Frontiers in Earth Science,
Год журнала:
2024,
Номер
12
Опубликована: Дек. 12, 2024
Experimental
variogram
modelling
is
an
essential
process
in
geostatistics.
The
use
of
artificial
intelligence
(AI)
a
new
and
advanced
way
automating
experimental
modelling.
One
part
this
AI
approach
the
population
search
algorithms
to
fine-tune
hyperparameters
for
better
prediction
performing.
We
Bayesian
optimization
first
time
find
optimal
learning
parameters
more
precise
neural
network
regressor
goal
leverage
capability
consider
previous
regression
results
improve
output
using
three
variograms
as
inputs
one
training,
calculated
from
ore
grades
four
orebodies,
characterised
by
same
genetic
aspect.
In
comparison
architectures,
Bayesian-optimized
demonstrably
achieved
superior
Coefficient
determination
validation
78.36%.
This
significantly
outperformed
non-optimized
wide,
bilayer,
tri-layer
configurations,
which
yielded
32.94%,
14.00%,
−46.03%
determination,
respectively.
improved
reliability
demonstrates
its
superiority
over
traditional,
regressors,
indicating
that
incorporating
can
advance
modelling,
thus
offering
accurate
intelligent
solution,
combining
geostatistics
specifically
machine
Minerals,
Год журнала:
2024,
Номер
14(4), С. 421 - 421
Опубликована: Апрель 19, 2024
The
conventional
Archie
formula
struggles
with
the
interpretation
of
water
saturation
from
resistivity
well
log
data
due
to
increasing
complexity
exploration
targets.
This
challenge
has
prompted
researchers
explore
alternative
physical
parameters,
such
as
acoustic
characteristics,
for
breakthroughs.
Clarifying
influencing
factors
porous
media
characteristics
is
one
most
important
approaches
help
understanding
mechanism
carbonate
reservoirs.
article
uses
digital
rock
technology
characterize
pore
structure,
quantitatively
identify
fractures
and
structures
in
rocks,
establish
models.
Through
testing,
pressure
wave
(P-wave)
shear
(S-wave)
velocities
samples
at
different
saturations
are
obtained,
dynamic
elastic
modulus
calculated.
A
finite
element
calculation
model
established
using
computational
provide
a
basis
fluid
methods.
Based
on
real
models,
combinations
virtual
constructed,
affecting
parameters
analyzed.
study
finds
that
porosity
increases,
velocity
difference
between
cores
fractured
also
increases.
These
findings
technical
support
theoretical
interpreting
logging
evaluating
reservoirs
fracture
types.
Frontiers in Earth Science,
Год журнала:
2024,
Номер
12
Опубликована: Ноя. 20, 2024
Identifying
lithology
is
crucial
for
geological
exploration,
and
the
adoption
of
artificial
intelligence
progressively
becoming
a
refined
approach
to
automate
this
process.
A
key
feature
strategy
leveraging
population
search
algorithms
fine-tune
hyperparameters,
thus
boosting
prediction
accuracy.
Notably,
Bayesian
optimization
has
been
applied
first
time
select
most
effective
learning
parameters
neural
network
classifiers
used
identification.
This
technique
utilizes
capability
utilize
past
classification
outcomes
enhance
models
performance
based
on
physical
calculated
from
well
log
data.
In
comparison
architectures,
Bayesian-optimized
(BOANN)
demonstrably
achieved
superior
accuracy
in
validation
significantly
outperformed
non-optimized
wide,
bilayer,
tri-layer
configurations,
indicating
that
incorporating
can
advance
lithofacies
recognition,
offering
more
accurate
intelligent
solution
identifying
lithology.
International Journal of Multiphase Flow,
Год журнала:
2024,
Номер
180, С. 104952 - 104952
Опубликована: Авг. 5, 2024
Fluid
Dynamics
is
a
key
scientific
field
to
multitudes
of
engineering
applications.
Experimental
work
in
this
requires
careful
set-up
and
expensive
image-capturing
equipment,
particularly
when
considering
the
finer
details
complex
phenomena.
In
work,
we
study
application
super-resolution
Generative
Adversarial
Networks
(GANs)
achieve
high-resolution
results
by
upscaling
lower-resolution
experimental
images.
We
train
GANs
proposed
for
natural
images
on
bubbly
flow
dataset
compare
common
evaluation
metrics
domain
expert
assessments
upscaled
find
that
these
models
promising
results,
as
evaluated
experts,
transfer
learning
from
translates
better
performance
overall.
Attention
mechanisms
are
found
be
useful
recreating
sharper
details.
On
other
hand,
traditional
align
poorly
with
perception
quality,
signaling
need
systematic
methodologies
domain.
