Semiautomated relative picking of microseismic events
Daniel Raymer*, Schlumberger Cambridge Research, James Rutledge, Los Alamos National Laboratory, and
Paul Jaques, Geoware Ltd.
Summary
A high-precision semiautomated relative picking
methodology is developed and applied to a dataset of
microseismic events from hydraulic fracturing. Comparison
with locations from manual high-precision picking is made
and found to give similar results, but with significantly
faster processing times. Applying relative picking
techniques improves consistency of picking between
events, therefore, improving the relative locations. This
results in improved resolution of the layers and faults
where the microseismic events are occurring, making
subsequent interpretation easier.
Introduction
When trying to interpret microseismic event locations, it is
desirable to have the highest-resolution event locations
possible. Multievent processing allows the information that
exists in having multiple, similar-waveform events to be
exploited to improve processing. Namely, the use of
higher-precision picking techniques that consider similarity
between events provides a method to improve the relative
locations.
Manual relative picking techniques (Rutledge and Phillips,
2003) were shown to provide very good results, but are
rather time consuming. Fully automated multievent, highprecision repicking methods such as Rowe et al. (2002)
require significant time to get parameters initialized. In this
work, an automation of the manual approach is developed
that retains a manual quality control (QC) role. This
provides a quick way to process events with little initial set
up while maintaining the understanding of the data that is
gained by going through the events manually.
Method
The method is based on using individual events or stack of
events that have been initially picked with high precision as
reference events. Picks on other events are then obtained
with precise times relative to the arrival times of the
reference event. The workflow is summarized in Figure 1.
Event Selection – Given a population of microseismic
events, the first stage of the process is selection of events to
work on. In the dataset described in this work, we worked
on a subset of events with high signal-to-noise ratio (SNR),
which typically included 10 – 15% of automatically located
events. Focusing on only “good” events helps to reveal
structure in the events. No matter how well you pick, the
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Event Selection
Depth selection
Sort by Location
Select reference event
Manual pick reference event
Data Preparation
• Interpolate
• Rotate into P, SH and SV
• Apply filtering
Predicted Picks
• Reference event pick
• Initial or predicted picks
Event similarity
• Extract data window
• Cross correlate & find lag
• Use lag to update pick
Repeat for each trace
QC
• Cross correlation threshold
• Lag variation tolerance
• Remove bad picks
Repeat for each trace
Next Event/Level
Figure 1: Example workflow for semiautomated relative picking.
User interaction mainly at the QC stage. This example could refer
to picking the same trace from multiple events or all levels from
a single event.
lower SNR events will have greater location error and,
therefore, are likely to reduce resolution of structures in the
data. The initial locations show a single linear feature with
the monitoring well orthogonal to the feature. In a situation
like this, the distance to event does not vary too much, so
the main variation of signal characteristic will result from
differences in depth and source characteristics. Previous
work on this dataset showed that the source-to-receiver
azimuth crosses a nodal plane for P-waves at the closest
point of fracture to the monitoring well (Fischer et al.,
2008). As we want to try and maximize the similarity of
events, the dataset is divided into different depth groupings
based on any grouping seen in the initial locations. The
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Relative picking of microseismic events
events are then sorted by horizontal location, in this case
from west to east. From the events to the west, a suitable
strong and typical event is selected as an initial reference
event. The reference event will then be picked manually on
an easily identified feature, such as first-arrival peak
amplitude, for correlation with lower SNR events.
An alternative to the spatial selection of events is to use
their waveform similarity. This can be done by carrying out
a full multiplet analysis and process groups of similar
events. Another option is to crosscorrelate a reference event
with all other events and then process the events starting
with the most similar. Once similarity is no longer good
enough for consistent picking, a new reference event can be
identified and used.
For real-time monitoring or ongoing reservoir monitoring,
each new event could be compared to a library of master
events; the master event with greatest correlation can then
be used as reference event for precise relative picking. If
the new event does not match any existing master events,
and is itself a high SNR event, then it can be used as a new
master event.
Data preparation – Before processing the data, it is upsampled by a factor of four using Fourier interpolation,
thereby removing waveform differences due to coarse
sampling rates (in this case, increasing sampling from 1
kHz to 4 kHz). The three-component data are rotated into
P, SH, and SV components using the arrival raypath from,
in our example, the reference event location, but the initial
location or another specified location could be used.
Filtering is also applied at this stage. For these data, a 40Hz and 4-pole high-pass Butterworth filter was applied.
Picking – The P and S phases of the reference events are
picked on the first peaks or troughs, providing picks that
are less influenced by the signal-to-noise ratio than onset
picking. Initial picks are then obtained for the event to be
a)
processed; if picks already exist, these are used; if at least
one pick exists, then the others are predicted using the
relative difference between pick times in the reference
event and the average lag between picks on the two events.
Event similarity and pick adjustment – The next
processing stage is to use the similarity between events to
make sure an event is picked in a consistent manner relative
to the reference event. In a manual approach, this is
achieved using visual correlation of the signals. Here, we
perform crosscorrelation of the waveform for the relevant
components from a window about the specified pick. The
point of maximum correlation identifies the lag between
current pick and an updated pick that is consistent with the
pick on the reference event. Initially, a large window is
used that covers the entire arrival and a period with no
signal before the pick. This will give an approximate
location of the required pick (the correct peak or trough),
but the differences between events gives a correlation that
best fits the whole arrival rather than the peak or trough
that has been picked. A second crosscorrelation is
performed using a smaller window about the new pick; this
then provides a pick whose position within the peak or
trough is consistent to the reference event. Manual picking
allows sub-sample picking by means of visual interpolation
to pick the peak or trough. The automatic process can only
pick on a sample; a final refinement of the picking is to
subsample further on the peak/trough then select the
appropriate sample for the final pick.
Quality Control – A number of quality control methods are
applied to ensure that we get the correct picks. The
similarity of the chosen phase for each level with that of the
reference event can be evaluated by the value of the
crosscorrelation function at the point of maximum
correlation. A value of 1 indicates perfect correlation. A
threshold is set for minimum acceptable correlation, and
below this, the pick will not be accepted. An additional QC
method used is based on the lag between pick on the
b)
c)
Figure 2: Figures showing SH traces for eight levels of a reference event and the event being processed (a) S-wave picks for the reference event
picked on the initial peak. (b) SH traces for event being processed overlaid in red by the reference event. (c) same as (b) but with traces aligned
on new picks. Time is on the horizontal axis in milliseconds and amplitude normalised per trace on vertical. Below each trace, there is a black +
indicating time of initial picks and a blue triangle for the new precise picks. Above each trace, the two crosscorrelation windows are shown; the
orange is the 1st stage crosscorrelation and the blue the 2nd stage. The bars on the left show a crosscorrelation coefficient-based quality control
measure; the vertical line in the box is the threshold, picks that have a bar extending beyond the threshold will be rejected (bottom level in this
example).
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Main Menu
Relative picking of microseismic events
reference event and on the current event for the specific
trace. The lag found for P and S picks across all levels
should be similar for the event; a difference in event
location produces some variation in lag. The lag for the
particular pick is compared to the median value on all
levels; and if it differs from this by more than a specified
amount, it is rejected.
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Figure 3: Plan view of Stage 3 events comparing intial automatic
event locations, locations from manual relative picking, and
locations from semiautomated relative picking
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Workflow implementation – The visualization of the data
plays an important part in manual QC of the data. The
workflow was implemented in an existing processing
package in a function that allows loading and visualizing a
cube of data. The traces from multiple events are loaded
and the user can view either many traces from a single
event or the same trace from many events. The initial
workflow loop is applied automatically when the event is
loaded. If automatic high-precision picking has worked as
required for an event, then as little user interaction as
possible (a single click) will save the processing and allow
the user to move to the next event. To determine if the
automated process was successful, the user must quickly
examine waveform displays as can be seen in Figure 2. The
reference event traces are overlaid and aligned with the
current event traces for visual confirmation of the
automated picking. The QC parameter based on the
crosscorrelation is displayed by each trace as a bar along
with an indication of the failure threshold for this
parameter. If too many traces have failed the specified
criteria, or if the user is not satisfied with the picks, then the
processing can be repeated with different parameters.
Levels that fail to obtain an acceptable pick on initial
processing may get one on a second pass, as the initial
predicted picks that are used to select the waveform
correlation window will differ from the first pass due the
contribution from picks on levels that were acceptable. The
crosscorrelation and pick adjustment can also be applied
again using different window selection parameters and,
Semi Automatic Relative
Picking Locations
Initial Automatic Locations
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The method is applied to a hydraulic fracturing dataset
from the Canyon Sands formation in west Texas (Fischer et
al., 2008). Events from two stages of fracturing have
previously been picked using the manual high-precision
picking method used in Rutledge and Philips (2003) and
allow for a suitable comparison to this methodology. The
same velocity model and tool orientations are used.
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We found that when signal characteristics for adjacent
sources are fairly distinct and stable, the most efficient
method of picking was to examine all levels for one event
at a time. However, when signal characteristics varied more
significantly between events, consistent picking can be
achieved more successfully while viewing the same level
from many spatially sequenced events.
Example
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after a few processing iterations, can provide satisfactory
picks.
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Figure 4: Plan views of fracturing stages 1 to 5 for the initial
automatic locations (left) and semiautomatic relative picking
locations (right). Stage 1 is at the top down to Stage 5 at the
bottom. Yellow squares indicate perforation locations.
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Relative picking of microseismic events
Comparison of results shows a very similar level of
improvement. Figure 3 shows a plan view of the 236
manually picked locations for Stage 3 and the 236 highest
SNR events from automatic processing and application of
semiautomated relative picking methodology described
here. Similar results with significant improvement in
linearity of fracture are obtained in the manual and
semiautomated approaches, but with at least a four-fold
increase in processing speed due to automation.
The technique has been applied to five stages of the
fracturing process. Initial automatic processing provided a
total of 5268 locations for these stages, and the relative
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picking method was applied to 742 of these with high SNR.
Figures 4 and 5 show the results compared to the same 742
initial automated locations. The plan views of the five
stages in Figure 4 show the improvement in resolution of
the relative picked locations. That is, the scatter of events is
reduced, resulting in a narrower fracture width and a more
linear pattern of events that is especially evident on the
most eastward extent of the seismicity. Figure 5 has two
plots comparing side views of the events. The vertical
resolution of locations is improved revealing finer structure
and more distinct vertical features that are not seen in the
initial locations. For example, the initial locations only
suggest three distinct layers in the Stage 2 events, but the
relative picking locations reveal four distinct layers with a
new layer being resolved at a depth of about 1180 m.
Conclusions
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A method for semiautomatic relative picking has been
shown to give locations that reveal structure similar to fully
manual high-precision picking while reducing processing
time significantly.
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Even a method such as this requires the initial reference
events to be picked well, that is, the phases that correspond
to those in forward modeling must be picked carefully.
Problems arise with emergent arrivals, head waves, and
anywhere there is not a clear and distinctive first peak or
trough that is consistent from event to event.
The method described provides consistent picks that reduce
relative location errors and improves the fracture images
revealed by microseismicity. By having precise picks, you
are in a good position to try to remove other factors
effecting the locations. For example, mismatches between
velocity model and reality can be compensated for using
techniques such as joint hypocenter determination to obtain
suitable station corrections.
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A semiautomated method such as described here may not
be ideal for rapidly processing large volumes of data, but it
provides a simple method for improving picks and
locations of events in areas of interest without requiring
much initial preparation. The method allows the manual
processor to observe the data characteristics and factors that
may be issues in processing and QC of the data.
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Acknowledgements
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Figure 5: Side view of Stages 1 to 5 for initial automatic locations
(top) and semiautomatic relative picking locations (bottom). View
is looking north. Events are colored by stage number.
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We thank the Dominion Exploration Company for
releasing the data for this study. We thank the European
Union for funding the IMAGES Transfer of Knowledge
project (MTKI-CT-2004-517242).
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