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Figure Captions
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 Seismic
velocity and density changes in a producing reservoir depend on rock
type, fluid properties, and the depletion mechanism. Time-lapse seismic
responses may be caused by:
-
Changes in
reservoir saturation. Displacement of oil by gas cap expansion,
gas injection or gas exsolution resulting from pressure decline
below bubble point; these decrease velocity and density. Water sweep
of oil increases velocity and density.
-
Pore fluid
pressure changes during fluid injection or depletion. Injection
will increase fluid pressure, decreasing the effective stress acting
on the rock frame and lowering seismic velocities. Compaction during
depletion reduces porosity and increases velocity and density.
-
Temperature
changes. An increase in temperature increases fluid
compressibility, and as a result decreases reservoir seismic
velocities and density.
Reservoir
factors that affect the seismic detectability of production changes can
be evaluated in order to determine which geological settings and
production processes are most suited for reservoir monitoring. Each
field is unique, and modeling of the seismic response to production,
based on reservoir flow simulation, is used to evaluate the
interpretability of seismic differences and to determine how early in
field life a time-lapse survey can be used to monitor reservoir changes.
The
optimal times for repeat seismic surveys depend on detectability and the
field's development and depletion plan. Planning for repeat surveys in
the context of field surveillance will maximize the value of the data.
Seismic Repeatability
The
difference between two seismic surveys is not only sensitive to changes
in reservoir rock properties but also to differences in acquisition and
processing. As suggested in Figure 2, the
chance of success for a 4-D project depends on both detectability and
seismic repeatability. Some of the factors that affect repeatability
include:
-
Acquisition geometry
differences such as sail line orientation and heading,
source-receiver spacing, streamer feather, and coverage due to
obstructions.
-
Near-surface
conditions resulting in statics and receiver coupling variations.
-
Sea level, sea state
and swell noise, water temperature and salinity.
-
Residual multiple
energy.
-
Ambient and
shot-generated noise.
-
Geological factors
such as shallow gas and steep geological dip.
4-D Seismic
Acquisition, Processing
The
objective of 4-D seismic acquisition and processing is to minimize
differences in the seismic data that are unrelated to production -- and
to preserve and resolve those differences in the reservoir that are due
to production. Four-D repeat survey acquisition attempts to match both
the source and receiver positions and signatures of the baseline survey.
Positional repeatability ensures the same raypaths for base and monitor
surveys. Tolerance to geometry deviations depends on the complexity of
the overburden; where there is rapid lateral change or anisotropy in the
overburden, raypaths need to be more similar.
A number
of strategies have been developed to maximize acquisition repeatability
for both land and marine data, and permanent monitoring systems -- such
as the BP's installation at Valhall -- can result in high repeatability.
While there is a large up-front cost associated with fixed receivers,
these systems can permit the acquisition of lower-cost monitor surveys
with short repeat intervals or "on demand."
Four-D
processing is best described as co-processing or parallel processing of
base and monitor surveys. This implies:
-
Controlled amplitude
and phase.
-
Early equalization of
geometry to facilitate QC comparisons.
-
Application of the
same algorithms and parameters where appropriate.
A key to
successful time-lapse processing is continual comparison of the base and
monitor surveys to ensure that repeatability is not being compromised.
Often, "fast track" data (e.g., decimated, post-stack migrated and/or
using parameters based on earlier processing) are used to evaluate the
processing flow and refine interpretation concepts.
Also, the
objective to maximize repeatability may be at the expense of other
processing objectives, such as high-resolution imaging. As a result, it
is not uncommon that separate flows are used for time-lapse data.
4-D Interpretation
The
interpretation of time-lapse seismic differences in terms of reservoir
changes requires integration of the data with detailed reservoir
characterization, fluid flow simulation, petrophysics, and conventional
reservoir surveillance data. Many companies use a model-based 4-D
interpretation workflow, where seismic differences are compared to
predicted differences based on seismic modeling of history-matched
reservoir flow simulations. The interpretation process is one of
comparing, contrasting, reconciling and validating these two images of
the production process.
This
approach is used because 4-D seismic interpretations are non-unique.
An example
of 4-D interpretation is from the North Sea Jotun Field, where oil is
being depleted through a strong natural water drive. Water sweep in the
reservoir results in a 10-12 percent increase in the seismic impedance.
Figure 3 compares the results of inverting
the seismic difference acquired after three years of production to
obtain impedance change with the oil saturation change predicted by the
reservoir flow simulation. At this location, the simulator suggests that
the reservoir is fully swept -- but the seismic data show that only one
reservoir zone has been swept and that internal shales act as barriers
or baffles to flow. This results in a flank rather than bottom water
drive.
Infill or
sidetrack opportunities are found where there is no change in the
seismic data and where reservoir characterization suggests there is high
net-to-gross sand. As a result of the 4-D survey at Jotun, three
successful infill wells were drilled and a potential dry hole was
avoided.
Other
published 4-D case studies show that seismic data can image production
changes in a variety of geological settings and production scenarios,
including water and gas sweep, pressure changes and compaction, and
enhanced recovery. Further, 4-D interpretation is evolving toward a more
quantitative analysis of the data. By incorporating time-lapse shear
wave information, either from AVO analysis, elastic inversion, or PS
data , it is possible to estimate saturation and pressure changes in the
reservoir. These estimates can be a strong history match constraint on
reservoir simulations.
More predictive simulations will result
in more efficient reservoir management.
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