National Data Repositories (NDRs) have long served as trusted systems for preserving, governing, and distributing subsurface data. For decades, success was measured by the ability to ingest, manage, and provide access to growing volumes of information. Today, a new opportunity is emerging. As artificial intelligence, cloud-scale computing, and standards-based data ecosystems mature, the NDR can become more than a repository. It can become the foundation for a new generation of data-driven workflows, analytics, and decision support across the energy industry. This presentation explores the evolution of the Norwegian National Data Repository, DISKOS, and examines how modern data architectures can transform national repositories from systems of record into platforms for intelligence. Drawing on operational experience from managing one of the world's largest subsurface data repositories, the presenters will discuss how trusted data, common standards, and scalable architectures create the conditions for advanced search, automated quality control, digital workflows, AI applications, and future agent-based systems. The session will also examine the strategic value of NDRs beyond regulatory compliance and archival storage. As energy companies seek to accelerate exploration, improve recovery, support carbon storage initiatives, and reduce operational risk, national repositories are uniquely positioned to provide a shared foundation for innovation across the industry. Attendees will gain practical insights into the technologies, governance models, and architectural decisions that can help transform NDRs from passive data stores into active enablers of industry intelligence.

Are we trying to run before we can walk? While AI offers vast opportunities for workflow efficiencies and enhanced insights, it needs to be built on a sound data foundation. Subsurface data are difficult and expensive to acquire, manage and store. To maximize the return on these investments, we need to ensure that the right data are at our fingertips; whether we’re running traditional subsurface workflows or complex machine learning algorithms, data is currency. AI adds to the jeopardy of poorly managed data, owing to its “black box” nature. We can rapidly build models and make decisions based on vast quantities of data that may never have been curated or verified. As we know it’s “garbage in, garbage out,” so the consequences could be disastrous. To mitigate this risk and safely leverage the power of AI, we need a subsurface data ecosystem that is fit-for-purpose — the right data, in the right place, deduplicated, curated. We can reach this goal by first scanning all data repositories to identify issues, then building a data strategy suited to our aims, and finally using a tech-driven approach to implement this accurately and efficiently. Only then can we fully embrace the value of AI without fear of the consequences.