Azure Data Manager for Energy Adds SLB ARM GA

Introduction

Microsoft and SLB have made agile reservoir modeling (ARM) generally available on Azure Data Manager for Energy (ADME). For energy organizations, this matters because reservoir uncertainty analysis often depends on complex infrastructure, fragmented data sources, and time-sensitive simulation workflows that can slow drilling and development decisions.

What’s new

The new GA integration combines SLB’s ARM with Microsoft’s Azure Data Manager for Energy to support cloud-scale ensemble reservoir modeling.

Key capabilities

• General availability of ARM on ADME for production use
• Elastic Azure high-performance computing to run many reservoir realizations in parallel
• Scalable Azure storage designed for high throughput and concurrent access
• Governed data foundation using ADME with OSDU-aligned services and DDMS
• Petrel integration so engineers can define uncertainty workflows and work directly with corporate subsurface data
• Entra ID-based access controls and compliance features such as OSDU legal tags

Why it matters

Reservoir teams often need to evaluate hundreds of subsurface model realizations to understand uncertainty and risk. Traditional infrastructure was built for smaller, sequential studies, making large ensemble modeling difficult to scale.

With ARM on Azure, simulation generation and execution can be distributed across Azure HPC while data remains connected to a governed enterprise platform. This reduces isolated project stores, manual orchestration, and duplicated data movement. Teams can also publish results back into ADME for discovery, filtering, and reuse across the organization.

Impact on IT administrators and data leaders

For Azure and energy IT teams, the biggest benefit is architectural consistency. The integration connects simulation workflows to an OSDU standard-aligned data platform with centralized identity, governance, search, and interoperability.

This can help administrators:

• Reduce disconnected subsurface data silos
• Improve access governance with Microsoft Entra ID
• Support compliance requirements across modeling workflows
• Scale compute, storage, throughput, and IOPS independently based on workload demand

Next steps

Organizations in the energy sector should evaluate whether current reservoir modeling workflows are constrained by on-premises infrastructure or fragmented data management. If so, this GA release is a strong signal to review ARM on ADME for ensemble modeling, simulation orchestration, and governed data reuse.

Microsoft also noted ongoing work with SLB and the OSDU community to advance more cloud-native reservoir data standards, which could further improve interoperability and support future AI-driven workflows.