Loading…

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...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical geosciences 2020-02, Vol.52 (2), p.213-231
Main Authors: Bubnova, Anna, Ors, Fabien, Rivoirard, Jacques, Cojan, Isabelle, Romary, Thomas
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-a448t-eb9066493b67363cff842a7c683a4d5134933062a2262289b77d118f85c16f243
cites cdi_FETCH-LOGICAL-a448t-eb9066493b67363cff842a7c683a4d5134933062a2262289b77d118f85c16f243
container_end_page 231
container_issue 2
container_start_page 213
container_title Mathematical geosciences
container_volume 52
creator Bubnova, Anna
Ors, Fabien
Rivoirard, Jacques
Cojan, Isabelle
Romary, Thomas
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.
doi_str_mv 10.1007/s11004-019-09793-w
format article
fullrecord <record><control><sourceid>proquest_hal_p</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_hal_02070537v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2343277451</sourcerecordid><originalsourceid>FETCH-LOGICAL-a448t-eb9066493b67363cff842a7c683a4d5134933062a2262289b77d118f85c16f243</originalsourceid><addsrcrecordid>eNp9kD1PwzAQhi0EEqXwB5giMTEEfLZjO2KqWqBIlRigYrTc1IZUSVxsl4p_j0tQ2Trd1_PenV6ELgHfAMbiNkAKLMdQ5rgUJc23R2gAUrBclgU93uccTtFZCCuMOdACBuhutImu1bGusomJxrd1lwrXZc5mL2ZZt6aL2n9n866OIbPetdmbaZpsoqM-RydWN8Fc_MUhmj_cv46n-ez58Wk8muWaMRlzsygx56ykCy4op5W1khEtKi6pZssCaBpRzIkmhBMiy4UQSwBpZVEBt4TRIbru937oRq193aaHlNO1mo5matfDBAtcUPEFib3q2bV3nxsTolq5je_Se4pQRokQrDhMQQkCJABJFOmpyrsQvLH744DVznbV266S7erXdrVNItqLQoK7d-P_Vx9Q_QCSFoGU</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2191718112</pqid></control><display><type>article</type><title>Automatic Determination of Sedimentary Units from Well Data</title><source>Springer Link</source><creator>Bubnova, Anna ; Ors, Fabien ; Rivoirard, Jacques ; Cojan, Isabelle ; Romary, Thomas</creator><creatorcontrib>Bubnova, Anna ; Ors, Fabien ; Rivoirard, Jacques ; Cojan, Isabelle ; Romary, Thomas</creatorcontrib><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.</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 &amp; 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. 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><subject>Algorithms</subject><subject>Analogs</subject><subject>Applied geology</subject><subject>Chemistry and Earth Sciences</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Computer Science</subject><subject>Computer simulation</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Ecological succession</subject><subject>Geostatistics</subject><subject>Geotechnical Engineering &amp; Applied Earth Sciences</subject><subject>Hydrogeology</subject><subject>Hydrology</subject><subject>Lithofacies</subject><subject>Machine Learning</subject><subject>Mathematics</subject><subject>Meandering</subject><subject>Methods</subject><subject>Mineralogy</subject><subject>Modeling and Simulation</subject><subject>Paleosols</subject><subject>Petrography</subject><subject>Physics</subject><subject>Probability</subject><subject>Reservoirs</subject><subject>Sand</subject><subject>Sciences of the Universe</subject><subject>Statistics</subject><subject>Statistics for Engineering</subject><subject>Stratigraphy</subject><subject>Well data</subject><subject>Well logging</subject><issn>1874-8961</issn><issn>1874-8953</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kD1PwzAQhi0EEqXwB5giMTEEfLZjO2KqWqBIlRigYrTc1IZUSVxsl4p_j0tQ2Trd1_PenV6ELgHfAMbiNkAKLMdQ5rgUJc23R2gAUrBclgU93uccTtFZCCuMOdACBuhutImu1bGusomJxrd1lwrXZc5mL2ZZt6aL2n9n866OIbPetdmbaZpsoqM-RydWN8Fc_MUhmj_cv46n-ez58Wk8muWaMRlzsygx56ykCy4op5W1khEtKi6pZssCaBpRzIkmhBMiy4UQSwBpZVEBt4TRIbru937oRq193aaHlNO1mo5matfDBAtcUPEFib3q2bV3nxsTolq5je_Se4pQRokQrDhMQQkCJABJFOmpyrsQvLH744DVznbV266S7erXdrVNItqLQoK7d-P_Vx9Q_QCSFoGU</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Bubnova, Anna</creator><creator>Ors, Fabien</creator><creator>Rivoirard, Jacques</creator><creator>Cojan, Isabelle</creator><creator>Romary, Thomas</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><general>Springer Verlag</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>1XC</scope><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></search><sort><creationdate>20200201</creationdate><title>Automatic Determination of Sedimentary Units from Well Data</title><author>Bubnova, Anna ; 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 &amp; 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 &amp; 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 &amp; Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy &amp; Non-Living Resources</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science &amp; 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>
fulltext fulltext
identifier ISSN: 1874-8961
ispartof Mathematical geosciences, 2020-02, Vol.52 (2), p.213-231
issn 1874-8961
1874-8953
language eng
recordid cdi_hal_primary_oai_HAL_hal_02070537v1
source Springer Link
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
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-26T22%3A51%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_hal_p&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Automatic%20Determination%20of%20Sedimentary%20Units%20from%20Well%20Data&rft.jtitle=Mathematical%20geosciences&rft.au=Bubnova,%20Anna&rft.date=2020-02-01&rft.volume=52&rft.issue=2&rft.spage=213&rft.epage=231&rft.pages=213-231&rft.issn=1874-8961&rft.eissn=1874-8953&rft_id=info:doi/10.1007/s11004-019-09793-w&rft_dat=%3Cproquest_hal_p%3E2343277451%3C/proquest_hal_p%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-a448t-eb9066493b67363cff842a7c683a4d5134933062a2262289b77d118f85c16f243%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2191718112&rft_id=info:pmid/&rfr_iscdi=true