CN116432975B - Exploitation management method, device, equipment and medium of shale gas multi-station platform - Google Patents
Exploitation management method, device, equipment and medium of shale gas multi-station platform Download PDFInfo
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Abstract
The invention relates to the technical field of shale gas exploitation, in particular to a exploitation management method, device, equipment and medium of a shale gas multi-station platform. The method comprises the following steps: acquiring real-time data of each well in each station; performing linear regression analysis on the production data and the pressure data of each well based on the production system of each well to perform a first adjustment on the production system parameters of each well; the production system comprises a predicted total production value, a production limit interval and a polynomial coefficient set of each day of the current well; determining target rules from a rule base of each well based on the linear regression analysis results, so as to carry out second adjustment on production system parameters of each well according to the target rules; when the well is abnormal, an alarm is sent to related personnel for manual intervention. According to the scheme, automatic analysis operation and monitoring management of each well in the multi-station can be realized, so that the monitoring management efficiency can be improved, and the labor cost can be reduced.
Description
Technical Field
The embodiment of the invention relates to the technical field of shale gas exploitation, in particular to a exploitation management method, device, equipment and medium of a shale gas multi-station platform.
Background
Most of the existing shale gas monitoring and management platforms pay attention to a plurality of platforms by monitoring staff and process data of the plurality of platforms, however, as the number of shale gas stations increases, the existing shale gas monitoring and management platforms cannot adapt to intelligent management requirements, manual processing is low in efficiency, and a large amount of labor cost is consumed.
Thus, a need exists for a new method of mining management for shale gas multi-station platforms.
Disclosure of Invention
In order to solve the problem that the unified monitoring management cannot be automatically performed on a large number of shale gas stations in the prior art, the embodiment of the invention provides a mining management method, device, equipment and medium for a shale gas multi-station platform.
In a first aspect, an embodiment of the present invention provides a method for mining management of a shale gas multi-station platform, where the method includes:
acquiring real-time data of each well in each station; the real-time data includes production data and pressure data;
performing a linear regression analysis on the production data and the pressure data for each well based on the production regime for each well to make a first adjustment to production system parameters for each well; the production system comprises a predicted total production value, a production limit interval and a polynomial coefficient set of each day of the current well;
determining a target rule from a rule base of each well based on the linear regression analysis result, so as to carry out second adjustment on production system parameters of each well according to the target rule;
when the well is abnormal, an alarm is sent to related personnel for manual intervention.
In a second aspect, an embodiment of the present invention further provides a mining management device for a shale gas multi-station platform, including:
an acquisition unit for acquiring real-time data of each well in each station; the real-time data includes production data and pressure data;
a first adjustment unit for performing a linear regression analysis on the production data and the pressure data of the well based on a production regime of the well to perform a first adjustment on production system parameters of the well; the production system comprises a predicted total production value, a production limit interval and a polynomial coefficient set of each day of the current well;
the second adjustment unit is used for determining a target rule from a rule base of the current well based on the linear regression analysis result so as to carry out second adjustment on the production system parameters of the current well according to the target rule;
and the alarm unit is used for sending an alarm to related personnel to perform manual intervention when the well is abnormal.
In a third aspect, an embodiment of the present invention further provides a computing device, including a memory and a processor, where the memory stores a computer program, and the processor implements a method according to any embodiment of the present specification when executing the computer program.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform a method according to any of the embodiments of the present specification.
The embodiment of the invention provides a exploitation management method, a device, equipment and a medium of a shale gas multi-station platform, wherein firstly, real-time data of each well in each station is obtained; the real-time data includes production data and pressure data; then, based on the production system of each well, performing linear regression analysis on the production data and the pressure data of each well to perform first adjustment on the production system parameters of each well; the production system comprises a predicted total production value, a production limit interval and a polynomial coefficient set of each day of the current well; meanwhile, determining target rules from a rule base of each well based on the linear regression analysis result, so as to carry out second adjustment on production system parameters of each well according to the target rules; then, when the well is abnormal, an alarm is sent to relevant personnel for manual intervention. According to the scheme, the production system of the well can be utilized to conduct linear regression analysis on the production data and the pressure data of the well, and then, the production system parameters of the well are subjected to first adjustment and second adjustment according to the linear regression analysis result, so that automatic analysis operation and monitoring management on each well in the multi-station are achieved, the monitoring management efficiency can be improved, and the labor cost can be reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention 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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for mining management of a shale gas multi-station platform according to an embodiment of the present invention;
FIG. 2 is a hardware architecture diagram of a computing device according to one embodiment of the present invention;
fig. 3 is a block diagram of a mining management device for a shale gas multi-station platform according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
As mentioned before, most of the existing shale gas monitoring and management platforms pay attention to a plurality of platforms by monitoring staff and process data of the plurality of platforms, however, as the number of shale gas stations increases, the existing shale gas monitoring and management platforms cannot adapt to intelligent management requirements, so that manual processing is low in efficiency and needs to consume a large amount of labor cost.
In order to solve the technical problems, the inventor can consider that each well in each station is automatically regulated and controlled and monitored through linear regression analysis and rule matching, so that not only can manual analysis be reduced, manual management cost be reduced, but also monitoring efficiency can be improved, and high-efficiency multi-station monitoring can be realized.
Specific implementations of the above concepts are described below.
Referring to fig. 1, an embodiment of the present invention provides a method for mining and managing a shale gas multi-station platform, which includes:
step 100, acquiring real-time data of each well in each station; the real-time data includes production data and pressure data;
step 102, performing linear regression analysis on the production data and the pressure data of each well based on the production system of each well so as to perform first adjustment on the production system parameters of each well; the production system comprises a predicted total production value, a production limit interval and a polynomial coefficient set of each day of the current well;
step 104, determining a target rule from a rule base of each well based on the linear regression analysis result, so as to carry out second adjustment on the production system parameters of each well according to the target rule;
and 106, when the well is abnormal, giving an alarm to related personnel for manual intervention.
In the embodiment of the invention, firstly, the real-time data of each well in each station is acquired; the real-time data includes production data and pressure data; then, based on the production system of the well, carrying out linear regression analysis on the production data and the pressure data of the well so as to carry out first adjustment on the production system parameters of the well; the production system comprises a predicted total production value, a production limit interval and a polynomial coefficient set of each day of the current well; meanwhile, determining a target rule from a rule base of the current well based on a linear regression analysis result, so as to carry out second adjustment on production system parameters of the current well according to the target rule; then, when the well is abnormal, an alarm is sent to relevant personnel for manual intervention. According to the scheme, the production system of the well can be utilized to conduct linear regression analysis on the production data and the pressure data of the well, and then, the production system parameters of the well are subjected to first adjustment and second adjustment according to the linear regression analysis result, so that automatic analysis operation and monitoring management on each well in the multi-station are achieved, the monitoring management efficiency can be improved, and the labor cost can be reduced.
For step 100:
in the embodiment of the invention, the management platform can acquire the real-time data of each well in each station transmitted by the lower computer system. The real-time data includes production data and pressure data, the production data can include water production and gas production of the well, and the pressure data can include pressure data such as tubing pressure, casing pressure, and export pressure.
The real-time data also includes operation data, process conditions and block data of the well, so as to analyze the working condition of the well in real time according to the operation data, process conditions and block data of the well.
For step 102:
in embodiments of the invention, each well will have a corresponding production schedule, and the production schedule will contain each day of schedule for future production times.
In some embodiments, the production schedule is generated by the manner shown in steps H1-H4:
step H1, determining the predicted total production of the current well in each day of the future recoverable time based on the historical production data of the current well and the predetermined recoverable residual quantity;
step H2, determining polynomial coefficients of a predicted production curve model of the current well on the basis of production data of the current well at each moment in a plurality of target historical days and the predicted total production of the current well on each day;
step H3, determining a total yield limit interval of each day and polynomial coefficients of each pressure curve model based on the predicted total yield of each day;
and step H4, determining polynomial coefficients of the corresponding yield limit curve model based on the total yield limit interval of each day and the polynomial coefficients of the predicted yield curve model of each day to generate a polynomial coefficient set.
In this embodiment, for each well, the following is performed: analyzing how long the recoverable residual quantity of the well is recovered based on the historical production data of the current well and the predetermined recoverable residual quantity, and determining a predicted total production for each day over a future recoverable time; then, a current well per day predicted production curve model may be determined based on the production data for each time of the current well for several target historical days and the predicted total production for each day; next, determining a total yield limit interval and each pressure curve model of each day based on the predicted total yield of each day; based on the total yield limit interval of each day and the predicted yield curve model of each day, a corresponding yield limit curve model is determined, so that an accurate production system of each well is generated, and the control management accuracy of the platform is improved. In addition, in order to save the storage space, polynomial coefficients can be generated based on each curve model, and when the rear side needs to be used, the curve models can be restored by analyzing the polynomial coefficients, so that the processing efficiency of the platform is improved.
The method for determining the predicted total yield of the current well in step H1 in each day in the future available time may be determined according to the decreasing productivity rule, or may be determined by training and predicting by using a long-short-term memory network, a random forest, or other machine learning algorithm, which is not limited to the specific method.
In some embodiments, step H2 may comprise:
acquiring yield data of target historical dates according to time sequence to determine yield slope of each target historical date and yield value of last time in the last target historical date; wherein the last target historical date is the day before the first day in the future acquirable time;
determining a predicted yield curve model for the first day in the future acquirable time based on the yield value at the last time in the last target historical date, the yield slope of each target historical date, and the predicted total yield for the first day in the future acquirable time;
taking the first day in the future acquirable time as a new target historical date, and determining a predicted yield curve model of the second day in the future acquirable time based on the yield value of the last moment in the new target historical date, the yield slope of each target historical date and the predicted total yield of the second day in the future acquirable time until the predicted yield curve model of each day in the future acquirable time of the current well is determined;
polynomial coefficients of the current well day-to-day predicted production curve model are generated based on the day-to-day predicted production curve model.
For example, if the future availability time is 2021, 4-1, 1-2031, 4-1, then the target history date may be 2021, 3-1, 3-3, 31, and the working time of each day is set from 0 point to 24 point, then the yield value at the last time of the initial final target history date is 2023, 3-31, 24-point yield value, it will be appreciated that the yield slope of each target history date may be determined from the yield data at each time of each target history date; then, based on the 24-point yield value at 3.31.2023, the yield slope for each target historical date, and the predicted total yield at 1.2021.4 in the future availability time, a predicted yield curve model for 1.2021.4 may be determined; then, taking the month 1 of 2021 as a new target history date, determining a predicted yield curve model of the month 2 of 2021 based on the yield value of 24 points of the month 1 of 2021, the yield slope of each target history date (including the month 1 of 2021) and the predicted total yield of the month 2 of 2021; and so on until a model of the predicted production curve for each of the current wells at 2021, 4-1, 4-2031, 1 is determined. Therefore, the accuracy of the predicted yield curve model of each day is improved, the predicted yield curve model of each day is stored in the form of polynomial coefficients, the space memory can be saved, the running efficiency of the management platform is improved, and the processing speed of the management platform is improved.
In some embodiments, the model of the predicted yield curve for each day at the future time of availability in step H2 is determined by the following equation:
wherein O (t) is the predicted yield at each time of each day in the future availability time, S isPredicted total yield per day for future time of availability, D is average deviation rate, O 0 For the yield value, k, at the last instant in the last target history date i For the yield slope of each target history date, n is the number of target history dates, and t represents each time.
In this embodiment, the number n of target history dates increases with the increase of new target history dates, and the average value of the slopes is also continuously adjusted, so that the prediction accuracy can be increased. D is the average deviation rate of the actual production and the predicted production of the well on a plurality of history dates, and taking the actual production condition of the well into consideration, the prediction accuracy of the predicted production curve model of each day can be increased.
In some embodiments, the yield limit interval for each day in step H3 is determined by the following formula:
wherein, max_output and min_output are the upper limit and the lower limit of the yield limit interval of each day respectively, S is the predicted total yield of each day in the future producible time, a is a set multiple, and var is the predicted total yield variance of the current well in the future producible time.
In this embodiment, the accuracy of the limit interval of the production of each day can be increased by taking the variance of the predicted total production of the current well in the future available time into consideration, and in addition, the limit interval of the total production of each day is determined first, instead of determining the limit interval of the production of each time in each day, the total production of each day can be more intuitively limited, so that the generated limit interval can control the total production more easily, and the feasibility and accuracy of the automatic regulation and control management of the platform can be improved.
In some embodiments, the yield limit curve model in step H4 is determined by the following formula:
wherein O is max (t) and O min (t) the upper and lower yield limit curve models for each time of each day in the future available time, D is the average deviation rate, O 0 For the yield value, k, at the last instant in the target history date i For the yield slope of each target history date, n is the number of target history dates, and t represents each time.
It should be noted that each time represented by t may be 1 minute or 1 second, and is not specifically limited herein according to the actual situation and the actual requirement.
In this embodiment, the limit interval of the total yield of each day is determined first, instead of directly limiting the limit interval of the yield of each time in each day, the total yield of each day can be more intuitively limited, so that the generated limit interval is easier to control the total yield, and the feasibility and accuracy of the automatic regulation and control management of the platform can be improved.
In some embodiments, step 102 may include:
for each well, perform:
acquiring a production system of a current well, and analyzing each polynomial coefficient of the day in a polynomial coefficient set of the production system to obtain a predicted yield curve model, a yield limit curve model and each pressure curve model of the day;
based on the predicted production curve model, the production limit curve model and each pressure curve model of the day, carrying out linear regression analysis on the production data and the pressure data of the current well received in real time so as to carry out first adjustment on the production system parameters of the well;
and determining that the current well is abnormal when the yield data of the current well received in real time exceeds the yield limit curve and/or the pressure data exceeds the set threshold value of the corresponding pressure curve model.
In this embodiment, a polynomial coefficient is used to analyze a predicted production curve model, a production limit curve model and each pressure curve model on the day, a curve is generated based on each curve model, linear regression analysis is performed on production data and pressure data of a current well received in real time, so as to obtain a linear regression analysis result of actual production data and pressure data, the linear regression analysis result in this embodiment is an actual production progressive rate, a deviation rate, a production deviation, an analysis gas production slope, a water production slope, a casing pressure slope, an oil pressure slope, an output pressure slope, and a gas-water ratio average value, and a first adjustment is performed on production system parameters of the well according to differences between the actual production data and pressure data and the predicted production data and pressure data. And determining that the current well is abnormal when the yield data of the current well received in real time exceeds the yield limit curve and/or the pressure data exceeds the set threshold value of the corresponding pressure curve model.
It will be appreciated that the production system parameter adjustments to the well may be adjusting the valve sizes of tubing, water tubing, etc., and may be other production related production equipment parameters.
The actual production data and the pressure data are analyzed for a short period and predicted for a long period at set time intervals. Specifically, the short period analysis can be to analyze real-time second and minute data (parameters such as oil pressure jacket pressure) and fit data values of one day to perform linear regression calculation to obtain gas-water ratio, absolute value of water quantity and trend of gas quantity in period, and output pressure trend, analyze strong positive/negative relation and perform linkage control by using a rule engine; the long period prediction is to analyze the hour and day data (parameters such as oil pressure casing pressure, etc.), fit the data of a time period (not less than 30 days), perform a linear regression calculation, predict the yield decreasing straight line, deviation rate, dynamically configure rules by using a rule engine technology, and adjust the production system of the time after the well.
For step 104:
in some embodiments, determining a target rule from a rule base of the current well based on the linear regression analysis results to make a second adjustment to the production system parameters of the current well according to the target rule comprises:
for each well, perform:
inputting the linear regression analysis result of the current well into a rule engine;
the rule engine performs rule condition matching on the linear regression analysis result to determine a target rule from a rule base of the current well; each rule in the rule base comprises rule conditions and adjustment modes, and the rule is generated by using an AI machine learning algorithm;
and performing second adjustment on the production system parameters of the current well based on the adjustment mode of the target rule.
In this embodiment, the rule engine performs rule condition matching on the linear regression analysis result, performs similarity calculation on a certain item of linear regression analysis result within the extracted preset time interval and rule conditions of each rule in the rule base, sorts rule conditions with similarity higher than sixty percent from high to low, determines rules corresponding to rule conditions with highest similarity as target rules, and uses other rules higher than sixty percent as preset options, when abnormality occurs in the set time after the second adjustment of the production system parameter by using the adjustment mode of the target rules, adjusts the adjustment mode of the target rules according to the adjustment mode of the rule of the preset options, and feeds back the rule of the preset options to the staff, so that the staff can perform manual intervention according to the rule of the preset options; if the similarity calculation result does not have a rule condition higher than sixty percent, the period is not subjected to second adjustment, and when an abnormality occurs in the subsequent setting time, the adjustment mode of manual intervention is automatically combined with a certain linear regression analysis result in the extracted preset time interval to generate a new rule and store the new rule into a rule base.
It should be noted that, the sixty percent similarity threshold may be set according to practical situations and requirements, and is not limited in particular.
In the embodiment of the invention, more analysis and adjustment rules can be produced by using an AI machine learning algorithm based on a decision tree, the fusion rule engine realizes data range adjustment in an adjustment mode, the more rules are established when the system operates for a long time, and the rules of each well can obtain dynamic range adjustment in automatic learning, so that one well can be realized.
For step 106:
in the embodiment of the invention, when an abnormality occurs in a certain well, the abnormality is pushed to a monitoring manager, a research and development personnel in real time through a short message, a WeChat and the like, the personnel can directly enter a real-time data interface and an operation interface of the well through clicking an alarm by app software, the data can be immediately analyzed and processed, and the solution period is reduced to the greatest extent.
And providing an abnormal well module in the interface display area, enabling an operator to directly click to check specific information of the well, and feeding back rules of the preset options to the operator, so that the operator can perform manual intervention according to the rules of the preset options, and adjusting parameters to solve the problem. And the system can also check the dynamic adjustment history event directly through the system function, assist manual intervention and adjust parameters or models so as to enable the system to process.
In addition, after the parameters are adjusted through manual intervention, the system can give out short-period analysis and long-period prediction, give out the estimated output situation after adjustment, help monitoring staff to analyze the result after operation, and reduce the knowledge reserve requirement of the monitoring staff.
As shown in fig. 2 and 3, the embodiment of the invention provides a exploitation management device of a shale gas multi-station platform. The apparatus embodiments may be implemented by software, or may be implemented by hardware or a combination of hardware and software. In terms of hardware, as shown in fig. 2, a hardware architecture diagram of a computing device where a shale gas multi-station platform mining management device provided by an embodiment of the present invention is located, where the computing device where the embodiment is located may include other hardware, such as a forwarding chip responsible for processing a message, besides a processor, a memory, a network interface, and a nonvolatile memory shown in fig. 2. Taking a software implementation as an example, as shown in fig. 3, as a device in a logic sense, the device is formed by reading a corresponding computer program in a nonvolatile memory into a memory by a CPU of a computing device where the device is located. The embodiment provides a exploitation management device of shale gas multi-station platform, the device includes:
an acquisition unit 301 for acquiring real-time data of each well in each station; the real-time data includes production data and pressure data;
a first adjustment unit 302 for performing a linear regression analysis on the production data and the pressure data of each well based on the production schedule of each well to perform a first adjustment on the production system parameters of each well; the production system comprises a predicted total production value, a production limit interval and a polynomial coefficient set of each day of the current well;
a second adjustment unit 303, configured to determine a target rule from the rule base of each well based on the linear regression analysis result, so as to perform a second adjustment on the production system parameter of each well according to the target rule;
and the alarm unit 304 is used for sending an alarm to related personnel for manual intervention when the well is abnormal.
In one embodiment of the present invention, the production schedule in the first adjustment unit 302 is generated by:
determining a predicted total production for each day of the current well over a future recoverable time based on the historical production data for the current well and the predetermined recoverable residual quantity;
determining polynomial coefficients of a predicted production curve model for each day of the current well based on the production data for each time of the plurality of target history days of the current well and the predicted total production for each day;
determining a total yield limit interval of each day and polynomial coefficients of each pressure curve model based on the predicted total yield of each day;
polynomial coefficients of the corresponding yield limit curve model are determined based on the total yield limit interval for each day and the polynomial coefficients of the predicted yield curve model for each day to generate a set of polynomial coefficients.
In one embodiment of the present invention, the first adjusting unit 302 is specifically configured to, when executing the determining the polynomial coefficients of the predicted production curve model for each day of the current well based on the production data for each time of the plurality of target history days of the current well and the predicted total production for each day:
acquiring yield data of target historical dates according to time sequence to determine yield slope of each target historical date and yield value of last time in last target historical date; wherein the last target historical date is the day before the first day in the future acquirable time;
determining a predicted yield curve model for the first day in the future acquirable time based on the yield value at the last time in the last target historical date, the yield slope of each target historical date, and the predicted total yield for the first day in the future acquirable time;
taking the first day in the future acquirable time as a new target historical date, and determining a predicted yield curve model of the second day in the future acquirable time based on the yield value of the last moment in the new target historical date, the yield slope of each target historical date and the predicted total yield of the second day in the future acquirable time until the predicted yield curve model of each day in the future acquirable time of the current well is determined;
polynomial coefficients of the current well day-to-day predicted production curve model are generated based on the day-to-day predicted production curve model.
In one embodiment of the present invention, the predicted yield curve model for each day in the future available time in the first adjustment unit 302 is determined by the following formula:
wherein O (t) is the predicted yield at each time of each day in the future time of availability and S is the future time of availabilityPredicted total yield per day in the interval, D is the average deviation rate, O 0 For the yield value, k, at the last instant in the last target history date i For the yield slope of each target history date, n is the number of target history dates, and t represents each time.
In one embodiment of the present invention, the yield limit interval of each day in the first adjustment unit 302 is determined by the following formula:
wherein, max_output and min_output are the upper limit and the lower limit of the yield limit interval of each day respectively, S is the predicted total yield of each day in the future producible time, a is a set multiple, and var is the predicted total yield variance of the current well in the future producible time.
In one embodiment of the present invention, the first adjusting unit 302 is configured to perform:
for each well, perform:
acquiring a production system of a current well, and analyzing each polynomial coefficient of the day in a polynomial coefficient set of the production system to obtain a predicted yield curve model, a yield limit curve model and each pressure curve model of the day;
based on the predicted production curve model, the production limit curve model and each pressure curve model of the day, carrying out linear regression analysis on the production data and the pressure data of the current well received in real time so as to carry out first adjustment on the production system parameters of the well;
and determining that the current well is abnormal when the yield data of the current well received in real time exceeds the yield limit curve and/or the pressure data exceeds the set threshold value of the corresponding pressure curve model.
In one embodiment of the present invention, the first adjusting unit 302 is configured to perform:
for each well, perform:
inputting the linear regression analysis result of the current well into a rule engine;
the rule engine performs rule condition matching on the linear regression analysis result to determine a target rule from a rule base of the current well; each rule in the rule base comprises rule conditions and adjustment modes, and the rule is generated by using an AI machine learning algorithm;
and performing second adjustment on the production system parameters of the current well based on the adjustment mode of the target rule.
It will be appreciated that the structure illustrated in the embodiments of the present invention does not constitute a specific limitation on the production management apparatus of a shale gas multi-station platform. In other embodiments of the invention, a shale gas multi-station platform production management apparatus may include more or fewer components than shown, or certain components may be combined, certain components may be split, or different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
The content of information interaction and execution process between the modules in the device is based on the same conception as the embodiment of the method of the present invention, and specific content can be referred to the description in the embodiment of the method of the present invention, which is not repeated here.
The embodiment of the invention also provides a computing device, which comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the exploitation management method of the shale gas multi-station platform in any embodiment of the invention is realized.
The embodiment of the invention also provides a computer readable storage medium, and the computer readable storage medium is stored with a computer program, when the computer program is executed by a processor, the processor is caused to execute the exploitation management method of the shale gas multi-station platform in any embodiment of the invention.
Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium.
In this case, the program code itself read from the storage medium may realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code form part of the present invention.
Examples of the storage medium for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer by a communication network.
Further, it should be apparent that the functions of any of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform part or all of the actual operations based on the instructions of the program code.
Further, it is understood that the program code read out by the storage medium is written into a memory provided in an expansion board inserted into a computer or into a memory provided in an expansion module connected to the computer, and then a CPU or the like mounted on the expansion board or the expansion module is caused to perform part and all of actual operations based on instructions of the program code, thereby realizing the functions of any of the above embodiments.
It is noted that relational terms such as first and second, and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: various media in which program code may be stored, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. The exploitation management method of the shale gas multi-station platform is characterized by comprising the following steps of:
acquiring real-time data of each well in each station; the real-time data includes production data and pressure data;
performing a linear regression analysis on the production data and the pressure data for each well based on the production regime for each well to make a first adjustment to production system parameters for each well; the production system comprises a predicted total production value, a production limit interval and a polynomial coefficient set of each day of the current well;
determining a target rule from a rule base of each well based on the linear regression analysis result, so as to carry out second adjustment on production system parameters of each well according to the target rule;
when the well is abnormal, giving an alarm to related personnel for manual intervention;
the production schedule is generated by the following steps:
determining a predicted total production for each day of the current well over a future recoverable time based on the historical production data for the current well and the predetermined recoverable residual quantity;
determining polynomial coefficients of a predicted production curve model for each day of the current well based on the production data for each time of the plurality of target history days of the current well and the predicted total production for each day;
determining a total yield limit interval of each day and polynomial coefficients of each pressure curve model based on the predicted total yield of each day;
determining polynomial coefficients of a corresponding yield limit curve model based on the total yield limit interval of each day and the polynomial coefficients of the predicted yield curve model of each day to generate a set of polynomial coefficients;
the linear regression analysis of the production data and the pressure data for each well based on the production regime for each well to make a first adjustment to production system parameters for each well, comprising:
for each well, perform:
acquiring a production system of a current well, and analyzing each polynomial coefficient of the day in a polynomial coefficient set of the production system to obtain a predicted yield curve model, a yield limit curve model and each pressure curve model of the day;
based on the predicted production curve model, the production limit curve model and each pressure curve model of the day, carrying out linear regression analysis on the production data and the pressure data of the current well received in real time to obtain linear regression analysis results of the production data and the pressure data, so as to carry out first adjustment on production system parameters of the well according to the difference value between the production data and the pressure data and the predicted production data and the pressure data;
and determining that the current well is abnormal when the yield data of the current well received in real time exceeds the yield limit curve and/or the pressure data exceeds a set threshold value of a corresponding pressure curve model.
2. The method of claim 1, wherein determining polynomial coefficients for a predicted production curve model for each day of the current well based on production data for each time of a number of target historical days of the current well and a predicted total production for each day comprises:
acquiring yield data of target historical dates according to time sequence to determine yield slope of each target historical date and yield value of last time in the last target historical date; wherein the last target historical date is the day before the first day of the future acquirable times;
determining a predicted yield curve model for the first day of the future acquirable time based on the yield value at the last time of the last target historical date, the yield slope of each target historical date, and the predicted total yield for the first day of the future acquirable time;
taking the first day of the future acquirability time as a new target historical date, and determining a predicted yield curve model of the second day of the future acquirability time based on the yield value of the last moment in the new target historical date, the yield slope of each target historical date and the predicted total yield of the second day of the future acquirability time until determining the predicted yield curve model of the current well in each day of the future acquirability time;
polynomial coefficients of the current well day-to-day predicted production curve model are generated based on the day-to-day predicted production curve model.
3. The method of claim 2, wherein the model of the predicted yield curve for each day of the future availability time is determined by the formula:
in the method, in the process of the invention,for the predicted yield at each time of each day of the future time of availability, S is the predicted total yield at each day of the future time of availability, D is the average deviation rate,/and/or>Yield value for last moment in last target history date,/->For the yield slope of each target history date, n is the number of target history dates, and t represents each time.
4. The method of claim 1, wherein the production limit interval for each day is determined by the following formula:
in the method, in the process of the invention,and->Upper and lower limits of the production limit interval per day, respectively, < >>For the predicted total yield per day of the future time of availability, < >>To set multiple->For the current wellPredicted total yield variance over future harvestable time.
5. The method of claim 1, wherein determining a target rule from a rule base for each well based on the linear regression analysis results to make a second adjustment to the production system parameters for each well based on the target rule comprises:
for each well, perform:
inputting the linear regression analysis result of the current well into a rule engine;
the rule engine performs rule condition matching on the linear regression analysis result to determine a target rule from a rule base of the current well; wherein each rule in the rule base comprises rule conditions and adjustment modes, and the rule is generated by using an AI machine learning algorithm;
and carrying out second adjustment on the production system parameters of the current well based on the adjustment mode of the target rule.
6. The utility model provides a exploitation management device of shale gas multi-station platform which characterized in that includes:
an acquisition unit for acquiring real-time data of each well in each station; the real-time data includes production data and pressure data;
a first adjustment unit for performing a linear regression analysis on the production data and the pressure data for each well based on a production regime for each well to perform a first adjustment on production system parameters for each well; the production system comprises a predicted total production value, a production limit interval and a polynomial coefficient set of each day of the current well;
a second adjustment unit, configured to determine a target rule from a rule base of each well based on a linear regression analysis result, so as to perform a second adjustment on a production system parameter of each well according to the target rule;
the alarm unit is used for giving an alarm to related personnel to perform manual intervention when the well is abnormal;
the production schedule in the first adjustment unit is generated by:
determining a predicted total production for each day of the current well over a future recoverable time based on the historical production data for the current well and the predetermined recoverable residual quantity;
determining polynomial coefficients of a predicted production curve model for each day of the current well based on the production data for each time of the plurality of target history days of the current well and the predicted total production for each day;
determining a total yield limit interval of each day and polynomial coefficients of each pressure curve model based on the predicted total yield of each day;
determining polynomial coefficients of a corresponding yield limit curve model based on the total yield limit interval of each day and the polynomial coefficients of the predicted yield curve model of each day to generate a set of polynomial coefficients;
the first adjusting unit is configured to perform:
for each well, perform:
acquiring a production system of a current well, and analyzing each polynomial coefficient of the day in a polynomial coefficient set of the production system to obtain a predicted yield curve model, a yield limit curve model and each pressure curve model of the day;
based on the predicted production curve model, the production limit curve model and each pressure curve model of the day, carrying out linear regression analysis on the production data and the pressure data of the current well received in real time to obtain linear regression analysis results of the production data and the pressure data, so as to carry out first adjustment on production system parameters of the well according to the difference value between the production data and the pressure data and the predicted production data and the pressure data;
and determining that the current well is abnormal when the yield data of the current well received in real time exceeds the yield limit curve and/or the pressure data exceeds a set threshold value of a corresponding pressure curve model.
7. A computing device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the method of any of claims 1-5 when the computer program is executed.
8. A computer readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform the method of any of claims 1-5.
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