As data continues to be amassed via ongoing operations and analyses, discovery of new backlogs of data and acquisition of new data, the need for an orchestrated and automated process transforming and synchronizing these datasets across applications and repositories into OSDU becomes significantly important. This work presents an evergreen data solution developed to address these challenges by ensuring that both newly generated (and/or discovered) and historical data are effectively curated, integrated, and utilized to support business objectives. At the core is a flexible, event-driven Consumer that connects and listens to changes across diverse data sources, platforms, and storage locations available within the operating environment. Detected changes initiate end-to-end orchestration of automated file classification and modular data transformation, contextualization, and ingestion into OSDU. The transformation modules perform an automated ETL process of pre-mapped files, application datasets, and structured databases into domain data models, ensuring full lineage, governance, and traceability maintained. The initial modules focus on interpretation data sourced from Petrel—such as horizons, faults, and wellbore markers—where changes occur continuously within the application by end-users and must be captured, technically assured, and integrated with other data in the platform. The architecture is designed for continuous extension, enabling the integration of new sources, transformation modules, and functional components over time, so that newly acquired and discovered datasets remain live, reliable, and readily accessible across OSDU-native applications and workflows. The first deployment demonstrates how the approach can move rapidly from concept to operational use, while also guiding its ongoing evolution.

The Energy Industry is entering a transformational era in how data is delivered, governed, and leveraged to make business decisions. At the center of this transformation is OSDU, the industry’s open, vendor-agnostic trusted data ecosystem designed to preserve and propagate the context of trust and uncertainty across applications, workflows, technologies, as well as across domains, scientific and technical disciplines, and organizational structures. OSDU demonstrates that standardization is not a constraint on innovation, but rather an enabler of trust, interoperability, scalability, and accelerated learning. As organizations increasingly adopt Artificial Intelligence, Machine Learning, and advanced analytics in their decision-making processes, the need for trusted data with embedded quality, usability, lineage, and purpose has become essential - because “garbage in, garbage out” still holds true, even in AI￾driven environments, particularly when data quality is poor, unknown or when input lacks sufficient context. In modern decision ecosystems, false confidence derived from poor-quality or context-deficient data is significantly more dangerous than acknowledged uncertainty – uncertainty that is understood, quantified, managed, and explicitly considered in decision-making processes.

Our technical presentation advances the concept that trust must extend beyond data accuracy to include contextual dimensions of quality and utility, enabling both human intelligence and artificial intelligence systems to learn, reason, and make decisions with measurable confidence — where those decisions are traceable and auditable, allowing continuous learning through analysis and reflection on past data, processes, and outcomes. We will showcase the foundation for modern decision ecosystems where standards improve decision quality, reduce uncertainty, enable interoperable data within a shared trust context, and accelerate enterprise learning from business decisions at scale.

Managing OSDU/ADME at scale requires visibility across governance, security, and data flows. Existing tools target developers. We built a custom UI layer for non-technical stakeholders: data stewards, platform operators, decisionmakers who need situational awareness without CLI access or code. This enables faster onboarding, stronger compliance visibility, fewer operational gaps. We'll share the design patterns that work for technical platforms and show how making information accessible shifts adoption and governance outcomes.

The adoption of OSDU has opened new opportunities for sharing seismic interpretation data across applications and analytics. Realizing these opportunities requires robust, vendor-independent software tools that can address the technical and architectural complexities of OSDU, its Reservoir Domain Data Management Service (RDDMS), the RESQML data exchange standard, and the Energistics Transfer Protocol (ETP). Equinor found the available open-source Python tooling for RESQML, RDDMS, and ETP to be insufficient.

This presentation shares Equinor’s experience developing rddms-io and pyetp, two open-source Python libraries designed to simplify interaction with the OSDU RDDMS using ETP and RESQML. Pyetp provides a modern, asynchronous ETP client implemented in pure Python, along with Pydantic models for all RESQML objects. Rddms-io offers higher-level abstractions for constructing RESQML-based objects from geophysical models. It leverages pyetp to manage their upload and download to and from RDDMS services. Together, these libraries aim to lower the barrier for Python-based applications and analytics to exchange interpretation data via RDDMS while remaining interoperable with established domain standards.

Furthermore, we describe the motivating use case of making interpretation data available to downstream applications and analytics through OSDU. We share practical lessons learned when modeling common geophysical objects in RESQML, where flexibility often conflicts with interoperability. Particular attention is given to the interaction between RDDMS and the OSDU Core Catalog, including trade-offs between metadata duplication, discoverability, and overall system complexity.

We also showcase results from an ongoing pilot implementation of automated delivery of interpretation data from vendor seismic interpretation software via OSDU to a third-party application used in Equinor’s well-planning workflow.

Structural interpretations, even of the same seismic survey, commonly reside in multiple siloed data stores. They are often created independently across projects, teams, applications or individuals, with different: goals, perspectives of geological context and, over time, availability of input or calibrating data (e.g. seismic, velocities, wells and production).

Even when discoverable across these silos, a lack of audit trail can result in absence of consistency and confidence in the interpretation data and its downstream use in modeling, resulting in unnecessary rework, inconsistent subsurface models, and delayed decision-making.

In the context of AI training, understanding of data context and quality are essential for good outcomes.

A vendor-agnostic governance framework enables interpretation discoverability with full context: who created it, with which seismic dataset and velocity model, what quality metrics were assigned, and how it was validated. Comprehensive information enables modelers to quickly assess if input interpretations are trustworthy and fit-for-purpose for their current project, eliminating unnecessary reinterpretation work.

We will demonstrate streaming seismic data directly from an OSDU® Data Platform, enabling high-performance visualization and interpretation without data duplication. Resulting interpretations and models are written back to the OSDU® Reservoir DMS with a clear record of inter-relationships and antecedents.

This capability proves particularly valuable when reconciling multiple interpretations created by different teams. Rather than engaging in subjective debates about which interpretation is "better," teams or agents can objectively compare interpretation contexts—examining which used more recent seismic data, which incorporated more well control, or which applied more sophisticated velocity modeling—and make data- and context- driven decisions about which to incorporate into an integrated subsurface model.

Data exchange between software and companies is a challenge:

Point-to-point connections

Complex to manage Proprietary API’s

Expensive to implement

Differences in data definitions

Hard to map Different approaches to governance

Difficult to get access

The oil and gas industry operates a large portfolio of software applications. These digital solutions help us achieve safer, more efficient planning and operations. As Equinor continues with our digital transformation, we have an increased need for data exchange. Most of our data currently resides in proprietary systems with custom APIs. For Equinor to quickly adopt new technologies, we must find more efficient ways of sharing data.

How do we create value through technical solutions, innovation, and lessons learned? How can you showcase innovative methods, practical experiences, and measurable impact.

OSDU aims to solve data exchange with their standardized data models and platform.

OSDU platform as a master data store, binding point for all applications.

Iterate data exchange more frequently.

Standardized API’s, publicly documented that can be implemented equally for all.

Standard data models, still hard to map, but only needs to be done once.

Easier to cross domain boundaries.

Equinor aims to solve the software interoperability issues by committing to the OSDU platform and standards. We’ve taken a proactive approach by migrating our drilling and well data to OSDU, allowing our partners faster access to our standardized data. We are also collaborating with our partners in making software OSDU compliant, opening up for new and more efficient work processes. Other technologies adopting the OSDU open standards will also benefit from much faster onboarding in our software portfolio.

Energy companies hold large volumes of operational and subsurface data in table-based formats such as spreadsheets, relational databases, and flat-file exports. Ingesting this data into an OSDU platform today often relies on bespoke, one-off solutions that are difficult to maintain, inconsistent in governance, and hard to reproduce across teams or data domains. A standardised, repeatable pattern is needed to turn bulk ingestion into a governed, scalable activity that any OSDU adopter can apply.

This work proposes a pattern that standardises bulk ingestion from table-based sources into OSDU by combining OSDU Data Definitions with the platform's native services.

The pattern is built around five elements:

1. Source dataset: the table-based data to be ingested.

2. Data mapping file: the core artefact of the pattern, defining how source columns map to a target Well-Known Schema or Custom Schema.

3. Data partition: determines tenancy and isolation within the OSDU platform instance.

4. Ingestion engine: an execution component that reads the mapping file and runs the transformation and loading algorithm against OSDU platform services.

5. Governance configuration: ACLs and Legal Tags applied to every ingested record, ensuring compliance from the moment data enters the platform.

By packaging these elements into a single repeatable pattern, organisations can onboard new table-based datasets with minimal custom development, while keeping governance and schema alignment consistent across data domains. The approach is platform-agnostic at the mapping layer and can be adopted by any OSDU-compliant deployment, helping the community reduce duplicated engineering effort around bulk ingestion.

In geotechnical engineering, the challenge is often not a lack of data, but the ability to find, access, and integrate it across workflows. This is particularly evident in offshore projects, where ground models depend on combining seismic data (2D and 3D) with large volumes of in-situ geotechnical measurements and laboratory results.

Field Manager is a cloud-based platform that supports the full lifecycle of geotechnical survey data, from collection and quality assurance to visualization. In this talk, we present recently developed data structures and workflows for linking geotechnical data (0D/1D) with spatial references to seismic data (2D and 3D), enabling these datasets to be explored and interpreted in context. The focus is on the design and implementation of the underlying data model, and on how to balance flexibility and structure when building systems for complex, multi-source data.

Effective subsurface data management plays a critical role in optimizing resources and supporting informed decisionmaking in geoscience operations. This study demonstrates a collaborative effort between Cegal and an upstream SEA NOC's innovation team to enhance data integrity and operational efficiency within the subsurface projects environment through the deployment of Blueback Project Tracker.

The initiative begins with a comprehensive evaluation of the existing data landscape, systematically identifying corrupt datasets, obsolete files, and coordinate reference system (CRS) inconsistencies across an extensive portfolio. During the initial phase, more than 5,000 subsurface projects spanning multiple software versions from 2007 to 2024 were assessed. This thorough inventory established the foundation for targeted data optimization activities.

A key focus involves addressing data duplication across well, interpretation, and seismic datasets. By implementing globally unique identifiers (GUIDs) and MD5 hashcode methodologies, redundant data entries were efficiently detected and eliminated. This systematic approach enhanced data integrity while achieving potential storage reduction of approximately 18 TB, significantly improving operational efficiency.

To sustain ongoing improvements, robust monitoring mechanisms were established to track project performance and data quality over time. A critical outcome of this continuous improvement process is maintaining a clean data environment, which enables the establishment of stronger governance protocols.

The results emphasize the value of modern data management methodologies in rationalizing subsurface data, maximizing operational efficiency, and reducing costs. This initiative demonstrates how structured approaches to data governance, combined with appropriate technological solutions, can transform geoscience data environments and support sustainable knowledge management practices.

Plug & Abandonment (P&A) represents a major technical, operational, and regulatory challenge for mature oil and gas provinces such as the Norwegian Continental Shelf, where large inventories of aging wells must be safely decommissioned under stringent environmental and regulatory requirements. A key obstacle to efficient P&A execution is the limited accessibility and quality of historical well information, which predominantly exists in unstructured legacy formats, including scanned reports and handwritten notes stored in heterogeneous file types (PDF, Word, PowerPoint). These limitations extend planning timelines, introduce uncertainty, and increase the risk of costly reabandonment. This paper presents a Generative AI driven solution that transforms legacy well documentation into a structured, engineering grade digital data foundation built for P&A workflows. The approach enables automated extraction and standardization of P&A relevant data and supports generation of consistent digital well schematics to inform risk based P&A design and regulatory compliance.

Extracted data is curated, quality controlled, standardized, and ingested into subsurface and engineering data platforms, ensuring traceability to source documents and enabling automated generation of P&A ready digital well schematics. The methodology was validated through a P&A case study involving more than 12,000 pages of multilingual legacy documentation across 9 wells from the Netherlands and the Gulf of America. Tasks that previously required 3 to 4 weeks of manual effort per well were completed in less than 2 days while maintaining high extraction accuracy. For operators and regulators, this GenAI enabled approach supports faster historical well assessments, earlier identification of integrity risks, standardized P&A decision making, and increased confidence in regulatory compliance, while converting legacy well data into a reliable, engineering grade foundation for risk based P&A execution.

Using Google Cloud's Gemini models with the Open Subsurface Data Universe (OSDU) platform.

* Multi-Agent Offset Well Analysis: This AI architecture is used to predict hydrocarbon production and run optimization scenarios for future field development based on multi-disciplinary datasets (geoscience, well design, operations). It uses a multi-agent architecture to orchestrate sub-agents: an Analytics Agent for data analysis and summarization, a Plotting Agent for generating Python data visualizations, an ML Agent for making predictions and evaluating ML models, and an Optimizer Agent for optimizing against constraints.

* Strata Scanner: An AI-driven multimodal intelligence solution designed to digitize dormant, unstructured historical data—such as well log images in .TIFF format—into modern OSDU formats in minutes.

* Automatic Schema Transformation: An interactive, conversational AI tool that helps users pipeline semi-structured database data into OSDU formats.

* Agents for Search & Automation: The integration deploys agents grounded in OSDU enterprise data to unlock conversational data access, allowing users to efficiently summarize complex technical documents, analyze large datasets, and automate workflows across the OSDU ecosystem.

Critical information for the evaluation of carbon storage prospects is held in the well abandonment documentation for historic wellbores. But extracting that information is a slow, and laborious exercise. Can LLMs do it better, faster, and cheaper? The NSTA and Flare Solutions have been working together to find out. We will share the outcome of work across over 1,000 well abandonment documents to extract cement plug and casing cutting parameters critical to understanding store integrity risk and the possibility of wellbore remediation. We'll also share what we've learned along the way about using commodity LLMs to answer domain-specific questions quickly, cheaply, and reliably.

Offset well analysis is one of the most valuable yet time-consuming workflows in drilling. Engineers often spend days or weeks selecting relevant analog wells, reviewing daily drilling reports, completion reports, end-of-well reports, and other semi-structured records, then manually translating that history into planning actions. This paper presents an AIaccelerated offset well analysis workflow that moves beyond data extraction to analysis and operational decision support.

The approach combines clustering-based well similarity methods with generative AI applied to unstructured and semistructured drilling data. Structured well attributes are first used to identify the most relevant offsets for a new plan. Large language models with OCR and document parsing then analyze drilling narratives to extract and interpret hazards, operational sequences, lessons learned, formation behavior, NPT drivers, losses, instability indicators, and mitigation actions. Rather than only producing structured fields, the workflow builds a customizable driller’s roadmap: a practical, well-specific summary of expected risks, depth-linked events, recommended mitigations, and execution guidance for the next well.

This reduces review time from weeks to days, improves consistency, and unlocks learning from large historical well populations that are too large for manual review. The paper will also present case studies showing how the workflow helps identify stuck-pipe risk, loss-zone patterns, and offset performance trends, demonstrating how generative AI can turn legacy well records into operational intelligence for faster, smarter, and safer well planning.

The study presents the development of an AI-based agent and software system designed to extract data from geothermal well reports and perform well nodal analysis. The main goal of the project was to create a tool capable of processing technical documentation (containing text, images, tables) and converting them into structured data used for optimizing the production of geothermal wells.

The system utilizes a Retrieval-Augmented Generation (RAG) approach combined with a Chroma vector database to efficiently retrieve relevant technical information. To ensure high-quality data extraction, a pre-trained YOLO model is used to filter images, focusing OCR efforts on relevant schematics while excluding unnecessary noise such as company logos. The AI agent identifies and extracts key parameters, including well trajectories and casing details, transforming them into a structured JSON format.

A dedicated nodal analysis module allows users to evaluate inflow and pressure conditions (IPR/VLP). The workflow is semi-automated, allowing engineers to review and adjust the extracted data before running calculations. This humanin-the-loop design preserves QA/QC reliability while significantly accelerating the transition from raw documentation to actionable insights.

The solution was developed during the SPE Europe Energy Geohackathon 2025, where it was awarded second place. The results demonstrate that the system effectively reduces manual workloads and supports more consistent analysis of geothermal assets, proving the practical value of combining Agentic AI tools with traditional engineering workflows.

This presentation introduces the Data Delivery Plan (DDP), a tool developed by Equinor to improve planning and reporting of wellbore data. The solution provides a structured overview of planned data acquisition aligned with regulatory requirements (Blue Book Table A-1) and the drilling programme, enabling consistent and compliant reporting.

The DDP application allows users to define, review, and export a complete data acquisition plan through a guided workflow covering wellbore information, dataset selection, and final validation. It ensures visibility of mandatory datasets and supports improved completeness, transparency, and communication across operators, service and log QC providers, Diskos and authorities.

Two implementation options are presented: a lightweight version for creating and exporting plans locally, and a more advanced version with user login, storage, and dashboard functionality for managing multiple plans. Overall, the DDP aims to standardise data planning, reduce errors and gaps in reporting, and enable more efficient, consistent, and compliant data delivery to authorities and internal systems.

Over the last 2 years, we have partnered with the NSTA to audit, reclassify, and re-tag nearly 1 million well data items. We will share what we learned about running a mega-scale classification project, including approaches to automation, quality control, and standardising approaches to classification across a diverse project team. During the project, the AI revolution started. We'll also share our thoughts about the impact AI has had, and will have in classification and taxonomy management.

Is Diskos an enabler for new developments and analysis of the Norwegian Continental Shelf, or are we just more of the same... Status of the now more than 31 years old colaboration project. And, maybe some interesting news on new ways of utilizing the vast amout of data and information that has been collected and shared for decades. Everybody wants to create value - can we help?

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.

The legislation that regulates recording and use of bathymetric data or information about the seabed has been updated and is not well known among some stakeholders. This presentation is primarily aimed at those who map bathymetric data or collect any type of information about the sea bed. The presentation will provide an overall introduction to the new regulation and what it entails, what type of mapping activity that requires an application or report, how the actors should relate to the legal framework and how to apply, how to get access to Kartverkets batymetric data, as well as a demo of our new digital application service. We belive this presentation will be useful for ensuring compliance with the regulation, while also contributing to safe and efficient development within the industry.

Data with geospatial context is the essential connective tissue linking subsurface and energy workflows, enabling efficient decision-making, operational efficiency, and supporting sustainability objectives. Despite its importance, handling geospatial data remains still one of the most complex elements within modern data ecosystems. When interoperability breaks down, the consequences may be rather silent but far-reaching, affecting data quality, workflows, and data platforms - including OSDU - and ultimately compromising the effectiveness of analytics and AI outcomes.

This presentation delves into the causes and failure points behind these geospatial interoperability challenges. Using seismic position data and metadata as an example, current data workflows - including data quality considerations - will be examined, along with their impact on diverse stakeholders, ranging from data and solution architects to end-users reliant on the data.

Attendees will gain insight into risks within geospatial workflows. Additionally, strategies to prevent the compromise of AI-driven insights, along with practical approaches to ensure data integrity, interoperability, and readiness for analytics, decision-making, and sustainable operations, will also be discussed.

Two years ago, at this conference, the argument was made that file format compliance remains indispensable even in the era of AI – that the foundation of reliable automation is a reliable benchmark. This argument holds, and we are beginning to see the tangible benefits of automation in data loading and QC.

Compliant and regularised subsurface data enables automated file identification and ingestion, systematic geodetic parameter validation, repeatable QC procedures, structured error reporting, and export to common exchange formats – all without manual intervention. The data lifecycle becomes more manageable, and each dataset is assigned an integrity indicator so that informed decisions are made. This process is established and in practical use today.

In this talk, we present practical experience of operating such workflows, examining both what is working and where the boundaries remain. Non-compliant data continues to present genuine challenges: CRSs that must be inferred when not explicitly declared, input files in formats that resist automation, poor quality scanned legacy data, and fundamental errors embedded invisibly within otherwise plausible datasets. In each of these cases, human expertise remains critical – and likely will for some time.

At these boundaries, there are feasible approaches to reduce the triage burden when data loading. Probabilistic, fuzzy CRS identification uses spatial and contextual clues to arrive at a reasoned prediction of positioning. Pattern recognition can identify fields within non-compliant files, and format fingerprinting can inform and refine future loading decisions.

Each improvement introduced into the automation workflow reduces the load on the analyst, allowing expertise to be directed toward genuine ambiguity rather than routine compliance checks. We explore the current industry state, emerging developments, and the challenges that still require solving before full automation can be achieved.

A growing number of maritime industries, including offshore wind, seabed mineral exploration, fisheries, and petroleum, are increasing the demand for high-quality geospatial data.

At the sime time, data silos remain a persistent barrier in both public and private sector, limiting the efficient use of geospatial information by restricting access, creating duplication of effort and reducing the ability to integrate data across maritime domains.

This presentation explores the underlying reasons these challenges remain, with particular emphasis on the gap between technical solutions and their implementation - and demonstrates how improved data sharing can unlock significant collective value across sectors.

Drawing on experiences from public geospatial data management in Norway, and informed by the author’s role as Marine Geodata Coordinator at the Norwegian Mapping Authority, this presentation highlights how clear roles, governance structures, and a national spatial data infrastructure have played a key role in breaking down data silos and enabling cross-sector data integration.

We argue that collaborative and governance-related factors, including data ownership, incentives and trust often is more critical than the enabling technology itself.

In Collaborative Well Planning (CWP), data delivery speed is often assumed to be an integration challenge. In practice, the dominant constraint is human behaviour under time pressure: last minute data submissions, informal handovers and unclear ownership that undermine trust and traceability. These behaviours routinely stretch data preparation from hours into days—and carry forward into downstream workflows. This presentation describes how a shift towards small, user prepared data packages reduced CWP data delivery from 1.5 days to as little as 1.5 minutes, without adding heavyweight process or central bottlenecks. The change was enabled by empowering users to take control of their data and thus bypassing the formal data preparation and readiness checks that were needed to ensure successful well planning sessions. The human dimension is at the core of this, and why users disengage with the prescribed methods, even though they are straightforward and simple. By designing data workflows around real behaviour—rather than perceived ideal behaviour—governed data packages become an enabler of speed and not an obstacle.

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

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.

The oil and gas industry continues to digitise subsurface data, yet a significant portion of high-value information enriched by subject matter experts remains trapped in unstructured formats such as scanned reports, well documents, and legacy PDFs. While the OSDU® Data Platform standardises structured datasets, unlocking value from unstructured content requires both semantic enrichment and rigorous, enterprise-grade security.

This presentation introduces a scalable, entitlement-first Retrieval Augmented Generation (RAG) architecture developed in collaboration with a major operator, transforming unstructured OSDU-referenced content into actionable, contextaware intelligence. The approach combines document reconstruction logic and header-aware chunking with a hybrid retrieval strategy, integrating vector search and keyword-based methods to improve retrieval quality. Evaluation on a curated set of subsurface-specific questions is used to guide the tuning of key system parameters and quantify performance improvements.

A key architectural principle is the enforcement of access control and watermarking through mapping user identity to OSDU Access Control Lists (ACLs), ensuring that only authorised data is retrieved and that both outputs and source documents remain traceable. The solution is designed for seamless integration into existing subsurface workflows, allowing users to access OSDU-sourced insights directly within their daily tools without introducing additional friction, thereby reducing the time spent on searching and validating data.

Beyond retrieval, the architecture establishes a foundation for future agent-based workflows, where AI systems can reason over both structured and unstructured data while maintaining full compliance with enterprise security requirements. The presentation highlights key lessons learned, challenges encountered, and the practical considerations required to scale the solution in an enterprise setting.

As energy companies modernise their data estates, one challenge remains: unlocking value from legacy Petrel project data. While the OSDU™ Data Platform provides a cloud-native, vendor-agnostic foundation for data access and reuse, Petrel data is often locked inside proprietary project structures that are difficult to search, govern, and integrate beyond the desktop.

This presentation shows how Petrel projects can be transformed into OSDU-ready data through automated extraction, enrichment, and metadata normalisation. Using domain-aware tools such as Blueback Project Tracker and purpose-built extraction pipelines, large volumes of Petrel projects can be scanned, assessed, and prepared for ingestion. The session highlights common challenges, including version inconsistencies, incomplete legacy archives, embedded unstructured content, and missing spatial metadata, and explains how validation, enrichment, and reference-data mapping improve quality and alignment with OSDU domain models.

A central theme is the shift from treating Petrel projects as static containers to viewing them as structured sources of reusable domain data. By breaking projects into independently managed data objects, organisations can enable lineage, governance, cross-project comparison, and more collaborative cloud workflows.

The talk shares practical lessons from real-world deployments, including archive scanning, project prioritisation, ingestion integration, and quality assurance feedback loops. Attendees will leave with a clearer view of how to approach legacy-to-cloud transformation at scale and turn trapped project data into governed, discoverable, cloud-ready assets.

Recent advances in agent-based artificial intelligence are transforming how subsurface professionals interact with enterprise data ecosystems, information models, and technical workflows. Instead of manually discovering, validating, and integrating data across multiple systems, intelligent agents act as context-aware assistants that interpret user intent, reason across distributed data sources, and orchestrate end-to-end data management processes.

This work presents a practical demonstration of an agent-driven approach applied to a subsurface use case using an open field dataset. Starting from a high-level objective, the agent discovers and evaluates available data assets across enterprise repositories, summarizes field and reservoir context, inventories wells and associated datasets, and assesses data completeness, consistency, and quality. The agent integrates information from both internal data platforms and external regulatory and public data services to identify and resolve gaps, including missing interpretation data.

Building on this foundation, the agent generates reproducible workflows for data ingestion, transformation, conditioning, and enrichment within a unified environment aligned with enterprise data models. It demonstrates how heterogeneous data can be harmonized through metadata-driven approaches and interoperable services. As part of the workflow, the agent constructs an analytical pipeline, including a machine learning model to estimate missing subsurface properties.

By interacting with data catalogs, services, orchestration frameworks, and computational platforms, the agent highlights the importance of interoperability and standardization. This work shows how agent-based systems bridge the gap between domain intent and governed, reproducible workflows, improving data accessibility, strengthening data quality, and accelerating integrated digital subsurface workflows.

The growing volume and complexity of geoscience data require scalable solutions for efficient data governance and workflows. This paper demonstrates how data-driven approaches and digital transformation principles can be applied in practice through a data management platform, using Blueback Project Tracker as a case study.

Blueback Project Tracker scans and structures geoscience data, enabling users to identify data duplication, mispositioned assets, and large datasets. Automated project size calculation and integration with visualization tools such as Power BI support data-driven decision-making.

Beyond monitoring, the platform is used in data assessment and consultancy workflows to extract Petrel metadata and support data rationalization. Using command-line (CLI) functionality, standardized workflows are applied, including consolidating data from multiple projects into a single environment, distributing data across projects, and reconnecting or externalizing seismic data to reduce redundancy.

The platform also supports key data management tasks such as project upgrading, archiving, and deletion. Batch processing with optional backup, rule-based validation, and flexible filtering improves efficiency and reduces operational risk. User activity monitoring and access control further support governance and data security.

Overall, this work demonstrates how systematic workflows powered by Blueback Project Tracker can turn data chaos into strategic advantage, eliminating redundancy, unlocking hidden storage savings, and support efficient collaboration across subsurface teams.