Sacrificing Surfactants to Improve Oil Recovery: A Fluid Density Functional Theory Study
Zhenghe Xu,
No information about this author
Jin Cheng,
No information about this author
Yuanlong Hu
No information about this author
et al.
Langmuir,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 20, 2024
In
the
chemically
enhanced
oil
recovery
(CEOR)
processes,
heavy
components
in
crude
oil,
such
as
asphaltenes,
adhere
to
reservoir
rocks,
significantly
impeding
extraction.
Surfactants
are
frequently
utilized
improve
due
their
ability
reduce
interfacial
tension
(IFT)
and
modify
surface
wettability.
Nevertheless,
indiscriminate
surfactant
usage
may
result
resource
wastage
hinder
attainment
of
optimal
outcomes.
Therefore,
it
is
urgent
accurately
efficiently
screen
out
surfactants
suitable
for
different
fields.
This
work
employs
fluid
density
functional
theory
(FDFT)
investigate
competitive
adsorption
mechanism
asphaltenes
on
rock
interfaces.
We
examined
impact
asphaltene
determined
concentration
chain
length
differing
electrical
properties
compositions.
Furthermore,
a
comprehensive
assessment
was
conducted,
considering
both
performance
economic
factors.
The
findings
contribute
deeper
comprehension
displacement
effect
offer
scientific
screening
solutions
processes.
Language: Английский
Thermodynamic Perturbation Theory for Charged Branched Polymers
Journal of Chemical Theory and Computation,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 18, 2024
Classical
density
functional
theory
(DFT)
provides
a
versatile
framework
to
study
the
polymers
with
complex
topological
structure.
Generally,
classical
DFT
describes
excess
Helmholtz
free
energy
of
nonbonded
chain
connectivity
due
excluded-volume
effects
and
electrostatic
correlations
using
first-order
thermodynamic
perturbation
(referred
as
DFT-TPT1).
Beyond
perturbation,
second-order
TPT
(TPT2)
captures
not
only
between
neighboring
monomers
but
also
interactions
within
three
consecutive
monomers,
playing
crucial
role
in
describing
polymer
topology.
However,
numerical
implementation
TPT2
is
limited
by
lack
an
effective
triple
correlation
function
(CF),
especially
for
charged
systems.
Here,
we
propose
CF
incorporate
it
into
DFT-eTPT2)
describe
correlations.
Using
data
from
molecular
dynamics
simulation
benchmark,
DFT-eTPT2
shows
clear
improvement
over
DFT-TPT1
predicting
profiles
both
neutral
branched
brushes,
accurately
capturing
key
structural
features,
such
significant
peaks
near
branching
point
profiles.
In
short,
this
work
precise
efficient
theoretical
tool
revealing
molecular-level
insights
their
brushes.
Language: Английский