Asset simulation is a cornerstone in prediction of petroleum reservoir performance and ultimate oil and gas recovery. However, the standalone predictions without optimization of field development would not produce the most optimal recovery strategy. Optimization of field recovery is crucial at every point of the reservoir cycle - from appraisal through production, until abandonment to get. the most output from the investments made. To optimize rate of returns and find the best field development strategy, the varying field development scenarios are simulated with different recovery methods and compared to each other. The field development optimization should be enhanced based on the latest algorithms and deployment technology to ensure efficient results.

The reservoir management workflows features provided as a part of cloud-based Full-Scale Asset Simulation, a DecisionSpace®365 cloud application, aim to address all these and more challenges related but not limited to workflow orchestration, field recovery optimization, management and analysis of simulation runs, sensitivity analysis, uncertainty quantification, and computational time reduction using the latest algorithms and technology with exceptional user experience.


Full-Scale Asset Simulation provides the capability to perform custom, flexible workflows to address any reservoir engineering need for building, validating, analyzing multiple models for a petroleum reservoir under study. It does this by providing several predefined reservoir engineering operations called tasks. These predefined task types are presented in Figure 1. In addition to predefined tasks, users have an option to build custom tasks with Python scripts in Jupyter Notebook. All tasks can be effortlessly run and rerun in the cloud in parallel as many times as required to derive complete and diverse insights into the petroleum reservoir. A history of all changes applied to the reservoir model is tracked and kept in the system for users to analyze, review, and derive insights. Analysis of large number of stored petroleum reservoir models enable reservoir engineers to maximize oil and gas recovery from a reservoir.

Figure 1: Workflow Task Modules. Red blocks represent task types available through Full-Scale Asset Simulation. Gray blocks stand for the upcoming links with other G&G, engineering, and economics applications.

Each predefined task has a particular goal. The Proxy Flow Modeling (PFM) task reduces the computational time of other tasks by replacing full-physics flow simulation runs by the surrogate model. The surrogate model predictions are as accurate as full-physics simulation predictions, but considerably less computationally expensive. The Sensitivity Analysis (SA) task allows engineers to identify important input model parameters. The Design of Experiments task (DOE) enables users to build multiple development scenarios for comparison purposes or for uncertainty quantification. The Assisted History Matching (AHM) task efficiently assimilates historical production data for improved petroleum reservoir predictions. The Field Development Optimization (FDO) task generates and analyzes many field development optimization scenarios.


All in all, a considerable number of the asset simulation runs should be performed, carefully managed, and thoroughly analyzed to obtain a clear understanding of how the field should be developed. The reservoir management workflows feature in Full-Scale Asset Simulation provides functionality to build sophisticated, yet flexible reservoir management workflows by putting all these predefined and custom tasks together, an example of which is shown in Figure 2.

Figure 2: An example of a possible reservoir engineering workflow built with Full-Scale Asset Simulation.


Building complex workflows and managing a large number of resulting reservoir models can be done with ease in Full-Scale Asset Simulation. The execution time is reduced due to cloud deployment, scalability, parallelization, and replacement of full-physics flow simulation with proxy flow or reduced order models where necessary and possible. The infusion of cutting-edge data science, optimization, model order reduction, design of experiments, and sampling algorithms ensures finding the most optimal asset development scenarios for implementation and maximizing reservoir recovery over the entire life of the asset.

Look for even more details about Full-Scale Asset Simulation’s features and functionality in our upcoming white paper, where we’ll explore

  • Ensemble-based Multi-Scenario Analysis
  • Sensitivity Analysis feature
  • Model Prediction Accuracy
  • Effective Reservoir Development Scenario
  • Computation Reduction through Proxy Modeling

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