I'm sure you know how to make good deals. So what I want to talk about is
what are some of the things that you want to think about the risk? Well, we're
small partners, small boutique shops, we acquire mature assets and operate and
monetize it on exit, so maybe drill some wells or do some ESP. But the thing I
learned quickly-- we never enjoy average wealth-- just the spread. So that's
the way to think about risk.
I'm a geophysicist, so how many others are geophysicists here? OK, good.
Thank you. But I'd like to quote the geologist of all geologists, Wallace
Pratt. He said that oil is finally in the minds of men. In fact, he added on
his 91st birthday-- and women. But if he were still here today, maybe given the
data that we'd have access to-- all these production information, maybe
learning with machine-based, machine-learning alone-- he might suggest that.
Now, let's talk about risk. If I substitute learning with flying-- flying
with a machine on a 737 MAX 8 beats machine flying alone. I think that's the way
I look at it. OK. So bear with me.
Now, what we're going to go through in the next few slides is just look at
risk, but how do people think about it, but particularly if we want to get a
better handle on it? Use the granularity of data, but also we want to be
transparent. We don't know-- just trust me, and do that, and do this, and put
it all together in an actionable wisdom, so we just make better-informed
Now, there are a lot of books you can read, but this one you read over the
weekend-- 138 pages. Bethany was the same author that wrote about Enron. She
was a financial analyst at Goldman Sachs for a while, so she knew what she was
talking about. But if I can help you buy just the book in one word-- it's about
volatility. It's not the price of oil. I mean, it goes up and down. I mean,
December was $44. And a couple of days ago, $60-some. But that's not what she's
talking about. She's talking about some of the things inherent in our industry,
or whether you rush in to go, and it's quite expensive-- acreage and drill and
maybe you can really make it work.
But let's see some of the quotes from there. You can read it. But one of the
things she pointed out was the valuation was based on multiple-- not multiple
profits, as we all now learn, but based on the multiple of acreage, which
didn't work quite well.
All right. So this is one that perhaps gets some attention-- based on
Goldman Sachs-- that in order to keep the same production level from the
conventional, you'd put a lot more capital to work. So let's say 2018, maybe
about $36 million, $38 million-- 2023 project about a little over $56 billion.
But if you use the same cumulative average growth rates-- could be a huge
amount by 2040.
So I don't know, if by 2050, we can switch to renewable, but it's a
tremendous amount to keep it at the current level-- not really about growing.
So what is sustainable? Not as you're a better judge than that.
Now, what I want to talk about is something about how do we use data. We use
a lot of data, but how? Well, as far as this other book talks about,
Bridgewater, I guess they run very successful hedge funds, but they really
built a database, and they use algorithms-- go through data. So they come up
with-- a way to look at it is always continuous learning, and so forth-- how to
make better decisions for the next round.
So the algorithm-- but the data-- now, everybody talks about Google, Amazon,
and so forth, but I think particularly for oil and gas, we do have some data,
but we need to think about it differently. The data, for example, you heard
about type curves, and here's one. Just, typically, we get all kinds of curves,
but we might average them out. We kind of have one representative of an area.
But that's done many, many times. So later you'll find out we've got better
ways to use machine learning to help you do that.
But having said that-- so that's a starting point. You get a type curve. And
then the data granularity-- so let's talk about spread-- this is the intrinsic
volatility in the initial production. Now, whatever the reason-- it so happened
this is in Eagle Ford-- you can look at the spread, you say, hey, well, I just
kind of fit three different distributions. These days, there's 80-some of these
you can fit. But if you look at the purple dash line, that's the normal
distribution. That's where we say, hey, we took the average, we have some feel
for if we drill enough wells, we'll hit that average. But if you drill only a
few wells, it might not work. But the actual distribution looks more like a lot
normal, so kind of skewed.
Now, geologists here-- what does it remind you-- the lot normal
distribution? Well, the grain size, the free reserves-- a lot of not normal
in-- mother nature doesn't play dice. This is not a normal distribution. So
what it really means-- the first thing is that if you take an average of
something-- you say, hey, that's a P50. No, that's not a P50. In fact, let's
say these are three areas you look at in Eagle Ford-- a lot of different levels
of production, productivity, but in any case, they all showed some kind of
So the first thing is that, hey, when we look at risk or whatever the kind
of when we want to get a handle on the return versus risk, maybe we should
think about how to incorporate the spread in there. OK. So just an example, so
let's say I go in, I say, well, I don't know, but I can put in-- I have $100
million. I put 1/3, 1/3, 1/3, to each equal split into those assets. And then I
can-- based on the decline curve-- I can do well because I get a distribution
of what that spread of the return might be. All right?
Now, looking at this spread is one thing, but how do I make sense of it? So
I borrowed a concept from the value-at-risk from the financial industry. That's
what people use to determine this with a bank or institution is safe under
But one other simple interpretation is this-- in the middle, the black dash
line-- that's, let's say 50% of time-- I might-- for this portfolio-- I might
lose no more than 10% of my investment. And you go all the way to the left, you
say, well, about 99% of the time, I would not lose any more than 82%. So it
gives you a feel for, hey, if I want to do some hedging, I say, well, I don't
want to lose more than 10% so maybe hedge 50% of my investment or 50% of my
But if I want to sleep like a baby maybe-- I want to hedge more because I
push it to the far left, but of course, it's like insurance-- I want more
coverage, I have to pay more. But, that's again, one way to look at if you look
in an area-- look at the distribution of production, you can have a way to
gauge what the spread is. That's just one scenario.
What if there's some other combination? So, for example, maybe for these
three assets, I might have different amounts that I put in. So if you go
through the scenario assimilation-- so each one of these spreads, then I say,
hey, I have the so-called variance and the expected return, and I plot it on
this with-- in this case, do 2,500 assimilations, so I get this little diagram.
But what's important is that in terms of the expected return versus expected
volatility, which is the risk, the spread, it's not random. It has this little
shape. It's like, you think of, it's like a little bow-shape if you turn it
sideways. But the key thing is that the end-flow-- the edge of that is what
some of the financial world calls the "efficient frontier," as some
of you probably have used them in your company.
But what it really means is that for the allocation along that efficient
frontier for a given return that you seek, there's a portfolio allocation of
those assets that you can achieve with minimum volatility. So where the black
arrow points to, that particular combination-- maybe half of that-- goes into
Asset A, 8% goes into B, 41% goes into C, and then you get about-- so the
return's roughly about 12%-- I mean, about less than-- slightly less than 10%,
but your volatility also is less than if you go to the right.
But if you really want to go with minimum volatility sometime, you might say,
hey, I sacrificed on the return. Now, this is based on average well cost and so
forth. So if you have the ability to improve the operating efficiency, lower
your cost, then you basically can move the thing up towards the upper left
quadrant. That's what you want.
OK. So I basically split this into four quadrants-- the upper-left,
upper-right, lower-left, lower-right, so in all that, you get a sense of what
that really means. I borrowed this concept from Boston Consulting Group, the
Matrix Unit. Lower-left-- you don't want to go there. Those are docs that, hey,
they're low return, low volatility, no chance of really turning around.
The upper-right is the stars. It's good return-- good risk-adjusted return.
But what you see is that they're all going to-- they are mostly the mirror
image-- the inverse of each other when you go diagonal. So, again, it gives you
a sense of, hey, how do I think about allocating my capital to work? Now,
there's only for 3. Now, you can do it for 80, you can do it for 100 and so
forth, but it's the same thing.
So that's using the granularity of data-- using all the distribution of the
initial production and giving you a sense of-- but still using the type curve.
So we'll come back to that. Now, as far as the data, we want transparent
process. So if somebody comes in, hey, this is a really good opportunity,
here's a return. But if a single number-- how to think about it. So here we're
going to talk about it just as an example.
If you look at the stock market or oil prices, today-- what they were-- and
over time, you have one variable. When you have a time series, you can fit the
trend. You can do some prediction or forecast. But if you have different-- more
than one variable, then you still can do that.
So in the machine-learning world, it's a multivariate regression. But in the
simple case where we want to think about in this application, hey, there's some
opportunity at Eagle Ford. I can go and restimulate, refract the well, and
maybe I'll get some decent uplift. So here we say, hey, look for those either
binary, yes, or no-- some places that I might get a before and after four
times, basically, 300% uplift and where but those places are.
Now, the features that you go in here is the pop-ins, the volume, the
fluids, and the chemicals, and the thing that you want to predict or you want
to use as criteria is the delta of initial production before and after. So the
vendor-- since we don't have the magic sauce-- so the vendor is a proxy for
that-- so there are about 12 features here and about 3,850 wells. So in green
are the ones, that, hey, we get good so-called test results that predict or
forecast about-- the red is off. But this is not about how good this is, but
it's just a way to say, hey, there's a way to use the algorithm with the data
coming from frac-focused data and be able to look at the opportunity and start
thinking about is that an area that's worth taking a second look.
A lot of geoscientists, when we talk of models, we think of earth models--
the properties associated with this at every location, the velocity or the
brittleness, the permeability, porosity-- but in the machine learning, when you
talk about really models-- data models or some kind of weight associated with
particular features whether that particular one is the pop-ins or the volume of
fluid and so forth. So we'll keep that in mind. So what we're really interested
is, we think about the decline curve for so much of the data, but are there
some ways that I can do better and put all this into work?
All right. So, again, instead of just algorithms, there's some first
principle. So here's, I think this is another one of those-- my go-to book-- I
think it was published about 1990, and it's out of print, but if you can get it
from a used bookstore, it's still worth taking a look. In there, there's a lot
of talk about risk but how we look at the opportunity. So, today, we just
simply take some of those ideas and put it to an algorithm and works. So here's
an example. It's a type curve, and in the current-- there are different maybe
modified the Ops equation that lets you look at the different fittings. And so
you can do that and play with it, and so you can get a fit. But the key is,
hey, maybe I can think about what can machine learning do with this information?
So here's one example. And in this example, we take four of the observations
and use it to predict a fifth. So think about it real long. I get all his
information. I organize it. So I can train it, let it come back and see how
well it does.
So you can see in the orange-- this curve is the modified up equation. So
it's like if I look at it, I try to fit it, and, hey, I get a good fit, so I
got a pretty good model. But if I go to any one of the wells, you start getting
data in, and I will have to do this again, right? So that might be a little
bit-- that's OK for 1/2 a dozen wells, but if you've got thousands of wells,
that might be a little bit difficult. So on the other hand, in the Neural
Network application we can take the information-- maybe the first six months or
so forth of the production-- and then we run through this, and we'll be able to
train this and be able to predict.
So what we want to show is that the Neural Network-- the yellow band is the
input data where we will use for training, and then the rest is how it would
use to predict and forecast. So you can see the green-dashed line does fit the
actual solid type curve reasonably well. The orange is an example-- just a
moving average. So you can see-- just get a flavor of what it's doing. But it's
better than moving averages because it keeps track of the detail.
Just for information, the Neural Network is the straightforward, simple
implementation and a more powerful one you might come across in literature.
This recurrent Neural Network is more using long and short-term memory. So it's
just more detailed. It keeps track of the same input, but it keeps track of the
case. So either way, there's two to automate that whole process, but it's
transparent. I can run through it-- could be thousands of wells, and I can get
So let's go back to the principle. I'd like to point out for these
unconventional wells because the client is significant. So what I'd like to--
sometimes look at the half-life-- that means half of the production is
whatever's captured within the first 8 or 10 or 11 months. But what it means is
that if we're doing a restimulating with fracking to move the needle on well
economics, then we have to do it before the half-life.
So let's go back to why we want to start having a tool like the Neural
Network so I can capture from early production and start projecting and look at
my remaining reserves in place-- recoverable the ow, ow, ow, so we can see,
hey, there's some opportunity. We can do it before it gets past this economic
All right. So these portfolio analytics-- at least it's in addition to
perform the allocation, and how do we do it better? Just a kind of quick
switch. I just want to show the same two where here is the hot energy. And on
the website, you can show they have the four major stocks. And if you look at
that, you say, hey, that's the efficient frontier that the yellow star is the
minimum portfolio, minimum variance, and the star is the best risk-adjusted
So that's the four stocks. But if I add one of the independents, it might
change and spread it out because there's a lot more volatility of stock prices.
But the underlying thing is the same, that you can get a feel for how you
allocate. Now, that's from a pure, simple stock portfolio. But if I take the
same approach, look at the production data-- for example, on the left, I have
the production data from the four basins-- they worked on 2014, '15-- back in
Eagle Ford, Marcellus and Permian-- you get a spread. You get the efficient
frontier envelope. On the right-- but if I drop back and I look at the
Permian-- the Eagle Ford, Marcellus, I got-- well, the maximum risk-adjust
return the red star and the yellow star-- they're still about the same in
place, but the spread is smaller. So if you're looking at your portfolio
strategies, this might be something to consider as a quick way of maybe
deciding how you fine-tune your allocation.
One last thing. Nobody is going to fund these all on their own from banks,
or so we always need to think about maybe a mix of equity and debt. So if you
have done the portfolio work, the idea is that given whatever the fed rate on
the intercept, you can say, hey, wherever that line I draw-- if it's 2%,
tangent to the efficient frontier is where the most robust. That means if I
have that allocation, I might be able to weather the storm a little better. But
if the federal fund rates keep rising, you can think of that little box to go
from the green to the red-- it starts getting smaller and smaller.
So at some point, this doesn't make sense to finance these projects. So
that's just a quantitative way of, again, using all the data-- the production
data-- going through this whole process with the machine-learning perhaps on
the decline curve part instead of just the type curve and come up with more
robust views of these risk and return profiles. Now, again, if I start doing
this, I can even classify it into three bands that have really saved-- the one
at risk, the one that's really in trouble, so you can just continuous update.
Every day you can look at it. But where I'm going with this is that when it
comes to asset pricing, one of the things is I can put together using the
quantitative technique with the fundamentals, with a production trend, I can
actually come up with a much better handle on where these asset prices may be.
Now, it's all good. But at the end, it's learning with the machine piece--
the machine learning alone. So I want to share with you. And I want to put this
out-- get more people with you think about get past using the averages-- that's
not your P50-- but one of the things is, if you go to Amazon Skills, you can
look up, Well Proved Undeveloped. There's a little thing you can download-- you
can play with. And you can give us some average-- some spread. And if you want
the P10, P50, P90, assuming it's a lot normal distribution. And you can really
gig it out. You can be a P66-- it will give you that too.
So at the end, what I really want to do is to think of developing a digital
assistant. What kind of digital assistant do I want? Is it a P10 guy, P50 or
P90? I really want him to help me to become the giant of whatever basin I'm
playing. This, by the way, that's James Dean, The Giant, in 1956. Thank you.
And thank you for your attention.