OSDU

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.