Sensors,
Journal Year:
2024,
Volume and Issue:
24(23), P. 7492 - 7492
Published: Nov. 24, 2024
Addressing
the
issues
of
low
prediction
accuracy
and
poor
interpretability
in
traditional
matte
grade
models,
which
rely
on
pre-smelting
input
assay
data
for
regression,
we
incorporate
process
sensors'
propose
a
temporal
network
based
Time
to
Vector
(Time2Vec)
convolutional
combined
with
multi-head
attention
(TCN-TMHA)
tackle
weak
characteristics
uncertain
periodic
information
copper
smelting
process.
Firstly,
employed
maximum
coefficient
(MIC)
criterion
select
strongly
correlated
grade.
Secondly,
used
Time2Vec
module
extract
from
variables,
incorporates
time
series
processing
directly
into
model.
Finally,
implemented
TCN-TMHA
specific
weighting
mechanisms
assign
weights
features
prioritize
relevant
key
step
features.
Experimental
results
indicate
that
proposed
model
yields
more
accurate
predictions
content,
determination
(
Processes,
Journal Year:
2024,
Volume and Issue:
12(10), P. 2088 - 2088
Published: Sept. 26, 2024
Productivity
prediction
has
always
been
an
important
part
of
reservoir
development,
and
tight
reservoirs
need
accurate
efficient
productivity
models.
Due
to
the
complexity
oil
reservoir,
data
obtained
by
detection
instrument
extract
features
at
a
deeper
level.
Using
Pearson
correlation
coefficient
partial
analyze
main
control
factors,
eight
characteristic
parameters
volume
coefficient,
water
saturation,
density,
effective
thickness,
skin
factor,
shale
content,
porosity,
permeability
were
obtained,
specific
production
index
was
used
as
target
parameter.
Two
sample
structures
pure
static
dynamic
(shale
permeability,
density
parameters,
thickness
parameters)
created,
corresponding
model
(BP
(Backpropagation),
neural
network
model,
LSTM-BP
(Long
Short-Term
Memory
Backpropagation)
model)
designed
compare
effects
models
under
different
structures.
The
mean
absolute
error,
root
square
relative
percentage
determination
evaluate
results.
predict
capacity
test
set.
results
showed
that
average
error
0.07,
0.10,
21%,
0.97.
wells
in
WZ
area
for
testing,
model’s
predictions
are
evenly
distributed
on
both
sides
45°
line,
separating
predicted
values
from
actual
values,
with
errors
line
being
relatively
small.
In
contrast,
BP
analytical
method
unable
achieve
such
even
distribution
around
line.
Experiments
show
can
effectively
parameter
stronger
generalization
ability.
SPE Annual Technical Conference and Exhibition,
Journal Year:
2024,
Volume and Issue:
191
Published: Sept. 20, 2024
Abstract
The
evolution
of
shale
gas
production
has
reshaped
North
America's
energy
profile.
Utilizing
the
vast
amounts
data
generated
from
and
operations,
machine
learning
offers
significant
advantages
in
forecasting
performance
optimization.
This
study
proposed
a
pioneering
hybrid
model
integrating
tabular,
spatial,
temporal
modalities
to
enhance
unconventional
reservoirs.
Despite
traditional
methods
such
as
artificial
neural
networks
(ANN)
XGBoost,
which
rely
solely
on
tabular
for
training
prediction,
this
proposes
novel
3D-parameterization
method.
approach
tokenizes
formation
property
distribution
into
3-axis
tensors,
enabling
more
comprehensive
representation
spatial
data.
Then,
3D-convolutional
network
(3D-CNN)
with
attention
mechanism
module
was
established
process
created
For
modality,
long
short-term
memory
(LSTM)
used
accept
dynamic
input
predict
monthly
simultaneously.
A
total
677
wells
Duvernay
collected,
pre-processed
fed
according
based
their
modality.
results
show
that
combined
three
achieved
an
impressive
level
accuracy,
coefficient
determination
(R2)
0.8771,
surpassing
(0.7841)
tabular-spatial
(0.8230)
models.
Additionally,
global
optimization
applied
further
by
optimizing
architecture
each
hyperparameters,
1.88%
improvement
empirical
design.
These
advancements
set
new
benchmark
predictive
modelling
reservoirs,
highlighting
importance
utilizing
different
improving
forecast
prediction.
Research Square (Research Square),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Oct. 21, 2024
Abstract
Predicting
the
price
and
volatility
of
international
crude
oil
futures
is
a
complex
task.
This
paper
presents
novel
hybrid
prediction
model,
VMD-BiTCN-BiGRU-Attention,
which
integrates
variational
mode
decomposition
(VMD)
advanced
deep
learning
techniques
to
forecast
nonlinear,
non-stationary,
time-varying
characteristics
sequences.
Initially,
sequence
decomposed
into
multiple
modes
using
VMD,
enabling
capture
different
frequency
components.
Each
independently
predicted
bidirectional
time
convolutional
network
(BiTCN),
captures
temporal
information
enhances
long-term
dependencies
through
dilated
convolution.
Subsequently,
gated
recurrent
unit
(BiGRU)
models
more
effectively,
while
an
attention
mechanism
adjusts
weights
BiGRU
outputs
emphasize
critical
information.
The
model’s
predictions
are
optimized
with
Adam
algorithm.
Empirical
results
demonstrate
that
model
adept
at
forecasting
non-stationary
nonlinear
prices.
Furthermore,
Diebold-Mariano
(DM)
test
confirms
this
surpasses
15
other
regarding
accuracy
performance,
achieving
optimal
key
metrics:
R²
=
0.9953,
RMSE
1.4417,
MAE
0.7973,
MAPE
1.5213%.
These
findings
underscore
its
potential
for
enhancing
prediction.
Energies,
Journal Year:
2024,
Volume and Issue:
17(22), P. 5674 - 5674
Published: Nov. 13, 2024
In
the
prediction
of
single-well
production
in
gas
reservoirs,
traditional
empirical
formula
reservoirs
generally
shows
poor
accuracy.
process
machine
learning
training
and
prediction,
problems
small
data
volume
dirty
are
often
encountered.
order
to
overcome
above
problems,
a
model
based
on
CNN-BILSTM-AM
is
proposed.
The
built
by
long-term
short-term
memory
neural
networks,
convolutional
networks
attention
modules.
input
includes
previous
period
its
influencing
factors.
At
same
time,
fitting
error
value
reservoir
introduced
predict
future
data.
loss
function
used
evaluate
deviation
between
predicted
real
data,
Bayesian
hyperparameter
optimization
algorithm
optimize
structure
comprehensively
improve
generalization
ability
model.
Three
single
wells
Daniudi
D28
well
area
were
selected
as
database,
was
production.
results
show
that
compared
with
network
(CNN)
model,
long
(LSTM)
bidirectional
(BILSTM)
test
set
three
experimental
reduced
6.2425%,
4.9522%
3.0750%
average.
It
basis
coupling
meets
high-precision
requirements
for
which
great
significance
guide
efficient
development
oil
fields
ensure
safety
China’s
energy
strategy.
Sensors,
Journal Year:
2024,
Volume and Issue:
24(23), P. 7492 - 7492
Published: Nov. 24, 2024
Addressing
the
issues
of
low
prediction
accuracy
and
poor
interpretability
in
traditional
matte
grade
models,
which
rely
on
pre-smelting
input
assay
data
for
regression,
we
incorporate
process
sensors'
propose
a
temporal
network
based
Time
to
Vector
(Time2Vec)
convolutional
combined
with
multi-head
attention
(TCN-TMHA)
tackle
weak
characteristics
uncertain
periodic
information
copper
smelting
process.
Firstly,
employed
maximum
coefficient
(MIC)
criterion
select
strongly
correlated
grade.
Secondly,
used
Time2Vec
module
extract
from
variables,
incorporates
time
series
processing
directly
into
model.
Finally,
implemented
TCN-TMHA
specific
weighting
mechanisms
assign
weights
features
prioritize
relevant
key
step
features.
Experimental
results
indicate
that
proposed
model
yields
more
accurate
predictions
content,
determination
(
