Pipeline Safety Management Systems (PSMS) itself is an evolution of how assets, safety, and operations are managed. While organizations are at different stages in their PSMS journey, one key element involves comparing risks across assets using similar methodologies. This alignment creates a strong foundation for moving toward a risk digital twin approach. PSMS also emphasizes the importance of data quality, using risk assessment results to prioritize additional data collection. This creates a cycle of continuous improvement where data maturity, risk maturity, and technology maturity evolve together.

Just like a risk program, a digital twin program should be helping you remove risk from the system. It should help you gain insights you didn’t have before. Value from digital twin implementation can be measured through both immediate and long-term benefits. In the short term, organizations often see efficiency gains through reduced time spent collecting and analysing asset information, making risk assessment processes more scalable. This enables more frequent risk assessments without proportional increases in personnel. Long-term benefits include reduced incidents, improved asset longevity, and optimized operation and maintenance spending. 

Digital twins may also enable new capabilities, such as using risk-based planning for asset replacement and more optimized design processes. Some benefits may not be apparent until the technology is implemented, and organizations discover new ways to leverage the capabilities.

The minimum data requirements should align with what information would be needed to make informed decisions about the asset. Essential data includes basic asset information such as location, operating conditions (pressures, flows), and material properties (steel grade, design strength). This aligns with industry requirements for traceable, verifiable, and complete records. 

Beyond these basics, information about exposure levels and threat mitigation measures becomes important for comprehensive risk assessment. The digital twin is particularly adept at providing real-time insights about asset conditions and their inherent ability to resist failure.

I would think not. The risk models don't necessarily need to be identical; but they should share some common elements that are translatable. It is of course far more streamlined, if the models use comparable units and quantitative measurements rather than relative rankings. It becomes particularly challenging to compare, for instance, a quantitative transmission model with a relative model for your mains and services. Translations between these models would be very difficult and nuanced as well.  

In effect, it boils down to the core fundamentals- verifiable units, quantitative models and evaluating the same types of consequence. For example, comparing one asset that only consider safety impacts against another that includes environmental, and business impacts would create an uneven comparison.

The primary challenges include resource constraints and competing priorities. Building a risk digital twin requires significant organizational effort and commitment. Strong leadership support is essential to maintain focus and resources through implementation. 

Organizations should avoid trying to implement everything at once - instead, start with focused implementations between two connected systems to demonstrate value early. The program should be designed for persistence, not depending on specific individuals, as organizations change over time. Proper tool selection and documentation are crucial for long-term success. 

Remember that this is an evolution toward safer and more efficient operations, not an overnight transformation.

The best way to ensure a successful collaboration is that everybody involved in that collaboration needs to be able to get something positive out of it. That logic applies to implementing Digital Twins as well. 

To successfully implement digital twins, all involved departments (IT, construction, operations, GIS, asset integrity, etc.) need to understand what they will gain from the implementation. Each team would like to invest their time in something that will pay off for them and will help them do the best job they can. So being able to paint a picture about what these programs are and what they're going to get out of it, will be important to make sure they're invested in that potential outcome.

This requires a champion who can effectively communicate the program's benefits, pain points it will address, and new insights it will provide. Leadership ownership is also crucial - having executive stakeholders who are committed to the program helps maintain momentum when competing priorities arise. Starting with smaller, focused implementations that can demonstrate early value helps build support for broader adoption.