Streamline Simulation

Referent:
Dr. Marco R. Thiele
StreamSim Technologies, Inc.
San Francisco, CA

StreamSim Technologies, Inc.

Streamline-based flow simulation (SL) is now accepted as an effective and complementary technology to more traditional flow modeling approaches such as finite differences (FD). This is because streamline-based flow simulation is particularly effective in solving large, geologically complex and heterogeneous systems, where fluid flow is dictated by well positions and rates, rock properties (permeability, porosity, and fault distributions), fluid mobility (phase relative permeabilities and viscosities), and gravity. These are the class of problems more traditional modeling techniques have difficulties with. Capillary pressure effects and expansion-dominated flow mechanism, on the other hand, are not modeled efficiently by streamlines.

Modern SL simulation rests on 6 key principles: (1) tracing three-dimensional (3D) streamlines in terms of time-of-flight (TOF); (2) recasting the mass conservation equations along streamlines in terms of TOF; (3) periodic updating of streamlines; (4) numerical 1D transport solutions along streamlines; (5) accounting for gravity effects using operator splitting; and (6) extension to compressible flow. These principles are reviewed here.

The usefulness and uniqueness of SL simulation is presented in the context of what are generally considered important issues in reservoir engineering: (1) flood optimization; (2) history matching; (3) uncertainty in reservoir performance; (4) upscaling; computational speed; and (5) miscible flooding. Novel, streamline-specific data is presented in the context of injector/producer efficiencies and as a unique aid in upscaling by allowing engineers to go beyond the usual approach of only matching reference solutions.

Finally, the outlook for streamline-based simulation is discussed in the context of compositional simulation, tracing streamlines through structurally complex geometries, fractured systems, and parallel computation. The speed and efficiency as well as the availability of new data make streamlines potentially the most significant tool for solving complex optimization problems related to history matching and optimal well placements.

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