FLEXIBILITY & INTEGRATION
DESIGNED FOR SPEED
CONNECT TO DATA SOURCE AND DATABASE
AR2GAS is built to connect to an external data source and only extract data as needed for efficient workflows on very large models (billion of cells). The data stream is optimized for each algorithm providing performance and scalability.
RUN ALGORITHMS AS PROCESSES
Easily build complex modelling algorithms in Python as processes with strong-typed parameters. This feature greatly decreases the chance of user errors when initializing algorithms and provides robustness during execution by isolating the execution of geostatistical algorithms from the main program. AR2GAS comes with processes for its geostatistical algorithms. Additionally, a user can build their own processes and inject them to remote (cloud) workers for execution.
STREAMING DATA TO ALGORITHMS
Whenever possible AR2GAS streams data to and from the data source to the algorithms. This allows 1) to avoid loading a large data set into memory 2) keep the memory requirement low, 3) with little or no compromise on performance on the desktop and 4) improve performance on the cloud.
With grid blocks of various geometric shapes and sizes, unstructured grids offer a better representation of geological complexities seen in deposits. The current standard of building block models with rectangular cuboids forces simplifying engineering concepts onto geological modeling processes. Unstructured grids separate the geologist's needs from the regularity of engineering grids though the use of flexible modelling frameworks.
AR2GAS provide an environment where a space is fully informed by geostatistical values. The user is then free to retrieve information on the support needed, be along a well, in a grid (cartesian, stratigraphic, or unstructured) or on any user defined volume. The geostatistical space ensures that all the values retrieved on any support are always consistent with one another and with all available data.
ACCESS TO GEOSTATISTICAL BUILDING BLOCKS
In addition to providing geostatistical algorithms, AR2GAS gives access to the building blocks of the algorithms. The Python interface provides access to low-level functionalities such as variograms, search neighborhood and kriging systems. Advanced users can build their own geostatistical algorithms or incorporate these low-level components into machine learning applications.
CREATION OF INTERACTIVE WEB-BASED DASHBOARD
AR2GAS creates dashboards to automatically analyse models and provide validation. These dashboards are designed to be actionable and provide real insight about the validity of a geostatistical models, all from an internet browser.
GEOSTATISTICS FOR EMBEDDED SYSTEMS (IOT)
The AR2GAS library can run on small device (such as Raspberry Pi) to provide advanced spatial algorithms to embedded systems. This is in active development.