A Permeability Prediction Model of Single-Peak NMR T2 Distribution in Tight Sandstones: A Case Study on the Huangliu Formation, Yinggehai Basin, China
Tight sandstone reservoirs have low porosity, low permeability, and a complex pore structure. The seepage from tight sandstones is a key factor in evaluating the oil and gas accumulation in these reservoirs. Therefore, reservoir permeability prediction has become the focus of researchers. Using nucl...
Gespeichert in:
Veröffentlicht in: | Mathematical geosciences 2024-08, Vol.56 (6), p.1303-1333 |
---|---|
Hauptverfasser: | , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 1333 |
---|---|
container_issue | 6 |
container_start_page | 1303 |
container_title | Mathematical geosciences |
container_volume | 56 |
creator | Zhao, Jing Huang, Zhilong Dong, Jin Zhang, Jingyuan Wang, Rui Ma, Chonglin Deng, Guangjun Xu, Maguang |
description | Tight sandstone reservoirs have low porosity, low permeability, and a complex pore structure. The seepage from tight sandstones is a key factor in evaluating the oil and gas accumulation in these reservoirs. Therefore, reservoir permeability prediction has become the focus of researchers. Using nuclear magnetic resonance (NMR), high-pressure mercury injection, scanning electron microscopy, and other experimental methods, scholars have established various permeability prediction models, which have obvious advantages and disadvantages. However, there is less research conducted on predicting the permeability of tight sandstone reservoirs according to their single-peak NMR
T
2
distribution. Based on NMR experiments and the bimodal Gaussian density formula, this study identified the criteria for determining the types of reservoir pore structures with single-peak NMR
T
2
distribution and established the parameters (
η
1
and
η
2
) that can be used in the evaluation of reservoir pore structure. A novel model for predicting the permeability of tight sandstone reservoirs was established using
η
1
and
η
2
. The results of the prediction model proposed in this study were found to be superior to the results of eight permeability prediction models established by other scholars in the studied case of the Huangliu Formation. However, permeability prediction models established using the NMR experimental results of different sources were found to be ineffective. Additionally, the new model is suitable for use with sandstone reservoirs with both single-peak and double-peak NMR
T
2
distributions in the studied case of the Yanchang Formation. Logging curves can be used to predict
η
1
and
η
2
, and the permeability of a single well of a tight sandstone reservoir. The study findings would be useful for predicting tight sandstone reservoir permeability. |
doi_str_mv | 10.1007/s11004-023-10118-1 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3093886906</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3093886906</sourcerecordid><originalsourceid>FETCH-LOGICAL-c200t-c7bfdd10cc67ec26c50074e74ec70fcd09fe98517f7ce7ccf53cb22d9d581f403</originalsourceid><addsrcrecordid>eNp9kM1OAjEURidGExF9AVc3cetoO8P8uUMUMRElggtXTae9HYowg21nwYv4vBYwujO5yb1pzvc1OUFwTskVJSS7ttSvXkiiOKSE0jykB0GH5lkvzIskPvy9U3ocnFi7ICSlcUI7wVcfJmhWyEu91G4DE4NSC6ebGsaNxCU0Cqa6rpYYTpB_wPP4FWYR3GnrjC7bHahrmOlq7mDKa2ldU6O9gT4MuEWYulZuwENujjBquW_SLQwbs-Lb7CW8-_IK51zDLbfaPwzmuuanwZHiS4tnP7sbvA3vZ4NR-PTy8DjoP4UiIsSFIiuVlJQIkWYoolQkXkYP_YiMKCFJobDIE5qpTGAmhEpiUUaRLGSSU9UjcTe42PeuTfPZonVs0bSm9l-ymBRxnqcFST0V7SlhGmsNKrY2esXNhlHCtv7Z3j_z_tnOP6M-FO9D1sN1heav-p_UN8LMiPc</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3093886906</pqid></control><display><type>article</type><title>A Permeability Prediction Model of Single-Peak NMR T2 Distribution in Tight Sandstones: A Case Study on the Huangliu Formation, Yinggehai Basin, China</title><source>SpringerLink Journals - AutoHoldings</source><creator>Zhao, Jing ; Huang, Zhilong ; Dong, Jin ; Zhang, Jingyuan ; Wang, Rui ; Ma, Chonglin ; Deng, Guangjun ; Xu, Maguang</creator><creatorcontrib>Zhao, Jing ; Huang, Zhilong ; Dong, Jin ; Zhang, Jingyuan ; Wang, Rui ; Ma, Chonglin ; Deng, Guangjun ; Xu, Maguang</creatorcontrib><description>Tight sandstone reservoirs have low porosity, low permeability, and a complex pore structure. The seepage from tight sandstones is a key factor in evaluating the oil and gas accumulation in these reservoirs. Therefore, reservoir permeability prediction has become the focus of researchers. Using nuclear magnetic resonance (NMR), high-pressure mercury injection, scanning electron microscopy, and other experimental methods, scholars have established various permeability prediction models, which have obvious advantages and disadvantages. However, there is less research conducted on predicting the permeability of tight sandstone reservoirs according to their single-peak NMR
T
2
distribution. Based on NMR experiments and the bimodal Gaussian density formula, this study identified the criteria for determining the types of reservoir pore structures with single-peak NMR
T
2
distribution and established the parameters (
η
1
and
η
2
) that can be used in the evaluation of reservoir pore structure. A novel model for predicting the permeability of tight sandstone reservoirs was established using
η
1
and
η
2
. The results of the prediction model proposed in this study were found to be superior to the results of eight permeability prediction models established by other scholars in the studied case of the Huangliu Formation. However, permeability prediction models established using the NMR experimental results of different sources were found to be ineffective. Additionally, the new model is suitable for use with sandstone reservoirs with both single-peak and double-peak NMR
T
2
distributions in the studied case of the Yanchang Formation. Logging curves can be used to predict
η
1
and
η
2
, and the permeability of a single well of a tight sandstone reservoir. The study findings would be useful for predicting tight sandstone reservoir permeability.</description><identifier>ISSN: 1874-8961</identifier><identifier>EISSN: 1874-8953</identifier><identifier>DOI: 10.1007/s11004-023-10118-1</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Chemistry and Earth Sciences ; Computer Science ; Earth and Environmental Science ; Earth Sciences ; Electron microscopy ; Experimental methods ; Geotechnical Engineering & Applied Earth Sciences ; Hydrogeology ; Magnetic permeability ; Magnetic resonance ; Membrane permeability ; Mercury ; NMR ; Normal distribution ; Nuclear magnetic resonance ; Parameter identification ; Permeability ; Physics ; Porosity ; Prediction models ; Predictions ; Research methodology ; Reservoirs ; Sandstone ; Scanning electron microscopy ; Sedimentary rocks ; Seepage ; Statistics for Engineering</subject><ispartof>Mathematical geosciences, 2024-08, Vol.56 (6), p.1303-1333</ispartof><rights>International Association for Mathematical Geosciences 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c200t-c7bfdd10cc67ec26c50074e74ec70fcd09fe98517f7ce7ccf53cb22d9d581f403</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/s11004-023-10118-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11004-023-10118-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27922,27923,41486,42555,51317</link.rule.ids></links><search><creatorcontrib>Zhao, Jing</creatorcontrib><creatorcontrib>Huang, Zhilong</creatorcontrib><creatorcontrib>Dong, Jin</creatorcontrib><creatorcontrib>Zhang, Jingyuan</creatorcontrib><creatorcontrib>Wang, Rui</creatorcontrib><creatorcontrib>Ma, Chonglin</creatorcontrib><creatorcontrib>Deng, Guangjun</creatorcontrib><creatorcontrib>Xu, Maguang</creatorcontrib><title>A Permeability Prediction Model of Single-Peak NMR T2 Distribution in Tight Sandstones: A Case Study on the Huangliu Formation, Yinggehai Basin, China</title><title>Mathematical geosciences</title><addtitle>Math Geosci</addtitle><description>Tight sandstone reservoirs have low porosity, low permeability, and a complex pore structure. The seepage from tight sandstones is a key factor in evaluating the oil and gas accumulation in these reservoirs. Therefore, reservoir permeability prediction has become the focus of researchers. Using nuclear magnetic resonance (NMR), high-pressure mercury injection, scanning electron microscopy, and other experimental methods, scholars have established various permeability prediction models, which have obvious advantages and disadvantages. However, there is less research conducted on predicting the permeability of tight sandstone reservoirs according to their single-peak NMR
T
2
distribution. Based on NMR experiments and the bimodal Gaussian density formula, this study identified the criteria for determining the types of reservoir pore structures with single-peak NMR
T
2
distribution and established the parameters (
η
1
and
η
2
) that can be used in the evaluation of reservoir pore structure. A novel model for predicting the permeability of tight sandstone reservoirs was established using
η
1
and
η
2
. The results of the prediction model proposed in this study were found to be superior to the results of eight permeability prediction models established by other scholars in the studied case of the Huangliu Formation. However, permeability prediction models established using the NMR experimental results of different sources were found to be ineffective. Additionally, the new model is suitable for use with sandstone reservoirs with both single-peak and double-peak NMR
T
2
distributions in the studied case of the Yanchang Formation. Logging curves can be used to predict
η
1
and
η
2
, and the permeability of a single well of a tight sandstone reservoir. The study findings would be useful for predicting tight sandstone reservoir permeability.</description><subject>Chemistry and Earth Sciences</subject><subject>Computer Science</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Electron microscopy</subject><subject>Experimental methods</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Hydrogeology</subject><subject>Magnetic permeability</subject><subject>Magnetic resonance</subject><subject>Membrane permeability</subject><subject>Mercury</subject><subject>NMR</subject><subject>Normal distribution</subject><subject>Nuclear magnetic resonance</subject><subject>Parameter identification</subject><subject>Permeability</subject><subject>Physics</subject><subject>Porosity</subject><subject>Prediction models</subject><subject>Predictions</subject><subject>Research methodology</subject><subject>Reservoirs</subject><subject>Sandstone</subject><subject>Scanning electron microscopy</subject><subject>Sedimentary rocks</subject><subject>Seepage</subject><subject>Statistics for Engineering</subject><issn>1874-8961</issn><issn>1874-8953</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kM1OAjEURidGExF9AVc3cetoO8P8uUMUMRElggtXTae9HYowg21nwYv4vBYwujO5yb1pzvc1OUFwTskVJSS7ttSvXkiiOKSE0jykB0GH5lkvzIskPvy9U3ocnFi7ICSlcUI7wVcfJmhWyEu91G4DE4NSC6ebGsaNxCU0Cqa6rpYYTpB_wPP4FWYR3GnrjC7bHahrmOlq7mDKa2ldU6O9gT4MuEWYulZuwENujjBquW_SLQwbs-Lb7CW8-_IK51zDLbfaPwzmuuanwZHiS4tnP7sbvA3vZ4NR-PTy8DjoP4UiIsSFIiuVlJQIkWYoolQkXkYP_YiMKCFJobDIE5qpTGAmhEpiUUaRLGSSU9UjcTe42PeuTfPZonVs0bSm9l-ymBRxnqcFST0V7SlhGmsNKrY2esXNhlHCtv7Z3j_z_tnOP6M-FO9D1sN1heav-p_UN8LMiPc</recordid><startdate>20240801</startdate><enddate>20240801</enddate><creator>Zhao, Jing</creator><creator>Huang, Zhilong</creator><creator>Dong, Jin</creator><creator>Zhang, Jingyuan</creator><creator>Wang, Rui</creator><creator>Ma, Chonglin</creator><creator>Deng, Guangjun</creator><creator>Xu, Maguang</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</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></search><sort><creationdate>20240801</creationdate><title>A Permeability Prediction Model of Single-Peak NMR T2 Distribution in Tight Sandstones: A Case Study on the Huangliu Formation, Yinggehai Basin, China</title><author>Zhao, Jing ; Huang, Zhilong ; Dong, Jin ; Zhang, Jingyuan ; Wang, Rui ; Ma, Chonglin ; Deng, Guangjun ; Xu, Maguang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c200t-c7bfdd10cc67ec26c50074e74ec70fcd09fe98517f7ce7ccf53cb22d9d581f403</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Chemistry and Earth Sciences</topic><topic>Computer Science</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Electron microscopy</topic><topic>Experimental methods</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Hydrogeology</topic><topic>Magnetic permeability</topic><topic>Magnetic resonance</topic><topic>Membrane permeability</topic><topic>Mercury</topic><topic>NMR</topic><topic>Normal distribution</topic><topic>Nuclear magnetic resonance</topic><topic>Parameter identification</topic><topic>Permeability</topic><topic>Physics</topic><topic>Porosity</topic><topic>Prediction models</topic><topic>Predictions</topic><topic>Research methodology</topic><topic>Reservoirs</topic><topic>Sandstone</topic><topic>Scanning electron microscopy</topic><topic>Sedimentary rocks</topic><topic>Seepage</topic><topic>Statistics for Engineering</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Jing</creatorcontrib><creatorcontrib>Huang, Zhilong</creatorcontrib><creatorcontrib>Dong, Jin</creatorcontrib><creatorcontrib>Zhang, Jingyuan</creatorcontrib><creatorcontrib>Wang, Rui</creatorcontrib><creatorcontrib>Ma, Chonglin</creatorcontrib><creatorcontrib>Deng, Guangjun</creatorcontrib><creatorcontrib>Xu, Maguang</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><jtitle>Mathematical geosciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Jing</au><au>Huang, Zhilong</au><au>Dong, Jin</au><au>Zhang, Jingyuan</au><au>Wang, Rui</au><au>Ma, Chonglin</au><au>Deng, Guangjun</au><au>Xu, Maguang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Permeability Prediction Model of Single-Peak NMR T2 Distribution in Tight Sandstones: A Case Study on the Huangliu Formation, Yinggehai Basin, China</atitle><jtitle>Mathematical geosciences</jtitle><stitle>Math Geosci</stitle><date>2024-08-01</date><risdate>2024</risdate><volume>56</volume><issue>6</issue><spage>1303</spage><epage>1333</epage><pages>1303-1333</pages><issn>1874-8961</issn><eissn>1874-8953</eissn><abstract>Tight sandstone reservoirs have low porosity, low permeability, and a complex pore structure. The seepage from tight sandstones is a key factor in evaluating the oil and gas accumulation in these reservoirs. Therefore, reservoir permeability prediction has become the focus of researchers. Using nuclear magnetic resonance (NMR), high-pressure mercury injection, scanning electron microscopy, and other experimental methods, scholars have established various permeability prediction models, which have obvious advantages and disadvantages. However, there is less research conducted on predicting the permeability of tight sandstone reservoirs according to their single-peak NMR
T
2
distribution. Based on NMR experiments and the bimodal Gaussian density formula, this study identified the criteria for determining the types of reservoir pore structures with single-peak NMR
T
2
distribution and established the parameters (
η
1
and
η
2
) that can be used in the evaluation of reservoir pore structure. A novel model for predicting the permeability of tight sandstone reservoirs was established using
η
1
and
η
2
. The results of the prediction model proposed in this study were found to be superior to the results of eight permeability prediction models established by other scholars in the studied case of the Huangliu Formation. However, permeability prediction models established using the NMR experimental results of different sources were found to be ineffective. Additionally, the new model is suitable for use with sandstone reservoirs with both single-peak and double-peak NMR
T
2
distributions in the studied case of the Yanchang Formation. Logging curves can be used to predict
η
1
and
η
2
, and the permeability of a single well of a tight sandstone reservoir. The study findings would be useful for predicting tight sandstone reservoir permeability.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s11004-023-10118-1</doi><tpages>31</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1874-8961 |
ispartof | Mathematical geosciences, 2024-08, Vol.56 (6), p.1303-1333 |
issn | 1874-8961 1874-8953 |
language | eng |
recordid | cdi_proquest_journals_3093886906 |
source | SpringerLink Journals - AutoHoldings |
subjects | Chemistry and Earth Sciences Computer Science Earth and Environmental Science Earth Sciences Electron microscopy Experimental methods Geotechnical Engineering & Applied Earth Sciences Hydrogeology Magnetic permeability Magnetic resonance Membrane permeability Mercury NMR Normal distribution Nuclear magnetic resonance Parameter identification Permeability Physics Porosity Prediction models Predictions Research methodology Reservoirs Sandstone Scanning electron microscopy Sedimentary rocks Seepage Statistics for Engineering |
title | A Permeability Prediction Model of Single-Peak NMR T2 Distribution in Tight Sandstones: A Case Study on the Huangliu Formation, Yinggehai Basin, China |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T12%3A33%3A01IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Permeability%20Prediction%20Model%20of%20Single-Peak%20NMR%20T2%20Distribution%20in%20Tight%20Sandstones:%20A%20Case%20Study%20on%20the%20Huangliu%20Formation,%20Yinggehai%20Basin,%20China&rft.jtitle=Mathematical%20geosciences&rft.au=Zhao,%20Jing&rft.date=2024-08-01&rft.volume=56&rft.issue=6&rft.spage=1303&rft.epage=1333&rft.pages=1303-1333&rft.issn=1874-8961&rft.eissn=1874-8953&rft_id=info:doi/10.1007/s11004-023-10118-1&rft_dat=%3Cproquest_cross%3E3093886906%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3093886906&rft_id=info:pmid/&rfr_iscdi=true |