Enhanced rig activity analysis with Machine Learning
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WellAct has a very effective algorithm.
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Like any mathematical function, it needs certain data to function. If they are missing, it will not run the analysis.
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ML helps "plugging" those holes as we have trained it to recognize patterns even if data are incomplete or not good quality.
Generative AI integrates automated Real Time reporting
Looking at the data in Real time.
Wondering how the rig is performing? Ask DrillBot!
Hello Drillbot.
Can you tell me what was the drilling connection performance today?
Weight To Weight Metrics
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Median Duration: 6.60 minutes
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Maximum Duration: 13.03 minutes
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Minimum Duration: 4.40 minutes
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KPI Target: 5 minutes
Performance Evaluation
Inconsistent Weight-to-Weight Durations
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Large variation between the longest and shortest intervals indicates performance inconsistencies.
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Significant potential for time savings in the remaining drilling section.
KPI Target Exceeded
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Median (+10%) is above the 5-minute KPI, signaling structural inefficiencies.
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Optimizing operations could yield substantial time & cost savings.
Most Time-Consuming Activity (within Weight-to-Weight)
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Circulating: The majority of connection time is used here.
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Should be reviewed for efficiency improvements.
We spent the day tripping out of hole.
I wonder if we did in it the best possible way?
Hello Drillbot.
can you plot duration of each tripping out versus block down versus bit depth?
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Results
Can we quantify improved performances?
Built a RT reporting system active 24hrs a day via chatbot
ML improved the accuracy of the automated rig activity identification by a margin between 5% and 10%. Bringing the overall rig activity identification between 85% and 90% of the total time.