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Automatic Determination of Sedimentary Units from Well Data
The issue of identifying stratigraphic units within a sedimentary succession is of prime importance for reservoir studies, because it allows splitting the reservoir into several units with specific parameters, thus reducing the vertical nonstationarity in simulations. A new method is proposed for se...
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Published in: | Mathematical geosciences 2020-02, Vol.52 (2), p.213-231 |
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description | The issue of identifying stratigraphic units within a sedimentary succession is of prime importance for reservoir studies, because it allows splitting the reservoir into several units with specific parameters, thus reducing the vertical nonstationarity in simulations. A new method is proposed for semi-automatic determination of the sedimentary units from well logging that uses a customized geostatistical hierarchical clustering algorithm. A new linkage criteria derived from the Ward criteria (cluster minimum variance) is proposed to enforce the monotonic increase of dissimilarities. The discretized proportion of sand lithofacies calculated from the vertical proportion curve of the well is taken as input data. At each step of the procedure, the algorithm merges the most similar of two consecutive units of sand lithofacies, ensuring stratigraphic consistency. Finally, the number of units is deduced from the first most important step of the dissimilarity. The user can investigate a larger number of units by considering the clusters with lower levels of dissimilarities. The method is validated using two synthetic cases built for a fluvial meandering reservoir analog containing three and five units. The results from the synthetic cases show that the units are identified when the sand proportion contrast between units is larger than the internal variability within the units. For low sand contrasts between units or for a small number of wells, sedimentary unit limits may be found for lower clustering dissimilarities. Finally, the method is successfully applied to a field study, where the resulting cluster units are found to be comparable to the field interpretation, suggesting a limit between units defined by paleosols rather than close overlying lacustrine levels. |
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A new method is proposed for semi-automatic determination of the sedimentary units from well logging that uses a customized geostatistical hierarchical clustering algorithm. A new linkage criteria derived from the Ward criteria (cluster minimum variance) is proposed to enforce the monotonic increase of dissimilarities. The discretized proportion of sand lithofacies calculated from the vertical proportion curve of the well is taken as input data. At each step of the procedure, the algorithm merges the most similar of two consecutive units of sand lithofacies, ensuring stratigraphic consistency. Finally, the number of units is deduced from the first most important step of the dissimilarity. The user can investigate a larger number of units by considering the clusters with lower levels of dissimilarities. The method is validated using two synthetic cases built for a fluvial meandering reservoir analog containing three and five units. The results from the synthetic cases show that the units are identified when the sand proportion contrast between units is larger than the internal variability within the units. For low sand contrasts between units or for a small number of wells, sedimentary unit limits may be found for lower clustering dissimilarities. Finally, the method is successfully applied to a field study, where the resulting cluster units are found to be comparable to the field interpretation, suggesting a limit between units defined by paleosols rather than close overlying lacustrine levels.</description><identifier>ISSN: 1874-8961</identifier><identifier>EISSN: 1874-8953</identifier><identifier>DOI: 10.1007/s11004-019-09793-w</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Analogs ; Applied geology ; Chemistry and Earth Sciences ; Cluster analysis ; Clustering ; Computer Science ; Computer simulation ; Earth and Environmental Science ; Earth Sciences ; Ecological succession ; Geostatistics ; Geotechnical Engineering & Applied Earth Sciences ; Hydrogeology ; Hydrology ; Lithofacies ; Machine Learning ; Mathematics ; Meandering ; Methods ; Mineralogy ; Modeling and Simulation ; Paleosols ; Petrography ; Physics ; Probability ; Reservoirs ; Sand ; Sciences of the Universe ; Statistics ; Statistics for Engineering ; Stratigraphy ; Well data ; Well logging</subject><ispartof>Mathematical geosciences, 2020-02, Vol.52 (2), p.213-231</ispartof><rights>International Association for Mathematical Geosciences 2019</rights><rights>Mathematical Geosciences is a copyright of Springer, (2019). All Rights Reserved.</rights><rights>2019© International Association for Mathematical Geosciences 2019</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a448t-eb9066493b67363cff842a7c683a4d5134933062a2262289b77d118f85c16f243</citedby><cites>FETCH-LOGICAL-a448t-eb9066493b67363cff842a7c683a4d5134933062a2262289b77d118f85c16f243</cites><orcidid>0000-0002-1616-8743 ; 0000-0002-3770-3370 ; 0000-0001-6281-9853</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27903,27904</link.rule.ids><backlink>$$Uhttps://minesparis-psl.hal.science/hal-02070537$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Bubnova, Anna</creatorcontrib><creatorcontrib>Ors, Fabien</creatorcontrib><creatorcontrib>Rivoirard, Jacques</creatorcontrib><creatorcontrib>Cojan, Isabelle</creatorcontrib><creatorcontrib>Romary, Thomas</creatorcontrib><title>Automatic Determination of Sedimentary Units from Well Data</title><title>Mathematical geosciences</title><addtitle>Math Geosci</addtitle><description>The issue of identifying stratigraphic units within a sedimentary succession is of prime importance for reservoir studies, because it allows splitting the reservoir into several units with specific parameters, thus reducing the vertical nonstationarity in simulations. A new method is proposed for semi-automatic determination of the sedimentary units from well logging that uses a customized geostatistical hierarchical clustering algorithm. A new linkage criteria derived from the Ward criteria (cluster minimum variance) is proposed to enforce the monotonic increase of dissimilarities. The discretized proportion of sand lithofacies calculated from the vertical proportion curve of the well is taken as input data. At each step of the procedure, the algorithm merges the most similar of two consecutive units of sand lithofacies, ensuring stratigraphic consistency. Finally, the number of units is deduced from the first most important step of the dissimilarity. The user can investigate a larger number of units by considering the clusters with lower levels of dissimilarities. The method is validated using two synthetic cases built for a fluvial meandering reservoir analog containing three and five units. The results from the synthetic cases show that the units are identified when the sand proportion contrast between units is larger than the internal variability within the units. For low sand contrasts between units or for a small number of wells, sedimentary unit limits may be found for lower clustering dissimilarities. 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Ors, Fabien ; Rivoirard, Jacques ; Cojan, Isabelle ; Romary, Thomas</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a448t-eb9066493b67363cff842a7c683a4d5134933062a2262289b77d118f85c16f243</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Analogs</topic><topic>Applied geology</topic><topic>Chemistry and Earth Sciences</topic><topic>Cluster analysis</topic><topic>Clustering</topic><topic>Computer Science</topic><topic>Computer simulation</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Ecological succession</topic><topic>Geostatistics</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Hydrogeology</topic><topic>Hydrology</topic><topic>Lithofacies</topic><topic>Machine Learning</topic><topic>Mathematics</topic><topic>Meandering</topic><topic>Methods</topic><topic>Mineralogy</topic><topic>Modeling and Simulation</topic><topic>Paleosols</topic><topic>Petrography</topic><topic>Physics</topic><topic>Probability</topic><topic>Reservoirs</topic><topic>Sand</topic><topic>Sciences of the Universe</topic><topic>Statistics</topic><topic>Statistics for Engineering</topic><topic>Stratigraphy</topic><topic>Well data</topic><topic>Well logging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bubnova, Anna</creatorcontrib><creatorcontrib>Ors, Fabien</creatorcontrib><creatorcontrib>Rivoirard, Jacques</creatorcontrib><creatorcontrib>Cojan, Isabelle</creatorcontrib><creatorcontrib>Romary, Thomas</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Mathematical geosciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bubnova, Anna</au><au>Ors, Fabien</au><au>Rivoirard, Jacques</au><au>Cojan, Isabelle</au><au>Romary, Thomas</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic Determination of Sedimentary Units from Well Data</atitle><jtitle>Mathematical geosciences</jtitle><stitle>Math Geosci</stitle><date>2020-02-01</date><risdate>2020</risdate><volume>52</volume><issue>2</issue><spage>213</spage><epage>231</epage><pages>213-231</pages><issn>1874-8961</issn><eissn>1874-8953</eissn><abstract>The issue of identifying stratigraphic units within a sedimentary succession is of prime importance for reservoir studies, because it allows splitting the reservoir into several units with specific parameters, thus reducing the vertical nonstationarity in simulations. A new method is proposed for semi-automatic determination of the sedimentary units from well logging that uses a customized geostatistical hierarchical clustering algorithm. A new linkage criteria derived from the Ward criteria (cluster minimum variance) is proposed to enforce the monotonic increase of dissimilarities. The discretized proportion of sand lithofacies calculated from the vertical proportion curve of the well is taken as input data. At each step of the procedure, the algorithm merges the most similar of two consecutive units of sand lithofacies, ensuring stratigraphic consistency. Finally, the number of units is deduced from the first most important step of the dissimilarity. The user can investigate a larger number of units by considering the clusters with lower levels of dissimilarities. The method is validated using two synthetic cases built for a fluvial meandering reservoir analog containing three and five units. The results from the synthetic cases show that the units are identified when the sand proportion contrast between units is larger than the internal variability within the units. For low sand contrasts between units or for a small number of wells, sedimentary unit limits may be found for lower clustering dissimilarities. Finally, the method is successfully applied to a field study, where the resulting cluster units are found to be comparable to the field interpretation, suggesting a limit between units defined by paleosols rather than close overlying lacustrine levels.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s11004-019-09793-w</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0002-1616-8743</orcidid><orcidid>https://orcid.org/0000-0002-3770-3370</orcidid><orcidid>https://orcid.org/0000-0001-6281-9853</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Analogs Applied geology Chemistry and Earth Sciences Cluster analysis Clustering Computer Science Computer simulation Earth and Environmental Science Earth Sciences Ecological succession Geostatistics Geotechnical Engineering & Applied Earth Sciences Hydrogeology Hydrology Lithofacies Machine Learning Mathematics Meandering Methods Mineralogy Modeling and Simulation Paleosols Petrography Physics Probability Reservoirs Sand Sciences of the Universe Statistics Statistics for Engineering Stratigraphy Well data Well logging |
title | Automatic Determination of Sedimentary Units from Well Data |
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