Scale reservoir uncertainty analysis with agile reservoir modeling
Turn subsurface uncertainty into decision-ready insight
Drilling campaigns are among the most expensive and operationally complex activities in the energy industry. Rigs, crews, equipment, and logistics are scheduled months in advance, leaving little room for delay. Every hour of downtime can carry significant costs. Geoscientists and engineers rely on reservoir models to understand the subsurface and guide drilling decisions, often under intense time pressure. Yet the current modeling infrastructure that many companies rely on for these critical decisions was designed for smaller datasets, simpler workflows, and siloed operations. As subsurface data grows in scale and complexity, teams need a faster, more connected way to transform uncertainty into actionable decisions.
Now generally available, agile reservoir modeling (ARM) from SLB, integrated with Azure Data Manager for Energy (ADME), is co-engineered for cloud scale to close that gap. The combined solution connects ensemble-centric reservoir modeling and simulation with a governed data foundation, elastic Azure high-performance computing, and scalable storage designed for the throughput and concurrency of large ensemble studies.
Learn more about Azure Data Manager for Energy
Scale ensemble modeling with ARM on Azure
A single reservoir model represents one plausible interpretation of the subsurface. Ensemble modeling evaluates hundreds of model realizations together, giving asset teams a broader view of potential outcomes and a stronger basis for understanding uncertainty and risk.
The industry has long recognized the value of ensemble modeling for risk-aware decision-making, but adoption has been constrained by infrastructure and software designed for smaller, sequential studies. Moving from a limited model set to hundreds of realizations multiplies the compute, storage, data movement, and results that must be managed. ARM addresses this barrier by distributing realization generation and simulation across elastic Azure high-performance computing, while scalable storage provides the capacity, throughput, and parallel access required by ensembles of different sizes.
Connect reservoir workflows to a governed data foundation
Building and updating reservoir models draws on many systems: seismic, wellbore, interpretation, and simulation data, plus the applications and compute that process them. When these pieces are fragmented, customers must connect and orchestrate them on their own, and large-scale simulation becomes a disconnected, one-off exercise.
SLB and Microsoft combined their respective technologies, ARM engineered by SLB and ADME engineered by Microsoft, to remove that burden. Rather than a set of loosely connected tools, customers get one integrated workflow in which each component has a clear role:
Azure Data Manager for Energy is the governed data foundation. OSDU® Technical Standard-aligned services and OSDU Domain Data Management Services (DDMS) manage seismic, wellbore, interpretation, and simulation data.
Petrel™ subsurface software is where engineers define interpretation and uncertainty workflows, connected directly to data in ADME.
Agile reservoir modeling is the ensemble workflow engine. It orchestrates the generation of reservoir realizations and simulation runs and supports discovery and analysis at enterprise scale.
As a result, teams build ensembles from trusted corporate data and publish results back for discovery and reuse, which reduces data movement and improves collaboration across asset teams.
Put ensemble modeling into practice with a connected workflow
Beyond integration and scale, ARM distinguishes itself by orchestrating all stages of an ensemble study across modeling, simulation, data management, and analysis. The workflow tightly connects the tools reservoir teams already use with Azure-scale infrastructure and the governed data services provided by Azure Data Manager for Energy.
Agile Reservoir Modeling workflow using cloud HPC to generate and simulate multiple reservoir-model realizations in parallel delivered to web-based interface for analysis.
A walkthrough of how this works today:
Reservoir engineers define uncertainty workflows in Petrel software and prepare simulation models for supported SLB simulators, including the Intersect™ reservoir simulator and Eclipse™ reservoir simulator. ARM generates the realizations, prepares the simulation cases, and coordinates their execution across the Azure environment.
Azure high-performance computing executes multiple realizations in parallel. A shared, high-performance Azure Files volume holds model inputs, grids, simulation decks, temporary files, and results so the simulation fleet can read and write concurrently. Because compute capacity, storage capacity, throughput, and IOPS can be scaled independently, the environment can be sized for the concurrency of the ensemble rather than the constraints of fixed infrastructure.
As simulations complete, SLB reservoir engineering services manage the resulting ensembles and use OSDU search capabilities on ADME to support discovery, filtering, and sorting across large studies. Teams can locate relevant realizations and supported, published results without manually navigating disconnected project stores.
Engineers can analyze ensembles in the reservoir engineering workspace or load them back into Petrel software for further interpretation, history matching, and reservoir engineering studies. This allows teams to work in the environment and application best suited to each task while maintaining continuity, without having to step outside their workflow to manage orchestration.
ADME provides entitlement-based access control integrated with Microsoft Entra ID and supports compliance controls such as OSDU legal tags. Connecting ARM workflows to this governed data foundation helps maintain consistent access and compliance as corporate subsurface data is used across modeling and simulation. Because ARM workflows are integrated through Petrel software in ADME, reservoir engineers can perform ensemble-scale studies leveraging their corporate-wide data stores and publish results for discovery, eliminating friction and driving collaboration. Microsoft and SLB engineering collaborated closely to build the integration between Petrel software and ARM for enterprise-scale performance.
For reservoir engineers and asset teams, ARM turns ensemble modeling into a repeatable workflow rather than a bespoke infrastructure project. Teams can stress-test development concepts against calibrated ensembles that honor observed production history, not simply run a larger number of models. As new wells are drilled and new data becomes available, they can update their reservoir understanding without waiting for a major model rebuild. For energy IT and data leaders, the workflow connects simulation to an OSDU standard-aligned architecture with consistent identity, governance, search, and interoperability, reducing isolated data copies and disconnected applications.
The future of the reservoir lifecycle
Building on this partnership, Microsoft and SLB are working within the OSDU community to advance a more cloud-native standard for reservoir data. This work will help models, simulation results, and engineering workflows operate more consistently across applications, improving interoperability while reducing dependence on proprietary formats and silos. A shared, governed data foundation also paves the way for AI and agentic experiences that can discover data, coordinate workflows, and surface insights across the reservoir lifecycle. Combined with Azure-scale compute and storage, this creates a path toward more automated and continuous reservoir modeling.
Get started with ARM on Azure Data Manager for Energy
Agile reservoir modeling on Azure Data Manager for Energy is generally available today. Learn more about agile reservoir modeling from SLB and Azure Data Manager for Energy. Contact your Microsoft and SLB account teams to identify an initial reservoir workflow and discuss the path to deployment.
Azure Data Manager for Energy
Accelerate your energy intelligence.
Get started
The post Scale reservoir uncertainty analysis with agile reservoir modeling appeared first on Microsoft Azure Blog.
Quelle: Azure
