This presentation details the implementation of the Onecheckshot methodology within subsurface data management at Equinor, focusing on the principles, governance, and practical challenges of managing checkshot data. The project is a joint effort between two teams within Exploration and Production Norway unit to deliver controlled quality data using an automatic or semi-automatic approach in the cloud, ensuring compliance with technical management criteria.

The Equinor data governance model is highlighted as a federated approach, distributing responsibilities across 13 specialized data offices and one enterprise data management group, with the Subsurface Data Office playing a central role in legal risk assessment, data cleanup, and reporting standardization. Onecheckshot is an example of how we can define systems to deliver products following this high-level model, while providing data in a more efficient way than traditional and error-prone manual methods. It addresses issues such as non-standard file formats and inconsistent quality procedures to provide approval or failure of specific quality control rules, which can be used to pinpoint the error for a specific checkshot file.

Key strategies for successful implementation include effective user onboarding, transparent communication, and continuous feedback mechanisms. The approach has resulted in improved data accuracy, increased efficiency, faster decision-making, and cost savings. By centralizing data in the cloud, advanced algorithms can refine sonic logs and support seismic imaging. The presentation concludes that effective partnerships, tailored solutions, and clear governance documentation are essential for scalable, high-quality data products and data-driven culture in subsurface operations.

As organisations accelerate their adoption of artificial intelligence, many are discovering that the true barrier to value creation is not the technology itself, but the absence of structured, trustworthy and accessible data. While AI tools are increasingly capable of generating high-quality insights, their effectiveness remains constrained when enterprise data lacks governance, context and ownership across organisational domains.

This talk explores how data governance can become a strategic differentiator in the age of AI. Building on the concept of data products, it argues that well-governed, certified data can be treated as an internal knowledge foundation— providing AI systems with reliable, contextualised information comparable to continuously updated, organisation-specific research.

Rather than focusing on technology, the session highlights the importance of establishing cross-functional processes that ensure data quality, clarity of ownership and shared understanding across business domains. The presentation will provide a practical perspective on how organisations can transition from fragmented data practices to a governed, product-oriented approach—enabling scalable and trusted AI adoption. Ultimately, it will demonstrate how the ability to embed data governance across the enterprise may define the next generation of competitive advantage.

Data stewardship is the foundation behind automation and digital twins Large unstructured data requires structured governance and access control Scripted QC ensures consistent, measurable data quality Contractor QC and master/reference data are critical to data integrity.

This project transforms Subsurface Data Management from a manual, service-intensive model into an automated, selfservice environment, balancing strict governance with operational accessibility. The scope includes deploying a frontend visualization platform integrated with a backend architecture to optimize data discovery while maintaining stringent security protocols.

The approach utilizes a visualization tool equipped with AI-driven search and summarization. This interface reduces time spent on data collection during subsurface studies, supported by a backend integration layer ensuring continuous quality assurance. Implementation is phased, beginning with strategic assets selected for data maturity. Subsequent phases incorporate Joint Venture and corporate subsurface data, evolving the system into a unified one-stop channel for all subsurface information.

A commercial platform was selected to ensure long-term sustainability, as internal solutions often struggle to keep pace with rapid technological shifts. Implementing this solution requires balancing standardized features with dynamic user requirements. While the project is ongoing, implementation demonstrates that transitioning data services requires significant investment in personnel, time, and workflow re-engineering; shortcuts create systemic hurdles.

While utilizing data visualization for data services is not new, its implementation within PTTEP’s heterogeneous data ecosystem is innovative. PTTEP’s landscape reflects decades of diverse E&P practices. Integrating and standardizing these varied ecosystems into a unified system governed by strict protocols requires creative problem-solving in a challenging environment—providing valuable lessons for future digital transformation.