US 12,612,849 B1 · Method for constructing high-conductivity fracture of carbonate reservoir stimulation

Abstract

The present application discloses a method for constructing a high-conductivity fracture of carbonate reservoir stimulation, and belongs to the field of oil and gas field stimulation. The method includes: S1. determining a lithology of a target layer, S2. preliminarily selecting a rock strengthening agent for in-situ modification of the fracture wall surfaces; S3. performing an experiment on in-situ modification of the fracture wall surfaces with the rock strengthening agent under a reservoir environment; S4. selecting the rock strengthening agent based on the experimental results; S5. constructing a mathematical model relating rock embedment strength to factors such as the rock strengthening agent; S6. constructing a mathematical model relating fracture conductivity to factors such as the rock embedment strength of the fracture wall surfaces; and S7. inversely designing parameters such as the rock strengthening agent based on the fracture conductivity required in the reservoir stimulation.

A method for constructing a high-conductivity fracture of carbonate reservoir stimulation, comprising the following steps:

S1:
determining a lithology at a target layer as carbonate rock;
S2:
theoretically analyzing and primarily selecting a rock strengthening agent for modifying fracture wall surfaces in situ;
S3:
performing in-situ modification experiments on the fracture wall surfaces under reservoir conditions by using the rock strengthening agent preliminarily selected in S2, and testing parameters such as rock sample hardness, permeability, mass, and three-dimensional spatial distribution of pores before and after the experiments;
S4:
based on the experimental results in S3, selecting a rock strengthening agent corresponding to a maximum comprehensive index Fr;
F
r
=
ϰ
⁢
HV
s
-
HV
o
HV
o
+
γ
⁢
k
s
-
k
o
k
o
+
ξ
⁢
L
s
-
Δ
⁢
L
L
s
+
λ
⁢
(
m
z
-
m
o
)
/
t
1
n
⁢
∑
i
=
l
n
[
(
m
si
-
m
oi
)
/
t
i
]
(
1
)
wherein HVs, kz, Ls, ms, and ti respectively represent hardness, permeability, length, mass, and treatment time of the rock sample after treatment with the rock strengthening agent;
the subscript i represents an i-th experiment, each rock strengthening agent is tested n times;
HVo, ko, and mo respectively represent hardness, permeability, and mass of an original rock sample;
ΔL is a penetration depth of the rock strengthening agent, obtained from the three-dimensional spatial distribution of pores reconstructed by CT before and after the experiment;
and χ, γ, ξ, and λ are coefficients, χ+γ+ξ+λ=1;
S5:
performing in-situ modification experiments of the fracture wall surfaces using the rock strengthening agent selected in S4 under different surface-to-volume ratios, rock strengthening agent concentrations, experimental temperatures, pressures, and times, measuring the rock sample hardness before and after the experiments, calculating the rock embedment strength before and after the experiments, and constructing a mathematical model relating the rock embedment strength to the surface-to-volume ratio, the rock strengthening agent concentration, the experimental temperature, the pressure, and the time;
wherein
a calculation formula for the rock embedment strength is as follows:
S
RE
=
gHV
sin
⁢
α
2
×
10
6
(
2
)
wherein SRE is the rock embedment strength;
g is the acceleration of gravity;
HV is the rock sample hardness;
α is a relative angle between an indenter of the hardness tester and the surface;
the mathematical model relating the rock embedment strength to the surface-to-volume ratio, the rock strengthening agent concentration, the experimental temperature, the pressure, and the time is constructed by using machine learning algorithms or multivariate nonlinear regression;
S
REs
=
f
⁢
(
S
V
,
C
z
,
T
,
P
,
t
,
S
REo
)
(
3
)
wherein SRE is the rock embedment strength of the rock sample after treatment with the rock strengthening agent;
S
V
is a ratio of a reaction area of the rock sample to a volume of the rock strengthening agent participating in the reaction;
Cz is a concentration of the rock strengthening agent;
T, P, and t are experimental temperature, pressure, and time, respectively;
and SREo is a rock embedment strength of the original rock sample;
S6:
performing fracture conductivity experiments before and after in-situ modification of the fracture wall surfaces using the rock strengthening agent selected in S4, obtaining fracture wall surface characteristic parameters and rock embedment strength before and after modification, and constructing a mathematical model relating the fracture conductivity to the rock embedment strength of the fracture wall surfaces, fracture morphology, proppant parameters, proppant placement concentration, and effective closure stress using multivariate nonlinear regression or machine learning algorithms, namely:
kwf=F(SREz,Ra,JRC,εcc,εcl,φz,Cpσca) (4)
wherein kwf is the fracture conductivity;
Ra is a root mean square elevation;
JRC is a roughness coefficient;
εα and δcl are transverse and longitudinal tortuosity ratios, respectively;
φp is a porosity of the proppant placed within the fracture;
Cp is the proppant placement concentration;
and σce is the effective closure stress of the fracture;
and
S7:
calculating the fracture conductivity required for carbonate reservoir stimulation based on the production capacity model, and, combined with reservoir parameters and fracture closure time, inversely designing the rock strengthening agent concentration, the fracture morphology, the proppant parameters, and the proppant placement concentration using the mathematical models established in S6 and S5.

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Method for constructing high-conductivity fracture of carbonate reservoir stimulation

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Method for constructing high-conductivity fracture of carbonate reservoir stimulation

Life of this patent

Abstract

Claims1