Location: Stavanger
Date: 3 - 4 May. 2023
Start Time: 09:00
Instructor: Martha Stunell

RPS is partnering with Resoptima to deliver 'Ensemble-Based Reservoir Modelling and Simulation' with Martha Stunell, who will be bringing to the classroom her exceptional breadth of expertise in the theory and practical application of the ensemble-based modelling to subsurface assets. The first run will take place in person in Stavanger, Norway from 3-4 May however the course is scheduled to be delivered again virtually in Q4.

Business impact: Ensemble-based modelling is grounded in a paradigm where all data that informs the subsurface understanding can be integrated in a consistent workflow, so that models are a reasonable representation using data from all disciplines. This course will show that ensemble-based modelling is about much more than building a set of models, but instead, at its core is a true integrated modelling vision.

Examples from field studies worldwide will be used to illustrate ensemble modelling in action, highlighting that an ensemble-based approach has many benefits in terms of enabling asset teams to continuously work on identifying potential risks and opportunities for the field, while capturing the subsurface uncertainty. Finally, the recommended steps for leveraging uncertainty-centric modelling will be discussed along with examples of how to evolve a company’s modelling processes to allow integrated ensemble-based modelling to flourish.  

Participants will learn to:

1. Evaluate the importance of establishing a robust modelling workflow to test hypothesis about the subsurface, considering uncertainties from all disciplines.
2. Apply the principles of the Ensemble smoother multiple data assimilation (ESMDA) approach to integrate all data in an iterative workflow.
3. Analyse and review results from an ensemble of models to enable effective communication with peers and decision makers, and to drive the concept of modelling as a continuous learning tool.
4. Analyse the changes to prior models required to accommodate the production, 4D, and other data sources from which the integrated team can learn about the unknown reservoir parameters.

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