When And Why To Use Streamlines

Streamlines are ideally suited for modeling large, geologically heterogeneous, multi-well systems where production is principally a result of fluid injection (water, polymer, CO2, etc.). Streamlines are less suited for situations where capillary crossflow, transverse diffusion, or counter-current flow are important and first-order production mechanisms.

Streamlines should be used for modeling production from brown fields undergoing waterflooding, WAG, and/or EOR injection using some sort of injector/producer pattern arrangement.

The following is a list of reservoir engineering applications where streamlines have shown to be effective.

  1. Production Surveillance Quickly identifying and quantifying injector/producer connectivity (well allocation factors) in brown fields is essential for proactive reservoir management. Use studioSL to quickly import production data and well locations from common databases such as OFM or geoSCOUT to estimate well allocation factors in the field. Because a surveillance model is not a simulation model, but simply a connectivity map and material balance model it is computationally light.  See here for more information.
  2. Pattern Flooding Streamlines identify injector producer pairs that result from the distribution of injection/production volumes across the field and the geological description given as input. It is therefore possible to quantify the efficiency of injector/producer pairs and determine the displacements efficiency on a per-pattern basis. See here for more information.
  3. Waterflood/Sweep Optimization With data known on a per-patterns basis, it is possible to optimize and pro-actively manage reservoirs on an individual pattern basis. This is a key distinguishing factor of streamline-based modeling. See floodOPT for a heuristic solution to manage floods using streamline data.
  4. Screening of Models Streamline-based reservoir simulation is ideal for screening large sets of geological models because of the its computational efficiency. For example, for heterogeneous systems where geological connectivity is a key input parameter, screening using an incompressible waterflood can be a good proxy for more complex displacements. See Sensitivity & Ranking for ways to analyze large sets of runs.
  5. History Matching Streamline-based history matching makes use of data provided by the streamlines, such as  reservoir volumes "seen" by the various wells and breakthrough times of individual streamlines. This in turn allows to selectively modify geological/flow parameters for history matching purposes. It is a powerful technique to allow well-level history matching effectively. Streamline-based history matching has been a major focus of research at Streamsim. See here for info.
  6. Uncertainty Quantification Using single models for forecasting purposes does not allow to quantify the uncertainty associated with the input data. A more powerful approach is to work with an ensemble of models for forecasting purposes. See the EVOLVE workflow on how to create such an ensemble of models.  

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