CN117670260B - Downhole drilling instrument use period management system based on data analysis - Google Patents
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Abstract
The invention discloses a downhole drilling instrument use period management system based on data analysis, which belongs to the technical field of drilling instrument use period management.
Description
Technical Field
The invention relates to the technical field of drilling instrument use period management, in particular to a downhole drilling instrument use period management system based on data analysis.
Background
Drilling equipment is used for drilling underground resources (such as oil, natural gas and the like), and during the use process, maintenance and repair of the drilling equipment are often involved, so that the normal drilling process is ensured. But the maintenance and the maintenance period of the drilling instruments with different delivery time, different use time and different use states are different, the conventional treatment means is to periodically overhaul the drilling instruments, so as to realize the maintenance and the maintenance of the drilling instruments, but the drilling instruments with long delivery time, the drilling instruments with long use time and the drilling instruments with poor use states are higher in maintenance and the maintenance frequency, but the prior art lacks a technical scheme capable of predicting the use period of the drilling instruments, thereby realizing the prediction of the use period of the different drilling instruments, realizing the maintenance and the maintenance as required, reducing the cost and guaranteeing the normal operation of the drilling work.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a downhole drilling instrument service period management system based on data analysis, which solves the problem of the lack of a technical scheme capable of predicting the service period of a drilling instrument.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a downhole drilling tool life cycle management system based on data analysis, comprising: the system comprises a plurality of sensors, a plurality of filter units, a plurality of sensor real-time state evaluation units, a drilling instrument real-time state evaluation unit, a drilling instrument historical state evaluation unit and a service cycle prediction unit;
Each of the sensors is used for collecting working use data of the drilling instrument;
Each filtering unit is used for filtering each working use data to obtain filtering data;
each sensor real-time state evaluation unit is used for acquiring a mechanical part real-time state evaluation value corresponding to the sensor according to the filtering data;
The real-time state evaluation unit of the drilling instrument is used for acquiring the real-time state evaluation value of the drilling instrument according to the real-time state evaluation values of the mechanical parts corresponding to the various sensors;
the historical state evaluation unit of the drilling instrument is used for acquiring an historical state evaluation value of the drilling instrument;
The use period prediction unit is used for predicting future use period of the drilling instrument according to the historical state evaluation value of the drilling instrument and the real-time state evaluation value of the drilling instrument.
The beneficial effects of the invention are as follows: according to the invention, various working use data are collected according to various sensors on a drilling instrument, filtering processing is carried out, the data precision is improved, then, according to the filtering data, the real-time state evaluation value of a mechanical part corresponding to the sensor is obtained, the real-time state evaluation value of the drilling instrument is obtained by combining the real-time state evaluation value of the mechanical part corresponding to various sensors, the current state of the drilling instrument is expressed, the historical state evaluation value of the drilling instrument is obtained, the historical state of the drilling instrument is expressed, the current state and the historical state are combined, the future use period of the drilling instrument is comprehensively predicted, the self working use data and the historical record of the drilling instrument are realized, the use period of different drilling instruments is predicted, maintenance and maintenance are realized according to the needs, the cost is reduced, and the normal running of the drilling work is ensured.
Further, the filtering unit includes: the first distance coefficient computing subunit, the second distance coefficient computing subunit and the data filtering subunit;
The first distance coefficient calculating subunit is used for calculating a first distance coefficient according to the distance between the work use data to be filtered and the historical filtering data;
the second distance coefficient calculation subunit is used for calculating a second distance coefficient according to the distance between the work use data to be filtered and the adjacent work use data to be filtered;
The data filtering subunit is used for processing the work use data to be filtered according to the first distance coefficient and the second distance coefficient to obtain filtering data corresponding to the work use data to be filtered.
Further, the expression of the first distance coefficient calculation subunit is:
，
Wherein f 1 is a first distance coefficient, g n is nth to-be-filtered work use data, g n-1,f is filtering data corresponding to n-1 th to-be-filtered work use data, g max is maximum to-be-filtered work use data, and n is a positive integer.
Further, the expression of the second distance coefficient calculation subunit is:
，
Wherein f 2 is a second distance coefficient, g n is the nth to-be-filtered work use data, g n-k is the nth to kth to-be-filtered work use data, g n+k is the (n+k) th to-be-filtered work use data, k is a positive integer, N is the number of data adjacent to the to-be-filtered work use data, and g max is the maximum to-be-filtered work use data.
The beneficial effects of the above further scheme are: in the filtering process, factors of two aspects are considered, on one hand, the distance between the work use data to be filtered and the historical filtering data is considered, and on the other hand, the distance between the work use data to be filtered and the work use data adjacent to the work use data to be filtered is considered, so that the first distance coefficient and the second distance coefficient are integrated, and the influence degree of noise on the work use data to be filtered is estimated.
Further, the expression of the data filtering subunit is:
，
wherein g n,f is the filtering data corresponding to the nth working use data to be filtered, f 1 is the first distance coefficient, f 2 is the second distance coefficient, and g n is the nth working use data to be filtered.
The beneficial effects of the above further scheme are: at the position ofThe larger the work usage data to be filtered is, the larger the noise influence degree is, and the larger the filtering degree is.
Further, the sensor real-time status evaluation unit includes: the system comprises a data average value extraction subunit, a data characteristic value extraction subunit and a real-time state evaluation subunit;
The data average value extraction subunit is used for calculating the average value of the filtering data according to the filtering data;
the data characteristic value extraction subunit is used for calculating a data characteristic value according to the filtering data;
The real-time state evaluation subunit is used for calculating a real-time state evaluation value of the mechanical part corresponding to the sensor according to the mean value and the data characteristic value.
Further, the expression of the data characteristic value extraction subunit is:
，
Wherein g b is a data characteristic value, ln is a logarithmic function, e is a natural constant, g m,f is mth filtering data, M is the number of filtering data, g c is the average value of the filtering data, and M is a positive integer.
The beneficial effects of the above further scheme are: according to the invention, according to the filtering data, the average value and the data characteristic value are obtained, the average value reflects the size of the whole filtering data, and the data characteristic value reflects the data characteristic of the whole filtering data.
Further, the expression of the real-time state evaluation subunit is:
，
Wherein Pr is a real-time state evaluation value of a mechanical part corresponding to the sensor, g b is a data characteristic value, g c is a mean value of filtering data, g c,s is a standard mean value, g b,s is a standard characteristic value, exp is an exponential function based on a natural constant.
The beneficial effects of the above further scheme are: the similarity between the mean value of the filter data and the standard mean value is calculated, and the similarity between the characteristic value of the data and the standard characteristic value is calculated, so that the state condition of the mechanical part corresponding to the sensor is evaluated through two aspects.
Further, the expression of the real-time state evaluation unit of the drilling instrument is as follows:
，
Wherein, P 1 is the real-time state evaluation value of the drilling instrument, P r,i is the real-time state evaluation value of the mechanical part corresponding to the ith sensor, K is the type number of the sensors, i is a positive integer, exp is an exponential function based on a natural constant, and L is the number of the real-time state evaluation values P r,i of the mechanical part corresponding to the sensors greater than 1.
The beneficial effects of the above further scheme are: the invention synthesizes the real-time state evaluation values of the mechanical parts corresponding to various sensors and sets the proportionality coefficientThe real-time state evaluation value of the well-conditioned drilling instrument is further improved.
Further, the expression of the historical state evaluation unit of the drilling instrument is:
，
Wherein P 2 is a historical state evaluation value of the drilling instrument, exp is an exponential function based on a natural constant, alpha is a historical state coefficient, T s is the service time of the drilling instrument, T c is the factory time of the drilling instrument, Z is the maintenance times of the drilling instrument, and Z c is the average maintenance times of the drilling instrument;
the expression of the use period prediction unit is:
，
Where h is the future usage period of the drilling instrument, w 1 is the weight of P 1, w 2 is the weight of P 2, P 1 is the real-time state evaluation value of the drilling instrument, and P 2 is the historical state evaluation value of the drilling instrument.
The beneficial effects of the above further scheme are: according to the method, the historical state of the drilling instrument is estimated according to the using time of the drilling instrument, the leaving time of the drilling instrument and the maintenance times of the drilling instrument, and the future using period of the drilling instrument is obtained by combining the historical state and the real-time state of the drilling instrument.
Drawings
FIG. 1 is a system block diagram of a downhole drilling tool life cycle management system based on data analysis.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, a downhole drilling tool life cycle management system based on data analysis includes: the system comprises a plurality of sensors, a plurality of filter units, a plurality of sensor real-time state evaluation units, a drilling instrument real-time state evaluation unit, a drilling instrument historical state evaluation unit and a service cycle prediction unit;
Each of the sensors is used for collecting working use data of the drilling instrument;
Each filtering unit is used for filtering each working use data to obtain filtering data;
each sensor real-time state evaluation unit is used for acquiring a mechanical part real-time state evaluation value corresponding to the sensor according to the filtering data;
The real-time state evaluation unit of the drilling instrument is used for acquiring the real-time state evaluation value of the drilling instrument according to the real-time state evaluation values of the mechanical parts corresponding to the various sensors;
the historical state evaluation unit of the drilling instrument is used for acquiring an historical state evaluation value of the drilling instrument;
The use period prediction unit is used for predicting future use period of the drilling instrument according to the historical state evaluation value of the drilling instrument and the real-time state evaluation value of the drilling instrument.
In this embodiment, the plurality of sensors includes: temperature sensor, vibration sensor, pressure sensor etc. temperature sensor can be used to monitor the temperature of transformer, engine part etc. vibration sensor can be used to monitor the vibration condition of each position when drilling instrument works, and pressure sensor can be used to monitor the pressure of parts such as water pump and compressor.
The filtering unit includes: the first distance coefficient computing subunit, the second distance coefficient computing subunit and the data filtering subunit;
The first distance coefficient calculating subunit is used for calculating a first distance coefficient according to the distance between the work use data to be filtered and the historical filtering data;
the second distance coefficient calculation subunit is used for calculating a second distance coefficient according to the distance between the work use data to be filtered and the adjacent work use data to be filtered;
The data filtering subunit is used for processing the work use data to be filtered according to the first distance coefficient and the second distance coefficient to obtain filtering data corresponding to the work use data to be filtered.
The expression of the first distance coefficient calculation subunit is as follows:
，
Wherein f 1 is a first distance coefficient, g n is nth work use data to be filtered, g n-1,f is filtering data corresponding to the nth-1 work use data to be filtered, namely historical filtering data, g max is maximum work use data to be filtered, and n is a positive integer.
The expression of the second distance coefficient calculation subunit is as follows:
，
Wherein f 2 is a second distance coefficient, g n is the nth to-be-filtered work use data, g n-k is the nth to kth to-be-filtered work use data, g n+k is the (n+k) th to-be-filtered work use data, k is a positive integer, N is the number of data adjacent to the to-be-filtered work use data, and g max is the maximum to-be-filtered work use data.
In the filtering process, factors of two aspects are considered, on one hand, the distance between the work use data to be filtered and the historical filtering data is considered, and on the other hand, the distance between the work use data to be filtered and the work use data adjacent to the work use data to be filtered is considered, so that the first distance coefficient and the second distance coefficient are integrated, and the influence degree of noise on the work use data to be filtered is estimated.
The expression of the data filtering subunit is:
，
wherein g n,f is the filtering data corresponding to the nth working use data to be filtered, f 1 is the first distance coefficient, f 2 is the second distance coefficient, and g n is the nth working use data to be filtered.
At the position ofThe larger the work usage data to be filtered is, the larger the noise influence degree is, and the larger the filtering degree is.
The sensor real-time state evaluation unit includes: the system comprises a data average value extraction subunit, a data characteristic value extraction subunit and a real-time state evaluation subunit;
The data average value extraction subunit is used for calculating the average value of the filtering data according to the filtering data;
the data characteristic value extraction subunit is used for calculating a data characteristic value according to the filtering data;
The real-time state evaluation subunit is used for calculating a real-time state evaluation value of the mechanical part corresponding to the sensor according to the mean value and the data characteristic value.
In the invention, each sensor real-time state evaluation unit is used for processing the filter data acquired by each sensor, and the filter data acquired by the same sensor are aimed at when the mean value and the data characteristic value are calculated.
The expression of the data characteristic value extraction subunit is as follows:
，
Wherein g b is a data characteristic value, ln is a logarithmic function, e is a natural constant, g m,f is mth filtering data, M is the number of filtering data, g c is the average value of the filtering data, and M is a positive integer.
According to the invention, according to the filtering data, the average value and the data characteristic value are obtained, the average value reflects the size of the whole filtering data, and the data characteristic value reflects the data characteristic of the whole filtering data.
The expression of the real-time state evaluation subunit is:
，
Wherein Pr is a real-time state evaluation value of a mechanical part corresponding to the sensor, g b is a data characteristic value, g c is a mean value of filtering data, g c,s is a standard mean value, g b,s is a standard characteristic value, exp is an exponential function based on a natural constant.
In the invention, the standard mean and the standard characteristic value are derived from the mean and the data characteristic value of the corresponding filtering data of the drilling instrument when leaving the factory.
The similarity between the mean value of the filter data and the standard mean value is calculated, and the similarity between the characteristic value of the data and the standard characteristic value is calculated, so that the state condition of the mechanical part corresponding to the sensor is evaluated through two aspects.
The expression of the real-time state evaluation unit of the drilling instrument is as follows:
，
Wherein, P 1 is the real-time state evaluation value of the drilling instrument, P r,i is the real-time state evaluation value of the mechanical part corresponding to the ith sensor, K is the number of types of sensors, i is a positive integer, exp is an exponential function based on a natural constant, L is the number of the real-time state evaluation values of the mechanical part corresponding to the sensors P r,i being greater than 1, i.e. the number of the mechanical parts corresponding to the sensors P r,i being greater than 1 in K P r,i.
The invention synthesizes the real-time state evaluation values of the mechanical parts corresponding to various sensors and sets the proportionality coefficientThe real-time state evaluation value of the well-conditioned drilling instrument is further improved.
The historical state evaluation unit of the drilling instrument has the following expression:
，
Wherein P 2 is a historical state evaluation value of the drilling instrument, exp is an exponential function based on a natural constant, alpha is a historical state coefficient, T s is the service time of the drilling instrument, T c is the factory time of the drilling instrument, Z is the maintenance times of the drilling instrument, and Z c is the average maintenance times of the drilling instrument;
the longer the service time of the drilling instrument is, the longer the delivery time of the drilling instrument is, and when the number of times of maintenance of the drilling instrument is larger, the smaller the historical state evaluation value of the drilling instrument is, and the worse the historical state is.
In this embodiment, the history state coefficient may be set experimentally or empirically.
The expression of the use period prediction unit is:
，
Where h is the future usage period of the drilling instrument, w 1 is the weight of P 1, w 2 is the weight of P 2, P 1 is the real-time state evaluation value of the drilling instrument, and P 2 is the historical state evaluation value of the drilling instrument.
According to the method, the historical state of the drilling instrument is estimated according to the using time of the drilling instrument, the leaving time of the drilling instrument and the maintenance times of the drilling instrument, and the future using period of the drilling instrument is obtained by combining the historical state and the real-time state of the drilling instrument.
According to the invention, various working use data are collected according to various sensors on a drilling instrument, filtering processing is carried out, the data precision is improved, then, according to the filtering data, the real-time state evaluation value of a mechanical part corresponding to the sensor is obtained, the real-time state evaluation value of the drilling instrument is obtained by combining the real-time state evaluation value of the mechanical part corresponding to various sensors, the current state of the drilling instrument is expressed, the historical state evaluation value of the drilling instrument is obtained, the historical state of the drilling instrument is expressed, the current state and the historical state are combined, the future use period of the drilling instrument is comprehensively predicted, the self working use data and the historical record of the drilling instrument are realized, the use period of different drilling instruments is predicted, maintenance and maintenance are realized according to the needs, the cost is reduced, and the normal running of the drilling work is ensured.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A downhole drilling tool life cycle management system based on data analysis, comprising: the system comprises a plurality of sensors, a plurality of filter units, a plurality of sensor real-time state evaluation units, a drilling instrument real-time state evaluation unit, a drilling instrument historical state evaluation unit and a service cycle prediction unit;
Each of the sensors is used for collecting working use data of the drilling instrument;
Each filtering unit is used for filtering each working use data to obtain filtering data;
each sensor real-time state evaluation unit is used for acquiring a mechanical part real-time state evaluation value corresponding to the sensor according to the filtering data;
The real-time state evaluation unit of the drilling instrument is used for acquiring the real-time state evaluation value of the drilling instrument according to the real-time state evaluation values of the mechanical parts corresponding to the various sensors;
the historical state evaluation unit of the drilling instrument is used for acquiring an historical state evaluation value of the drilling instrument;
The using period prediction unit is used for predicting the future using period of the drilling instrument according to the historical state evaluation value of the drilling instrument and the real-time state evaluation value of the drilling instrument;
The sensor real-time state evaluation unit includes: the system comprises a data average value extraction subunit, a data characteristic value extraction subunit and a real-time state evaluation subunit;
The data average value extraction subunit is used for calculating the average value of the filtering data according to the filtering data;
the data characteristic value extraction subunit is used for calculating a data characteristic value according to the filtering data;
The real-time state evaluation subunit is used for calculating a real-time state evaluation value of the mechanical part corresponding to the sensor according to the mean value and the data characteristic value;
The expression of the data characteristic value extraction subunit is as follows:
，
Wherein g b is a data characteristic value, ln is a logarithmic function, e is a natural constant, g m,f is mth filtering data, M is the number of the filtering data, g c is the average value of the filtering data, and M is a positive integer;
the expression of the real-time state evaluation subunit is:
，
Wherein Pr is a real-time state evaluation value of a mechanical part corresponding to the sensor, g b is a data characteristic value, g c is a mean value of filtering data, g c,s is a standard mean value, g b,s is a standard characteristic value, exp is an exponential function based on a natural constant;
The expression of the real-time state evaluation unit of the drilling instrument is as follows:
，
Wherein, P 1 is the real-time state evaluation value of the drilling instrument, P r,i is the real-time state evaluation value of the mechanical part corresponding to the ith sensor, K is the type number of the sensors, i is a positive integer, exp is an exponential function based on a natural constant, and L is the number of the real-time state evaluation values P r,i of the mechanical part corresponding to the sensors greater than 1;
the historical state evaluation unit of the drilling instrument has the following expression:
，
Wherein P 2 is a historical state evaluation value of the drilling instrument, exp is an exponential function based on a natural constant, alpha is a historical state coefficient, T s is the service time of the drilling instrument, T c is the factory time of the drilling instrument, Z is the maintenance times of the drilling instrument, and Z c is the average maintenance times of the drilling instrument;
the expression of the use period prediction unit is:
，
Where h is the future usage period of the drilling instrument, w 1 is the weight of P 1, w 2 is the weight of P 2, P 1 is the real-time state evaluation value of the drilling instrument, and P 2 is the historical state evaluation value of the drilling instrument.
2. The downhole drilling tool life management system of claim 1, wherein the filtering unit comprises: the first distance coefficient computing subunit, the second distance coefficient computing subunit and the data filtering subunit;
The first distance coefficient calculating subunit is used for calculating a first distance coefficient according to the distance between the work use data to be filtered and the historical filtering data;
the second distance coefficient calculation subunit is used for calculating a second distance coefficient according to the distance between the work use data to be filtered and the adjacent work use data to be filtered;
The data filtering subunit is used for processing the work use data to be filtered according to the first distance coefficient and the second distance coefficient to obtain filtering data corresponding to the work use data to be filtered.
3. The downhole drilling tool life management system of claim 2, wherein the first distance coefficient calculation subunit is expressed as:
，
Wherein f 1 is a first distance coefficient, g n is nth to-be-filtered work use data, g n-1,f is filtering data corresponding to n-1 th to-be-filtered work use data, g max is maximum to-be-filtered work use data, and n is a positive integer.
4. The downhole drilling tool life management system of claim 3, wherein the second distance coefficient calculation subunit is expressed as:
，
Wherein f 2 is a second distance coefficient, g n is the nth to-be-filtered work use data, g n-k is the nth to kth to-be-filtered work use data, g n+k is the (n+k) th to-be-filtered work use data, k is a positive integer, N is the number of data adjacent to the to-be-filtered work use data, and g max is the maximum to-be-filtered work use data.
5. The downhole drilling tool life management system of claim 4, wherein the expression of the data filtering subunit is:
，
wherein g n,f is the filtering data corresponding to the nth working use data to be filtered, f 1 is the first distance coefficient, f 2 is the second distance coefficient, and g n is the nth working use data to be filtered.
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