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Welcome to our new website highlighting our Rezen and Review software products. Rezen provides reservoir simulation run management capabilities along with simulation deck generation, uncertainty modelling, history matching and data analysis/post-processing. Review is a 2D and 3D visualiser of reservoir simulation output data that is being integrated within Rezen.

Take a look under the Technology menu tab at the descriptions of the experimental design algorithms that are implemented in Rezen


 

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Rezen

Objective Functions

The user can define one or more objective functions to calculate from the output data of  the simulation decks that have been generated and successfully run through the simulator

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Rezen

Simulation Project Manager

Rezen provides a hierarchical data structure for managing all the simulation runs in a reservoir simulation project. A project contains one or more ensembles, each ensemble representing a scenario the engineer wishes to investigate. Within each ensemble a number of simulation decks can be created, edited and run. The output data vectors can be analysed and plotted on an ensemble or deck basis.

What is Rezen?

Rezen is a software tool for managing multiple reservoir simulation runs. Functionality includes :-

  • simulation run management
  • simulation decks generation
  • uncertainty modelling
  • history matching
  • data analysis and post-processing
Rezen aims to support industry standard reservoir simulators, providing a user friendly environment for aiding the design, management and investigation of a reservoir simulation scenario.

A scenario may consist of multiple related instances of a reservoir simulation model, these instances or decks being collectively known as an ensemble.

Rezen supports managing a forward prediction or uncertainty modelling scenario where multiple simulation decks are generated using experimental design algorithms prior to submission to the simulator.

The aim of using an experimental design algorithm is to extract information from a smaller number of simulation runs.

Additionally Rezen supports managing a history matching ensemble where simulation runs are made in serial or parallel, each set of new simulation decks being dependent on the prior runs. The user defines a goodness of fit function as an optimization criterion.

 

Rezen is written primarily in Python and uses the Enthought Tool Suite (code.enthought.com) extensively, in particular, Traits for the GUI, Chaco for line plots, bar charts and 2D plots and Mayavi for 3D. NumPy is used for fast and efficient array handling and SciPy for statistical and optimization functions. 

All these Python packages are open source projects with an extensive number of contributors. Visual Reservoir acknowledges this excellent work. 

Traits can use either Qt or wxPython as an underlying layer. Rezen uses the wxPython layer.

Mayavi uses the VTK visualization toolkit which itself uses OpenGL.

The Python code also interfaces to some Fortran and C legacy code.

A small amount of Tcl scripting is also used. 

A framework diagram is shown below.

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