Rapid inversion method for logging-while-drilling information based on transfer learning
The invention discloses a transfer learning-based logging-while-drilling information rapid inversion method, which comprises the following steps of: constructing a logging-while-drilling azimuth electromagnetic wave logging forward model, and simulating logging-while-drilling response of a multi-com...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Patent |
| Sprache: | chi ; eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | GAO MUZHI ZHU GAOYANG |
| description | The invention discloses a transfer learning-based logging-while-drilling information rapid inversion method, which comprises the following steps of: constructing a logging-while-drilling azimuth electromagnetic wave logging forward model, and simulating logging-while-drilling response of a multi-component logging-while-drilling electromagnetic wave logging instrument under different stratum parameter conditions by utilizing the logging-while-drilling azimuth electromagnetic wave logging forward model; the method comprises the following steps: constructing a logging-while-drilling response database, combining a convolutional neural network with an LSTM network to construct a logging-while-drilling azimuth electromagnetic wave logging information inversion model, training and optimizing logging-while-drilling response data in the logging-while-drilling response database to obtain a source domain network model, and constructing a target domain network model based on transfer learning. After the target domain net |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN116992754A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN116992754A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN116992754A3</originalsourceid><addsrcrecordid>eNrjZIgISizITFHIzCtLLSrOzM9TyE0tychPUUjLL1LIyU9Pz8xL1y3PyMxJ1U0pyszJAXKBaoGSuYklINVJicWpKQpARklRYl5xWipQU2piUR5QGQ8Da1piTnEqL5TmZlB0cw1x9tBNLciPTy0uSExOzUstiXf2MzQ0s7Q0Mjc1cTQmRg0A6e068g</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Rapid inversion method for logging-while-drilling information based on transfer learning</title><source>esp@cenet</source><creator>GAO MUZHI ; ZHU GAOYANG</creator><creatorcontrib>GAO MUZHI ; ZHU GAOYANG</creatorcontrib><description>The invention discloses a transfer learning-based logging-while-drilling information rapid inversion method, which comprises the following steps of: constructing a logging-while-drilling azimuth electromagnetic wave logging forward model, and simulating logging-while-drilling response of a multi-component logging-while-drilling electromagnetic wave logging instrument under different stratum parameter conditions by utilizing the logging-while-drilling azimuth electromagnetic wave logging forward model; the method comprises the following steps: constructing a logging-while-drilling response database, combining a convolutional neural network with an LSTM network to construct a logging-while-drilling azimuth electromagnetic wave logging information inversion model, training and optimizing logging-while-drilling response data in the logging-while-drilling response database to obtain a source domain network model, and constructing a target domain network model based on transfer learning. After the target domain net</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; EARTH DRILLING ; EARTH DRILLING, e.g. DEEP DRILLING ; ELECTRIC DIGITAL DATA PROCESSING ; FIXED CONSTRUCTIONS ; MINING ; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR ASLURRY OF MINERALS FROM WELLS ; PHYSICS</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20231103&DB=EPODOC&CC=CN&NR=116992754A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20231103&DB=EPODOC&CC=CN&NR=116992754A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>GAO MUZHI</creatorcontrib><creatorcontrib>ZHU GAOYANG</creatorcontrib><title>Rapid inversion method for logging-while-drilling information based on transfer learning</title><description>The invention discloses a transfer learning-based logging-while-drilling information rapid inversion method, which comprises the following steps of: constructing a logging-while-drilling azimuth electromagnetic wave logging forward model, and simulating logging-while-drilling response of a multi-component logging-while-drilling electromagnetic wave logging instrument under different stratum parameter conditions by utilizing the logging-while-drilling azimuth electromagnetic wave logging forward model; the method comprises the following steps: constructing a logging-while-drilling response database, combining a convolutional neural network with an LSTM network to construct a logging-while-drilling azimuth electromagnetic wave logging information inversion model, training and optimizing logging-while-drilling response data in the logging-while-drilling response database to obtain a source domain network model, and constructing a target domain network model based on transfer learning. After the target domain net</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>EARTH DRILLING</subject><subject>EARTH DRILLING, e.g. DEEP DRILLING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>FIXED CONSTRUCTIONS</subject><subject>MINING</subject><subject>OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR ASLURRY OF MINERALS FROM WELLS</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZIgISizITFHIzCtLLSrOzM9TyE0tychPUUjLL1LIyU9Pz8xL1y3PyMxJ1U0pyszJAXKBaoGSuYklINVJicWpKQpARklRYl5xWipQU2piUR5QGQ8Da1piTnEqL5TmZlB0cw1x9tBNLciPTy0uSExOzUstiXf2MzQ0s7Q0Mjc1cTQmRg0A6e068g</recordid><startdate>20231103</startdate><enddate>20231103</enddate><creator>GAO MUZHI</creator><creator>ZHU GAOYANG</creator><scope>EVB</scope></search><sort><creationdate>20231103</creationdate><title>Rapid inversion method for logging-while-drilling information based on transfer learning</title><author>GAO MUZHI ; ZHU GAOYANG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116992754A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>EARTH DRILLING</topic><topic>EARTH DRILLING, e.g. DEEP DRILLING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>FIXED CONSTRUCTIONS</topic><topic>MINING</topic><topic>OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR ASLURRY OF MINERALS FROM WELLS</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>GAO MUZHI</creatorcontrib><creatorcontrib>ZHU GAOYANG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>GAO MUZHI</au><au>ZHU GAOYANG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Rapid inversion method for logging-while-drilling information based on transfer learning</title><date>2023-11-03</date><risdate>2023</risdate><abstract>The invention discloses a transfer learning-based logging-while-drilling information rapid inversion method, which comprises the following steps of: constructing a logging-while-drilling azimuth electromagnetic wave logging forward model, and simulating logging-while-drilling response of a multi-component logging-while-drilling electromagnetic wave logging instrument under different stratum parameter conditions by utilizing the logging-while-drilling azimuth electromagnetic wave logging forward model; the method comprises the following steps: constructing a logging-while-drilling response database, combining a convolutional neural network with an LSTM network to construct a logging-while-drilling azimuth electromagnetic wave logging information inversion model, training and optimizing logging-while-drilling response data in the logging-while-drilling response database to obtain a source domain network model, and constructing a target domain network model based on transfer learning. After the target domain net</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN116992754A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING EARTH DRILLING EARTH DRILLING, e.g. DEEP DRILLING ELECTRIC DIGITAL DATA PROCESSING FIXED CONSTRUCTIONS MINING OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR ASLURRY OF MINERALS FROM WELLS PHYSICS |
| title | Rapid inversion method for logging-while-drilling information based on transfer learning |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T02%3A10%3A08IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=GAO%20MUZHI&rft.date=2023-11-03&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN116992754A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |