Drilling leakage crack width prediction method based on neural network data mining
The embodiment of the invention provides a drilling leakage crack width prediction method based on neural network data mining. The method comprises the steps that historical drilling data, a leakage stoppage case, an imaging logging real crack width and other data information of a target block are c...
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| creator | LI FANG LI LIZONG ZUO FUYIN SU JUNLIN LUO PINGYA HUANG JINJUN QIN ZUHAI ZHAO YANG |
| description | The embodiment of the invention provides a drilling leakage crack width prediction method based on neural network data mining. The method comprises the steps that historical drilling data, a leakage stoppage case, an imaging logging real crack width and other data information of a target block are collected; data preprocessing is carried out on the collected data information, wherein the preprocessing content comprises data cleaning, integration and conversion; the preprocessed historical drilling data is used as input, the crack width is used as output, the imaging logging real crack width isused as a standard value, and a crack width prediction neural network model is obtained through supervision training and optimization; and the drilling instant data related to the target positive drilling well is imported into the neural network model, and the model automatically judges the corresponding well depth crack width at the moment. By utilizing the technical scheme provided by the embodiment of the invention, t |
| format | Patent |
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The method comprises the steps that historical drilling data, a leakage stoppage case, an imaging logging real crack width and other data information of a target block are collected; data preprocessing is carried out on the collected data information, wherein the preprocessing content comprises data cleaning, integration and conversion; the preprocessed historical drilling data is used as input, the crack width is used as output, the imaging logging real crack width isused as a standard value, and a crack width prediction neural network model is obtained through supervision training and optimization; and the drilling instant data related to the target positive drilling well is imported into the neural network model, and the model automatically judges the corresponding well depth crack width at the moment. By utilizing the technical scheme provided by the embodiment of the invention, t</description><language>chi ; eng</language><subject>EARTH DRILLING ; EARTH DRILLING, e.g. DEEP DRILLING ; FIXED CONSTRUCTIONS ; MINING ; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR ASLURRY OF MINERALS FROM WELLS</subject><creationdate>2020</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=20200403&DB=EPODOC&CC=CN&NR=110952978A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200403&DB=EPODOC&CC=CN&NR=110952978A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LI FANG</creatorcontrib><creatorcontrib>LI LIZONG</creatorcontrib><creatorcontrib>ZUO FUYIN</creatorcontrib><creatorcontrib>SU JUNLIN</creatorcontrib><creatorcontrib>LUO PINGYA</creatorcontrib><creatorcontrib>HUANG JINJUN</creatorcontrib><creatorcontrib>QIN ZUHAI</creatorcontrib><creatorcontrib>ZHAO YANG</creatorcontrib><title>Drilling leakage crack width prediction method based on neural network data mining</title><description>The embodiment of the invention provides a drilling leakage crack width prediction method based on neural network data mining. The method comprises the steps that historical drilling data, a leakage stoppage case, an imaging logging real crack width and other data information of a target block are collected; data preprocessing is carried out on the collected data information, wherein the preprocessing content comprises data cleaning, integration and conversion; the preprocessed historical drilling data is used as input, the crack width is used as output, the imaging logging real crack width isused as a standard value, and a crack width prediction neural network model is obtained through supervision training and optimization; and the drilling instant data related to the target positive drilling well is imported into the neural network model, and the model automatically judges the corresponding well depth crack width at the moment. By utilizing the technical scheme provided by the embodiment of the invention, t</description><subject>EARTH DRILLING</subject><subject>EARTH DRILLING, e.g. DEEP DRILLING</subject><subject>FIXED CONSTRUCTIONS</subject><subject>MINING</subject><subject>OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR ASLURRY OF MINERALS FROM WELLS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZAhyKcrMycnMS1fISU3MTkxPVUguSkzOVijPTCnJUCgoSk3JTC7JzM9TyE0tychPUUhKLE5NUQDy81JLixJzgFRJeX5RtkJKYkmiQm5mHtAkHgbWtMSc4lReKM3NoOjmGuLsoZtakB-fWlyQmJwK1BXv7GdoaGBpamRpbuFoTIwaAA2TOAs</recordid><startdate>20200403</startdate><enddate>20200403</enddate><creator>LI FANG</creator><creator>LI LIZONG</creator><creator>ZUO FUYIN</creator><creator>SU JUNLIN</creator><creator>LUO PINGYA</creator><creator>HUANG JINJUN</creator><creator>QIN ZUHAI</creator><creator>ZHAO YANG</creator><scope>EVB</scope></search><sort><creationdate>20200403</creationdate><title>Drilling leakage crack width prediction method based on neural network data mining</title><author>LI FANG ; LI LIZONG ; ZUO FUYIN ; SU JUNLIN ; LUO PINGYA ; HUANG JINJUN ; QIN ZUHAI ; ZHAO YANG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN110952978A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2020</creationdate><topic>EARTH DRILLING</topic><topic>EARTH DRILLING, e.g. DEEP DRILLING</topic><topic>FIXED CONSTRUCTIONS</topic><topic>MINING</topic><topic>OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR ASLURRY OF MINERALS FROM WELLS</topic><toplevel>online_resources</toplevel><creatorcontrib>LI FANG</creatorcontrib><creatorcontrib>LI LIZONG</creatorcontrib><creatorcontrib>ZUO FUYIN</creatorcontrib><creatorcontrib>SU JUNLIN</creatorcontrib><creatorcontrib>LUO PINGYA</creatorcontrib><creatorcontrib>HUANG JINJUN</creatorcontrib><creatorcontrib>QIN ZUHAI</creatorcontrib><creatorcontrib>ZHAO YANG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LI FANG</au><au>LI LIZONG</au><au>ZUO FUYIN</au><au>SU JUNLIN</au><au>LUO PINGYA</au><au>HUANG JINJUN</au><au>QIN ZUHAI</au><au>ZHAO YANG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Drilling leakage crack width prediction method based on neural network data mining</title><date>2020-04-03</date><risdate>2020</risdate><abstract>The embodiment of the invention provides a drilling leakage crack width prediction method based on neural network data mining. The method comprises the steps that historical drilling data, a leakage stoppage case, an imaging logging real crack width and other data information of a target block are collected; data preprocessing is carried out on the collected data information, wherein the preprocessing content comprises data cleaning, integration and conversion; the preprocessed historical drilling data is used as input, the crack width is used as output, the imaging logging real crack width isused as a standard value, and a crack width prediction neural network model is obtained through supervision training and optimization; and the drilling instant data related to the target positive drilling well is imported into the neural network model, and the model automatically judges the corresponding well depth crack width at the moment. By utilizing the technical scheme provided by the embodiment of the invention, t</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | EARTH DRILLING EARTH DRILLING, e.g. DEEP DRILLING FIXED CONSTRUCTIONS MINING OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR ASLURRY OF MINERALS FROM WELLS |
| title | Drilling leakage crack width prediction method based on neural network data mining |
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