The Company assets average life span have reached 50 years. Which over such a long period creates challenges in maintaining the information changes during the plant life cycle, the change in terminology, formats, standards, and systems. According to today’s digital twin and operational excellence mandates, it is required to manage and represent the physical asset information. For that, the company implemented state of the art technology, which is intelligent, open and scalable data-model. This technology is based on web ontology, and it defines the formal naming, types, properties, and interrelationships of entities that fundamentally exist to represent the physical asset information during the asset life cycle from concept and design to operate stage. The objective of this paper is to share ADNOC Offshore experience, challenges and the benefits gained by implementing asset information Class Library, and the Reference Data Library as aligned with ISO15926 that is known as Data-Model, which describes the engineering Tags classification networks and ultimately represents the physical assets intelligent data and attributes. The data-model fundamentally is based on entities > relationships > values formula, where entities can lead to various values with unlimited relationships allowing any solution to quickly identify all related data in real-time. The formulas allow automatic extraction and identification of entities to facilitate the recognition and associations. This data management approach depends on Ontologies that are a formal way to describe taxonomies, essentially defining the structure of data, where it can be fully utilized to build the digital assets knowledge. Thus, the solution is designed to be flexible and allow reading from various data sources, which enriched the digital assets information and provided business, operations and integrity values. The challenges came from the rapidly growing Data, the newly engaged young engineers, and the demand for new digital technologies such as artificial intelligence, which is adding another layer of complexity.
In the recent years, it has proven that this data model is expandable, agile and able to accommodate both small and large variances from projects and changes in standards into one consolidated master model. This in turn acts as the core of enterprise Digital Twin, which provide end users with a unified experience in accessing engineering and operation data. Ontologies are meant to represent information widely and are expected to be evolving frequently based on technical and business requirements in flexible manner and easy to maintain. Unlike the conventional data-models, which is based on database and class hierarchy representing structured data that evolve slowly with complicated constraints. Whereas, with the implemented data-model we were able to add new data sources and classes instantly to represent information for an operation business case.