You can quickly quantify and monitor the predictive power of a conditioned ensemble using a graphical analysis, and drill down on individual reservoir parameters to measure predictability and improve your knowledge. Ensemble validation allows for an efficient inspection of forecasted production, immediately indicating the quality of the ensemble.

Improve your understanding of the subsurface

IRMA is the essential toolkit to make working with ensembles as easy as working in a traditional single model paradigm. It improves the quality of your ensemble with modelling recommendations, and improves your understanding of the subsurface so that you gain confidence in your decisions. You automatically explore your ensembles, extract key statistics, and highlight the drivers for the dynamic response throughout the reservoir. IRMA helps you perform quality assurance against input data to detect potential inconsistencies in your ensembles and identify possible modelling solutions.

IRMA takes out the guesswork about what is a good model to use for well target selection since you plan with a full ensemble. Why run the risk of taking a base case approach when you can build your ensemble with ResX and rapidly get your well target analytics with IRMA? As a dedicated analytics tool IRMA can automatically identify robust well targets based on an ensemble of models. Users define well target selection criteria based on reservoir properties and get feedback on the associated risks. Therefore, identified targets are ranked according to their capacity to add value and their risk.

If you want to experiment, develop algorithms and gain even more insights from your ensemble of models, you can use IRMA Lab. IRMA Lab is an open and extensible environment based on the Jupyter™ Notebook.