Journal of the Geological Society,
Journal Year:
2024,
Volume and Issue:
181(6)
Published: June 17, 2024
The
quantification
of
total
organic
carbon
(TOC)
and
the
free
hydrocarbon
content
(S
1
)
is
crucial
for
evaluating
shale
oil
generation
bearing
properties
source
rocks.
This
study
aimed
to
enhance
accuracy
TOC
S
in
evaluations.
scope
encompassed
development
a
novel
deep
learning
framework
overcome
limitations
traditional
physical
machine
or
methods.
paper
proposes
an
integrated
swarm
optimization
algorithm–convolutional
neural
network/machine
framework.
uses
algorithm
hyperparameter
convolutional
network
framework,
utilizing
experimental
data
from
core
samples
preserved
liquid
nitrogen
alongside
well
logging
data.
application
proposed
H11
Subei
Basin,
China,
using
110
samples,
demonstrated
superior
performance.
results
validate
framework's
effectiveness
predicting
contents
at
various
depths.
stands
out
its
convenient
methodology,
wide
range
high
precision
prediction.
These
attributes
contribute
significantly
field
petroleum
engineering
development,
offering
approach
that
promises
both
efficiency
evaluation.
SPE Journal,
Journal Year:
2025,
Volume and Issue:
unknown, P. 1 - 19
Published: Feb. 1, 2025
Summary
Reservoir
characterization
is
critical
to
the
oil
and
gas
industry,
influencing
field
development,
production
optimization,
hydraulic
fracturing,
reserves
estimation
decisions.
Accurately
estimating
porosity
crucial
for
reservoir
characterization,
well
planning,
optimization
in
industry.
Traditional
determination
methods,
such
as
porosimetry,
geostatistical,
core
analysis,
often
involve
complex
geological
geophysical
models,
which
are
expensive
time-consuming.
This
study
used
integrated
machine
learning
model
of
differential
evolution
(DE)
with
group
method
data
handling
(GMDH-DE)
estimate
using
log
from
Mpyo
field,
Uganda.
The
GMDH-DE
demonstrates
superior
performance
compared
conventional
GMDH,
support
vector
regression
(SVR),
random
forest
(RF),
achieving
a
coefficient
(R2)
0.9925
root
mean
square
error
(RMSE)
0.0017
during
training,
an
R²
0.9845
RMSE
0.0121
testing,
when
validated
R2
was
0.9825
0.00018.
A
key
novelty
this
work
integration
Shapley
additive
explanations
(SHAP),
provides
interpretable
analysis
model’s
input
features.
SHAP
reveals
that
bulk
density
(RHOB)
neutron
(NPHI)
most
parameters
estimation,
offering
valuable
insight
into
features
importance.
proposed
represent
novel
independent
approach
accurate
interpretability,
significantly
enhancing
efficiency
reliability
hydrocarbon
exploration
development.
