Revisiting Reservoir Flood-Surveillance Methods Using Streamlines
- Rod P. Batycky (StreamSim Technologies, Inc.) | Marco R. Thiele (Streamsim Technologies, Inc.) | Richard O. Baker (Epic Consulting Services Ltd.) | Shelin Chugh (Epic Consulting Services Ltd.)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- April 2008
- Document Type
- Journal Paper
- 387 - 394
- 2008. Society of Petroleum Engineers
- 5.5.8 History Matching, 5.1.1 Exploration, Development, Structural Geology, 5.5 Reservoir Simulation, 5.7.2 Recovery Factors, 6.5.2 Water use, produced water discharge and disposal, 5.8.6 Naturally Fractured Reservoir, 5.4.9 Miscible Methods, 3.3 Well & Reservoir Surveillance and Monitoring, 5.4.1 Waterflooding, 4.3.4 Scale, 5.1 Reservoir Characterisation, 2.4.3 Sand/Solids Control, 5.5.7 Streamline Simulation
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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, 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 labor-intensive, 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.
The surveillance of production data is fundamental to good reservoir management of waterfloods and miscible floods. This type of surveillance is useful to understand flood performance to date and can highlight good vs. poor recovery areas. In particular, surveillance can identify areas of extreme water cycling, patterns with poor sweep, or local voidage imbalances, providing "real-time?? monitoring of a flood without having to construct a detailed flow-simulation model. The specifics of standard surveillance methods, such as voidage plots or pattern recovery plots, are discussed in detail by Baker (1997, 1998).
The basic element of all surveillance diagnostics is the association of produced volumes with injected volumes through WAFs. A WAF defines how much flow at a producer is caused by each offset injector, or alternatively, how much injection goes to each offset producer. However, WAFs are also the key weakness of surveillance methods because the results are heavily dependent on the assumptions made to compute the WAFs. Traditionally, the WAFs have been based on well-pattern geometry or were computed from simple 2D streamline models and usually assumed fixed through time. Field simulations routinely show that WAFs are neither fixed nor solely a function of well-pattern geometry and that there is substantial flow between well pairs outside of predefined patterns (Baker et al. 2002; Chapman and Thompson 1989; Flanders and Bates 1987; Grinestaff and Caffery 2000). Additionally, for large, multiwell floods, pattern definition and WAF calculations are a time-consuming process. As a result, classic surveillance on a pattern-by-pattern basis is known to have significant limitations and therefore is practiced rarely.
Although flow-based WAFs are more realistic, their calculation for surveillance purposes has never been the main goal of streamline-based simulation. However, Grinestaff (1999) demonstrated the qualitative use of streamlines to estimate WAFs for flood management, and Thiele and Batycky (2006) used streamline-derived WAFs in a workflow to set well-rate targets to reduce fluid cycling.
In this paper, we step back from flow simulation and focus on the use of streamline-based WAFs in reservoir surveillance of production data. First, we show how streamlines allow standard surveillance techniques to be implemented efficiently on a pattern-by-pattern basis, and how streamline-derived WAFs compare with traditional geometric WAFs. Second, we discuss the limitations of fixed-pattern surveillance, regardless of how the WAFs are computed. Third, we propose injector-centered, streamline-derived patterns that change with time as the basic pattern element. Last, we discuss the validity of flow-based WAFs and the quantity of data required to build a surveillance model that is useful for reservoir-engineering purposes. We argue that flow-based WAFs are mainly a function of historical well rates and gross geological features.
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