Journal of energy resources technology.,
Journal Year:
2024,
Volume and Issue:
1(1)
Published: May 20, 2024
Abstract
Separate-layer
injection
technology
is
a
highly
significant
approach
for
enhancing
oil
recovery
in
the
later
stages
of
oilfield
production.
Both
separate-layer
and
general
information
are
crucial
parameters
multi-layer
systems.
However,
significance
usually
overlooked
during
optimization
process
injection.
Moreover,
conventional
schemes
fail
to
meet
immediate
dynamic
demands
well
Consequently,
method
based
on
artificial
neural
network
residual
(ANN-Res)
model
was
proposed.
Firstly,
primary
controlling
factors
production
were
identified
through
grey
correlation
analysis
ablation
experiments.
Then,
data-driven
established
with
an
(ANN),
which
block
utilized
incorporate
information,
eventually
forming
ANN-Res
that
integrates
information.
Finally,
workflow
designed
association
model.
Analysis
factor
shows
combination
prediction
leads
redundancy.
The
results
injection–production
demonstrate
significantly
better
than
ANN
only
inputs
or
Furthermore,
result
proves
proposed
can
be
successfully
applied
optimization,
realizing
purpose
increasing
decreasing
water
cuts,
thereby
improving
development.
Geomechanics and Geophysics for Geo-Energy and Geo-Resources,
Journal Year:
2024,
Volume and Issue:
10(1)
Published: Aug. 1, 2024
Abstract
The
oil
and
gas
industry
relies
on
accurately
predicting
profitable
clusters
in
subsurface
formations
for
geophysical
reservoir
analysis.
It
is
challenging
to
predict
payable
complicated
geological
settings
like
the
Lower
Indus
Basin,
Pakistan.
In
complex,
high-dimensional
heterogeneous
settings,
traditional
statistical
methods
seldom
provide
correct
results.
Therefore,
this
paper
introduces
a
robust
unsupervised
AI
strategy
designed
identify
classify
zones
using
self-organizing
maps
(SOM)
K-means
clustering
techniques.
Results
of
SOM
provided
potentials
six
depositional
facies
types
(MBSD,
DCSD,
MBSMD,
SSiCL,
SMDFM,
MBSh)
based
cluster
distributions.
MBSD
DCSD
exhibited
high
similarity
achieved
maximum
effective
porosity
(PHIE)
value
≥
15%,
indicating
good
rock
typing
(RRT)
features.
density-based
spatial
applications
with
noise
(DBSCAN)
showed
minimum
outliers
through
meta
attributes
confirmed
reliability
generated
Shapley
Additive
Explanations
(SHAP)
model
identified
PHIE
as
most
significant
parameter
was
beneficial
identifying
non-payable
zones.
Additionally,
highlights
importance
managing
distribution
across
various
formations,
going
beyond
simple
characterization.
