SYSTEMS AND METHODS FOR AUTOMATIC GENERATION OF DRILLING SCHEDULES USING MACHINE LEARNING
Systems and methods for automatically generating drilling schedules are disclosed. According to one embodiment, a method of predicting rig movement between wells includes receiving historical well data regarding individual well types and historical rig data regarding individual rigs, and generating...
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| creator | Al-Shahri, Ali M Zahrani, Aidah G Nooruddin, Hasan A Shahri, Mohammed A |
| description | Systems and methods for automatically generating drilling schedules are disclosed. According to one embodiment, a method of predicting rig movement between wells includes receiving historical well data regarding individual well types and historical rig data regarding individual rigs, and generating a Markov Chain model from the historical well data and the historical rig data. The Markov Chain model includes a plurality of states and a plurality of links between states. Each state of the plurality of states is a well class derived from the historical well data. Each link indicates a number of rigs that traveled between individual well classes. The method further includes determining, using the Markov Chain model, a probability of rigs moving between individual well classes, and predicting movement of individual rigs of a plurality of rigs between future wells based at least in part on the Markov Chain model. |
| format | Patent |
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According to one embodiment, a method of predicting rig movement between wells includes receiving historical well data regarding individual well types and historical rig data regarding individual rigs, and generating a Markov Chain model from the historical well data and the historical rig data. The Markov Chain model includes a plurality of states and a plurality of links between states. Each state of the plurality of states is a well class derived from the historical well data. Each link indicates a number of rigs that traveled between individual well classes. The method further includes determining, using the Markov Chain model, a probability of rigs moving between individual well classes, and predicting movement of individual rigs of a plurality of rigs between future wells based at least in part on the Markov Chain model.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; ELECTRIC DIGITAL DATA PROCESSING ; HANDLING RECORD CARRIERS ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2021</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=20211111&DB=EPODOC&CC=US&NR=2021350335A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20211111&DB=EPODOC&CC=US&NR=2021350335A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Al-Shahri, Ali M</creatorcontrib><creatorcontrib>Zahrani, Aidah G</creatorcontrib><creatorcontrib>Nooruddin, Hasan A</creatorcontrib><creatorcontrib>Shahri, Mohammed A</creatorcontrib><title>SYSTEMS AND METHODS FOR AUTOMATIC GENERATION OF DRILLING SCHEDULES USING MACHINE LEARNING</title><description>Systems and methods for automatically generating drilling schedules are disclosed. According to one embodiment, a method of predicting rig movement between wells includes receiving historical well data regarding individual well types and historical rig data regarding individual rigs, and generating a Markov Chain model from the historical well data and the historical rig data. The Markov Chain model includes a plurality of states and a plurality of links between states. Each state of the plurality of states is a well class derived from the historical well data. Each link indicates a number of rigs that traveled between individual well classes. The method further includes determining, using the Markov Chain model, a probability of rigs moving between individual well classes, and predicting movement of individual rigs of a plurality of rigs between future wells based at least in part on the Markov Chain model.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>HANDLING RECORD CARRIERS</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2021</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNiksKwjAURTNxIOoeHjgW2oYuICQvTSAfyEsGHZVS4ki0UPePFVyAo3vO4R7ZSCNl9AQiKPCYTVQEOiYQJUcvspUwYMC0UwwQNahknbNhAJIGVXFIUOjrXkhjA4JDkcIezuxwnx9bvfz2xK4aszS3ur6muq3zUp_1PRXqmq7lfcN5L1r-3-sD9FYycg</recordid><startdate>20211111</startdate><enddate>20211111</enddate><creator>Al-Shahri, Ali M</creator><creator>Zahrani, Aidah G</creator><creator>Nooruddin, Hasan A</creator><creator>Shahri, Mohammed A</creator><scope>EVB</scope></search><sort><creationdate>20211111</creationdate><title>SYSTEMS AND METHODS FOR AUTOMATIC GENERATION OF DRILLING SCHEDULES USING MACHINE LEARNING</title><author>Al-Shahri, Ali M ; Zahrani, Aidah G ; Nooruddin, Hasan A ; Shahri, Mohammed A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2021350335A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2021</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>HANDLING RECORD CARRIERS</topic><topic>PHYSICS</topic><topic>PRESENTATION OF DATA</topic><topic>RECOGNITION OF DATA</topic><topic>RECORD CARRIERS</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><toplevel>online_resources</toplevel><creatorcontrib>Al-Shahri, Ali M</creatorcontrib><creatorcontrib>Zahrani, Aidah G</creatorcontrib><creatorcontrib>Nooruddin, Hasan A</creatorcontrib><creatorcontrib>Shahri, Mohammed A</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Al-Shahri, Ali M</au><au>Zahrani, Aidah G</au><au>Nooruddin, Hasan A</au><au>Shahri, Mohammed A</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>SYSTEMS AND METHODS FOR AUTOMATIC GENERATION OF DRILLING SCHEDULES USING MACHINE LEARNING</title><date>2021-11-11</date><risdate>2021</risdate><abstract>Systems and methods for automatically generating drilling schedules are disclosed. According to one embodiment, a method of predicting rig movement between wells includes receiving historical well data regarding individual well types and historical rig data regarding individual rigs, and generating a Markov Chain model from the historical well data and the historical rig data. The Markov Chain model includes a plurality of states and a plurality of links between states. Each state of the plurality of states is a well class derived from the historical well data. Each link indicates a number of rigs that traveled between individual well classes. The method further includes determining, using the Markov Chain model, a probability of rigs moving between individual well classes, and predicting movement of individual rigs of a plurality of rigs between future wells based at least in part on the Markov Chain model.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | SYSTEMS AND METHODS FOR AUTOMATIC GENERATION OF DRILLING SCHEDULES USING MACHINE LEARNING |
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