The SEG-Y revision 2 specification opens up new possibilities for seismic data exchange. This presentation draws on hands-on experience implementing the specification, illustrated with practical examples and tips for adding software support. Along the way, we'll examine the benefits the latest revision brings — and the challenges it introduces.

LUMIN, Kadme's data and insights engine for the oil and gas industry, has expanded significantly over the past year — moving beyond search and data discovery into active quality assurance workflows and AI-native data access. This presentation covers two major additions to the LUMIN product suite.

The first is the Well Log QC module, now in active deployment for a major national oil company in Latin America. The module automates quality control of well log data (in LAS and DLIS formats) by applying a configurable set of business rules that validate completeness, range compliance, curve consistency, and cross-well integrity.

The second highlight is the LUMIN MCP Server. MCP works as a universal connector between AI clients and data sources. The LUMIN MCP Server exposes LUMIN's lexical search, semantic search, and API query capabilities directly to any MCP-compatible AI agent, without custom integrations.

Together, these developments reflect Kadme's direction: a platform where data quality and AI accessibility reinforce each other at every layer of the subsurface workflow.

In this talk we will look at what is required for the digital transformation of seismic data and how this can be achieved and why we have not got there - yet.

The O&G industry generates vast amounts of data across the exploration, development, and production lifecycle. However, transforming this data into actionable insights and business value still remains a significant challenge. Many analytics initiatives never progress beyond the Proof of Concept (PoC) stage. Success requires more than advanced analytics or black-box models; it demands a deep understanding of the underlying physics, engineering constraints, and operational realities that shape decision-making in E&P.

In this presentation, Expert Analytics will showcase a series of real-world customer use cases in production optimization, demonstrating how industrial data science can bridge the gap between raw data and operational impact. By combining domain expertise, physics-based understanding, advanced analytics, and robust software engineering, we help E&P operators tackle complex challenges and accelerate the adoption of data-driven solutions.

While operators embrace OSDU as an open data platform to liberate data for new insight and application interoperability, the journey is taking time and effort from scarce expert resources. Petrabytes have developed an customer-adaptable OSDU data ingestion platform with a Lakehouse Architecture, deployable in any cloud or on-premise environment. The presenters will share lessons learned from projects and how the approach follows a Medallion Design Pattern to provision subsurface data as products. While OSDU is a destination as new System of Records, the approach also allows for synergies though “dual destinations” to analytics platforms, like Databricks and Fabric.

A subsurface data-pipeline is a business-critical element in your data foundation. Reliability may come from design, but availability and serviceability requires management and attention. The presentation will illustrate a concept of Observability which allows for automation of remedial actions if data sources, ETL, enrichment or provisioning is indicating a problem.

* 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.