Information Mgt and Compliance (Tuesday) and Information Mgt (Wednesday)

Energy companies hold decades of legacy subsurface data – acquired at significant cost but often trapped in inconsistent formats, disconnected from modern databases, or stored on aging physical media. With rising energy demand and increasing acquisition costs, recovering value from this existing data is a strategic priority. This presentation shares practical experience from two complementary projects tackling different facets of the legacy data challenge with a major operator’s data quality programme.

The first project addressed the identification problem: thousands of legacy seismic survey records with inconsistent naming conventions, missing identifiers, and format variations could not be reliably matched to the master database. Manual matching yielded accuracy in the single-digit percentages. An iterative approach was adopted – combining fuzzy string matching algorithms with systematic ID normalization rules. This pragmatic approach delivered a step change from single digits to near-complete match accuracy – without even requiring generative AI.

The second project tackled the verification problem: hundreds of physical data tapes required full reconciliation against a multi-million-row digital archive prior to safe decommissioning. The tooling evolved from naïve linear scanning to hash-indexed lookups, establishing efficient large-scale verification across hundreds of tapes and ensuring no data was lost before physical media disposal.

Together the projects demonstrate a full legacy data lifecycle: first establishing what you have, then confirming it is all accounted for – enabling confident migration to modern platforms and responsible decommissioning of physical media. Key takeaways include the power of well-engineered deterministic methods before reaching for AI, the importance of domain expert validation alongside automated pipelines, and the transferability of these approaches to similar legacy data challenges across the energy sector.

The Geological Survey of the Netherlands (TNO-GSN), acting as the statutory delegated data custodian, plays a central role in management of data that must be provided to the state under the Dutch Mining Act. In this context well statuses like ‘drilling’, ‘closed-in’ and ‘plugged and abandoned’ are reported to TNO-GSN. Triggered by the European methane regulation and the need for consistent aftercare of wells, a comprehensive project focused on restructuring the definition and management of well statuses was undertaken. Years of evolving regulations, data standards and management perspectives had resulted in unclear terminology. This lead to inconsistent application of well statuses among various parties which in turn hindered proper data management and usability. To rectify this situation, seven well statuses were defined through systematic information analysis of international standards of the PPDM, requirements of the Dutch Mining Act and internal workflows. These seven well statuses now have clear definitions, assignment and data management criteria, and hierarchy rules. The legacy statuses were mapped to this new set and updated accordingly, removing redundant or ambiguous entries. Definitions are now publicly accessible via the TNO-GSN data portal NLOG, supporting transparency and interoperability between the various organisations active in the Dutch subsurface and TNO-GSN. Additionally, a semi-automated ETL process was implemented to support a consistent work process. The new well status definition framework streamlines data management, minimises miscommunication and facilitates effective knowledge sharing. This work can serve as best practice for organisations across the energy sector facing similar challenges related to metadata management or setting definitions and standards within existing data sets.

Moving from applications to a shared data platform changes access control at its core. When data lived inside an application, access to the application was effectively access to the data. On a platform like OSDU, many users need the platform but must not automatically see all data on it. Access must instead be derived from the context and obligations attached to each dataset.

That context is rarely simple. Access decisions in the energy domain are shaped by sensitivity, licence and joint venture terms, service provider agreements, national regulation, operatorship, and commercial constraints. These dimensions overlap, evolve, and do not map cleanly to static roles or groups — leaving a gap between how legal and governance stakeholders reason about access and how platforms enforce it.

Equinor and Tieto Tech Consulting are closing this gap with a rule-based approach in OSDU. Legal and commercial intent is expressed as declarative rules. For each request, the platform gathers the relevant context across related data, evaluates the applicable rules, and produces an outcome that can be explained and audited. Rules are treated as data — authored, versioned, and refined iteratively as real-world complexity emerges.

The session will demonstrate the concept through a purpose-built application based on work underway at Equinor. Attendees will leave with a clear mental model: platform access is not data access; data access must follow governance context; and rules can bridge legal intent and technical enforcement.

Presented jointly by Equinor and Tieto Tech Consulting, drawing on work currently being implemented and validated against real-world data.

As upstream oil and gas partnerships grow increasingly data-intensive, national oil companies face a strategic imperative to govern and leverage E&P data as a foundation for competitive partnership engagement. Kuwait Oil Company (KOC) has commissioned a digital transformation of its Data Control and Reporting (DC&R) function, establishing a five-year strategic roadmap across four capability pillars: technology, data, process, and people. The roadmap was developed through a four-phase engagement comprising discovery, assessment, analysis, and design, applying a five-level maturity model across six dimensions: organization, governance, processes, technology, data management, and reporting/analytics. The roadmap defines a structured path to full operational maturity through five progressive implementation stages, underpinned by a digital platform architecture aligned with KOC's IT strategy. KOC's DC&R transformation establishes a four-function operating model comprising Data Management, Data Governance, Data Analytics and Visualization, and Reporting and Performance Management, supported by three digital components: a governed enterprise data management system; an AI-ready analytics platform; and a Data Room environment for secure, controlled sharing of asset and partnership data. These three components are unified through the Partnership and Oversight Portal, a governed digital access layer providing IOC partners, regulators, and stakeholders with role-based access to partnership data, real-time dashboards, KPI frameworks, scenario-based insights, and Data Room content. Outcomes include compressed due diligence timelines, strengthened licensing accountability, and real-time performance oversight building IOC confidence across the partnership lifecycle. This paper presents a replicable E&P partnership data governance framework, addressing a domain underrepresented in NOC data management practice, applicable across the Middle East upstream community.

In PETRONAS, Petroleum Arrangement Contractors are required to meet defined data management obligations as stipulated in PETRONAS data governance standards, including requirements for data submission, data quality, and data release. These obligations are critical to regulatory compliance, operational assurance, and data‑driven decision‑making. However, contractors often face challenges arising from complex submission processes and manual compliance checks. This disconnect between governance requirements, usable information, and day‑to‑day workflows increases effort and delays governance compliance execution. To address these challenges, PETRONAS implemented NDex (National Data Excellence), a unified platform designed to simplify compliance by connecting data, information, and people through embedded and streamlined processes. NDex integrates modular capabilities including StarPAC, Data Uploader, InstaPAC, and Intelligent Approval and Assurance for Release. StarPAC embeds governing standard checks directly into submission workflows to validate compliance at the point of entry. Data Uploader supports governed bulk and system‑based ingestion with metadata validation. InstaPAC strengthens data culture by promoting timely, right‑first‑time submissions and clearer ownership. Intelligent Approval and Assurance for Release applies machine learning to prioritise reviews, identify potential risks, and accelerate data release decisions. By integrating governance controls, behavioural enablement, and intelligent assurance within a single platform, NDex reduces process complexity, improves data quality, and shortens data release cycles. This presentation shares lessons from PETRONAS’ experience in simplifying compliance within contractor‑driven, regulated environments.

TGS' internal Knowledge Platform (TKP) is a practical example of how Information Management in the E&P industry can move from fragmented, system-specific work toward a unified, AI-enabled operating model. Positioned internally as "AI for Efficiency and Growth," TKP is designed not as a narrow point solution, but as a truly cross-organizational platform. This presentation will focus on real-world implementations and workflows that have been supercharged with AI to deliver efficiency gains, turning work that previously took days or weeks into something that can be progressed in minutes. In practice, TKP brings together internal knowledge and external sources of information in a manner that is seamless and invisible to the end-user, and without duplicating or transforming data. Access to data and information living in silos created intentionally and unintentionally, such as contracts, transactions, shipments, and financial information, is democratized and augmented with external intelligence such as industry news in one secure framework, allowing users to query, synthesize, and generate reusable outputs through natural-language, agent-powered workflows. We will also touch on the actual infrastructure and implementation behind a genAI-based framework, including its agent orchestration layer.

Centralising subsurface data alone is not enough. This presentation shows how data discovery, archive modernisation, and AI-assisted enrichment improve data context, quality, and usability, helping organisations build trusted subsurface datasets across physical and digital archives and prepare them for modern workflows. Subsurface data environments have evolved over decades of exploration and production, creating a complex mix of physical archives, file-based repositories, and application-specific data stores. Seismic, well, and related datasets are often distributed across these environments with limited understanding of their content, condition, and context. Incomplete metadata and missing documentation mean significant effort is spent locating, validating, and preparing data before it can be reused with confidence. This presentation describes a practical approach that connects subsurface data discovery with archive modernisation, supported by AI-assisted techniques. Based on implementations using platforms such as Exploration Archives and Interica DataCentre, it shows how organisations can move from fragmented archives to more structured and usable data environments. The approach begins with metadata-driven discovery to establish understanding across datasets and documentation. AI-assisted techniques support metadata extraction, dataset classification, and identification of gaps, with human-in￾the-loop validation. Archive modernisation activities, including retrieval, quality control, format standardisation, and metadata enrichment, then improve readiness for downstream workflows. By linking discovery, AI-assisted enrichment, and preparation into a continuous workflow, organisations can reduce manual effort, improve access to trusted subsurface data, and increase confidence across interpretation, regulatory, migration, and analytical use cases.

Through years of operations, acquisitions and mergers, organizations are often left with a portfolio of outdated applications - each holding critical pieces of asset history that cannot be lost. Yet legacy systems are often kept alive far longer than necessary, not for their functionality, but for the data they contain. This session explores how Aker BP separates data from their systems, decommission legacy applications, and still retain access to trusted information across the full asset lifecycle. By consolidating fragmented data landscapes into a structured, searchable archive, Aker BP reduces technical debt, supports compliance, and unlocks new value from historical data. Because the real challenge is not retiring systems, but ensuring the information they contain remains available.

This presentation shows how large language models (LLMs) can be used to navigate complex data on the Norwegian Continental Shelf (NCS). Central to this is the NOD FactPages, available through REST APIs/Data serivce, which provide structured data.

The data service offers rich metadata describing the data itself and rich metadata about the relationships between tables and data is provided. This makes it possible for LLMs to act as an interface, where users can ask complex questions and get answers based on AI combining relevant data through "navigating" the APIs.

Sodir, together with FabriqAI, has applied this approach in the Knowledge Navigator. This solution uses LLM agents to retrieve and combine information from all public NOD data sources, both structured and unstructured, Tthe FactMaps API and data service forming a key backbone to navigating . To the right unstructured data/information, if the Factpages is not itself sufficient to provide an answer.

Good metadata provided in a format that is AI-native through well-described APIs is grounds for value creation by navigating and receiving relevant information using LLMs.