IRMA is designed for ensembles from the ground-up, making it easy to break the habit of thinking of cases.
It’s the environment required to make uncertainty centric modelling a reality.
IRMA applies analytics, data exploration, machine learning and visualization techniques to enable you to easily use ensembles of models to explore the subsurface, identifying new opportunities and associated risks.
Automatically ingest and manage your ensemble data from ResX or from other ensemble sources. Coherently manage your ensembles as an integrated model of the reservoir under uncertainty.
The solution to your ensembles is IRMA. Giving you the full benefits of the aggregated statistics of the models and providing specific ensemble-oriented analytics.
IRMA handles, visualizes and explores ensembles of reservoir models as if they represented a single model of the reservoir under uncertainty. In other words, the tasks of forecasting, optimizing, exploring opportunities, improving the understanding of the reservoir and developing insights for improving the models are made easy.
Powered by fit-for-purpose machine learning algorithms for ensemble-based modelling and analytics, ResX and IRMA deliver speed, control and power to look at all available data as a whole. Immediately assessing how modelling choices and data interpretation affect the resulting ensembles of models and enabling teams to iterate quickly and continuously learn and improve.
IRMA is an open application based on a microservice oriented architecture. The microservices can run on all types of cloud-based architectures as well as on premise.
The services connect to data sources via open APIs and are accessed by users through a modern web interface. In addition to providing automatic integration with ResX for ensemble data, IRMA provides adapters for other data sources. IRMA also connects to multiple reservoir simulator infrastructures, enabling ease of access to simulation capacity when needed.
Is your ensemble up-to-date?
Quickly quantify and monitor the predictive power of a conditioned ensemble through a graphical analysis. Drill down on individual reservoir parameters to measure predictability and improve understanding. Ensemble validation allows for efficient inspection of forecasted production through the validation period, immediately indicating the quality of the ensemble.
Learn, review assumptions, update
IRMA Model Validation tracks the current state of the ensemble and alerts when model updates are needed.
Furthermore, ResX enables rapid model updates by streamlining modelling and data integration, dramatically reducing time spent building models, fostering collaboration and increasing your teams subsurface understanding.
Experience planning with an ensemble
No more guessing about what is a good model to use for well target selection – plan with a full ensemble. Why take the risk of taking a base case approach? Build your ensemble with ResX, get your well target analytics with IRMA – fast.
Easy, robust, fast
An analytics tool for automatically identifying 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 risk. Therefore, identified targets are ranked according to their added value potential and risk.
Understand risk & opportunity – improve your decision making process
Now you can inform decision makers about potential targets for wells, as well as give a clear ranking based on your own preferences. Consequently enabling consistently good judgments on how to take action.
Optimize your development plan
Use fit for purpose algorithms to explore how to further improve recoverable volume, net present value, and/or CO2 emission while accounting for the uncertainty in the subsurface. Evaluate the full potential of different development options, with their associated risks and opportunities. Let algorithms quickly explore alternative options to gain new understanding of your reservoir.
The algorithm behind Drainage Strategy Optimization is implemented with flexibility in mind, this to ensure generic application to your development challenges.
Expand on the native functionality to address your needs by combining Drainage Strategy Optimization with IRMA Lab.
Improve quality & subsurface understanding – gain confidence
The essential toolkit to make working with ensembles as easy as working in a traditional single model paradigm. Improve quality of your ensemble with modelling recommendations, improve subsurface understanding & gain confidence in your decisions.
Explore & learn
Automatically explore your ensembles, extract key statistics, highlight the drivers for the dynamic response throughout the reservoir. Perform quality assurance against input data to detect potential inconsistencies in your ensembles and identify potential modelling solutions.
Open & Extensible
Experiment, develop algorithms and gain even more insights from your ensemble of models with IRMA Lab.
IRMA Lab is an open and extensible environment based on the Jupyter™ Notebook. In order for you to integrate all your available ensemble data with popular data science languages (Python, Julia, R, Scala) and third-party libraries
(MLlib, NumPy, SciPy,scikit-learn,and pyc3).
Fasttrack your personalized analysis
Write IRMA plug-ins that add new components and integrate with existing ones.
Ultimately, explore and use your ensemble of models to the fullest.
IRMA automatically ingests and manages ensemble data from ResX.
ResX and IRMA deliver speed, control and power to look at all available data as a whole. Immediately assessing how modelling choices and data interpretation affect the resulting ensembles of models and enabling teams to quickly iterate and continuously learn and improve.
ResX and IRMA deliver an uncertainty-centric approach based on an end-to-end integrated and automated workflow that quickly closes the loop between input data and ensemble, providing analytics for insights and decision support.