Streamline your asset information management with our recommended practice for asset information modelling framework (DNV-RP-0670)

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Complexities in managing engineering assets

Engineering assets, whether in the energy sector or other industries, are characterized by high complexity. The flow of information between organizations, people, and IT applications is challenging due to the lack of standardization and extensive use of documents as the carriers of information in the value and supply chains. This leads to several issues: 

  • Interoperability issues: Different formats, methods, tools and work processes cause loss of information, duplication of work and extensive manual mapping. 
  • Information silos: Information is often locked into documents and not readily available for automated processing by computers. 
  • Limited reuse: Reduced possibilities for the reuse of concepts and designs, leading to inefficiencies and increased costs. 
  • Application lock-in: Dependency on specific applications with company-specific requirements. 

Our asset information modelling framework

To address the challenges in the management of complex engineering assets, we offer the asset information modelling framework (IMF). This framework supports key engineering activities such as design, procurement, commissioning and operation. It helps share and exchange information throughout the asset’s lifecycle, ensuring reliable data during design, constructions, operations and decommissioning. 

This document explains the basic concepts and provides guidelines for creating machine-readable asset information models. The IMF describes a systematic method to develop asset information models, promoting the use of standards, shared reference data libraries and common digital vocabularies for seamless information exchange (interoperability). 

The development of the IMF was progressed through the READI joint industry project. This recommended practice has been developed in partnership with the DISC digital collaboration project members (Aker BP, Aker Solutions, Aibel and Equinor) and the SIRIUS centre at the University of Oslo. 

Benefits of implementing IMF

You can expect the following benefits of implementing IMF: 

  1. Save time: Reduce the time spent on managing and exchanging asset information, allowing you to focus on more critical tasks.
  2. Enhance efficiency: Streamline workflows and improve collaboration among team members, ensuring that everyone is on the same page.
  3. Ensure compliance and improve safety: Ensure that assets are designed and operated according to industry standards and regulatory requirements, minimizing risks and enhancing reliability.
  4. Improve data quality: Maintain high-quality, accurate data that supports better decision-making and operational efficiency.
  5. Promote interoperability: The framework promotes the use of standards, shared libraries, and common definitions to enable seamless enhancement of information.

Proof of concept developed in 2022 points to a 50% decrease in human errors in manual updates, alterations, and modifications of engineering information when IMF is implemented. For the detailed engineering of an offshore asset (in this case an FPSO) this corresponds to a savings of around 50 million USD. These findings are supported by the paper “A Framework for Trustworthy Digital Twins Over their Lifecycle”, presented at the Offshore Technology Conference Brasil in 2023.

Who will benefit from this recommended practice and how can it be used?

 

This document can be relevant for:

  • asset operators 
  • asset owners 
  • engineering, procurement, construction and installation (EPCI) 
  • original equipment manufacturers (OEMs) 
  • all types of suppliers 
  • service providers
  • IT vendors 
  • regulators

This document can be used:

  • as a reference in contract(s)
  • to gain an understanding of what is required to change to asset information models based on the IMF 
  • to promote shared reference data libraries 
  • to define, develop and implement cross-industry interoperability 
  • to develop internal company manuals describing how to implement and use the IMF 
  • as a basis for documentation of new work processes 
  • to be supplemented by formal specifications, examples, etc. 

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Read frequently asked questions about data interoperability:

Data interoperability in the energy industry refers to the seamless exchange and use of data among various systems, devices and stakeholders within the energy sector. It ensures that data related to energy generation, distribution and consumption can be shared and utilized effectively across different platforms and technologies. Learn more about how DNV can help you with data interoperability here.

Data interoperability is crucial, as it enables efficient communication and collaboration between diverse systems and stakeholders. It facilitates the integration of renewable energy sources, smart grids and energy management systems, leading to optimized operations, better decision-making and improved reliability of energy supply. more cost-efficient and faster in this video.

To succeed with data interoperability, the energy industry should prioritize standardized data formats, encourage collaboration among stakeholders, invest in interoperable technologies and establish robust data management systems. Promoting data sharing initiatives and maintaining data security are also crucial for success.

Data interoperability can be tested through interoperability assessments, compatibility testing and interoperability trials where systems exchange data to verify seamless communication. Additionally, conducting interoperability pilots and implementing standardized testing frameworks can help evaluate the effectiveness of data interoperability solutions.

Key use cases include integrating renewable energy sources into the grid, optimizing energy distribution and demand response, facilitating energy trading and market transactions, enabling predictive maintenance of infrastructure, and supporting regulatory compliance and reporting requirements. Learn how our customers are working with data interoperability here.

Common information models in the energy industry offer benefits such as improved data interoperability and integration across systems, streamlined decision-making, reduced duplication of efforts, and better alignment with industry standards and regulations.

Risks and challenges include compatibility issues between different systems, data security and privacy concerns, potential data inaccuracies or inconsistencies, complexity in integrating diverse data sources and the need for ongoing maintenance and updates to ensure continued interoperability.

To manage risks associated with data interoperability in the energy sector, employ robust data governance frameworks, conduct thorough risk assessments, ensure regulatory compliance, utilize encryption and anonymized data, establish clear data sharing agreements and regularly monitor and audit data exchange processes.

Key regulations and standards for data interoperability in the energy industry include GDPR (General Data Protection Regulation), ISO 27001 (Information Security Management), IEC 61850 (Communication Networks and Systems for Power Utility Automation), CIM (Common Information Model), and OpenADR (Open Automated Demand Response).

Asset information modelling in the energy industry involves creating and managing digital representations of physical assets throughout their lifecycle. It integrates various data types such as design, construction, and operational information to support asset management and decision-making processes.

Asset information modelling is crucial for the energy industry as it enhances decision-making processes, improves maintenance planning, optimizes asset performance and supports risk management. For instance, an effective asset management strategy could involve leveraging digital asset representations to predict maintenance needs, thereby minimizing downtime and optimizing asset performance. Conversely, a less effective strategy may involve reactive maintenance, addressing repairs only after equipment failures occur, leading to increased downtime and higher maintenance costs.

To succeed with asset information modelling, the energy industry should implement robust data management systems, foster collaboration among stakeholders, ensure comprehensive asset data integration, leverage predictive analytics for maintenance planning and invest in training personnel to effectively utilize technologies and processes for managing asset information.

DNV is developing a recommended practice for asset information modelling framework (DNV-RP-0670).

Asset information models can be tested through various methods, such as validation against real-world asset data, simulation of different operational scenarios, verification of model accuracy through field tests, and peer review by domain experts. Additionally, conducting usability testing with end-users can help identify any usability issues and ensure the effectiveness of the models.

Key use cases include predictive maintenance to anticipate equipment failures, optimizing asset performance through data-driven insights, facilitating regulatory compliance by maintaining accurate asset records, enabling informed decision-making for asset investments and upgrades, and supporting energy efficiency initiatives through better asset utilization and management.

Common asset information models in the energy industry offer enhanced data interoperability and integration across systems, streamlined decision-making processes, reduced duplication of efforts, better alignment with industry standards, improved regulatory compliance, and optimized asset performance through comprehensive and accurate asset data management.

Risks and challenges of asset information modelling range over data security and privacy concerns, potential inaccuracies or inconsistencies in asset data, complexity in integrating diverse data sources, compatibility issues with existing systems and the need for ongoing maintenance and updates to ensure relevance and accuracy. Additionally, organizational resistance to change can pose significant challenges.

Risk management involves implementing robust data governance frameworks, conducting thorough risk assessments, ensuring compliance with relevant regulations and standards, employing encryption and data anonymization techniques, establishing clear data sharing agreements and regularly monitoring and auditing asset information modelling processes. Additionally, fostering a culture of collaboration and communication among stakeholders can help mitigate risks effectively.

Key regulations and standards for asset information modelling include the ISO 55000 series (Asset Management), IEC 81346 (Industrial systems, installations, and equipment), PAS 1192 (Building Information Modelling), CIM (Common Information Model), ISO 15926 (Industrial automation systems), IEC 61970 (Energy Management), ISO/DIS 16739-1 (Industry Foundation Classes), CFIHOS (Capital Facilities Information), DEXPI (Process Industry Data Exchange), and ISO/IEC 21838 (Product Life Cycle Support). They guide asset management practices, data interoperability and information modelling in the energy sector. 

The ongoing development of ISO/WD 23726-3 (Automation systems and integration — Ontology based interoperability) deserves special mention. Centered on the Industrial Data Ontology (IDO), it enables automated reasoning over complex asset data, providing engineers with accessible language and relevant modelling patterns. IDO addresses industry demands for semantic interoperability, improving efficiency and accuracy in asset information management across the energy sector and beyond.