Shale brittleness index prediction method based on improved KNN algorithm
The invention discloses a shale brittleness index prediction method based on an improved KNN algorithm. The method comprises the following steps: determining a training well and a test well; performing correlation analysis and comparison on each logging parameter in the training well and the brittle...
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| creator | LYU XIAONAN LI YONG DUAN ZHENGXIN HUANG KAIXING WU CHAORONG |
| description | The invention discloses a shale brittleness index prediction method based on an improved KNN algorithm. The method comprises the following steps: determining a training well and a test well; performing correlation analysis and comparison on each logging parameter in the training well and the brittleness index, selecting three logging parameters with first three absolute values of correlation coefficients as independent variables, and taking the brittleness index as a dependent variable; constructing a training sample and a training database by using data in the training well; iteratively optimizing the training database by using a KNN algorithm to obtain an optimal training database; taking the data in the optimal training database as training data, taking the corresponding data in the test well and the training well as test data, and obtaining an optimal K value by adopting a cross validation method; and establishing a KNN model by using the optimal training database and the optimal K value to obtain a predi |
| format | Patent |
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The method comprises the following steps: determining a training well and a test well; performing correlation analysis and comparison on each logging parameter in the training well and the brittleness index, selecting three logging parameters with first three absolute values of correlation coefficients as independent variables, and taking the brittleness index as a dependent variable; constructing a training sample and a training database by using data in the training well; iteratively optimizing the training database by using a KNN algorithm to obtain an optimal training database; taking the data in the optimal training database as training data, taking the corresponding data in the test well and the training well as test data, and obtaining an optimal K value by adopting a cross validation method; and establishing a KNN model by using the optimal training database and the optimal K value to obtain a predi</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; DETECTING MASSES OR OBJECTS ; GEOPHYSICS ; GRAVITATIONAL MEASUREMENTS ; HANDLING RECORD CARRIERS ; MEASURING ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS ; TESTING</subject><creationdate>2022</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=20220513&DB=EPODOC&CC=CN&NR=114492627A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220513&DB=EPODOC&CC=CN&NR=114492627A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LYU XIAONAN</creatorcontrib><creatorcontrib>LI YONG</creatorcontrib><creatorcontrib>DUAN ZHENGXIN</creatorcontrib><creatorcontrib>HUANG KAIXING</creatorcontrib><creatorcontrib>WU CHAORONG</creatorcontrib><title>Shale brittleness index prediction method based on improved KNN algorithm</title><description>The invention discloses a shale brittleness index prediction method based on an improved KNN algorithm. 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The method comprises the following steps: determining a training well and a test well; performing correlation analysis and comparison on each logging parameter in the training well and the brittleness index, selecting three logging parameters with first three absolute values of correlation coefficients as independent variables, and taking the brittleness index as a dependent variable; constructing a training sample and a training database by using data in the training well; iteratively optimizing the training database by using a KNN algorithm to obtain an optimal training database; taking the data in the optimal training database as training data, taking the corresponding data in the test well and the training well as test data, and obtaining an optimal K value by adopting a cross validation method; and establishing a KNN model by using the optimal training database and the optimal K value to obtain a predi</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTING COUNTING DETECTING MASSES OR OBJECTS GEOPHYSICS GRAVITATIONAL MEASUREMENTS HANDLING RECORD CARRIERS MEASURING PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS TESTING |
| title | Shale brittleness index prediction method based on improved KNN algorithm |
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