Parameter Identification for a Slug Test in a Well with Finite-Thickness Skin Using Extended Kalman Filter
Yeh and Chen (J Hydro 342(3–4):283-294, 2007 ) integrated a slug test solution for a well having a finite-thickness skin with the simulated annealing (SA) to determine the hydraulic parameters of the skin zone and formation zone. Some results obtained in positive-skin scenarios are however not accur...
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| creator | Huang, By Yen-Chen Yeh, Hund-Der |
| description | Yeh and Chen (J Hydro 342(3–4):283-294,
2007
) integrated a slug test solution for a well having a finite-thickness skin with the simulated annealing (SA) to determine the hydraulic parameters of the skin zone and formation zone. Some results obtained in positive-skin scenarios are however not accurate if compared with the target values of the parameters. This study first employs the sensitivity and correlation analyses to quantify the relationship between two normalized sensitivities and analyze the resulting errors in parameter estimates. It is found that the inaccuracy in parameter estimates can be attributed to following two problems: (1) the normalized sensitivities of the skin thickness and hydraulic conductivity are highly correlated and (2) the SA algorithm is very sensitive to round-off error in well-water-level (WWL) data. A parameter identification approach is thus developed based on the extended Kalman filter (EKF) coupled with the solution used by Yeh and Chen (J Hydro 342(3–4):283-294,
2007
) to determine the parameters in six positive-skin scenarios where the parameters were not accurately determined before. We show that previous two problems can be overcome by the proposed approach because it is designed to account for uncertainties of measurements. Moreover, the EKF can save 99.8% and 99.9% computing time when compared with the results using the SA in analyzing 20 WWL data and 47 WWL data, respectively. |
| doi_str_mv | 10.1007/s11269-012-0128-8 |
| format | Article |
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2007
) integrated a slug test solution for a well having a finite-thickness skin with the simulated annealing (SA) to determine the hydraulic parameters of the skin zone and formation zone. Some results obtained in positive-skin scenarios are however not accurate if compared with the target values of the parameters. This study first employs the sensitivity and correlation analyses to quantify the relationship between two normalized sensitivities and analyze the resulting errors in parameter estimates. It is found that the inaccuracy in parameter estimates can be attributed to following two problems: (1) the normalized sensitivities of the skin thickness and hydraulic conductivity are highly correlated and (2) the SA algorithm is very sensitive to round-off error in well-water-level (WWL) data. A parameter identification approach is thus developed based on the extended Kalman filter (EKF) coupled with the solution used by Yeh and Chen (J Hydro 342(3–4):283-294,
2007
) to determine the parameters in six positive-skin scenarios where the parameters were not accurately determined before. We show that previous two problems can be overcome by the proposed approach because it is designed to account for uncertainties of measurements. Moreover, the EKF can save 99.8% and 99.9% computing time when compared with the results using the SA in analyzing 20 WWL data and 47 WWL data, respectively.</description><identifier>ISSN: 0920-4741</identifier><identifier>EISSN: 1573-1650</identifier><identifier>DOI: 10.1007/s11269-012-0128-8</identifier><identifier>CODEN: WRMAEJ</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Algorithms ; Aquifers ; Atmospheric Sciences ; Civil Engineering ; Correlation analysis ; Earth and Environmental Science ; Earth Sciences ; Earth, ocean, space ; Environment ; Error detection ; Estimates ; Exact sciences and technology ; Extended Kalman filter ; Fluid flow ; Geotechnical Engineering & Applied Earth Sciences ; Hydraulics ; Hydrogeology ; Hydrology. Hydrogeology ; Hydrology/Water Resources ; Kalman filters ; Mathematical models ; Parameter estimation ; Parameter identification ; Skin ; Slugs ; Studies ; Water resources ; Water resources management</subject><ispartof>Water resources management, 2012-11, Vol.26 (14), p.4039-4057</ispartof><rights>Springer Science+Business Media B.V. 2012</rights><rights>2015 INIST-CNRS</rights><rights>Springer Science+Business Media Dordrecht 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a435t-e836231e3783bab10e06e98b2f9f77744d7e6f0a7d7bb169b10dbda63915dd553</citedby><cites>FETCH-LOGICAL-a435t-e836231e3783bab10e06e98b2f9f77744d7e6f0a7d7bb169b10dbda63915dd553</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/s11269-012-0128-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11269-012-0128-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=26442609$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Huang, By Yen-Chen</creatorcontrib><creatorcontrib>Yeh, Hund-Der</creatorcontrib><title>Parameter Identification for a Slug Test in a Well with Finite-Thickness Skin Using Extended Kalman Filter</title><title>Water resources management</title><addtitle>Water Resour Manage</addtitle><description>Yeh and Chen (J Hydro 342(3–4):283-294,
2007
) integrated a slug test solution for a well having a finite-thickness skin with the simulated annealing (SA) to determine the hydraulic parameters of the skin zone and formation zone. Some results obtained in positive-skin scenarios are however not accurate if compared with the target values of the parameters. This study first employs the sensitivity and correlation analyses to quantify the relationship between two normalized sensitivities and analyze the resulting errors in parameter estimates. It is found that the inaccuracy in parameter estimates can be attributed to following two problems: (1) the normalized sensitivities of the skin thickness and hydraulic conductivity are highly correlated and (2) the SA algorithm is very sensitive to round-off error in well-water-level (WWL) data. A parameter identification approach is thus developed based on the extended Kalman filter (EKF) coupled with the solution used by Yeh and Chen (J Hydro 342(3–4):283-294,
2007
) to determine the parameters in six positive-skin scenarios where the parameters were not accurately determined before. We show that previous two problems can be overcome by the proposed approach because it is designed to account for uncertainties of measurements. Moreover, the EKF can save 99.8% and 99.9% computing time when compared with the results using the SA in analyzing 20 WWL data and 47 WWL data, respectively.</description><subject>Algorithms</subject><subject>Aquifers</subject><subject>Atmospheric Sciences</subject><subject>Civil Engineering</subject><subject>Correlation analysis</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Earth, ocean, space</subject><subject>Environment</subject><subject>Error detection</subject><subject>Estimates</subject><subject>Exact sciences and technology</subject><subject>Extended Kalman filter</subject><subject>Fluid flow</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Hydraulics</subject><subject>Hydrogeology</subject><subject>Hydrology. Hydrogeology</subject><subject>Hydrology/Water Resources</subject><subject>Kalman filters</subject><subject>Mathematical models</subject><subject>Parameter estimation</subject><subject>Parameter identification</subject><subject>Skin</subject><subject>Slugs</subject><subject>Studies</subject><subject>Water resources</subject><subject>Water resources management</subject><issn>0920-4741</issn><issn>1573-1650</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFkV9LHDEUxUOp0K36AXwLFKEvY_M_k0cRrVJBwV18HDKTO2vW2YxNZtF--95ll1IKxYdLCPmdc084hJxwdsYZs98K58K4inGxnbqqP5AZ11ZW3Gj2kcyYE6xSVvFP5HMpK8ZQ5diMrO599muYINObAGmKfez8FMdE-zFTTx-GzZLOoUw0Jrw-wjDQ1zg90auY4gTV_Cl2zwlKoQ_PSCxKTEt6-TZBChDoDz-sfUJ2wAVH5KD3Q4Hj_XlIFleX84vr6vbu-83F-W3lldRTBbU0QnKQtpatbzkDZsDVrehdb61VKlgwPfM22LblxiER2uCNdFyHoLU8JF93vi95_LnB6M06lg6D-wTjpjTcWiZxlHof5UILDFI7RL_8g67GTU74kYYzp63TTlqk-I7q8lhKhr55yXHt8y-Emm1Rza6oBkvaTt3UqDndO_vS-aHPPnWx_BEKo5QwbJtA7LiCT2kJ-e8E_zP_DSAMoPE</recordid><startdate>20121101</startdate><enddate>20121101</enddate><creator>Huang, By Yen-Chen</creator><creator>Yeh, Hund-Der</creator><general>Springer Netherlands</general><general>Springer</general><general>Springer Nature B.V</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>3V.</scope><scope>7QH</scope><scope>7ST</scope><scope>7UA</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>H97</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>KR7</scope><scope>L.-</scope><scope>L.0</scope><scope>L.G</scope><scope>L6V</scope><scope>LK8</scope><scope>M0C</scope><scope>M2P</scope><scope>M7P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope><scope>7TG</scope><scope>H96</scope><scope>KL.</scope></search><sort><creationdate>20121101</creationdate><title>Parameter Identification for a Slug Test in a Well with Finite-Thickness Skin Using Extended Kalman Filter</title><author>Huang, By Yen-Chen ; Yeh, Hund-Der</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a435t-e836231e3783bab10e06e98b2f9f77744d7e6f0a7d7bb169b10dbda63915dd553</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Algorithms</topic><topic>Aquifers</topic><topic>Atmospheric Sciences</topic><topic>Civil Engineering</topic><topic>Correlation analysis</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Earth, ocean, space</topic><topic>Environment</topic><topic>Error detection</topic><topic>Estimates</topic><topic>Exact sciences and technology</topic><topic>Extended Kalman filter</topic><topic>Fluid flow</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Hydraulics</topic><topic>Hydrogeology</topic><topic>Hydrology. Hydrogeology</topic><topic>Hydrology/Water Resources</topic><topic>Kalman filters</topic><topic>Mathematical models</topic><topic>Parameter estimation</topic><topic>Parameter identification</topic><topic>Skin</topic><topic>Slugs</topic><topic>Studies</topic><topic>Water resources</topic><topic>Water resources management</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huang, By Yen-Chen</creatorcontrib><creatorcontrib>Yeh, Hund-Der</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</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 Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>ABI/INFORM Global</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><jtitle>Water resources management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Huang, By Yen-Chen</au><au>Yeh, Hund-Der</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Parameter Identification for a Slug Test in a Well with Finite-Thickness Skin Using Extended Kalman Filter</atitle><jtitle>Water resources management</jtitle><stitle>Water Resour Manage</stitle><date>2012-11-01</date><risdate>2012</risdate><volume>26</volume><issue>14</issue><spage>4039</spage><epage>4057</epage><pages>4039-4057</pages><issn>0920-4741</issn><eissn>1573-1650</eissn><coden>WRMAEJ</coden><abstract>Yeh and Chen (J Hydro 342(3–4):283-294,
2007
) integrated a slug test solution for a well having a finite-thickness skin with the simulated annealing (SA) to determine the hydraulic parameters of the skin zone and formation zone. Some results obtained in positive-skin scenarios are however not accurate if compared with the target values of the parameters. This study first employs the sensitivity and correlation analyses to quantify the relationship between two normalized sensitivities and analyze the resulting errors in parameter estimates. It is found that the inaccuracy in parameter estimates can be attributed to following two problems: (1) the normalized sensitivities of the skin thickness and hydraulic conductivity are highly correlated and (2) the SA algorithm is very sensitive to round-off error in well-water-level (WWL) data. A parameter identification approach is thus developed based on the extended Kalman filter (EKF) coupled with the solution used by Yeh and Chen (J Hydro 342(3–4):283-294,
2007
) to determine the parameters in six positive-skin scenarios where the parameters were not accurately determined before. We show that previous two problems can be overcome by the proposed approach because it is designed to account for uncertainties of measurements. Moreover, the EKF can save 99.8% and 99.9% computing time when compared with the results using the SA in analyzing 20 WWL data and 47 WWL data, respectively.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11269-012-0128-8</doi><tpages>19</tpages></addata></record> |
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| subjects | Algorithms Aquifers Atmospheric Sciences Civil Engineering Correlation analysis Earth and Environmental Science Earth Sciences Earth, ocean, space Environment Error detection Estimates Exact sciences and technology Extended Kalman filter Fluid flow Geotechnical Engineering & Applied Earth Sciences Hydraulics Hydrogeology Hydrology. Hydrogeology Hydrology/Water Resources Kalman filters Mathematical models Parameter estimation Parameter identification Skin Slugs Studies Water resources Water resources management |
| title | Parameter Identification for a Slug Test in a Well with Finite-Thickness Skin Using Extended Kalman Filter |
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