Two Takeaways
- Survey verification should not require expertise in surveying Error-model-based
- QC should be possible using the Error model
Expertise Requirement
- Most people who drive cars are not mechanics
- There are warning signs when you need one
- Most who drill and survey wellbores are not survey experts
- Consumers of the data may be even less of an expert
- How do they know when there is a problem?
- Importance of error-model-based QC
Error-Model-Based QC: A Brief History
- Pre-Error Model
- Measure deviation from references
- Many standards, usually fixed thresholds
- SPE 103734, Ekseth, et al (2006)
- Define weighting functions, Root-Sum-Square
- Dynamic QC – Changes with orientation
- Maus, et al (2017)
- Account for error covariance
- Compute “sigma distance”
Shortcomings of These Methods
- Focus on single survey evaluation
- User is interested in the set as a whole
- Exception: MSE in 103734, but use and interpretation requires a knowledgeable user
- Real-World workflows can lead to complacency
- Once one survey fails, all the rest will likely fail
- “Drill ahead, this always happens near vertical!”
- Escalation procedures often assume some level of expertise
- “If you identify interference from an offset well, notify town”
- Assumes that they already know if the survey is good or bad
Decision Making With QC
- When do I stop drilling?
- When do I need to resurvey the well?
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View the entire Presentation:
Survey QC, Decision Making, and a Modest Proposal for Error Models
Marc Willerth, MagVar