With digital twins, energy companies can supercharge risk-based inspection by enabling capabilities like real-time visibility and AI/ML-driven predictive maintenance.

While digital twins have been utilized in various industries for years, their application in risk-based inspection (RBI) is a groundbreaking development within the energy sector. Energy companies can now enhance their RBI capabilities by leveraging real-time visibility and AI/ML-driven predictive maintenance through digital twins.

Maintaining the integrity of assets is crucial for operational success in the energy industry. However, traditional RBI approaches often need help providing real-time visibility to tackle complex operational reliability and compliance challenges. Integrating digital twin technology with RBI is a pioneering approach that addresses the unique demands of the energy sector, particularly with the added complexity of aging infrastructure. By adopting digital twins in the context of RBI, energy companies can seamlessly incorporate real-time insights, enabling proactive risk identification and more intelligent mitigation strategies. This sets the stage for elevated asset management outcomes.

This is the right time for introducing digital twin technology to enhance risk-based inspection (RBI) for several reasons. Recent technological advancements have made it possible to develop and implement digital twins more quickly and efficiently. Also, the availability of powerful computing capabilities, improved data collection methods, and advancements in artificial intelligence and machine learning have all contributed to the feasibility of integrating digital twins into RBI.

The energy industry faces increasingly complex asset integrity and operational reliability challenges. Digital twins offer a unique opportunity to address these challenges by providing real-time visibility, predictive maintenance capabilities, and the ability to simulate and analyze asset behavior under various conditions. The convergence of technological advancements, industry challenges, and the proven success of digital twins in other domains make it an opportune time to introduce this technology to enhance RBI in the energy industry.

The Challenges of Traditional Risk-Based Inspection (RBI) in the Energy Sector

While RBI has solved some crucial asset challenges, many challenges remain, including:

  • They have limited real-time visibility. Traditional RBI approaches need to deliver real-time insights, hampering efforts to identify and promptly address potential risks to asset integrity.
  • Compliance struggles. Traditional RBI methods need more agility to align swiftly with evolving regulatory frameworks.
  • Aging infrastructure. In the energy sector, aging infrastructure is prevalent—traditional RBI struggles to address complexities associated with older assets and their maintenance.
  • Data silos and integration issues. Compartmentalized data and integration challenges hinder the holistic view needed for practical risk assessment and mitigation.
  • Resource-intensive processes. Traditional RBI methods often require significant resources in terms of time and personnel, making them less efficient in managing assets within tight operational schedules.
  • They have limited predictive capabilities. Traditional RBI does not enable the advanced predictive insights that allow enterprises to address potential issues before they escalate.
While RBI frameworks help structure and prioritize asset maintenance, they are quickly being surpassed by new technology-driven approaches.  

Digital Twins for Risk-Based Inspection

A digital twin is a virtual representation of a physical asset or system. Digital twins can mirror their real-world counterparts because they are continuously updated with real-time data. As such, a digital twin provides a dynamic and comprehensive view of the asset's current performance, condition, and behavior.

Digital twins can mitigate each of the RBI challenges outlined above. By providing real-time visibility, they enable proactive risk identification. Because they are also comprehensive, they significantly improve compliance posture and can model all assets — including older legacy assets. By offering a comprehensive, integrated view of assets, they break down silos and facilitate a more holistic understanding of the asset risk landscape. Digital twins drive automation and associated efficiency gains, opening up new AI and ML capabilities that support genuinely predictive analytics.

Integration Challenges and Considerations

Integrating risk-based inspection (RBI) with digital twins presents numerous benefits. Still, there are also some considerations and challenges that energy companies should be aware of when incorporating this technology into their existing RBI strategies. One key aspect of the integration process is data acquisition and management. Energy companies must ensure that they have access to accurate and comprehensive data about their assets to create a practical digital twin. This may involve implementing new data collection methods or integrating existing data sources into a centralized system. Data quality and consistency are also crucial for accurate simulations and predictive maintenance. Another challenge is the development and calibration of the digital twin itself. Creating an accurate representation of assets requires a deep understanding of their behavior and characteristics. Energy companies may need to invest time and resources in developing models and algorithms that can accurately simulate asset performance and predict potential risks and associated failures.

Furthermore, integrating digital twins into existing RBI strategies may require organizational processes and workflow adjustments. Energy companies must ensure their teams have the necessary skills and knowledge to utilize digital twin technology effectively. This may involve training and upskilling employees or collaborating with external experts specializing in digital twin implementation. It is also important to note that implementing digital twins in RBI is an iterative process. Energy companies should start with a phased approach, focusing on specific assets or areas with the most significant benefits. This allows testing, learning, and refining the integration strategy before scaling it across the organization.

Digital twins are bringing a paradigm shift in asset management. From real-time insights to predictive analytics, this transformative approach unlocks efficiency, resilience, and proactive risk mitigation in the ever-evolving landscape of the energy sector.

About the Authors

Ankit Shah
Managing Consultant, Downstream Oil and Gas

Ankit has nearly 17 years of experience with static equipment maintenance and reliability in petroleum refining and corrosion analysis services. He is a professional corrosion and inspection specialist with a demonstrated history of working in the oil and gas downstream industry. Ankit is skilled in asset reliability, risk-based inspection, and turnaround inspection. He is a certified API 510, API 570, and API 580 inspector with practical experience in all the modules of APM-MERIDIUM, such as reliability, integrity, asset strategy, and asset health.

Guru Prasath
Principal Consultant, Downstream Oil and Gas

Guru has more than 14 years of diverse experience in refinery, pet-chem, metals, and consulting.  His expertise lies in Asset Performance Management (APM) and Enterprise Asset Management (EAM). He provides reliability improvement solutions, digital transformations, and predictive solutions leveraging AI/ML and digital twins. He is also a certified API 580 RBI analyst.