Methods for characterization of petroleum fluid and application thereof
An improved method that performs downhole fluid analysis of the fluid properties of a reservoir of interest and that characterizes the reservoir of interest based upon such downhole fluid analysis.
1. A computer-implemented method for characterizing petroleum fluid in a reservoir traversed by a wellbore, the method comprising:
(a) for each given measurement station in a first set of one or more measurement stations within the wellbore, acquiring at least one fluid sample at the given measurement station and performing downhole fluid analysis of the fluid sample to measure properties of the fluid sample, the properties including measured total asphaltenes of the fluid sample at the given measurement station;
(b) for a reference measurement station within the wellbore, using at least one probability distribution function to derive an estimated molar distribution of a plurality of asphaltene pseudocomponents at the reference measurement station;
(c) using the estimated molar distribution as derived in (b) in conjunction with an analytical model to derive predicted properties of the plurality of asphaltene pseudocomponents at varying locations in the wellbore, wherein the analytical model is of the form:
ϕ
ai
(
h
2
)
ϕ
ai
(
h
1
)
=
exp
{
[
(
v
ai
v
-
1
)
]
h
2
-
[
(
v
ai
v
-
1
)
]
h
1
}
exp
{
[
(
v
ai
RT
(
δ
ai
-
δ
)
2
]
h
2
-
[
(
v
ai
RT
(
δ
ai
-
δ
)
2
]
h
1
}
exp
{
v
ai
g
(
ρ
-
ρ
ai
)
(
h
2
-
h
1
)
RT
}
,
where φai (h1) is the volume fraction for the asphaltene pseudocornponent i at depth h 1 , φai (h2) is the volume fraction for the asphaltene pseudocomponent i at depth h 2 , v ai is the partial molar volume for the asphaltene pseudocomponent i, v is the molar volume for the bulk fluid, δ ai , is the solubility parameter for the asphaltene pseudocomponent i, δ is the solubility parameter for the bulk fluid, ρ ai is the partial density for the asphaltene pseudocomponent i, ρ is the density for the bulk fluid, R is the universal gas constant, and T is the absolute temperature of the reservoir fluid;
(d) for each given measurement station in the first set, using the predicted properties of the plurality of asphaltene pseudocomponents as derived in (c) to derive predicted total asphaltenes at the given measurement station, and comparing the predicted total asphaltenes at the given measurement station to the measured total asphaltenes for reservoir analysis.
2. A method according to claim 1 , wherein in (d), the results of the comparing are used to determine reservoir architecture.
3. A method according to claim 1 , wherein the probability distribution function is based on the Gamma function.
4. A method according to claim 1 , wherein the at least one probability distribution function is selected from the group including a first probability distribution function and a second probability distribution function, wherein the first probability distribution function is adapted to generate data representing an estimated molar distribution of a plurality of asphaltene nanoaggregate pseudocomponents, and the second probability distribution function is adapted to generate data representing an estimated molar distribution of a plurality of asphaltene nanoaggregate pseudocomponents as well as a plurality of asphaltene cluster pseudocomponents.
5. A method according to claim 4 , wherein the first probability distribution function is unimodal and evaluated over a single interval bounded by a first minimum molar mass for the set of asphaltene nanoaggregate pseudocomponents.
6. A method according to claim 5 , wherein:
the first probability distribution function is of the form:
p
(
x
)
=
(
x
-
m
min
I
)
α
-
1
exp
[
-
(
x
-
m
min
I
)
/
β
]
β
α
Γ
(
α
)
,
where α, β, and m min I are parameters defining the first probability density function, Γ represents the Gamma function, and m min I represents the first minimum molar mass.
7. A method according to claim 6 , wherein the parameter m min I is in the range of 500-1000 g/mol.
8. A method according to claim 6 , wherein the parameter β is estimated by
β
=
(
m
avg
I
-
m
min
I
)
α
,
wherein m min I represents the first minimum molar mass and m avg I represents an average molar mass for the set of asphaltene nanoaggregate pseudocomponents.
9. A method according to claim 4 , wherein the second probability distribution function is bimodal and evaluated over first and second intervals, the first interval bounded by a first minimum molar mass for the set of asphaltene nanoaggregate pseudocomponents, and the second interval bounded by a second minimum molar mass for the set of asphaltene cluster pseudocomponents, the second minimum molar mass being greater than the first minimum molar mass.
10. A method according to claim 9 , wherein the second probability density function is of the form
p ( x )=[ z I p I ( x )]+[ z II p II ( x )],
where
p
I
(
x
)
=
[
(
x
-
m
min
I
)
α
-
1
exp
[
-
(
x
-
m
min
I
)
/
β
]
β
α
Γ
(
α
)
]
,
p
II
(
x
)
=
[
z
II
(
x
-
m
min
II
)
α
-
1
exp
[
-
(
x
-
m
min
II
)
/
β
]
β
α
Γ
(
α
)
]
,
and
α, β, m min I , and m min II are parameters defining the second probability density function, m min I represents the first minimum molar mass, and m min II represents the second minimum molar mass.
11. The method according to claim 1 , wherein the analytical model is based on an assumption of connectivity and thermodynamic equilibrium of reservoir fluids in the wellbore, and the comparison of (d) is used to validate this assumption to determine that the reservoir fluids in the wellbore are connected and in thermodynamic equilibrium.
12. The method according to claim 11 , wherein the comparison of (d) is used to invalidate this assumption to determine that the reservoir fluids in the wellbore are compartmentalized or not in thermodynamic equilibrium.
13. The method according to claim 1 , wherein the analytical model is an equation of state model that predicts compositional gradients with depth.
14. The method according to claim 13 , wherein the equation of state model takes into account the impacts of gravitational forces, chemical forces, and thermal diffusion.
15. The method according to claim 1 , wherein the analytical model is a solubility model that characterizes relative concentrations of asphaltene pseudocomponents as a function of location in the wellbore as related to relative solubility and density of the asphaltene pseudocomponents at varying location.
16. The method according to claim 15 , wherein the solubility model treats the reservoir fluid as a mixture of two component groups: a solvent group (non-asphaltene components or maltene) and a solute group (asphaltene), wherein the asphaltenes include a number of asphaltene pseudo components.
17. A method according to claim 1 , further comprising performing multiple iterations of the operations of (b), (c) and (d) while varying at least one parameter of a given probability distribution function until a match is found in the comparison of (d).
18. A method according to claim 17 , wherein the at least one parameter includes a parameter representing an average molar mass for a set of asphaltene pseudocomponents.