Threat category
Threat Identification / Data Quality Support
This support family covers uncertain features, ILI data quality, and reconciliation workflows that help the engineer decide which real threat category should control the next step.
Quick scan
Category summary
3 topics currently available in this threat family.
Common concern drivers
- Weak classification confidence or conflicting labels
- Run-quality issues, matching drift, or weak field correlation
- Missing pipe attributes or prior validation
- Decisions that depend on exact size, location, or threat family
Common data gaps
- Missing vendor notes, run logs, or qualification context
- Poor weld alignment or feature matching confidence
- Sparse dig, NDE, or prior-run reconciliation history
Common decision pitfalls
- Treating the report table as ground truth
- Forcing a feature into a threat family without enough context
- Using a headline validation statistic while ignoring local outliers and run issues
Field verification themes
- Field verification may be driven by the need to reconcile the tool call with reality, not just by the reported severity.
Quick Methods and Reference Cards
API 1163 and tool-to-field reconciliation
Use qualification, validation, and field correlation review to decide whether the data are decision-grade.
In-Line Inspection of Pipelines
AMPP / NACE
Why it fits: Useful for corrosion review context, inspection capability questions, and understanding tool limitations.
Limitation: Provides broad inspection context rather than a topic-by-topic workflow for every anomaly.
In-line Inspection Systems Qualification Standard
API
Why it fits: Useful for data quality checks, feature confidence review, matching questions, and any topic driven by ILI limitations.
Limitation: This is a qualification and use framework, not a defect-specific engineering decision tool by itself.
Pipeline Data Quality and Reconciliation Practices
Internal / Program Guidance
Why it fits: Useful for classification uncertainty, matching issues, and when decisions depend on reconciling multiple data sources.
Limitation: Replace this placeholder with your organization’s actual SOP or governance document.
References and Further Reading
Core applicable standards
Core Applicable Standards
Most directly relevant to this topic and commonly used to frame the main review path.
In-line Inspection Systems Qualification Standard
API
Why it applies: Useful for data quality checks, feature confidence review, matching questions, and any topic driven by ILI limitations.
What it generally addresses: Foundational guidance for understanding ILI system qualification, performance, validation, and responsible use of inspection outputs.
Limitations: This is a qualification and use framework, not a defect-specific engineering decision tool by itself.
Pipeline Data Quality and Reconciliation Practices
Internal / Program Guidance
Why it applies: Useful for classification uncertainty, matching issues, and when decisions depend on reconciling multiple data sources.
What it generally addresses: Placeholder entry for company or program-level practices covering reconciliation, validation, and data governance.
Limitations: Replace this placeholder with your organization’s actual SOP or governance document.
Supporting context
Supporting / Cross-Discipline References
Helpful when the review needs integrity-management, regulatory, or cross-discipline context beyond the primary method family.
In-Line Inspection of Pipelines
AMPP / NACE
Why it applies: Useful for corrosion review context, inspection capability questions, and understanding tool limitations.
What it generally addresses: Reference material related to selecting, planning, and interpreting in-line inspection programs.
Limitations: Provides broad inspection context rather than a topic-by-topic workflow for every anomaly.
API 579
API
Why it applies: Useful as high-level fitness-for-service context when the condition needs broader damage-mechanism framing, documentation discipline, or escalation beyond simple screening.
What it generally addresses: General FFS mindset, damage-mechanism identification, and structured assessment thinking across multiple degradation types.
Limitations: It is not a pipeline integrity management rulebook and does not replace pipeline-specific methods, regulations, or company procedures.
API RP 1160
API
Why it applies: Provides integrity-management process context for anomaly prioritization, remediation planning, and defensible documentation.
What it generally addresses: Workflow discipline, repair scheduling context, and record quality rather than defect mechanics alone.
Limitations: Guidance framework only; enforceable timing comes from applicable CFR requirements and operator procedures.
PRCI research and guidance
PRCI
Why it applies: Useful when operator workflows need research-backed context on defect interaction, assessment limits, or field validation practice.
What it generally addresses: Industry best-practice and research support for complex or uncertain conditions.
Limitations: Research context is not itself an operating procedure or repair criterion.
49 CFR Parts 192 and 195
PHMSA
Why it applies: Provide the U.S. regulatory framework that operators commonly review when anomaly evaluation, remediation, documentation, and timing decisions need to be tied back to pipeline safety rules.
What it generally addresses: High-level regulatory context for integrity management, repair timing, maintenance, evaluation, and documented response.
CSA Z662 Oil and Gas Pipeline Systems
CSA Group
Why it applies: Provides Canadian technical and program context where the operator or jurisdiction uses CSA Z662 to frame integrity, maintenance, repair, and evaluation practices.
What it generally addresses: Canadian pipeline systems context for integrity management, maintenance expectations, and defect-related technical framework.
Managing System Integrity for Hazardous Liquid Pipelines
API
Why it applies: Useful when data quality affects prioritization, remediation planning, and how the operator documents confidence limits in integrity workflows.
What it generally addresses: Integrity-management process discipline and documentation context.
Additional learning
Additional Learning Resources
Good places to deepen understanding of practical behavior, research context, and broader industry guidance.
Pipeline Research Council International (PRCI)
PRCI
Why it applies: Publishes research that helps engineers understand real-world behavior, inspection limitations, interaction effects, and emerging practices across many threat types.
What it generally addresses: Research-backed context for defect behavior, validation limits, and applied integrity practice.
PHMSA and CER public guidance resources
PHMSA / CER
Why it applies: Useful for public advisories, guidance notes, and regulator-facing context that help explain where industry attention has been focused.
What it generally addresses: Public guidance, advisories, and oversight context for integrity programs and field response.
Drill Down by Workflow
Data Quality / ILI Review
Interaction Issues
Topics
Browse this threat family
Each topic follows the same summary-plus-accordion guidance model, but the drill-down is organized by sub-workflow.
Data Quality / ILI Review
Data Quality / ILI Review
Data quality and ILI review is the workflow used to decide whether the inspection data are trustworthy enough to support a real engineering decision. In practice this means checking whether the reported feature, location, sizing, and classification are still inside the tool's qualified capability and whether the tool call is unified with field reality, prior runs, weld alignment, and any excavation history.
Interaction Issues
Gouge
A gouge is localized mechanical damage where metal has been displaced, torn, or removed from the pipe surface. It may occur with a dent, but it can also exist without obvious gross deformation and still be important because the local notch effect, wall disturbance, and potential crack initiation can govern the integrity concern.
Data Quality / ILI Review
Unknown / Unclassified Feature
Unknown or unclassified feature guidance is for anomalies that cannot yet be confidently routed into a defect mechanism or engineering workflow because the data are incomplete, conflicting, or ambiguous.