CN117420150A - Analysis and prediction system and prediction method based on drilling parameters - Google Patents
Analysis and prediction system and prediction method based on drilling parameters Download PDFInfo
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Abstract
The application provides an analysis and prediction system based on drilling parameters, which comprises a wear degree monitoring module and a risk parameter monitoring module; the wear degree monitoring module is used for early warning the wear degree of the drilling component and comprises a characteristic acquisition module and a wear early warning module, wherein the characteristic acquisition module acquires characteristic data of the drilling component, and the wear early warning module judges whether the drilling component is subjected to wear risk early warning according to the acquired characteristic data; the risk parameter monitoring module is used for predicting the risk trend of the drilling component with the drilling parameter changed and comprises a risk parameter acquisition module and a detection period adjustment module; the risk parameter acquisition module is used for evaluating risk amplification generated by the drilling component by using the drilling parameters; and the risk parameter monitoring module adjusts the detection period of the drilling component according to the evaluation result of the risk parameter acquisition module. The accuracy and the security that this application had improved drilling part and detected.
Description
Technical Field
The present disclosure relates to the field of analysis and prediction based on drilling parameters, and in particular, to an analysis and prediction system and a prediction method thereof based on drilling parameters.
Background
In petroleum exploration engineering, in order to find and obtain underground resources such as oil gas, drilling is needed, a large amount of rock is crushed and drilled through stratum, during the drilling process, the rock is firstly crushed by a mechanical impact mode to form a borehole, in the drilling process, a drill bit is needed to be used as a main tool of the mechanical impact, so that the quality and performance of the drill bit have remarkable influence on the drilling speed, the drilling quality and the drilling cost, the drill bit is continuously worn in the working process due to the high-strength pressure use of the crushed rock and various severe application environments, and once the drill bit is damaged, the times of starting and tripping are increased, and the well construction time and the well construction danger are increased. Therefore, in the current oilfield system, the inspection of the drilling tool, especially the inspection of the drill bit, is very important, because even if the inspection of the drill bit is qualified when leaving the factory, after the drill bit works downhole for a period of time, quality degradation or invisible lacerations may be caused by a plurality of external or internal reasons, and if the drill bit is not found and adjusted in time, the drill bit may fail, even a downhole accident may be caused. However, the current inspection generally needs to be automatically detected after recovery, so that the round trip transportation inspection is carried out regularly, and a great amount of manpower and material resources are consumed, so that the current development trend is to accurately inspect the drill bit in use on site, and in order to realize accurate inspection and improve convenience and intelligent effect, an inspection method which can uniformly store and analyze drilling safety data by combining a big data technology on the basis of digital construction of petroleum drilling is needed, so that the purposes of reducing drilling cost and improving drilling safety are achieved through scientific analysis.
Disclosure of Invention
The following description is presented to enable one of ordinary skill in the art to make and use the invention. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
In order to solve the problems, the application provides an analysis and prediction system based on drilling parameters, which comprises a wear degree monitoring module and a risk parameter monitoring module;
the wear degree monitoring module is used for early warning the wear degree of the drilling component and comprises a characteristic acquisition module and a wear early warning module, wherein the characteristic acquisition module acquires characteristic data of the drilling component, and the wear early warning module judges whether the drilling component is subjected to wear risk early warning according to the acquired characteristic data;
the risk parameter monitoring module is used for predicting the risk trend of the drilling component with the drilling parameter changed and comprises a risk parameter acquisition module and a detection period adjustment module;
the risk parameter acquisition module is used for evaluating risk amplification generated by the drilling component by using the drilling parameters; the detection period adjustment module adjusts the detection period of the drilling component according to the evaluation result of the risk parameter acquisition module.
The drilling component comprises a drill bit, wherein the characteristic acquisition module acquires crack characteristic data, pit characteristic data and thickness characteristic data of the drill bit through acquiring images, and the abrasion early warning module detects and judges the abrasion degree of the drill bit through a convolutional neural network model after carrying out data normalization processing by using standard balance measurement.
Wherein the drilling component is associated with a number of drilling parameters, the drilling component is associated with the drilling parameters, and the risk weights of the drilling parameters to the drilling component are recorded in a data storage platform.
The present application also provides a prediction method using the analysis prediction system based on drilling parameters as described above, the steps comprising:
s1, setting data of n types of drilling components R in a data storage platform, wherein R= [ R ] 1， R 2， R 3， …，R n ]Wherein, the i-th type well drilling component R i There are associated m drilling parameters WI, wi= [ WI ] 1， WI 2， WI 3， …，WI m ]Wherein WI j Is R and i setting a risk parameter WI according to the j-th drilling parameter j The corresponding risk weight is ψ j ；
S2, with A-th detection period omega A To the drilling part R i To detect the degree of wear, in the case of drilling components R i At T A Early warning the abrasion degree occurs in the detection time, and then alarming and ending the steps;
if the drilling component R i At T A The early warning of the abrasion degree does not occur in the detection time, and the step S3 is carried out;
s3, obtaining and drilling a well component R i Associated jth drilling parameter WI j At T A Parameter value J of detection time A WI j At T A-1 Parameter value J of detection time A-1， Wherein T is A-1 =T A -ω A；
Obtaining R i Is to be used for the drilling parameters WI j At T A Detecting time to T A-1 Period a risk difference θ=j for detection time A -J A-1，
Setting drilling parameters WI j Risk difference threshold value theta of (2) 0，
When theta is less than or equal to theta 0 Continuing with the A-th detection period omega A As R i Ending the step;
when theta > theta 0 When drilling parameter WI j Adding drilling parts R as risk parameters i The step S4 is carried out by MRI of the A-th period risk parameter list;
s4, obtaining R i Setting up an MRI including r risk parameters, wherein a z-th risk parameter WI is set up z The period A risk difference value of (2) is theta z， Obtaining R i Is the A-th cycle risk amplification of (2)Wherein, psi is z Is a risk parameter WI z Corresponding risk weights;
setting drilling parameters WI j Risk of (2)Threshold delta of amplification 0 ；
When delta is less than or equal to delta 0 Continuing with the A-th detection period omega A For R i Detecting; when delta > delta 0 When R is taken i Is adjusted to the A+1 detection period omega A+1， Wherein omega A+1 ＜ ω A， Turning to step S5;
s5, for R i Performing wear detection as drilling component R i When early warning of the abrasion degree occurs, the warning condition is given and the step is finished; when drilling component R i When the early warning of the abrasion degree does not occur, the step S6 is carried out;
s6, obtaining R i Associated jth drilling parameter WI j At T A+1 Parameter value J of detection time A+1， According to the drilling parameters WI j At T A Parameter value J of detection time A， Obtaining R i Is to be used for the drilling parameters WI j From T A Detecting time to T A+1 Detection time a+1st cycle risk difference θ' =j A+1 -J A ；
When theta' > theta 0 When the process proceeds to step S61;
when theta' is less than or equal to theta 0 When the process proceeds to step S62;
s61, drilling parameters WI j Adding drilling parts R i A +1st cycle risk parameter list MRI-A1;
obtaining R i Setting an MRI-A1 including r1 risk parameters in the (A+1) th cycle risk parameter list MRI-A1, wherein the z1 st risk parameter WI z1 The risk difference in the A+1 cycle is θ' z1 Obtaining R i Risk amplification in the A+1 cycleWherein, psi is z1 Is a risk parameter WI z1 Corresponding risk weights; go to step S611;
s611 when delta' is less than or equal to delta 0 Continuing with cycle A+1 A+1 For R i Detecting; when delta' > delta 0 When R is taken i Is adjusted to omega A+1+1， Wherein omega A+1+1 ＜ω A+1； Go to step S5；
S62, obtain R i Is to be used for the drilling parameters WI j From T A-1 Detecting time to T A+1 Compensation risk difference θ″ =j for the (a+1) th cycle of the detection time A+1 -J A-1，
When theta' is less than or equal to theta 0 At the time of omega A+1 Continuing the drilling of the well component R for the test period i Detecting the abrasion degree;
when θ″ is greater than θ 0 When drilling parameter WI j Adding drilling parts R i A compensating risk parameter list MRI-A2 for the a+1 th cycle of (c);
setting r2 risk parameters in the MRI-A2 to obtain z2 risk parameters WI in the MRI-A2 z2 From T A-1 Detecting time to T A+1 Compensation risk difference θ 'for the A+1th cycle of the detection time' z2， Obtaining R i Compensation risk amplification for cycle A+1Wherein μ is a compensation coefficient, ψ z2 Is a risk parameter WI z2 Corresponding risk weights; go to step S621;
s621, when delta' is less than or equal to delta 0 Continuing with cycle A+1 A+1 For R i Detecting;
when delta '' > delta 0 When R is taken i Is adjusted to omega A+1+1， Wherein omega A+1+1 ＜ω A+1； The process proceeds to step S5.
Wherein, in step S4,wherein delta 0 ＞1；
Wherein, in step S611,；
in step S621 of the process of the present invention,；
wherein a drilling component R is arranged i Is the minimum period of (2)ω min In step S4, when the adjusted detection period ω A+1 ＜ω min At the time of omega min For periodic pairs of drilling members R i And (5) detecting the abrasion degree.
In step S62, a compensation period Δt=t is obtained A+1 -T A-1， Setting the maximum compensation time period to be delta T max ；
When DeltaT is less than or equal to DeltaT max Drilling parameter WI j The acquisition time of the compensation risk parameter list in the A+1st period is T A-1 To T A+1 ；
△T＞△T max Drilling parameter WI j The acquisition time of the compensation risk parameter list in the A+1st period is T A+1 -△T max To T A+1。
The beneficial effects realized by the application are as follows:
according to the method, the risk of the drilling component can be estimated and predicted in advance, the risk trend which possibly occurs is estimated and judged in advance on the basis of early warning of the abrasion degree, the safety is increased, the intelligent flexible treatment can be carried out on the periodic inspection, manpower and material resources and data computing power are saved, the drilling component which is working is accurately inspected, convenience and intelligent effect are improved, a big data technology is combined, the drilling safety data are uniformly stored and analyzed, and the purpose of reducing the drilling cost and improving the drilling safety is achieved through scientific analysis.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings to those skilled in the art.
FIG. 1 is a flow chart of the steps of a prediction method of the analysis and prediction system based on drilling parameters of the present application.
Detailed Description
The following description of the embodiments of the present application, taken in conjunction with the accompanying drawings, clearly and completely describes the technical solutions of the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The method is based on data construction of a digital oil field, analysis and collection, data processing and quantitative statistics of oil field safety data are carried out, a data model technology and an intelligent modeling technology are used, an intelligent sensing and decision analysis scheme with parameter guidance is constructed in a digital oil field system, the method can carry out data analysis and prediction on the collected data according to drilling parameters through unified centralized operation and real-time efficient and optimizing means, so that a drilling detection scheme is optimized, the detection efficiency is improved, the labor cost is reduced, the safety is increased, and meanwhile, reliable data support can be made for realizing integrated intelligent drilling overall decision.
Based on the above objects, the present application provides an analysis and prediction system based on drilling parameters, where a database adopted by the analysis and prediction system is based on a drilling big data acquisition platform and a storage platform, and when data are analyzed, the drilling parameters are recorded as important data analysis bases, the drilling parameters are parameters that can be controlled and adjusted by the system in the drilling process, the drilling parameters are closely related to various parts of the drilling, for example, drilling parameters related to a drill bit are drilling pressure and rotation speed, and parameters related to a drilling pump are pump quantity, pump pressure and pump rate; because of different properties of rock crushed by the drill bit, different drilling purposes and different requirements, various drill bits of different types are used in petroleum drilling, such as a drag bit applied to oil fields of soft stratum, a cone bit applied to medium hard stratum and deep well and a diamond bit with high crushing efficiency applied to extremely hard stratum and deep well, different drill bits are provided with different drilling parameters under different environments, a large number of drilling parameters related to each drilling component are recorded in a storage platform, and a data basis is provided for analyzing and excavating drilling big data.
The drilling components are subject to constant wear during operation, and once damaged, the time to build the well is increased, and the risk of building the well is increased. Therefore, in the current oilfield system, the inspection of the drilling component is very important, because even if the inspection of the drilling component is qualified when leaving the factory, after the drilling component works downhole for a period of time, the quality of the drilling component may be degraded or invisible lacerations may be generated due to a plurality of external or internal reasons, and if the drilling component is not found and adjusted in time, the drilling component may be invalid, or even a downhole accident may be caused. Taking the drill bit as an example, if the quality of the drill bit can be ensured, the number of times of starting and tripping in the whole drilling process can be reduced, the well construction speed is increased, and the well drilling cost is reduced. In order to improve the service life of the drill bit, the drill bit needs to be reasonably checked, and the checking method can be comprehensively evaluated through appearance, thread abrasion state, internal and external damage, corrosion condition and the like.
According to different working conditions, field working requirements and component materials, drilling parameters can change when the drilling component works, and the change of the drilling parameters can also have a certain influence on the quality and aging speed of the drilling component, so that the risk of the drilling component can be predicted in advance according to the drilling parameters stored in the storage platform as a data basis for drilling condition data analysis. The preprocessing method generally comprises dimension reduction, normalization and the like, firstly, the data are arranged into uniform and optimized modes with the same dimension, the data dimensions are mutually unified and standardized, and abnormal data and noise data are filtered to improve the accuracy of operation results. The building of drilling parameters generally comprises neural network, clustering, inductive learning and the like, data quantization analysis is carried out, a corresponding model is generated through program operation, information such as geological parameters, environmental factors and the like can be comprehensively analyzed and processed, first drilling parameters are determined, when drilling work changes, for example, due to complexity and diversity of underground environment, and an uncertainty data system of many factors in the drilling process can also adjust and control the working process by adjusting parameters and the like in the drilling process.
When the drilling parameters are adjusted, the working strength of the corresponding drilling components is also changed, and the aging speed and the abrasion progress of the drilling components are also influenced due to the change of the working strength, and under the condition of changing the existing drilling parameters and the working strength, the prediction of what kind of problems the drilling tool generates is more dependent on the experience of experts, so that the scientificity is poor. Along with the continuous increase of drilling data volume, the traditional data processing model has a certain limitation on unstructured data processing, so that how to collect, integrate and optimize various data of petroleum drilling by using advanced technology and management means has visual significance on predicting the development trend of drilling tool quality under the condition of changing the existing drilling parameters and working strength.
Based on the theoretical basis, the application provides an analysis and prediction system based on drilling parameters, which comprises a wear degree monitoring module and a risk parameter monitoring module, wherein the wear degree monitoring module comprises a feature acquisition module and a wear early warning module, the wear degree monitoring module is used for early warning the wear degree of a drilling component, the feature acquisition module is used for acquiring the features such as cracks, pits and thickness of a drill bit by the method such as an image, the acquired features are analyzed by the wear early warning module, and after the feature data are subjected to data normalization processing by using the same standard balance measure, whether the wear risk early warning occurs can be judged through the existing convolutional neural network model.
The risk parameter monitoring module comprises a risk parameter acquisition module and a detection period adjustment module. The risk parameter monitoring module is used for predicting and monitoring the risk trend of the drilling component with the changed drilling parameter, wherein the risk parameter obtaining module is used for evaluating the drilling parameter as the risk parameter of the current detection period of the drilling component when the risk amplification generated by the change of the drilling parameter exceeds a set risk amplification threshold value during detection of the drilling component, obtaining the risk amplification trend according to the evaluation of the risk parameter, and the detection period adjusting module is used for adjusting the detection period of the drilling component according to the amplitude of the risk amplification when the risk amplification trend exceeds the threshold value, so that the detection safety of the drilling component can be improved by adopting the early prediction mode.
In one embodiment, the evaluation prediction method is: setting the data of n types of drilling components R in a storage platform, wherein R= [ R ] 1 ，R 2 ，R 3 ，…，R n ]Wherein, the i-th type well drilling component R i There are associated m drilling parameters WI, wi= [ WI ] 1 ，WI 2 ，WI 3 ，…，WI m ]Wherein WI j Is R and i the j-th drilling parameter is correlated to obtain the risk parameter WI in the database j Corresponding risk weights ψ j 。
Setting drilling parts R i Is ω 1 I.e. in ω 1 For detecting period of drilling parts R i Data acquisition and analysis and prediction are carried out according to the data, and the analysis and prediction system comprises a drilling component R i The method comprises the steps of (1) predicting the abrasion degree and predicting risk parameters, wherein an abrasion degree monitoring module judges the abrasion degree of a drilling component, and when the abrasion degree monitoring module gives an early warning, the abrasion degree is indicated to reach the dangerous degree, an alarm condition is given on a system, and the step is finished; when the abrasion early warning module does not generate early warning, the drilling component R is described i The abrasion degree of the drilling component R does not reach the dangerous degree, and the risk parameter monitoring module is used for further controlling the drilling component R i Performing evaluation and prediction of risk trend, and judging whether to perform drilling component R according to evaluation results i Is of the first detection period omega 1 And (5) adjusting.
The specific risk trend assessment and prediction method comprises the following steps:
first, it is determined whether to drill the member R i Is of the first detection period omega 1 And (3) adjusting:
for the drilling member R at T1 test time i When detection is carried out, the abrasion degree monitoring module does not generate early warning, and the drilling component R is obtained i Associated jth drilling parameter WI j Parameter value J at T1 detection time T1， AndWI j At a time of T0 detection (t0=t1- ω 1 ) Parameter value J of (2) T0， Obtaining R i Is to be used for the drilling parameters WI j In the first detection period omega 1 （ω 1 Risk difference θ=j=t1-T0) T1- J T0 ；
Setting a risk difference threshold θ for a drilling parameter WIj 0 ；
When theta is less than or equal to theta 0 Continuing with the first detection period omega 1 Ending the step as a detection period of Ri
When theta > theta 0 When drilling parameter WI j Adding drilling parts R as risk parameters i Is a first periodic risk parameter list MRI;
obtaining R i Setting an MRI comprising r risk parameters, wherein the z-th risk parameter WI z In the first detection period omega 1 The risk difference value is theta z， According to risk parameter WI z Corresponding risk weights ψ z Obtaining R i In the first detection period omega 1 Risk amplification of (a)；
Setting a risk amplification threshold delta 0 （δ 0 > 1), when delta is less than or equal to delta 0 When the representative risk parameter is changed, the abrasion risk is lower, and the first detection period omega is continued 1 For R i Detecting;
when delta is less than or equal to delta 0 Continuing with the first detection period omega 1 Ending the step as a detection period of Ri;
when delta > delta 0 In the case of changing the representative risk parameters, the wear risk is increased, and the detection frequency is increased correspondingly to ensure the safety, at this time, R is increased i Is adjusted to omega 2 Wherein omega 2 ＜ω 1 Specifically, in the present embodiment,；
setting drilling parts R i Is of minimum period omega min When the adjusted detection period omega 2 ＜ω min At the time of omega min For periodic pairs of drilling members R i And (5) detecting the abrasion degree.
Adjusting R i After the detection period of (2), at a T2 detection time (t2=t1+ω 2 ) For R i Detecting, when the abrasion degree monitoring module gives early warning, indicating that the abrasion degree reaches the dangerous degree, alarming on the system and ending the prediction step; if the abrasion degree monitoring module does not generate abrasion risk early warning, using the risk parameter monitoring module to detect R i The analysis and prediction are carried out again, and the method for judging again is as follows:
when drilling component R i At T2 (t2=t1+ω 2 ) When the detection time is detected, R is obtained i Associated jth drilling parameter WI j Parameter value J at T2 detection time T2 Parameter value J according to T1 detection time T1 Obtaining R i Is to be used for the drilling parameters WI j In the second detection period omega 2 Risk difference θ' =j T2 -J T1 ；
When theta' > theta 0 When drilling parameter WI j Adding drilling parts R i A second periodic risk parameter list MRI-1;
acquisition of R obtained at T2 detection time i A second periodic risk parameter list MRI-1;
setting an MRI-1 including r1 risk parameters, wherein the z1 st risk parameter WI z1 The risk difference in the second period is θ' z1 Risk parameter WI z1 The corresponding risk weight is ψ z1 Obtaining a second period risk increase；
When delta is less than or equal to delta 0 Continuing with the second detection period omega 2 For R i Detecting;
when delta' > delta 0 When R is taken i Is adjusted to a third detection period omega 3 Wherein omega 3 ＜ω 2 ；
This practice isIn one embodiment of the present invention,；
with a third detection period omega 3 Continuing the detection as a detection period, and circulating the step;
when theta' is less than or equal to theta 0 When R is obtained i Is to be used for the drilling parameters WI j At detection time T2 (t2=t1+ω 2 ) With detection time T0 (t0=t1- ω 1 ) Compensation risk difference θ' =J T2 -J T0，
Wherein when theta' is less than or equal to theta 0 When the prediction step is finished, omega is adopted 2 For detecting period of drilling parts R i Continuing to detect;
when θ″ is greater than θ 0 When drilling parameter WI j Adding drilling parts R i Is a second period compensating risk parameter list MRI-2;
acquiring a second period compensation risk parameter list MRI-2: setting an MRI-2 including r2 risk parameters, wherein the z 2-th risk parameter WI z2 The compensation risk difference between the detection time T2 and the detection time T0 is theta z2， Risk parameter WI z2 The corresponding risk weight is ψ z2， Obtaining R i Compensating for risk amplification in the second periodWherein μ is a compensation coefficient, ψ z2 Is a risk parameter WI z2 Corresponding risk weights;
when delta' is less than or equal to delta 0 When the risk parameter is changed, the abrasion risk is lower, and the second detection period omega is continued 2 For R i Detecting;
when delta '' > delta 0 In order to ensure safety, the detection frequency needs to be correspondingly increased, and R is increased i Is adjusted to omega 3 Wherein omega 3 ＜ω 2 Specifically, in the present embodiment,the method comprises the steps of carrying out a first treatment on the surface of the With a third detection period omega 3 The detection is continued as a detection cycle and this step is cycled.
When the detection period omega is adjusted 3 ＜ω min At the time of omega min For periodic pairs of drilling members R i And (5) detecting the abrasion degree.
In addition, in one embodiment, a compensation period Δt=t is obtained 2 -T 0 Setting the maximum compensation time period as DeltaT max ；
When DeltaT is less than or equal to DeltaT max Drilling parameter WI j The acquisition time of the compensating risk parameter list of the second period is T 0 To T 2 ；
△T＞△T max Drilling parameter WI j The acquisition time of the compensating risk parameter list of the second period is T 2 -△T max To T 2 。
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (9)
1. An analysis and prediction system based on drilling parameters is characterized by comprising a wear degree monitoring module and a risk parameter monitoring module;
the wear degree monitoring module is used for early warning the wear degree of the drilling component and comprises a characteristic acquisition module and a wear early warning module, wherein the characteristic acquisition module acquires characteristic data of the drilling component, and the wear early warning module judges whether the drilling component is subjected to wear risk early warning according to the acquired characteristic data;
the risk parameter monitoring module is used for predicting the risk trend of the drilling component with the drilling parameter changed and comprises a risk parameter acquisition module and a detection period adjustment module;
the risk parameter acquisition module is used for evaluating risk amplification generated by the drilling component by using the drilling parameters; the detection period adjustment module adjusts the detection period of the drilling component according to the evaluation result of the risk parameter acquisition module.
2. The analysis and prediction system based on drilling parameters according to claim 1, wherein the drilling component comprises a drill bit, the feature acquisition module acquires crack feature data, pit feature data and thickness feature data of the drill bit by acquiring images, and the wear early warning module detects and judges the wear degree of the drill bit through a convolutional neural network model after performing data normalization processing by using a standard balance measure.
3. The analysis and prediction system based on drilling parameters of claim 1, wherein the drilling component is associated with a plurality of drilling parameters, the drilling component is associated with the drilling parameter, and the risk weighting of the drilling component by the drilling parameter is recorded in the data storage platform.
4. A prediction method using the analysis and prediction system based on drilling parameters of any one of claims 1-3, the steps comprising:
s1, setting data of n types of drilling components R in a data storage platform, wherein R= [ R ] 1 ，R 2 ，R 3 ，…，R n ]Wherein, the i-th type well drilling component R i There are associated m drilling parameters WI, wi= [ WI ] 1 ，WI 2 ，WI 3 ，…，WI m ]Wherein WI j Is R and i setting a risk parameter WI according to the j-th drilling parameter j The corresponding risk weight is ψ j ；
S2, with A-th detection period omega A To the drilling part R i To detect the degree of wear, in the case of drilling components R i At T A Detecting the degree of wear at the timeEarly warning, namely, alarming and ending the steps;
if the drilling component R i At T A The early warning of the abrasion degree does not occur in the detection time, and the step S3 is carried out;
s3, obtaining and drilling a well component R i Associated jth drilling parameter WI j At T A Parameter value J of detection time A WI j At T A-1 Parameter value J of detection time A-1 Wherein T is A-1 =T A -ω A ；
Obtaining R i Is to be used for the drilling parameters WI j At T A Detecting time to T A-1 Period a risk difference θ=j for detection time A -J A-1 ，
Setting drilling parameters WI j Risk difference threshold value theta of (2) 0 ，
When theta is less than or equal to theta 0 Continuing with the A-th detection period omega A As R i Ending the step;
when theta > theta 0 When drilling parameter WI j Adding drilling parts R as risk parameters i The step S4 is carried out by MRI of the A-th period risk parameter list;
s4, obtaining R i Setting up an MRI including r risk parameters, wherein a z-th risk parameter WI is set up z The period A risk difference value of (2) is theta z Obtaining R i Is the A-th cycle risk amplification of (2)Wherein, psi is z Is a risk parameter WI z Corresponding risk weights;
setting drilling parameters WI j Risk-increase threshold delta of (2) 0 ；
When delta is less than or equal to delta 0 Continuing with the A-th detection period omega A For R i Detecting; when delta > delta 0 When R is taken i Is adjusted to the A+1 detection period omega A+1 Wherein omega A+1 ＜ ω A Step S5 is carried out;
s5, for R i Performing wear detection as drilling component R i When early warning of the abrasion degree occurs, the warning condition is given and the step is finished; when drilling component R i When the early warning of the abrasion degree does not occur, the step S6 is carried out;
s6, obtaining R i Associated jth drilling parameter WI j At T A+1 Parameter value J of detection time A+1 According to the drilling parameters WI j At T A Parameter value J of detection time A Obtaining R i Is to be used for the drilling parameters WI j From T A Detecting time to T A+1 Detection time a+1st cycle risk difference θ' =j A+1 -J A ；
When theta' > theta 0 When the process proceeds to step S61;
when theta' is less than or equal to theta 0 When the process proceeds to step S62;
s61, drilling parameters WI j Adding drilling parts R i A +1st cycle risk parameter list MRI-A1;
obtaining R i Setting an MRI-A1 including r1 risk parameters in the (A+1) th cycle risk parameter list MRI-A1, wherein the z1 st risk parameter WI z1 The risk difference in the A+1 cycle is θ' z1 Obtaining R i Risk amplification in the A+1 cycleWherein, psi is z1 Is a risk parameter WI z1 Corresponding risk weights; go to step S611;
s611 when delta' is less than or equal to delta 0 Continuing with cycle A+1 A+1 For R i Detecting; when delta' > delta 0 When R is taken i Is adjusted to omega A+1+1 Wherein omega A+1+1 ＜ω A+1 The method comprises the steps of carrying out a first treatment on the surface of the Turning to step S5;
s62, obtain R i Is to be used for the drilling parameters WI j From T A-1 Detecting time to T A+1 Compensation risk difference θ″ =j for the (a+1) th cycle of the detection time A+1 -J A-1 ，
When theta' is less than or equal to theta 0 At the time of omega A+1 To checkThe measuring period continues to be applied to the drilling component R i Detecting the abrasion degree;
when θ″ is greater than θ 0 When drilling parameter WI j Adding drilling parts R i A compensating risk parameter list MRI-A2 for the a+1 th cycle of (c);
setting r2 risk parameters in the MRI-A2 to obtain z2 risk parameters WI in the MRI-A2 z2 From T A-1 Detecting time to T A+1 Compensation risk difference θ 'for the A+1th cycle of the detection time' z2 Obtaining R i Compensation risk amplification for cycle A+1Wherein μ is a compensation coefficient, ψ z2 Is a risk parameter WI z2 Corresponding risk weights; go to step S621;
s621, when delta' is less than or equal to delta 0 Continuing with cycle A+1 A+1 For R i Detecting;
when delta '' > delta 0 When R is taken i Is adjusted to omega A+1+1 Wherein omega A+1+1 ＜ω A+1 The method comprises the steps of carrying out a first treatment on the surface of the The process proceeds to step S5.
5. A prediction method using the analysis and prediction system based on drilling parameters according to claim 4, wherein, in step S4,wherein delta 0 ＞1。
6. A prediction method using the analysis and prediction system based on drilling parameters as claimed in claim 5, wherein, in step S611,。
7. a prediction method using the drilling parameter based analysis system of claim 5, wherein, in step S621,。
8. a prediction method using the analysis prediction system based on drilling parameters according to claim 5, wherein the drilling component R is set up i Is of minimum period omega min In step S4, when the adjusted detection period ω A+1 ＜ω min At the time of omega min For periodic pairs of drilling members R i And (5) detecting the abrasion degree.
9. A prediction method using the analysis prediction system based on drilling parameters according to claim 5, wherein, in step S62, a compensation period Δt=t is obtained A+1 -T A-1 Setting the maximum compensation time period as DeltaT max ；
When DeltaT is less than or equal to DeltaT max Drilling parameter WI j The acquisition time of the compensation risk parameter list in the A+1st period is T A-1 To T A+1；
△T＞△T max Drilling parameter WI j The acquisition time of the compensation risk parameter list in the A+1st period is T A+1 -△T max To T A+1。
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