Recent advances in machine learning and big data analytics, combined with Frank’s proprietary intelligent algorithms, offer a superior method to analyze and utilize the large volumes of data generated by connection make-up operations.

The iCAM® Connection Analyzed Make-up System represents a game-changing disruption in tubular connection analysis and control technology. The iCAM® system’s artificial intelligence engine and machine learning capabilities provide automated evaluation of connection make-up data, offering flexible integration with equipment such as power tongs and CRTs to produce optimal connection integrity and accelerate the make-up process, with the potential to reduce personnel on board.

The iCAM® system draws upon a dynamic trove of data consisting of millions of connection make-ups to positively identify anomalies, including thread and seal interference, dope squeeze, galling and other factors outside of conventional make-up criteria. This enables the iCAM® system to evaluate each connection for proper make-up against more robust and descriptive criteria than possible with traditional OEM rules-based parameters and to do so objectively. The result is a more accurate, reliable, and consistent assessment of tubular connection integrity.

 

Benefits:

  • Integrates readily with multiple equipment packages for both land and offshore operations
  • Available now, everywhere
  • Potentially reduces personnel on board
  • Increases well integrity and safety
  • Improves accuracy, consistency and reliability in connection integrity assessment
  • Has the potential to increase efficiencies through faster graph disposition
  • Data-driven recommendations result in fewer rejects and increase connection integrity
  • When integrated with the DISPLAY™ Digital Application, allows remote, real-time monitoring of the make-up process. Customers can view live and historical data using a web enabled device anywhere in the world

Watch a video on Machine Learning and the iCAM® system.

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Industry Publications

Read a December 2018 article on AI and Machine Learning in Oil & Gas Engineering magazine, “Machine learning streamlines tubular connection analysis” by Brennan Domec, Ph.D., P.E.

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