Experience Details

  *   Minimum of 5 year experience.

  *   Proficiency in Qt/PythonQt Widgets UI development and designs (version 5 and newer).

  *   Proficiency in C++, with fair knowledge of other languages.

  *   Thorough knowledge of the C++ standard libraries, 2D plotting libraries ...etc.

  *   Proficiency in Linux environment and scripting (Bash, csh, python).

  *   Proficient understanding of code versioning tools such as Git.

  *   Familiarity with continuous integration.

  *   Experience using Microsoft Team Foundation Server.

  *   Excellent analytical and problem solving skills.

  *   Excellent interpersonal skills to operate effectively as part of a team (team sizes range from 5-8 persons).

  *   Excellent oral and written communication skills to clearly explain identified problems.

  *   Excellent analytical and problem solving skills.

Role

Automate GCT-MOGA-Inversion workflow.

2. Develop GUI to configure the workflows and manage the simulation runs.

3. Enhance Parametrization, MOGA, and Inversion performance (parallelism and/or threading).

4. Improve the Parameterization, Multi-Objective Genetic Algorithms (MOGA) and Inversion algorithms to scale to Giga-Cell model sizes

5. Develop diagnostic tools to aid with quantifying the efficiency of Parametrization, and evaluate multi-objective function of MOGA optimizer.

6. Tight integration with reservoir Simulation Engineering Environment (SEE)

Full Details

Automate the in-house developed assisted history matching workflows including Parameterization, Multi-Objective Genetic Algorithms (MOGA) and streamline-based Inversion techniques. This will be accomplished through the development of a series of user-friendly graphical interfaces that hides the complexity of the process and provides the necessary integration between the different workflow processes. In addition, the workflow will be integrated with the reservoir simulation environment through SEE (Simulation Engineering Environment). It is also of great importance for Parameterization, Multi-Objective Genetic Algorithms (MOGA) and Inversion algorithms to scale to Giga-Cell model sizes. Also, parametrization and MOGA performance issues need to be resolved (parallelization or multi-threading). Furthermore, The developer will optimize Destiny PEBI gridding streamline tracing need either by parallelization or multi-threading.

Project Resources