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Predicting Drilling Dysfunction With AI-powered Automation

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Course Credit: 0.15 CEU, 1.5 PDH

To adapt to a rapidly changing market, oil and gas operators above all need to seek out stability in their operations, and that means eliminating, or at least minimizing, drilling dysfunction. Artificial intelligence applications offer the industry new ways to reach this goal, including:

Prescriptive maintenance: Using machine learning algorithms, prescriptive analytics use historical and current data to predict an impending asset failure, pinpoint when and why it will happen, and recommend potential plans of action.

Monitoring downhole conditions: AI algorithms use sensor data to analyze downhole conditions and immediately alert personnel when an issue arises, increasing speed and accuracy of detection.

Increased operational safety and insights: Natural language processing technology is able to extract information from unstructured data, including insights on well and reservoir performance and on potential safety hazards.

Cyber threat detection: Anomaly detection software monitors the behavior of devices within a network and flag any unusual behaviors or abnormal signals being sent out, allowing them to catch even subtler cyber attacks.

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Course Chapters

  • 1Predicting Drilling Dysfunction With AI-powered Automation - Chapter 1
    Media Type: Video

Credits

Earn credits by completing this course0.15 CEU credit1.5 PDH credits

Speakers

Philippe Herve