SYSTEMS AND METHODS FOR AUTOMATIC GENERATION OF DRILLING SCHEDULES USING MACHINE LEARNING

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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Bibliographische Detailangaben
Hauptverfasser: Al-Shahri, Ali M, Zahrani, Aidah G, Nooruddin, Hasan A, Shahri, Mohammed A
Format: Patent
Sprache:eng
Schlagworte:
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
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Beschreibung
Zusammenfassung: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.