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AI/ML Drilling Systems Need Timely Trusted Data to Deliver Trusted Results

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Course Credit: 0.15 CEU, 1.5 PDH

Real-time data now represents a growing stream of scores of channels of digital data being fed concurrently to numerous receiving entities in the operator’s remote monitoring amenities, at contractor centers and other facilities. Analytics are applied to data channels to signal or predict deviations from expected readings that require attention. For such systems to work effectively and reliably the input data must be trustworthy.

Establishing trust in a digital, i.e. binary, manner requires more rigor than what would be performed by a human observer. The boundary conditions for trust must be codified into rules that are grouped in a policy suitable for each data stream. Such conditions could include e.g. temperature range for a sensor outside of which readings are invalid.

The standards-based transmittals of WITSML data can be augmented with Data Assurance that codifies the rules that have established each data sample’s “pass” or “fail” status. To avoid cluttering the transmission channels, samples are transmitted with a blank Data Assurance field if the relevant policy was satisfied; the metadata indicating the rule or rules that were failed and the policy they are attached to are sent only with samples that failed. This information can be ingested by automated analytical tools. All content contained within this webinar is copyrighted by Jay Hollingsworth and its use and/or reproduction outside the portal requires express permission from Jay Hollingsworth.

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Course Chapters

  • 1AI/ML Drilling Systems Need Timely Trusted Data to Deliver Trusted Results - Chapter 1
    Media Type: Video

Credits

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

Speakers

Jay Hollingsworth