Both UPDM and PODS data models are industry standards for storing pipeline asset and operational data. PODS is more specialized for transmission pipelines, while UPDM can more generally model everything from the wellhead to the meter. There's technically no real limit to the data volume, but be aware of the constant growth issues associated with storing inspection data in your GIS. If you don't have a comprehensive integrity management software solution with a dedicated repository to manage inspection data, then using GIS is certainly recommended.

The biggest advantage of storing ILI data in GIS is leveraging everything GIS provides natively. GIS offers powerful 2D/3D visualization tools, allowing you to create a detailed thematic mapping of assets, defect locations, and other visual representations of inspection data with robust native labelling and symbology. Editing is managed via ESRI APR and easily handles updates to data when re-stationing, adjusting, or rerouting pipelines.

Some other Advantages

1. Spatial and other Analysis: GIS allows for advanced spatial analysis, enabling you to visualize and analyze the geographic distribution of ILI data
2. Data Integration: GIS can integrate ILI data with other spatial datasets, such as land use, environmental data, and infrastructure. This greater integrated view can improve decisionmaking and risk assessment
3. Data Quality: There is some enforcement of data quality and tools to identify issues
4. Accessibility: Storing ILI data in GIS can make it more accessible to various users, but this could be undesired
5. Data Management: GIS can handle large volumes of data and offers robust data management capabilities, including versioning, metadata management, and data validation

Disadvantages

The primary disadvantage is that GIS is not an integrity management software solution alone. You will need other software tools or a comprehensive integrated integrity management product to manage processes and perform specialized actions and analysis. GIS lacks the specialized visualizations needed for defect assessment results and the ability to manage activities and anomaly lifecycles without specialized tools.

1. Cost: Implementing and maintaining a GIS could be expensive. This includes software licenses, hardware, and training costs if not already utilized in the organization
2. Complexity: GIS systems require specialized knowledge and training to edit and manage
3. Interoperability: Seamlessly integrating GIS with other systems and data formats can be challenging, potentially leading to compatibility issues

Implementing predictive maintenance using ILI data starts with collecting comprehensive and high-quality ILI data. This includes data on pipeline conditions, defect types, and historical ILI inspection records. It's crucial to integrate ILI data with other relevant data sources, such as GIS asset and geospatial sources, sensor data, maintenance records, and environmental data. This broader view helps in creating accurate predictive models.

Once you have collected the data, check its quality for any inconsistencies or errors to maximise the accuracy of your predictive models. Choose appropriate and validated machine learning algorithms for your predictive maintenance models, then train them using historical data and validate them to ensure they accurately predict failures. It's important to keep refining and expanding the models and data collection to improve accuracy and insights over time.

With sufficient ILI data to train these models, they could potentially be utilized to predict corrosion on unpiggable pipe sections as well. This approach allows for a more proactive maintenance strategy, helping to identify potential issues before they become critical and optimizing maintenance schedules and resource allocation.

To ensure a successful inline inspection (ILI), start with a comprehensive threat assessment. Identify the typical features and anomalies you're looking for in your pipeline. This will help you shortlist the technologies capable of detecting these specific threats. Next, understand the strengths and limitations of each technology, potentially conducting pull tests if needed to validate performance specifications.

Set clear objectives for the inspection, defining what degradation mechanisms or threats you want to locate and, if possible, the minimum dimensions of the anomalies you want to detect. Consider any operational restrictions that might limit your technology options, such as UT only working in liquid lines.

Ensure proper pipeline preparation, including cleaning, to minimize the risk of tool failure due to debris or obstructions. Pay attention to the flow conditions and tool speed during the inspection, as these factors can significantly impact data quality. Finally, focus on good data alignment postinspection to extract the maximum insight from your inspection data and properly validate the tool
performance with adequate field verification and defect comparison. Following these steps will help ensure a successful ILI campaign and provide valuable data for your integrity management program.

ILI data should be integral to risk assessments and is often expected by regulatory requirements and audits. Various pipeline defects, such as corrosion, cracks, and dents, along with field measurements and dig data, are excellent sources of information for time-dependent threats such as corrosion and cracking. This data helps pinpoint these defects' exact locations and severity, allowing for more localized and focused risk assessments.

In risk analysis, ILI data can be used to dynamically segment the pipeline, providing a more granular view of risk along the pipeline length. This approach helps quickly identify and more accurately prioritize areas that need attention. While immediate indications and defects will always be addressed promptly, risk analysis incorporating ILI data can help prioritize scheduled or monitored defects by considering how these threats interact with other conditions.

Risk analysis provides a bigger picture view of all threats impacting the pipeline, accounting for how these threats are interacting, integrating more sources of data, and what the consequences of failure are in localized areas. This comprehensive approach drives dig and repair priority more effectively. The resulting risk assessments can be trended over time and used to identify and prioritize appropriate preventive and mitigative measures with higher safety and return on investment.

Recommendations for using ILI data in risk analysis include ensuring data quality and consistency, integrating it with other relevant data using appropriate risk models that can incorporate detailed ILI data, and regularly updating the risk assessment as new ILI data becomes available. This approach supports risk-based management by prioritizing repairs and maintenance based on the severity and potential impact of detected defects, ultimately optimizing resource allocation and minimizing the risk of pipeline failures.

The PODS data model is well suited and richly attributed to store this data with some customizations, but access to the data and analytic capabilities will depend on the Pipeline Integrity Management (PIM) software used. A software product such as DNV’s Synergi Pipeline maintains a large, open database repository that can store the history of inspection data, asset details, operational and geospatial data together with analysis results from risk, defect assessments, scenarios and other data, effectively reducing data silos and streamlining source integration.

Pipeline Integrity Management (PIM) software typically provides a range of map visuals and other tools specifically designed to support integrity engineers in making informed decisions. Our solution, Synergi Pipeline, offers detailed geospatial mapping that integrates various data layers, allowing you to visualize pipeline routes alongside inspection results, risk results, high-consequence areas (HCAs), environmental factors, and many more (depending on what you have in your DB). You can easily overlay ILI data, such as corrosion and crack locations, using color-coded indicators for anomaly severity. We also provide risk assessment heat maps to help you quickly spot high-risk pipeline sections. You’ll also find tools that allow you analyse historical inspection data, repair history, and even geohazard information, helping you see trends and make proactive decisions. Plus, our maps are interactive and synchronized with the table data, so you can zoom in on specific areas and explore the relevant data in more detail in the associated tables.