Note: Descriptions are shown in the official language in which they were submitted.
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SYSTEM AND METHOD FOR ASSESSING THE PRESENCE OF
HYDROCARBONS IN A SUBTERRANEAN RESERVOIR BASED ON
SEISMIC INVERSIONS
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation-in-part of U.S. Application
15/794,058, filed
October 26, 2017.
STATEMENT REGARDING FEDERALLY SPONSORED
RESEARCH OR DEVELOPMENT
[0002] Not applicable.
TECHNICAL FIELD
[0003] The present disclosure relates generally to methods and systems
for
probabilistic analysis of geologic features using seismic data and, in
particular, methods and
systems for assessing the probability of hydrocarbons in a subterranean
reservoir based on
seismic inversions generated from two or more seismic surveys performed at
different times.
BACKGROUND
[0004] Seismic exploration involves surveying subterranean geological
media for
hydrocarbon deposits. A survey typically involves deploying seismic sources
and seismic
sensors at predetermined locations. The sources generate seismic waves, which
propagate
into the geological medium creating pressure changes and vibrations.
Variations in physical
properties of the geological medium give rise to changes in certain properties
of the seismic
waves, such as their direction of propagation and other properties.
[0005] Portions of the seismic waves reach the seismic sensors. Some
seismic sensors
are sensitive to pressure changes (e.g., hydrophones), others to particle
motion (e.g.,
geophones), and industrial surveys may deploy one type of sensor or both. In
response to the
detected seismic waves, the sensors generate corresponding electrical signals,
known as
traces, and record them in storage media as seismic data. Seismic data will
include a plurality
of "shots" (individual instances of the seismic source being activated), each
of which are
associated with a plurality of traces recorded at the plurality of sensors.
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[0006] In some cases, it is desirable to analyze the recorded seismic
amplitudes. This
may be done in many ways. One step in conventional processing of seismic
reflection data
involves adding multiple seismic traces that share a common mid-point, but
have different
source-receiver offsets. This is commonly called "stacking". Stacking
generally improves the
signal to noise ratio, but can result in ambiguity surrounding the cause of
the seismic
amplitudes. For example, a high seismic amplitude could indicate either the
presence of
fluids or the presence of a particular lithology.
[0007] One conventional technique that can provide an improved method of
delineating between lithology and fluids is employment of amplitude versus
offset (AVO) or
angle (AVA) for a representative offset/angle gather. Those of skill in the
art would be aware
that amplitude versus angle (AVA) is often used interchangeably with amplitude
versus offset
(AVO).
[0008] During processing, this type of AVA data may not be stacked
thereby to
preserve information that can be used to distinguish indicators of fluids from
indicators of
lithology. For example, considering a seismic trace, in one scenario, a
hydrocarbon-bearing
sand may generally have an increasingly negative seismic amplitude at further
source-
receiver offsets compared to a water-bearing sand which may be indicated by a
decrease in
positive seismic amplitude at further source-receiver offsets.
[0009] The production of hydrocarbons causes changes in the elastic
parameters of
the earth. These changes may occur due to water displacing oil (or vice
versa), water
displacing gas (or vice versa), or gas displacing oil (or vice versa), within
the reservoir
interval. In other cases, the changes in the elastic parameters may occur due
to enhanced
hydrocarbon recovery operations, CO2 injection, or clathrate dissociation from
solid to gas.
Time-lapse (4D) seismic data is acquired to compare seismic data at different
times via two
or more seismic surveys, a seismic survey at time one (Ti) and another seismic
survey from
time two (T2), conducted months or years apart. The differences in the seismic
responses for
Ti and T2 are at least partially due to fluid movement and/or pressure changes
due to
production or injection of water or gas. Conventionally, these differences in
seismic response
are qualitatively interpreted relative to modeled response behaviors due to
fluid and pressure
changes. Typically, the seismic survey from Ti is referred to as the baseline
survey, and the
seismic survey from T2 is referred to as the monitor survey. However, in the
case for more
than one monitor survey we could be analyzing two monitor surveys, where the
seismic
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survey from Ti is an early monitor survey and the seismic survey from T2 is
another monitor
survey recorded at some time T2 where T2 is months or years after Ti
[0010] The above methods may however often be biased and may not truly
represent
the geologic features. In addition, conventional methods may fail where
seismic data quality
is low, such as where random and/or coherent noise is prevalent, or where
seismic gathers are
not flat. The ability to define the location of rock and fluid property
changes in the subsurface
is crucial to our ability to make the most appropriate choices for purchasing
materials,
operating safely, and successfully completing projects. Project cost is
dependent upon
accurate prediction of the position of physical boundaries and fluid content
within the Earth.
Decisions include, but are not limited to, budgetary planning, obtaining
mineral and lease
rights, signing well commitments, permitting rig locations, designing well
paths and drilling
strategy, preventing subsurface integrity issues by planning proper casing and
cementation
strategies, and selecting and purchasing appropriate completion and production
equipment.
These decisions also include identifying locations for producing wells and
injection wells, as
well as how to adjust production rates or injection rates to optimize
production over time.
[0011] There exists a need for seismic processing methods capable of
producing
improved time-lapse AVA information that may be used for analysis of geologic
features of
interest.
SUMMARY
[0012] In accordance with some embodiments, a computer-implemented method
of
reservoir property assessment in a subterranean volume of interest including
receiving a first
digital seismic attribute volume inverted from a seismic dataset recorded at a
first time
representative of a subsurface volume of interest, a second digital seismic
attribute volume
inverted from a seismic dataset recorded at a second time representative of
the subsurface
volume of interest, and a range of geological and geophysical parameters
possible in the
subsurface volume of interest; identifying at least one layer of interest;
identifying at least
one spatial area of interest for the at least one layer to define a reservoir
volume of interest;
computing an attribute difference volume from the first digital seismic
attribute volume and
the second digital seismic attribute volume within the reservoir volume of
interest;
performing probabilistic attribute analysis within the reservoir volume of
interest of at least
two of the first digital seismic attribute volume, the second digital seismic
attribute volume,
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and the attribute difference volume using the range of geological and
geophysical parameters;
estimating time-lapse reservoir properties within the within the reservoir
volume of interest
based on the probabilistic attribute analysis; and outputting visual
information depicting the
time-lapse reservoir properties via a user interface is disclosed.
[0013] In another aspect of the present invention, to address the
aforementioned
problems, some embodiments provide a non-transitory computer readable storage
medium
storing one or more programs. The one or more programs comprise instructions,
which when
executed by a computer system with one or more processors and memory, cause
the
computer system to perform any of the methods provided herein.
[0014] In yet another aspect of the present invention, to address the
aforementioned
problems, some embodiments provide a computer system. The computer system
includes one
or more processors, memory, and one or more programs. The one or more programs
are
stored in memory and configured to be executed by the one or more processors.
The one or
more programs include an operating system and instructions that when executed
by the one or
more processors cause the computer system to perform any of the methods
provided herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 illustrates a flowchart of a method of analyzing geologic
features
using seismic data, in accordance with some embodiments;
[0016] Figure 2 is a flowchart of one step from an embodiment;
[0017] Figures 3 - 6 are examples of other steps from various
embodiments;
[0018] Figure 7 illustrates steps and results from an embodiment;
[0019] Figure 8 is a block diagram illustrating a time-lapse fluid
assessment system,
in accordance with some embodiments;
[0020] Figure 9 illustrates a flowchart of a method of analyzing geologic
features
using seismic attributes calculated by seismic inversions, in accordance with
other
embodiments; and
[0021] Figure 10 is a flowchart of one step from an embodiment.
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[0022] Like reference numerals refer to corresponding parts throughout
the drawings.
DETAILED DESCRIPTION OF EMBODIMENTS
[0023] Described below are methods, systems, and computer readable
storage media
that provide a manner of geologic analysis using seismic data. These
embodiments are
designed to calculate probabilities of hydrocarbons (i.e. fluid property
estimation) in
subsurface geologic features and changes in those probabilities after
production and/or
injection. Industry standard techniques use deterministic estimation of the
underlying
geologic and geophysical parameters which contribute to the amplitude versus
angle response
utilizing forward modeling or inversion. The subsurface parameters of interest
are the
thickness, pore fluid (brine, oil, gas), hydrocarbon saturation, porosity,
etc. The present
method combines probabilistic AVA/AVO (amplitude versus angle/amplitude versus
offset)
and spatial summation of amplitude versus offset gathers with a Bayesian
analysis to
determine the range of geologic and geophysical parameters that will fit a
user-selected range
of measured field responses with selected areas. The probabilistic estimation
builds a model
space with a regular grid, then a singular bin is located for a given seismic
trace and the
property estimation is based on counting models in that singular bin. The
present invention
allows boxes based on the seismic data to be defined in the model space based
on the
probabilistic analysis from which the property estimation is done by counting
models in the
boxes.
[0024] Reference will now be made in detail to various embodiments,
examples of
which are illustrated in the accompanying drawings. In the following detailed
description,
numerous specific details are set forth in order to provide a thorough
understanding of the
present disclosure and the embodiments described herein. However, embodiments
described
herein may be practiced without these specific details. In other instances,
well-known
methods, procedures, components, and mechanical apparatus have not been
described in
detail so as not to unnecessarily obscure aspects of the embodiments.
[0025] Seismic imaging of the subsurface is used to identify potential
hydrocarbon
reservoirs. Seismic data is acquired at a surface (e.g. the earth's surface,
ocean's surface, or
at the ocean bottom) as seismic traces which collectively make up the seismic
dataset. The
seismic dataset may be processed and imaged via a pre-stack method in order to
analyze the
seismic amplitude versus angle (AVA) or offset (AVO). Seismic surveys,
generally called a
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baseline survey and one or more monitor surveys, conducted at different times
(months or
years apart) are used to monitor changes in the subsurface and are processed
and imaged to
create images that will show differences in seismic amplitudes.
[0026] The present invention includes embodiments of a method and system
for
assessing changes in reservoir properties over a period of time in a
subterranean reservoir to
determine the probability of hydrocarbons remaining after production and/or
injection, in
some embodiments estimating the probability of various saturation changes
and/or pressure
changes. Saturation changes are used to describe fluid changes in reservoirs
that contain
more than one type of fluid or gas or reservoirs where one fluid is partially
replacing another
as a result of hydrocarbon production or injection. Reservoir properties may
include at least
one of pore fluid content, porosity, brine composition, hydrocarbon
composition, pressure,
temperature, or any combination thereof Determining the most probable changes
in
reservoir properties in a geologic feature and a range of possible changes in
reservoir
properties allows strategic planning around budgetary planning, obtaining
mineral and lease
rights, signing well commitments, permitting rig locations, designing well
paths and drilling
strategy, preventing subsurface integrity issues by planning proper casing and
cementation
strategies, selecting and purchasing appropriate completion and production
equipment, and
enhanced production strategies such as water or steam injection, as well as
ultimately drilling
into an optimum location to produce the hydrocarbons.
[0027] Figure 1 illustrates a flowchart of a method 100 for time-lapse
geologic
analysis of a subsurface volume of interest. At operation 10, at least two
seismic datasets that
were recorded at different times (i.e., baseline and monitor datasets),
generally months or
years apart, are received. The earlier seismic dataset is generally referred
to as the baseline
dataset and the subsequent datasets are monitor datasets. As previously
described, a seismic
dataset includes a plurality of traces recorded at a plurality of seismic
sensors. Due to
changes in the reservoir properties caused by hydrocarbon production and/or
injection, the
seismic responses recorded at the two different times will be different in
affected areas.
[0028] Method 100 moves on to process the seismic datasets 11 using
substantially
similar processing flows to create digital seismic images. These datasets may
be subjected to
a number of seismic processing steps, such as deghosting, multiple removal,
spectral shaping,
and the like, before undergoing a pre-stack seismic imaging process. These
examples are not
meant to be limiting. Those of skill in the art will appreciate that there are
a number of useful
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seismic processing steps that may be applied to seismic data. The processing
should preserve
the seismic signal and reduce noise. The resultant digital seismic images may
be, for
example, a pre-stack seismic image, one or more seismic angle stacks, or one
or more digital
seismic horizon amplitude maps. The seismic horizon amplitude maps may have
been
computed at a series of angles (or summation of adjacent angles) in place of
migrated seismic
gathers. The seismic amplitude maps are computed by extracting the seismic
amplitude from
the migrated seismic gathers (either exact amplitude, or a computation of
seismic amplitude
at times around the horizon computed as average, absolute, rms, maximum,
minimum, or
other computational method) at the interpreted horizon time. The seismic
horizons may be
represented in time or depth, being optionally flattened. As is known,
flattening of seismic
data is used to remove the influence of geological processes such as folding
and faulting in
one or more the lithological interfaces from the data, enabling images
produced from the
seismic data to be processed into horizontal layers, e.g., for easier
interpretation. The
flattening of seismic data is an optional step. The seismic image and seismic
horizons may
be two-dimensional (2-D) (e.g., a horizontal dimension "x" and a time or depth
dimension
"z") or three-dimensional (3-D) data sets (e.g., two perpendicular horizontal
dimensions "x"
and "y" and a time or depth dimension "z"). In some embodiments, the seismic
horizon may
be representative of the top of a hydrocarbon reservoir (top sand) and/or the
base of the
hydrocarbon reservoir (base sand).
[0029] At operation 12, the seismic images are interpreted to identify at
least one
spatial area on a seismic horizon that has differing amplitudes between the
two seismic
images. The seismic horizon should be representative of the reservoir that is
being
monitored. An example of this can be seen in Figure 3. Figure 3 shows map-view
panels of
a seismic horizon from a first time 30 and the same seismic horizon from a
second time 32.
In order to create these map views 30 and 32, a full range of seismic
amplitude data has been
stacked, which in this example embodiment is seismic amplitude data between
angles 4 and
60 , as part of a data preprocessing step. The map indicates different regions
of varying
seismic amplitudes (indicated in differing shades) mostly correlating with the
distribution of
lithology, as well as liquids and gas, e.g., hydrocarbons. Interpretation of
the seismic
horizons shows that most of the amplitudes do not change between the two
surveys. In one
spatial area 33, the amplitudes do change, so this area is selected for
analysis by the rest of
method 100. The amplitude difference in this area is calculated at operation
13. In an
embodiment, one or more areas of interest are identified on the seismic
horizons. In an
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embodiment using 3-D data, the areas of interest may be identified on a map
view of the one
or more seismic horizons, e.g., as polygons, wherein the map view may be
colored (or shaded
or contoured) to indicate the seismic amplitudes along the particular horizon.
[0030] At operation 14, a probabilistic analysis is performed for the
seismic
amplitude versus angle (AVA) responses in at least one spatial area identified
in the seismic
image on at least one seismic horizon. An example of a method for doing this
probabilistic
analysis is shown in Figure 2 as method 200. This method may include, for
example, using
the method of US 2016/0209531, System and Method for Analyzing Geologic
Features
Using Seismic Data, which is incorporated herein in its entirety. A pre-stack
seismic image
contains multiple seismic horizons that represent seismic events identified or
selected, in an
embodiment, by a user as being of interest. These seismic horizons may
represent a single
thin lithology, such as a sand layer or a shale layer, or an interface within
one or between two
or more lithologies.
[0031] In some embodiments, each area of interest may encase a large
number of
seismic trace locations. In terms of the present disclosure, it is important
to include a
sufficient number of seismic trace locations (resulting in a sufficient number
of seismic traces
or data sets to be processed) thereby to ensure statistical stability of the
resulting AVA
curves. By way of example and not limitation, a sufficient number of seismic
trace locations
may be on the order of thousands of trace locations.
[0032] The statistical data ranges are influenced and determined by a
range of
geology enclosed in the selected area of interest (i.e. polygon) and noise.
The range of
geology may include, for example, changes in thickness, porosity, grain size,
cementation,
mineralogical composition, or the like. Statistical stability of the data is
ensured by making
the area of interest (polygon) sufficiently large to ensure that the noise is
averaged out, as
well as large enough to contain a representative sampling of the geology.
[0033] Referring again to Figure 1, in operation 14 statistical data
ranges are
computed for the seismic amplitudes in each of the areas of interest, shown in
the example of
Figure 4. These computations and calculations may be performed by reading
seismic angle
gathers, i.e. all of the seismic traces at a particular angle for an area of
interest, identifying a
time gate centered on the seismic horizon, and computing the aggregated
amplitudes at each
angle. The time gate has the effect of isolating a portion of each selected
trace around a
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feature of interest in time. This process of computing the statistical data
ranges for the
seismic amplitudes in each of the areas of interest is computationally
expensive.
[0034] A person skilled in the art would appreciate that the computation
and
calculations of statistical data ranges can be performed using pre-stack
seismic data in depth
coordinates, rather than time coordinates, and identifying a depth gate
centered on the seismic
horizon.
[0035] In terms of the present method it is advantageous to calculate the
probability
of various seismic amplitudes within the area of interest, thereby allowing
the statistical data
ranges of seismic amplitudes to be determined. In some embodiments, the
statistical data
ranges may be represented by P50 and an upper and a lower probabilistic value
for seismic
amplitudes, each of the upper and lower values being similarly offset from the
P50 value. For
example, the upper and lower probabilistic values may respectively be selected
as a P10 and a
P90 probabilistic value, a P20 and a P80 probabilistic value, a P30 and a P70
probabilistic
value, or the like. These values are provided by way of example only and are
not meant to be
limiting.
[0036] Typically, the P50 probabilistic value represents the underlying
signal, while
the upper and lower probabilistic values are indicative of a probabilistic
range which
represents the variable geology and/or noise. A variety of statistics may be
computed from
the aggregated seismic amplitudes, i.e. in addition, or alternatively, to the
probabilistic values
mentioned above. For example, the statistical data ranges may include one or
more of an
average or mean (such as an average absolute amplitude), a mode, or a standard
deviation
such as RMS amplitude. It will be appreciated that other statistical measures
may also be
used. The use of many seismic trace locations from the areas of interest may
assist in
obtaining statistically significant data, in that the data may be more stable
and distinct.
[0037] In addition, in another embodiment, angle stacks may be created by
summing
the seismic traces for each time or depth sample at two or more angles, e.g.,
adjacent angles.
The angle stacks may be narrow, summing over a few adjacent angles, or broad,
summing
over many angles such as 100 - 20 . Additionally, the ranges of angles summed
over may
overlap (e.g., 10 - 20 and 15 - 25 ). A normalization based on the number
of traces
summed may be used in order to obtain an optimum presentation of the results.
In other
words, these narrow angle stacks may in some instances stabilize the trend of
the AVA
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curves produced. It will however be appreciated that in many cases there may
be no need for
this type of stacking. As an alternative to using the AVA responses at
particular angles or
angle stacks, the statistical data ranges may be based on other criteria such
as the gradient or
rate of change of the seismic amplitude response with angle or other industry-
recognized
measurements in the field (e.g., fanfar, grenv).
[0038] Figure 2 shows an embodiment of a method 200 for performing
operation 14
of Figure 1. Operation 20 receives the baseline, monitor, amplitude difference
datasets from
the previous operations of method 100. Operation 21 of method 200 determines
possible
ranges of geological and geophysical parameters expected in the reservoir zone
being
analyzed that affect the seismic amplitude versus angle response. The expected
ranges of
geological and geophysical parameters are determined by the user based on
nearby known
information (e.g., previously drilled wells), estimated from theoretical
equations, or other
such information sources to provide results which may best characterize the
expected
geological and geophysical parameters expected in the reservoir zone.
Geophysical
parameters may include elastic properties such as P-wave velocity (Vp), S-wave
velocity
(Vs), and density. Geological parameters may include brine composition,
hydrocarbon
composition, pressure, temperature, porosity, reservoir thickness,
mineralogical composition,
and other factors. These determinations may be done by regional analysis,
geologic inference
or analogs, petrophysical analysis from analog well logs, or other means. In
one
embodiment, those of skill in the art will be aware that there are a number of
ways of
determining reasonable ranges of geological and geophysical parameters for a
particular
subterranean volume.
[0039] Geological parameters may be determined, for example, for a
situation in
which there is advance knowledge of the deposition environment of the
material. In this case,
that knowledge may allow the user to determine information regarding what
types of
materials are likely to be present as well as what relationship various layers
are likely to have.
By way of example, an eolian deposition environment would tend to include
sandstones that
are relatively free of clay and relatively well-sorted. In contrast, deltaic
sandstones would
tend to be higher in clay content. In order to render the hypothetical
physical properties more
relevant to the analysis of the acquired seismic data, the types of sandstone
generated would
depend, at least in part, on whether the region under investigation includes
wind-deposited or
river delta deposited material and could be further differentiated based on
specifics of the
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deposition environment. Geophysical parameters may be determined, for example,
where
there is local information available, such as from well cores or well logs
from nearby wells.
[0040] At operation 22, the seismic amplitude versus angle (AVA)
responses are
calculated in at least one spatial area for each of the baseline, monitor, and
amplitude
difference datasets. This may be done, for example, using the method of US
2016/0209531,
System and Method for Analyzing Geologic Features Using Seismic Data, which is
incorporated herein in its entirety. A pre-stack seismic image contains
multiple seismic
horizons that represent seismic events identified or selected, in an
embodiment, by a user as
being of interest. These seismic horizons may represent a single thin
lithology, such as a sand
layer or a shale layer, or an interface within one or between two or more
lithologies. At
operation 24, AVA probabilities are calculated for the baseline, monitor, and
amplitude
difference AVA responses. These computations and calculations may be performed
by
reading seismic angle gathers, i.e. all of the seismic traces at a particular
angle for an area of
interest, identifying a time gate centered on the seismic horizon, and
computing the
aggregated amplitudes at each angle. The time gate has the effect of isolating
a portion of
each selected trace around a feature of interest in time. This process of
computing the
statistical data ranges for the seismic amplitudes in each of the areas of
interest is
computationally expensive. A person skilled in the art would appreciate that
the computation
and calculations of statistical data ranges can be performed using pre-stack
seismic data in
depth coordinates, rather than time coordinates, and identifying a depth gate
centered on the
seismic horizon.
[0041] In terms of the present method it is advantageous to calculate the
probability
of various seismic amplitudes within the area of interest, thereby allowing
the statistical data
ranges of seismic amplitudes for each of the datasets to be determined. In
some embodiments,
the statistical data ranges may be represented by P50 and an upper and a lower
probabilistic
value for seismic amplitudes, each of the upper and lower values being
similarly offset from
the P50 value. For example, the upper and lower probabilistic values may
respectively be
selected as a P10 and a P90 probabilistic value, a P20 and a P80 probabilistic
value, a P30
and a P70 probabilistic value, or the like. These values are provided by way
of example only
and are not meant to be limiting.
[0042] Once the ranges of possible geological and geophysical parameters
are
determined, operation 23 proceeds to perform a full range of 2-layer or 3-
layer forward
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modeling with all combinations of the geological and geophysical parameters.
This may be
done, for example, using a method such as that described in US Patent
7,869,955, Subsurface
Prediction Method and System, which is incorporated herein in its entirety. By
way of
example and not limitation, pseudo-wells including multiple types of synthetic
well logs may
be generated. Pseudo-wells may include physical properties such as Vp, Vs,
density,
porosity, shale volume (Vshale), saturation, pore fluid type or other
properties. In an
embodiment, seismic models for the reservoir response first at conditions
represented by the
first seismic survey and then at a range of conditions representing expected
changes in the
reservoir properties that encompass the expected or measured properties
represented by the
time of the second seismic survey. These properties can be fluid saturation
(brine, oil, gas),
pressure, temperature, etc. These property changes should be represented by a
number of
discrete changes. In an embodiment, this may be a small number of discrete
changes such as
2 ¨ 5. The modeling of the reservoir at the initial state may include
variations in reservoir
thickness, porosity, and other properties. Using the forward modeling, this
operation may also
construct a series of results of the amplitude difference between the
reservoir properties
corresponding to the first (i.e., baseline) seismic data set and the suspected
discrete parameter
changes represented by the second (i.e., monitor) seismic data set. Once the
synthetic AVA
responses have been calculated at operation 23, the AVA probabilities are
calculated at
operation 25.
[0043] The pseudo-wells may be generated using a partially random
approach. Rather
than using a simple stochastic approach, in which any particular physical
model is equally
likely, the generation of the pseudo-wells may be constrained by physical
constraints. The
constraining may take place prior to the generating, or alternately, purely
stochastic pseudo-
wells may be later constrained (e.g., by eliminating wells having
characteristics outside the
constraints). As will be appreciated, it is likely to be more efficient to
first constrain, then
generate, the wells, but either approach should be considered to be within the
scope of the
present invention.
[0044] The forward modeling of operation 23 will produce modeled (i.e.
synthetic)
seismic gathers containing AVA effects for the various combinations of
geological and
geophysical parameters. Forward modeling may be done, for example, using some
form of
the Zoeppritz equation, full waveform modeling, or other such seismic modeling
method that
may be appropriate including that explained by US Patent 7,869,955. Then at
operation 25,
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these synthetic seismic gathers are used to calculate the probability of
various seismic
amplitudes within the area of interest, thereby allowing the statistical data
ranges of seismic
amplitudes to be determined. For example, Figure 4 shows AVA curves for three
different
fluid contents (brine/wet, fizz, and gas), including the P50 values (the
triangle, star, and
square symbols) with range bars indicating the P20 ¨ P80 ranges. Fizz is
generally considered
to be a low saturation, non-commercial amount of hydrocarbon gas (1% to 15%
gas
saturation) contained in the rock pores along with formation water. The
seismic amplitude
responses should be determined separately for brine, low and high hydrocarbon
saturation,
and different hydrocarbon fluids. The measured response ranges may also be
segregated by
different geological assessment of the mineralogical composition of the
reservoir and non-
reservoir rocks (i.e. facies) simulated in the forward modeling step. Other
examples of the
forward modeled responses can be seen in Figure 5. In Figure 5, the different
grayscale dots
indicate amplitudes as very-far-stack vs. near-stack for different fluid
contents. Boxes
defining the baseline amplitudes (amplitudes at the earlier time), monitor
amplitudes
(amplitudes at the later time), and difference amplitudes are based on the AVA
probabilities
calculated in operation 24, calculated from the input digital seismic images,
are shown.
Figure 6 shows a similar plot of the baseline and monitor boxes but the
forward modeled
results have been simplified to the modeled fluid vector rather than the
grayscale dots. To
one skilled in the art, it would obvious that instead of defining a box around
the P50
amplitudes at each measured angle to represent the range of possible models,
one could also
use an ellipse or other such shape to represent the spatial distribution of
the data about the
central value. Alternatively, a mathematical distribution characterizing the
distribution of the
data around the P50 amplitude could be estimated and used from operation 24
and forward in
the analysis. Moreover, although Figure 6 shows the box in two dimensions, the
box (or
ellipse or mathematical distribution) may be multi-dimensional. For example,
if statistical
data ranges are found for four different angles, the box would have four
dimensions.
[0045] Method
200 can now proceed to operation 26, estimating the probability for
changes in pore fluid saturation based on comparison of the calculated AVA
probabilities
from operation 24 and the calculated modeled AVA probabilities from operation
25. This
estimation is done by comparing the amplitude difference AVA responses and the
baseline
and monitor AVA responses. By way of example, this may be done by using a two-
box or
three-box test, to estimate the change in reservoir properties in each polygon
separately by
considering the difference in the measured seismic amplitude versus angle
responses between
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the first (baseline) and second (monitor) seismic survey within a single
spatial polygon. This
may be done by first determining the number of forward model responses which
represent a
reservoir in the initial state of the time of the first (i.e., baseline)
seismic survey that have
responses which fit into a box centered on the P50 response at each measured
parameter and
an extent determined by statistical measurements (e.g., P20 ¨ P80, standard
deviation, etc.).
The successful seismic models must have a calculated response which fits all
of the measured
response ranges. Next determine from this sub-class of forward model
responses, those
models which have a calculated response in the box centered on the P50
response from the
monitor seismic survey at each measured parameter and an extent determined by
statistical
measurements (e.g., P20 ¨ P80, standard deviation, etc.). Next determine from
this smaller
sub-class of forward model responses, those models which have a calculated
response in the
box centered on the P50 response from the amplitude difference (baseline minus
monitor or
vice versa, as long as the order of subtraction is the same for the recorded
seismic dataset
sand the modeled/synthetic seismic datasets) at each measured parameter and an
extent
determined by statistical measurements (e.g., P20 ¨ P80, standard deviation,
etc.). Any two or
all 3 of the tests above can be used to determine the final subset of
successful forward
models. When doing only a two-box test, one of the tests should be using the
amplitude
difference data set. Analyze the total number of successful forward model
responses which fit
either the two or three tests used. The probability of each reservoir property
change (e.g.
saturation case) at the time of the second (i.e., monitor) seismic survey
according to this
hypothesis is the number of successful responses for that saturation case
divided by the total
number of successful responses.
[0046] Referring again to method 100 of Figure 1, at operation 15 the
result of
method 200 can be used to estimate changes in reservoir properties. Seismic
models can be
used to estimate other reservoir properties. The estimated reservoir
properties may include
pore fluid content, porosity, brine composition, hydrocarbon composition,
pressure,
temperature, or any combination thereof These estimated reservoir properties
are estimates
of the average geology in the spatial area of interest. Reservoir properties
such as porosity,
thickness, and Vshale can be estimated from these seismic models. Statistical
measurements
can be computed and summarized.
[0047] Although the embodiments above describe a method based on seismic
horizons, a similar method may be used to allow interval (i.e.layer-based)
analysis, which
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would include a probabilistic analysis of the reservoir thickness as well as
other reservoir
properties. This may be done by including a seismic attribute for an interval.
For example,
time thickness is a candidate for the seismic attribute, or average amplitude
for an interval.
Time thickness could be used as a seismic attribute to assure that the model
and seismic
thickness are broadly matched. Alternatively, time thickness for the seismic
traces could be
used to adjust the priors for the reservoir thickness of the models (greater
than tuning
thickness, less than tuning thickness, a mixture). Average amplitude for an
interval could be
useful for estimation of NTG (net to gross).
[0048] Other seismic attributes for layer-based analysis may be used,
including the
results of seismic inversions. Those of skill in the art are aware that there
are many types of
seismic inversion that may be used to invert for different seismic attributes,
such as acoustic
impedance (AI), shear impedance (SI), density (p), and elastic impedance (El),
as well as
other attributes that may be calculated by simultaneous inversions such as X.
and II. (first and
second Lame parameters). The inversions may include single angle stack
inversions,
simultaneous inversions, geostatistical inversions, and 4D joint inversions.
Figure 9 is a
flowchart illustrating method 900 for estimating changes in reservoir
properties from seismic
attributes calculated by seismic inversion.
[0049] At operation 90 of Figure 9, at least two seismic attribute
volumes
representative of the subsurface volume of interest as recorded at a first
time (baseline
attribute volume) and a second time (monitor attribute volume) are received.
As previously
explained, these seismic attribute volumes may include, by way of example and
not
limitation, one or more of Al, SI, p, El, Xi) and II., as calculated by
seismic inversions of the
seismic datasets recorded at the first and second times. There may be one
seismic attribute
volume (e.g., Al) for each recording time (baseline and monitor), or there may
be multiple
seismic attribute volumes (e.g., Al and SI; Al, SI, and p) for each recording
time (baseline
and monitor). The same type of seismic attribute volumes must be received for
both the
baseline and monitor times.
[0050] At operation 91, a spatial area and layer is identified where the
seismic
attribute changes between the baseline and monitor volumes. This is similar to
operation 12
of method 100 from Figure 1. However, in method 100, the spatial area is
selected on a
given seismic layer (i.e. interval). In general, this layer may be a reservoir
layer such as a
sand layer. By way of example and not limitation, the layer could be defined
based on
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seismic amplitudes or seismic impedance. For a low-impedance sand layer, the
layer top
would be defined as being at the high-to-low impedance transition and the
layer base would
be at the low-to-high impedance transition. In many cases, the layer thickness
may be greater
than one sample, so an average attribute value or other representative value
(e.g., root-mean-
square (RMS)) may be used to represent the seismic attribute for the layer at
a particular
location.
[0051] When the spatial area for the layer has been identified, operation
92 calculates
the difference between the baseline attribute volume and the monitor attribute
volume. This
is done as a point-by-point subtraction to produce an attribute difference
volume. The
attribute difference volume, baseline attribute volume, and monitor attribute
volume for the
spatial area are input to operation 93 which performs probabilistic attribute
analysis.
[0052] Figure 10 illustrates a method 1000 for performing probabilistic
attribute
analysis. Operation 1002 receives the attribute difference volume, baseline
attribute volume,
and monitor attribute volume for the spatial area. Operation 1006 performs
statistical
analysis of the volumes to calculate statistical data ranges for the attribute
volumes. These
computations and calculations may be performed by reading all of the
attributes for an area of
interest and within the layer and then determining statistical data ranges of
the attributes. In
some embodiments, the statistical data ranges may be represented by P50 and an
upper and a
lower probabilistic value for seismic attributes, each of the upper and lower
values being
similarly offset from the P50 value. For example, the upper and lower
probabilistic values
may respectively be selected as a P10 and a P90 probabilistic value, a P20 and
a P80
probabilistic value, a P30 and a P70 probabilistic value, or the like. These
values are provided
by way of example only and are not meant to be limiting.
[0053] Operation 1001 of method 1000 determines possible ranges of
geological and
geophysical parameters expected in the reservoir layer being analyzed that
affect the seismic
attributes. The expected ranges of geological and geophysical parameters are
determined by
the user based on nearby known information (e.g., previously drilled wells),
estimated from
theoretical equations, or other such information sources to provide results
which may best
characterize the expected geological and geophysical parameters expected in
the reservoir
zone. Geophysical parameters may include elastic properties such as P-wave
velocity (Vp),
S-wave velocity (Vs), and density. Geological parameters may include brine
composition,
hydrocarbon composition, pressure, temperature, porosity, reservoir thickness,
mineralogical
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composition, and other factors. These determinations may be done by regional
analysis,
geologic inference or analogs, petrophysical analysis from analog well logs,
or other means.
In one embodiment, those of skill in the art will be aware that there are a
number of ways of
determining reasonable ranges of geological and geophysical parameters for a
particular
subterranean volume.
[0054] Geological parameters may be determined, for example, for a
situation in
which there is advance knowledge of the deposition environment of the
material. In this case,
that knowledge may allow the user to determine information regarding what
types of
materials are likely to be present as well as what relationship various layers
are likely to have.
By way of example, an eolian deposition environment would tend to include
sandstones that
are relatively free of clay and relatively well-sorted. In contrast, deltaic
sandstones would
tend to be higher in clay content. In order to render the hypothetical
physical properties more
relevant to the analysis of the acquired seismic data, the types of sandstone
generated would
depend, at least in part, on whether the region under investigation includes
wind-deposited or
river delta deposited material and could be further differentiated based on
specifics of the
deposition environment. Geophysical parameters may be determined, for example,
where
there is local information available, such as from well cores or well logs
from nearby wells.
[0055] Once the ranges of possible geological and geophysical parameters
are
determined, operation 1003 proceeds to generate synthetic seismic attributes
with all
combinations of the geological and geophysical parameters. This may be done,
for example,
using a method such as that described in US Patent 7,869,955, Subsurface
Prediction Method
and System, which is incorporated herein in its entirety. By way of example
and not
limitation, pseudo-wells including multiple types of synthetic well logs may
be generated.
Pseudo-wells may include physical properties such as Vp, Vs, Al, SI, p, El,
X.p and II.,
porosity, shale volume (Vshale), saturation, pore fluid type or other
properties. In an
embodiment, seismic attributes are calculated for the reservoir first at
conditions represented
by the first seismic survey and then at a range of conditions representing
expected changes in
the reservoir properties that encompass the expected or measured properties
represented by
the time of the second seismic survey. These properties can be fluid
saturation (brine, oil,
gas), pressure, temperature, etc. These property changes should be represented
by a number
of discrete changes. In an embodiment, this may be a small number of discrete
changes such
as 2 ¨ 5. The parameters of the reservoir at the initial state may include
variations in
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reservoir thickness, porosity, and other properties. In a particular
embodiment, it may be
desirable to create sets of pseudowells for multiple saturation cases, such as
for a change of
water saturation from 10% to 40%, a change of water saturation from 10% to
60%, and a
change of water saturation from 10% to 20%. Operation 1003 will calculate the
synthetic
seismic attributes that were the output from seismic inversions, such as one
or more of AT, SI,
p, El, X.p and II., and the difference between the reservoir properties
corresponding to the first
(i.e., baseline) seismic data set and the suspected discrete parameter changes
represented by
the second (i.e., monitor) seismic data set. Once the synthetic attributes
have been calculated
at operation 1003, the synthetic attributes are used in operation 1007 to
determine
probabilities of changes in the reservoir.
[0056] The pseudo-wells may be generated using a partially random
approach. Rather
than using a simple stochastic approach, in which any particular physical
model is equally
likely, the generation of the pseudo-wells may be constrained by physical
constraints. The
constraining may take place prior to the generating, or alternately, purely
stochastic pseudo-
wells may be later constrained (e.g., by eliminating wells having
characteristics outside the
constraints). As will be appreciated, it is likely to be more efficient to
first constrain, then
generate, the wells, but either approach should be considered to be within the
scope of the
present invention. Additionally, if the seismic attribute volumes received in
operation 90 of
method 900 are bandlimited, the pseudowells can be filtered to match the
bandwidth of the
seismic attribute volumes.
[0057] Operation 1003 will produce synthetic (i.e. modeled) seismic
attributes for the
various combinations of geological and geophysical parameters. Examples of the
synthetic
attributes can be plotted similarly to those seen in Figure 5. In Figure 5,
the different
grayscale dots indicate attributes as average shear impedance (SI) vs. average
acoustic
impedance (Al) for different fluid contents. In this context, "average" means
the average
value for the layer being analyzed, which is typically a reservoir layer. For
other seismic
attributes, these plots could instead be Al vs. El or some other plot of at
least two seismic
attributes for each of the monitor and baseline datasets. Boxes defining the
baseline
attributes (attributes at the earlier time), monitor attributes (attributes at
the later time), and
difference attributes are based on the statistical data ranges calculated in
operation 1006,
calculated from the baseline, monitor, and difference attribute volumes, could
be drawn. The
boxes may be centered at the P50 values and the width of the boxes may be
determined by
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the statistical ranges, such as but not limited to P80-P20. P50 values and
statistical ranges
for 2 attributes define a box. The boxes can have higher dimensions, where a N-
dimensional
box is defined by N inversion attributes. To one skilled in the art, it would
obvious that
instead of defining a box around the P50 attribute values to represent the
range of possible
models, one could also use an ellipse or other such shape to represent the
spatial distribution
of the data about the central value. Alternatively, a mathematical
distribution characterizing
the distribution of the data around the P50 attribute value could be estimated
and used from
operation 1006 and forward in the analysis. Moreover, the box (or ellipse or
mathematical
distribution) may be multi-dimensional. For example, if statistical data
ranges are found for
four different attributes, the box would have four dimensions.
[0058] Method
1000 can now proceed to operation 1007, estimating the probability
for changes in reservoir properties based on comparison of the calculated
attribute statistical
data ranges from operation 1006 and the calculated synthetic attributes from
operation 1003.
This estimation is done by comparing the attribute differences and the
baseline and monitor
attributes. By way of example, this may be done by using a two-box or three-
box test, to
estimate the change in reservoir properties in each polygon separately by
considering the
difference in the measured attributes between the first (baseline) and second
(monitor)
seismic survey within a single spatial polygon for the layer of interest. This
may be done by
first determining the number of synthetic attributes which represent a
reservoir in the initial
state of the time of the first (i.e., baseline) seismic survey that have
attributes which fit into a
box centered on the P50 attribute value at each measured parameter and an
extent determined
by statistical measurements (e.g., P20 ¨ P80, standard deviation, etc.). The
successful seismic
models must have a calculated attribute which fits all of the measured
attribute ranges. Next
determine from this sub-class of synthetic attributes, those models which have
a calculated
attribute in the box centered on the P50 attribute value from the monitor
seismic survey at
each measured parameter and an extent determined by statistical measurements
(e.g., P20 ¨
P80, standard deviation, etc.). Next determine from this smaller sub-class of
synthetic
attributes, those models which have a calculated attribute in the box centered
on the P50
attribute value from the attribute difference (baseline minus monitor or vice
versa, as long as
the order of subtraction is the same for the recorded seismic dataset and the
modeled/synthetic seismic attribute volumes) at each measured parameter and an
extent
determined by statistical measurements (e.g., P20 ¨ P80, standard deviation,
etc.). Any two or
all 3 of the tests above can be used to determine the final subset of
successful models. When
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doing only a two-box test, one of the tests should be using the attribute
difference data set.
Analyze the total number of successful synthetic attributes which fit either
the two or three
tests used. The probability of each reservoir property change (e.g. saturation
case) at the time
of the second (i.e., monitor) seismic survey according to this hypothesis is
the number of
successful attributes for that saturation case divided by the total number of
successful
attributes.
[0059] Referring again to Figure 9, at operation 94 the result of method
1000 can be
used to estimate changes in reservoir properties in addition to saturation
changes. Those of
skill in the art understand that seismic attributes can be used to estimate
other reservoir
properties. The estimated reservoir properties may include pore fluid content,
porosity, brine
composition, hydrocarbon composition, pressure, temperature, or any
combination thereof
These estimated reservoir properties are estimates of the average geology in
the spatial area
of interest. Reservoir properties such as porosity, thickness, and Vshale can
be estimated
from these seismic attributes. Statistical measurements can be computed and
summarized.
[0060] Figure 7 is an example of the steps of method 100 using method
200. The
structure maps show the baseline map 71A and the monitor map 71B. Diagram 72
shows
AVA probability curves created at operation 24 of method 200. Diagram 73 shows
the AVA
curves for the baseline seismic data, the monitor seismic data, and the
amplitude difference
between the baseline and monitor seismic data with angles selected for use in
subsequent
steps of method 200. Diagram 74 shows a box for known baseline oil, meaning
that at the
time of the baseline survey, the reservoir contained oil. Seismic models
generated by method
200 at operations 23 and 25. Diagram 75 shows the three-box test described
above. Diagram
76 shows the probabilities calculated as a result of method 100. Although
these results are
displayed graphically, this is not meant to be limiting. Other methods of
presenting the
results, such as in a spreadsheet format, are possible.
[0061] Figure 8 is a block diagram illustrating a time-lapse reservoir
property
assessment system 500, in accordance with some embodiments. While certain
specific
features are illustrated, those skilled in the art will appreciate from the
present disclosure that
various other features have not been illustrated for the sake of brevity and
so as not to
obscure more pertinent aspects of the embodiments disclosed herein.
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[0062] To that end, the reservoir property assessment system 500 includes
one or
more processing units (CPUs) 502, one or more network interfaces 508 and/or
other
communications interfaces 503, memory 506, and one or more communication buses
504 for
interconnecting these and various other components. The reservoir property
assessment
system 500 also includes a user interface 505 (e.g., a display 505-1 and an
input device 505-
2). The communication buses 504 may include circuitry (sometimes called a
chipset) that
interconnects and controls communications between system components. Memory
506
includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other
random access solid state memory devices; and may include non-volatile memory,
such as
one or more magnetic disk storage devices, optical disk storage devices, flash
memory
devices, or other non-volatile solid state storage devices. Memory 506 may
optionally include
one or more storage devices remotely located from the CPUs 502. Memory 506,
including the
non-volatile and volatile memory devices within memory 506, comprises a non-
transitory
computer readable storage medium and may store seismic data, velocity models,
seismic
images, and/or geologic structure information.
[0063] In some embodiments, memory 506 or the non-transitory computer
readable
storage medium of memory 506 stores the following programs, modules and data
structures,
or a subset thereof including an operating system 516, a network communication
module 518,
and a reservoir property module 520.
[0064] The operating system 516 includes procedures for handling various
basic
system services and for performing hardware dependent tasks.
[0065] The network communication module 518 facilitates communication
with other
devices via the communication network interfaces 508 (wired or wireless) and
one or more
communication networks, such as the Internet, other wide area networks, local
area networks,
metropolitan area networks, and so on.
[0066] In some embodiments, the time-lapse module 520 executes the
operations of
method 100. Time-lapse module 520 may include data sub-module 525, which
handles the
seismic image including seismic gathers 525-1 through 525-N. This seismic data
is supplied
by data sub-module 525 to other sub-modules.
[0067] AVA (amplitude versus angle) sub-module 522 contains a set of
instructions
522-1 and accepts metadata and parameters 522-2 that will enable it to execute
operation 11,
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12, 13, and parts of 14 of method 100. The forward modeling function sub-
module 523
contains a set of instructions 523-1 and accepts metadata and parameters 523-2
that will
enable it to execute parts of operation 14 of method 100. The fluid content
sub-module 524
contains a set of instructions 524-1 and accepts metadata and parameters 524-2
that will
enable it to execute at least operation 15 of method 100. Although specific
operations have
been identified for the sub-modules discussed herein, this is not meant to be
limiting. Each
sub-module may be configured to execute operations identified as being a part
of other sub-
modules, and may contain other instructions, metadata, and parameters that
allow it to
execute other operations of use in processing seismic data and generate the
seismic image.
For example, any of the sub-modules may optionally be able to generate a
display that would
be sent to and shown on the user interface display 505-1. In addition, any of
the seismic data
or processed seismic data products may be transmitted via the communication
interface(s)
503 or the network interface 508 and may be stored in memory 506.
[0068] Although time-lapse reservoir property assessment system 500 is
designed for
method 100, those of skill in the art will appreciate that a very similar
system including an
attribute sub-module rather than an AVA sub-module can be used for method 900.
[0069] Method 100 is, optionally, governed by instructions that are
stored in
computer memory or a non-transitory computer readable storage medium (e.g.,
memory 506
in Figure 8) and are executed by one or more processors (e.g., processors 502)
of one or more
computer systems. The computer readable storage medium may include a magnetic
or optical
disk storage device, solid state storage devices such as flash memory, or
other non-volatile
memory device or devices. The computer readable instructions stored on the
computer
readable storage medium may include one or more of: source code, assembly
language code,
object code, or another instruction format that is interpreted by one or more
processors. In
various embodiments, some operations in each method may be combined and/or the
order of
some operations may be changed from the order shown in the figures. For ease
of
explanation, method 100 is described as being performed by a computer system,
although in
some embodiments, various operations of method 100 are distributed across
separate
computer systems.
[0070] While particular embodiments are described above, it will be
understood it is
not intended to limit the invention to these particular embodiments. On the
contrary, the
invention includes alternatives, modifications and equivalents that are within
the spirit and
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scope of the appended claims. Numerous specific details are set forth in order
to provide a
thorough understanding of the subject matter presented herein. But it will be
apparent to one
of ordinary skill in the art that the subject matter may be practiced without
these specific
details. In other instances, well-known methods, procedures, components, and
circuits have
not been described in detail so as not to unnecessarily obscure aspects of the
embodiments.
[0071] The terminology used in the description of the invention herein is
for the
purpose of describing particular embodiments only and is not intended to be
limiting of the
invention. As used in the description of the invention and the appended
claims, the singular
forms "a," "an," and "the" are intended to include the plural forms as well,
unless the context
clearly indicates otherwise. It will also be understood that the term "and/or"
as used herein
refers to and encompasses any and all possible combinations of one or more of
the associated
listed items. It will be further understood that the terms "includes,"
"including," "comprises,"
and/or "comprising," when used in this specification, specify the presence of
stated features,
operations, elements, and/or components, but do not preclude the presence or
addition of one
or more other features, operations, elements, components, and/or groups
thereof
[0072] As used herein, the term "if' may be construed to mean "when" or
"upon" or
"in response to determining" or "in accordance with a determination" or "in
response to
detecting," that a stated condition precedent is true, depending on the
context. Similarly, the
phrase "if it is determined [that a stated condition precedent is truer or "if
[a stated condition
precedent is truer or "when [a stated condition precedent is truer may be
construed to mean
"upon determining" or "in response to determining" or "in accordance with a
determination"
or "upon detecting" or "in response to detecting" that the stated condition
precedent is true,
depending on the context.
[0073] Although some of the various drawings illustrate a number of
logical stages in
a particular order, stages that are not order dependent may be reordered and
other stages may
be combined or broken out. While some reordering or other groupings are
specifically
mentioned, others will be obvious to those of ordinary skill in the art and so
do not present an
exhaustive list of alternatives. Moreover, it should be recognized that the
stages could be
implemented in hardware, firmware, software or any combination thereof.
[0074] The foregoing description, for purpose of explanation, has been
described with
reference to specific embodiments. However, the illustrative discussions above
are not
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intended to be exhaustive or to limit the invention to the precise forms
disclosed. Many
modifications and variations are possible in view of the above teachings. The
embodiments
were chosen and described in order to best explain the principles of the
invention and its
practical applications, to thereby enable others skilled in the art to best
utilize the invention
and various embodiments with various modifications as are suited to the
particular use
contemplated.
24
