Note: Descriptions are shown in the official language in which they were submitted.
CA 02791982 2012-10-05
=
HYBRID SEISMIC SENSOR NETWORK
FIELD
[0001] This document describes methods and systems for monitoring
seismicity, including reservoir-induced seismicity, using a hybrid seismic
network.
BACKGROUND
[0002] Hydraulic fracturing is used to fracture rock surrounding a
treatment
well and pump the created fractures with a mixture of fluid and granular media
(proppant) to enhance the permeability of the rock formation adjacent the
treatment well. If the formation contains a hydrocarbon reservoir, treatments
such
as hydraulic fracturing seek to increase the production of the reservoir by
creating
pathways through which the hydrocarbons can flow to the treatment well. A
typical scenario is in gas-bearing shale formations where the inherent
permeability
of the rock is too low to allow for efficient drainage of the reservoir.
Hydraulic
fracturing allows for the gas trapped in pore spaces of the shale to be
produced,
even from long distances from a production well, due to the enhanced
permeability
of the hydrocarbon-bearing formation that the injected proppant imparts.
[0003] In the process of creating and reactivating cracks in the
formation,
hydraulic fracturing generates small-scale seismic events. This seismic energy
generated by these events propagates away from the location of the fracture,
which is known as the hypocenter. These seismic events, called microseismic
events, typically measure less than Mw0 on the moment magnitude scale. In
contrast, earthquakes that are felt by humans and reported on surface
typically
reach magnitudes of Mw3 or more. Moment magnitude (Mw) is a parameter that
involves characterization of the low-frequency spectrum of the seismic or
microseismic event.
[0004] Many injection processes, including for example hydraulic
fracturing
and cyclic steaming, are monitored through the use of microseismic monitoring.
Hydraulic fracturing and cyclic steaming are capable of generating thousands
of
micro-earthquakes with magnitudes typically ranging from -Mw4 to -Mwl. The
1
CA 02791982 2012-10-05
instrumentation and configuration of the microseismic monitoring networks are
typically chosen with this magnitude range in mind, and the relatively high
frequency signals are recorded with geophones with the bandwidth appropriate
for
accurate spectral characterization.
[0005] A seismic monitoring network that enhances frequency range for
monitoring seismic events while maintaining the location accuracy from
monitoring
proximal to the reservoir is desirable.
SUMMARY
[0006] In one aspect there is described a system for monitoring seismicity
during fluid injection at or near a hydrocarbon reservoir comprising: a first
set of
seismic sensors for deployment at a site for collecting seismic data; a second
set
of seismic sensors for sub-surface deployment at the site at a depth lower
than the
first set of seismic sensors for collecting seismic data, the first set of
seismic
sensors having a lower frequency response than that of the second set of
seismic
sensors; and a data collection system in communication with the first and
second
set of sensors.
[0007] In another aspect there is described a method for monitoring
seismic
events induced at or near a hydrocarbon reservoir, comprising: deploying a
first
set of seismic sensors at a site for collecting seismic data; deploying a
second set
of seismic sensors at the site at a depth lower than the first set of seismic
sensors
for collecting seismic data, the first set of seismic sensors having a lower
frequency response than that of the second set of seismic sensors; and
collecting
seismic data generated by the first set and second sets of seismic sensors for
seismic events.
[0008] Other aspects and embodiments, such as for example systems
operating in accordance with above methods, and computers and stored algorithm
embodying instructions to operate in accordance with the above methods, will
be
evident from the brief description, detail description and accompanying FIGS.
BRIEF DESCRIPTION OF THE DRAWINGS
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[0009] Reference will now be made, by way of example, to the accompanying
drawings which show example embodiments of the present description, and in
which:
[00010] FIG. 1 is a schematic illustration of an example hydraulic
fracturing
monitoring system deployed to collect microseismic data caused by hydraulic
fracturing from a stimulation well of a reservoir.
[00011] FIG. 2A is a schematic cross-sectional view showing alternate
seismic
sensor couplings to affix the sensors to a borehole of an observation well in
the
hydraulic monitoring system of FIG. 1.
[00012] FIG. 2B is a schematic cross-sectional view showing how a seismic
sensor may be positioned on the ground surface above a reservoir in the
hydraulic
monitoring system of FIG. 1.
[00013] FIG. 3 is a graph showing an example seismic velocity model showing
seismic velocity as a function of depth for a reservoir similar to the
reservoir of
FIG 1.
[00014] FIG. 4A is a schematic illustration of a deployed array of sensors
for
the system of FIG. 1 and apparent hodogram azimuths for a known shot location
prior to sensor calibration.
[00015] FIG. 4B is a schematic illustration of the deployed array of
sensors of
FIG 4 A and the corrected hodogram azimuths after sensor calibration using the
known shot location.
[00016] FIG. 5 is an illustration representing an example seismic waveform
and an example STA/LTA function derived from the waveform, which example
function represents a microseismic event.
[00017] FIG. 6 is a time-based graph depicting microseismic data recorded
by
the sensors of FIG. 1 and potential microseismic events identified, for
example,
using the trigger logic of FIG. 5.
[00018] FIG. 7 is a graphical representation of travel time and direction
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information for individual sensors of FIG. 1 for an example microseismic event
identified in FIG. 6 derived from P and S waves utilizing Sv and SH
components.
[00019] FIG. 8 is a graphical representation of a hypocenter of an example
microseismic event derived from travel time and direction information like
that
shown in FIG. 7.
[00020] FIG. 9 is graphical illustration of corner frequency determination
employing a Brune model fit for the P wave of a microseismic event of.
[00021] FIG. 10 is a graphical illustration of an example polarity
assignment
for P. Sv, and SH waves for a microseismic event.
[00022] FIG. 11A is a graphical illustration of reflecting and refracting
microseismic energy radiating from a hypocenter.
[00023] FIG. 11B is a contour plot of the P wave showing positive and
negative
polarity P waves of FIG. 11A mapped on a focal sphere, with the projection of
the
applicable sensors on the focal sphere.
[00024] FIG. 12 is a block diagram illustrating an example of how sensors
can
be used to collect microseismic data.
[00025] FIG. 13 is a block diagram illustrating an example of how to
determine
source radius data from trigger logic processed data.
[00026] FIG. 14 is a block diagram illustrating an example of how to
determine
the SMTI from data processed using a non-linear search algorithm to determine
the hypocenter of the microseismic event.
[00027] FIG. 15 illustrates spectral modelling of SH waves recorded at
three
different sensors: (left) accelerometer FBA sensor; (middle) 4.5 Hz geophone
sensor and; (right) 15 Hz geophone sensor.
[00028] FIG. 16 illustrates an example comparison of the magnitudes of
several seismic events as calculated from data from each sensor type including
FBA sensors 102, 4.5 Hz geophone sensors 103 and 15Hz geophone sensors 104.
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[00029] FIG. 17 illustrates a magnitude scale saturation for short-period
sensors (15hz and 4.5 Hz geophone sensors) as well as for FBA sensors for
synthetic events.
DETAILED DESCRIPTION
[00030] As noted above, the instrumentation and configuration of
microseismic monitoring networks are typically chosen to monitor microseismic
events having a relatively high frequency and low magnitude. The signals
generated by such events are often measured with seismic sensors such as
geophones having a sensitive bandwidth appropriate for accurate spectral
characterization of signals within the typical microseismic event range.
[00031] Commonly, geophones are passive mechanical velocity sensing
devices based on a mass-spring system where movement of a reference mass is
measured. In the absence of movement the geophone reference mass remains at
rest and therefore does not provide any signal relating to the physical
orientation
of the device. A geophone's ability to detect low frequencies is governed by
the
physics of a mass-spring system and typically requires physically larger
devices to
detect increasingly lower frequencies. A similar limitation exists for
specific types
of accelerometers (e.g., a piezoelectric based accelerometer only outputs
charge
relative to the changing compression of the crystal). By way of example, 15Hz
geophones are commonly used for microseismic event monitoring in the context
of
hydraulic fracturing. Such geophones have a corner frequency of 15Hz and are
likely to experience magnitude saturation when used to measure a seismic event
that generates signals that are lower than 15Hz.
[00032] Additionally, the recording parameters used in microseismic
monitoring systems typically trigger only short-time measurement windows once
an event has been detected. While these parameters may be acceptable for the
characterization of small, higher frequency magnitude events, they are not
ideal
for the characterization of larger magnitude events with Mw>0 to approximately
Mw3 because the lower frequency signals emitted by these events will not
faithfully be recorded. While the majority of events detected will have moment
magnitudes between -Mw2 to MwO, when relatively uncommon macro events with
CA 02791982 2012-10-05
magnitudes Mw>0 to approximately Mw>3 do occur, it is useful in at least some
applications to understand their behavior and accurately obtain estimates of
magnitude for any risk and hazard assessments. For example, the injection of
fluids during a hydraulic fracture treatment may cause fault structures in the
area
to slip resulting in the occasional felt earthquake.
[00033] Accordingly, the present disclosure describes a hybrid sensor array
with both high-frequency and lower-frequency seismic sensors that may in at
least
some environments mitigate against the spectral bandwidth and time window
limitations of existing microseismic monitoring systems. In at least some
applications, the system described herein may assist in characterizing events
both
within conventional microseismic magnitude range as detected using
conventional
downhole geophone arrays and larger events that are out of conventional range
and that occur as a result of reservoir injection activities, thereby
enhancing the
overall reservoir management system in steam or hydraulic fracturing
applications.
System Overview
[00034] FIG. 1 is a schematic illustration of a hydraulic fracturing
monitoring
system 100, according to an example embodiment, deployed to collect seismic
data from seismic events caused by fluid injection at or near a hydrocarbon
reservoir. Fluid injection could for example include hydraulic fracturing at a
stimulation zone 120 of a stimulation well 118 at the site of the reservoir.
Referring
to FIG. 1, a plurality of seismic sensors 102, 103, 104 measuring ground
displacement or one of its derivatives (e.g., velocity or acceleration) are
deployed
at the site in the vicinity of the expected microseismic activity. The
plurality of
sensors 102, 103, 104 are deployed throughout the vicinity of the expected
microseismic activity either at ground level (surface sensors 102), or below
ground
level (downhole sensors 103, 104) down one or more observation wells 106. Out
of use stimulation wells can be used as observation wells if available. The
seismic
sensors 102, 103, 104 can include, but are not limited to, geophones,
accelerometers, or any other device that measures ground motion. For example,
downhole sensors 103, 104 that are deployed in observation wells 106 may
include
three-component geophone arrays. The sensors 102, 103, 104 are configured to
record data corresponding to ground motion corresponding to the elastic waves
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generated by the microseismic activity (notably the Primary (P) and Secondary
(5)
waves). As will be explained in greater detail below, at least some of the
sensors
have different sensitive frequency and magnitude ranges than other sensors
within
the system.
[00035] The sensors 102, 103, 104 are connected to provide signals to a data
collection system 130 that includes at least a computing device 108 such as a
central processing unit (CPU), for example a Dell R300, operating in
accordance
with computer program instructions stored in memory, such that the electronic
signals generated by the sensors can be captured on a local storage device
(for
example, persistent storage 113 associated with computing device 108), or
transmitted for remote storage. The data collection system 130 can include one
or
more digitizers 110 for digitizing data collected by the plurality of sensors
102,
103, 104. For example, digitizers 110 could be implemented using a digitizer
sold
under the trademark Paladin by ESG Solutions Inc., of Kingston, Ontario,
Canada,
Digitizers 110 can time-stamp collected data using a GPS synchronized time
source 112 so that the data collected are precisely time-synchronized across
all
sensors 102, 103, 104. The time stamped data collected by the digitizers 110
from
the plurality of sensors 102, 103, 104 can be transmitted to the local data
storage
device 113 where the data from the plurality of sensors 102, 103, 104 are
combined in computer 108 to arrive at a time-synchronized record of the
microseismic activity captured by the plurality of sensors 102, 103, 104.
[00036] Referring to FIG. 2A, in some applications the microseismic
activity
the sensors 103, 104 can be mechanically or magnetically affixed to the casing
202 of the borehole of the observation well 106. For example, FIG. 2A shows
three
possible ways of affixing the sensors 103, 104 to the borehole casing 202,
including : (i) the use of a coupling arm 204, (ii) a bowspring (bowspring
206A
unsprung for deployment; bowspring 206B sprung to couple to borehole), or
(iii)
magnets 208. Other means of affixing a sensor 103, 104 to the borehole casing
202 could also be used - for example, the sensors 103, 104 could be fixed in
place
with concrete. As shown in FIG 2B, surface sensors 102 can be enclosed in a
protective vault or case 210, which may for example be located on a concrete
platform and buried at surface.
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[00037] Operating algorithms and data, such as models, can be stored and
processed locally on the memory, CPU and storage device of on-site computing
device 108 previously mentioned, or alternatively, the collected seismic data
can
be transmitted or otherwise transported to a remote location, for example
across a
computer network 116 such as the Internet, for processing on a remote computer
114 having associated memory and storage device for the algorithms and data.
The algorithms may be stored in memory in the form of computer programs which
computer programs when operated on the computer cause the computer 108, 114
to carry out the algorithms using stored or received data, and storing the
results of
such algorithms following processing. The computers 108, 114 may have an
associated monitor to allow an operator to view the data or graphical
representations thereof and human interface devices such as a pointing device
(for
example, a mouse) and a keyboard for operator control, such as requests for
particular graphical representations generated by the algorithms, and a
display
screen 118 for viewing of the data or graphical representations. It is
recognized
that the various functions of the computers 108, 114 could be distributed
across
more than one computer 108, 114, and such distributed computers could interact
locally or remotely, for example through a computer network such as the
Internet.
Furthermore, the algorithms described in this description can operate
independent
of the sensing system described in this description. The algorithms can be
operated in a central location for a plurality of remote sensing systems. The
algorithms can be used in realtime as data is collected provided that
computers
and communication networks of sufficient speed and capacity are available.
Alternatively, sensed data can be stored for later use in conjunction with the
algorithms.
[00038] As noted above, a passive geophone's ability to detect low
frequencies
is governed by the physics of a mass-spring system and typically physically
larger
devices are required to detect lower frequencies. An enhancement to lower
frequency detection from a physically small device is to measure the force it
takes
to hold the mass still. A further enhancement is to ensure that the mass is
held in
its centre position, referred to as force balancing. There are a variety of
force-
balanced technologies available ranging from enhanced geophone performance at
low frequencies to MEMS (micro-electronic mechanical machines) accelerometers
8
CA 02791982 2012-10-05
capable of measuring the static force of gravity. In the latter case, the
effort taken
to keep the reference device centred is proportional to the gravitational
vector. The
final selection of an appropriate device for low-frequency detection depends
on
factors ranging from physical size to expected reliability when installed.
[00039] Accordingly, in an example embodiment surface or near surface
sensors 102 are implemented in the form of force balanced accelerometer (FBA)
sensors in order to provide lower frequency measurements, and downhole sensors
103 and 104 are implemented using geophones that have different frequency
responses and placed at different depths - for example the frequency corner or
minimum frequency of the geophones used for downhole sensors 104 can be
higher than that of the geophones used for downhole sensors 103, with the
higher
frequency geophone sensors 104 being located at a deeper level than the lower
frequency geophone sensors 102. FBA sensors 102 have an even lower minimum
frequency response than both sets of geophone sensors 103 and 104.
[00040] By way of non-limiting example, in one embodiment the downhole
sensors 103 of monitoring system 100 are implemented in the form of eight-
level
arrays of 4.5 Hz three-component geophones close to surface (for example,
within
150 m) and sensors 104 are implemented in the form of 15 Hz three-component
omni-directional geophones deployed deeper than geophone sensors 103, in 11
vertical downhole observation wells 106. A network of five surface deployed,
force-
balanced accelerometer (FBA) sensors 102 augment the downhole array, two of
which are collocated with observation wells 106. In one example, the
approximate
total areal extent of this array of sensors 102, 103 and 104 could be
approximately 150 km2 (12.7 km x 12.2 km). In a typical configuration,
geophone
sensors 104 may be deployed at or near the depth of the stimulation zone 120,
with geophone sensors 103 located between the surface and the stimulation zone
120.
[00041] When a sensor 102, 103, 104 is triggered, the recording windows for
the respective sensors are a function of the type of sensor 102, 103, 104. By
way
of non limiting example, in one possible application, for the 15 Hz and 4.5 Hz
geophone sensors 103, 104, the recording window is 6.5 sec long while the FBA
sensors 102 employ recording window lengths from 1 min to 5 min, depending on
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CA 02791982 2012-10-05
the separation between the P and the S waves. These longer windows ensure that
the waveforms from more distant events are captured. Events located in the
reservoir may for example have a total location accuracy from around 50 m to
100
m, although when events are detected on certain combinations of arrays, event
locations may become more accurate.
[00042] In an example embodiment, the FBA sensors 102 have a flat response
from 0 Hz to the Nyquist frequency. Active electronic devices inherently add
their
own noise signature to the system; often the noise signature is more
significant at
lower frequencies (referred to as 1/f noise). Accordingly, to mitigate against
the
noise-floor of the system 100 being raised by the FBA sensors 102 and
obscuring
the signals of interest, a low frequency limit on the FBA sensors 102 can be
imposed through the respective digitizer 110 (for example, 0.7Hz). Geophones
are
typically quieter than FBAs because they do not generate electronic noise, but
as
frequency increases the advantage of the geophone is lost because velocity
rolls
off from velocity at 20 dB per decade. However, in the frequency band of
interest
for microseismic events, typically with dominant frequencies up to 300 Hz -
500
Hz, geophone elements can faithfully reproduce incoming signals.
[00043] In an example embodiment, during operation of system 100, signals
from sensors 102, 103 and 104 are continuously recorded at their respective
distributed digitizers 110 as an independent data stream for each class or
type of
sensors (which for example may include 32 bit data recorders at each network
node location), with sampling carried out at 1/4 ms or 4 kHz for all sensor
data
streams. In an example embodiment, signals from the FBA sensors 102 are
further
decimated, for example to a 1k Hz sampling rate, to improve the dynamic range.
In some example applications, all recorded signals, including GPS time stamps
for
timing accuracy and triggering, are processed using a simple long-term average
to
short term average approach.
[00044] In an example embodiment, the measured moment magnitudes are
initially determined for each sensor class or type - for example a sensor
class
specific Mw is determined for each event is determined for (i) the FBA sensors
102; (ii) the 4.5 Hz geophone sensors 103 and (iii) the 15 Hz geophone sensors
104. Over a large network of stations, the estimates from each class of sensor
can
CA 02791982 2012-10-05
be averaged together, with some weights that can be applied to account for any
unique instrument features or a number of other factors (e.g., corrections for
recording on the ground-air interface and attenuation). In some embodiments, a
determination is made if the bandwidth for a particular sensor type does not
include a sufficient range of frequencies around the corner frequency of the
seismic event, and in such cases, the measurements from such sensor types are
excluded from the calculation of the source parameters. Hanks and Kanamori
(1979) stipulate how to calculate moment magnitude from seismic moment, which
itself is measured from the long-period spectral amplitudes of the
displacement
spectrum (see also Baig and Urbancic, 2010, for an overview of these
calculations
as applied to microseismic data) corrected for focal mechanism, source and
site
conditions, and geometrical spreading (Brune, 1970). This low-frequency
plateau
is a feature of many source models (e.g., Brune,1970; Boatwright, 1980) that
characterize the spectrum by the long-period level, corner frequency, and
attenuation quality factor. From these quantities assessed from the
displacement
spectrum, the source parameters such as seismic moment, radiated energy,
source
radius can be calculated.
Processing Sensor Data
[00045] In order to provide an example of how various quantities can be
determined from the data streams recorded from sensors 102, 103, 104, a
description of how sensor data from sensors 102, 103 and 104 can be processed
will now be provided. In an example embodiment the data streams acquired from
the different types of instruments are combined and the quantities described
below
are calculated using the data from one or more of the sensor specific data
streams
depending on which of the sensor types is or are the most appropriate
instrument(s) given the frequency content of the data.
[00046] Referring now to FIG. 3, a model of seismic velocities that is
predetermined for the monitoring site can be used to locate accurately
microseismic events. This seismic velocity model 300 can be constructed from
well log information where a sensor commonly referred to as a dipole sonic
logger
11
CA 02791982 2012-10-05
(not shown) measures wave velocities in the vicinity of the borehole 106 in
which
it is located. A model of velocities can also be provided by other means, such
as a
vertical seismic profile or by seismic profiling through reflection/refraction
surveys.
This information can be used in determining the composition and structure of
the
reservoir in the vicinity of the borehole 106. As shown in FIG. 3, the seismic
velocity model 300 will show the measured seismic velocity of both the P and S
waves in relation to its depth (S wave sonic log velocity 302, S wave block
velocity
304, P wave sonic log velocity 306 and P wave block velocity 308).
[00047] Referring to FIGS. 4A and 4B, prior to recording microseismic
activity
the sensors 102, 103, 104 are calibrated. In some example embodiments, surface
sensors 102 may include physical markings that allow them to be manually
oriented in a known orientation. Typically, sub-surface sensors 103, 104 will
need
to be electronically calibrated by firing a test shot in a known location and
measuring the result. In this regard, FIG. 4A is a graphical representation of
data
collected by the sensors 104 in an uncalibrated system. FIG. 4B is a graphical
representation of data collected by the sensors 104 in a calibrated system.
Calibration is usually accomplished by recording the microseismic signals from
an
event with a known location 402, such as a perforation shot in a well, an
explosive
charge placed in a downhole well or on the surface, or a seismic vibrator
(vibroseis) truck on the surface. Knowing that the primary (P) wave energy
from
these sources will be aligned with the direction to the source, the previously
unknown orientation of a sensor can be determined. For example, a rotation
matrix can then be determined for each of the sensors 104 to apply to
subsequently measured signals and correct for any variations in the
orientations of
the respective sensors 104. Sensors 103 can be calibrated in a similar manner,
as
can surface sensors 102 if required.
[00048] Referring now to FIG. 5, trigger logic can be used for automated
identification of when microseismic events occur in signals collected by the
sensor
arrays. For example, an algorithm can determine a short term averaging/long
term
averaging (STA/LTA) function from a microseismic waveform (signal) by taking
the
root means square (RMS) average of the signal over a short window and a long
window. The short term average is divided by the long term average for each
12
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= channel to obtain the function. Potential events are identified when this
function is
strongly peaked over a number of channels. Because different size events will
have different dominant periods, different sensor types can have variably-
sized
STA/LTA windows, appropriate to the range of magnitudes that the particular
sensor instrument is most attuned to. Other types of trigger logic can be used
to
identify potential events, usually consisting of scanning the data for
relatively large
amplitudes on a number of different channels. Manual intervention by operators
through the human interface device of computer device 108, 114 in response to
data displayed on a display can allow for manual confirmation to the algorithm
of
automated identification of when microseismic events occur, or manual
identification to the algorithm of when microseismic events occur.
[00049] Referring now to FIG. 6, the time-stamped data collected
by the
plurality of sensors 102, 103, 104 is analyzed to identify the time of
potential
microseismic events. Using the STA/LTA algorithm as described above (see also
A
Comparison of Select Trigger Algorithms for Automated Global Seismic Phase and
Event Detection, Withers et al., Bulletin of the Seismological Society of
America,
Vol. 85, No. 1, pp 95-106, February 1998, the contents of which are
incorporated
by reference into this detailed description), potential microseismic events
602 are
detected when this function is strongly peaked over a number of channels,
wherein
a channel is the data collected by a single sensor.
[00050] Referring now to FIG. 7, the sensor data corresponding
to the timing
of the microseismic events 602 identified in FIG. 6 is analyzed for as many of
the
sensors 102, 103, 104 as the signal-to-noise ratios will allow. The three-
component signal captured by the selected sensor 102, 103, 104 is analyzed to
determine the direction the waves are propogating, as well as the source of
the
microseismic activity. When a P wave pick is available for the sensors 102,
103,
104, the window will be placed after this arrival and the three-components of
the
particle motion should align with the direction of propagation. For secondary
(S)
waves, the particle motion in the window will be in a plane perpendicular to
the
particle motion so the normal vector to this plane can be used to determine
the
direction of propagation. In example embodiments, only one estimate of the
particle motion will be assigned to each sensor, and can be variably weighted
to
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= between using only the P wave hodograms to only using the S wave
hodograms.
= [00051] Referring now to FIG. 8, the objective function is a
measure of how
well a potential location fits the data measured from the plurality of sensors
102,
103, 104. The objective function is formed according to the description given
by
Microearthquake Location: A Nonlinear Approach That Makes Use of a Simplex
Stepping Procedure (Prugger and Gendzwill, Bulletin of the Seismological
Society
of America, Vol. 78, No. 2, pp. 799-815, April 1988;) and modified to include
S
wave traveltimes and hodogram information. The objective function is searched
using the simplex algorithm discussed by Prugger and Gendzwill to find the
best
fitting location, known as the hypocenter 802, based on the data. For example,
a
search algorithm is applied to locate the area of least misfit between
theoretical
information and measured data.
[00052] Referring now to FIG. 9, once a hypocenter 802 has been
determined,
the source parameters can be calculated from the data collected by the sensors
102, 103, 104. Automatic Time-Domain Calculation of Source Parameters for the
Analysis of Induced Seismicity (Urbancic et at., Bulletin of the Seismological
Society of America, Vol. 86, No. 5, pp. 1627-1633, October 1996; the contents
of
which are incorporated by reference into this detailed description) outlines
examples of algorithms used to calculate source parameters like seismic
moment,
energy, corner frequency, and a number of other parameters. Integrals in
windows after the P and S waves are calculated in the time domain and related
to
each of these parameters. In the case of source radius, the corner frequency
902
is related to this parameter like those presented by Tectonic Stress and the
Spectra of Seismic Shear Waves from Earthquakes (Brune, Journal of Geophysical
Research, Vol. 75, No. 26, September 10, 1970) or Spectra of Seismic Radiation
From a Tensile Crack (Walter and Brune, Journal of Geophysical Research, Vol.
98,
No. b3, Pages 4449-4459, March 10 1993).
[00053] Depending on where the corner frequencies of the
measured seismic
events fall with respect to the bandwidth of the sensors 102, 103, 104, the
source
parameters determined from inappropriate sensor types can be biased due to
saturation effects. Accordingly, the sensor data that is used for source
parameter
estimation should be selected from the sensor types having the correct
bandwidth
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CA 02791982 2012-10-05
= or frequency response that is appropriate for the seismic event in order
to provide
accurate source parameter estimation. Certain source parameters require an
estimate of the radiation pattern imposed by the seismic moment tensor to be
determined to correct for the effect of the source mechanism on the amplitude
of
the waveforms. In cases where the moment tensor cannot be determined (due to
unfavourable sensor/event geometry), averaged values of the radiation patterns
may be used as illustrated by Boore and Boatwright (1984, Average body-wave
radiation coefficients, Bulletin of the Seismological Society of America,
Volume 74).
[00054] Referring now to FIG. 10, once the hypocenters 802 have
been
located the moment tensor can be determined by further examining for the
polarities and amplitudes of the different seismic phases for each sensor 102,
103,
104. The data collected by the sensors 102, 103, 104 is analyzed to determine
its
polarity. The S wave motion takes place in a plane perpendicular to the
direction of
propagations. A common convention is to decompose this plane into SH
(horizontal direction) 1006 and SV (perpendicular to SH) 1004. The polarities
are
measured on each channel with sufficient signal-to-noise ratios, and an
uncertainty
to this polarization can be assigned. Generally, different phases will be
polarized
along differing directions, but looking at the onset of these phases, the
first motion
will be defined as being either positive aligned or negative aligned along
these
polarization directions.
[00055] The data collected by the sensors is also analyzed to
determine the
amplitude. The amplitudes are in a window following the P 1002, SV 1004 and SH
1006 waves can be calculated by integrating the waveforms in the frequency
domain. This polarity and amplitude data of these phases of seismic activity
make
up the seismic moment tensor, and is the first step in determining the seismic
moment tensor inversion (SMTI).
[00056] Referring now to FIG. 11A, the velocity model defines
how to project
the amplitude and polarity data as determined in FIG. 10 back to the
hypocentre
802 in order to determine the radiation pattern for P, SV, and SH waves. The
waves reflect and refract from the source 802 to the sensors 102, 103, 104
according to the velocity model, and the measured amplitudes and polarities
are
projected back to the source 802 along these reflecting and refracting
raypaths. In
CA 02791982 2012-10-05
FIG. 11A layered velocity mode11100 includes a plurality of layered velocity
interfaces 1104 and lines 1102 represent ray paths refracting through the
layered
velocity module.
[00057] Referring now to FIG. 11B, the radiation pattern is the projected
P, SV
and SH wave polarities and amplitudes projected back to the source. FIG. 11B
is a
contour plot of the P wave showing positive and negative polarity P waves of
FIG.
mapped on a focal sphere, with the projection of the applicable sensors on the
focal sphere. In particular, a lower hemisphere stereographic projection of
the P
wave radiation pattern is used to display the moment tensor. White areas 1106
on
the plot represent negative polarity P waves; blue (or shaded) areas 1108 on
the
plot represent positive polarity P waves; symbols 1110 represent projection of
the
seonsors 102, 103, 104 on the focal sphere. With a good spatial sampling
around
the event, the measured waveform polarities and amplitudes can determine these
radiation patterns then determine the moment tensor. An algorithm to perform
the moment tensor inversion from waveforms is described in A Fast Evaluation
of
the Seismic Moment Tensor for Induced Seismicity (Trifu et al., Bulletin of
the
Seismological Society of America, 90, 6, pp. 1521-1527, December 2000).
[00058] The moment tensor inversion consists of six parameters, and as such
at least six observations of waveform characteristics need to be made to
calculate
a solution. However, due to the non-uniqueness of waveform characteristics
when
only observed from one azimuth, the stability of the moment tensor inversion
is
improved with increased sampled solid angle of the focal sphere created from
the
projection of the amplitude and polarization directions along the rays back to
the
source. That is, the better the azimuthal coverage of the observation wells,
the
higher degree of the focal sphere will be covered and the more robust the
moment
tensor solution.
[00059] To resolve this potential non-uniqueness the sensors 102, 103, 104
are deployed such that a sufficient degree of azimuthal coverage is achieved.
This
can be accomplished by deploying arrays of sensors 103, 104 in non-producing
or
non-treatment wells 106, deploying sensor arrays 102 on or near the surface,
or
combinations of the above as suited to the local geology. A well 106 providing
coverage for more than one azimuth (e.g., a well with a substantial vertical
and
16
CA 02791982 2012-10-05
=
= substantial horizontal component relative to the surface) could also be
used.
Modeling of the condition numbers of the moment tensor inversion gives an idea
of
where the moment tensors will behave the most stably.
[00060] FIG. 12 provides an example embodiment of how three component
sensors 102, 103, 104 can be used to record microseismic data. Three component
FBA sensors 102 deployed on the surface coupled to the ground, and geophone
sensors 103, 104 are deployed downhole, coupled to the borehole. The sensors
are
manually or electronically oriented as described above in respect of FIGs 4A
and
4B. The sensors 102, 104 detect microseismic activity, which is comprised of
three
components of ground velocity (digitized at digitizers 110) (Action 2106).
This data
is then time synched with GPS time (from GPS devices 112) (Action 2110) and
transmitted to a central processor 108 (Action 2110). Trigger logic such as
the
STA/LTA logic described above in conjunction with FIGs. 5 and 6 is then used
to
identify potential seismic events (Action 2008), and the resulting trigger
logic
processed data 2114 is then further processed as shown in FIG. 13.
[00061] FIG. 13 describes how the source parameters including
the source
radius 2006 are determined from the trigger logic processed data 2114. Once
the
data has been processed using the trigger logic as seen in FIG. 12, travel
times
and directional information for each primary (P) and secondary (S) wave can be
determined for those microseismic events determined by the trigger logic
(Action
2204) . Seismic velocity model information 2210, which provides information
regarding the geographic composition of the reservoir, is then incorporated.
The
objective function for each microseismic event is then determined using the
velocity model, the time picks, and the rotations (hodograms) (Action 2206). A
nonlinear search algorithm is then used to find the hypocenter of the
microseismic
event (Action 2208). This results in a set of source radius parameters 2006
associated with the seismic event including, but not limited to, the estimated
moment (magnitude) and the source radius. The resulting data 2211 of the
nonlinear search are also used in determining the SMTI 2008 as decribed in
further
detail below. The method of Figure 13 corresponds to the activities described
above in respect of Figures 7-10.
[00062] FIG. 14 describes how to determine the SMTI data 2008
from the data
17
CA 02791982 2012-10-05
2211 processed by the nonlinear search algorithm 2208. Each P, SH, and SV
waveform is assigned a polarity as described above in respect of FIG. 10
(Action
2302), and the amplitude of these waveforms are also determined (Action 2304).
This information is correlated with the seismic velocity model 2210 to
determine
how to project the amplitude and polarity data back to the hypocenter to
determine the radiation pattern of the P, SV, and SH waves from the hypocenter
(Action 2308) as described above in respect of Figure 11A. The radiation
patterns
are then used to constrain the moment tensor (Action 2308) as described above
in
respect of Figure 11B, and a condition number is determined for each SMT
inversion to assess the stability of the solution(Action 2310). The well-
conditioned
events are then selected as SMTI data 2008 to be analyzed further.
[00063] Once the fault plane has been determined using one of the two
procedures described above, this information is combined with the source
radius
data to arrive at the Sensor Type Specific Data 2012. The data 2012 includes
information regarding event location, event type, fracture orientations,
spacing,
moment (magnitude) and the source radius.
Differentiation Between Data Streams From Different Sensor Types
[00064] To facilitate an understanding of how different sensor types
measure a
seismic event in an example system 100, Figure 15 shows an example fit of a
Brune spectrum to the signals recorded from (i) FBA sensors 102, (ii) 4.5 Hz
geophone sensors 103 and (iii) 15 Hz geophone sensors 104 for a larger
microseismic event with Mw=2.3. This example features the spectra of the P
waves as seen on all three sensor types all associated with the same
observation
well, with the 4.5 Hz and 15 Hz geophone sensors 103, 104 deployed downhole
and the FBA sensor 102 on the surface, proximal to the well. An attenuation
factor
is applied to all of the spectra based on a model of the seismic attenuation
for both
P and S waves, but the influence of this model is to attenuate the high
frequencies
preferentially and does not affect necessarily the estimates of the long-
period
plateau in this example. Figure 15 illustrates how the short-period sensors
(the
geophone sensors) underestimate the moment magnitudes of the large event -
while the FBA sensor 102 accurately recovers the magnitude of Mw1.8, the other
geophone sensors 104, 104 show saturation around Mw=1.6 and Mw=1.8,
18
CA 02791982 2012-10-05
respectively. This depletion of low frequencies in the geophone records (ii)
and (iii)
can also be observed by the breakdown of the noise signal around the natural
period of the sensor instruments which is not observed at the FBA record (i).
[00065] Figure 16 illustrates an example comparison of the magnitudes of
several seismic events as calculated from data from each sensor type including
FBA sensors 102, 4.5 Hz geophone sensors 103 and 15Hz geophone sensors 104.
As can be seen from Figure 16, there is a definite systematic bias toward
lower
magnitudes for the large events in the dataset (Mwl-Mw2) when only the shorter-
period geophone sensors are used in the calculation. There is a similar bias
towards lower magnitudes when comparing the magnitudes determined from the
15 Hz geophone sensors versus the 4.5 Hz geophone sensors. The median values
of the independent magnitude datasets capture the effect of these biases: 0.74
for
the FBA sensors; 0.65 for the 4.5 Hz geophone sensors; and 0.43 Hz for the 15
Hz
geophone sensors. This comparison of datasets highlights how accurately
capturing the long-period spectrum can mitigate against underestimating the
magnitudes.
[00066] The effects observed in Figure 16 are known as magnitude saturation
- in at least some applications, the natural frequency of the recording
sensors that
causes the calculated magnitudes to saturate.To illustrate this effect, Figure
17
illustrates a magnitude scale saturation for short-period sensors (15hz and
4.5 Hz
geophone sensors 104, 103) as well as for FBS sensors 102 for synthetic
events.
Synthetic source spectra are computed from -Mw2 to Mw3 events in increments of
half magnitude units, with an assumed constant stress drop of 0.1 MPa used in
the
calculations, and the median stress drop of the events estimated from the FBA
sensor data. For events with the same seismic moment, a higher stress drop
event
will have a higher corner frequency and vice-versa. The representation of the
synthetic spectra computed with a higher stress drop in Figure 17 would be
equivalent in displacing the spectra to the right along the x axis and to the
left for
a lower stress drop. Saturation of the magnitude scale occurs when the long-
period spectral plateaus fall completely outside the recording bandwidth.
Accordingly, when the event corner frequency is below the natural frequency of
the
sensor instrument, then magnitude saturation occurs and the source parameters
19
CA 02791982 2012-10-05
determined from such instruments will be inaccurate. In one example
configuration, the magnitudes start to saturate at around Mw 0.4 for the 15 Hz
geophone, around Mw1.4 for the 4.5 Hz geophone sensors, and at about Mw3 for
the FBA sensors (considering the FBA sensors in the described example are
calibrated with a cut-off frequency of 0.7 Hz). The scale will be fully
saturated at
higher magnitudes. In practice, recording and analysis of data would occur
below
these saturation limits.
[00067] For these largest events, the FBA data returns accurate source
parameters showing how longer-period sensors are necessary to adequately
characterize larger-magnitude, induced events. Accordingly, in at least some
applications the system 100 which utilizes a hybrid system of vertical
borehole
arrays of geophone sensors 103, 104 and FBA surface sensors 102 may facilitate
more accurate magnitude estimates across a range of seismic event sizes,
including larger events. The inclusion of longer period sensors such as FBA
sensors
may assist in avoiding the scale saturation effects that bias magnitude
estimates
to lower values for shorter period sensors such as geophones, thereby allowing
the
system 100 to avoid underestimating larger seismic events.
[00068] In some example embodiments, different weighting can be applied to
the data streams received from different sensor types in dependence on the
measured results. For example, if the magnitude for a seismic event is
determined
to be above a predetermined threshold that is associated with magnitude
saturation for the higher frequency geophone sensors 103, 104, the data from
such sensors may be ignored for the a particular seismic event in favour of
the
data from FBA sensors 102. Conversely, for smaller magnitude events, the data
from FBA sensors 102 may be given little or no weight relative to data from
geophone sensors 103, 104, particularly since it would be unlikely that a
coherent
signal will be observed on the surface.
[00069] In some example embodiments, data streams from different sensor
types may be combined to optimize the resulting information - for example, for
a
higher magnitude event, data from the higher frequency geophone sensors 103,
104 may be used to calculate a location for the event, And this location
information combined with signals from the FBA sensors 102 to determine a
CA 02791982 2012-10-05
magnitude for the event. By modeling the spectra for all of the observable
signals
on the different types of sensor instruments, the corner frequency for the
waveforms on each sensor 102, 103, 104 can be determined. If the corner
frequency determination for a lower frequency sensor is sufficiently near or
below
the low frequency corner of the bandwidth of the higher frequency sensor, then
the higher frequency sensor will be saturated and the resulting source
parameter
calculations from that instrument will be biased. Only the lower frequency
sensor
instrumentation will return accurate source parameters in this case, and as
such
the magnitudes, radiated energies, corner frequencies, stress drops and other
such source parameters will be calculated using only the data streams acquired
from the lower frequency sensors.
[00070] In some example systems, the sensors may include just two types of
sensors - for example FBA sensors and 15Hz geophone sensors. In some
examples, the system may include more than three types or classes or of
sensors.
Furthermore, the frequency response ranges for the sensors could be different
than that stated above. By way of non-limiting example, surface or near-
surface
sensors 102 could have a low frequency cutoff of anywhere from 0Hz to 3Hz,
subsurface geophone sensors 103 could have a low frequency cutoff of anywhere
from 1Hz to 15Hz and; geophone sensors 104 could have a low frequency cutoff
of
anywhere from 10Hz to 30Hz.
[00071] All numeric examples and numeric ranges specified herein in respect
of numbers and location of sensors and sensor frequencies and periods are
illustrative examples - other numeric values and numeric ranges may be used as
appropriate. While embodiments of the present invention have been shown and
described herein, it will be obvious that each such embodiment is provided by
way
of example only. Numerous variations, changes, and substitutions will occur to
those skilled in the art without departing from the invention disclosed.
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