WF</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Fattahi, Hadi</creator><creator>Zandy Ilghani, Nastaran</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M2P</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope></search><sort><creationdate>2021</creationdate><title>Hybrid wavelet transform with artificial neural network for forecasting of shear wave velocity from wireline log data: a case study</title><author>Fattahi, Hadi ; Zandy Ilghani, Nastaran</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a342t-c0e7eefd31192b6c03eeef4b34c92dc21e13feca087b2f32b77a8218fe1090913</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial neural networks</topic><topic>Biogeosciences</topic><topic>Bulk density</topic><topic>Compressional wave velocities</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Environmental Science and Engineering</topic><topic>Field tests</topic><topic>Geochemistry</topic><topic>Geology</topic><topic>Geomechanics</topic><topic>Hydrology/Water Resources</topic><topic>Model accuracy</topic><topic>Neural networks</topic><topic>Original Article</topic><topic>Preprocessing</topic><topic>Reservoirs</topic><topic>S waves</topic><topic>Shear wave velocities</topic><topic>Terrestrial Pollution</topic><topic>Velocity</topic><topic>Wave velocity</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fattahi, Hadi</creatorcontrib><creatorcontrib>Zandy Ilghani, Nastaran</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Science Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><jtitle>Environmental earth sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fattahi, Hadi</au><au>Zandy Ilghani, Nastaran</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hybrid wavelet transform with artificial neural network for forecasting of shear wave velocity from wireline log data: a case study</atitle><jtitle>Environmental earth sciences</jtitle><stitle>Environ Earth Sci</stitle><date>2021</date><risdate>2021</risdate><volume>80</volume><issue>1</issue><artnum>5</artnum><issn>1866-6280</issn><eissn>1866-6299</eissn><abstract>Shear wave velocity (
V
s
) is an important variable for performing geomechanical and geophysical modeling and reservoir studies. Field tests to measure this variable directly are high costs and time consuming. Due to the operational difficulties mentioned above, it is more convenient estimating
V
s
without direct measurements from conventional log data. In this research, the hybrid of wavelet transform with artificial neural network is utilized to estimate the
V
s
. To input variables (log gamma, log compressional wave velocity, and log bulk density), preprocessing is done using wavelet transform and then variables are entered to artificial neural network model. The estimation abilities of the hybrid artificial neural network with wavelet transform were substantiated using field data achieved from Marun reservoir, Iran. The results obtained in this study show a positive effect of input parameters’ preprocessing using wavelet transform in the estimation of
V
s
, and it has led to noticeable increase in the accuracy of model calculations.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12665-020-09320-9</doi></addata></record> |
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| subjects | Artificial neural networks Biogeosciences Bulk density Compressional wave velocities Earth and Environmental Science Earth Sciences Environmental Science and Engineering Field tests Geochemistry Geology Geomechanics Hydrology/Water Resources Model accuracy Neural networks Original Article Preprocessing Reservoirs S waves Shear wave velocities Terrestrial Pollution Velocity Wave velocity Wavelet transforms |
| title | Hybrid wavelet transform with artificial neural network for forecasting of shear wave velocity from wireline log data: a case study |
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