U.S. patent application number 16/747294 was filed with the patent office on 2020-07-30 for method for intelligently determining hydrate drilling and production risks based on fuzzy judgment.
The applicant listed for this patent is SOUTHWEST PETROLEUM UNIVERSITY. Invention is credited to Zhenjun CUI, Yinghe HONG, Lin JIANG, Haitao LI, Luling LI, Xi LI, Yu QIAO, Wantong SUN, Na WEI, Luyue YANG, Jinzhou ZHAO.
Application Number | 20200240243 16/747294 |
Document ID | 20200240243 / US20200240243 |
Family ID | 1000004667203 |
Filed Date | 2020-07-30 |
Patent Application | download [pdf] |
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United States Patent
Application |
20200240243 |
Kind Code |
A1 |
LI; Haitao ; et al. |
July 30, 2020 |
METHOD FOR INTELLIGENTLY DETERMINING HYDRATE DRILLING AND
PRODUCTION RISKS BASED ON FUZZY JUDGMENT
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.
Inventors: |
LI; Haitao; (Chengdu City,
CN) ; WEI; Na; (Chengdu City, CN) ; ZHAO;
Jinzhou; (Chengdu City, CN) ; LI; Luling;
(Chengdu City, CN) ; CUI; Zhenjun; (Chengdu City,
CN) ; JIANG; Lin; (Chengdu City, CN) ; SUN;
Wantong; (Chengdu City, CN) ; YANG; Luyue;
(Chengdu City, CN) ; LI; Xi; (Chengdu City,
CN) ; HONG; Yinghe; (Chengdu City, CN) ; QIAO;
Yu; (Chengdu City, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
SOUTHWEST PETROLEUM UNIVERSITY |
Chengdu City |
|
CN |
|
|
Family ID: |
1000004667203 |
Appl. No.: |
16/747294 |
Filed: |
January 20, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
E21B 49/00 20130101;
E21B 2200/22 20200501; E21B 49/08 20130101; E21B 47/00 20130101;
E21B 41/0092 20130101 |
International
Class: |
E21B 41/00 20060101
E21B041/00; E21B 47/00 20060101 E21B047/00; E21B 49/00 20060101
E21B049/00; E21B 49/08 20060101 E21B049/08 |
Foreign Application Data
Date |
Code |
Application Number |
Jan 29, 2019 |
CN |
201910086578.6 |
Claims
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.sub.2S 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 .about. = [ a .about. 11 a
.about. 1 m a .about. m 1 a .about. mm ] = ( a .about. ij ) m
.times. m ##EQU00017## 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)}.sub.M: A .about. M = 1 n [ A .about. 1 + A .about. 2 + +
A .about. n ] ##EQU00018## wherein {tilde under (A)}.sub.1, {tilde
under (A)}.sub.2 and {tilde under (A)}.sub.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)}.sub.M is:
r.sub.i=(a.sub.i1.times.a.sub.i2.times.a.sub.i3.times. . . .
.times.a.sub.im).sup.1/m a relative fuzzy weight value of the i-th
evaluation factor is:
w.sub.i=r.sub.i.times.(r.sub.1+r.sub.2+r.sub.3+ . . .
+r.sub.m).sup.-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.sub.i of the
i-th evaluation factor in the form of a triangular fuzzy number,
wherein w.sub.i=(R.sub.i, M.sub.i, L.sub.i), L.sub.i is left
extension of the triangular fuzzy number, R.sub.i is right
extension of the triangular fuzzy number, and M.sub.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.sub.i of the i-th evaluation factor: DF i = [ ( R i
- L i ) + ( M i - L i ) ] 3 + L i ; ##EQU00019## 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 .SIGMA. DF ij ; ##EQU00020##
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'.sub.Ti of the i-th secondary evaluation factor:
w'.sub.Ti=w'.sub.1i.times.w.sub.2i w'.sub.1i is the relative weight
value of the primary evaluation factor corresponding to the i-th
secondary evaluation factor, and w'.sub.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.sub.T
of hydrate drilling and production risks, wherein A.sub.T is shown
as follows: A T = [ w T 11 ' w T 1 e ' w Tm 1 ' w Tme ' ]
##EQU00021## e is the number of risks, e=8; step 7: constructing a
monitoring parameter change vector b i = .DELTA. S i S iL ( a ) b i
= S ic - ( S iL + .DELTA. H i ) ( S iL + .DELTA. H i ) ( b ) b i =
S ic - ( S iL - .DELTA. H i ) ( S iL - .DELTA. H i ) ( c )
##EQU00022## wherein b.sub.i is a relative change rate of the i-th
monitoring parameter; .DELTA.S.sub.i is a variation of a value of
the i-th monitoring parameter; S.sub.ic is a measured value of the
i-th monitoring parameter; S.sub.iL is a theoretical value of the
i-th monitoring parameter; .DELTA.H.sub.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.sub.1b.sub.2. . . b.sub.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 ' ]
##EQU00023## 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 ) . ##EQU00024##
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)}.sub.M in step 3 is as follows: A .about. M = 1 n [ A .about. 1
+ A .about. 2 + + A .about. n ] ##EQU00025## wherein {tilde under
(A)}.sub.1, {tilde under (A)}.sub.2 and {tilde under (A)}.sub.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)}.sub.k=[a.sub.cd.sup.k], a.sub.cd.sup.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 .about. 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 ) ] . ##EQU00026##
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] Not applicable.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] Not applicable.
NAMES OF THE PARTIES TO A JOINT RESEARCH AGREEMENT
[0003] Not applicable.
INCORPORATION-BY-REFERENCE OF MATERIALS SUBMITTED ON A COMPACT
DISC
[0004] Not applicable.
TECHNICAL FIELD
[0005] The present invention relates to the technical field of
intelligent judgment and research on natural gas hydrate drilling
and production risks, and in particular to a method for
intelligently determining hydrate drilling and production risks
based on fuzzy judgment.
BACKGROUND
[0006] Natural gas hydrate is a non-stoichiometric clathrate
crystal substance generated by water and natural gas in a
high-pressure and low-temperature environment. It is unconventional
energy with high density and high heat value, mainly distributed in
marine and terrestrial permafrost sediments. The amount of marine
natural gas hydrate resources is about 100 times that of
terrestrial permafrost. The exploitation of the marine natural gas
hydrate has attracted much attention. The natural gas hydrate is
generally considered to be the most potential replacement energy in
the 21st century and is also new energy with the largest reserves
yet to be developed.
[0007] For such a huge amount of resources, the drilling safety of
natural gas hydrate reservoirs has become a major problem that
restricts the development of a natural gas hydrate drilling and
production technology. Hydrate drilling and production are often
faced with eight types of risks, which are formation gas
production, borehole instability, hydrate production, drill string
fracture, H.sub.2S production, sticking, bit balling and
piercing-caused leakage of a drilling tool. Basic risk monitoring
and judgment methods have been established in the drilling process
of conventional oil and gas reservoirs, but the methods are not
perfect. At present, no scholars have proposed a method for
determining risks in the natural gas hydrate drilling and
production process. In order to ensure the safe and efficient
exploitation of natural gas hydrate, there is an urgent need to
provide a method for intelligently determining risks of natural gas
hydrate during drilling.
BRIEF SUMMARY OF THE INVENTION
[0008] The present invention provides a method for intelligently
determining hydrate drilling and production risks based on fuzzy
judgment. The method has a reliable working principle and simple
and convenient operations, and can quickly and accurately determine
a risk type and generate an alarm when risks occur in the hydrate
drilling and production process. The method enables a hydrate
drilling and production operation process to be monitored in real
time, thereby ensuring safe hydrate drilling and production, and
filling the gap in intelligently determining risks in hydrate
drilling and production.
[0009] To achieve the foregoing objective, the present invention
adopts the following technical solutions.
[0010] Monitoring parameters in a hydrate drilling and production
process are first hierarchically structured by using a fuzzy
analytic hierarchy process and then classified into layers from top
to bottom, including a target layer (composed of 8 risks), a
primary evaluation factor layer (composed of monitoring parameter
types, where a primary evaluation factor is a monitoring parameter
type), and a secondary evaluation factor layer (composed of
monitoring parameters, where a secondary evaluation factor is a
monitoring parameter). Then a relative weight value of each primary
evaluation factor is calculated (for example, when a certain risk
occurs, the stronger the response of a certain primary evaluation
factor to the risk, the greater the relative weight of this primary
evaluation factor to this risk, that is, the greater the relative
weight value of this primary evaluation factor). Then a relative
weight value of each secondary evaluation factor contained in each
primary evaluation factor is calculated respectively (for example,
when a certain risk occurs, the stronger the response of a certain
secondary evaluation factor to the risk, the greater the relative
weight of this secondary evaluation factor to this risk, that is,
the greater the relative weight value of this secondary evaluation
factor). Then the relative weight value of each primary evaluation
factor is respectively connected in series with the relative weight
values of all secondary evaluation factors included in the primary
evaluation factor (that is, the relative weight value of each
primary evaluation factor is respectively multiplied with the
relative weight values of all secondary evaluation factors included
in this primary evaluation factor), and the relative weight values
of the secondary evaluation factors connected in series are overall
weight values of the secondary evaluation factors (that is, when
the risk occurs, the greater the intensity of the comprehensive
response of which secondary evaluation factor to the risk, the
greater the overall weight value of this secondary evaluation
factor). The foregoing steps are repeated to calculate a relative
weight value of each primary evaluation factor of the remaining
risks in the target layer and respectively calculate a relative
weight value of each secondary evaluation factor contained in each
primary evaluation factor and overall weight values of the
secondary evaluation factors. Finally the overall weight values of
the secondary evaluation factors of each risk are constructed into
column vectors in the same order, and a comprehensive determining
weight matrix, namely a comprehensive determining weight matrix of
hydrate drilling and production risks, is constructed after the
constructed column vectors are arranged in sequence, and the risks
in the hydrate drilling and production process are quickly,
accurately and intelligently determined by combining monitoring
parameter change vectors.
[0011] In the specification, if a factor does not specifically
refer to an evaluation factor, it is simply referred to as an
evaluation factor.
[0012] A method for intelligently determining hydrate drilling and
production risks based on fuzzy judgment includes the following
steps in sequence.
[0013] Step 1: building a hierarchical structure model
hierarchically structuring monitoring parameters in a hydrate
drilling and production process by using a fuzzy analytic hierarchy
process and classifying into layers from top to bottom, which
include a target layer, a primary evaluation factor layer and a
secondary evaluation factor layer, where the target layer is
composed of 8 risks which are formation gas production, borehole
instability, hydrate production, drill string fracture, H.sub.2S
production, sticking, bit balling and piercing-caused leakage of a
drilling tool respectively; the primary evaluation factor layer is
composed of 3 kinds of monitoring parameters, 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.
[0014] Step 2: constructing a determining matrix
[0015] in the constructed hierarchical structure model,
constructing a sub-region according to each primary evaluation
factor (monitoring parameter type) of a selected risk and the next
evaluation factor layer (monitoring parameters) dominated by this
primary evaluation factor, establishing a determining matrix for
the sub-region, and evaluating relative importance of each
evaluation factor in the sub-region by a nine-scale method, with
the process as follows: based on a selected risk in the target
layer (namely a first layer), first using the nine-scale method to
compare primary evaluation factors of the primary evaluation factor
layer (namely a second 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 (namely a third layer) contained
in each primary evaluation factor.
[0016] Scale values of each primary evaluation factor and each
secondary evaluation factor are determined by using the nine-scale
method. An example of determining a scale value is as follows: 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 (namely importance) 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 ) . ##EQU00001##
The scale value is a judgment result of the importance of the
monitoring parameter i and the monitoring parameter j to this risk.
According to the scale values of the primary evaluation factor
layer and the secondary evaluation factor layer, a primary
evaluation factor determining matrix and a secondary evaluation
factor determining matrix are respectively constructed, the
constructed determining matrixes of the primary evaluation factors
and the secondary evaluation factors are expressed by {tilde under
(A)}, and an example of {tilde under (A)} is as follows:
A .about. = [ a .about. 11 a .about. 1 m a .about. m 1 a .about. mm
] = ( a .about. ij ) m .times. m ##EQU00002##
[0017] where 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 of j
is 1, 2, 3, . . . , m), and in refers to the number of primary
evaluation factors or the number of secondary evaluation
factors.
[0018] Step 3: establishing a comprehensive determining matrix and
calculating a fuzzy weight value
[0019] setting the number of judging experts to be n to obtain a
comprehensive determining matrix by using a fuzzy average method,
as shown in the following formula:
A .about. M = 1 n [ A .about. 1 + A .about. 2 + + A .about. n ]
##EQU00003##
[0020] where {tilde under (A)}.sub.M refers to the comprehensive
determining matrix, and {tilde under (A)}.sub.1, {tilde under
(A)}.sub.2, and {tilde under (A)}.sub.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.
[0021] Further, a determining matrix established by the k-th expert
by evaluation is expressed as
A .about. k = [ a cd k ] , a cd k ##EQU00004##
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 .about. 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 ) ]
##EQU00005##
[0022] Further, a geometric average fuzzy weight calculation method
(similar to an nth root method) is used to calculate the relative
fuzzy weight value of each evaluation factor in the matrix (the
relative fuzzy weight of each evaluation factor has already taken
the normalization of a fuzzy number into account).
[0023] A geometric mean of the i-th evaluation factor in the
comprehensive determining matrix {tilde under (A)}.sub.M is:
r.sub.i=(a.sub.i1.times.a.sub.i2.times.a.sub.i3.times. . . .
.times.a.sub.im).sup.1/m
[0024] A relative fuzzy weight value of the i-th evaluation factor
is:
w.sub.i=r.sub.i.times.(r.sub.1+r.sub.2+r.sub.3+ . . .
+r.sub.n).sup.-1
[0025] Step 4: converting the relative fuzzy weight value of the
i-th evaluation factor into an explicit value
[0026] expressing the relative weight fuzzy weight value w.sub.i of
the i-th evaluation factor in the form of a triangular fuzzy
number, where w.sub.i=(R.sub.i, M.sub.i, L.sub.i), L.sub.i is left
extension of the triangular fuzzy number, R.sub.i is right
extension of the triangular fuzzy number, and M.sub.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.sub.i of the i-th evaluation factor, where a
calculation formula of DF.sub.i is as follows:
DF i = [ ( R i - L i ) + ( M i - L i ) ] 3 + L i ##EQU00006##
[0027] Step 5: normalizing the explicit weight value of the i-th
evaluation factor
[0028] In order to compare the relative importance of each primary
evaluation factor (including an injection parameter, a drilling
parameter and a return parameter) and secondary evaluation factors
(including 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), an explicit weight
value of the i-th evaluation factor is normalized, and a
normalization formula is:
w i ' = DF ij .SIGMA. DF ij ##EQU00007##
[0029] w'.sub.i is the relative weight value of the normalized i-th
evaluation factor.
[0030] Step 6: connecting relative weight values of each interlayer
evaluation factor in series
[0031] respectively connecting in series the relative weight value
of each primary evaluation factor with the relative weight values
of all secondary evaluation factors contained in the primary
evaluation factor (namely 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), where the relative weight values of the
secondary evaluation factors connected in series are the overall
weight values of the secondary evaluation factors.
[0032] w'.sub.Ti is the overall weight value of the i-th secondary
evaluation factor, w'.sub.1i is the relative weight value of the
primary evaluation factor corresponding to the i-th secondary
evaluation factor, and w'.sub.2i is the relative weight value of
the i-th secondary evaluation factor. The overall weight of the
i-th secondary evaluation factor relative to a certain risk is as
follows:
w'.sub.Ti=w'.sub.1i.times.w'.sub.2i
[0033] Further, steps 2-6 are repeated, a relative weight value of
each primary evaluation factor of the remaining risks in the target
layer is calculated, a relative weight value of each secondary
evaluation factor contained in each primary evaluation factor and
overall weight values of the secondary evaluation factors are
respectively calculated. Then the overall weight values of the
secondary evaluation factors of each risk are constructed into
column vectors in the same order, and a comprehensive determining
weight matrix, namely a comprehensive determining weight matrix
A.sub.T of hydrate drilling and production risks, is constructed
after the constructed column vectors are arranged in sequence,
where A.sub.T is shown as follows:
A T = [ w T 11 ' w T 1 e ' w Tm 1 ' w Tme ' ] ##EQU00008##
[0034] where e is the number of risks (e=8).
[0035] Step 7: constructing a monitoring parameter change
vector
[0036] When a risk occur, what kind of risk occurs underground is
determined based on a change trend of monitoring parameter values
and the magnitude of the relative change rate of monitoring
parameter values. The relative change rate of each monitoring
parameter value at a certain well depth is used as a constituent
element of the monitoring parameter change vector, and the relative
change rate of monitoring parameter values reflects the response
intensity of monitoring parameters to the risk. Since the
monitoring parameters such as 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
fluctuate within a normal range during normal construction (during
construction without risks), in order to avoid the influence of
fluctuation within the normal range of each monitoring parameter on
risk judgment, a reasonable change range of monitoring parameters
is established by analyzing monitoring data of a large number of
drilled wells and combining the experience of field engineers. When
the monitoring parameters fluctuate within this range, it is
determined that the monitoring parameters do not change, otherwise,
it is determined that the monitoring parameters have changed.
During the construction process, there are two changes of the
monitoring parameter value: increase and decrease. The "+"
indicates the increase of the monitoring parameter value and the
"-" indicates the decrease of the monitoring parameter value. In
the calculation process, an initial value of the monitoring
parameter falls into two conditions: "0" and "not 0". Based on the
above principle, the calculation formula for a constituent element
of a monitoring parameter change vector is established as
follows:
b i = .DELTA. S i S iL ( a ) b i = S ic - ( S iL + .DELTA. H i ) (
S iL + .DELTA. H i ) ( b ) b i = S ic - ( S iL - .DELTA. H i ) ( S
iL - .DELTA. H i ) ( c ) ##EQU00009##
[0037] where b.sub.i is a relative change rate of the i-th
monitoring parameter (namely the i-th evaluation factor);
.DELTA.S.sub.i is a variation of a value of the i-th monitoring
parameter; S.sub.ic is a measured value of the i-th monitoring
parameter; S.sub.iL is a theoretical value of the i-th monitoring
parameter; and .DELTA.H.sub.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; and a decrease of the measured value of the
i-th monitoring parameter is calculated by using formula c. When
the value of the i-th monitoring parameter changes within a
reasonable change range, it is defined as 0; when the change range
of the i-th monitoring parameter is greater than or equal to 100%,
it is defined as 1; and when the change range of the i-th
monitoring parameter is between the reasonable change range and
100%, the value is taken as b.sub.i.
[0038] Elements of the monitoring parameter change vector are
sorted according to the arrangement sequence of the monitoring
parameter in the column vector of the constructed comprehensive
determining weight matrix, and finally the monitoring parameter
change vector is constructed. The monitoring parameter change
vector is expressed as follows:
B=(b.sub.1b.sub.2. . . b.sub.m)
[0039] Step 8: determining a risk
[0040] After the comprehensive determining weight matrix and
monitoring parameter change vector are established, the product
between the two is a judgment result of hydrate drilling and
production risks, as shown in the following formula:
Z = BA T = ( b 1 b 2 b m ) [ w T 11 ' w T 1 e ' w Tm 1 ' w Tme ' ]
##EQU00010##
[0041] where a value in Z indicates a possibility of each kind of
risk; apparently, the greater the value of the element in Z, the
greater the possibility of the corresponding risk; and in contrast,
the smaller the value, the smaller the possibility of the
corresponding risk.
[0042] In view of the risk judgment problem faced in the natural
gas hydrate drilling and production process, according to the
present invention, the method for intelligently determining hydrate
drilling and production risks based on fuzzy judgment is
established by using the fuzzy analytic hierarchy process. This
method can quickly and accurately realize functions of intelligent
judgment, alarm and the like, and is used to monitor and determine
in real time whether underground risks occur during the natural gas
hydrate drilling and production operation, thereby ensuring the
safety of the natural gas hydrate drilling and production
operation.
BRIEF DESCRIPTION OF THE DRAWINGS
[0043] FIG. 1 is a hierarchical structure diagram of a method for
intelligently determining hydrate drilling and production risks
based on fuzzy judgment according to the present invention;
[0044] FIG. 2 is a risk and monitoring parameter response diagram
of the present invention; and
[0045] FIG. 3 is a judgment result diagram of the present
invention.
DETAILED DESCRIPTION
[0046] The following further describes the present invention in
detail with reference to the accompanying drawings and
embodiments.
Embodiment 1
[0047] A method for intelligently determining hydrate drilling and
production risks based on fuzzy judgment specifically includes the
following steps.
[0048] A hierarchical structure model is built.
[0049] As shown in FIG. 1, a target layer is composed of 8 risks
which are formation gas production, borehole instability, hydrate
production, drill string fracture, H.sub.2S production, sticking,
bit balling and piercing-caused leakage of a drilling tool
respectively. A primary evaluation factor layer is composed of an
injection parameter, a drilling parameter and a return parameter
respectively. A secondary evaluation factor layer is composed of
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.
[0050] A determining matrix is constructed.
[0051] With formation gas production an example, a sub-region is
constructed according to each primary evaluation factor of this
risk and the next evaluation factor layer dominated by this primary
evaluation factor, and a determining matrix is established for this
sub-region (see Table 1): based on the formation gas production in
the target layer (namely a first layer), the nine-scale method is
first used to compare primary evaluation factors of the primary
evaluation factor layer (namely a second layer) and determine a
scale value, then a primary evaluation factor determining matrix of
the formation gas production is established based on the determined
scale value, and then based on each primary evaluation factor of
the primary evaluation factor layer respectively, a secondary
evaluation factor determining matrix of the formation gas
production is established for secondary evaluation factors of the
secondary evaluation factor layer (namely a third layer) contained
in each primary evaluation factor. Scale values of each primary
evaluation factor and each secondary evaluation factor are
determined by using the nine-scale method to construct an
evaluation matrix A as follows:
A .about. 1 = [ ( 1 , 1 , 1 ) ( 0.14 , 0.17 , 0.2 ) ( 0.14 , 0.17 ,
0.2 ) ( 5 , 6 , 7 ) ( 1 , 1 , 1 ) ( 0.33 , 0.5 , 1 ) ( 5 , 6 , 7 )
( 1 , 2 , 3 ) ( 1 , 1 , 1 ) ] ##EQU00011## A .about. 2 = [ ( 1 , 1
, 1 ) ( 1 , 1 , 1 ) ( 0.33 , 0.5 , 1 ) ( 1 , 1 , 1 ) ( 1 , 1 , 1 )
( 1 , 1 , 1 ) ( 1 , 2 , 3 ) ( 1 , 1 , 1 ) ( 1 , 1 , 1 ) ]
##EQU00011.2## A .about. 3 = [ ( 1 , 1 , 1 ) ( 0.33 , 0.5 , 1 ) (
0.14 , 0.17 , 0.2 ) ( 1 , 2 , 3 ) ( 1 , 1 , 1 ) ( 0.33 , 0.5 , 1 )
( 5 , 6 , 7 ) ( 1 , 2 , 3 ) ( 1 , 1 , 1 ) ] ##EQU00011.3## A
.about. 4 = [ ( 1 , 1 , 1 ) ( 0.14 , 0.17 , 0.2 ) ( 0.14 , 0.17 ,
0.2 ) ( 5 , 6 , 7 ) ( 1 , 1 , 1 ) ( 0.33 , 0.5 , 1 ) ( 5 , 6 , 7 )
( 1 , 2 , 3 ) ( 1 , 1 , 1 ) ] ##EQU00011.4## A .about. 5 = [ ( 1 ,
1 , 1 ) ( 0.2 , 0.25 , 0.33 ) ( 0.14 , 0.17 , 0.2 ) ( 3 , 4 , 5 ) (
1 , 1 , 1 ) ( 0.33 , 0.5 , 1 ) ( 5 , 6 , 7 ) ( 1 , 2 , 3 ) ( 1 , 1
, 1 ) ] ##EQU00011.5## A .about. 6 = [ ( 1 , 1 , 1 ) ( 0.14 , 0.17
, 0.2 ) ( 0.14 , 0.17 , 0.2 ) ( 5 , 6 , 7 ) ( 1 , 1 , 1 ) ( 0.33 ,
0.5 , 1 ) ( 5 , 6 , 7 ) ( 1 , 2 , 3 ) ( 1 , 1 , 1 ) ]
##EQU00011.6## A .about. 7 = [ ( 1 , 1 , 1 ) ( 0.14 , 0.17 , 0.2 )
( 0.14 , 0.17 , 0.2 ) ( 5 , 6 , 7 ) ( 1 , 1 , 1 ) ( 1 , 1 , 1 ) ( 5
, 6 , 7 ) ( 1 , 1 , 1 ) ( 1 , 1 , 1 ) ] ##EQU00011.7## A .about. 8
= [ ( 1 , 1 , 1 ) ( 0.2 , 0.25 , 0.33 ) ( 0.14 , 0.17 , 0.2 ) ( 3 ,
4 , 5 ) ( 1 , 1 , 1 ) ( 0.33 , 0.5 , 1 ) ( 5 , 6 , 7 ) ( 1 , 2 , 3
) ( 1 , 1 , 1 ) ] ##EQU00011.8## A .about. 9 = [ ( 1 , 1 , 1 ) (
0.14 , 0.17 , 0.2 ) ( 0.2 , 0.25 , 0.33 ) ( 5 , 6 , 7 ) ( 1 , 1 , 1
) ( 1 , 1 , 1 ) ( 3 , 4 , 5 ) ( 1 , 1 , 1 ) ( 1 , 1 , 1 ) ]
##EQU00011.9## A .about. 10 = [ ( 1 , 1 , 1 ) ( 0.2 , 0.25 , 0.33 )
( 0.14 , 0.17 , 0.2 ) ( 3 , 4 , 5 ) ( 1 , 1 , 1 ) ( 1 , 1 , 1 ) ( 5
, 6 , 7 ) ( 1 , 1 , 1 ) ( 1 , 1 , 1 ) ] ##EQU00011.10##
TABLE-US-00001 TABLE 1 Evaluation scale table of a nine-scale
method Scale value Meaning (1, 1, 1) Factors i and j are of equal
importance. (1, 2, 3) The factor i is slightly more important than
the factor j. (3, 4, 5) Compared with the factor j, the factor i is
of great importance. (5, 6, 7) Compared with the factor j, the
factor i is very important. (7, 8, 9) Compared with the factor j,
the factor i is absolutely important.
[0052] A comprehensive determining matrix is established and a
fuzzy weight value is calculated.
[0053] The established comprehensive determining matrix is as
follows:
A .about. M = [ ( 1 , 1 , 1 ) ( 0.26 , 0.31 , 0.4 ) ( 0.17 , 0.21 ,
0.29 ) ( 3.6 , 4.5 , 5.4 ) ( 1 , 1 , 1 ) ( 0.53 , 0.65 , 1 ) ( 4 ,
5 , 6 ) ( 1.4 , 2 , 2.6 ) ( 1 , 1 , 1 ) ] ##EQU00012##
[0054] A geometric mean of each primary evaluation factor
(monitoring parameter type) of the comprehensive determining matrix
is solved:
r.sub.1=[(1.times.0.26.times.0.17),(1.times.0.31.times.0.21),(1.times.0.-
4.times.0.29)].sup.1/3=(0.354,0.402,0.488)
r.sub.2=[(3.6.times.1.times.0.53),(4.5.times.1.times.0.65),(5.4.times.1.-
times.1)].sup.1/3=(1.240,1.430,1.754)
r.sub.3=[(4.times.1.4.times.1);(5.times.2.times.1),(6.times.2.6.times.1)-
].sup.1/3=(1.776,2.154,2.499).sub..
[0055] The sum of the geometric mean is:
r=r.sub.1+r.sub.2+r.sub.3=(3.37,3.987,4.741)
[0056] The relative fuzzy weight value of each primary evaluation
factor calculated by formula (5) is as follows:
w 1 = r 1 r = ( 0.075 , 0.101 , 0.145 ) ##EQU00013## w 2 = r 2 r =
( 0.262 , 0.359 , 0.521 ) ##EQU00013.2## w 3 = r 3 r = ( 0.375 ,
0.540 , 0.742 ) ##EQU00013.3##
[0057] The relative fuzzy weight value of each evaluation factor is
converted by formula (6) into an explicit value of the evaluation
factor as follows:
DF 1 = [ ( R 1 - L 1 ) + ( M 1 - L 1 ) ] 3 + L 1 = [ ( 0.145 -
0.075 ) + ( 0.101 - 0.075 ) ] 3 + 0.075 = 0.107 ##EQU00014##
[0058] Similarly, the following can be obtained: DF.sub.2=0.38, and
DF.sub.3=0.552.
[0059] The explicit weight value is normalized by formula (7) as
follows:
w 1 ' = 0.107 0.107 + 0.38 + 0.552 = 0.103 ##EQU00015##
[0060] Similarly, the following can be obtained: w'.sub.2=0.366,
and w'.sub.3=0.531.
[0061] Calculation results of relative weight values of the
foregoing primary evaluation factor layer (monitoring parameter
type) of formation gas production are summarized as shown in Table
2.
TABLE-US-00002 TABLE 2 Summary table of calculation results of
relative weight of the primary evaluation factor layer (monitoring
parameter type) of formation gas production Injection parameter
Drilling parameter Return parameter Geometric mean r.sub.i (0.354,
0.402, 0.488) (1.24, 1.43, 1.754) (1.766, 2.154, 2.499) Fuzzy
weight w.sub.i (0.075, 0.101, 0.145) (0.262, 0.359, 0.521) (0.375,
0.54, 0.742) Explicit normalized 0.103 0.366 0.531 weight
w.sub.i'
[0062] The weight calculation of the secondary evaluation factor
layer is carried out in sequence, and then series connection is
carried out between various layers, and finally the risk weight
value of formation gas production is obtained as shown in Table
3.
TABLE-US-00003 TABLE 3 Summary table of risk weight of formation
gas production Relative weight Relative weight Secondary Primary
value of the Secondary value of the evaluation evaluation primary
evaluation evaluation secondary evaluation factor Overall Target
factor factor factor factor weight value Formation Injection 0.103
Injection fluid 1 0.103 gas parameter pressure production Injection
fluid 0 0 flow Drilling 0.366 Hanging load 0.548 0.201 parameter
Drilling time 0.452 0.165 Torque 0 0 Rotational speed 0 0 Return
0.531 Total 0.625 0.332 parameter hydrocarbon value Hydrogen
sulfide 0 0 concentration Return fluid flow 0.126 0.067 Return
fluid 0.249 0.132 pressure Return fluid 0 0 temperature
[0063] Finally, the comprehensive determining weight matrix of
hydrate drilling risks is obtained as follows:
A T = [ w T 11 ' w T 1 e ' w Tm 1 ' w Tme ' ] = [ 0.103 0.154 0 -
0.17 0 0.185 0.224 - 0.796 0 - 0.035 0 0 0 0 0 0 - 0.201 0.109 0 -
0.637 0 0.199 0.103 0 - 0.165 0.074 - 0.165 0.087 0 0 0.432 0.063 0
0.207 0 - 0.106 0 0.407 0.241 0.091 0 - 0.084 0 0 0 - 0.041 0 -
0.05 0.332 0 0.389 0 0 0 0 0 0 0 0 0 1 0 0 0 0.067 - 0.121 0.067 0
0 0 0 0 0.132 - 0.216 0.059 0 0 - 0.168 0 0 0 0 - 0.32 0 0 0 0 0 ]
##EQU00016##
[0064] Columns of the comprehensive determining weight matrix
sequentially represent eight risk types which are formation gas
production, borehole instability, hydrate production, drill string
fracture, H.sub.2S production, sticking, bit balling and
piercing-caused leakage of a drilling tool. In each column, overall
weight values of 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 are
represented sequentially.
[0065] A well A is a deep water well located in the South China
Sea. Take the well A as an example for trial calculation. Basic
data of this well is as follows:
TABLE-US-00004 Parameter name Data Parameter name Data Water depth
(m) 1000 Geothermal gradient (.degree. C./m) 0.025 Well depth (m)
5100 Submarine temperature (.degree. C.) 4 Inlet temperature
(.degree. C.) 22 Outer diameter of drill string 127 (mm) Diameter
of choke 76.2 Inner diameter of riser (mm) 472 manifold (mm)
Drilling fluid density 1.3 Thermal conductivity of 2.25
(g/cm.sup.3) formation ( W/(m .degree. C.)) Displacement (L/s) 30
Thermal conductivity of 1.5 drilling fluid (W/(m .degree. C.)) Bit
size (mm) 215.9 Specific heat of drilling fluid 1675 (J/(kg
.degree. C.)) Bit pressure (kN) 40 Rotational speed (rad/min) 40
-50
[0066] Downhole anomalies occurred when the well was drilled to a
depth of 4833.7 m. Theoretical values of various monitoring
parameters at 4833.7 m were calculated through the model. During
the construction process, an on-site monitoring device acquired
measured values of various monitoring parameters at the well
section at a depth of 4833.7 m. Table 4 shows the theoretical
values and the measured values corresponding to various monitoring
parameters when the well was drilled to a depth of 4833.7 m.
[0067] b.sub.i is a relative change rate of the i-th monitoring
parameter (namely the i-th evaluation factor); .DELTA.S.sub.i is a
variation of a value of the i-th monitoring parameter; S.sub.ic is
a measured value of the i-th monitoring parameter; S.sub.iL is a
theoretical value of the i-th monitoring parameter; .DELTA.H.sub.i
and is a reasonable change range of the value of the i-th
monitoring parameter.
TABLE-US-00005 TABLE 4 Comparison table of model calculation values
and field measured values at a drill depth of 4833.7 m Monitoring
parameter Injection fluid Injection Hanging Drilling Rotational
pressure fluid flow load time Torque speed Parameter (MPa)
(m.sup.3/min) (kN) (min/m) (kN m) (rad/min) Theoretical 15.296 1.80
1745 5.5 11.85 50 value Measured 13.85 1.824 1653.55 6.3 6.82 50
value Monitoring parameter Total Hydrogen sulfide Return Return
fluid Outlet Parameter hydrocarbon value concentration fluid flow
pressure temperature value type (%) (ppm) (m.sup.3/min) (kPa)
(.degree. C.) Theoretical 4.06 0 1.80 14.57 22 value Measured 3.35
0 1.862 14.55 22 value
[0068] The relative change rate of each monitoring parameter was
calculated and the monitoring parameter change vector was
constructed by using the obtained monitoring parameter related data
at the well depth of 4833.7 m (as shown in Table 4). The finally
obtained judgment result is as follows:
Z=BA.sub.T=(0.604 0 0 0 71.572 0 0 27.824)
[0069] The foregoing results correspond to the risk types to draw a
histogram of risk occurrence probability (as shown in FIG. 3).
Through fuzzy judgment, it can be seen that the possibilities of
piercing-caused leakage of a drilling tool and drill string
fracture are relatively high, with the possibilities being 27.824%
and 71.572% respectively. It can be determined that drill string
fracture occurs when it is drilled to a depth of 4833.7 m. In
actual drilling engineering, when it was drilled to 4833.7, a drill
string fracture accident occurred. The results obtained by the
method for intelligently determining hydrate drilling and
production risks based on fuzzy judgment are consistent with the
actual monitoring results on site.
[0070] The foregoing descriptions are only preferred
implementations of the present invention. It should be noted that
for a person of ordinary skill in the art, several improvements and
modifications may further be made without departing from the
principle of the present invention. These improvements and
modifications also fall within the protection scope of the present
invention.
* * * * *