Data/AI Governance & Strategy

We introduce a federated European innovation ecosystem for subsurface data and intelligence, enabled through use case orchestration as a coordination mechanism, to address fragmentation in AI‑enabled subsurface innovation without centralisation or loss of national or organisational autonomy. Rather than creating new institutions or shared operational platforms, the proposed ecosystem establishes a joint innovation capacity that enables organisations to collaboratively develop reusable capabilities while preserving national mandates, infrastructures, and data sovereignty. The novelty of the approach lies in combining a federated ecosystem model with use case orchestration as the primary means of alignment. Concrete subsurface use cases, rather than projects or programmes, form the unit of coordination across organisations. This enables early identification of shared needs, data and technical dependencies, and reuse opportunities, while allowing each organisation to deploy and operate solutions independently within its own context.

Within the ecosystem, participants jointly develop shared components, standards, and AI‑enabled workflows that strengthen individual organisational capabilities while improving interoperability and coherence at European scale. Practical applications demonstrate that use‑case‑driven orchestration significantly reduces duplicated research and innovation effort, enables effective co‑funding, and accelerates the delivery of high‑impact subsurface intelligence capabilities.

By providing an operationally feasible alternative to both isolated national initiatives and centralised European solutions, the proposed ecosystem offers a scalable and governance‑compatible pathway for coordinated subsurface data and intelligence innovation across Europe.

Operational reporting is some of the highest-volume data a drilling operation produces, and some of the least governed. Daily activity remarks are entered by hand under time pressure, in free text, with missing dates, inconsistent depths, merged operations, and activity codes that are frequently wrong or missing. The result is a record that is hard to standardize, hard to compare across wells, and hard to load into a corporate data platform.

We built a set of grounded GenAI agents that turn this raw operational narrative into standardized, OSDU-compliant well operations records, piloted with Equinor FLX across four North Sea wells (a representative well file holds around 175 operational rows spanning roughly a month). The agents reconstruct a coherent chronological daily report from scattered remarks; ground each activity to the correct code within a three-level controlled vocabulary of roughly 3,700 codes (99 main activities, 590 sub-groups) using retrieval and a confidence score; and derive risk-aware performance targets from the statistical distribution of historical execution data instead of subjective benchmarks. The output maps directly to OSDU WellOperationsReport 2.1.0, verified against OSDU reference-data manifests.

The information-management lesson is that text generation is the easy part.The hard parts are binding model output to a controlled vocabulary, quality-assuring records that an AI wrote, and proving they conform to the schema and reference data the business depends on. We share what worked, where output had to be checked and corrected (including a cautionary case of fabricated reference values), and a repeatable path from freeform human reports to governed, queryable records. In the pilot, once the inputs are clean, report generation runs in one to two minutes, against the hours a night supervisor would otherwise spend reconstructing the day by hand.

This presentation showcases Equinor’s initiative to strengthen well data reporting by taking ownership of and improving Standard Operating Procedures (SOPs) with Service providers.

Through extensive insight work, including interviews with more than 50 stakeholders across disciplines and companies, we identified key pain points across the data lifecycle. These included poor communication, insufficient quality-control routines, inconsistent file structures, unclear task ownership, and excessive iteration cycles in end-of-well (EOW) deliveries and authority reporting.

The findings highlight systemic challenges throughout the process including lack of standard templates, insufficient metadata governance, fragmented workflows, and limited transparency in data sharing and version control.

To address these issues, Equinor has established a comprehensive SOP framework with clearly defined requirements, standardized data formats and workflows, improved QC processes, better role clarity, and enhanced collaboration between all stakeholders. New elements include structured data delivery overview, standardized plots and metadata, shared platforms, defined timelines, and aligned QC responsibilities (Equinor, LogQC and Service providers).

The overall objective is to enable “correct data delivered the first time,” reducing rework, improving efficiency, ensuring compliance with Equinor and authority requirements, and ultimately making well data reporting simpler, faster, and more reliable.

As organizations adopt OSDU as the foundation for energy data management, establishing scalable governance and data quality processes becomes increasingly important. While OSDU provides standards for metadata, Data Quality Rules, RuleSets, and quality assessments, organizations still face challenges in operationalizing these capabilities across enterprise data platforms.

This presentation shares lessons learned from a joint Equinor–Microsoft collaboration focused on bridging governance and data quality within an OSDU system.

The initiative explored two complementary areas. The first focused on governance alignment, evaluating how OSDU governance concepts, including Data Quality Rules, RuleSets, scoring mechanisms, and validation requirements, could be represented within an enterprise governance platform. This work provided insights into the alignment between OSDU governance requirements and enterprise governance capabilities and identified key considerations for future integration.

The second focused on implementing an end-to-end data quality workflow using OSDU, Microsoft Fabric Lakehouse, and Microsoft Purview. OSDU data quality rules were translated, managed, and executed through Purview's Data Quality capabilities, while assessment results were generated and written back to OSDU. This established a closed-loop process linking governance definitions, quality execution, and quality reporting.

The session will present the architecture, implementation approach, lessons learned, and practical considerations from integrating OSDU, Fabric Lakehouse, and Purview. It will discuss how governance and data quality capabilities can work together to support trusted, reusable, and well-governed energy data, while highlighting opportunities and challenges in aligning enterprise governance platforms with OSDU standards.

Effective management and use of subsurface data is becoming a key enabler for improved decision-making and operational efficiency in the energy sector. This paper presents Petoro’s experience adopting SLB’s Lumi™ Data & AI platform as part of a transition to a cloud-based, OSDU-aligned data ecosystem. The implementation focuses on building a scalable and flexible foundation for subsurface data, enabling accessibility, interoperability and governance. Core capabilities for data management, seismic access and data exchange have been established alongside structured processes for data quality and lifecycle management.

By reducing manual data handling and improving data availability, the approach enables domain experts to work more efficiently and focus on high-value interpretation tasks. The platform enables integration with broader AI innovation ecosystems, facilitating experimentation and the development of new data-driven use cases in collaboration with technology partners. A key outcome is the already developed integration of AI-driven use cases leveraging Lumi’s AI Workspace and conversational data capabilities (Tela™), including well benchmarking and automated document insights, demonstrating end-to-end value from data ingestion to decision support. This case study highlights practical experience with platform adoption, demonstrating the importance of governance, standardization and incremental implementation in strengthening the integration between enterprise data platforms and AI innovation ecosystems.

Over the past two years, "Equinor’s seismic journey to OSDU® Data Platform" presentations has focused on establishing core platform capabilities, including metadata ingestion, metadata enrichment, seismic file ingestion and governance. This presentation continues that journey by exploring the use of OSDU External Data Sources (EDS) as a mechanism to extend data discovery beyond the platform boundary.

The session shares practical experiences from enabling discovery and access to third‑party seismic metadata through EDS, allowing external metadata to be exposed and searched alongside native OSDU data without full migration. EDS proved practical to adopt for early collaboration scenarios, supporting faster onboarding and improved visibility across data domains.

The experience also highlighted important considerations for production readiness, including alignment on reference data, missing metadata strategy, metadata quality, and operational governance as integrations scale. The presentation summarizes key lessons learned and demonstrates how EDS can be used as a standardized mechanism to share and discover metadata across partners, enabling efficient exchange with external data providers.

REVEAL — "Subsea Revealed" — is the current phase of Aker BP's Subsea Transformation and a fundamental rethink of how subsea integrity is managed. It shifts the model from inspection campaigns and reports toward visible, trusted, decision ready insight: raw data captured by next generation sensor carriers is turned into clear evidence engineers can act on, served directly into a 3D field twin environment, to compe current observations against historic baselines. The aim is to bring hardware, engineering, visualisation, AI, and integrity workflows into a single end to end approach that reduces manual interpretation and prepares the ground for autonomous operations. None of this is possible without a deliberate data foundation. This presentation argues that data, data governance, and data management are the true enablers of REVEAL — the difference between data engineers merely receive and data they can trust and act on. It shows how Aker BP structures core subsea data products, governs geospatial products in ArcGIS, establishes a federated governance model across the GIS, Field Twin, and Subsea Data Management teams with clear owner/steward/custodian roles, and runs a single data issue service loop. Attendees will see why embedding governance from the outset makes a sensor driven subsea transformation deliverable

As part of Equinor’s initiative to increase data governance maturity, an updated enterprise data strategy has been developed to address both evolving external demands and internal capability gaps. The existing data strategy, established in 2019, laid the foundation for a federated data governance model and the development of initial data capabilities. While progress has been achieved since then, maturity and adoption remain uneven across the organization. At the same time, rapid advancements in the digital landscape—including increased use of artificial intelligence alongside stricter regulatory and operational requirements—have created a need to reassess and strengthen the strategic direction.

This presentation describes the development of the updated data strategy, created through a six-month co-creation process involving key stakeholders across the enterprise and guided by Equinor’s standard strategy development framework. The resulting strategy defines three enterprise-level strategic focus areas, each supported by clear ambitions and goals that aim to drive consistency, scalability, and value realization from data.

The presentation will outline the development process, including the applied framework, stakeholder engagement approach, and key work methods, as well as challenges encountered along the way. It will also introduce the strategy on it's current state and its delivery package, including a proposed path forward for implementation. Finally, lessons learned from the process will be shared to provide insights for similar initiatives.

Many organizations are rich in data, systems, and digital tools, yet struggle to realize their full potential. This presentation introduces the concept of the “gap to potential” — the difference between current ways of working and what becomes possible when data is effectively utilized.

Drawing on experience from Digital Twin implementations and Information Management in operations, the session highlights how existing data foundations — structured engineering data, historical records, and integrated platforms — already enable significant value creation. It shows how these foundations support new ways of working, reduce manual effort, and shift focus toward better decision-making.

The presentation emphasizes that high-quality, governed data is the key enabler for AI. Without it, AI amplifies inefficiencies; with it, AI can connect fragmented information, reuse historical data, and deliver insights at scale. The key message: AI creates value by closing the gap to potential — enabled by strong Information Management, robust data foundations, and a focus on scalable impact.