Summary
This paper describes a novel approach to predict injection- and
production-well rate targets for improved management of waterfloods. The
methodology centers on the unique ability of streamlines to define dynamic well
allocation factors (WAFs) between injection and production wells. Streamlines
allow well allocation factors to be broken down additionally into phase rates
at either end of each injector/producer pair. Armed with these unique data, it
is possible to define the injection efficiency (IE) for each injector and for
injector/producer pairs in a simulation model. The IE quantifies how much oil
can be recovered at a producing well for every unit of water injected by an
offset injector connected to it. Because WAFs are derived directly from
streamlines, the data reflect all the complexities impacting the dynamic
behavior of the reservoir model, including the spatial permeability and
porosity distributions, fault locations, the underlying computational grid,
relative permeability data, pressure/volume/temperature (PVT) properties, and
most importantly, historical well rates.
The possibility to define IEs through streamline simulation stands in
contrast to the ad hoc definition of geometric WAFs and simple surveillance
methods used by many practicing reservoir engineers today. Once IEs are known,
improved waterflood management can be implemented by reallocating injection
water from low-efficiency to high-efficiency injectors. Even in the case in
which water cannot be reallocated because of local surface-facility
constraints, knowing IEs on an injector/producer pair allows the setting of
target rates to maintain oil production while reducing water production.
We demonstrate this methodology by first introducing the concept of IEs,
then use a small reservoir as an example application.
Introduction
Local areas of water cycling and poor sweep exist as a flood matures.
Current flood management is restricted to surveillance methods or workflows
centered on finite-difference (FD) simulation, where areas of bypassed oil are
identified and then rate changes, producer/injector conversions, or
infill-drilling scenarios are tested. However, identifying and testing improved
management scenarios in this way can be laborious, particularly for waterfloods
with a large number of wells and/or a relatively high-resolution numerical
grid.
For mature fields that have potential for improved production without
introducing new wells or producer/injector conversions, the main goal is to
manage well rates so as to reduce cycling of the injected fluid while
maintaining or even increasing oil production.
Reservoir engineers have no easy or automated way to identify injection
patterns, well-pair connections, or areas of inefficiency beyond simple
standard fixed-pattern surveillance techniques (Baker 1997; Baker 1998; Batycky
et al. 2005). Such methods are approximate at best owing to the need to define
geometric allocation factors and fixed patterns, which suffer from
“out-of-pattern” flow. These limitations are removed through streamline-based
surveillance models (Batycky et al. 2005). By adding a transport step along
streamlines, streamline simulation (3DSL 2006) can additionally identify how
much oil production results from an associated injector, quantifying the
efficiency down to an individual injector/producer pair. It is this crucial
piece of information—the efficiency of an injector/producer pair—that allows an
improved estimation of future target rates, leading to improved reservoir flood
management.
© 2006. Society of Petroleum Engineers
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History
- Original manuscript received:
13 February 2004
- Revised manuscript received:
2 February 2006
- Manuscript approved:
10 February 2006
- Version of record:
20 April 2006
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