CN116976680B - Pressure early warning method for natural gas hydrate drilling and production shaft - Google Patents
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Abstract
The invention relates to the field of risk early warning, and provides a pressure early warning method for a drilling and production shaft of natural gas hydrate. The method can monitor the wind pressure change in the shaft in real time, captures potential dangerous case signals by utilizing the change of wind pressure data in the drilling and production process, and simultaneously gives early warning to the potential dangerous case, so that the probability of accident occurrence and the loss caused by the accident are reduced to the greatest extent, more reflecting time is reserved for facing the dangerous case, and the natural gas hydrate drilling and production process can be safely and reliably carried out.
Description
Technical Field
The invention relates to the field of risk early warning, in particular to a pressure early warning method for a natural gas hydrate drilling and production shaft.
Background
Natural gas hydrate is a solid compound formed under low temperature and high pressure conditions, also called combustible ice, and is an ice-like structure formed by combining natural gas and water molecules under proper temperature and pressure, and the distribution of the natural gas hydrate is mainly formed by deep sea sediment and frozen soil in polar regions, and part of resources are also present in the bottom of ocean cold springs or frozen soil layers on land.
The natural gas hydrate is used as a huge potential energy resource, has huge reserves and wide application potential, can be used as a substitute product of traditional natural gas and coal in the field of energy supply, has fewer pollutants and lower carbon emission when being burnt compared with coal and oil fuels, and has great advantages in the aspects of long-distance transportation, cross-regional trade and the like, however, the exploitation process of the natural gas hydrate is often accompanied with extreme conditions such as high pressure, low temperature and the like, the parameter change related to pressure in a shaft needs to be monitored at any time in the process of drilling the natural gas hydrate, and once the pressure in the shaft has severe fluctuation or other abnormality, the probability of serious accidents such as shaft rupture, blowout, leakage and the like in the drilling process can be greatly increased due to the reasons such as hydrate dissociation, gas release or rock stratum rupture and the like, so that the process of drilling the natural gas hydrate is greatly harmed to staff and equipment.
The traditional pressure monitoring method is mostly dependent on arranging a large number of sensors in a shaft, manually observing or making experience judgment on dangerous situations of the shaft based on parameters returned by the sensors, the method is easy to be influenced by subjective factors so as to misjudge, and secondly, potential dangerous signals in the shaft are difficult to capture only by simply and directly reading data, and real-time monitoring and early warning of pressure abnormality in the shaft cannot be realized, so that a pressure early warning method based on internet of things sensing and data analysis is needed to accurately predict the dangerous situations of the pressure in the shaft, and the probability of accident risk in the drilling and production process is reduced.
Disclosure of Invention
The invention aims to provide a pressure early warning method for a natural gas hydrate drilling and production shaft, which aims to solve one or more technical problems in the prior art and at least provides a beneficial selection or creation condition.
The invention provides a pressure early warning method for a drilling and production shaft of natural gas hydrate, which comprises the steps of arranging a wind pressure sensor in the shaft, acquiring wind pressure data through the wind pressure sensor, transmitting the wind pressure data to a server in the drilling and production process of the natural gas hydrate, preprocessing the wind pressure data in the server to obtain anti-disturbance data, screening out heterogeneous data segments in the anti-disturbance data, and establishing a pressure early warning model by utilizing the heterogeneous data segments. The method can monitor the wind pressure change in the shaft in real time, captures potential dangerous case signals by utilizing the change of wind pressure data in the drilling and production process, and simultaneously gives early warning to the potential dangerous case, so that the probability of accident occurrence and the loss caused by the accident are reduced to the greatest extent, more reflecting time is reserved for facing the dangerous case, and the natural gas hydrate drilling and production process can be safely and reliably carried out.
To achieve the above object, according to an aspect of the present invention, there is provided a pressure pre-warning method of a natural gas hydrate drilling wellbore, the method comprising the steps of:
s100, arranging a wind pressure sensor in a shaft, and acquiring wind pressure data through the wind pressure sensor;
s200, transmitting wind pressure data to a server in the drilling and production process of the natural gas hydrate;
s300, preprocessing wind pressure data in a server to obtain anti-disturbance data;
s400, screening out heterogeneous data segments in the anti-disturbance data, and establishing a pressure early warning model by utilizing the heterogeneous data segments.
Further, in step S100, a wind pressure sensor is disposed in the wellbore, and the method for acquiring wind pressure data by using the wind pressure sensor specifically includes: the method comprises the steps that a wind pressure sensor is arranged on the inner wall of a shaft, a diaphragm of the wind pressure sensor is in contact with air in the shaft, wind pressure data in the shaft are detected in real time by utilizing the diaphragm, jt is taken as any moment in the drilling process of natural gas hydrate in the shaft, wind (jt) is recorded as the wind pressure in the shaft monitored by the wind pressure sensor at the moment jt, wind (jt) is taken as the wind pressure data, and the unit of wind (jt) is pascal (Pa).
Further, in step S200, in the drilling process of the natural gas hydrate, the method for transmitting the wind pressure data to the server specifically includes:
starting a wind pressure sensor, connecting the wind pressure sensor to an Internet of things gateway, wherein the Internet of things gateway is used for transmitting wind pressure data detected by the wind pressure sensor in the drilling process of the natural gas hydrate into a server, and the Internet of things gateway and the server realize data transmission in a wireless mode;
and taking the N values wind (1), wind (2), … and wind (N) as wind pressure data and remotely transmitting the wind pressure data into a server through an Internet of things gateway.
Further, the method for acquiring wind pressure data comprises the following steps: taking the starting moment of the wind pressure sensor as moment jt1, taking the T second after the moment jt1 as moment jt2, marking a period formed by the moment jt1 and the moment jt2 as T0, marking the length of the period T0 as N (the total size of the period T0 is N seconds), marking the wind pressure detected by the wind pressure sensor in the ith second in the period T0 as wind (i), wherein i is a sequence number, the value range of i is i=1, 2, … and N, and then wind (i) =wind (1), wind (2), … and wind (N); wherein, t is any integer (the number of seconds from 1 to 2 hours) in the interval [3600,7200 ].
Further, in step S300, the method for preprocessing wind pressure data in the server to obtain anti-disturbance data includes: creating a blank array wind [ ], adding N values wind (1), wind (2), …, wind (N) of wind pressure data into the array wind [ ] in sequence, wherein wind (i) represents an i-th element in the array wind [ ], i=1, 2, …, N;
defining a first equation as:
wherein P is 0 Is an array wind]The element with the smallest element value, P1 is the group wind [ []The element with the largest element value;
traversing the sequence number i from i=1 to i=N in the first equation to obtain N values sus (1), sus (2), …, sus (N), creating a blank array sus with the length of N, adding all the N values sus (1), sus (2), …, and sus (N) into the array sus in sequence, sorting all elements in the array sus in ascending order, and storing the array sus after the ascending order;
setting a variable j, wherein the value range of the variable j is the same as the value range of the serial number i; representing the j-th element corresponding to the variable j in the array wind [ ] by wind (j), and creating a blank array den [ ];
defining a first algorithm as: in array wind []In which all elements with element values smaller than wind (j) are formed into a sequence seq<j>Sequence seq is recorded<j>The number of all elements in the composition is K 0 Array sus []K of (2) 0 +1 element sus (K) 0 +1) adding an array den []In the clear sequence seq<j>(resetting K0, avoiding the situation of repeated addition of K0 during j value iteration);
traversing the value of the variable j from j=1 to j=n in a first algorithm, and further obtaining an array den [ ] with the length of N, wherein den (i) represents an ith element in the array den [ ];
defining a second equation as:；
traversing the sequence number i from i=1 to i=n in the second equation, thereby obtaining N values dis (1), dis (2), …, dis (N), creating a blank array dis [ ] with a length of N, adding all the N values dis (1), dis (2), …, dis (N) into the array dis [ ] in sequence, and taking all elements in the array dis [ ] as anti-disturbance data.
The beneficial effects of this step are: because the wind pressure data obtained by the sensor has interference data, including noise, a large number of outliers, or data missing caused by communication errors, and the like, the modeling quality of the model data is affected.
Further, in step S400, the method for screening out heterogeneous data segments in the anti-disturbance data and establishing the pressure early warning model by using the heterogeneous data segments specifically includes:
s401, reading an array dis [ ], using dis (i) to represent the i-th element in the array dis [ ], wherein i=1, 2, …, N and N are the number of all elements in the array dis [ ], performing function fitting on the array dis [ ] to obtain a function F (D), screening all inflection points of the function F (D) in a definition domain D of the function F (D), recording the number of all inflection points as M, D= (0, dis (N) ], and turning to S402;
s402, setting a variable j, wherein the value range of the variable j is the same as that of a sequence number i, traversing the variable j from j=1, representing a j-th element corresponding to the variable j in an array dis [ ] by dis (j), creating a blank set aut { }, and turning to S403;
s403, in array dis [ []In which, the M numbers of dis (j), dis (j+1), … and dis (j+M-1) are formed into a set dis j { } calculate the current set dis j The isomer value isom (j) of { } goes to S404;
wherein dis (j+1) represents the j+1th element in the array dis [ ], dis (j+M-1) represents the j+M-1th element in the array dis [ ], and the values of dis (j+1), dis (j+M-1) vary with the value of variable j;
s404, if the value of the current variable j is smaller than N1, the process goes to S405; if the value of the current variable j is equal to or greater than N1, go to S406; n1=n-2×m+1;
s405, if the value of current isom (j) is greater than 0, the current set dis j All numbers within { } are added to the set aut { } while increasing the value of variable j by M-1, and go to S403;
if the value of current isom (j) is less than or equal to 0, the value of j is incremented by 1, and the process goes to S403;
s406, using the aggregate aut { as a heterogeneous data segment, and establishing a pressure early warning model stress_M (T).
Further, the method for establishing the pressure early warning model stress_M (T) comprises the following steps:
wherein R is 0 For the element with the largest median of the aggregate aut { }, T is a model input variable of the pressure early warning model, aut (x) is the xth element in the aggregate aut { }, x=1, 2, …, N2 is the number of all elements in the aggregate aut { }.
Further, the current set dis is calculated j The calculation method of the isomerism value isom (j) is specifically as follows: in dis j (a) Representing a collection dis j The a-th element in { } a=1, 2, …, N (j) being the set dis j The number of all elements within { } the isomerism value isom (j) is calculated by:
；
in ave (dis) j { }) represents the set dis j Average value of all elements in { and max (dis) j { }) represents the set dis j The element with the largest value in { dis, min (dis j { }) represents the set dis j { } element with minimum value, dis j (a-1) represents the set dis j A-1 st element in { }.
The beneficial effects of this step are: in the drilling and mining process of natural gas hydrate, dangerous situation early warning in wind pressure is required to be ensured first, the earlier the dangerous situation signal is captured, the smaller the loss caused by dangerous situation accident is, when the pressure in a shaft rises sharply, the early warning signal is always too late, meanwhile, the data characteristics such as high-frequency wind pressure fluctuation or sudden change of wind pressure and the like can possibly indicate the danger situation in the shaft, therefore, the capturing of the potential dangerous situation signal before the accident is the key for reducing the loss caused by the accident, the method of the step selects out the heterogeneous state data segment through screening so as to establish a pressure early warning model, the heterogeneous state data segment is the data part which can reflect the wind pressure abnormality most effectively, the potential dangerous situation signal in the drilling and mining shaft can be captured effectively through the pressure early warning model, the safety problem caused by the delay of the early warning signal is solved, the potential drilling and mining signal is identified by a certain advance, and the continuous development and progress of the drilling and mining process are ensured.
The beneficial effects of the invention are as follows: the method can monitor the wind pressure change in the shaft in real time, captures potential dangerous case signals by utilizing the change of wind pressure data in the drilling and production process, and simultaneously gives early warning to the potential dangerous case, so that the probability of accident occurrence and the loss caused by the accident are reduced to the greatest extent, more reflecting time is reserved for facing the dangerous case, and the natural gas hydrate drilling and production process can be safely and reliably carried out.
Drawings
The above and other features of the present invention will become more apparent from the detailed description of the embodiments thereof given in conjunction with the accompanying drawings, in which like reference characters designate like or similar elements, and it is apparent that the drawings in the following description are merely some examples of the present invention, and other drawings may be obtained from these drawings without inventive effort to those of ordinary skill in the art, in which:
fig. 1 is a flow chart of a method for pressure warning in a natural gas hydrate drilling wellbore.
Detailed Description
The conception, specific structure, and technical effects produced by the present invention will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present invention. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
In the description of the present invention, a number means one or more, a number means two or more, and greater than, less than, exceeding, etc. are understood to not include the present number, and above, below, within, etc. are understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
Referring to fig. 1, a flow chart of a pressure pre-warning method for a natural gas hydrate drilling and production well bore according to the present invention is shown, and a pressure pre-warning method for a natural gas hydrate drilling and production well bore according to an embodiment of the present invention is described below with reference to fig. 1.
The invention provides a pressure early warning method for a natural gas hydrate drilling and production shaft, which comprises the following steps:
s100, arranging a wind pressure sensor in a shaft, and acquiring wind pressure data through the wind pressure sensor;
s200, transmitting wind pressure data to a server in the drilling and production process of the natural gas hydrate;
s300, preprocessing wind pressure data in a server to obtain anti-disturbance data;
s400, screening out heterogeneous data segments in the anti-disturbance data, and establishing a pressure early warning model by utilizing the heterogeneous data segments.
Further, in step S100, a wind pressure sensor is disposed in the wellbore, and the method for acquiring wind pressure data by using the wind pressure sensor specifically includes: the method comprises the steps that a wind pressure sensor is arranged on the inner wall of a shaft, a diaphragm of the wind pressure sensor is in contact with air in the shaft, wind pressure data in the shaft are detected in real time by utilizing the diaphragm, jt is taken as any moment in the drilling process of natural gas hydrate in the shaft, wind (jt) is recorded as the wind pressure in the shaft monitored by the wind pressure sensor at the moment jt, wind (jt) is taken as the wind pressure data, and the unit of wind (jt) is pascal (Pa).
Further, in step S200, in the drilling process of the natural gas hydrate, the method for transmitting the wind pressure data to the server specifically includes:
starting a wind pressure sensor, connecting the wind pressure sensor to an Internet of things gateway, wherein the Internet of things gateway is used for transmitting wind pressure data detected by the wind pressure sensor in the drilling process of the natural gas hydrate into a server, and the Internet of things gateway and the server realize data transmission in a wireless mode;
taking the starting moment of the wind pressure sensor as moment jt1, taking the T second after the moment jt1 as moment jt2, marking a period formed by the moment jt1 and the moment jt2 as T0, marking the length of the period T0 as N (the total size of the period T0 is N seconds), marking the wind pressure detected by the wind pressure sensor in the ith second in the period T0 as wind (i), wherein i is a sequence number, the value range of i is i=1, 2, … and N, and then wind (i) =wind (1), wind (2), … and wind (N); wherein, the value of t is 3600;
and taking the N values wind (1), wind (2), … and wind (N) as wind pressure data and remotely transmitting the wind pressure data into a server through an Internet of things gateway.
Further, in step S300, the method for preprocessing wind pressure data in the server to obtain anti-disturbance data includes: creating a blank array wind [ ], adding N values wind (1), wind (2), …, wind (N) of wind pressure data into the array wind [ ] in sequence, wherein wind (i) represents an i-th element in the array wind [ ], i=1, 2, …, N;
defining a first equation as:
wherein P is 0 Is an array wind]The element with the smallest element value, P1 is the group wind [ []The element with the largest element value;
traversing the sequence number i from i=1 to i=N in the first equation to obtain N values sus (1), sus (2), …, sus (N), creating a blank array sus with the length of N, adding all the N values sus (1), sus (2), …, and sus (N) into the array sus in sequence, sorting all elements in the array sus in ascending order, and storing the array sus after the ascending order;
setting a variable j, wherein the value range of the variable j is the same as the value range of the serial number i; representing the j-th element corresponding to the variable j in the array wind [ ] by wind (j), and creating a blank array den [ ];
defining a first algorithm as: in array wind []In which all elements with element values smaller than wind (j) are formed into a sequence seq<j>Recording the sequenceColumn seq<j>The number of all elements in the composition is K 0 Array sus []K of (2) 0 +1 element sus (K) 0 +1) adding an array den []In the clear sequence seq<j>；
Traversing the value of the variable j from j=1 to j=n in a first algorithm, and further obtaining an array den [ ] with the length of N, wherein den (i) represents an ith element in the array den [ ];
defining a second equation as:；
traversing the sequence number i from i=1 to i=n in the second equation, thereby obtaining N values dis (1), dis (2), …, dis (N), creating a blank array dis [ ] with a length of N, adding all the N values dis (1), dis (2), …, dis (N) into the array dis [ ] in sequence, and taking all elements in the array dis [ ] as anti-disturbance data.
Further, in step S400, the method for screening out heterogeneous data segments in the anti-disturbance data and establishing the pressure early warning model by using the heterogeneous data segments specifically includes:
s401, reading an array dis [ ], using dis (i) to represent the i-th element in the array dis [ ], wherein i=1, 2, …, N and N are the number of all elements in the array dis [ ], performing function fitting on the array dis [ ] to obtain a function F (D), screening all inflection points of the function F (D) in a definition domain D of the function F (D), recording the number of all inflection points as M, D= (0, dis (N) ], and turning to S402;
s402, setting a variable j, wherein the value range of the variable j is the same as that of a sequence number i, traversing the variable j from j=1, representing a j-th element corresponding to the variable j in an array dis [ ] by dis (j), creating a blank set aut { }, and turning to S403;
s403, in array dis [ []In which, the M numbers of dis (j), dis (j+1), … and dis (j+M-1) are formed into a set dis j { } calculate the current set dis j The isomer value isom (j) of { } goes to S404;
wherein dis (j+1) represents the j+1th element in the array dis [ ], dis (j+M-1) represents the j+M-1th element in the array dis [ ], and the values of dis (j+1), dis (j+M-1) vary with the value of variable j;
s404, if the value of the current variable j is smaller than N1, the process goes to S405; if the value of the current variable j is equal to or greater than N1, go to S406; n1=n-2×m+1;
s405, if the value of current isom (j) is greater than 0, the current set dis j All numbers within { } are added to the set aut { } while increasing the value of variable j by M-1, and go to S403;
if the value of current isom (j) is less than or equal to 0, the value of j is incremented by 1, and the process goes to S403;
s406, using the aggregate aut { as a heterogeneous data segment, and establishing a pressure early warning model stress_M (T).
Further, the method for establishing the pressure early warning model stress_M (T) comprises the following steps:
wherein R is 0 For the element with the largest median of the aggregate aut { }, T is a model input variable of the pressure early warning model, aut (x) is the xth element in the aggregate aut { }, x=1, 2, …, N2 is the number of all elements in the aggregate aut { }.
Further, the current set dis is calculated j The calculation method of the isomerism value isom (j) is specifically as follows: in dis j (a) Representing a collection dis j The a-th element in { } a=1, 2, …, N (j) being the set dis j The number of all elements within { } the isomerism value isom (j) is calculated by:
；
in ave (dis) j { }) represents the set dis j Average value of all elements in { and max (dis) j { }) represents the set dis j The element with the largest value in { dis, min (dis j { }) represents the set dis j { } element with minimum value, dis j (a-1) represents the set dis j A-1 st element in { }.
Optionally, the method for performing function fitting on the array dis [ ] to obtain the function F (d) specifically includes: dis (1), dis (2), …, dis (N) and 1,2, …, N are respectively input in the Y_data option and the X_data option in the Curve_fit toolbox through the Curve_fit toolbox in MATLAB, the Fit type is selected from Interpolant, and the button Fit is clicked to generate a function F (d).
Further, in step S400, heterogeneous data segments in the anti-disturbance data are screened out, and a pressure early warning model is established by using the heterogeneous data segments, and further, an early warning signal is sent out through the pressure early warning model, specifically:
in the drilling and production process of the natural gas hydrate, N3 moments jt (1), jt (2), … and jt (N3) are selected at will, N3 is set to be 12, wind pressure in a shaft at the N3 moments is obtained through a wind pressure sensor, wind (jt (1)), wind (jt (2)), … and wind (jt (N3)), wind (jt (1)), wind (jt (2)), … and wind (jt (N3)) are sequentially input into a pressure early warning model stress_M (t), and therefore N3 pressure early warning data are obtained:
Stress_M(wind(jt(1))),Stress_M(wind(jt(2))),…,Stress_M(wind(jt(N3)))；
if the N3 pressure early-warning data satisfy the ascending order (i.e., for any one data stress_M (K) in the N3 pressure early-warning data, the value of the data positioned behind stress_M (K) is larger than the value of stress_M (K)), sending an early-warning signal to the terminal through the server;
the terminal is a physical host of the monitoring center.
The invention provides a pressure early warning method for a drilling and production shaft of natural gas hydrate, which comprises the steps of arranging a wind pressure sensor in the shaft, acquiring wind pressure data through the wind pressure sensor, transmitting the wind pressure data to a server in the drilling and production process of the natural gas hydrate, preprocessing the wind pressure data in the server to obtain anti-disturbance data, screening out heterogeneous data segments in the anti-disturbance data, and establishing a pressure early warning model by utilizing the heterogeneous data segments. The method can monitor the wind pressure change in the shaft in real time, captures potential dangerous case signals by utilizing the change of wind pressure data in the drilling and production process, and simultaneously gives early warning to the potential dangerous case, so that the probability of accident occurrence and the loss caused by the accident are reduced to the greatest extent, more reflecting time is reserved for facing the dangerous case, and the natural gas hydrate drilling and production process can be safely and reliably carried out. Although the present invention has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any such detail or embodiment or any particular embodiment so as to effectively cover the intended scope of the invention. Furthermore, the foregoing description of the invention has been presented in its embodiments contemplated by the inventors for the purpose of providing a useful description, and for the purposes of providing a non-essential modification of the invention that may not be presently contemplated, may represent an equivalent modification of the invention.
Claims (3)
1. A pressure pre-warning method for a natural gas hydrate drilling and production shaft, which is characterized by comprising the following steps of:
s100, arranging a wind pressure sensor in a shaft, and acquiring wind pressure data through the wind pressure sensor;
s200, transmitting wind pressure data to a server in the drilling and production process of the natural gas hydrate;
s300, preprocessing wind pressure data in a server to obtain anti-disturbance data;
s400, screening out heterogeneous data segments in the anti-disturbance data, and establishing a pressure early warning model by utilizing the heterogeneous data segments;
the method for acquiring the wind pressure data comprises the following steps: taking the starting moment of the wind pressure sensor as moment jt1, taking the T second after the moment jt1 as moment jt2, marking a period formed by the moment jt1 and the moment jt2 as T0, marking the length of the period T0 as N, marking the wind pressure detected by the wind pressure sensor in the ith second in the period T0 as wind (i), wherein i is a sequence number, the value range of i is i=1, 2, … and N, and then wind (i) =wind (1), wind (2), … and wind (N); wherein, the value of t is any integer in the interval [3600,7200 ];
in step S300, preprocessing wind pressure data in a server to obtain anti-disturbance data, wherein the method comprises the following steps: creating a blank array wind [ ], adding N values wind (1), wind (2), …, wind (N) of wind pressure data into the array wind [ ] in sequence, wherein wind (i) represents an i-th element in the array wind [ ], i=1, 2, …, N;
acquiring an array sus [ ];
setting a variable j, wherein the value range of the variable j is the same as the value range of the serial number i; representing the j-th element corresponding to the variable j in the array wind [ ] by wind (j), and creating a blank array den [ ];
traversing the value of the variable j from j=1 to j=n in a first algorithm, and further obtaining an array den [ ] with the length of N, wherein den (i) represents an ith element in the array den [ ];
traversing the sequence number i from i=1 to i=n in the second equation, thereby obtaining N values dis (1), dis (2), …, dis (N), creating a blank array dis [ ] with the length of N, adding all the N values dis (1), dis (2), …, dis (N) into the array dis [ ] in sequence, and taking all elements in the array dis [ ] as anti-disturbance data;
wherein the second equation is defined as:；
the method for obtaining the array sus is specifically as follows:
traversing the sequence number i from i=1 to i=N in the first equation to obtain N values sus (1), sus (2), …, sus (N), creating a blank array sus with the length of N, adding all the N values sus (1), sus (2), …, and sus (N) into the array sus in sequence, sorting all elements in the array sus in ascending order, and storing the array sus after the ascending order;
wherein, the first equation is defined as:wherein P is 0 Is an array wind]The element with the smallest element value, P1 is the group wind [ []The element with the largest element value;
the calculation method of the first algorithm specifically comprises the following steps:
defining a first algorithm as: in array wind []In which all elements with element values smaller than wind (j) are formed into a sequence seq<j>Sequence seq is recorded<j>The number of all elements in the composition is K 0 Will beArray sus []K of (2) 0 +1 element sus (K) 0 +1) adding an array den []In the clear sequence seq<j>；
In step S400, heterogeneous data segments in the anti-disturbance data are screened out, and a pressure early warning model is established by using the heterogeneous data segments, and the method further comprises the step of sending out an early warning signal through the pressure early warning model, specifically:
in the drilling and production process of the natural gas hydrate, N3 moments jt (1), jt (2), … and jt (N3) are selected at will, N3 is set to be 12, wind pressure in a shaft at the N3 moments is obtained through a wind pressure sensor, wind (jt (1)), wind (jt (2)), … and wind (jt (N3)), wind (jt (1)), wind (jt (2)), … and wind (jt (N3)) are sequentially input into a pressure early warning model stress_M (t), and therefore N3 pressure early warning data are obtained:
Stress_M(wind(jt(1))),Stress_M(wind(jt(2))),…,Stress_M(wind(jt(N3)))；
if the N3 pressure early warning data meet the ascending order, sending an early warning signal to the terminal through the server; the terminal is a physical host of the monitoring center;
in step S400, the method for screening out heterogeneous data segments in the anti-disturbance data and establishing the pressure early warning model by using the heterogeneous data segments specifically comprises the following steps:
s401, reading an array dis [ ], using dis (i) to represent the i-th element in the array dis [ ], wherein i=1, 2, …, N and N are the number of all elements in the array dis [ ], performing function fitting on the array dis [ ] to obtain a function F (D), screening all inflection points of the function F (D) in a definition domain D of the function F (D), recording the number of all inflection points as M, D= (0, dis (N) ], and turning to S402;
s402, setting a variable j, wherein the value range of the variable j is the same as that of a sequence number i, traversing the variable j from j=1, representing a j-th element corresponding to the variable j in an array dis [ ] by dis (j), creating a blank set aut { }, and turning to S403;
s403, in array dis [ []In which, the M numbers of dis (j), dis (j+1), … and dis (j+M-1) are formed into a set dis j { } calculate the current set dis j The isomer value isom (j) of { } goes to S404;
wherein dis (j+1) represents the j+1th element in the array dis [ ], dis (j+M-1) represents the j+M-1th element in the array dis [ ], and the values of dis (j+1), dis (j+M-1) vary with the value of variable j;
s404, if the value of the current variable j is smaller than N1, the process goes to S405; if the value of the current variable j is equal to or greater than N1, go to S406; n1=n-2×m+1;
s405, if the value of current isom (j) is greater than 0, the current set dis j All numbers within { } are added to the set aut { } while increasing the value of variable j by M-1, and go to S403;
if the value of current isom (j) is less than or equal to 0, the value of j is incremented by 1, and the process goes to S403;
s406, using the aggregate aut { } as a heterogeneous data segment, and establishing a pressure early warning model stress_M (T);
wherein the current set dis is calculated j The calculation method of the isomerism value isom (j) is specifically as follows: in dis j (a) Representing a collection dis j The a-th element in { } a=1, 2, …, N (j) being the set dis j The number of all elements within { } the isomerism value isom (j) is calculated by:
；
in ave (dis) j { }) represents the set dis j Average value of all elements in { and max (dis) j { }) represents the set dis j The element with the largest value in { dis, min (dis j { }) represents the set dis j { } element with minimum value, dis j (a-1) represents the set dis j A-1 st element in { }.
2. The method for pre-warning the pressure of a natural gas hydrate drilling and production well bore according to claim 1, wherein in step S100, a wind pressure sensor is arranged in the well bore, and the method for acquiring wind pressure data through the wind pressure sensor specifically comprises the following steps: the method comprises the steps that a wind pressure sensor is arranged on the inner wall of a shaft, a diaphragm of the wind pressure sensor is in contact with air in the shaft, wind pressure data in the shaft are detected in real time by utilizing the diaphragm, jt is taken as any moment in the drilling process of natural gas hydrate in the shaft, wind (jt) is recorded as the wind pressure in the shaft monitored by the wind pressure sensor at the moment jt, wind (jt) is taken as the wind pressure data, and the unit of wind (jt) is pascal.
3. The method for pre-warning the pressure of a drilling and production well bore of a natural gas hydrate according to claim 1, wherein in step S200, the method for transmitting wind pressure data to a server during the drilling and production process of the natural gas hydrate is specifically as follows:
starting a wind pressure sensor, connecting the wind pressure sensor to an Internet of things gateway, wherein the Internet of things gateway is used for transmitting wind pressure data detected by the wind pressure sensor in the drilling process of the natural gas hydrate into a server, and the Internet of things gateway and the server realize data transmission in a wireless mode;
wind (1), wind (2), … and wind (N) are taken as wind pressure data and are remotely transmitted into a server through an Internet of things gateway.
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