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 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.
Introduction
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.
© 2008. Society of Petroleum Engineers
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History
- Original manuscript received:
13 July 2005
- Meeting paper published:
9 October 2005
- Revised manuscript received:
22 August 2007
- Manuscript approved:
15 January 2008
- Version of record:
25 April 2008
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