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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, timevarying 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 waterfloodscapturing 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.

24Journal of Canadian Petroleum Technology FIGURE 1: Difference between finite difference model vs. streamline model for transporting fluids [adapted from Grinestaff, SPE 54616 (20) .] FIGURE 2: Example of typical reservoir flows. (Streamline Time = 01/01/1991.) FIGURE 3: Example of typical reservoir flows. (Streamlines Times = 12/31/1999.) April 2001, Volume 40, No. 4 25 FIGURE 4: Identification of history match regions. FIGURE 5: Relationship between (watercut or water rate) vs. time and spatial position of streamlines (map view).

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Forward This paper is the second part of a two-part article (part one published April 1997). Waterflood management is critical, particularly for poor quality or geologically complex reservoirs. In part one, we examined oil production response to a waterflood. In the second part, we investigate gas and water production response as well as injection analysis and reservoir pressure response. Gas-oil Ratio and Water-oil Ratio An indicator of bypassing is a premature drop in gas-oil ratio; i.e., earlier than expected collapse of gas saturation. Early gas collapse (water fillup) may indicate that channeling has occurred. In layered reservoirs with no or little vertical crossflow, water injection in an initially depressurized layer will cause GOR to drop rapidly. Often naturally fractured reservoirs exhibit fast gas collapse because water fills up the fracture system and does not initially invade the matrix, the desired target for waterflooding. Figure 1 shows an example of a pattern where channeling has occurred. This type of pattern should be reviewed geologically to attempt to identify the thief zones/natural fractures. Other key performance indicators are water breakthrough times and subsequent WOR trends, which also can be indicative of channeling and bypassing problems. However, since wells or patterns showing high WOR rise or quick gas collapse may simply be due to high injection rates, one should plot WOR and GOR versus hydrocarbon pore volume injected (HCPVI). In general, if water breakthrough occurs before 20% hydrocarbon pore volume injected (HCPVI), channeling or bypassing due to heterogeneity is likely occurring. Like the WOR or GOR versus time plots, the log of WOR versus cumulative oil produced (Np) is used as an indication of channeling and heterogeneity (Figures 2 – 4).(1, 2) In an unfavourable mobility ratio situation (M >), the late time slope of the graph is primarily controlled by the oil water relative permeability curves; therefore, volumetric sweep efficiency can be derived from this plot.(2) In a favourable mobility ratio situation (M (1), the late time slope of the graph is controlled primarily by permeability heterogeneity or fluid segregation. In layered systems, the WOR versus Np plot may have a stair-type profile as various layers breakthrough (Figure 4). Plotting WOR versus Np and comparing individual patterns against a group average (e.g., for an entire unit operation) gives a qualitative indicator of volumetric sweep efficiency. This should be evaluated in the context of known or suspected geological trends. Stylized representations of waterflood performance in simplistic geological cross sections are depicted in the companion insets to Figures 3 and 4. Extrapolation of the WOR versus Np plot and changes in its slope can indicate incremental oil recovery. Therefore, an examination of the log of WOR versus Np plot is useful in determining the incremental recovery due to infill drilling or operational changes, as shown in Figure 5. The changing slope of the curve indicates increased reserves after infill drilling.