Drilling fluids touch just about everything in the
drilling process.
We're here to deconstruct the drilling process and
drilling fluid concepts
to provide a deeper understanding of our industry.
In each episode, we'll share information,
talk to interesting people, and maybe share a
few stories along the way. Welcome to the
flow line, a production of AES Drilling Fluids
brought to you by Matt Offenbacher
and Justin Gauthier.
Hey, everyone. Welcome back to another episode of
the flow line. Believe it or not, this
episode is actually being generated by AI. Can
you tell? Yes.
Just kidding. Tried to sound like a robot.
Beep boop beep boop. No. It's actually an
interesting conversation, Matt. We've talked about this internally,
and
if you've been online over the last year,
you've probably seen the word AI at least
a trillion times. It's a buzzword, but there's
actually some interesting things happening. I'm sure if
you did some googling or some chat GPT
ing, you've probably got an understanding of what
it is. But coming from our side of
the world here in the mud world, Matt
and I thought it'd be good to talk
about
what is AI first by kind of defining
it for those who may not be as
interested in it, but just to learn, and
then how we might apply it in the
mud world, Matt? What do you think? I
like it. Cool. Well, first and foremost, Matt,
how would you define AI? What is it?
AI, according to this definition I looked up,
the practice of getting machines to mimic human
intelligence to perform tasks.
Artificial intelligence is super broad. I found
it was a fairly cynical
cartoon online on Twitter and x. Sorry. But
it was like a graph on the wall,
and then somebody put a frame around it,
and it was like machine learning, and then
they just changed the label to artificial intelligence.
Uh-huh. But, originally, it was like statistics. Right?
So a lot of predictive modeling, that kind
of thing,
and a lot of people use machine learning
or they were using that sort of interchangeably
where machine learning is a subset of artificial
intelligence,
and a lot of that is models learning
from data patterns without any direction.
There's also sort of AI that just learns
from pools of data. It doesn't need them
to be organized in any way.
I thought that the journal Petroleum Technology
had a good article, and they sort of
broke down just data science produces insights,
machine learning produces predictions, and artificial intelligence produces
actions.
And I kinda like that explanation. So data
science is the visualizations
where you can understand large volumes of data.
Machine learning says,
with all that information, the next likely thing
to happen is this. And then artificial intelligence
is,
because I know the next likely thing to
happen, I'm gonna go ahead and do it.
Yeah. That's actually a really simple way of
kind of the flow of how that all
kinda works. I like that. Yeah. One of
the big areas that people are talking about
is an area called deep learning,
which is
a subset of AI and a subset of
machine
learning that uses what are called artificial neural
networks. And, basically, they're trying to model the
way the brain works, the way our neurons
interact
to
basically model our behavior,
model the way we think.
Probably the biggest thing that everybody's talking about
with AI right now is large learning model
or generative AI. So this is your chat
GPT,
your Claude, your Gemini. Mhmm. And we see
the good and the bad, but we can't
seem to stop talking about it. Well, it
keeps evolving at such a rapid pace. So
many companies are using
the backbone of OpenAI or Chai ChiBT to
then build their own. That's sort of the
framework, and then you can build things on
top of it for whatever it is you're
trying to do, which I know sounds very
broad, and I can picture it in my
head. But a lot of times, I have
a hard time articulating that. But is that
right, Matt? Because, like, a lot of times
people are using you can subscribe to ChatGPT
Mhmm. But there's a lot of people who
have built their own ChatGPTs for, like, business
intelligence or DALL E uses that open AI
to generate images, and that's like the, I
don't know, like the c or, I don't
know, hub, and then there's spokes around it
to create all these other things. Yeah. Well,
especially with generative AI, you can train it
with additional datasets.
Some of the discussions are like a pharmaceutical
company might spend 1,000,000,000
coming up with their own generative AI
to come up with new drugs, cure things,
or what have you. And they may need
that to just use their own pool of
knowledge.
But most of these AI tools
are trained on the Internet.
They learn from everything that's out there, and
then you can say, okay. Well, here's some
extra information I'm giving you
that is proprietary to me or what have
you. And some of the discussion is even
like chat bots and stuff where we can
get them to imagine at your bank
how they really stink right now and they
go through all it, but, like, you can
say it in the form of like, I
am having trouble
accessing my account and instead of it being
like, well, here are the 5 steps, it
will actually say, well, I've looked into this,
this, and this. Do you want me to
do this for you? Do you want me
to reset your password?
Yes.
Okay. I've reset your password. Now go try
and log in again.
Instead of going through the,
let's talk to 5 different people about a
very basic problem because it wasn't on the
prompts and the keypad or what have you.
Yeah. I'm excited about that aspect of it.
Yeah. So what what is exciting about AI
from your standpoint? I think one part of
it is it's new. We're in the middle
of the hype cycle,
which means
we're still learning ways that it's useful. Yeah.
There's all this exciting stuff on the horizon.
Could I do this? If I can do
this, can I do this?
At least in my world,
there's just all of these opportunities
to get excited about where it's like, well,
if this stinks, could AI fix it?
And I think we're also just the first
time you use a large language like chat
gpt and you prompt it, and it 2
seconds later spits out something that's
a paragraph that makes some sense.
Sounds like a human, and you're like, wow.
This is very different than what I've used
before. Yeah. We're still learning what it can
do. And a lot of the experts are
saying, you need to keep playing with this
to find out what it can do because
nobody's figured out the exact limits. They found
ways to use it, but they haven't found
every way to use it. Yeah. No. That's
true. And with a lot of
conversation
around AI, a lot of it comes down
to what are the risks, And I think
that's important to also discuss is from your
standpoint,
what's risky about all this stuff and because
everyone seems to wanna jump in, but I
think it's important to talk about the unintended
consequences.
Yeah. I mean, we'll start with the hallucinations,
which I think it doesn't seem that anybody
really understands with generative AI
how good you can get it and if
you can get it to stop hallucinating.
And it hallucinates less, but in effect,
it's not human. Right? What it is doing
is is using a probabilistic model to guess
what part of a word follows another
word, and it is probabilistically
possible
that it can say something totally off the
wall very confidently
that is complete nonsense
surrounded by things that make perfectly good sense.
Right. And at that point, if you're not
keenly aware of this,
you can fall down some pretty bad rabbit
holes with
just believing something that is written in a
way that sounds credible.
So I think there's a lot of interest
in trying to figure that
out, and some of these things get better.
But Gemini, Google's
AI, bunch of people were putting up when
they indexed Reddit, they signed a deal with
Reddit to index everything.
Somebody put in, I'm trying to get the
cheese to stick to my pizza, and it
said, well, you could use a glue stick.
No. And you're able to find the Reddit
post where somebody sarcastically said that. Right? Like
but it it says it it it was
saying some crazy stuff. No way. And this
stuff's learning off the Internet. So
you can chat with it, then it can
start saying horribly inappropriate things.
Do you have any examples of where it
hallucinated on you? Like, I use chat GBT
all the time. Yeah. I mean, when I
ask technical drilling fluids information, there's a lot
of times where it's like, that's not true.
Yeah. Okay. It was funny. I got into
an argument with Gemini about something a while
ago. Like, it was no No. That is
incorrect, and it was actually like, no. You're
right, actually. Oh, wow. Can you provide any
detail? That's interesting. So Do you remember? Without
going into too much detail, I was trying
to put together a slide pack to explain
how certain high temperature products work. Okay. And
there's a certain process that you can use
to
make things more temperature resilient,
and it said, no. That actually makes them
degrade faster. And I got into an argument,
well, what about this and this? And then
it was, like,
actually, no. You're right. I mean, blah blah
blah. It was something I knew enough to
I was, like, well, we're using this stuff
so I know it works, but I don't
know why, and I'd love to be able
to explain why to somebody.
So there's just a weird tension there.
So there's hallucinations.
I think
artificial general intelligence, like, can we make these
models as smart as a flawed person, and
does it make us useless?
I think that that is both scary but
a little overblown.
You can go through history and talk about
how cars were gonna eliminate all these jobs.
I was just gonna yeah. Robots and automotive
factories. Almost every time, what it led to
was
machines complementing people and making them more efficient.
I'm a little skeptical of that, but I
understand the fear.
And then I think tying back to some
of the training,
there are aspects of
nobody understands how these models work. It's a
black box. Besides its hallucinations, you don't know
where it's getting this information.
And it'll say, I can't really tell you
where I got it because
I'm just using probability to mash words together.
And the problem is, are you violating copyright?
Are you stealing somebody's IP and not knowing
it? Was it reading a patent that you
go you do that and you're in direct
violation? Mhmm. There's a lot of concerns with,
especially, derivative works and content creation and stuff
where it's like, hey, you're actually taking somebody's
original work and you can't do that. Yeah.
And the law is not clear either. Right?
Yep. And then kind of the last one
on the risk side is just bad training.
And by that, I mean, I search for
technical drilling fluids information on the Internet,
and there's a lot of academic work, stuff
that was never used in the field for
very good reason.
Most companies keep a lot of their very
nuanced stuff in house.
Searching for it, you're not gonna find what
you're looking for, but it's going to give
you an answer.
There's an argument that's being made that
as people sort of pass off writing articles
to AI,
what's gonna happen is AI is gonna be
training AI, and it's all just gonna be
the same. There's gonna be very little new
original content.
Right. To train these models any further.
There's just a lot of risks over
not paying attention to what it gives you
because it's so convincing.
Understanding those risks
could be pretty important, and people are being
pretty greedy with your data.
Keep in mind that a public ChatGPT account,
you're training the model.
So anything you tell it or if you
ask it to review some numbers, anything along
those lines,
I think it was Samsung inadvertently released a
bunch of trade secrets
because people were using the public version of
ChatcheBT,
and it was training the model, and then
you could query
specific things about Samsung products, and it was
telling you for stuff that wasn't out yet.
There's a lot of these unintended consequences.
There was one by the DOD where they
trained
they trained some drones in simulation. There was,
like, your mission is this. And
they tried to shut them down, basically call
them back before the mission was over,
and the AI was like, well, my job
is to complete the mission. So it tried
to attack the command center What? Because the
idea was you're getting in the way of
my mission.
Oh my goodness. So it's like you have
to think of making sure that you tell
the AI not to do that. Yeah. And
so what is the stuff we miss?
Jeez. I mean, there's all kinds of stories
about that stuff. But I think for the
sake of our episode,
have you used any AI tools to just
kinda wrap your head around some drilling fluid
stuff? On the drilling fluid side, not specific
to anything technical. It's been more to help
organize thoughts for presentations or
reports or
certain things that sort of deliverables that help
me fine tune the delivery. I'll ask you
to maybe review. So not, like, specific to
I was trying to solve a problem in
drilling fluids, and Chatt GPT helped me. So,
no, to answer your question, I do use
it. I have it
basically replacing
Kahugel. And so if I have questions or
part even 2 is going into a
a meeting. I'll kinda outline the meeting, and
I'll say my objective this meeting is to
do x, y, or z. Can you give
me any suggestions on how to structure the
meeting or any good questions that I can
ask to help get there? In my mind,
I already kinda know how I'm gonna approach
it, but it's even helped me just improve
that piece of my job or whatever the
case is. So I'm using it more on
that kind of things, but not as specific
for, like, technical drilling fluid stuff. I think
that's a huge part of it though is
the creativity where it's like sort of having
a friend you can bounce ideas off of
and
you can even say look
Somebody in that meeting room is
going to be somewhat hostile.
What can I do to start off the
meeting
to release the tension? Yeah. And it might
throw something out and you could disagree with
it. You'd be like, well, but I'm not
gonna do that, but I'm gonna do this.
Yeah. It can be kind of like a
little friend that you can bounce ideas off
of and you can tell it, act this
way, pretend to be this kind of person,
and
then answer my question. Yeah. That's true. And
even you can reference if you let's say
you read a book and and one actually
and you suggest this, unreasonable
It's one that I'm reading in almost, I
would say, close, you know, around halfway through.
But it's interesting because of the Internet and
there's probably so much stuff around that book
or any book, you could say based off
principles
that outlined in this book, how would you
handle this situation
if you believe that that, say, book or
that material or that author or that individual
has a good way of handling whatever. And
so, again, the creativity I think is where
the differentiator is for a lot of this
stuff because you could say act like if
there's someone out there or, like, there's a
hypothetically speaking. Pretend you're Elon Musk. How would
you handle this conversation? And it'll give you
a much different
response than saying, how would you handle this
conversation? And so, again, if you really get
creative and kind of expand
the box in which you sort of think
in, you can get pretty interesting results. Yeah.
I think the creative process in general, that
can be r and d, that can be
communication.
It can be very helpful for that. Yeah.
Let's focus in on the drilling fluid side
of things, Matt. I think that's something that
you've probably played with a lot more than
myself. How can AI help in drilling fluids?
I mean, a big part is troubleshooting. I
think institutional knowledge where
you can
much more easily search and compare
problems you've had in the past with problems
you have now and be like, oh, this
is just the same thing.
The first thing I like to think about
large language models is they're, like, really awesome
search engines.
Faster search and better access to knowledge,
I think, is kinda the first and foremost
a big one.
R and d is actually quite interesting just
because, like, mud is pretty complicated.
It would be very difficult to be like,
tell me how these 3 molecules are gonna
interact in an emulsion and but I think
kicking around ideas has been pretty helpful,
and there's a lot of technical literature out
there. And being able to, like, summarize this
paper. Yeah. Does it have what I'm looking
for?
Those things, it's been it's been really helpful.
The big win or kinda equalizer I see
is in modeling though.
There were a lot of hydraulic models and
other types of models that were developed,
and a lot of it was just sort
of, like, calibrating things to the rig.
But nowadays, you might not need some elaborate
equation. You could just say tell the AI,
like, this is my standpipe pressure, and these
are my mud properties. Yeah. What is it
across temperatures? What is it across
these other conditions?
And it requires a lot less
research
to
get the outcome or create a model that
will generate a consistent outcome. Yeah. I think
there's just a lot of time savings in
that where your pattern recognition and that kind
of thing.
Even well planning, like, hey,
write me up a mud program based upon
all these other wells we've drilled, that stuff
we've been playing with. Yeah. It could save
a lot of time. Yeah. The way I
typically describe it to people who are like,
oh, I just don't know how to use
chatJPGT or how to really implement AM, just
like think of it as a super powered
productivity tool. Again, like you said, it's like
your little friend. And so if you get
used to being able to
use it to help increase your productivity, I
think right now that is a win. And
then anyone that I'm working around, like our
techs and stuff like that,
like, hey, play with it. Mhmm. It's yeah.
It just maybe seems like you're putting stuff
into Google search bar, but if you look
at some of the things that people have
built to help with whatever and too so,
like, perfect example. I met with a customer
of ours who's on the executive level, and
he was a very small operator. And so
a lot of their stuff, they're bringing in
scout data or something, whatever. Anyway, we were
talking about this exact topic over lunch yesterday,
and he said, man, you will not believe
how much it's helped me. I guess he
had all this data and all these reports,
and he dumped them into ChatGBT
and said organize it in this fashion, blah
blah blah blah blah, gave it all his
input, and he said it would have taken
him a week. He did it in a
matter of, like, an hour Mhmm. And he
brought it to the CEO.
And the guy was, like, blown away how
he was able to organize it, collect it,
have the insights and the answer and kind
of the overall, like, here's what we need
to do moving forward,
and it was just, like, blown away. So,
again, like productivity, that's where I think right
now there's a huge benefit. But being careful
what you obviously provided,
going back to the risk,
I don't know what this gentleman like, how
much sensitivity this information had, but he's dumping
data into this I don't know if it
was public, but anyway. Well and think about,
like, are there hallucinations where it's like, okay.
It all looked good, but, like, you gotta
read every page you generated and make sure
that And that's what I had said. And
he said for the information he was providing,
it would have been easy for him to
identify an outlier or something that was not
correct, but
he was quite confident
based off his initial assessment that it didn't
mess it up too bad. So No. That's
good. I think that, once again, we'll play
with it more. We'll learn more. Yeah. There
have been a lot of interesting studies
that have shown where it's been a huge
benefit in certain areas. In other areas, it
hasn't been as helpful. Sure. But the important
thing is to try it. We haven't figured
out everywhere
it can be useful. Mhmm. Again, in 5
years from now, we're gonna look back, and
it'll probably look so much different than what
we think of it today. I mean, this
stuff is moving at a rapid pace, and
so I'm excited. I always like to kinda
see what the future holds. And, again, you're
pretty tech savvy, and you kinda keep your
finger on the pulse of all this stuff.
So
and just AES in general, we love new
technology. We love staying in the cutting edge,
but we thankfully do our due diligence and
make sure we don't overstep our boundaries and
keep forging ahead. But I'm curious if anyone
out there in the audience has any experience
with AI and if it's been revolutionary for
them or how it's maybe improved some of
their things at work or something like that,
Matt. I mean, any closing last words or
requests for the audience? I'm curious too just
because there's so many ideas out there. We're
already seeing it. A lot of these drilling
simulators, some of these other things. Yeah.
But with a hype cycle, I think it's
also interesting to recognize
I remember Nanotech and how everybody was trying
to put nanoparticles in fluids, nanoparticles in everything,
and
then it got quiet. Well,
a lot of the exciting
good applications for Nanotech just became
things you
did. At least what we're seeing on some
of the AI side with software and stuff
is, like, a lot of the security stuff
has AI stuff built into it. Right. That's
one way to do security better.
You're not buying software because it has AI
in it. It has AI in it. Like,
that's the latest release. Leverages that. Yeah. So
I think there will be a lot where
it'll just be, like, normal standard of business
is you're using AI more. Yeah. And even
then, like, this is a super simple example,
but, like, now in emails,
someone will send you an email, and then
out at the top, it'll give you, like,
4 or 5 different options, like, great, comma,
thanks, exclamation mark. Oh, that sounds good. Whatever.
That's AI. And now it's just become like,
oh, I don't actually have to type thank
you. You can just hit a button that
says thank you because it's recognizing the pattern
of, like, this is an email that you
would typically respond by saying thank you. Yes.
Lee told me the other day, he was
replying to an email in a somewhat colorful
way, and it said, perhaps consider a less
aggressive approach or something.
That's funny. Yeah. But to your point, I
think now it's becoming almost
part of this regular routine,
part of the workflow. Mhmm. But then there's
some obviously powerful things out there too that
are moving the needle. But with that said,
again, anyone out there has any questions or
thoughts or any examples of how they're using
it, I would love to hear about it.
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