Good morning, everybody. Today’s quick announcement.
Next week’s seminar, I’ll do a talk. July the 5th, this auditorium.
And for today, we introduce Martin Schoenball from Stanford University.
This is – he’s a second postdoc. Previously, he’s done his
bachelor’s degree in physics at the University of Dresden and
his master’s and Ph.D. in geophysics at the University of Karlsruhe.
And then he did his first postdoc here at the USGS called GMAC.
And his background – he’s done earthquake processes,
induced seismicity, geothermal systems, borehole geomechanics,
and as well, hydromechanics. For further ado, Martin.
- Good morning, everyone. You can hear me? Yeah.
Yeah, thank you very much for coming here, and thank you for
giving me the opportunity to present you some of my research
that I’ve been doing the last year together with Bill Ellsworth.
So I was – I will talk today about the spatiotemporal evolution
of induced seismicity clusters in Oklahoma and southern Kansas.
Fluid-induced seismicity has been observed in a wide range of settings,
ranging from reservoir impoundment to geothermal systems, hydraulic fracturing,
and injection of CO2 and wastewater disposal, but also natural causes
such as rainfall events, snow melt and volcanic swarms.
And basically, all of these observations of fluid-induced
seismicity can be explained by one of – both of two mechanisms.
The first one is the direct effect of the elevated fluid pressure,
which decreases the effective stress on a fault.
It unclamps the fault but maintains the shear stress.
So in this case – [clears throat] sorry – the Mohr’s circle moves to the left,
touching the failure envelope, and we get an earthquake.
And the second effect is that of changes to the solid stress, either
communicated by poor elastic stressing or by changes in the gravitational load.
And in this case, the Mohr’s circle changes its size
and may touch the failure envelope this way.
If we look at the earthquake rate in the United States in the last couple years,
we’ve noticed this uptick in 2009.
And in 2013, the seismicity rate basically exploded.
And most of this seismicity has been observed in the
Oklahoma and Kansas area. So what’s been going on there?
You all are very familiar with this. So with the advent of hydraulic
fracturing or improvement of hydraulic fracturing, formations that
were previously uneconomical are now being produced economically.
But these formations have a huge water cut,
so the produced fluid is mostly water and contains a little bit of oil.
And this water is considered saltwater, so it has to be disposed of.
And in Oklahoma and Kansas, it is disposed of in the Arbuckle Formation,
which is just overlying the crystalline basement.
The Arbuckle Formation typically lies at depths of about 1 to 2 kilometers.
And it’s a naturally under-pressured formation, very permeable, so injection
pressures are actually very small. But the disposal volumes are huge.
So we get a very large area that is perturbed.
In this study by Katie Keranen and co-workers, they showed that
seismicity occurred at distances greater than 20 kilometers from
injection wells and modeled pressure changes at
hypocentral locations on the order of a tenth of a megapascal.
This other study from Nick van der Elst and co-workers showed that the area is
susceptible to remote dynamic triggering. So stress changes on the order of
kilopascals from remote earthquake waves trigger earthquakes in this case.
In this study, I show that earthquakes occur typically about 4 kilometers below
the injection horizon, so again, this is where the pressure perturbation is
subsided quite substantially, just because we are far away from the injection.
And this recent study by Andy Barbour and co-workers showed – they were
looking at the Pawnee earthquake and made a hydromechanical model for it.
And they showed that the stress changes at hypocentral depth
are again on the order of tens of kilopascals.
So all of these observations tell us that these earthquakes that we see,
widespread in the region of about 200 kilometers,
are triggered by very low stress perturbations.
Although now the earthquake rate is in decline in Oklahoma,
there is still a lot of challenges ahead to understand
what’s happening there and to prevent similar cases in the future.
The big looming question, of course, is why are we seeing this uptick of –
have we been seeing this uptick in seismicity in Oklahoma
and not in other areas of significant wastewater disposal?
And the operators want to know,
can we mitigate induced seismicity but still dispose of wastewater?
And why, in particular, the – interested and excited about
this injection experiment is that it provides us
great opportunities to learn about the natural system as well.
So what can we learn about the state of stress
in these dormant faults that are probably everywhere in the basement?
How are these basement faults
hydraulically connected to the sedimentary overburden?
And how do fluid-driven earthquake swarms work?
And this would be also interesting to understand volcanic systems
where we do see these fluid-driven earthquake swarms but don’t really
know what the source, or how to characterize the source, of these swarms.
And then lastly, I’m interested in understanding the role of
the various triggering processes that go into the earthquake nucleation.
This is the current earthquake map of the area.
So the black line here shows the Kansas-Oklahoma state border.
The thin lines are the counties.
And dots, of course, earthquakes color-coded by time.
Early earthquakes – I will just use the same color bar for my talk for time.
Early earthquakes are in blue, and then later, more recent, earthquakes in yellow.
And you basically see that we have this carpet of earthquakes
spanning about 200 kilometers in the region.
If we look a little bit closer, we see that many of these earthquakes
are actually occurring in distinct clusters.
So with the study, I wanted to understand, what are these clusters?
What are their driving mechanisms? And what is their collective behavior?
Do they follow some empirical relations that we can use to describe them?
And also, what is their relation to natural seismicity in the area?
And by analyzing all of these clusters together, I was – I was hoping
to use some stacking techniques to inform the temporal evolution
of these sequences and also the evolution of the largest earthquakes –
those earthquakes which do drive the seismic hazard in the region.
So my take-home points for today are the following.
These earthquake sequences are initiated by anthropogenic stressing,
but they seem to sustain rupture through earthquake interactions.
Most of these sequences initiate months before they rise to peak activity.
And the largest events result from stronger perturbations
but not from re-activating more fresh asperities.
But to get to these points, I first had to do an earthquake relocation study.
Since what I wanted to do – the earthquake catalog
was just not suitable for what I planned.
And so what we benefited from is that data from 40 three-component
stations that were operated by the industry were made available to us.
Ten of those stations are proprietary, and they are – they were provided
by sponsors of SCITS – the Stanford Center for
Induced and Triggered Seismicity.
And 30 other stations are now available on IRIS for a timespan
of two and a half years, I think. And it’s the network NX.
And so these 30 stations are in this area here. Ten other stations are in this area.
So with these stations, we have a relatively even
station coverage of stations basing on the order of 25 kilometers
or better for the whole region, starting in about 2013.
So for this relocation procedure, I used the ANSS catalog and its phase arrivals.
I also used the detections of the Oklahoma Geological Survey catalog.
I did not detect any other earthquakes. I didn’t attempt to.
And then, using an automatic picker for P and S wave arrivals, I queried those
40 stations. I used a very simple 1D velocity model, which is closely
modeled after the one that the Oklahoma Geological Survey is using.
And then I’m using HypoInverse for absolute locations, and I refined those
locations using the double-difference method in the software HypoDD.
And here I’m using differential times from the catalog picks, and as well as
waveform cross-correlation where I get waveforms to cross-correlate.
And overall, we covered the period of May 2013, which is the time
when the 30 stations in this area came online, until November last year.
So with this, we covered the major uptick after the
first uptick until the beginning of the decline in last year.
And, in total, these are 18,600 earthquakes now.
So, again, this is the catalog that we started with.
After relocating, all the seismicity collapses quite a bit,
and we see some structural grain in the area.
We see that these faults – or these earthquakes tend to
align in linear features that we can interpret as faults.
But to really show the quality of the data, we have to zoom in.
So let’s go into the Kansas part where we do have the best network,
of course, because we have the closest station spacing there.
But it’s still sort of representative of what we have in the rest of the area.
And we see that earthquakes here align in these pretty linear features
that can be interpreted as the vertical faults.
But then we still have some cloudy structure in some other areas.
So we can zoom in again. And if you look at it in 3D,
we see that there are – in this case, we have two beautifully resolved
normal faults in an abutting relationship.
Here I show also the comparison of the focal mechanisms from Bob Hermann
from St. Louis University, and they are in very good agreement
with strike and dip of what we resolved just from the hypocenters here.
So that was very encouraging for us.
The location uncertainties now are on the order of a couple hundred meters
in the horizontal and typically better than 1 kilometer in the vertical.
And the relative locations are about one order of magnitude better than that.
In this slide, I would like to show you a movie of a subset of the earthquakes.
So in yellow, I show injection wells, and the symbol size scales
with the injection volume. And it will change with time.
And then earthquakes will start to appear in red and then fade to – fade to gray.
All right.
So now, in about summer of 2013, you see that earthquakes start to
appear basically everywhere in the region.
We see that some wells are changing their injection schedule a little bit.
We have some sequences that evolve gradually in all kinds of
directions with several faults being activated here.
We have some sequences that are pretty slow, developing over a long time.
And we have this really nice, intriguing alignment here
that develops into the Fairview sequence.
And now, in 2016, we have daily injection data.
And you see just how these wells over here –
you don’t see this – how they are pulsating.
Let’s try this again.
Doesn’t work.
So just play it again because there’s so much to see here.
[ Silence ]
So now we start to see this alignment over here which turns into Fairview.
[ Silence ]
And then notice the pulsation of the injections.
So this injection schedule is actually varying on a daily basis, or probably
even on an hourly basis by orders of magnitude from zero to full injection.
And we don’t really understand yet what this pulsation, or these rapid
changes of injection rate, actually mean for the mechanical processes there.
If we look at depth of these earthquakes –
I showed this slide – this figure before.
Here I plot depth relative to the top of the basement that
we get from well bore locks.
And just as a reminder, the Arbuckle Formation is the one that –
where the fluid injection is occurring. And most of these earthquakes
occurred about 4 kilometers below this top of the basement.
And this is still kind of a mystery to me why this is occurring.
And since you are going to ask anyway, my speculation for this is that it
seems to be kind of a sweet spot between the
brittleness of the crust and how much stress is stored at greater depth.
But then, at shallower depth, we also have higher pressure changes.
So my guess is that, at 4-kilometer depth,
we are just somehow in a sweet spot, and I think this is also supported by
laboratory studies from Oklahoma University – Brett Carpenter.
All right, so now we have earthquakes relocated.
Now we want to study the sequences. So the first step is to
identify those sequences here. For that, I’m using the
DBSCAN algorithm, which recursively identifies clusters.
It only requires two input parameters, which are the minimum number of points
that are required to call it a cluster – 10 in my case –
and then a search radius – 700 meters. These 700 meters are
kind of a magic number. But it seems to work quite nicely to pick
out those clusters that we see readily by eye and divide – pick up these clusters.
So on the left-hand side, this is one of those cluster candidates.
Then in the second step, I manually review those clusters
in a 3D view, and I interpret faults here.
So in this case I showed you before, we have these two beautifully resolved
faults and a couple scattered earthquakes
around that we cannot attribute to a fault.
And then, in the second step, we can take hypocenters from each fault and
compute the covariance matrix, and the eigenvectors give us a strike and dip.
And we did here in a study from Fred on the Pawnee earthquake.
Okay, to summarize the relocation procedure,
this is what we started out with.
By adding much more data, we have much better control of the
absolute and relative locations now. We identified sequences and clusters.
We can now compare these alignments also of the Oklahoma fault map,
and we basically see no agreement. Only does – I only know one case
where we actually have earthquakes on a previously known fault strand.
Importantly, also the default trends that we see already just by looking at
these lineaments here are not apparent in the Oklahoma fault map.
So we do have probably a biased view from both ends as to the
orientation or preferential orientation of faults that we have in the basement.
We do see a pretty good agreement with these focal mechanisms that
are provided by Bob Herrmann. So that was very encouraging for us.
And the catalog will be available very soon.
We have a paper in press in SRL. And if you’re interested in
working with this data set, I can also just send you the data set right now.
Overall, we identified 300 faults ranging from about a couple hundred
meters to more than 10 kilometers in length for –
and with strikes that are clearly preferentially aligned.
I just recently looked at even smaller clusters with less than 10 earthquakes,
and it seems to be even a couple hundred – maybe 200 more
that are convincingly elongated to be called a fault, but I don’t show this here.
So we can compare the fault strikes now
with what we know about the stress field.
So on the left-hand side, I show a figure by Alt and Zoback,
who looked at the stress field in the area, and their main conclusion is that
the stress field is pretty constant throughout the area, and the maximum
horizontal stress is oriented at 85 degrees east of north.
And the fault strikes that we observe are pretty much consistent
with what we would expect in a strike-slip faulting
regime with this kind of stress orientation.
All right. Let’s have a look at how these sequences evolve.
At first, I was interested in migration patterns.
So I was looking – I was hoping to find horizontal migration patterns
that would show up as some coherent domains that
several clusters migrate in the same direction.
To cut a long story short, I didn’t find any of these.
Maybe I have to look a little bit – a little bit harder, but for now, I stop there.
I did, however, see some systematics in the vertical migration.
This is what I would like to show you here.
So this is an example from the Woodward sequence in westernmost –
the westernmost area of the crisis area.
And, again, I color-code earthquakes by time beginning in blue.
The sequence systematically migrates to the left in this case.
If we look at it in depth view, we see, of course, the same behavior.
But then there’s a second migration direction overlaying.
As a particular strike of a fault was activated, later earthquakes
appear to be at greater depth. And then later, another strike
was activated, and seismicity migrates downward.
And in the second half of the sequence,
we saw reactivation of a shallow part of this fault strand.
But once this part was activated, again, seismicity migrated downward here.
So this is already a relatively complex behavior to capture with a simple
algorithm that tries to quantify diverging migrations either up or down.
Because we have these shallow earthquakes in the end,
but still this downward migration, an algorithm that would compare
just the hypocentral depth of earthquakes of the first and second half
would call this sequence migrating upward, which it clearly does not.
So this is why I opted for a manual interpretation of this vertical migration.
And in this manual interpretation, I also tried to differentiate
between individual faults in single clusters.
So some of these clusters have several faults being activated in succession.
So in this case, I interpret first the vertical migration
along this fault and then on this fault here.
And the result of this interpretation is that the first faults that are
being activated tend to show a preference for downward migration,
which suggests that their driving source, it’s above these faults.
But for second faults that are being activated, I do not see
this preferential migration direction downward.
So this suggests that, for second faults being activated,
a different process is coming to play, such as a fault interaction that would
maybe not have this preferential vertical migration direction imposed.
If we look at this sequence that I showed you before,
it ruptured with a couple of magnitude 3 earthquakes
in the first three months until it hit this second fault here.
And then, once it hit this fault, seismicity evolved in a burst,
reaching magnitude 4.3 earthquake and the highest activity.
So here we have a delay from the initiation –
the first recorded earthquake – until the maximum activity
we observed in this sequence of about three months.
And it turns out that this delay is actually very characteristic
of earthquakes in the area. Only about a quarter of sequences
reached their maximum activity within the first month of initiation.
And many sequences reached their maximum activities, or the maximum
earthquake rate, delayed by several months or even longer than a year.
So here we do have some kind of warning time
even at this relatively coarse completeness monitoring that we had.
Then we might be interested in looking when the largest earthquakes
occur and see if there’s a development there or if these –
if the largest events occur at a random time.
So they’re just a random Gutenberg-Richter draw.
And I looked at it in two ways here for all sequences that had
at least 10 earthquakes, which is the light gray data set here.
And it looks like, in this cumulative density plot,
that it follows very closely aligned and would correspond
to a random occurrence of the largest earthquakes.
So this would follow a uniform distribution,
and the Kolmogorov-Smirnov test actually does not reject a null hypothesis
that the largest events are uniformly distributed
over time or along their position.
If we only look at the larger sequences that have at least 30 earthquakes,
there seems to be some tendency for the largest events
to occur during the middle of the sequences.
And in this case, the Kolmogorov- Smirnov test actually does reject
the null hypothesis that these events are randomly distributed.
So we can continue analyzing earthquake rates and how they
develop towards the main shock. So if we consider, for each cluster,
the largest event to be the main shock and all the events that occurred before,
we can call them pre-shocks, and then all the events after
the largest event are aftershocks.
So, in this case, I stacked earthquake rates along the
largest event at zero and any earthquakes at negative time are pre-shocks.
Any events at positive time are aftershocks.
If I did this – or, if I do this, we get these kind of plots.
So in this case, the main shock would be in between the two plots here.
And let’s look first at the aftershocks. We do see, in this log-log plot
of the number and time that we have this kind of linear decay,
which means that we can approximate the earthquake rate
after the main shock by an Omori-type decay.
The P value, which describes the
decay behavior, has a relatively low value of about 0.6.
So typical single main shock-aftershock sequences show larger values of
about 0.8 to 1.2 or something. So these are relatively low values.
But there has been also other studies where this kind of stacking technique
was used that show similarly small values here.
If we look at the foreshocks on the left – or, pre-shocks on the left side,
we first notice that we have a lot of pre-shocks.
In fact, cutting the catalog in two parts of pre-shocks
and aftershocks basically cuts the catalog in half.
We have the same number of pre-shocks as aftershocks.
We still see kind of this increasing behavior that we can describe with –
sort of with a reverse Omori law.
But it doesn’t follow this linear trend as closely.
Instead, we have this – appear to have this jump of
activity a month before the main shock is occurring.
And then pre-shock activity seems to be on a relatively constant high level.
[ Silence ]
Okay, now we may be interested in how do – or, why do these
largest earthquakes occur? Do they occur because we are –
continue pumping after the sequence has been initiated?
Or do they occur because we reactivate fresh fault asperities
that have not been activated before, and we basically just have a larger chance of
finding a nice asperity that is well-suited for rupturing in a big earthquake?
And to follow up on this question, I was studying each sequence
and then looking at when the currently largest earthquake
occurred and the extent of these sequences.
So in this case here, for the Woodward sequence,
the second earthquake occurs 600 meters away from the
first earthquake, and the larger of the two is a magnitude 2.8.
And then the next-largest earthquake was a 3.5
once the sequence spread to 2-1/2 kilometers.
Then we get a 3.7 once we have 4 kilometers reactivated,
and a 3.8 once the whole sequence is 8 kilometers long.
The Fairview sequence evolved quite differently here.
So here we observe a 3.1 earthquake already
at a distance of 10 – or, spread out over 10 kilometers.
With the same extent, we get a 3.4.
Just growing the sequence a little bit gives us a 4.3.
Still kind of in the center of what’s been activated before.
Same extent, we get a 4.4.
And then almost the same extent, we culminate in a 5.1 here.
So these are pretty different behaviors.
And I plot them in this graph here of cluster length that has been
reactivated and the maximum magnitude that has been observed.
And for Woodward, we get this upward trajectory here.
And for Fairview, we get this sort of horizontal –
or, yeah, constant trajectory there. So the Fairview sequence basically
increased its effective stress drop throughout the sequence,
while the Woodward sequence decreased its effective stress drop.
So it’s getting – well, less energetic, if you want.
And we can interpret these kind of trajectories.
The steeper trajectories as resulting from – the largest events result
from reactivating longer fault strands, while the horizontal trajectories
would result from stronger perturbation of a continued injection.
If I do this for all of these studies – all of these sequences,
we get this pretty messy graph here.
But on the right-hand side, I show this simplified in a histogram.
And we see that we have this preference for sequences to occur,
or to show the largest events because of stronger perturbation.
So we have these horizontal trajectories here and only
a smaller set that is actually resulting from longer reactivation.
If we look at the largest earthquakes, so earthquakes greater than 4, or even
greater than 4-1/2, this preference for the stronger perturbation increases.
So there’s, in fact, no earthquake larger than 4.5 that was occurring
because we activated a new fault strand or new part of a fault.
All right.
Then I was interested in quantifying the earthquake interactions.
And I’m using – I’m trying to quantify this using temporal clustering.
Temporal clustering is a fundamental property of earthquakes
as we see in mainshock-aftershock sequences, for instance.
So if we have a catalog that does not show temporal clustering,
this can be interpreted as – events can be interpreted as
independent background events with no interaction.
But if there is temporal clustering occurring –
so many events occur at a small section of time,
and then we have large stretches in time where you don’t. So if an earthquake –
this means that earthquake interactions are important,
and we do get this temporal clustering.
And to quantify this temporal clustering for these relatively small subsets here,
I’m using the coefficient of variation, which is – of the inter-event times,
which is defined as the standard deviation
of inter-event times divided by the mean.
And this quantity has a value of about 1 for
uniform distributed occurrence times, so a Poissonian process.
And it takes larger values for sequences where we do have temporal clustering,
such as a mainshock-aftershock sequence that I use as an example here.
If I do this for the sequences, we basically get the full spectrum
from sequences that show no temporal clustering –
so they behave as background events – as independent background events.
And then many sequences which have
a pretty strong temporal clustering component.
So these sequences seem to be strongly influenced by earthquake interactions.
And I’ll come back to this in just a bit.
But now I would like to talk briefly about activity fronts.
So the current paradigm with current understanding for these
sequences is that they are triggered by pore pressure perturbations
which propagate by a diffusive process.
And if so, we can describe the outer envelope of quakes as a distance or of
a time by this relationship here – square root of 4-pi times diffusivity times time.
And we can use this to estimate diffusivity in the underground.
And I did this for the larger sequences here,
and we get diffusivity values that span about three orders of magnitude.
And they are sort of in agreement with diffusivity values
that have been obtained in this kind of manner from reservoir impoundment –
typically larger values or volcanic swarms.
In this case, I should compare with Dave Shelley’s work here.
And then also lower values that we see in southern California,
tectonic swarms, and even smaller values than this.
And so here I plot – a cross-plot of the diffusivity on the right-hand side,
and on the top, the temporal clustering,
quantified by the coefficient of variation.
And with this figure, I want to suggest that there might be
a correlation between the two in the way that sequences that
have no temporal clustering, they do seem to propagate slower.
So their seismic diffusivity, as I call it now, is relatively small.
And then sequences that have strong temporal clustering
seem to propagate also much faster.
So in this case, the seismic diffusivity that I get is larger.
And I would suggest that these sequences are propagating
through a stress transfer, while the slower sequences are
propagating through slower processes such as pore pressure diffusion.
If we look closely, we also see, in some sequences,
what can be called a cessation front. So we have – from the same origin,
we have a spreading, so of quiescence, behind which
almost no earthquakes occur.
So this corresponds to some kind of relaxation process here.
This is another example where – see a very well-developed cessation front.
But in this case, a triggering front is actually not very well-developed,
where it’s actually kind of interrupted by this burst of activity
in the middle of the sequence where seismicity ruptured outward
by a couple kilometers from the origin in a very short time.
But again, the cessation front was pretty striking to me.
So I had a closer look onto this example here.
So here are a couple panels. In this map view, I show the
earthquakes like dots and the crosses correspond to injection wells
color-coded by the distance from the first earthquake
that we record in this sequence.
So wells that are within 15 kilometers
of the origin have this kind of injection schedule here.
In red, I show the injection schedule for wells within 6 kilometers.
And then, in green, it’s just this one single well which is less than
a kilometer from what we determined as the origin of this sequence here.
And if we zoom into this gray area on the right and look at –
so this is the same timeframe now – and compare the occurrence of
earthquakes with the injection schedule for this one particular well which sits
close at the origin, we see a pretty good agreement with the
ramp-up of this injection well to much higher values than it was used before
and the occurrence of these earthquakes.
And in this case, the operator realized that they have a well which is
very well-correlated with seismicity, so they decided to shut it down.
And, as an effect, we do have this declining pressure now spreading
from this injection well. And as a – and we see this cessation front.
So just to explain it a little further, if we start injecting – and in this cartoon,
the black line represents the shear stress on a fault,
and the blue line is the pore pressure perturbation.
And in cases where the pore pressure perturbation crosses,
or touches, the residential strength of this fault, we do get earthquakes.
If you continue injecting, we continue driving seismicity outward,
but we still get earthquakes behind the triggering front
because we are still increasing pressure there.
However, if we stop injecting, we still get the triggering front,
and then we stop, so we are still propagating the triggering front outward.
But behind, the pressure is actually declining, so we do not get any more
earthquakes there. This is why we do observe this cessation front here.
So these observations together let me conclude that the
triggering front actually characterizes the seismogenic process.
So it is a convolution of the pore pressure diffusion
and other processes that come into play,
such as static and dynamic stress transfer or other kind of earthquake interactions.
And the cessation front actually characterizes hydraulic diffusivity since
it shows us when the pore pressure falls behind its previous maximum value.
And we see that in this kind of example here
that it spreads over periods longer than a year.
What is also interesting to notice that the diffusivity that we get
from these cessation fronts fall into a pretty narrow range
of 1 to – 1 to 3, 10 to the minus 3 square meters per second.
And incidentally, these are the lowest values we also get
for the triggering front for these very slow sequences
where we do not see earthquake interactions to occur.
All right. So just to repeat my key observations,
seismicity occurs in distinct clusters that may span several faults.
These sequences typically occur at about 4 kilometers below the top
of the basement, which means that small stress perturbations
at depth are sufficient to get widespread earthquake triggering.
The majority of cluster shows a downward migration,
which indicates that the driving source is above.
The position of the largest events appears to be random for the smaller sequences,
but there seems to be some kind of development for the larger sequences.
So in order to reduce risk for large earthquakes,
we have to reduce the overall activity.
Events are triggered by fluid diffusion and by faster earthquake interactions.
This means that faults are critically stressed throughout and are
capable of sustaining rupture once they get initiated.
No major sequence actually starts with the largest earthquakes,
and many – and often many months before they rise to peak activity.
So with an improved seismic monitoring here, we can actually have
some kind of early warning where we see that there’s something developing.
And to conclude, earthquake sequences are initiated by anthropogenic stressing
but sustain rupture through earthquake interactions.
Sequences initiate months before they rise to peak activity.
And the largest earthquakes result from stronger perturbations
but not from reactivating more fresh asperities. Thank you.
[ Applause ]
- Questions?
- Thank you. That was a fantastic talk. Can you show the last slide again?
Say that the rate peaks months after this first event.
How – can you tell how much later than when they start the injection?
Or you don’t often know when they started the injection.
It could be years after they start the injection, right?
- So in most cases, it’s just impossible to connect a
single well with a single sequence. - Too many wells?
- We have several hundred wells in the area that have been injecting for decades
before this crisis actually occurred. And then there was just a ramp-up in,
I don’t know, 2012 or so that caused these many earthquakes.
So I think that’s just – it’s really difficult.
- It’s just great to see the insights that are emerging from all this work.
So there’s been a major decrease in seismicity in the last year or so.
Is that – does that owe to management practices? Or what’s going on there?
- So earthquake injection rates have been declining through
mandated reductions, but also through, I guess, market forces.
This is probably the major cause. So we are – I’m not quite sure
how it’s going to develop after now, but it’s definitely reductions
in injection rate that do cause this decline.
- Yeah. Really nice body of work there, Martin.
I was going to ask about the last conclusion,
which was the larger earthquakes that are resulting from
stronger perturbations rather than reacting fresh asperities.
So you showed the kind of – the, I guess, map view of the
length of the sequence versus time and whether that’s expanding or not.
How do you account for changes either in depth or in –
I think in some cases, there’s evidence that, you know, the fluid pressure
changes are – result from injection from wells in multiple locations.
So, like in the upper-left plot there, do we know that that event in the
lower left – sorry, the – yeah, the upper- left plot – the event in the lower left,
do we know that that one is caused sort of by the same fluid pressure front?
Or could that be a fluid pressure perturbation coming from a different
well and they’re sort of meeting in the middle of that fault?
- So for this sequence, actually, this is the Fairview sequence,
which is actually very far away from major injection wells.
So it’s a pretty interesting sequence by itself just because it was triggered
so far away from injection wells. So I’m pretty confident that
the earthquake over here is driven by the same source as this one.
As to the other comment on depth, for now, I don’t look at depth.
And I know this is a problem in this characterization,
so I might have to think about how to incorporate depth here.
But I …
I think for these larger sequences, they don’t – well, so typically,
the depth extent of the sequences are on the order of 2 kilometers,
so this would go to magnitude 4. So maybe there is definitely –
there might be a component of depth in there I should look into, yeah.
- In this case, is it – it looks like it – the sequence broadens a bit toward the end.
Is that … - It’s a dipping structure.
- Okay. - Yep. It’s like a dip of 60, 70 degrees.
- Okay. So there is – it’s maybe showing an increase in the depth extent there
that’s reflected by that broadening, or at least filling out that …
- Potentially, yeah.
- What’s the time span for that figure?
- I think it’s something like 1-1/2 years, maybe. I’m not quite sure.
[ Silence ]
- So I’m curious about the plot of the earthquake locations versus depth.
And first question is, so I think you mentioned it, but you’re taking account
of the change in depth of the Arbuckle or the basement contact there?
- Yep. - Okay.
And then, you know, if you put that in together with
how you’ve actually defined your faults, do you find that some of the seismicity
in those clusters would reach up to the Arbuckle contact in most of those cases?
Or are these faults – seem more confined to that
kind of 4 kilometers below the contact?
Or could you comment a little bit on what you see there?
- We do see single sequences that rupture probably to the contact –
Cushing, for instance. I think most of the sequences, though, do stay deep. Yeah.
So I guess, for most of sequences, we can say that their vertical extent
is about 2-ish kilometers except for the very big ones.
So they would stay at depth.
- Nice talk, Martin.
I actually want to return to what Dave and Randy were asking you about.
I was – I was sort of wondering, when you – when you have these sort of longer
fault structures, I would expect that you have more earthquakes in general.
And so couldn’t – just to play devil’s advocate, couldn’t you
just be seeing the biggest earthquakes in these sort of sequences due to
a statistical effect, basically? The more earthquakes you have,
the more likely you are to have big earthquakes, and it’s
not that it’s a pressure effect. It’s just a statistical result.
- Certainly. [laughter]
I mean, yes, if we have more earthquakes, we also
increase just the likelihood of rolling the dice with a better number.
But then the question is, why are we seeing more earthquakes? [laughs]
So, yeah.
- Yeah, is there a depth dependence of magnitude or a magnitude
dependence on depth? And in particular, in the next slide,
what are the depths of those four or five earthquakes that go from 3.1 to 4.4?
In the next slide?
- Next – I’m not sure which one is the next slide you meant.
- Well, the one where … - This one?
- The Fairview slide. - Oh.
- The – yeah, that one.
So what are the depths of – are they all at the same depth, or …
- I didn’t look into depth too much for magnitudes for now.
I should look into this depth dependence of magnitude. I still have to do that.
- And then in the following slide, how did you estimate the stress drop
for those various earthquakes? - So these lines of stress drop are
just kind of equivalent stress drop taking the size of the reactivated fault,
taking this as – diameter of a rupture source.
And then, in this case, the magnitude gives us the …
- Okay, so you spread the slip over the whole length of the …
- Right. Yes. - … fault as it existed at that time.
- So these have nothing to do with actual stress drops of earthquakes.
It’s just kind of the – a proxy for the energy release in a single sequence.
- Okay. Thank you.
[ Silence ]
- I’m guessing there’s no observation wells that go
3 or 4 kilometers into basements. So these changes in conditions
of seismogenic depths are inferred from models?
- We wish. [laughs] - Did you have – aren’t there data from –
are there downhole data from, say, the injection depth
these days that are available?
- Well, Andy or Bob, do you want to comment on this? [laughs]
- The answer is, it’s complicated. We have one monitoring well
that’s basically outside of where earthquakes are at this point.
And the OJS has monitoring wells that is inside the seismically active zone.
So these are old injection wells that have been instrumented.
Although we haven’t seen any of those data basically, so …
- No? [laughter]
- We’re waiting for more. - Yeah. We’re trying for more.
[ Silence ]
- Any more – any more questions?
All right, well, let’s thank our speaker.
[ Applause ]
If you guys want to grab lunch with Martin,
we’ll be outside the pole, like, around 11:40.
Thank you.
[ Silence ]