[applause]
Can everyone hear me okay
okay?
This afternoon we're going to talk about a
new software I've been working on for
the past couple years
that has been started by Tom Smith.
We're doing unsupervised neural
analysis
using seismic attributes and trying to
find anomalies in seismic data.
Okay, a little bit about Geophysical Insights, it's a new company
like I said Tom Smith was
Dr. Smith was a gentleman who started
seismic microtechnology.
Most of you know or use Kingdom software.
He was a guy that started Kingdom and
when the
Capital Ventures bought him out
about five years ago
he was too smart to kinda sit around and just retire. So he started tinkering with
unsupervised neural networks. He and
Terry Tenor
who is now deceased but he worked with
Terry at
Chevron. Started working on building
an engine. He built his engine in
Fortran. Now we put
a car body on it in C++ but
to look at how we could better use
seismic data
and find oil and gas. What we're doing
now is we're putting the UI together.
We're testing what we're doing. We're
looking for test cases
and we're taking up to 20 square miles of data or 2D Data
if you want and you give us a problem we
try to find a solution.
Go to our website (geoinsights.com)  it's all for free but we're looking for test data.
So that's about Geophysical Insights
right now.
What we're doing, and what I want to talk about today, is self-organizing maps
and a SOM is a type of artificial neural
network
that trains without supervision on
unclassified data to produce
a map. Now people are not as familiar with
unsupervised maps as they are supervised
maps an example of a supervised neural
analysis might be
how many of you are familiar with some software called Strata Magic?
okay Strata Magic is a supervised neural analysis where you have
known well data, so you have a good well
and a bad well,
and you want it to map the changes
in the seismic waveform
between the good well and the map well at the bad well. And it will actually give you a map
channels or whatever like that can be mapped
and thats an example of supervised
neural networks.
What we're doing is unsupervised.
what neural networks do for you in
general in the public they've been
around for a long time
cluster analyses classification
regression
forecasting dimensionality reduction in
data compression
and the data compression is kind of what
we're into.
okay This first example
is is from Kahonen's book
on unsupervised neural analysis and what
he did is he
put together 39 attributes, much like we
would look at attributes in seismic data
he put together 39 attributes which
represent the quality of life
issues around each country in the world
most countries in the world.
and he had 39
he went through 126 different countries
to do an analysis.
He put the date in the spreadsheet and
he looked at quality of
live attributes in the country and
ended up with a 9x13 hexagonal
neuron topology and i'll
show you what the hexagonal neuron topology is later.
This is a list to the country's that
he used
in his study and by time he did the
analysis, the neuron analysis, it was very
interesting how the
countries with very similar attributes
tended to the lineup
and he put it in color so it's really
easy for people to see but if you look
at
quality of life say, for Canada and US, very
similar.
Israel Australia and New Zealand and
then you get over into the African
countries and
and other parts of the world but but
they all lining up
according to the similarity that they
had in the quality of life attributes.
And this is kind of a concept that we're
taking in, to using this in seismic
and I'm not gonna read all
this but
the key elements here are self-organizing map, neural map
what's what's an anomaly here and the
important thing about anomaly is that
we're looking for probability of fit
and when you have something as anomalous
it has a very low probability.
You have a neuron out there that's
looking for something
different in the seismic
attributes from every other part of
the seismic data. Classification. When
we run a SOM analysis on seismic data
we end up with two
new volumes. One is called a classification
volume,
one is called a probability volume and
that's where we see our anomalies and
that's where we can highlight them
and use them for different reasons and
that classification and probability cut
off
are part of the things that we're showing.
Okay, this is the slide
I have the movie hopefully it'll work
its gonna work, I see it thinking! Okay, if you go to the
Geophysical Insights website (geoinsights.com) we have
a little example of how 
the SOM
neural analysis works, maybe might have to click it again
um its supposed, if you just double-click
on that pad, there we go now what I'm doing here
is I have put this together with
three attributes and I'm using 200 data points
this is a free download that you can get
on our website is called SOM-Lite
I'm showing you the different data
points for the three different
attributes that we're gonna use in this
analogy.
Or in this SOM map that we're going to make.
We can look at it in 2D or we can look at
and 3D.
and part of this is built into the
new software that we're working on
and those little triangles out
there represent the data points.
I'm setting this up to be an 8x8 neural analysis so I have 64 neurons.
That are going to go in
to these 200 data points and you
can look at it in
random or you can set it to 0.
I'm gonna set this to 0, because I don't
want any
kind of bias out there for the neurons.
Then I'm going to tell it to
to start training and I'm going to run through ten different
epics, and the epics are the training process, the neurons go through the data
ten different times and they're gonna
analyze the different data points.
And you'll notice as we go through down
here at the bottom
I'm showing this, I had this timed really well.
I'm going through Epic's, each epic they they train a little bit better so they start
slowing down.
They start out with jumping all around
and then as they get through the
training process they slow down.
And they start figuring out which of the
data points they're going to attach themselves to.
See they're not moving around near as much now and I'm at epic
7, 8, and
and you'll see that up in the corner
there's a color topology map,
similar to the country map that was
in colors
these things are lining themselves up in colors according to the data points
not stopped and you'll see
the vectors that they've attached
themselves to the data points
and I'll just rotate it a little bit for
you see can see how each of the neurons,
64 neurons, have analyzed and attached themselves to the 200 data points.
We can look at it in the 3D or we can
look at it in 2D view
and that's kind of a top-down view.
So what's anomalous in here
we want to look for the neurons which
attach themselves to no data points.
Those are the three little 
anomalies in the seismic data.
And that's just kind of a brief little
show-and-tell of
SOM-Lite, like I said it's free to
download on our website,
and it's fun to play with you know. We
only have it going up to three attributes
but you can put a thousand data points
in there and you can have any, you can have a
16x16 or any kind number of
neurons that you want to do
just to watch it go through.
Okay, so the first kind of
cases where I want to go to
is, a client of mine came to me,
they had shot a 3D, a 10 square mile 3D
trying to extend the Eagle Ford into
North East or, North-Central
Texas and the critical elements about
looking at
unconventional reservoirs is you've got to
understand the reservoir geology
you gotta understand the geochemistry
you gotta understand the geomechanics
and this is where the geomechanics and a lot of the acoustic impedence
inversion the inversion volumes
really
become important for looking for sweet
spots in
seismic data and certainly there's
attributes that are involved in faults
and fractures and stress regimes.
Coherency, curvature those things
are also very important.
With our client we ran
th normal attributes that I
could run and
I start out with the project in Kingdom and
I'll run through the stuff that's
that's available in Kingdom and then I have
Rock-solid so I'll do that
and then Tom's new software also runs its own suite of attributes
we're working on new attributes just to bring to that.
But in addition to the normal attribute suite that we ran
immediately because they had
well control they had done some
extra processing on the data and they
brought us final density, landrow, muerow
Poissons’ brittleness
Poissons’ ratio, shear impedence and brittleness
to the table. So,
we ran a SOM analysis, and
, need some water, sorry about that guys,
this is kinda the outcome where we thought the sweet spot was for the
Eagle Ford in this particular area
They had already drilled a couple
wells in here, in fact they had drilled
this guy right here which had very few
shows
and they had some mechanical issues with
with drilling the well and didn't end up
producing anything out of it
so the second well they drilled it's
kinda hard to tell because it's,
laterally going in here, they start
getting some really good shows
and again they had some mechanical
issues and
and my client I have concurred that maybe their engineer
is not the great engineer he thinks he is
but that's another story.
And that's not disparaging
to any, or all engineers, there's just a couple
maybe
are not quite up to snuff. Anyway so but
they had excellent shows and excellent
shows when they start getting into this
sweet spot right here.
They are drilling some other wells this is
an area that they haven't really
started drilling in for the Eagle Ford yet
but the second part the story which
is more conventional
is for the Buda and they're
looking at this area for the Buda.
So if you looked at it with
say a sweetness volume the sweetness
volume wouldn't have shown all of
the sweet spots
for the Eagle Ford, that we found
when we ran a series of
attributes into a self-organizing Map (SOM)
So here is the SOM analysis
in the classification mine and it's kinda
hard to see but the well is right here
and it comes in here it got its first
show at that little red spot
and it started getting better and
better shows as I got into this red area
right here.
This is the top the Eagle Ford right
here this is
a zone I mapped kinda the oil window
and then this is the top of the Buda so this
area would be good for the
Eagle Ford and the Buda in this
particular instance and they had shows
that this little
area in this area right here before they
had mechanical problems and stop
well again if you look at it in the
conventional attribute
this being sweetness you
wouldn't necessarily know
where they were getting their shows. Now
the Buda still shows up a sweet but
I think that's a
that's a frequency based issue with the Buda being a limestone in here
to see that the attribute analysis brings out
information in the data that you
wouldn't necessarily see if you just
looked at each attribute
on its own. This is a summation
of the SOM analysis. I did a 12x16
a grid. These are a list of the attributes
I put in there.
Some of which are conventional attributes
like, envelope slope
or the PSTN volume but a lot of them
are the specialized impetence volumes
that they gave to us to use for the
analysis.
so part 2 this particular clients
problem
was trying to identify something in the Buda
which is more of a conventional
reservoir in here even though they're
doing laterals in the Buda
there have been some excellent Buda wells completed nearby
the first two wells which were Eagle Ford test did not hit the Buda
but obviously there's a little bit of a
Buda sweet spot where the
where the Eagle Ford sweet spot was but
this is the area
that we determine was the Buda sweet
spot. And indeed they were drilling a Buda
well right in here.
And they got to a certain point they
were having excellent shows two to four
thousand units of gas
they were heavy
on their mud and they were still getting
those kinda
those kinda gas shows and the pressures
were so high, I can't remember the total
story but again and
they had some mechanical problems and
they had to stop
they realize that they need to set a
liner and they were gonna
set the liner then drill out and keep on
going
which they ultimately did. They drilled
about another 150 feet
past this point right here but ended up
completing the well,
or the last thing I heard they had
completed the well naturally
for about three hundred barrels a day. They haven't fracted
to my knowledge but I think that
there's more running room for them
here in in the Buda according to our
SOM analysis
and again the sweet spot in Buda and I
think this is probably
an amplitude driven or a frequency driven
issue
with the sweetness, but obviously there
in a sweet spot, no pun intended, anyway
but you wouldn't maybe have noticed the
rest of the area of the sweet spot in the
Buda
if you hadn't combined and done the SOM analysis
Here's the well. The well is coming down here
it's turning and coming down here there's
another fault block there and there
that look like they're also good.
But this is where the first well went.
and this is where they stop.
And, again, the Buda on a sweetness volume
see the sweetness volume here looks good in there anyway
this is the result the SOM analysis,
now because this is more of a
conventional reservoir
I ended up using more conventional type
attributes I used
attenuation, I used average energy I did some
dip variants looking at some of the
fractures stuff
I did use some of their inversion
poissance, brittleness and stuff but I put up a different mix of attributes in here
for a more conventional reservoir than I did for the unconventional reservoir.
Okay case number two and, this is
an area that Brian Calhoun and I have been working on
in Lavaca County and I was fortunate
in our little piece of 3D to have gathers, so I
created some very specialized
AVO type volumes and the far minus nears
far minus nears times fars, in this area
data quality was really good we had
really good
resolution in the gathers out to about 45
degrees
and it took getting out past 35 or 40
degrees before you saw
the results of this type 2 AVO, so you probably wouldn't have seen it
on the stack data not as obvious anyway.
These are these are the volumes and I
created
and this was my original mapping,
before the well was there, and I looked
at it and said so well you know I like
that amplitude but how many of us have
drilled amplitudes that turned up
to be fizz water or something less than
desirable. You're looking for
an analysis that can
probably help reduce the risk
in type 2 and type 3 reservoirs and
I told Brian at the time I said, I
give that about seventy acres
And he says, we'll see.
okay so this is what it looked like on
the PSTN raw volume
trough over P you know nice little thing but you know
it's not anything to write home to
mother about
And there's still a lot of risk with these
kinds of reservoirs.
so when I did the SOM analysis I use the
AVO volumes
I use the PSTN Raw amplitude. I used
average energy, I used sweetness in the
pot and the results are kinda
interesting because
my seventy acres now turns into about
180 to 200 acres because these are 
depletion drive reservoirs in the Yegua
is a finpay probably about 18 feet
somewhere between 24 and 28 percent
porosity
but all these little guys appear to be
linked. And sure enough,
I have to give Brian credit on this he
told me a long time ago when we first
started producing the
well he says this is a bigger reservoir because of the pressures
and then the seventy acres and I have to
say he's right
hurts to say that but I have to say it. Okay
This is what it looks like in the SOM analysis, it's you know
it's really isolated this is the map
again and this is
this is a line through the
well
now there was another anomaly that we
did see
in the area if you look up here the time
slice
that shows this anomaly really well is just
coming through the bottom of
this one because it's a slightly
different elevation
this is down thrown and and this is
another channel coming through here but you
can see
obviously that this one is linked in a
channel system this
and this guy just he hit the fault and kinda spread out.
the two wells show up quite well
we did drill this one
and it was successful and the nice thing
about
the software is we can go in and we
have a 2D and 1D colorbar
and we can isolate these things in the
3D viewer by just picking on certain
neurons and
bring out and what we're learning you
know the more data we work with
more tests we do the more we're learning
there's anomalies in the low probability
but when you click on and put a combination of neurons together
sometimes you can bring out channel
sequences sometimes you can bring out
stratigraphic information that may not
be hydrocarbon bearing but may
impact what you're looking for in the
area
on the next thing we're working on is
principal component analysis (PCA) and that's
it's just kinda the next thing past
SOM and what Principal
Component Analysis is is if you have a
lot of attributes and
you want to find out the ones that give
you the most bang for your buck
or make the most impact in your
interpretation and that's what principal
component analysis does.
in this particular example
I use 25 attributes over here the list
over there
and I did a PCA and I did on a line by line
basis
in the dataset that Apache gave us. Apache
gave me
150 curvature volumes and 150
spectral decomposition volumes and they
specifically wanted me to run a PCA
on those volumes to find out which curvature volumes and
which spectral decomp volumes had the most impact
so what this kinda does is this little
bar right here
this first line and the second line and
so on and so forth
is a combination of these twenty five
attributes
that make the most impact. The highest, what they call, eigenvalue.
and the software they we're working
on and it's changed its look even since I made
these slides
now we have a graph below and we're
showing the percent contribution
of each of the attributes in each one of these bars you can cursor over each of these bars and
these numbers will all change
and we can go and different lines and
say look at the different line in
a whole different set of attributes
comprised of
different values in these lines and
again this will change
but the idea is to take this green
line
and raise or lower it to include like the top
10 eigenvalues
and then run a SOM on
those so
everything that's very similar is down
below the curves
if you've got 150 attributes and you only
want to run a SOM to see what the top 10 do
you wanna run a principal component
analysis (PCA) to get rid of all the riff-raff
and only run it on the good stuff
so in conclusion unsupervised neural
networks analysis or SOM
SOM maps can solve a lot of problems
they can get in and look at attributes to
reduce risk and
identify specific solutions and and I guess
another example I have of this
a recent
problem we had with some Repsol data offshore Guiana
is they hit a zone of high-pressure when they were drilling a hundred million dollar well
and the pressure got so great they had
to stop the well
and they could not see the pressure in
the seismic data
so they gave us 10 square miles offshore
Guiana they gave us the well information
they gave us the DSP all the time depth data, they gave us the
the pressure profiles and they said run
some analysis to see if you can see the
pressure
and we used a specific set of analysis
employing curvature and similarity
and some specialized volumes that we run in the software and sure enough when you put in
the 3D volume and you
isolate, you make transparent you know
certain neurons
we knew exactly where they hit pressure they stopped the well three hundred feet short of
going out of pressure and so now they have us doing an analysis in the Gulf of Mexico
where they're having some problems
and we're gonna try to see if the same
sweet of attributes also worked for
pressure
identification but there was something
that their geophysicists and their
engineers could not see in the regular
data
that we were able to pull out using the
SOM analysis and the more information
you have
wells, production, the better the
solution could be to specific attribute
selections
neural analysis can be done on 2D or 3D data and a principal component analysis
if you have lots of attributes going in, will save you time and possibly give you better results
and finally, we're going to the SEG
for the first time so if you're going to
SEG come visit us
and thank you guys very much.
[applause]
Any questions?
Yes, Sir?
[sound not picked up in video]
well in the case of the Eagle Ford
I ran it from 1.2 to 1.6 seconds
on the Repsol data we ran it over two
time windows
trying to get an upper or lower boundary
for the pressure
a lot of volumes and I'll start out running
the whole darn volume
and then see if I knew need to narrow
down the window in the case of the
Yegua AVO's, we had so much interference
from
the shallow frio production that I had
to narrow that window down as well
to isolate the Yegua better
No, a volume-based
analysis and not necessarily a timed
now, as we improve the
software we're gonna start being able to
bring horizons in
and then do the analysis and time
windows above and below horizons or
between horizons and things like that
but we're not quite there yet.
Another question?
[sound not picked up in video]
Regular 3D data
