Scale reservoir uncertainty analysis with agile reservoir modeling

Transforming Subsurface Uncertainty into Actionable Insights

Running drilling campaigns is one of the most costly and complex ventures in the energy sector. Everything from rigs and crews to equipment and logistics is meticulously planned months ahead, leaving little room for delays. Just an hour of downtime can incur major expenses. Geoscientists and engineers use reservoir models to gain insights into the subsurface and inform their drilling choices, often facing tight deadlines. Unfortunately, the current modeling systems many companies depend on were initially built for smaller datasets and simpler workflows, which means they’re struggling to keep up with the increasing scale and complexity of subsurface data. What teams now need is a faster, more integrated method for turning uncertainty into sound decisions.

Introducing agile reservoir modeling (ARM) from SLB, now widely available and designed to work with Azure Data Manager for Energy (ADME). This collaborative effort is crafted for cloud scalability, bridging the gap in your modeling needs. By connecting ensemble-driven reservoir modeling and simulation with a structured data foundation, flexible Azure high-performance computing, and scalable storage capabilities, teams can efficiently manage large-scale ensemble studies.

Enhance Ensemble Modeling with ARM on Azure

Each reservoir model provides just one possible interpretation of the subsurface. In contrast, ensemble modeling examines hundreds of model scenarios collectively, allowing asset teams to gain a more comprehensive understanding of potential outcomes, thereby improving risk assessment and decision-making.

While the value of ensemble modeling for informed decision-making is well acknowledged in the industry, its adoption has been hampered by infrastructure that was not designed for extensive studies. Transitioning from a limited model set to hundreds of realizations demands increased computational power, storage, and careful data management. ARM addresses this issue by distributing the generation of realizations and simulations across elastic Azure high-performance computing resources. This scalable storage can accommodate the capacity and throughput necessary for ensembles of varying sizes.

Link Reservoir Workflows to a Structured Data System

Creating and updating reservoir models requires input from diverse systems, including seismic, wellbore, interpretation, and simulation data, along with the applications and computing power needed to process them. When these elements are disconnected, it falls on the customer to connect and manage them, making large-scale simulations isolated and inefficient.

To resolve this, SLB and Microsoft have combined their respective strengths—ARM from SLB and ADME from Microsoft. This integration simplifies the workflow, ensuring that each element plays a specific role:

• Azure Data Manager for Energy serves as the governed data platform, adhering to the OSDU® Technical Standard with services that manage seismic, wellbore, interpretation, and simulation data.
• Using Petrel subsurface software, engineers define workflows for interpretation and uncertainty, directly linked to data stored in ADME.
• Agile reservoir modeling operates as the ensemble workflow engine, orchestrating the generation and simulation of reservoir realizations while facilitating discovery and analysis on an enterprise level.

This means teams can create ensembles from reliable corporate data and share results efficiently, reducing the need for excessive data movement and fostering collaboration among asset teams.

Implement Ensemble Modeling with a Cohesive Workflow

In addition to integration and scalability, ARM excels in coordinating every aspect of an ensemble study, including modeling, simulation, data management, and analysis. The workflow seamlessly connects the tools that reservoir teams are already familiar with to the robust Azure infrastructure and the structured data services provided by Azure Data Manager for Energy.

Here’s How It Works:

Reservoir engineers establish uncertainty workflows in Petrel software, preparing simulation models suitable for various SLB simulators, like the Intersect and Eclipse reservoir simulators. ARM generates the model realizations, handles simulation cases, and smoothly coordinates the execution within the Azure environment.

Azure high-performance computing allows for multiple realizations to run simultaneously. A shared, high-performance Azure Files volume stores model inputs, grids, simulation decks, temporary files, and results, enabling concurrent access for the simulation fleet. Because compute, storage, throughput, and IOPS can be scaled independently, the overall environment can be tailored for the concurrency of the ensemble, free from limitations of traditional infrastructure.

Once simulations are complete, SLB’s reservoir engineering services oversee the resulting ensembles and leverage OSDU search features in ADME for efficient discovery, filtering, and sorting across extensive studies. Teams can effortlessly find relevant realizations and access published results without the hassle of navigating disjointed project databases.

Engineers have the option to analyse ensembles directly in the reservoir engineering workspace or import them back into Petrel software for further interpretation, history matching, and reservoir studies. This integration allows teams to operate in their preferred environments while ensuring continuity without having to break away from their workflow for orchestration.

ADME offers entitlement-based access control integrated with Microsoft Entra ID, ensuring compliance with regulations like OSDU legal tags. Connecting ARM workflows to this structured data foundation ensures that consistent access and compliance are maintained as corporate subsurface data is employed across modeling and simulation tasks. With ARM integrated through Petrel software in ADME, reservoir engineers can carry out ensemble-scale studies using their corporate data stores while publishing results for easy discovery, reducing friction and enhancing collaborative efforts. The close collaboration between Microsoft and SLB engineering teams has led to the development of this robust integration.

For both reservoir engineers and asset teams, ARM transforms ensemble modeling from an ad-hoc infrastructure project into a repeatable workflow. Teams can rigorously test development concepts against calibrated ensembles that accurately reflect observed production history rather than simply running more models. As new wells are drilled and fresh data is gathered, teams can refine their reservoir understanding promptly without waiting for massive model overhauls. For energy IT and data leaders, the workflow links simulation to an OSDU-standard architecture, providing consistent identity, governance, search, and interoperability, while minimising isolated data copies and fragmentation of applications.

Envisioning the Future of Reservoir Management

Building on their alliance, Microsoft and SLB are actively participating in the OSDU community to refine a cloud-native standard for reservoir data. This initiative will facilitate a more consistent operation of models, simulation outputs, and engineering workflows across various applications, enhancing interoperability while decreasing reliance on proprietary formats and data silos. A shared, governed data foundation also sets the stage for AI-enabled solutions that can help discover data, coordinate workflows, and extract insights throughout the reservoir lifecycle. Together with the computational and storage capabilities of Azure, this advancement paves the way for more automated and perpetual reservoir modeling.

Begin Your Journey with ARM on Azure Data Manager for Energy

Agile reservoir modeling on Azure Data Manager for Energy is now available. To find out more about agile reservoir modeling from SLB and Azure Data Manager for Energy, reach out to your Microsoft and SLB account representatives to pinpoint an initial reservoir workflow and discuss how to proceed with deployment.

FAQs

What is agile reservoir modeling?

Agile reservoir modeling (ARM) helps energy companies manage and model subsurface data effectively by integrating advanced computational resources and structured data management, enabling quicker and more informed decision-making during drilling operations.

How does ARM improve collaboration among asset teams?

ARM streamlines workflows by connecting all necessary data and tools into a single integrated system, reducing data silos and promoting teamwork between different asset teams while working on reservoir projects.

What role does Azure Data Manager for Energy play in reservoir modeling?

Azure Data Manager for Energy acts as a governed data foundation that organizes and manages seismic, wellbore, interpretation, and simulation data, ensuring that users can easily access and analyze relevant information throughout their workflows.

What benefits does ensemble modeling offer?

Ensemble modeling evaluates multiple scenarios to provide a more comprehensive view of possible subsurface conditions, improving risk assessment and aiding teams in making better-informed drilling decisions.