Hello, everybody. Let’s get started.
Thank you all for coming out to this week’s Earthquake
Science Center Seminar. Today we have our very own
Rob Skoumal, but before we get started, I have a few announcements.
So we have an ESC Coffee Hour today here in Menlo Park, and then tomorrow
in Moffett Field, both at 2:00 p.m. And then tomorrow, we have a public
lecture on the Loma Prieta earthquake anniversary.
So there will be one talk tomorrow at noon and then one at 7:00 p.m.
here in Menlo Park. And then Susan and others are working
very hard at getting the webstreaming and seminar all set up for Moffett Field,
but next week, our seminar will still be here in Menlo.
Okay. And so today, we have Rob. Most of you know him.
He got his B.S. in marine science at Eckerd College and his M.S.
and Ph.D. in geology at Miami University.
And he has been a Mendenhall postdoc with us since 2016.
So take it away, Rob.
- Thanks a lot. And thanks, everyone,
for making the journey up to Menlo today.
So I’d like to talk about some of our current and ongoing research looking at
induced earthquakes in the south-central United States.
So the work that I’ll be talking about has benefited greatly from the
contributions and discussions with pretty much our entire induced
seismicity project team, including Andy, Elizabeth, Ole, Art, and Justin,
as well as our friends at Miami University,
Mike Brudzinski, Shannon Fasola, and Rosie Ries.
Okay. So just a very quick outline of the stuff that I’ll be talking about today.
First, I’ll be talking about kind of our efforts to characterize the
earthquakes in the subsurface. So kind of improving detections
and locations of these events.
Then using those earthquakes, we’re going to start to map
the seismogenic faults that are largely responsible for these earthquakes
in the central United States. Then I’ll talk about how we can
use those faults to actually estimate SHmax and other things
that we can learn from those types of analyses.
Once we’ve kind of characterized the earthquakes, then we hope to
learn about them. So I’ll be talking about how we
can distinguish different types of injection-induced earthquakes.
So most of the earthquakes in the United States are induced by wastewater
disposal, but hydraulic fracturing- induced earthquakes are probably
kind of the secondary most common source. So distinguishing those is
very important for our understanding of what’s actually going on.
Then finally, I’ll talk about the industrial and geologic factors
that control whether or not a well will induce earthquakes,
which I think is a very important question.
Okay. So to start with, I’ll talk about Oklahoma.
So what we did was took this – all the cataloged earthquakes from the
Oklahoma Geological Survey and the ComCat catalogs and ran it through a
large-scale template-matching routine. So this took about 20,000 cataloged
events and turned this into about a quarter million newly detected events.
Okay, so with these, I took about a million manually selected phase picks
on those cataloged events, and I cross-correlated those phase picks
against all of our new detections in order to propagate those manual phase
picks throughout our entire catalog. Once we had phase picks for our
catalog, we then did a double-difference relocation
using the correlation coefficients and differential phase arrival times
in order to – the relative locations for these events.
For that, I used GrowClust. It’s very similar to HypoDD.
I don’t know if you’ve used that before.
Just GrowClust has some algorithmic advantages that allows us to relocate
catalogs that are a bit larger with greater ease.
So an example of the relocations. So we did the relocations for
the entire state of Oklahoma. This is just kind of a zoomed-in
view around the magnitude 5 Cushing sequence.
So, on the left here is the OGS catalog. So it’s kind of a distributed
cluster of earthquakes. Now, previously, Martin Schoenball
and Bill Ellsworth took that OGS catalog, did automatic phase picks,
and then relocated with HypoDD.
So their results in the – are in the middle.
And then our results – again, using the template matching,
using the manual phase picks, and GrowClust – are on the right.
So in general, our catalog is pretty similar to what Martin and Bill found.
Just we have an order of magnitude more events.
All right, so with this, we can see additional fault complexity that’s
going on, as well as we can see additional faults that
could not have been identified before.
All right, so fairly similar to other catalogs, just, you know,
it’s a little bit better. So what we’d like to do is take
this catalog and start to map these seismogenic faults that are responsible.
Because the vast majority of earthquakes – induced earthquakes
have occurred along mapped – faults that were previously unmapped.
So if we’re interested in just this area, we could probably identify these
faults by hand relatively easily. What we want to do is identify the
faults over a much larger area, and that becomes a much more onerous task.
So, to help us with that, we’re going to turn to an algorithm, right?
So I’ll kind of briefly run through just kind of the – give an overview
of how this algorithm works.
And I’ll be using this cartoon map of seismicity to try to explain that.
So our algorithm has two primary steps. The first step is that we cluster
the seismicity. And we do this in a density-based approach.
This approach requires two different parameters. We need to know
some sort of distance metric, and we use that to define the radius of a circle.
We then draw out those circles around all of our earthquakes.
If an earthquake falls within the radius of another circle –
or, another event, those events become kind of linked together,
and we form these chains of earthquakes.
Then we need to know the minimum number of events to define a cluster.
All right, so if a cluster has fewer than those events, we discard that cluster.
Okay. So every type of clustering algorithm is limited in the extent
that the result is always dependent on your input parameters.
Right, so in this case, we’re also limited by those input parameters.
Our solution to try to limit that limitation is to use a whole bunch
of different parameters. We follow the general scheme of,
we consider large distances with large cluster sizes first.
And then we iteratively consider smaller and smaller clusters.
All right. Now, between each iteration, we’re going to take these clusters
that we identified and try to identify faults that are within them.
All right, so let’s imagine we had that cluster in the lower left there.
What we’re going to do is then use random sample consensus.
With this approach, we randomly selected two points –
or, two earthquakes in that cluster. We draw a rectangle around them.
We then see how many other earthquakes fall within that rectangle,
and we define those earthquakes as inliers.
All right, in order to determine which model works best,
we look for the model that has the largest number of inliers.
So this initial model, right – hopefully you can see it.
You know, this does not represent the seismicity very well.
But what we’re going to do is, we’re going to run through all possible
combinations to try to evaluate and see what kind of fault plane
will represent the seismicity the best.
Once we identify that best case, we run some quality control checks.
If it passes those checks, those faults become associated with this kind of
fault and are sort of removed from this catalog.
We then apply – oops. We then apply random
sample consensus iteratively. So you can see that there’s these
kind of five earthquakes that are still left. We’ll apply random sample
consensus on those events, and maybe we’ll identify a fault like this.
Once we’ve gone through all those earthquakes,
we’ll return to that clustering. We’ll use those smaller set of
parameters. Maybe we’ll identify that cluster on the top right there.
Then we’ll repeat the random sample consensus.
All right, so this is obviously a very elementary view of what’s going on.
There’s some nuances that I’m not describing.
And this also only shows it in two dimensions. All right,
but hopefully this kind of gives you an idea of what we’re doing here.
So here’s an application. So this is that Cushing sequence that I showed earlier.
This was our map of relocated events in black. And, on top of this,
I’m going to show our automatically detected faults by these red lines.
So would you have picked slightly different faults?
All right, I hope so. All right, I know there’s some faults that I’d like to tweak.
You know, there’s some that – you know, is that even a fault, right?
But, in general, these look like faults that we might pick.
Right, not just for this area, but for the entire state.
We can do other things to try to, you know, improve our confidence
in these events. We can compare them to
focal mechanisms, which, in general, agree pretty well.
We can also start to look at the orientations of these faults.
Right, so when we look at the orientations, I’m going to
start to zoom out and look at larger areas.
So here are three rose diagrams showing the orientations of
our automatically detected faults, with each diagram representing
about a 400-square-kilometer area.
Now, in the center of each of these rose – or, the center of
each of these areas, there’s an SHmax measurement, which was determined
from drilling-induced tensile fractures. All right, so if we estimate the optimal
orientation from that SHmax, shown in kind of pink here, we see
there’s a good agreement between the optimal orientations and our faults.
Now, this kind of raises an interesting question.
If the vast majority of our faults are optimally oriented to the original stress
field, can we use our fault orientations to estimate the stress orientations?
And I would argue that we could. All right, so here’s an example
of a different area. So this is the SHmax measurement for
one of those areas that I showed earlier.
And around that are earthquakes and the faults that we identified.
So what I’d like to do is compare our fault orientations –
all right, those red lines against this SHmax measurement.
All right, so to do that, we’ll be doing these little geographical bins
and kind of running them over our fault areas to look at what types
of orientations we have. And those results are shown here by these colors.
All right, so the yellow areas represent that our faults are 30 degrees away
from the SHmax measurement. Right, so assuming a coefficient
of friction of 0.6 and that these are vertical strike-slip faults,
30 degrees would be optimally oriented. So this is an example from central
Oklahoma, where pretty much the stress is all kind of in this east-west direction
within 5 degrees or so, which is kind of on the order of our uncertainty.
But there are areas of Oklahoma that have a little bit
more exciting stress regimes. So this is an area in northern Oklahoma.
In this area, there are actually three stress measurements, kind of going
clockwise from the top left here, this is 79 degrees, 68 degrees, and 59 degrees.
So we’re going to apply the same kind of methodology, but this time, we’re
going to be using that top left SHmax measurement as a reference point.
So when we do that, we see that the faults over there are 30 degrees
away from that. But, as you move away from that measurement, for every
1 kilometer you travel, there’s a 1 degree rotation in the stress field.
So that may not be very large, depending on where you work
in the field. In California, they have some pretty extreme rotations.
But, by Oklahoma standards, you know, this is – this is pretty exciting stuff.
A 1-degree rotation on a kilometer basis is pretty large.
So our measurements are in great agreement with these
other actual measurements. Also, you can start to see that we can,
you know, estimate SHmax in areas that do not have measurements.
So this is an interesting area because it’s right next to this Nemaha Fault.
So we can – and we’ll start to look at questions.
You know, is this rotation related to that ridge?
You know, I think that there’s a lot that we can do now that we have
these orientations. All right, but just as a reminder, all right,
our stress measurements come only from earthquake locations.
We don’t know anything about the mechanisms or any kind of
borehole measurements. All of this information is gleaned
just from the earthquake locations and are automatically determined faults.
Which I think is – which is pretty cool, right?
So even if we have a fairly limited but high-quality data set,
I think we can still do some pretty cool science.
Okay. So what I’ve shown so far are just, you know, two-dimensional
fault lines that represent these vertical strike-slip faults.
But obviously not all earthquakes are vertical strike-slip faults.
So, to represent those, we need to look at fault planes.
All right, so this is an application of the same kind of algorithm that
I showed before, but we’ve scaled it up one dimension,
so now we can tackle three-dimensional fault planes.
All right, and so we applied this to Elizabeth’s Prague catalog.
So, following the magnitude 5.7 in Prague, a local network was deployed.
And Elizabeth spent a lot of time relocating the events,
and her catalog are shown by these black dots.
All right, now our fault planes are represented by these lines.
It’s pretty difficult to represent planes with lines.
So I have this little animation. All right, so in this view, they’re the
same kind of map view, but when I run this animation, all right, it’ll be going
from kind of the map view into profile view, and then it’ll rotate around.
All right, so I think we can see more complexity with this type of analysis.
There’s certainly still a lot of advancements that we want to make
with this algorithm, but we can go very quickly from just having
a catalog of events to start to identify these fault planes very rapidly.
So this is – this is highly scalable. We can, you know, identify planes
from millions of earthquakes within about a minute or so.
So I think it’s a pretty promising technique.
All right. Now, for the – for this actual Prague – oops.
For this Prague sequence – and Elizabeth found a pretty
interesting observation. All right, so Elizabeth had to look
at shear wave splitting to estimate the SHmax orientations.
The idea that the upper crust is composed of these numerous fluid-filled
cracks that will be preferentially oriented in the direction of SHmax.
All right, so her results are shown by these little green lines.
And she found that, in general, the SHmax is fairly constant –
this standard east-west direction.
Now, if we compare our fault planes against those orientations,
we see that this kind of primary fault that hosted the magnitude 5.7
is optimally oriented to those – to those stresses estimates.
But there are other faults that are poorly oriented.
Right, they’re pointed 30 degrees away from the optimal orientation.
All right, so I think this – you know, is a good reminder that, although
we may be able to identify the faults and determine the stress state such that
we could maybe determine the likelihood of certain faults that
would slip, we can still have these faults that aren’t poorly – gosh – that are
poorly oriented that are still close to failure that can still induce earthquakes.
All right, so we just – I think this is a great data set, and we can do
a lot of cool things with it. We just have to be careful in how
we interpret those results and how other people interpret those results.
Okay. So most of the earthquakes in Oklahoma and in the central
United States are induced by wastewater disposal, but hydraulic
fracturing is probably the second most likely cause of earthquakes.
So a couple of years ago in my seminar, I talked about kind of our plans to
look at this in Oklahoma, and I’m talking about the results.
It turned out that there’s a lot more [laughs] hydraulic fracturing-
induced earthquakes than we thought.
So the project kind of became a pretty large beast by the end.
So what this is showing – each one of these boxes is a region that we found
to be dominated by hydraulic fracturing-induced seismicity.
On the right, this represents each of these geographical areas,
and it shows the percentage of earthquakes in those areas
that we’re able to attribute directly to hydraulic fracturing.
So, in many of these areas, more than 80% of the earthquakes
we could tie to individual wells. Which I think is a pretty remarkable
feat considering all the uncertainties in the industry data.
There are, you know, certainly misreported wells
and wells that are missing. So to get – still to get associations
of 80-plus percent I think is pretty neat.
So you may notice that there’s this big blob of seismicity in kind of
central northern Oklahoma that we do not have hydraulic fracturing
induced earthquakes. So this is largely because there’s just
so many wastewater disposal-induced earthquakes, it kind of obscures the
signal of hydraulic fracturing. All right, so in kind of even the
best-case scenarios for the wells, we usually just have a start day and an end
day of the stimulation with really no knowledge of what occurs in between.
So it makes it pretty difficult to, you know, drive statistically significant
correlations between earthquakes and those wells with just those data.
So it’s expected that we do have hydraulic fracturing-induced
earthquakes within that big blob. Just they’re kind of hidden up.
So, just from our results, we found that there were 274 wells that
had induced earthquakes. And kind of after this time
window of our study, there have been larger – kind of magnitude 3.7.
But so they’re – the magnitudes are fairly small from hydraulic
fracturing-induced seismicity in the United States.
So the largest earthquake from hydraulic fracturing that we’ve –
that we’re confident about in the United States is
a magnitude 4.0 in Texas. So fairly small.
Now, I’m not trying to downplay the significance or the risk or hazard
of hydraulic fracturing-induced earthquakes. Internationally,
they have had larger cases. So, in Canada – western Canada,
they’ve had earthquakes up in the mid-magnitude 4s.
And overseas in China, there have been deaths related to hydraulic fracturing.
So the past December, there was a magnitude 5.2 that killed a couple
people that was induced by hydraulic fracturing.
And more recently, this past June, there was a magnitude 5.8
that killed about a dozen people. And it’s also been suggested
that earthquake was also induced by hydraulic fracturing.
But in the United States, we’re pretty fortunate that the
earthquakes from hydraulic fracturing tend to be quite small.
Now I think one of the reasons for these relatively small events is that both the
industry and operators are aware of these events, and they’re interested in
learning what’s going on and how to reduce the likelihood of those events.
So fairly recently, the Oklahoma Corporation Commission,
which is the body that’s in charge of regulations in Oklahoma, put in some
regulations targeting the SCOOP and STACK play in kind of central and
western Oklahoma, which is where a lot of induced earthquakes have occurred.
So, from these regulations – they kind of follow a stoplight pattern,
such that, if a well induces a magnitude 2, the operator
has to implement a predetermined mitigation plan.
If a magnitude 3 is induced, they have to pause operations for a few hours.
And they induce a magnitude 3.5, they have to suspend operations and
wait for the Corporation Commission to give them approval to resume.
All right, so if these regulations had been in effect, you know,
during our study window, you know, hundreds of wells would have
triggered various levels of this. But the red light of the magnitude 3.5
would have only been reached a couple times.
So, after these regulations went into effect, there have been earthquakes
that have been induced, and the wells have been suspended.
So these are – from what we can tell, these regulations have been successful
at at least limiting or reducing the number of hydraulic
fracturing-induced earthquakes.
Now, it’s interesting because these regulations only apply
to the SCOOP and STACK play. We have this kind of Arkoma Basin
in the southeastern Oklahoma, where we have, you know,
very prominent cases of hydraulic fracturing-induced seismicity.
The regulations do not apply to these cases.
So even if they induce a magnitude 3.5, these –
any wells sort of – this down there, that would not be affected.
Okay, so I mentioned before that, you know, all we really have
is a start day and an end day of these stimulation windows.
The exception is that we have information about two different wells.
So, in this Manning-Irene well – this is a horizontal well,
and the operator voluntarily provided us with the stimulation times.
So we don’t know, you know, the volumes or rates, but we know
when they were stimulating in kind of a minute-by-minute resolution.
All right, so with that, we can make some pretty interesting
temporal correlations, and that’s very important.
Just to the northwest, there’s the Eagleton Well Pad.
So this is a vertical well. And we were – we were able
to determine injection rates because those rates were published
in an Open-File report. So I was able to digitize those data,
and then we were interested in creating a poroelastic model for those earthquakes.
All right, so our earthquake catalog – these gray symbols are the initial
earthquake locations. And the colored symbols are improved locations.
So southern Oklahoma, the geology is pretty complex.
There’s not a lot that’s known about the velocity model,
so it’s definitely a struggle to locate the earthquakes.
So our earthquake uncertainties are still kind of on the order of about
a kilometer or so, which is about the distance away from the well.
All right, so kind of – but we’re still pretty confident the earthquakes
are within a kilometer of these stimulations.
So what we’d like to do is take this – there it is – take this Eagleton Well Pad
and start to model to see what actually happened with these earthquakes.
So here are the actual injection parameters that I was able to digitize.
All right, so there are four stimulations that were done.
So in a vertical well – so this first stimulation was the deepest.
And then each sequential stimulation represented by
these lines were slightly shallower.
Our earthquakes represented by – represented by these gray boxes,
with the largest magnitude occurring in this first stimulation
with about a magnitude 3.2.
All right, so when we stimulate, we had a bunch of earthquakes
following the stimulation. Kind of get this – kind of the aftershock
decay following a stimulation. So this is a fairly typical example of
hydraulic fracturing-induced seismicity, where the earthquakes and the
aftershocks only last a few days or so. We do have examples where aftershocks
from hydraulic fracturing can last months. But those are fairly rare.
Okay, so what we’d like to do is take these injection parameters,
take these earthquakes, and estimate some geologic parameters and provided
these to Andy Barbour, who was able to put this into a relatively simple axial
symmetrical model to estimate the poroelastic and pore pressure effects.
All right, so those results are shown here.
So this is just showing the estimates in the Lower Viola, which is the
formation that being stimulated. These yellow dots are our earthquakes.
And then these – the black dashed line is the model poroelastic effects.
So we observed that there’s a pretty good correlation between
the onset of our earthquakes and the poroelastic stress increases.
And we also noticed that the pore pressure effects were
only significant within 100 meters or so of the well.
All right, so if these earthquakes were induced by pore pressure,
the earthquakes would be very close to the well itself.
Whereas, if it was poroelastic effects, the earthquakes could be, you know,
further way, on the order of hundreds of meters away.
So we do have other examples in the Appalachian Basin, where,
following the stimulation, within minutes, you know,
we’ll have earthquakes about 700 meters away.
So, in those cases, we do believe that poroelastic effects are the
primary cause of those seismicity, but we can’t rule out pore pressure
in this – in this particular case.
Okay, so up until now, I think a lot of the stuff that I’ve been talking about
is sort of reactionary, right? So people caused earthquakes.
We want to learn about those earthquakes as much as we can.
The end goal of what I’d like to get to is to hopefully start to understand
why these earthquakes were induced, such that we can tell operators and
tell regulators, you know, these are the parameters that largely drive whether
or not the well will induce earthquakes. All right, now this starts to get pretty
complicated because there are [chuckles] very rarely independent parameters.
Right, a lot of these parameters depend on one another.
So we generated large odds ratio tables, which are pretty dull.
So I won’t bore you with that, but what I’d like to do is try to qualitatively walk
you through kind of four of the most intuitive parameters that we’ve found.
All right, so the first parameter that we’d like to look at is the formation, right?
So for this, we’ll be looking at our catalogs, both in Oklahoma and Texas,
to try to understand what factors are important.
All right, so this is sort of a geologic figure of Oklahoma.
All right, so this – these are showing the formations with the shallow
formations on top, deeper formations on the bottom.
And the formations are colored by the formation type.
All right, so yellow are sandstones. Blue are carbonates.
And the gray is a shale. All right.
And these little brackets represent the percentage of wells that were
stimulated in those formations that we were able to induce –
that we associated with induced earthquakes.
So we don’t really see a correlation between the formation type and
the percentage of earthquakes.
But you may start to notice that we do see a correlation with depth, right?
So we don’t really see a correlation directly with formation, right,
but we do see a correlation with depth. All right, so this figure on the right
is showing the depth of the wells and the probability that those
wells induce earthquakes. So both in the SCOOP and STACK,
which is kind of in the western part of Oklahoma, and that Arkoma Basin,
which is in the southeast, we see a very significant statistical correlation
between the depths of these wells and the probabilities that
they induce earthquakes.
Now, there’s a couple explanations for why depth matters.
One, it could just be that the wells are closer to the seismogenic faults.
But it could also be a factor of the over-pressurization.
So previously, up in Canada, Dave Eaton and Ryan Schultz
found that shales that were over-pressurized had a much
higher likelihood of being associated with induced earthquakes.
In Oklahoma, we don’t have as good of pressure data.
This is pretty much a cross-section of the SCOOP and STACK plays
showing the pressure gradient. This is really all the pressure data
that we have in the state. But, from this study that was done
a couple decades ago, we do see a great correlation between the areas that are
over-pressurized and the occurrence of induced earthquakes.
So, on this pressure gradient – so the red have higher over-pressurizations,
and the cooler colors have lower pressures – we kind of plotted this
Woodford Shale formation. So, as you can see, you know,
at these deeper formations, it’s going to be higher –
more over-pressurized and could be more likely to induce earthquakes.
Okay.
Another factor that we can look at is the volume of wells.
All right, so here’s a figure of this. All right, so this figure shows the
volume at an individual stimulated well. So we see a – or, so Ryan Schultz
and others found a correlation between injection volume and the probability
that those wells induced earthquakes up in the Duverney in Canada.
From our previous work, found a correlation between volume and
earthquakes in the Appalachian Basin. And it’s not shown here because it’s
in review, but we also see a correlation between volume and earthquakes
in the Eagle Ford Shale of Texas.
You might notice that we do not see a correlation in the Arkoma Basin
and SCOOP and STACK. All right, so this is where things
start to get a little bit muddy. Because this is where different
parameters start being influenced. All right, so in Oklahoma, a lot of
these wells are just vertical wells that are hydraulically stimulated.
And these are spatiotemporally isolated from other wells that might be nearby.
However, in the Duvernay, in the Appalachian Basin, and Eagle Ford in
Texas, a lot of these are horizontal well, right, with multiple laterals in
close proxy to each other, but all associated with a single well pad.
All right, so although the amount of fluid injected at an individual well is
similar in Oklahoma to these other areas, I think we need to start to consider
the influence of injection from nearby wells as well, right?
So it’s really – we need to consider the injection into a volume of area
rather than individual wells, right? But, for simplicity, I’m just going to
say that volume can sometimes play a role in induced earthquakes.
All right. This kind of leads me into the final topic that I want to talk about,
which is the influence of operations on seismicity.
All right, so, in this kind of cartoon view, let’s just pretend that this operator
had two horizontal wells that they wanted to stimulate.
So this is a map view. So we have the toe of the well
at the very top and the heel at the bottom.
And these triangles are trying to represent the stimulations.
So this operator may decide to first stimulate this Well #1, and then they
may switch over to Well #2. Right, so immediately after the
stimulation, they go to Well #2. After they finish stimulation at Well #2,
they may go back to #1 and kind of go in a zipper pattern.
All right, so this is called a zipper frack. There’s variations of this.
For example, they could stimulate multiple stimulations
at the same time at different wells. And we call that a simulfrack.
But, in general, all the wells kind of in this family of where you’re
stimulating multiple wells at the same time we’re going to
call simultaneous stimulations, okay?
Now, there’s other types of strategies that the operator could decide to use.
For example, in this same kind of cartoon view, the operator may
just decide to stimulate one well. All right, when they finish that well,
they may then move on to the second well, and then stimulate that way.
Right, because these wells are sequentially stimulated,
we’re going to call those sequentially stimulated wells.
All right, and then finally, we can have a well that’s all by itself.
All right, they may just stimulate like that.
And these are little isolated well, and it’s sad because it’s all alone.
So, with just this kind of cartoon view, right, could you make a guess as to
what type of well would be more likely to induce earthquakes?
I’m sure you can, right, but if you think simultaneous wells,
you would be correct, right? So we found that simultaneous
stimulated wells were more than three times as likely than isolated wells
to induce earthquakes. At the same time, simultaneous wells
were more than twice than likely than sequential wells to induce earthquakes.
All right, so I think this has to do with the reservoir pressures.
Right, so when you’re doing multiple stimulations in close spatiotemporal
proximity to each other, the reservoir pressures will be
elevated for a longer period of time.
All right, we’re going to compare that to isolated wells that are stimulated,
you know, kind of sequentially with further timing between the stimulations.
That gives the time for the reservoir and the pressures
and fluids to kind of diffuse out.
So this idea is kind of corroborated by these other findings that we found.
So most unconventional oil wells are hydraulically fractured with
a frack prop composed largely of water. So these wells are called kind of a
slickwater proppant, and we found that these were 1-1/2 times more likely
than when a gelling additive was used.
All right, so previously, models have determined that this gel increases the
viscosity of the material such that it’ll limit the extent fluids and
stresses are able to propagate. Right, so it’s kind of limited to a –
to a smaller region. As I kind of showed before,
wells that have multiple laterals that are stimulated in close proximity to each
other, the more laterals you have, the higher probability that you
induce earthquakes. And similarly, wells that inject
at a much higher rate also have an increased likelihood.
Okay, so kind of the downside is that we don’t have any actual pressure
measurements in these reservoirs. All right, so industry definitely
has those data, but they’re not publicly available.
But from these observations, all right, I think it’s fairly reasonable to make
the conclusion that stimulation strategies that cause higher reservoir
pressures for longer periods of time could lead to increase in the likelihood
of hydraulic fracturing- induced seismicity.
So I think this is a fairly common-sense statement, but I think
it’s an important one to make. Because this is really the first time
that we’re even able to investigate these type of parameters on this
kind of large regional scale. And, you know, this is not a model.
This is looking at real-world parameters to see what
actually happened from these types of operations.
So certainly there are – this is a very complicated issue.
There’s a lot of questions left to answer.
But hopefully, you know, we’re headed in the right direction
with this type of work. Okay.
So the final thing that I’d like to talk about – oops.
Oh, yeah. So the stimulation type is also induced. All right.
So the final thing I’d like to talk about is our efforts
in the Delaware Basin in Texas.
So I like to think of the Delaware Basin as a baby Oklahoma.
All right, we’re not quite sure what it’ll grow up into,
but it’s a sketchy little baby. You know, we need to keep an eye on it.
Okay, so this is a map of western Texas. The upper left here is New Mexico.
The lower left is Mexico.
And this region largely encompasses the Permian Basin.
So the Permian Basin has been in the news a lot because it’s undergoing
a lot of hydrocarbon development. But this basin also a history
of documented cases of induced seismicity.
So Cliff Frohlich has done a great job to characterize induced earthquakes
over the past century or so. And he’s found a lot of
production-related earthquakes in the Permian Basin.
There’s also examples in Snyder. In the ’70s and ’80s, earthquakes were
induced by water flooding, which is an enhanced oil recovery technique.
And about 10 years ago, there were some more earthquakes
in Snyder induced by CO2 injection.
For the west, there’s the Dagger Draw field that was
induced by wastewater disposal. But really, in the past five years or so,
in this – kind of this southern portion of the Delaware Basin, there’s been
a large increase in the number of earthquakes.
All right, so this is kind of our study area that I will be focusing on.
So if we zoom into this area, these black dots are representative
of our earthquakes. The orange dots are the
hydraulic fractured wells. And the blue are wastewater disposal wells. All right,
so there’s a tremendous amount of industry operations in this area.
So I won’t be talking about it, but we’ve done analysis of the
hydraulic fracturing-induced earthquakes and kind of about
5% of the earthquakes, represented by the green stars,
are induced by hydraulic fracturing. Okay.
Okay, so much like Oklahoma, we’ve done template matching.
So this is showing our regional template matching.
So we took a regional catalog and correlated events
over the past decade or so.
We also have access to the TexNet data. All right, so these are more local
station deployments in this area. And from that, we can get a much
better catalog, but we’re limited in time. Then also I’ll talk about this later,
but we also are interested in improving the catalog in this northwest area.
But in general, we’ve identified about 40,000 events, but the largest of which
is kind of in the mid-magnitude 3 range. All right, so we have a lot of
earthquakes, but the earthquake magnitudes are still fairly small.
If we look at the depths of these operations – so on the left
are the wastewater disposal. The middle is hydraulic fracturing,
and the right are the cataloged earthquake locations.
This is a little bit different than Oklahoma.
So, in Oklahoma, most of the wastewater disposal is into the
Arbuckle, which is right above the basement.
But here in the Delaware Basin, a lot of the injection is into this
Delaware Mountain Group, which is a sandstone formation,
which has been used a lot for conventional oil exploration,
and now it’s being used as a reservoir to dispose of the wastewater.
The earthquakes – or the hydraulic fracturing that’s occurred is largely
done in the Wolfcamp or Bone Spring Formations,
which is below the Delaware Mountain Group.
And interestingly, a lot of the earthquake hypocenters,
those reported depths are below these shales.
So, if these earthquake locations are reliable, it raises some questions.
Now, if the injection is occurring shallower, right, is there some kind of
permeated pathway through these shales that allows those
fluids to reach these faults? All right, rather – or, I don’t know,
is it just a stress transfer, you know, through those formations?
Or possibly – I think the most likely explanation is that these earthquakes are
actually a little bit shallower, and we should probably shift them up.
All right, so just kind of make this point in that the earthquake hypocenter
depths are going to be very important for our understanding
of what’s going on in the Delaware Basin.
Okay, so another comparison with Oklahoma.
So some people have found that there are poroelastic effects that
can induce earthquakes tens of kilometers away from wells.
I think one of the best examples of this is done by Thomas Goebel and others,
where they found that the Fairview and Woodward sequences in western
Oklahoma were induced by injection wells that were
kind of 30 to 40 kilometers away from those sequences.
It should be noted that there are disposal wells that are within
10 kilometers of these sequences. Just the volumes are much less.
All right, so I think everyone in this room is aware of Art’s theory
that the cumulative seismic moment from induced seismicity can be related
and constrained by the cumulative injection volume.
So this Fairview example that Thomas Goebel found, it did seem to
follow along Art’s relationship, considering only wells that are
within 10 kilometers.
All right, so this a – kind of a [chuckles] controversial topic.
I won’t get into debating whether or not these are poroelastic effects.
I just more raise this point that observations have been made,
but it’s not a super clear-cut example.
All right, hopefully I can do that to help motivate our work.
All right, so in this green box in the northwest region, you know, I looked
for some geographical landmark in this area. There’s really nothing.
So I just called it the northwest region. If we zoom in on this area shown on
the right, we said that these earthquakes are about 25 kilometers away from
the nearest injection well. All right, these injection wells are pretty
shallow and relatively low volume. So pretty much the largest
deep injection well is about 35 kilometers away. All right.
So the earthquake uncertainties horizontally are kind of on the
order of about a kilometer or so. So we have, you know, pretty good
constraints that these earthquakes are at least, you know, a couple tens of
kilometers away from the injection.
All right, so I think that that’s a pretty interesting observation.
So you may also notice that there’s a lot of wells in kind of this
more northern part of the Delaware Basin.
But there aren’t any earthquakes. All right, so this kind of same kind of
data could be shown by these figures. So this is showing kind of the –
kind of the spatial grid of wastewater disposal volumes.
So you can see that there’s a lot of injection occurring throughout this area.
But the earthquake – that looks kind of blue. [laughs]
That’s supposed to be purple.
But, yeah, a lot of the earthquake counts are all south in this basin.
There aren’t really any earthquakes in this more northern part.
And I think answering this question of why we have earthquakes in
the southern part but not the northern part is going to be very important.
All right, so we started to work and try to look at why this is.
So I’ve been looking largely at the geology.
All right, so this is showing the basement contours –
all right, so the depths of the basement.
And these lines are showing mapped faults in this area.
So, to the east, we have this central basin platform.
All right, I think I can make a fairly convincing argument that this basin –
that this platform will inhibit the horizontal migration of fluids.
But a real question is, you know, why do we have this kind of
north-south division? So it’s – you know, potentially,
this is just a spurious correlation.
But we do see that the earthquakes stop right at this Grisham Fault.
And along this fault, we see that there’s a rotation of the stress field.
All right, so kind of east-west, north of this fault, and then
just to the south, the stress rotates towards kind of a southeast direction.
So this raises some interesting questions, right?
Is there also a magnitude of stress change along this fault?
Maybe, does this fault inhibit the flow of fluid across the fault?
Maybe there are different reservoir pressures in these areas.
I think there’s a lot of potential possibilities.
But we’ve kind of reached the limits of what we can do
observationally with the data that we have available. All right.
So we’re working on a finite element model for this entire Delaware Basin to
look at the influences that the structure will have on the seismicity.
All right, so we want to look at the interplay of both the production and
disposal to see – to see to what degree these both contribute.
And to also see what influences these operations have on the
pressures and stresses in this basin.
All right, so if you have ideas of [chuckles] why the earthquakes will
suddenly stop, you know, let me know. All right, but hopefully that’s
a good question to leave you guys with. All right.
So, in conclusion, this FaultID algorithm that I talked about,
I think, is a – is a pretty powerful tool. I think there’s still plenty of room
for improvement and advancements to this technique.
But, so far, most of the earthquakes that we’ve found in Oklahoma have
occurred along faults that are optimally oriented to the original stress field.
And I would even argue that we could those stress orientations
to estimate the SHmax in areas that we don’t have measurements.
So hydraulic fracturing is quite a bit more common than we thought
even just a few years ago. And there are various factors that will
influence the likelihood of whether or not a well will induce earthquakes.
So I think – largely due to stimulation depth of –
you know, over-pressurization, as well as operational decisions
made by the operators.
And then finally, the seismicity rate in the Delaware Basin
will likely continue as long as the operations are not altered.
But hopefully we can start to answer some of these questions
before that baby Oklahoma grows up. All right, so with that, I’ll be
happy to answer any questions. All right, thanks.
[Applause]
[Silence]
- Okay. Do we have any questions?
- Uh-oh.
- [laughs]
[Silence]
- Nice talk, Rob. - [laughs]
- Can we go back to the Delaware Basin where you’re plotting the depths
sort of against injection … - Oh, yeah, yeah.
- And here you said that you think that the depths need to be higher.
And I guess … - It’s one possibility, yes.
- I guess I don’t – I mean, I guess – can you justify that a little bit better?
Why shouldn’t they be that deep? - Yep. So – I mean, so, if they are
that deep, I mean, how do you explain why there were induced, right?
So if it was – if it was induced by wastewater disposal, is there
some permeability pathway through the shales?
Is the stress being transmitted?
- Why should this be any different than Oklahoma?
- So, in that – in the Delaware Basin, we have these shales, right,
which are relatively impermeable. So, in Oklahoma, it’s injecting into
the basal sediments, right, into the Arbuckle, which is right above the
basement, which is where a lot of these earthquakes and faults are located.
But here, in the Delaware Basin, all right, we have those shales.
All right, so is this acting as some impermeable layer?
All right, are there the pathways that allows the fluids through?
I mean, there’s questions. So preliminary work that’s not done
by me that has not been published yet do indicate that those earthquakes
tend to be shallower, even into these – kind of the shale formations, which is
where they think a lot of the faults are.
But I would say those are not published yet, so …
- Any additional questions?
- It was very interesting, Rob.
My question concerns – I think you showed some faults that you defined
in the Cushing area, and – early in your talk, in the first topic.
- [inaudible] - Right.
- Or the Prague? Or Cushing? - Cushing.
- Yep.
- Right. Now, I was wondering why we don’t see another fault to the
south of the main one you showed toward the bottom of your figure there,
which has kind of a conjugate orientation if the horizontal stresses
are – the maximum horizontal stress is approximately east-west there.
- So where are you …
Like here?
- Dan McNamara did a study that – on earthquakes that were, I think, in 2014,
mostly, in the Cushing area before … - Oh, so …
- … and he showed faults that those earthquakes …
- Yes. So I think that – that’s actually just off scale –
that’s just, I think, to the south. - Oh, it’s off the bottom? Okay.
- Yeah. So it – yeah, it’s down here, but it’s off this map.
- Yeah, south of Cushing, in fact. - Yeah. I think I know what
you’re talking about, yeah. - Oh, okay. Thanks a lot.
- Yes. Yeah. There are definitely earthquakes down there.
- Okay. Thanks a lot. [laughs] - Yeah, sure.
[Silence]
- Hi, Rob. Nice talk. - Thanks, Steve.
- Getting back to the question that Justin just asked, if you have –
going back to that figure, if you have poroelastic effects, wouldn’t those –
expected to change the stress below a relatively permeable shale there …
- Yes. Yep. - So if you don’t believe in short-circuit
fluid pathways through the shale,
does this suggest that poroelastic effects might be important?
If the earthquakes are really where they appear to be?
- Yeah. So I tried to list that as one of the three possibilities.
Right, either they have the pathways that stress is transmitted, or that
the earthquakes are closer. All right, so it’s one of those three.
And I’m just – I’m just listing those questions, right, out there.
I’m not stating what’s the case. But I think it is – it is possible.
- It’s a pretty short distance, and it’d be something to model.
- Yep. So that’s what we’re working on, yep.
- Yeah. And you’d also want to look at whether the shales –
are they presumably seismogenic? Depending – you know, some shales
are, some aren’t, so those are the kind of issues you could look at too.
Another question I have had to do with the operational thing.
- Yep.
- Being an operational kind of guy, I latch onto these.
So actually, no, it was the one – yeah, this one. This is good.
So what is – so the fact that slickwater is at 1-1/2 times,
I totally get the simultaneous thing. You’re building up fluid pressure over
a larger volume – or poroelastic effects. What do you think the fact that gel
is less likely to produce events than slickwater is?
Is that – does that tell us it’s a fluid pressure effect?
Does it rule out poroelasticity? Does it help discriminate between
those two by looking at the viscosity and the kind of fractures you would
stimulate and inflate with these different kinds of fluids?
Because the gels, I presume, have huge viscosities.
- Yeah. Very large. Yep. - Yeah.
- So I’m only aware of a few modeling-type studies.
I mean, so certainly the industry has done those analyses.
They actually have measurements. But the stuff that’s public,
I believe it’s just kind of models. But, from those highly viscous gels,
it really limits the spatial extent that those stresses and fluids propagate.
Right, it’s really just isolated, you know, close to the well itself
and doesn’t really diffuse out as slickwater would.
So that’s the – kind of our understanding of it.
- I was going to also point out, I was reassured to see that the top one –
simultaneously – three times more than isolated.
Because that’s the guiding principle for enhanced geothermal system
stimulations now. You’re more likely to get permeability
creation through micro-earthquakes by injecting into multiple zones
at one time. - Yes.
- If you can. - So, I mean, that’s one of the reasons
why industry does that, right, so … - Thanks.
- Yep.
- Do we have any other questions?
Okay, great. Let’s thank Rob one more time.
- All right. Thanks, guys.
[Applause]
[Silence]
- And if you want to join us and Rob for lunch, please come up,
and we can make a plan to do that.