Unsupervised learning of well drilling operations: Fuzzy rule-based approach
The paper proposes a method for operation identification in the context of well drilling. This task is usually trusted to domain experts, however, we introduce a fuzzy rule-based classifier for the automatic detection of ongoing operations at a drilling site. The operations of the drilling rig are u...
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| creator | Riid, A. Saadallah, N. |
| description | The paper proposes a method for operation identification in the context of well drilling. This task is usually trusted to domain experts, however, we introduce a fuzzy rule-based classifier for the automatic detection of ongoing operations at a drilling site. The operations of the drilling rig are usually monitored and sensory data is stored. The proposed classifier is identified from real data from an already drilled well via unsupervised learning. The results of our experiment are encouraging, since we manage to separate a number of distinct drilling operations and the classifier is transparent to interpretation thus its decisions are understandable to domain experts. |
| doi_str_mv | 10.1109/INES.2012.6249862 |
| format | Conference Proceeding |
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This task is usually trusted to domain experts, however, we introduce a fuzzy rule-based classifier for the automatic detection of ongoing operations at a drilling site. The operations of the drilling rig are usually monitored and sensory data is stored. The proposed classifier is identified from real data from an already drilled well via unsupervised learning. The results of our experiment are encouraging, since we manage to separate a number of distinct drilling operations and the classifier is transparent to interpretation thus its decisions are understandable to domain experts.</description><subject>Accuracy</subject><subject>Conferences</subject><subject>Couplings</subject><subject>Joints</subject><subject>Rocks</subject><subject>Unsupervised learning</subject><issn>1543-9259</issn><issn>2767-9462</issn><isbn>9781467326940</isbn><isbn>1467326941</isbn><isbn>9781467326957</isbn><isbn>9781467326933</isbn><isbn>1467326933</isbn><isbn>146732695X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVUMlOwzAUNJtEVPoBiEt-wMXP--OGqhYqRXCgnCs3ccDIpJHTgNqvb4BemMtIsx2GkGtgEwCGt4un2cuEM-ATzSVazU_IGI0FqY3gGpU5JRk32lCUmp_98yQ7JxkoKShyhZdk3HUfbMCQACsyUrw2Xd_69BU6X-XRu9SE5i3f1Pm3jzGvUojxVxgybhs2TXeXz_v9fpenPnq6dj8117Zp48r3K3JRu9j58ZFHZDmfLaePtHh-WEzvCxqQbalTHpmzAo0BoTTIyjKsrXasxNJwkJKvKwWssrAulfde1NqURihAg95IMSI3f7NhMFdtCp8u7VbHa8QBQRNS5g</recordid><startdate>201206</startdate><enddate>201206</enddate><creator>Riid, A.</creator><creator>Saadallah, N.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201206</creationdate><title>Unsupervised learning of well drilling operations: Fuzzy rule-based approach</title><author>Riid, A. ; Saadallah, N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-a5e90a83977135614d809f86a0c9c721442bd510d81bc5eee3f67c7351979e743</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Accuracy</topic><topic>Conferences</topic><topic>Couplings</topic><topic>Joints</topic><topic>Rocks</topic><topic>Unsupervised learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Riid, A.</creatorcontrib><creatorcontrib>Saadallah, N.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Riid, A.</au><au>Saadallah, N.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Unsupervised learning of well drilling operations: Fuzzy rule-based approach</atitle><btitle>2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES)</btitle><stitle>INES</stitle><date>2012-06</date><risdate>2012</risdate><spage>375</spage><epage>380</epage><pages>375-380</pages><issn>1543-9259</issn><eissn>2767-9462</eissn><isbn>9781467326940</isbn><isbn>1467326941</isbn><eisbn>9781467326957</eisbn><eisbn>9781467326933</eisbn><eisbn>1467326933</eisbn><eisbn>146732695X</eisbn><abstract>The paper proposes a method for operation identification in the context of well drilling. This task is usually trusted to domain experts, however, we introduce a fuzzy rule-based classifier for the automatic detection of ongoing operations at a drilling site. The operations of the drilling rig are usually monitored and sensory data is stored. The proposed classifier is identified from real data from an already drilled well via unsupervised learning. The results of our experiment are encouraging, since we manage to separate a number of distinct drilling operations and the classifier is transparent to interpretation thus its decisions are understandable to domain experts.</abstract><pub>IEEE</pub><doi>10.1109/INES.2012.6249862</doi><tpages>6</tpages></addata></record> |
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| identifier | ISSN: 1543-9259 |
| ispartof | 2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES), 2012, p.375-380 |
| issn | 1543-9259 2767-9462 |
| language | eng |
| recordid | cdi_ieee_primary_6249862 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy Conferences Couplings Joints Rocks Unsupervised learning |
| title | Unsupervised learning of well drilling operations: Fuzzy rule-based approach |
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