A novel data-driven model for real-time prediction of static Young's modulus applying mud-logging data
Earth Science Informatics,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 11, 2024
Язык: Английский
Integrating geophysical logs for reservoir assessment of paleocene reservoir, Manzalai gas field, Kohat Basin, Pakistan
Carbonates and Evaporites,
Год журнала:
2025,
Номер
40(2)
Опубликована: Май 6, 2025
Язык: Английский
Horizontal well flow rate prediction applying machine-learning model
Bulletin of the Tomsk Polytechnic University Geo Assets Engineering,
Год журнала:
2024,
Номер
335(5), С. 118 - 130
Опубликована: Май 29, 2024
Relevance.
The
need
to
accurately
and
quickly
predict
flow
rates
of
horizontal
wells.
This
allows
optimizing
drilling
schedules,
enhanced
oil
recovery
programs,
field
development
strategy,
as
well
making
the
economic
model
more
accurate
predictable.
Currently,
analytical
calculations
numerical
modeling
methods
are
used
production
rates.
These
have
limitations
in
both
accuracy
time.
To
solve
this
problem,
it
is
proposed
use
machine
learning,
which
due
its
accuracy,
adaptability,
speed,
excluding
disadvantages
above-mentioned
methods.
Aim.
create
a
machine-learning
quantify
gas
based
on
geological
properties
at
different
time
steps.
Object.
Stock
wells
condensate
Western
Siberia.
Methods.
Mathematical
modelling,
learning
statistical
Results.
authors
carried
out
300
iterations
hydrodynamic
simulator.
They
collected
an
initial
data
set
with
following
parameters:
step,
porosity,
permeability,
water
saturation,
reservoir
thickness,
bottom
hole
pressure
distances
from
wellbore,
rate.
Machine
models
random
forest
gradient
boosting
algorithms
were
created
ratios
testing/training
samples.
able
rate
well.
Gradient
showed
better
prediction
results
compared
forest:
root
mean
square
error
equal
8440
std.
m3/day,
absolute
percentage
3,95%,
coefficient
determination
(R2)=0,991.
Язык: Английский
Multiscale Characterization of Fractures and Analysis of Key Controlling Factors for Fracture Development in Tight Sandstone Reservoirs of the Yanchang Formation, SW Ordos Basin, China
Applied Sciences,
Год журнала:
2024,
Номер
14(21), С. 9676 - 9676
Опубликована: Окт. 23, 2024
Tight
sandstone
reservoirs,
despite
their
low
porosity
and
permeability,
present
considerable
exploration
potential
as
unconventional
hydrocarbon
resources.
Natural
fractures
play
a
crucial
role
in
migration,
accumulation,
engineering
challenges
such
late-stage
reformation
these
reservoirs.
This
study
examines
the
seventh
member
of
Triassic
Yanchang
Formation’s
tight
within
Ordos
Basin
using
range
methods,
including
field
outcrops,
core
samples,
imaging
conventional
logging,
thin
sections,
scanning
electron
microscopy.
The
clarifies
characteristics
fracture
development
evaluates
relationship
between
dynamic
static
rock
mechanics
parameters,
calculation
brittleness
index.
Primary
factors
influencing
were
quantitatively
assessed
through
combination
outcrop,
core,
mechanical
test
data.
Findings
reveal
that
high-angle
structural
are
predominant,
with
some
bedding
diagenetic
also
present.
Acoustic,
spontaneous
potential,
caliper
conjunction
data,
enabled
comprehensive
probabilistic
index
for
identification,
which
produced
favorable
results.
analysis
identifies
four
key
development:
stratum
thickness,
index,
lithology,
stratigraphy.
Among
factors,
thickness
is
negatively
correlated
development.
Conversely,
positively
correlates
significantly
influences
length,
aperture,
linear
density.
Fractures
most
prevalent
siltstone
fine
sandstone,
minimal
mudstone.
Different
layer
types
impact
These
insights
into
controlling
anticipated
to
enhance
efforts
contribute
similar
Язык: Английский