CN109933745B - Hydrate drilling and production risk intelligent judgment method based on fuzzy judgment - Google Patents
Hydrate drilling and production risk intelligent judgment method based on fuzzy judgment Download PDFInfo
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- 238000005553 drilling Methods 0.000 title claims abstract description 59
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000011156 evaluation Methods 0.000 claims abstract description 181
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- 238000010276 construction Methods 0.000 claims abstract description 10
- 239000012530 fluid Substances 0.000 claims description 30
- 238000002347 injection Methods 0.000 claims description 17
- 239000007924 injection Substances 0.000 claims description 17
- 239000007789 gas Substances 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 10
- 230000004044 response Effects 0.000 claims description 8
- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical compound S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 claims description 7
- 230000015572 biosynthetic process Effects 0.000 claims description 7
- 229910000037 hydrogen sulfide Inorganic materials 0.000 claims description 7
- 239000004215 Carbon black (E152) Substances 0.000 claims description 6
- 229930195733 hydrocarbon Natural products 0.000 claims description 6
- 150000002430 hydrocarbons Chemical class 0.000 claims description 6
- NMJORVOYSJLJGU-UHFFFAOYSA-N methane clathrate Chemical compound C.C.C.C.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O NMJORVOYSJLJGU-UHFFFAOYSA-N 0.000 description 12
- 230000008569 process Effects 0.000 description 11
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Natural products C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 5
- 239000003345 natural gas Substances 0.000 description 3
- 239000000470 constituent Substances 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000013078 crystal Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 150000004677 hydrates Chemical class 0.000 description 1
- 230000036571 hydration Effects 0.000 description 1
- 238000006703 hydration reaction Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- -1 natural gas hydrates Chemical class 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
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- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
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- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
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- E21B41/0099—Equipment or details not covered by groups E21B15/00 - E21B40/00 specially adapted for drilling for or production of natural hydrate or clathrate gas reservoirs; Drilling through or monitoring of formations containing gas hydrates or clathrates
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Abstract
The invention discloses a hydrate drilling and production risk intelligent judgment method based on fuzzy judgment, which comprises the following steps of: firstly, stratifying monitoring parameters in a hydrate drilling and production process from top to bottom: a target layer, a main evaluation factor layer and a secondary evaluation factor layer; secondly, calculating the relative weight value of each main evaluation factor and each secondary evaluation factor contained in the main evaluation factor; then, respectively connecting the relative weight values of the main evaluation factors with the relative weight values of the secondary evaluation factors in series to obtain the overall weight values of the secondary evaluation factors; repeating the above steps; and finally, constructing the integral weight values of the evaluation factors of each risk into column vectors, thereby obtaining a comprehensive judgment weight matrix of the hydrate drilling and production risk, and judging the risk in the hydrate drilling and production process by combining the monitoring parameter change vectors. The method has reliable principle, and can quickly and accurately realize the functions of risk type judgment, alarm and the like when the risk occurs in the drilling and production process of the hydrate, thereby ensuring the safe operation of the drilling and production construction of the hydrate.
Description
Technical Field
The invention relates to the technical field of intelligent judgment and research of drilling and production risks of natural gas hydrates, in particular to an intelligent judgment method of drilling and production risks of hydrates based on fuzzy judgment.
Background
The natural gas hydrate is a non-stoichiometric cage-shaped crystal substance generated by water and natural gas in a high-pressure low-temperature environment, is an unconventional energy source with high density and high heat value, is mainly distributed in marine and land permafrost zone sediments, wherein the amount of marine natural gas hydration resources is about one hundred times of that of a land permafrost zone, the exploitation of the marine natural gas hydrate is concerned, and the natural gas hydrate is widely considered to be a substitute energy source with the highest potential in the 21 st century and also be a new energy source with the largest reserve which is not developed at present.
In the face of such huge resource amount, the well drilling safety problem of the natural gas hydrate reservoir becomes a great problem restricting the development of the natural gas hydrate drilling and production technology, and the hydrate drilling and production process is often confronted with formation gas production, borehole wall instability, hydrate production, drill string fracture and H production2S, 8 risks of drill sticking, drill bit mud bags and drill bit puncture. Basic methods for risk monitoring and judgment have been established in conventional reservoir drilling processes, but are not perfect. For gas hydrate drilling and production processThe risk judgment method is not established by scholars at present, and in order to ensure the safe and efficient exploitation of the natural gas hydrate, an intelligent natural gas hydrate risk judgment method while drilling is urgently needed.
Disclosure of Invention
The invention aims to provide an intelligent hydrate drilling and production risk judgment method based on fuzzy judgment, which has reliable principle and simple and convenient operation, can quickly and accurately realize the functions of risk type judgment, alarm and the like when risks occur in the hydrate drilling and production process, monitors the hydrate drilling and production construction operation process in real time, ensures the safe operation of hydrate drilling and production construction, and fills the blank of the intelligent hydrate drilling and production risk judgment method.
In order to achieve the technical purpose, the invention adopts the following technical scheme.
Firstly, carrying out layered structural processing on monitoring parameters in the drilling and production process of the hydrate by using a fuzzy analytic hierarchy process, and dividing the levels from top to bottom, wherein the levels comprise a target layer (consisting of 8 risks), a main evaluation factor layer (consisting of monitoring parameter types, one main evaluation factor is one monitoring parameter type) and a secondary evaluation factor layer (consisting of monitoring parameters, and one secondary evaluation factor is one monitoring parameter); secondly, calculating relative weight values of the main evaluation factors (for example, when a certain risk occurs, the stronger the response of a certain main evaluation factor to the risk is, the larger the relative weight value of the main evaluation factor to the risk is, that is, the larger the relative weight value of the main evaluation factor is); thirdly, respectively calculating the relative weight value of each secondary evaluation factor contained in each primary evaluation factor (for example, when a certain risk occurs, the stronger the response of a certain secondary evaluation factor to the risk is, the larger the relative weight value of the secondary evaluation factor to the risk is, that is, the larger the relative weight value of the secondary evaluation factor is); then, respectively connecting the relative weight value of each main evaluation factor with the relative weight values of all secondary evaluation factors contained in the main evaluation factor in series (i.e. multiplying the relative weight value of each main evaluation factor with the relative weight values of all secondary evaluation factors contained in the main evaluation factor), wherein the relative weight value of the secondary evaluation factor after connection in series is the overall weight value of the secondary evaluation factor (i.e. when the risk occurs, the greater the intensity of the comprehensive response of which secondary evaluation factor to the risk is, the greater the overall weight value of the secondary evaluation factor is); repeating the steps, calculating the relative weight value of each main evaluation factor of the rest risks in the target layer, and respectively calculating the relative weight value of each secondary evaluation factor contained in each main evaluation factor and the overall weight value of the secondary evaluation factors; and finally, constructing the integral weight values of each secondary evaluation factor of each risk into column vectors according to the same sequence, arranging the constructed column vectors according to the sequence to construct a comprehensive judgment weight matrix, namely a hydrate drilling and production risk comprehensive judgment weight matrix, and combining the monitoring parameter change vectors to quickly, accurately and intelligently judge the risk in the hydrate drilling and production process.
Unless a certain evaluation factor is specified, the evaluation factor is simply referred to herein.
An intelligent hydrate drilling and production risk judgment method based on fuzzy judgment sequentially comprises the following steps:
step 1, establishing a hierarchical structure model
Carrying out layered structural treatment on monitoring parameters in the drilling and production process of the hydrate by using a fuzzy analytic hierarchy process, and dividing the levels from top to bottom, wherein the levels comprise a target layer, a main evaluation factor layer and a secondary evaluation factor layer, and the target layer consists of 8 risks, namely formation gas production, borehole wall instability, hydrate production, drill string fracture and H production2S, sticking a drill, wrapping a drill bit mud bag and piercing a drilling tool, wherein a main evaluation factor layer is composed of 3 monitoring parameter types, namely an injection parameter, a drilling parameter and a return parameter, and a secondary evaluation factor layer is composed of 11 monitoring parameters, namely an injection fluid pressure, an injection fluid flow, a hanging weight, a drilling time, a torque, a rotating speed, an all hydrocarbon value, a hydrogen sulfide concentration, a return fluid flow, a return fluid pressure and a return fluid temperature, so that a hierarchical structure model is constructed.
Step 2, constructing a judgment matrix
In the constructed hierarchical structure model, a sub-region is formed according to each main evaluation factor (monitoring parameter type) of the selected risk and the next-level evaluation factor layer (monitoring parameter) governed by the main evaluation factor, a judgment matrix is established for the sub-region, and the relative importance of each evaluation factor of the sub-region is evaluated by adopting a nine-scale method, wherein the process is as follows: firstly, taking the selected risk in a target layer (namely a first layer) as a reference, applying a nine-scale method to compare the main evaluation factors of the main evaluation factor layer (namely a second layer) and determining a scale value, then establishing a main evaluation factor judgment matrix according to the determined scale value, and then respectively establishing a secondary evaluation factor judgment matrix for the secondary evaluation factors of the secondary evaluation factor layer (namely a third layer) contained in each main evaluation factor by taking each main evaluation factor of the main evaluation factor layer as a reference.
And the scale values of each main evaluation factor and each secondary evaluation factor are determined by adopting a nine-scale method. An example of scale value determination is as follows: when the monitoring parameter i corresponding to the selected risk is compared with the monitoring parameter j, determining a scale value according to the response strength (namely importance) of the monitoring parameter i and the monitoring parameter j to the risk, wherein the scale value uses a triangular fuzzy number
And quantitatively expressing, wherein the scale value is a judgment result of the importance of the monitoring parameter i and the monitoring parameter j to the risk. Respectively constructing a main evaluation factor judgment matrix and a secondary evaluation factor judgment matrix according to the scale values of the main evaluation factor layer and the secondary evaluation factor layer, and using the constructed judgment matrixes of the main evaluation factor and the secondary evaluation factor
It is shown that,
examples are as follows:
i refers to the ith evaluation factor (i is 1, 2, 3 … m) of a certain layer in the hierarchical structure model, j refers to the jth evaluation factor (j is 1, 2, 3 … m) of the same layer in the same hierarchical structure model as i, and m refers to the number of main evaluation factors or the number of secondary evaluation factors.
Step 3, establishing a comprehensive judgment matrix and calculating a fuzzy weight value
The number of experts to be evaluated is n, and the comprehensive judgment matrix can be obtained by a fuzzy average method, as shown in the following formula:
further, a judgment matrix is established according to the evaluation of the kth expert
Expressed as a scale value determined by the kth expert according to the importance of the evaluation factor c of the same layer to the evaluation factor d, the comprehensive judgment matrix is calculated as follows:
further, a relative fuzzy weight value of each evaluation factor in the matrix is then calculated by using a geometric mean fuzzy weight calculation method (similar to a root method) (the relative fuzzy weight of each evaluation factor has been normalized by considering a fuzzy number).
Comprehensive judgment matrix
The geometric mean of the ith evaluation factor in (a) is:
ri＝(ai1×ai2×ai3×…×aim)1/m
the relative blur weight value for the ith evaluation factor is:
wi＝ri×(r1+r2+r3+…+rm)-1
step 4, converting the relative fuzzy weight value of the ith evaluation factor into a definite value
Relative weight fuzzy weight value w of ith evaluation factoriIs expressed in the form of triangular fuzzy numbers, i.e. wi＝(Ri，Mi，Li)，LiLeft extension for triangular blur number, RiFor right extension of the triangular blur number, MiFor the intermediate value of the triangular fuzzy number, the relative weight fuzzy weight value of the ith evaluation factor is converted into the definite weight value DF of the ith evaluation factori，DFiThe calculation formula of (a) is as follows:
step 5, normalizing the definite weight value of the ith evaluation factor
To compare the relative importance of each primary evaluation factor (including injection parameters, drilling parameters, return parameters) and secondary evaluation factors (including injection fluid pressure, injection fluid flow rate, suspended weight, time to drill, torque, rotational speed, total hydrocarbon value, hydrogen sulfide concentration, return fluid flow rate, return fluid pressure, and return fluid temperature), the definite weight value of the ith evaluation factor is normalized by the normalization formula:
w′ia relative weight value that is a normalized ith evaluation factor.
Step 6, the relative weight values of the evaluation factors between the layers are connected in series
The relative weight value of each main evaluation factor is respectively connected in series with the relative weight values of all the secondary evaluation factors contained in the main evaluation factor (i.e. the relative weight value of each main evaluation factor is respectively multiplied by the relative weight values of all the secondary evaluation factors contained in the main evaluation factor), and the relative weight value of the secondary evaluation factor after being connected in series is the overall weight value of the secondary evaluation factor.
w’TiIs the overall weight value, w 'of the ith secondary assessment factor'1iIs a relative weight value of a primary evaluation factor corresponding to the ith secondary evaluation factor, w'2iFor the ith sub-assessment factor relative weight value, the overall weight of the ith sub-assessment factor relative to a certain risk is as follows:
w′Ti＝w′1i×w′2i
further, repeating the steps 2-6, calculating the relative weight values of all main evaluation factors of the rest risks in the target layer, calculating the relative weight values of all secondary evaluation factors contained in each main evaluation factor and the overall weight values of the secondary evaluation factors, then constructing the overall weight values of all secondary evaluation factors of each risk into column vectors according to the same sequence, and then constructing the constructed column vectors into a comprehensive judgment weight matrix which is a hydrate drilling risk comprehensive judgment weight matrix A after arranging the constructed column vectors in sequenceT，ATAs follows:
e refers to the risk number (e ═ 8).
Step 7, construction of monitoring parameter change vector
When the risk occurs, the risk occurring underground is judged based on the change trend of the monitoring parameter values and the relative change rate of the monitoring parameter values, the relative change rate of each monitoring parameter value at a certain well depth is used as a constituent element of a monitoring parameter change vector, and the relative change rate of the monitoring parameter values reflects the response strength of the monitoring parameter to the risk. In the normal construction process (in the construction process without risk), monitoring parameters such as injection fluid pressure, injection fluid flow, suspended load, drilling time, torque, rotating speed, total hydrocarbon value, hydrogen sulfide concentration, return fluid flow, return fluid pressure and return fluid temperature fluctuate within a normal range, in order to avoid the influence of fluctuation within the normal range of each monitoring parameter on risk judgment, a reasonable variation range of the monitoring parameters is established by analyzing a large amount of drilled monitoring data and combining with the experience of a field engineer, when the monitoring parameters fluctuate within the range, the monitoring parameters are determined not to change, otherwise, the monitoring parameters are determined to change. During construction, the monitoring parameter value has two changes of increase and decrease, the increase of the monitoring parameter value is represented by "+", and the decrease of the monitoring parameter value is represented by "-". In the calculation process, the initial value of the monitoring parameter is divided into two conditions of '0' and not '0', and based on the principle, the calculation formula for establishing the constituent elements of the variation vector of the monitoring parameter is as follows:
wherein: biThe relative change rate of the ith monitoring parameter (i.e. the ith evaluation factor); delta SiThe variation of the ith monitoring parameter value; sicThe measured value of the ith monitoring parameter is obtained; siLThe theoretical value of the ith monitoring parameter; Δ HiIs a reasonable variation range of the ith monitoring parameter value. When the initial value of the ith monitoring parameter is not 0, calculating the relative change rate of the ith monitoring parameter by using a formula a; when the initial value of the ith monitoring parameter is '0', the measured value of the ith monitoring parameter should be increasedThe measured value of the ith monitoring parameter is reduced by using the formula b, and the formula c is used for calculation. Defining the parameter value of the ith monitoring parameter as '0' when the parameter value of the ith monitoring parameter is changed within a reasonable change range, defining the parameter value of the ith monitoring parameter as '1' when the change range of the ith monitoring parameter is more than or equal to 100%, and defining the value of the ith monitoring parameter as b when the change range of the ith monitoring parameter is between the reasonable change range and 100%i。
Sequencing the elements of the monitoring parameter variation vector according to the sequence of constructing the monitoring parameters in the comprehensive judgment weight matrix array vector, and finally constructing the monitoring parameter variation vector, wherein the monitoring parameter variation vector is expressed as:
B＝(b1 b2 … bm)
step 8, risk judgment
After the comprehensive judgment weight matrix and the monitoring parameter variation vector are established, the product of the comprehensive judgment weight matrix and the monitoring parameter variation vector is the judgment result of the hydrate drilling and production risk, and the judgment result is shown as the following formula:
wherein: the numerical value in Z represents the probability of each risk, obviously, the larger the numerical value of the element in Z is, the higher the probability of the corresponding risk is, and conversely, the lower the probability of the occurrence is.
Aiming at the problem of risk judgment in the natural gas hydrate drilling and production process, the fuzzy analytic hierarchy process is utilized to establish the intelligent natural gas hydrate drilling and production risk judgment method based on fuzzy judgment, the method can quickly and accurately realize the functions of intelligent judgment, alarm and the like, and can monitor and judge whether underground risks occur during the natural gas hydrate drilling and production operation in real time, thereby ensuring the safe operation of the natural gas hydrate drilling and production construction operation.
Drawings
FIG. 1 is a layered structure diagram of the hydrate drilling and production risk intelligent judgment method based on fuzzy judgment.
FIG. 2 is a graph of risk versus monitoring parameter response for the present invention.
FIG. 3 is a diagram of the determination result of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and examples.
Example 1
An intelligent hydrate drilling and production risk judgment method based on fuzzy judgment comprises the following specific processes:
establishing a hierarchical structure model:
as shown in figure 1, the target layer produces gas from the stratum, the borehole wall is unstable, hydrate is produced, the drill string is broken, and H is produced2S, 8 risks of drill sticking, bit balling and drill stabbing are formed, a main evaluation factor layer is formed by injection parameters, drilling parameters and return parameters, and a secondary evaluation factor layer is formed by injection fluid injection pressure, injection fluid flow, hanging weight, drilling time, torque, rotating speed, total hydrocarbon value, hydrogen sulfide concentration, return fluid flow, return fluid pressure and return fluid temperature.
Constructing a judgment matrix:
taking gas production from the formation as an example, each main evaluation factor of the risk and the next-level evaluation factor layer governed by the main evaluation factor form a sub-region, and a judgment matrix is established for the sub-region (see table 1): firstly, taking the gas produced by the stratum in a target layer (namely a first layer) as a reference, comparing the main evaluation factors of a main evaluation factor layer (namely a second layer) by applying a nine-scale method, determining a scale value, establishing a main gas produced by the stratum evaluation factor judgment matrix according to the determined scale value, and respectively establishing a secondary gas produced by the stratum evaluation factor judgment matrix for the secondary evaluation factors of a secondary evaluation factor layer (namely a third layer) contained in each main evaluation factor by taking each main evaluation factor of the main evaluation factor layer as a reference. The scale values of all the main evaluation factors and all the secondary evaluation factors are determined by adopting a nine-scale method, and a judgment matrix A is formed as follows:
TABLE 1 nine-Scale evaluation Scale Table
Establishing a comprehensive judgment matrix and calculating a fuzzy weight value:
the established comprehensive judgment matrix is as follows:
and (3) solving the geometric mean value of each main evaluation factor (monitoring parameter type) of the comprehensive judgment matrix:
the sum of the individual geometric means is:
r＝r1+r2+r3＝(3.37,3.987,4.741)
the relative fuzzy weight value for each primary evaluation factor is calculated from equation (5) as follows:
the relative fuzzy weight values for each evaluation factor are converted by equation (6) into explicit values for each evaluation factor as follows:
the same can be obtained: DF (Decode-feed)2＝0.38，DF3＝0.552。
The weight values are normalized explicitly by equation (7) as follows:
the same can be obtained: w'2＝0.366，w′3＝0.531。
The results of calculating the relative weight values of the main evaluation factor layers (monitoring parameter types) of gas production from the formation are summarized in table 2.
Table 2 summary table of relative weight calculation results of main evaluation factor layer (monitoring parameter type) of gas production from formation
And sequentially carrying out weight calculation on the secondary evaluation factor layers, and then carrying out series connection among all the layers to finally obtain the gas production risk weight value of the stratum as shown in a table 3.
TABLE 3 summary table of gas production risk weights for formation
Finally, the obtained hydrate drilling risk comprehensive judgment weight matrix is as follows:
wherein, each row of the comprehensive judgment weight matrix sequentially represents eight risk types of gas production of a stratum, instability of a well wall, hydrate production, drill stem fracture, hydrogen sulfide production, drill sticking, bit balling and drill puncture, and each row sequentially represents the integral weight values of injection fluid pressure, injection fluid flow, hanging weight, drilling time, torque, rotating speed, total hydrocarbon value, hydrogen sulfide concentration, return fluid flow, return fluid pressure and return fluid temperature.
The well A is a deep well in south China sea, taking the well A as an example for trial calculation, and the basic data of the well is as follows:
when the well drilling is constructed to the well depth of 4833.7m, the underground abnormity occurs, theoretical values of all monitoring parameters at 4833.7m are calculated through a model, in the construction process, the field monitoring equipment acquires measured values of all monitoring parameters of a 4833.7m well section, and table 4 shows the theoretical values and the measured values corresponding to all monitoring parameters when the well drilling is carried out to the well depth of 4833.7 m.
biThe relative change rate of the ith monitoring parameter (i.e. the ith evaluation factor); delta SiThe variation of the ith monitoring parameter value; sicThe measured value of the ith monitoring parameter is obtained; siLThe theoretical value of the ith monitoring parameter; Δ HiIs a reasonable variation range of the ith monitoring parameter value.
TABLE 4 comparison table of model calculated value at drill depth 4833.7m and field measured value
Table 4 table for comparing model calculated value at 4833.7m drilling depth with field measured value
The obtained data (as shown in table 4) related to the monitoring parameters at the well depth of 4833.7m is applied to calculate the relative change rate of each monitoring parameter and construct the change vector of the monitoring parameters, and the final judgment result is as follows:
Z＝BAT＝(0.604 0 0 0 71.572 0 0 27.824)
and (3) corresponding the results to the risk types, drawing a risk occurrence probability histogram (shown in figure 3), wherein the probability of occurrence of drilling tool puncture and drill string breakage is high through fuzzy judgment, the probability is 27.824% and 71.572%, respectively, and the drill string breakage can be judged when the drill is drilled to 4833.7 m. In the actual drilling engineering, when the drilling reaches 4833.7, a drill string breakage accident occurs, and the result obtained by the hydrate drilling and production risk intelligent judgment method based on fuzzy judgment is consistent with the actual monitoring result on site.
While the preferred embodiments of the present invention have been described, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the following claims.
Claims (3)
1. An intelligent hydrate drilling and production risk judgment method based on fuzzy judgment sequentially comprises the following steps:
step 1, establishing a hierarchical structure model
The monitoring parameters in the hydrate drilling and production process are classified from top to bottom into levels comprising a target layer, a main evaluation factor layer and a secondary evaluation factor layer, wherein the target layer consists of 8 risks, namely formation gas production, borehole wall instability, hydrate production, drill string fracture and H production2S, sticking a drill, wrapping a drill bit mud bag and piercing a drilling tool, wherein a main evaluation factor layer consists of 3 monitoring parameter types, namely an injection parameter, a drilling parameter and a return parameter, and a secondary evaluation factor layer consists of 11 monitoring parameters, namely injection fluid pressure, injection fluid flow, hanging weight, drilling time, torque, rotating speed, total hydrocarbon value, hydrogen sulfide concentration, return fluid flow, return fluid pressure and return fluid temperature, so that a hierarchical structure model is constructed;
step 2, constructing a judgment matrix
Firstly, taking the selected risk in a target layer as a reference, applying a nine-scale method to compare main evaluation factors of a main evaluation factor layer, determining a scale value, establishing a main evaluation factor judgment matrix according to the determined scale value, and establishing secondary evaluation on secondary evaluation factors of secondary evaluation factor layers contained in each main evaluation factor by taking each main evaluation factor of the main evaluation factor layer as a referenceFactor judgment matrix, and judgment matrix for primary evaluation factor and secondary evaluation factor
Represents:
i refers to the ith evaluation factor of a certain layer in the hierarchical structure model, the value of i is 1, 2, 3 … m, j refers to the jth evaluation factor of the same layer in the same hierarchical structure model as i, the value of j is 1, 2, 3 … m, and m refers to the number of main evaluation factors or the number of secondary evaluation factors;
step 3, establishing a comprehensive judgment matrix and calculating a fuzzy weight value
Setting the number of the experts to be evaluated as n, and solving a comprehensive judgment matrix
comprehensive judgment matrix
The geometric mean of the ith evaluation factor in (a) is:
ri＝(ai1×ai2×ai3×…×aim)1/m
ɑi1、ɑi2、ɑi3、ɑimrespectively means the importance determined scale values of the ith evaluation factor compared with the 1 st, the 2 nd, the 3 rd and the mth evaluation factors of the same layer;
the relative blur weight value for the ith evaluation factor is:
wi＝ri×(r1+r2+r3+…+rm)-1；
step 4, converting the relative fuzzy weight value of the ith evaluation factor into a definite value
Relative weight fuzzy weight value w of ith evaluation factoriExpressed in the form of triangular fuzzy numbers, wi＝(Ri，Mi，Li)，LiLeft extension for triangular blur number, RiFor right extension of the triangular blur number, MiFor the intermediate value of the triangular fuzzy number, the relative weight fuzzy weight value of the ith evaluation factor is converted into the definite weight value DF of the ith evaluation factori：
Step 5, normalizing the definite weight value of the ith evaluation factor
Normalizing the explicit weight values of the ith evaluation factor, wherein the normalized relative weight values of the ith evaluation factor are as follows:
DFijthe definite weight value determined by the ith evaluation factor and the jth evaluation factor of the same layer is referred to;
step 6, the relative weight values of the evaluation factors between the layers are connected in series
Multiplying the relative weight value of each main evaluation factor by the relative weight values of all secondary evaluation factors contained in the main evaluation factor to obtain the integral weight value w 'of the ith secondary evaluation factor'Ti：
w′Ti＝w′1i×w′2i
w’1iIs a relative weight value of a primary evaluation factor corresponding to the ith secondary evaluation factor, w'2iEvaluating the factor relative weight value for the ith time;
respectively calculating the relative weight values of all main evaluation factors of other risks in the target layer, the relative weight values of all secondary evaluation factors contained in each main evaluation factor and the overall weight values of the secondary evaluation factors, constructing the overall weight values of all secondary evaluation factors of each risk into column vectors according to the same sequence, and constructing a comprehensive judgment weight matrix after arranging the column vectors in sequence, namely the comprehensive judgment weight matrix A of the hydrate drilling and production riskT，ATAs follows:
e is the risk number, e-8;
step 7, construction of monitoring parameter change vector
Wherein: biThe relative change rate of the ith monitoring parameter; delta SiThe variation of the ith monitoring parameter value; sicThe measured value of the ith monitoring parameter is obtained; siLThe theoretical value of the ith monitoring parameter; Δ HiA reasonable variation range of the ith monitoring parameter value; when the initial value of the ith monitoring parameter is not 0, calculating the relative change rate of the ith monitoring parameter by using a formula a; when the initial value of the ith monitoring parameter is '0', the measured value of the ith monitoring parameter is added with the calculation of an application formula b; reducing the measured value of the ith monitoring parameter by using an application formula c for calculation;
constructing a monitoring parameter change vector:
B＝(b1 b2…bm)；
step 8, judging the risk of drilling and production of hydrate
The numerical value in Z represents the possibility of each risk, the larger the numerical value is, the higher the possibility of the corresponding risk is, and conversely, the lower the possibility of the risk is.
2. The hydrate drilling and production risk intelligent judgment method based on fuzzy judgment as claimed in claim 1, wherein the scale values of each primary evaluation factor and each secondary evaluation factor in step 2 are determined by a nine-scale method, and when the monitoring parameter i corresponding to the selected risk is compared with the monitoring parameter j, the response intensity of the monitoring parameter i and the monitoring parameter j to the risk is determined according to the response intensity of the monitoring parameter i and the monitoring parameter j to the riskDetermining a scale value using a triangular fuzzy number
And (4) quantitatively expressing.
3. The hydrate drilling and production risk intelligent judgment method based on fuzzy judgment as claimed in claim 1, wherein the comprehensive judgment matrix in the step 3
the judgment matrix established by the evaluation of the kth expert is
Expressed as a scale value determined by the kth expert according to the importance of the evaluation factor c of the same layer to the evaluation factor d, the comprehensive judgment matrix is calculated as follows:
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| CN113236195B (en) * | 2021-06-15 | 2022-04-19 | 中国矿业大学 | Method for cooperatively regulating and controlling hydrate generation risk of drilling shaft by using three factors |
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| CN117726173A (en) * | 2023-12-19 | 2024-03-19 | 雄安中科雄创科技有限公司 | Building construction safety risk early warning method and device, electronic equipment and storage medium |
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