How Ruhr Oel improves data quality and decision-making with DIMATE PACS

In this interview, Frederik Worpenberg-Theil, Integrity Improvement Lead at Ruhr Oel GmbH shares his perspective on current inspection workflows, data handling and the role of DIMATE PACS in supporting these processes.

When you look at your inspection and integrity workflow: where does DIMATE PACS support you the most today?

When we compare our current workflow to the past, the biggest differences become clear in two key areas.

Previously, the inspection scope was typically handed over to the NDT contractor via Excel spreadsheets. Important metadata was often missing or had to be added manually. With DIMATE PACS, this process is now structured and clearly defined - all relevant information is provided upfront, ensuring a clear and unambiguous assignment from the start.

A similar improvement can be seen in how measurement data is transferred back. In the past, multiple manual steps were required. Today, data is fed back directly and in a structured way into the Asset Performance Management System (APM) for thickness monitoring. The result is a much more robust process: fewer manual interventions, fewer sources of error, and significantly improved data quality.

Where do you see the greatest time savings in this process?

The biggest time savings come from the immediate availability of data.

Currently, inspection reports are still received via SharePoint and evaluated manually. When data is available directly in the system, this intermediate step is eliminated - saving significant time, especially when transferring measurement data, which is otherwise highly manual and time-consuming.

Another key factor is the significantly improved image quality. Previously, images were embedded in PDFs and compressed due to storage limitations, which restricted proper evaluation. Today, we work with original image data. This makes a major difference: we can adjust contrast, identify details more clearly and even re-measure if needed. This also allows us to assess anomalies outside the actual measurement area - such as early signs of corrosion - much more reliably. Overall, this not only saves time but also significantly improves the quality of analysis.

How has data integrity and traceability improved through the centralized storage of NDT evidence in DIMATE PACS?

A key benefit is the significant improvement in data integrity and traceability.

One example is the use of calibration spheres in images. With DIMATE PACS, we can measure directly within the system and verify whether calibration aligns with the captured data at a specific inspection point. This makes it possible to confirm whether measurement values are plausible and accurate. The ability to re-measure directly within the image provides additional confidence. If an area appears suspicious, we can analyze and assess it more precisely afterward.

Another advantage - which we expect to leverage even more in the future - is access to historical image data. This allows us to track damage development over time. Instead of manually searching through reports, we can access original images directly and identify changes much more clearly.

Where do you see the greatest quality lever: data completeness or clear assignment of data to components?

The biggest lever is clearly the unambiguous assignment. If we want to assess the lifetime of a component over many years, it is essential that each measurement value is clearly assigned to that specific component. Without this clarity, the data quickly loses its value. With structured metadata, assignment is now precise and mix-ups are almost completely eliminated. This is the foundation for working reliably with data over the long term.

How does DIMATE PACS support you in evaluating findings and making decisions?

The key factor is, again, significantly improved data quality. Since we work in a risk-based manner, our assessments depend heavily on accurate measurement data - for example, when calculating corrosion rates. If the data is incorrect, the results and resulting decisions will also be unreliable. A reliable data foundation enables clean trend analysis, which is essential for lifetime assessments. Especially with corrosion, it is crucial to understand whether it progresses steadily or accelerates. The data provides the foundation, while the evaluation is still performed by engineers. However, with consistent and high-quality data, trends can be identified much more clearly and decisions can be made faster and with greater confidence.

Does a better data set lead to faster decisions or to better-justified ones?

In practice, it’s both.

When historical data is readily available, we can better understand developments – for example, whether corrosion was already present years ago and how it has evolved. This allows us to justify decisions more clearly, such as whether a repair is required or further inspection is needed.

At the same time, direct data availability speeds up the process. In many cases, we reach decisions much faster. This is particularly valuable in unexpected situations: instead of searching for information, the relevant data is immediately available.

How has collaboration - internally and with contractors - changed with DIMATE PACS?

The workflow has changed significantly and is still partly evolving. Previously, NDT reports and images were shared via SharePoint, often with limited image quality. Follow-ups or clarifications were typically handled informally via phone or email.

Today, contractors submit their data directly into the system with clear assignment. This allows immediate evaluation and targeted feedback. The key advantage is that we can now refer directly to specific points - for example, when a re-inspection is required or when inconsistencies are identified. This makes coordination much more precise and efficient.

Has the new workflow improved the quality and usability of your data?

Yes, definitely. Areas such as thickness monitoring and risk-based inspection (RBI) benefit significantly from improved data quality. Reliable corrosion data enables much more accurate lifetime assessments. This directly impacts planning: when high corrosion rates are identified, we can adjust the inspection scope accordingly and focus on critical areas more frequently.

Data quality is equally critical in RBI. Since we always work with the last known condition, it is essential that data is correctly assigned to the respective component. Only then is risk assessment reliable. Especially in cases of wall thickness reduction, this has a major impact: if the data is correct, the resulting decisions are also accurate.

Has this also improved data quality and structure in your APM?

Yes, absolutely. In the past, measurement points sometimes appeared multiple times within a short period. Even small deviations could lead to unrealistic corrosion rates over time.

Today, such inconsistencies can be identified and tracked much more effectively - supported by modern data analysis and visualization tools that make large data volumes transparent.

In the long term, this creates a continuous data flow: from the APM to DIMATE PACS, to the contractor, and back into the system, followed by analysis and targeted flagging. Unusual values or high corrosion rates become visible much faster. This level of transparency is essential when dealing with large data volumes.

What are the key prerequisites for ensuring that APM/Integrity systems and DIMATE PACS work as a closed loop?

The foundation is always a clean data structure. Everything starts in the APM or integrity system, where data must be structured to ensure clear assignment to equipment and measurement locations. Based on this, it is essential that all relevant metadata is transferred completely and correctly during commissioning.

Next comes proper and simple assignment in the field. The more intuitive this process is for contractors, the lower the risk of errors. Assigning images directly to the correct point instead of manual entry significantly improves quality.

In the end, everything comes down to two factors: clean metadata and clear, simple assignment. Only when both are ensured can measurement data be correctly returned. If not, uncertainties arise - even to the point of questioning whether a measurement is valid. In the worst case, this can lead to incorrect decisions and actions.

If you were speaking to someone unfamiliar with DIMATE PACS: which KPIs would you use to demonstrate its value?

A key aspect is transparency in identifying errors. With a structured data foundation, it becomes possible to trace exactly where an error occurred in the process. Another important indicator is rework or follow-up requests. These clearly show how often data is incomplete or incorrect - and therefore how well the overall process is functioning.

Ultimately, the most important factor is data integrity. To measure this, we focus on three key metrics.

• number of errors
• number of follow-up requests
• correctness of data assignment

These KPIs provide a clear picture of how overall data quality is improving.