Revisiting Reservoir Flood-Surveillance Methods Using Streamlines

Summary This paper revisits classic flood-surveillance methods applied to injection/production data and demonstrates how such methods can be improved with streamline-based calculations. Classic methods rely on fixed patterns and geometric-based well-rate allocation factors (WAFs). In this paper, we compare conclusions about pattern performance from classic surveillance calculations to conclusions about pattern performance from a streamline surveillance model using flow-based WAFs. We show that very different conclusions on pattern performance can be reached, depending on which approach is used. We introduce streamline-defined, time-varying injector-centered patterns as the basic pattern unit, with offset producers being those to which the injector is connected. Such patterns give a better measure of an injector's true effectiveness because of the improved estimation of offset oil production compared to fixed, predefined patterns. In the second part of this paper, we illustrate how to build a relevant streamline-based surveillance model. We compare WAFs and offset oil production computed from much more laborintensive, history-matched flow-simulation models to that from much simpler surveillance models and illustrate the difference with a field example. As long as offset-well rates are a function of neighboring-well rates—as is typical in many waterfloods— capturing first-order flow effects is sufficient to produce a surveillance model that is useful for reservoir-engineering purposes. Properly accounting for well locations, historical rates, gross geological bodies, and major flow barriers is generally sufficient to produce a useful surveillance model that replicates well pairs and total interwell fluxes that are similar to those of more-complex and more-expensive history-matched models. We believe that this similarity arises because historical well rates already mirror reservoir connectivity, and it is well rates that mainly impact how the streamlines connect well pairs.