US2020240243A1PendingUtilityA1
Method for intelligently determining hydrate drilling and production risks based on fuzzy judgment
Est. expiryJan 29, 2039(~12.5 yrs left)· nominal 20-yr term from priority
Inventors:Haitao LiNa WeiJinzhou ZhaoLuling LiZhenjun CuiLin JiangWantong SunLuyue YangXi LiYinghe HongYu Qiao
E21B 2200/22E21B 44/00E21B 41/0099E21B 49/08E21B 2041/0028E21B 41/0092E21B 49/00E21B 47/00
36
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Claims
Abstract
A method for intelligently determining hydrate drilling and production risks based on fuzzy judgment. First classifying monitoring parameters in a hydrate drilling and production process into layers from top to bottom: a target layer, a primary evaluation factor layer and a secondary evaluation factor layer; then calculating relative weight values of each primary evaluation factor and each secondary evaluation factor contained therein; then connecting in series the relative weight values of the primary evaluation factors with the relative weight values of the secondary evaluation factors to obtain an overall weight value of the secondary evaluation factors; repeating the foregoing steps; finally constructing the overall weight value of each secondary evaluation factor of each risk into a column vector to obtain a comprehensive determining weight matrix of hydrate drilling and production risks, and determining the risks in the hydrate drilling and production process by combining monitoring parameter change vectors.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1 . A method for intelligently determining hydrate drilling and production risks based on fuzzy judgment, comprising the following steps in sequence:
step 1: building a hierarchical structure model based on monitoring parameters in a hydrate drilling and production process, classifying into layers from top to bottom, which comprise a target layer, a primary evaluation factor layer and a secondary evaluation factor layer, wherein the target layer is composed of 8 risks which are formation gas production, borehole instability, hydrate production, drill string fracture, H 2 S production, sticking, bit balling and piercing-caused leakage of a drilling tool respectively; the primary evaluation factor layer is composed of 3 monitoring parameters types which are an injection parameter, a drilling parameter and a return parameter respectively; the secondary evaluation factor layer is composed of 11 monitoring parameters, which are injection fluid pressure, injection fluid flow, hanging load, drilling time, torque, rotational speed, total hydrocarbon value, hydrogen sulfide concentration, return fluid flow, return fluid pressure and return fluid temperature respectively, to construct a hierarchical structure model; step 2: constructing a determining matrix based on a selected risk in the target layer, first using a nine-scale method to compare primary evaluation factors of the primary evaluation factor layer and determine a scale value, then establishing a primary evaluation factor determining matrix based on the determined scale value, and then based on each primary evaluation factor of the primary evaluation factor layer respectively, establishing a secondary evaluation factor determining matrix for secondary evaluation factors of the secondary evaluation factor layer contained in each primary evaluation factor, wherein the determining matrixes of the primary evaluation factors and the secondary evaluation factors are expressed with {tilde under (A)}:
A
∼
=
[
a
∼
11
⋯
a
∼
1
m
⋮
⋱
⋮
a
∼
m
1
⋯
a
∼
mm
]
=
(
a
∼
ij
)
m
×
m
i refers to the i-th evaluation factor of a certain layer in the hierarchical structure model (a value of i is 1, 2, 3, . . . , m), j refers to the j-th evaluation factor of the same layer in the same hierarchical structure model as j (a value off is 1, 2, 3, . . . , m), and in refers to the number of primary evaluation factors or the number of secondary evaluation factors;
step 3: establishing a comprehensive determining matrix and calculating a fuzzy weight value
setting the number of judging experts to be n to obtain a comprehensive determining matrix {tilde under (A)} M :
A
∼
M
=
1
n
[
A
∼
1
+
A
∼
2
+
⋯
+
A
∼
n
]
wherein {tilde under (A)} 1 , {tilde under (A)} 2 and {tilde under (A)} n refer to determining matrixes constructed according to scale values determined by judgment results of the first expert, the second expert and the n-th expert respectively;
a geometric mean of the i-th evaluation factor in the comprehensive determining matrix {tilde under (A)} M is:
r i =( a i1 ×a i2 ×a i3 × . . . ×a im ) 1/m
a relative fuzzy weight value of the i-th evaluation factor is:
w i =r i ×( r 1 +r 2 +r 3 + . . . +r m ) −1 ;
step 4: converting the relative fuzzy weight value of the i-th evaluation factor into an explicit value
expressing the relative weight fuzzy weight value w i of the i-th evaluation factor in the form of a triangular fuzzy number, wherein w i =(R i , M i , L i ), L i is left extension of the triangular fuzzy number, R i is right extension of the triangular fuzzy number, and M i is a median of the triangular fuzzy number; converting the relative weight fuzzy weight value of the i-th evaluation factor into an explicit weight value DF i of the i-th evaluation factor:
DF
i
=
[
(
R
i
-
L
i
)
+
(
M
i
-
L
i
)
]
3
+
L
i
;
step 5: normalizing the explicit weight value of the i-th evaluation factor
normalizing the explicit weight value of the i-th evaluation factor, wherein the relative weight value of the normalized i-th evaluation factor is:
w
i
′
=
DF
ij
Σ
DF
ij
;
step 6: connecting relative weight values of each interlayer evaluation factor in series
multiplying the relative weight value of each primary evaluation factor respectively with the relative weight values of all secondary evaluation factors contained in this primary evaluation factor to obtain an overall weight value w′ Ti of the i-th secondary evaluation factor:
w′ Ti =w′ 1i ×w 2i
w′ 1i is the relative weight value of the primary evaluation factor corresponding to the i-th secondary evaluation factor, and w′ 2i is the relative weight value of the i-th secondary evaluation factor;
respectively calculating a relative weight value of each primary evaluation factor of the remaining risks in the target layer, a relative weight value of each secondary evaluation factor contained in each primary evaluation factor and overall weight values of the secondary evaluation factors, constructing the overall weight values of the secondary evaluation factors of each risk into column vectors in the same order, and constructing a comprehensive determining weight matrix after the column vectors are arranged in sequence, namely a comprehensive determining weight matrix A T of hydrate drilling and production risks, wherein A T is shown as follows:
A
T
=
[
w
T
11
′
⋯
w
T
1
e
′
⋮
⋱
⋮
w
Tm
1
′
⋯
w
Tme
′
]
e is the number of risks, e=8;
step 7: constructing a monitoring parameter change vector
b
i
=
Δ
S
i
S
iL
(
a
)
b
i
=
S
ic
-
(
S
iL
+
Δ
H
i
)
(
S
iL
+
Δ
H
i
)
(
b
)
b
i
=
S
ic
-
(
S
iL
-
Δ
H
i
)
(
S
iL
-
Δ
H
i
)
(
c
)
wherein b i is a relative change rate of the i-th monitoring parameter; ΔS i is a variation of a value of the i-th monitoring parameter; S ic is a measured value of the i-th monitoring parameter; S iL is a theoretical value of the i-th monitoring parameter; ΔH i is a reasonable change range of the value of the i-th monitoring parameter; when an initial value of the i-th monitoring parameter is not 0, the relative change rate of the i-th monitoring parameter is calculated by formula a; when the initial value of the i-th monitoring parameter is 0, an increase of the measured value of the i-th monitoring parameter is calculated by using formula b; a decrease of the measured value of the i-th monitoring parameter is calculated by using formula c;
constructing a monitoring parameter change vector:
B =( b 1 b 2 . . . b m );
step 8: obtaining a judgment result of hydrate drilling and production risks
Z
=
BA
T
=
(
b
1
b
2
⋯
b
m
)
[
w
T
11
′
⋯
w
T
1
e
′
⋮
⋱
⋮
w
Tm
1
′
⋯
w
Tme
′
]
wherein a value in Z indicates a possibility of each kind of risk; the greater the value, the greater the possibility of the corresponding risk; and in contrast, the smaller the value, the smaller the possibility of the corresponding risk.
2 . The method for intelligently determining hydrate drilling and production risks based on fuzzy judgment according to claim 1 , wherein
the scale values of each primary evaluation factor and each secondary evaluation factor in step 2 are determined by the nine-scale method; when the monitoring parameter i corresponding to the selected risk is compared with the monitoring parameter j, the scale value is determined according to a response intensity of the monitoring parameter i and the monitoring parameter j to the risk, and the scale value is quantitatively expressed by the triangular fuzzy number
a
ij
%
=
(
a
1
,
a
2
,
a
3
)
.
3 . The method for intelligently determining hydrate drilling and production risks based on fuzzy judgment according to claim 1 , wherein
the comprehensive determining matrix {tilde under (A)} M in step 3 is as follows:
A
∼
M
=
1
n
[
A
∼
1
+
A
∼
2
+
⋯
+
A
∼
n
]
wherein {tilde under (A)} 1 , {tilde under (A)} 2 and {tilde under (A)} n refer to determining matrixes constructed according to scale values determined by judgment results of the first expert, the second expert and the n-th expert respectively;
a determining matrix established by the k-th expert by evaluation is expressed as {tilde under (A)} k =[a cd k ], a cd k indicates a scale value determined by the k-th expert according to the importance of a same layer evaluation factor c relative to an evaluation factor d, and a comprehensive determining matrix is calculated as follows:
A
∼
M
=
1
n
[
∑
k
=
1
n
a
cd
k
]
=
[
(
1
,
1
,
1
)
⋯
1
n
[
∑
k
=
1
n
a
1
m
k
]
⋮
⋱
⋮
1
n
[
∑
k
=
1
n
a
m
1
k
]
⋯
(
1
,
1
,
1
)
]
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