Underground
hydrogen
storage
will
be
essential
to
enabling
a
economy,
given
the
need
store
very
large
gas
volumes
safely
and
cost-effectively.
This
work
focuses
on
challenge
of
identifying
screening
candidate
systems,
unique
behavior
in
subsurface.
Here,
we
describe
resource
assessment
methodology
apply
it
Alaska's
Cook
Inlet
region.
Alaska
provides
an
interesting
case
study
because
its
abundant
renewable
energy
resources,
relatively
low
demand,
isolated
electrical
grid.
The
framework
considers
each
site's
ability
1)
specific
volume,
2)
physically-contain
stored
gas,
3)
limit
biogeochemical
activity.
We
estimate
that
reservoirs
area
could
theoretically
total
286
TWh
(or
8.6
million
tonnes
[Mt])
working
92
pools.
is
likely
sufficient
meet
both
local
demand
support
array
exportable
products.
further
identify
seven
pools
may
especially
well
suited
for
sites.
Broadly,
this
demonstrates
regional
assessments.
On
finer
scale,
enables
next
steps
underground
–
i.e.
reservoir-specific
characterization
development
proceed
area.
Fuel,
Год журнала:
2024,
Номер
364, С. 131038 - 131038
Опубликована: Янв. 28, 2024
With
the
long-standing
efforts
of
green
transition
in
our
society,
underground
hydrogen
storage
(UHS)
has
emerged
as
a
viable
solution
to
buffering
seasonal
fluctuations
renewable
energy
supplies
and
demands.
Like
operations
hydrocarbon
production
geological
CO2
storage,
successful
UHS
project
requires
good
understanding
subsurface
formations,
while
having
different
operational
objectives
practical
challenges.
Similar
situations
problems,
information
formations
at
field
level
cannot
be
obtained
through
direct
measurements
due
resulting
high
costs.
As
such,
there
is
need
for
characterization
monitoring
scale,
which
uses
certain
history
matching
algorithm
calibrate
numerical
model
based
on
available
data.
Whereas
have
been
widely
used
activities
better
reservoirs,
best
knowledge,
present
it
appears
relatively
less
touched
area
problems.
This
work
aims
narrow
this
noticed
gap,
investigates
use
an
ensemble-based
workflow
3D
case
study.
Numerical
results
study
indicate
that
works
reasonably
well,
also
identifying
some
particular
challenges
would
relevant
real-world
Process Safety and Environmental Protection,
Год журнала:
2024,
Номер
183, С. 99 - 110
Опубликована: Янв. 4, 2024
The
effective
detection
and
prevention
of
CO2
leakage
in
active
injection
wells
are
paramount
for
safe
carbon
capture
storage
(CCS)
initiatives.
This
study
assesses
five
fundamental
machine
learning
algorithms,
namely,
Support
Vector
Regression
(SVR),
K-Nearest
Neighbor
(KNNR),
Decision
Tree
(DTR),
Random
Forest
(RFR),
Artificial
Neural
Network
(ANN),
use
developing
a
robust
data-driven
model
to
predict
potential
incidents
wells.
Leveraging
wellhead
bottom-hole
pressure
temperature
data,
the
models
aim
simultaneously
location
size
leaks.
A
representative
dataset
simulating
various
leak
scenarios
saline
aquifer
reservoir
was
utilized.
findings
reveal
crucial
insights
into
relationships
between
variables
considered
characteristics.
With
its
positive
linear
correlation
with
depth
leak,
could
be
pivotal
indicator
location,
while
negative
relationship
well
demonstrated
strongest
association
size.
Among
predictive
examined,
highest
prediction
accuracy
achieved
by
KNNR
both
localization
sizing.
displayed
exceptional
sensitivity
size,
able
identify
magnitudes
representing
as
little
0.0158%
total
main
flow
relatively
high
levels
accuracy.
Nonetheless,
underscored
that
accurate
sizing
posed
greater
challenge
compared
localization.
Overall,
obtained
can
provide
valuable
development
efficient
well-bore
systems.
Engineering Science & Technology Journal,
Год журнала:
2024,
Номер
5(5), С. 1588 - 1605
Опубликована: Май 5, 2024
Geotechnical
assessments
are
crucial
for
ensuring
the
stability
and
longevity
of
renewable
energy
infrastructure,
particularly
in
wind
solar
projects.
This
review
explores
significance
geotechnical
these
projects,
highlighting
key
considerations
challenges.
play
a
critical
role
design,
construction,
operation
providing
essential
information
about
subsurface
conditions
that
can
impact
performance
These
involve
evaluation
soil,
rock,
groundwater
to
assess
their
suitability
supporting
structures.
In
determining
foundation
design
turbines.
The
soil
rock
at
site
significantly
load-bearing
capacity
foundation,
affecting
overall
safety
turbine.
Similarly,
necessary
designing
panels
support
structures,
they
withstand
environmental
loads
maintain
efficiency
over
time.
One
challenges
projects
is
variability
conditions.
Soil
properties
vary
short
distances,
requiring
detailed
investigations
accurately
characterize
Additionally,
presence
natural
hazards
such
as
landslides,
earthquakes,
floods
further
complicate
assessments,
necessitating
robust
risk
mitigation
strategies.
Despite
challenges,
long-term
infrastructure.
By
valuable
insights
into
conditions,
help
developers
engineers
make
informed
decisions
selection,
management,
ultimately
contributing
successful
implementation
conclusion,
vital
mitigate
risks
ensure
safe
efficient
Keywords:
Assessments,
Renewable
Energy,
Infrastructure,
Stability,
Wind
Solar
Projects.
