How streamline simulation can be employed for managing and improving flood performance of brown fields.
This talk is aimed at giving a high-level description of Streamline Simulation (SLS) and focusing on the use of the technology to help manage and optimize flood performance. Advantages and disadvantages of SLS are highlighted throughout the presentation for a balanced presentation of the methodology. The talk concludes with a look to the future by commenting on the directions SLS is evolving towards.
Since streamlines connect source (injectors) and sinks (producers), the bundle of streamlines connecting quantifies the volumetric flux between the two, a key piece of information when considering flood management. The most valuable information that can be extracted from SLS for flood management is instantaneous injection efficiencies and conformance plots for each injector. A significant paradigm shift caused by SLS is looking at well patterns as being time-varying and centered on an injector and its off-set producers it is connected to. Moving away from having to define static well patterns is a significant development that has significant repercussions as to how one might go about improving performance of a flood.
SLS hinges on moving components along 1D, time-varying streamlines rather than on a static 3D structured or unstructured grid, and often there is a significant speed advantage that becomes essential when trying to optimize field performance. Speed together with the novel information provided by the streamlines is a significant aid to flood optimization. However, SLS also makes some significant assumptions that might not be appropriate for all reservoirs and this will be highlighted throughout the talk.
SLS has been maturing since the early 90’s and the next decade should bring further developments into sharper focus. The talk concludes with a look to the future discussing the topic of hybrid flow simulation-a mix of traditional modeling and streamline modeling, SLS as an enabler for uncertainty quantification through ensemble modeling, and finally SLS as a visual stronghold that allows multidisciplinary teams to find a common language for making better decisions.