Summary
This paper presents a new procedure to quantify communication between
vertical wells in a reservoir on the basis of fluctuations in production and
injection rates. The proposed procedure uses a nonlinear signal-processing
model to provide information about preferential-transmissibility trends and the
presence of flow barriers.
Previous work used a steady-state (purely resistive) model of interwell
communication. Data in that work often had to be filtered to account for
compressibility effects and time lags. Even though it was often successful, the
filtering required subjective judgment as to the goodness of the
interpretation. This work uses a more complicated model that includes
capacitance (compressibility) as well as resistive (transmissibility)
effects.
The procedure was tested on rates obtained from a numerical flow simulator.
It was then applied to a short-time-scale data set from an Argentinean field
and a large-scale data set from a North Sea field. The simulation results and
field applications show that the connectivity between wells is described by
model coefficients (weights) that are consistent with known geology, the
distance between wells, and their relative positions. The developed procedure
provides parameters that explicitly indicate the attenuation and time lag
between injector and producer pairs in a field without filtering. The new
procedure provides a better insight into the well-to-well connectivities for
both fields than the purely resistive model.
The new procedure has several additional advantages. It
-
can be applied to fields in which wells
are shut in frequently or for long periods of time.
-
allows for application to fields in
which the rates have a remnant of primary production.
-
has the capability to incorporate
bottomhole-pressure (BHP) data (if available) to enhance the investigation
about well connectivity.
Introduction
Production and injection rates are the most abundant data available in any
injection project. Valuable and useful information about interwell connectivity
can be obtained from the analysis of these data. The information may be used to
optimize subsequent oil recovery by changing injection patterns, assignment of
priorities in operations, recompletion of wells, and infill drilling.
A variety of methods have been used to compare the rate performance of a
producing well with that of the surrounding injectors. Heffer et al. (1997)
used Spearman rank correlations to relate injector/producer pairs and
associated these relations with geomechanics. Refunjol (1996), who also used
Spearman analysis to determine preferential-flow trends in a reservoir, related
injection wells to their adjacent producers and used time lags to find an
extreme coefficient value. De Sant’Anna Pizarro (1998) validated the Spearman
rank technique with numerical simulation and pointed out its advantages and
limitations. Panda and Chopra (1998) used artificial neural networks to
determine the interaction between injector/producer pairs. Soeriawinata and
Kelkar (1999), who also used Spearman rank analysis, suggested a statistical
approach to relate injection wells and their adjacent producing wells. They
applied superposition to introduce concepts of constructive and destructive
interference. See also the works of Araque-Martinez (1993) and Barros-Griffiths
(1998).
Albertoni and Lake (2003) estimated interwell connectivity on the basis of a
linear model with coefficients estimated by multiple linear regression (MLR).
The linear-model coefficients, or weights, quantitatively indicate the
communication between a producer and the injectors in a waterflood. Filters
were adopted to account for the time lag between producer and injector.
In this work, as in Albertoni and Lake (2003), the reservoir is viewed as a
system that converts an input signal (injection) into an output signal
(production). However, we use a more complete model that includes capacitance
(compressibility) as well as resistive (transmissibility) effects. For each
injector/producer pair, two coefficients are determined; one parameter (the
weight) quantifies the connectivity, and another (the time constant) quantifies
the degree of fluid storage between the wells. This work shows that the new
model better captures the true attenuation and time lag between injector and
producer pairs.
The new procedure resolves several limitations of the previous methods and
extends the applications to a wide range of real cases. It can be applied to
fields in which wells are shut in frequently or for long periods of time, it
allows for application to fields in which the rates have a remnant of primary
production, and it has the capability to use BHP data (if available) to enhance
the investigation of the well’s connectivity.
© 2006. Society of Petroleum Engineers
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History
- Original manuscript received:
13 July 2005
- Revised manuscript received:
10 July 2006
- Manuscript approved:
18 July 2006
- Version of record:
20 December 2006
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