CN109933745A - A kind of hydrate drilling risk intelligent determination method based on fuzzy Judgment - Google Patents
A kind of hydrate drilling risk intelligent determination method based on fuzzy Judgment Download PDFInfo
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- 238000005553 drilling Methods 0.000 title claims abstract description 47
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- 238000011156 evaluation Methods 0.000 claims abstract description 158
- 238000012544 monitoring process Methods 0.000 claims abstract description 92
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- 239000010410 layer Substances 0.000 claims description 47
- 239000012530 fluid Substances 0.000 claims description 29
- 239000007789 gas Substances 0.000 claims description 26
- 238000002347 injection Methods 0.000 claims description 14
- 239000007924 injection Substances 0.000 claims description 14
- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical compound S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 claims description 11
- 229910000037 hydrogen sulfide Inorganic materials 0.000 claims description 11
- 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
- 230000008014 freezing Effects 0.000 claims description 5
- 238000007710 freezing Methods 0.000 claims description 5
- 238000010606 normalization Methods 0.000 claims description 5
- 241001074085 Scophthalmus aquosus Species 0.000 claims description 4
- 230000004044 response Effects 0.000 claims description 4
- 102000002274 Matrix Metalloproteinases Human genes 0.000 claims description 2
- 108010000684 Matrix Metalloproteinases Proteins 0.000 claims description 2
- 239000011229 interlayer Substances 0.000 claims description 2
- 230000009467 reduction Effects 0.000 claims description 2
- 150000004677 hydrates Chemical class 0.000 description 12
- 230000008569 process Effects 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 5
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012821 model calculation Methods 0.000 description 2
- 239000003345 natural gas Substances 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 238000012935 Averaging Methods 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
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- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000036571 hydration Effects 0.000 description 1
- 238000006703 hydration reaction Methods 0.000 description 1
- 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 1
- 239000000203 mixture Substances 0.000 description 1
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Classifications
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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
- 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
-
- 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
-
- 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
- E21B41/00—Equipment or details not covered by groups E21B15/00 - E21B40/00
- 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
-
- 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
- E21B47/00—Survey of boreholes or wells
-
- 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
- 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
- E21B49/08—Obtaining fluid samples or testing fluids, in boreholes or wells
-
- 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
- E21B2200/00—Special features related to earth drilling for obtaining oil, gas or water
- E21B2200/22—Fuzzy logic, artificial intelligence, neural networks or the like
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Abstract
The invention discloses a kind of hydrate drilling risk intelligent determination method based on fuzzy Judgment, comprising the following steps: firstly, to the top-down division layer of monitoring parameters during hydrate drilling: destination layer, main evaluation factor layer, secondary evaluation factor layer；Secondly, calculate each main evaluation factor and it includes each secondary evaluation factor relative weight value；Then, the relative weight value of main evaluation factor is connected with the relative weight value of secondary evaluation factor respectively, obtains time evaluation factor entirety weighted value；It repeats the above steps；Finally, each secondary evaluation factor entirety weighted value of every kind of risk is configured to column vector, so that obtaining hydrate drilling risk integrative judges weight matrix, the risk during hydrate drilling is judged in conjunction with monitoring parameters change vector.The principle of the invention is reliable, during hydrate drilling when occurrence risk, can quickly and accurately realize the functions such as risk classifications judgement, alarm, ensure that hydrate drilling construction safety carries out.
Description
Technical field
The present invention relates to gas hydrates drilling risk intelligent decision studying technological domains, especially a kind of based on fuzzy
The hydrate drilling risk intelligent determination method of judgement.
Background technique
Gas hydrates are the non-stoichiometry caged crystal generated under high pressure low temperature environment by water and natural gas
Substance is the unconventional energy resource of a kind of high density, high heating value, is distributed mainly in ocean and land permafrost band deposit,
Wherein ocean natural gas hydration stock number is about 100 times of land tundra, and the exploitation of ocean gas hydrate is by pass
Note, gas hydrates be generally considered will be 21 century it is most potential take over the energy, while being also still undeveloped at present
A kind of maximum new energy of reserves.
In face of such huge stock number, the drilling safety problem of gas hydrates, which becomes, restricts gas hydrates
One great difficult problem of drilling and production technology development, often face during hydrate drilling stratum produce gas, borehole well instability, produce hydrate,
Drill string fracture produces H2S, 8 kinds of bit freezing, bit balling and drill stem washout risks.Base has had been established in conventional oil gas reservoir drilling process
The basic skills of Risk Monitoring originally and judgement, but it is also and not perfect.Risk during gas hydrates drilling is sentenced
Disconnected method does not have scholar's foundation at present, and in order to ensure the safe and efficient progress of exploitation of gas hydrates, there is an urgent need to build
Vertical gas hydrates are with brill risk intelligent determination method.
Summary of the invention
The purpose of the present invention is to provide a kind of hydrate drilling risk intelligent determination method based on fuzzy Judgment, the party
Method principle is reliable, easy to operate, during hydrate drilling when occurrence risk, can quickly and accurately realize risk classifications
The functions such as judgement, alarm, real-time monitoring hydrate drilling construction operation process ensure that hydrate drilling construction safety carries out, fill out
The blank of hydrate drilling process risk intelligent determination method is mended.
To reach the above technical purpose, the present invention uses following technical scheme.
Firstly, being carried out at hierarchical structured using Fuzzy AHP to the monitoring parameters during hydrate drilling
Reason, top-down division level, including destination layer (by 8 kinds of the risk forms), main evaluation factor layer are (by monitoring parameters type structure
At a main evaluation factor is a kind of monitoring parameters type), secondary evaluation factor layer (is made of, one assessment monitoring parameters
The factor is a kind of monitoring parameters)；Secondly, calculate each main evaluation factor relative weight value (such as: when a certain risk occurs,
Some main evaluation factor responds stronger column to risk, then this main evaluation factor is also bigger for the relative weighting of this risk,
That is the relative weight value of this main evaluation factor is bigger)；Again, it calculates separately each time that each main evaluation factor is included
Evaluation factor relative weight value (such as: when a certain risk occurs, some time evaluation factor responds stronger column to risk, then this
A secondary evaluation factor is also bigger for the relative weighting of this risk, i.e. the relative weight value of this secondary evaluation factor is bigger)；
Then, all time assessments for being included with this main evaluation factor respectively by the relative weight value of each main evaluation factor because
The relative weight value series connection of son (is wrapped the relative weight value of each main evaluation factor with this main evaluation factor respectively
The relative weight values of all secondary evaluation factors contained is multiplied), secondary evaluation factor relative weight value after series connection be time assess because
Son entirety weighted value (when i.e. this risk occurs, which time evaluation factor is bigger to the comprehensive intensity responded of this risk,
This secondary evaluation factor entirety weighted value is also bigger)；It repeats the above steps, each master for calculating remaining risk in destination layer comments
Estimate the relative weight value of the factor, calculate separately each main evaluation factor each secondary evaluation factor for being included relative weight value,
Secondary evaluation factor entirety weighted value；Finally, each secondary evaluation factor entirety weighted value of every kind of risk is pressed the equal structure of same sequence
Cause column vector, then the column vector that will be configured to be arranged in order after be configured to comprehensive descision weight matrix be hydrate bore
Collect folk songs dangerous comprehensive descision weight matrix, in conjunction with monitoring parameters change vector to the risk during hydrate drilling carry out quickly,
Accurately, intelligent decision.
It is such as not specific to certain evaluation factor, abbreviation evaluation factor herein.
A kind of hydrate drilling risk intelligent determination method based on fuzzy Judgment, successively the following steps are included:
Step 1. establishes hierarchy Model
Hierarchical structured processing is carried out to the monitoring parameters during hydrate drilling using Fuzzy AHP, from top
Level, including destination layer, main evaluation factor layer, secondary evaluation factor layer are divided downwards, and wherein destination layer is divided by 8 kinds of the risk forms
It is not that stratum produces gas, borehole well instability, produces hydrate, drill string fracture, produces H2S, bit freezing, bit balling and drill stem washout, main assessment
Because sublayer is made of 3 kinds of monitoring parameters types, is injection parameter, drilling parameter respectively, returns out parameter, secondary evaluation factor layer by
11 kinds of monitoring parameters are constituted, be respectively injection Fluid pressure, injection fluid flow, suspending weight, when boring, torque, revolving speed, total hydrocarbon value,
Concentration of hydrogen sulfide returns out fluid flow, returns out Fluid pressure and returns out fluid temperature (F.T.), constructs hierarchy Model.
Step 2. Judgement Matricies
In the hierarchy Model of construction, according to each main evaluation factor (monitoring parameters type) of selected risk and it is somebody's turn to do
Next hierarchy that main evaluation factor dominates establishes the subregion because sublayer (monitoring parameters) constitute a sub-regions
Judgment matrix evaluates the relative importance of each evaluation factor of subregion using Method of nine marks, and process is as follows: first
First on the basis of selected risk in destination layer (i.e. first layer), using Method of nine marks to main evaluation factor layer (i.e. the second layer)
Main evaluation factor is compared, determines scale value, establishes main evaluation factor judgment matrix further according to determining scale value, then divide
Not on the basis of each main evaluation factor of main evaluation factor layer, the secondary evaluation factor that is included to each main evaluation factor
The secondary evaluation factor of layer (i.e. third layer) establishes time evaluation factor judgment matrix.
The scale value of each main evaluation factor and each secondary evaluation factor is all made of Method of nine marks and is determined.Scale value is true
It is as follows to determine example: when the corresponding monitoring parameters i of selected risk is compared with monitoring parameters j, according to monitoring parameters i and monitoring parameters j
Scale value is determined for the response intensity (i.e. importance) of this risk, the scale value Triangular Fuzzy Number
Quantificational expression, the scale value are the judging result of monitoring parameters i and monitoring parameters j for this Risk importance.According to master
The scale value of evaluation factor layer and time evaluation factor layer constructs main evaluation factor respectively and judges that judgment matrix and time evaluation factor are sentenced
The judgment matrix of disconnected matrix, the main evaluation factor of construction and time evaluation factor is usedIt indicates,Example is as follows:
I refer to a certain layer in hierarchy Model i-th of evaluation factor (i value be 1,2,3 ... m), j refers to and i
(j value is 1,2,3 ..., and m), m refers to main evaluation factor number to j-th of evaluation factor of same layer in same hierarchy Model
Amount or secondary evaluation factor quantity.
Step 3. establishes Synthetic Judgement Matrix and calculates fuzzy weight weight values
If the expert's quantity judged is n, Synthetic Judgement Matrix can be acquired with the method for fuzzy averaging value, such as following formula institute
Show:
Refer to Synthetic Judgement Matrix；Refer to respectively according to the 1st expert, the 2nd expert, n-th
The judgment matrix for the scale value construction that the judging result of expert determines；
Further, it is according to the judgment matrix that kth position expert evaluation is established It is special to be expressed as kth position
The scale value that importance of the family according to same layer evaluation factor c than evaluation factor d determines, Synthetic Judgement Matrix calculate as follows:
Further, it then asks each in matrix using geometric mean fuzzy weighted values calculation method (similar root method) to comment
Estimate the Relative Fuzzy weighted value of the factor (the Relative Fuzzy weight of each evaluation factor has been contemplated that fuzzy number normalization).
Synthetic Judgement MatrixIn i-th of evaluation factor geometrical mean are as follows:
ri=(ai1×ai2×ai3×…×aim)1/m
The Relative Fuzzy weighted value of i-th of evaluation factor are as follows:
wi=ri×(r1+r2+r3+…+rm)-1
The Relative Fuzzy weighted value of i-th of evaluation factor of step 4., which is converted into, to be clearly worth
I-th of the opposite of evaluation factor weighs fuzzy weight values wiIt is to be indicated with Triangular Fuzzy Number form, i.e. wi=(Ri, Mi,
Li), LiFor the left extension of Triangular Fuzzy Number, RiFor the right extension of Triangular Fuzzy Number, MiFor Triangular Fuzzy Number median, i-th is commented
Estimate the opposite clear weighted value DF for weighing fuzzy weight values and being converted into i-th of evaluation factor of the factori, DFiCalculation formula it is as follows:
The clear weighted value of step 5. i-th of evaluation factor of normalization
For more each main evaluation factor (including injection parameter, drilling parameter, return out parameter) and time evaluation factor (including note
When entering to inject Fluid pressure, injection fluid flow, suspending weight, brill, torque, revolving speed, total hydrocarbon value, concentration of hydrogen sulfide, return out fluid
Flow returns out Fluid pressure and returns out fluid temperature (F.T.)) relative importance, the clear weighted value of i-th of evaluation factor is regular
Change, regular formula are as follows:
wi' be normalized i-th of evaluation factor relative weight value.
The relative weight value of each interlayer evaluation factor of step 6. is connected
All time assessments that the relative weight value of each main evaluation factor is included with this main evaluation factor respectively because
The relative weight value series connection of son (is wrapped the relative weight value of each main evaluation factor with this main evaluation factor respectively
The relative weight values of all secondary evaluation factors contained is multiplied), secondary evaluation factor relative weight value after series connection be time assess because
Sub- entirety weighted value.
w’TiFor the whole weighted value of i-th evaluation factor, w '1iFor the corresponding main assessment of i-th evaluation factor because
Sub- relative weight value, w '2iFor i-th evaluation factor relative weight value, i-th evaluation factor is relative to a certain risk
Whole weight is as follows:
w’Ti=w '1i×w’2i
Further, it repeats the above steps 2~6, calculates the opposite of each main evaluation factor of remaining risk in destination layer
Weighted value, relative weight value, the secondary evaluation factor for calculating separately each secondary evaluation factor that each main evaluation factor is included are whole
Then each secondary evaluation factor entirety weighted value of every kind of risk is configured to column vector by same sequence by body weighted value,
The column vector that will be configured to again is configured to comprehensive descision weight matrix after being arranged in order be that hydrate drilling risk integrative is sentenced
Disconnected weight matrix AT, ATIt is as follows:
E refers to risk amount (e=8).
The building of step 7. monitoring parameters change vector
When risk occurs, judged based on monitoring parameters value variation tendency and the size of monitoring parameters value relative change rate
Underground happens is which kind of risk, use at a certain well depth each monitoring parameters value relative change rate as monitoring parameters change to
The constitution element of amount, monitoring parameters value relative change rate have reacted monitoring parameters to the response intensity of risk.Due to normal construction
In the process (in the work progress of non-occurrence risk), when injecting Fluid pressure, injection fluid flow, suspending weight, brill, torque, turn
Speed total hydrocarbon value, concentration of hydrogen sulfide, returns out fluid flow, returns out Fluid pressure and returns out the monitoring parameters such as fluid temperature (F.T.) normal
Fluctuation in range, influence of the fluctuation to risk judgment in the normal range (NR) in order to evade each monitoring parameters, by largely
Drilling well Analysis on monitoring data and combination field engineer's experience establish monitoring parameters rational change range, and monitoring parameters are at this
In range when fluctuation, regards as monitoring parameters and do not change, changed conversely, then regarding as monitoring parameters.It constructed
Cheng Zhong, monitoring parameters value increase and decrease two kinds of situations of change, and the increase of monitoring parameters value is indicated with "+", and "-" indicates prison
Survey the reduction of parameter value.In calculating process, monitoring parameters initial value is divided into two kinds of situations of " 0 " He Buwei " 0 ", is based on above-mentioned original
Reason, the calculation formula for establishing monitoring parameters change vector constitution element are as follows:
Wherein: biFor i-th of monitoring parameters (i.e. i-th of evaluation factor) relative change rate；ΔSiFor i-th of monitoring parameters
It is worth variable quantity；SicFor the measured value of i-th of monitoring parameters；SiLFor the theoretical value of i-th of monitoring parameters；ΔHiIt is monitored for i-th
The rational change range of parameter value.When i-th of monitoring parameters initial value is not " 0 ", i-th of monitoring parameters relative change rate is answered
It is calculated with formula a；When i-th of monitoring parameters initial value is " 0 ", the measured value of i-th of monitoring parameters increases applying equation b and calculates, the
The measured value of i monitoring parameters reduces applying equation c and calculates.Determine when i-th of monitoring parameters value changes in rational change range
Justice is " 0 ", " 1 " is defined as when the variation range of i-th of monitoring parameters is greater than or equal to 100%, when i-th of monitoring parameters
Variation range between rational change range and 100% when, value bi。
It puts in order according to monitoring parameters in construction comprehensive descision weight matrix column vector to monitoring parameters change vector
Element be ranked up, it is ultimately constructed go out monitoring parameters change vector, monitoring parameters change vector indicate are as follows:
B=(b1 b2 … bm)
Step 8. risk judgment
After establishing comprehensive descision weight matrix and monitoring parameters change vector, product between the two is hydrate
The judging result of drilling risk, is shown below:
Wherein: numerical value indicates a possibility that every kind of risk occurs size in Z, it is clear that the numerical value of element is bigger in Z, corresponds to
Risk occur a possibility that it is also bigger, conversely, occur a possibility that it is smaller.
The present invention utilizes Fuzzy Level Analytic Approach for the risk judgment problem faced during gas hydrates drilling
Method, establishes a kind of gas hydrates drilling risk intelligent determination method based on fuzzy Judgment, this method can quickly,
Accurately realize functions, the real-time monitorings such as intelligent decision, alarm judge whether well occurs when gas hydrates drilling operation
Lower risk ensures that the safety of gas hydrates drilling construction operation carries out.
Detailed description of the invention
Fig. 1 is that the present invention is based on the hierarchical diagrams of the hydrate drilling risk intelligent determination method of fuzzy Judgment.
Fig. 2 is risk and monitoring parameters response diagram of the invention.
Fig. 3 is judging result figure of the invention.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.
Embodiment 1
A kind of hydrate drilling risk intelligent determination method based on fuzzy Judgment, detailed process is as follows:
Establish hierarchy Model:
As shown in Figure 1, destination layer produces gas, borehole well instability by stratum, produces hydrate, drill string fracture, production H2S, bit freezing, drill bit
8 kinds of the risk forms of mud drum and drill stem washout, main evaluation factor layer by injection parameter, drilling parameter, return out parameter and constitute, it is secondary to comment
Estimate because sublayer by injection injection Fluid pressure, injection fluid flow, suspending weight, bore when, torque, revolving speed, total hydrocarbon value, hydrogen sulfide it is dense
Spend, return out fluid flow, return out Fluid pressure and return out fluid temperature (F.T.) composition.
Judgement Matricies:
By taking stratum produces gas as an example, next level that each main evaluation factor of this risk and the main evaluation factor dominate is commented
Estimate because sublayer constitutes a sub-regions, judgment matrix (being shown in Table 1) is established to the subregion: first with destination layer (i.e. first layer)
On the basis of middle stratum produces gas, it is compared, really using main evaluation factor of the Method of nine marks to main evaluation factor layer (i.e. the second layer)
Angle value is calibrated, stratum is established according to determining scale value and produces the main evaluation factor judgment matrix of gas, respectively with main evaluation factor layer
On the basis of each main evaluation factor, the secondary of secondary evaluation factor layer (i.e. third layer) for being included to each main evaluation factor is commented
Estimate the factor and establishes stratum production gas time evaluation factor judgment matrix.The scale value of each main evaluation factor and each secondary evaluation factor
It is all made of Method of nine marks to be determined, it is as follows to constitute jdgement matrix A:
1 Method of nine marks opinion scale table of table
It establishes Synthetic Judgement Matrix and calculates fuzzy weight weight values:
The Synthetic Judgement Matrix of foundation is as follows:
Seek the geometrical mean of each main evaluation factor (monitoring parameters type) of Synthetic Judgement Matrix:
The sum of each geometrical mean are as follows:
R=r1+r2+r3=(3.37,3.987,4.741)
Ask the Relative Fuzzy weighted value of each main evaluation factor as follows by formula (5):
The Relative Fuzzy weighted value of each evaluation factor is converted into the clearly value of each evaluation factor such as by formula (6)
Under:
It can similarly obtain: DF2=0.38, DF3=0.552.
It is as follows by the clear weighted value normalization of formula (7):
It can similarly obtain: w'2=0.366, w'3=0.531.
Above-mentioned stratum is produced the main evaluation factor layer of gas (monitoring parameters type) relative weight value calculated result to summarize such as 2 institute of table
Show.
2 stratum of table produces the main evaluation factor layer of gas (monitoring parameters type) relative weighting calculated result summary sheet
Time evaluation factor layer weight calculation is successively carried out, then carries out connecting between each level, stratum is finally obtained and produces gas
Risk rated ratio value is as shown in table 3.
3 stratum of table produces gas Risk rated ratio summary sheet
Finally obtain hydrate drilling risk comprehensive descision weight matrix are as follows:
Wherein, each Leie of comprehensive descision weight matrix time represent stratum produce gas, borehole well instability, produce hydrate, drill string fracture,
Hydrogen sulfide, eight kinds of bit freezing, bit balling and drill stem washout risk classifications are produced, successively represents injection Fluid pressure, note in each column
When entering fluid flow, suspending weight, boring, torque, revolving speed, total hydrocarbon value, concentration of hydrogen sulfide, returns out fluid flow, returns out Fluid pressure
And return out the whole weighted value of fluid temperature (F.T.).
A well is to be located at South Sea deep well, tentative calculation is carried out by taking A well as an example, the basic data of the well is as follows:
It is abnormal that underground occurs when at the well wellbore construction to well depth 4833.7m, is calculated by model each at 4833.7m
The theoretical value of monitoring parameters, in the construction process, field monitoring equipment have collected each monitoring ginseng of 4833.7m well section
Several measured values, table 4 are the corresponding theoretical value of monitoring parameters each when being drilled at well depth 4833.7m and measured value.
biFor i-th of monitoring parameters (i.e. i-th of evaluation factor) relative change rate；ΔSiChange for i-th of monitoring parameters value
Amount； SicFor the measured value of i-th of monitoring parameters；SiLFor the theoretical value of i-th of monitoring parameters；ΔHiFor i-th of monitoring parameters value
Rational change range.
Table 4 bores model calculation value and field measurement value contrast table at deep 4833.7m
Continued 4 bores model calculation value and field measurement value contrast table at deep 4833.7m
Using monitoring parameters related data (such as table 4) at the well depth 4833.7m of acquisition, the phase of each monitoring parameters is calculated
To change rate and monitoring parameters change vector is constructed, finally obtains judging result are as follows:
Z=BAT=(0.604 000 71.572 00 27.824)
The above results are corresponding with risk classifications, it draws risk possibility occurrence histogram (as shown in Figure 3), by fuzzy
A possibility that drill stem washout known to judgement and drill string fracture occur is larger, and possibility is respectively 27.824% and 71.572%, can
Judge that drill string is broken when at drilling to 4833.7m.In actual well drilled engineering, when at drilling to 4833.7, drill string fracture occurs
Accident, the actual monitoring knot of the result obtained with the hydrate drilling risk intelligent determination method based on fuzzy Judgment and scene
Fruit is consistent.
Above-described is the preferred embodiment of the present invention, it should be pointed out that the ordinary person of the art is come
It says, can also make several improvements and retouch under the premise of not departing from principle of the present invention, these improvements and modifications also exist
In protection scope of the present invention.
Claims (3)
1. a kind of hydrate drilling risk intelligent determination method based on fuzzy Judgment, successively the following steps are included:
Step 1. establishes hierarchy Model
To the top-down division level of monitoring parameters during hydrate drilling, including destination layer, main evaluation factor layer, secondary comment
Estimate because of sublayer, wherein destination layer is that stratum produces gas, borehole well instability, produces hydrate, drill string fracture, produces respectively by 8 kinds of the risk forms
H2S, bit freezing, bit balling and drill stem washout, main evaluation factor layer are made of 3 kinds of monitoring parameters types, be respectively injection parameter,
Drilling parameter returns out parameter, and secondary evaluation factor layer is made of 11 kinds of monitoring parameters, is injection Fluid pressure, injection fluid respectively
When flow, suspending weight, brill, torque, revolving speed, total hydrocarbon value, concentration of hydrogen sulfide, fluid flow is returned out, returns out Fluid pressure and return out and flow
Temperature constructs hierarchy Model；
Step 2. Judgement Matricies
First on the basis of risk selected in destination layer, compare using main evaluation factor of the Method of nine marks to main evaluation factor layer
Compared with, determine scale value, establish main evaluation factor judgment matrix further according to determining scale value, then respectively with main evaluation factor layer
On the basis of each main evaluation factor, the secondary evaluation factor for the secondary evaluation factor layer that each main evaluation factor is included is established secondary
The judgment matrix of evaluation factor judgment matrix, main evaluation factor and time evaluation factor is usedIt indicates:
I refer to a certain layer in hierarchy Model i-th of evaluation factor (i value be 1,2,3 ... m), j refers to i same
In one hierarchy Model same layer j-th of evaluation factor (j value be 1,2,3 ... m), m refer to main evaluation factor quantity or
Secondary evaluation factor quantity；
Step 3. establishes Synthetic Judgement Matrix and calculates fuzzy weight weight values
If the expert's quantity judged is n, Synthetic Judgement Matrix is acquired
Refer to the scale determined respectively according to the judging result of the 1st expert, the 2nd expert, n-th expert
It is worth the judgment matrix of construction；
Synthetic Judgement MatrixIn i-th of evaluation factor geometrical mean are as follows:
ri=(ai1×ai2×ai3×…×aim)1/m
The Relative Fuzzy weighted value of i-th of evaluation factor are as follows:
wi=ri×(r1+r2+r3+…+rm)-1；
The Relative Fuzzy weighted value of i-th of evaluation factor of step 4., which is converted into, to be clearly worth
I-th of the opposite of evaluation factor weighs fuzzy weight values wiIt is indicated with Triangular Fuzzy Number form, wi=(Ri, Mi, Li), LiFor triangle
The left extension of fuzzy number, RiFor the right extension of Triangular Fuzzy Number, MiFor Triangular Fuzzy Number median, i-th of the opposite of evaluation factor is weighed
Fuzzy weight values are converted into the clear weighted value DF of i-th of evaluation factori:
The clear weighted value of step 5. i-th of evaluation factor of normalization
By the clear weighted value normalization of i-th of evaluation factor, the relative weight value of normalized i-th of evaluation factor are as follows:
The relative weight value of each interlayer evaluation factor of step 6. is connected
All secondary evaluation factors for being included with this main evaluation factor respectively by the relative weight value of each main evaluation factor
Relative weight value be multiplied, obtain the whole weighted value w ' of i-th evaluation factorTi:
w′Ti=w '1i×w′2i
w’1iFor the corresponding main evaluation factor relative weight value of i-th evaluation factor, w '2iIt is weighed for i-th evaluation factor is opposite
Weight values；
Calculating separately the relative weight value of each main evaluation factor of remaining risk, each main evaluation factor in destination layer is included
Each secondary evaluation factor relative weight value, secondary evaluation factor entirety weighted value, by each secondary evaluation factor of every kind of risk
Whole weighted value is configured to column vector by same sequence, and comprehensive descision weight square is configured to after column vector is arranged in order
Battle array, as hydrate drilling risk integrative judges weight matrix AT, ATIt is as follows:
E is risk amount, e=8；
The building of step 7. monitoring parameters change vector
Wherein: biFor i-th of monitoring parameters relative change rate；ΔSiFor i-th of monitoring parameters value variable quantity；SicIt is monitored for i-th
The measured value of parameter；SiLFor the theoretical value of i-th of monitoring parameters；ΔHiFor the rational change range of i-th of monitoring parameters value；The
When i monitoring parameters initial value is not " 0 ", i-th of monitoring parameters relative change rate's applying equation a is calculated；At the beginning of i-th of monitoring parameters
When initial value is " 0 ", the measured value of i-th of monitoring parameters increases applying equation b and calculates；The measured value reduction of i-th of monitoring parameters is answered
It is calculated with formula c；
Construct monitoring parameters change vector:
B=(b1 b2 … bm)；
The judging result of step 8. hydrate drilling risk
A possibility that numerical value indicates a possibility that every kind of risk occurs size in Z, and numerical value is bigger, and corresponding risk occurs is got over
Greatly, conversely, a possibility that occurring is smaller.
2. a kind of hydrate drilling risk intelligent determination method based on fuzzy Judgment as described in claim 1, feature exist
In the scale value of each main evaluation factor and each secondary evaluation factor is all made of Method of nine marks and is determined in the step 2, institute
When the corresponding monitoring parameters i of risk being selected to compare with monitoring parameters j, according to monitoring parameters i and monitoring parameters j for this risk
Response intensity determines scale value, the scale value Triangular Fuzzy NumberQuantificational expression.
3. a kind of hydrate drilling risk intelligent determination method based on fuzzy Judgment as described in claim 1, feature exist
In Synthetic Judgement Matrix in the step 3
Refer to the scale value determined respectively according to the judging result of the 1st expert, the 2nd expert, n-th expert
The judgment matrix of construction；
Kth position expert evaluation establish judgment matrix be Kth position expert is expressed as according to same layer evaluation factor c
The scale value that importance than evaluation factor d determines, Synthetic Judgement Matrix calculate as follows:
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| US16/747,294 US20200240243A1 (en) | 2019-01-29 | 2020-01-20 | Method for intelligently determining hydrate drilling and production risks based on fuzzy judgment |
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| CN113236195A (en) * | 2021-06-15 | 2021-08-10 | 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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| 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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