CN112598262B - Oil-gas well maintenance task scheduling processing method and device - Google Patents
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Abstract
The invention discloses a method and a device for scheduling and processing maintenance tasks of an oil and gas well, wherein the method comprises the following steps: acquiring oil and gas well maintenance task scheduling basic data; inputting maintenance task scheduling basic data into a pre-established oil and gas well maintenance task scheduling optimization model based on continuous time expression, and obtaining an optimal maintenance task scheduling strategy; and carrying out oil and gas well maintenance task scheduling treatment according to the optimal maintenance task scheduling strategy. The pre-established oil and gas well maintenance task scheduling optimization model is a maintenance task scheduling optimization model based on continuous time expression, namely a multi-oil and gas well site, a multi-maintenance skill level and a multi-task priority, an optimal maintenance task scheduling strategy can be obtained through the oil and gas well maintenance task scheduling optimization model, oil and gas well maintenance task scheduling processing is carried out according to the optimal maintenance task scheduling strategy, the rationality and the efficiency of the oil and gas well maintenance task scheduling processing can be improved, and the cost of the oil and gas well maintenance task scheduling processing is reduced.
Description
Technical Field
The invention relates to the technical field of oil and gas exploitation, in particular to a method and a device for scheduling and processing maintenance tasks of an oil and gas well.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
At present, the international oil and gas resources are more and more competitive, and compared with the foreign countries, the average single well yield of the oil and gas fields on the land and the country is generally lower, the regional distribution of the oil and gas resources is also more dispersed, and most of the oil and gas resources are distributed in remote and less developed economic areas of China. The lack of informatization in most fields results in low production efficiency, resulting in relatively high proportion of human expenditure, which is about 26% of the total cost of oil and gas operations. In addition, due to the restriction of geological development conditions and geographical environment, the oil and gas exploitation on land in China is still a labor intensive production operation mode, and the personnel expense accounts for more than 30% of the oil and gas operation cost.
In order to improve the core competitiveness of enterprises, more and more petroleum enterprises begin to accelerate the steps of updating equipment and production facilities, and radically change the modes of the facilities and the equipment, thereby improving the overall production efficiency of the enterprises and reducing the cost. Oil and gas equipment and production facilities are important components of oil and gas production, and the quality of the oil and gas equipment and the production facilities directly influence the quality and efficiency of oil and gas enterprise production, the enterprise operation cost and the core competitiveness. Information technology has revolutionized the spread and popularity of oil and gas fields and the processing modes of oil and gas facilities and related production facilities. Scientific equipment treatment and production facility maintenance, high-efficient trouble emergency treatment not only reduces equipment maintenance and production facility maintenance work's cost, more importantly can show the accident rate that reduces because of oil gas field equipment performance problem brings, and then improves oil gas production's efficiency effectively, reduction in production cost.
With the continuous improvement of the integration degree of equipment and operation, the maintenance tasks are more complex and various. These tasks are of different types, with different emergency requirements, and are also diverse in terms of technical capabilities, professional requirements of the maintenance technician. In the face of these complex and diverse maintenance tasks and maintenance technicians with varying capabilities, a need exists for a reasonably efficient maintenance strategy. Obviously, this is a typical scheduling optimization problem, which is currently done manually. With the diversity, complexity and number of maintenance tasks becoming higher and higher, the manual scheduling mode becomes very challenging, and the maintenance efficiency is seriously affected.
In conclusion, the existing oil-gas well maintenance task scheduling treatment is unreasonable, the efficiency is low, and the cost of the scheduling treatment is high.
Disclosure of Invention
The embodiment of the invention provides a method for scheduling and processing oil and gas well maintenance tasks, which is used for improving the rationality and efficiency of the scheduling and processing of the oil and gas well maintenance tasks and reducing the cost of the scheduling and processing, and comprises the following steps:
acquiring oil and gas well maintenance task scheduling basic data;
Inputting the scheduling task basic data into a pre-established oil and gas well maintenance task scheduling optimization model based on continuous time expression, and obtaining an optimal maintenance task scheduling strategy; the oil and gas well maintenance task scheduling optimization model based on continuous time expression is a maintenance task scheduling optimization model based on multiple oil and gas well sites, multiple maintenance skill levels and multiple task priorities of continuous time expression;
And carrying out oil and gas well maintenance task scheduling treatment according to the optimal maintenance task scheduling strategy.
The embodiment of the invention also provides an oil and gas well maintenance task scheduling processing device, which is used for improving the rationality and efficiency of oil and gas well maintenance task scheduling processing and reducing the cost of scheduling processing, and comprises the following steps:
The acquisition unit is used for acquiring the oil and gas well maintenance task scheduling basic data;
The determining unit is used for inputting the basic data of the scheduling tasks into a pre-established oil and gas well maintenance task scheduling optimization model based on continuous time expression, so as to obtain an optimal maintenance task scheduling strategy; the oil and gas well maintenance task scheduling optimization model based on continuous time expression is a maintenance task scheduling optimization model based on multiple oil and gas well sites, multiple maintenance skill levels and multiple task priorities of continuous time expression;
And the processing unit is used for carrying out oil and gas well maintenance task scheduling processing according to the optimal maintenance task scheduling strategy.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the oil and gas well maintenance task scheduling processing method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium which stores a computer program for executing the oil and gas well maintenance task scheduling processing method.
In the embodiment of the invention, compared with the technical proposal that the oil-gas well maintenance task scheduling processing is unreasonable, the efficiency is low and the cost of the scheduling processing is high in the prior art, the oil-gas well maintenance task scheduling processing scheme is characterized by comprising the following steps: acquiring oil and gas well maintenance task scheduling basic data; inputting maintenance task scheduling basic data into a pre-established oil and gas well maintenance task scheduling optimization model based on continuous time expression, and obtaining an optimal maintenance task scheduling strategy; and carrying out oil and gas well maintenance task scheduling treatment according to the optimal maintenance task scheduling strategy. The pre-established oil and gas well maintenance task scheduling optimization model is a maintenance task scheduling optimization model based on continuous time expression, namely a multi-oil and gas well site, a multi-maintenance skill level and a multi-task priority, an optimal maintenance task scheduling strategy can be obtained through the oil and gas well maintenance task scheduling optimization model, oil and gas well maintenance task scheduling processing is carried out according to the optimal maintenance task scheduling strategy, the rationality and the efficiency of the oil and gas well maintenance task scheduling processing can be improved, and the cost of the oil and gas well maintenance task scheduling processing is reduced.
Drawings
In order to more clearly illustrate the embodiments of the 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, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a schematic illustration of a simple repair task route in an embodiment of the invention;
FIG. 2 is a schematic diagram of an objective function according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating the definition of time slots and event points according to an embodiment of the present invention;
FIGS. 4-7 are Gantt charts of various example maintenance task assignments in accordance with embodiments of the present invention;
FIG. 8 is a schematic diagram of a maintenance task scheduling process for an oil and gas well according to an embodiment of the present invention;
FIG. 9 is a schematic flow chart of a method for scheduling maintenance tasks for an oil and gas well according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of an oil-gas well maintenance task scheduling device 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 embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
In recent years, there have been many achievements in terms of maintenance scheduling models. In consideration of the technical problems of the oil and gas well maintenance task scheduling processing scheme in the prior art, the inventor provides an oil and gas well maintenance task scheduling processing scheme which is based on a continuous time expressed scheduling optimization model for solving the problem of oil and gas well equipment maintenance tasks, so that accurate and efficient optimization decisions on planning of oil and gas well maintenance tasks, personnel scheduling and maintenance paths are realized, rationality and efficiency of oil and gas well maintenance task scheduling processing are improved, and cost of oil and gas well maintenance task scheduling processing is reduced. The following describes the oil and gas well maintenance task scheduling scheme in detail.
Fig. 9 is a flow chart of a method for scheduling maintenance tasks for oil and gas wells according to an embodiment of the present invention, as shown in fig. 9, the method includes the following steps:
step 901: acquiring oil and gas well maintenance task scheduling basic data;
Step 902: inputting the scheduling task basic data into a pre-established oil and gas well maintenance task scheduling optimization model based on continuous time expression, and obtaining an optimal maintenance task scheduling strategy; the oil and gas well maintenance task scheduling optimization model based on continuous time expression is a maintenance task scheduling optimization model based on multiple oil and gas well sites, multiple maintenance skill levels and multiple task priorities of continuous time expression;
step 903: and carrying out oil and gas well maintenance task scheduling treatment according to the optimal maintenance task scheduling strategy.
In order to overcome the inefficiency of manual scheduling of the existing oil and gas well maintenance tasks, the embodiment of the invention provides a scheduling optimization method of the oil and gas well maintenance tasks based on continuous time expression. The model can make decisions on maintenance tasks in a short time on the basis of maximizing the task amount and minimizing the routing time. The method has positive effects on improving the level of scientific scheduling decisions of a plurality of petroleum enterprises, promoting the popularization and popularization of information technology in oil and gas fields, improving the overall production efficiency of the enterprises, reducing the operation management cost and the like.
The embodiment of the invention uses the idea of global event points to treat the working state of maintenance technicians as event points, and the starting time and the ending time of each event point are also used as pending variables, so that two types of events exist in the problems described by the invention: the maintenance personnel are performing the task or are en route to the task switch. The model of continuous time representation adopts an asynchronous time period method to realize the correspondence of time periods and event points, divides the working time axis of each maintainer into a plurality of time periods with the same number and different lengths, and is used for distributing different maintenance tasks and task conversion events to realize the matching of the event points and the time periods, so that the starting and ending moments of the time periods with the same number of different maintainers are allowed to be different. The advantage of this expression is that the number of time periods is reduced, and thus the scale of the mathematical model is reduced, and the accuracy of the time expression is improved. Each serviceman schedule time axis is divided into the same number of non-uniform time periods, the number of time periods being determined by the serviceman, the number of tasks to be serviced. Since there are only two event types, the two event types on the time axis show alternate occurrence states, so the value of the time period must be an odd number. Moreover, the scale of the problem depends largely on the value of the time period, the total amount of tasks to be serviced and the number of maintenance personnel.
The following describes in detail the steps involved in the embodiments of the present invention.
1. Firstly, the step of pre-establishing an oil and gas well maintenance task scheduling optimization model based on continuous time expression is introduced.
The establishment of the oil and gas well maintenance task scheduling optimization model based on continuous time is as follows:
The maintenance tasks of the oil and gas well considered by the model are as follows: one is a fault type emergency maintenance task; the method is an important maintenance task of key equipment for guaranteeing the output and the production efficiency of the oil and gas well; one type is the general maintenance task associated with daily management of the wellsite. For a fault task, maintenance personnel need to deal with in time to ensure that production is smoothly carried out so as to avoid causing larger economic loss; for planned maintenance work performed by guaranteeing the performance of production equipment, the important maintenance planning task is generally determined by a maintenance scheme formulated by a long-term maintenance plan and needs to be completed in a regular period; for normal maintenance tasks of conventional management, the greatest possible completion is required, although indefinitely. Thus, maintenance tasks may be classified into three priorities according to the importance level of the task: emergency tasks, important tasks, and general tasks. The following principle of operation was followed: the emergency tasks are preferably completed, the important tasks must be completed on the same day, and the general tasks are selectively completed as much as possible. The maintenance personnel are classified by skill level, the higher the skill level, the shorter the time required to complete the same task, and vice versa. In addition, considering practical situations, it is required that the serviceman should return to the service center before lunch time.
Specifically, as shown in fig. 8, the steps for establishing the oil and gas well maintenance task scheduling optimization model based on continuous time expression are as follows:
step 1: reading basic data of a scheduling task:
Step 1.1: reading the data of the scheduling task and the person on duty on the same day:
A set P of service technician numbers; an event point set N, where N 1 represents a maintenance personnel transfer event and N 2 represents a maintenance task event; a maintenance task skill level requirement or a skill level set L possessed by a maintenance person;
step 1.1.1: scheduling task name, scheduling task priority.
TL j maintenance task j skill level requirement; importance priority of maintenance tasksK 1 represents a task with priority 1, a task with priority 2 of K 2 and a task with priority 3 of K 3; h is the scheduling period, BL is the interrupt task start time, and BU is the interrupt task end time.
Step 1.1.2: class of person on duty skill:
The skill level of the maintenance person is PL i, which indicates the skill level of PL i or higher possessed by the maintenance person i.
Step 1.2: and reading the basic data of the scheduling model related to the daily scheduling task and the on-duty personnel.
The time T ij for each maintenance person i to complete the maintenance task j; the shortest path time T ji′ (basic attribute data) required for the maintenance task j to transition to the maintenance task j' where no maintenance action occurs.
That is, in one embodiment, obtaining oil and gas well servicing task scheduling basis data may include: and acquiring the data of the current day scheduling task and the current day attendant and the basic attribute data related to the current day scheduling task and the current day attendant.
Step 2: and (5) establishing a maintenance task scheduling optimization model based on continuous time description (expression).
In one embodiment, the oil and gas well maintenance task scheduling processing method further includes: the method comprises the following steps of establishing an oil and gas well maintenance task scheduling optimization model based on continuous time expression:
describing and processing preset problems in the dispatching optimization of the maintenance tasks of the oil and gas well;
giving premise assumption features to all preset problems of description processing;
According to the premise assumption characteristics and the number of event points set for the preset type of event, establishing an objective function with the minimum path, the shortest total task time and the maximum number of completed tasks;
Obtaining a constraint condition expression of the objective function according to the initial position of the maintenance personnel, the time consumption of the maintenance task, the priority of the maintenance task and the skill level requirement of the maintenance personnel;
And obtaining the oil and gas well maintenance task scheduling optimization model based on continuous time expression according to the objective function and the constraint condition expression.
The steps for the model creation are described in detail below.
2.1, Setting the number of event points:
As described above, the problems described by the present invention exist for two types of events: the maintenance personnel are performing the task or are en route to the task switch. Referring to fig. 3, each serviceman schedule time axis is divided into the same number of non-uniform time periods, the number of time periods being determined by the serviceman, the number of tasks to be serviced. Since there are only two event types, these two event types show alternate occurrence states on the time axis, the value of the time period must be an odd number, where the state transition belongs to the set N 1 and the maintenance personnel performing the task belongs to the set N 2. The scale of the problem depends largely on the value of the time period, the total amount of tasks to be maintained and the number of maintenance personnel, and the value is manually assigned according to the actual situation.
In one embodiment, the precondition hypothesis feature may include:
The problem only considers whether the maintenance task is within the repair capability of a maintenance technician, and does not consider whether needed equipment and devices are in shortage;
The problem goes to the position where the maintenance task is located, uses the same transportation means and runs at a constant speed, and determines the running time by combining the actual running condition;
grouping the problem maintenance personnel according to skill level, distributing maintenance tasks for a study object by using maintenance groups, and regarding the grouped maintenance groups as single maintenance personnel;
in the implementation process of maintenance tasks, each maintenance personnel independently completes the maintenance tasks allocated to each maintenance personnel, the maintenance tasks are mutually independent, and the situation of mutual support does not exist;
Each maintainer of the problem executes the maintenance task on the same day according to the scheduling decision result, and the midway adjustment condition and the emergency of the task are not considered;
the problem maintenance tasks are non-preemptive tasks, and all maintenance tasks have no front-back dependency relationship and are independent;
The well where the problem maintenance task is located, the time for each maintenance technician to complete the maintenance task, the importance priority of the task, the skill level requirement of the maintenance task and the related information of the skill level of the maintenance personnel are all known;
Each maintenance group of the problem starts from a maintenance station, and after the maintenance task in the morning is completed, the maintenance station needs to be returned to rest in the noon, and after the maintenance task in the afternoon is completed, the maintenance group returns to the maintenance station;
the problem is that a plurality of maintenance tasks exist in the same well, the importance of the tasks is divided by priority, and the priority of the tasks of the same maintenance well is not unique;
the start time and end time of the problem maintenance task can occur at any point on the time axis, as determined by the results of the oil and gas well maintenance task scheduling optimization model solution, and the length of time is determined by the assigned skill level of the maintenance personnel.
2.2 Establishing an objective function
Referring to fig. 1 to 2, the objective function of the model (equation (1) below) aims at minimizing cost and maximizing the amount of tasks, which are both unified as cost problems in terms of time for descriptive convenience. The final objective function contains four pieces of information, the first term is to minimize the journey time of all technicians, M 1 determines the cost coefficient of the total journey time, tf in represents the end time of maintenance technician i at event point n, and Ts ib represents the start time of maintenance technician i at event point n; the second term is a soft constraint, namely, tasks with priority of 1 in a task list of each maintenance technology must be completed preferentially, M 2 is a time normalization coefficient with priority penalty, XS ijn =1 indicates that maintenance personnel do maintenance tasks j at an event point n, otherwise, the maintenance tasks j are 0; the third term is to maximize the number of scheduled tasks, i.e. schedule as many maintenance tasks as possible on the premise of meeting the constraint condition, M 3 represents the task amount normalization time coefficient; the fourth term is also a soft constraint, i.e. to ensure that the maintenance work is ended as early as possible with a small number of tasks, M 4 being the time-normalized coefficient of the term.
2.3 Establishing constraint expressions (e.g., equations (2) - (19) below):
2.3.1 time constraint
Defining the time sequence relationship between the beginning and the end of the time period on the time axis of each maintenance personnel. For each serviceman, the start time of the next event point must lag the end time of the previous event point and neither must exceed the scheduling period range.
The duration of the event point is determined by the time length of each time period, wherein the time length of each time period is determined by the assigned maintenance personnel and the maintenance tasks processed, wherein X ijj′n =1 represents that the maintenance technician i transitions from maintenance task j to maintenance task j', otherwise is 0.
Event points include two types: during task progress and task conversion, two types of tasks alternately appear on the time axis, so that adjacent time periods XS ijn and X ijj′n of the same time axis can only take on values of {0,1} or {1,0}. Equations (7) and (8) express this idea, but since the two equations are nonlinear polynomials in the form of the product of two discrete variables, they are linearized into (9) - (13), where AXS ijn represents the auxiliary variable 1 used for linearization.
2.3.2 Task allocation constraints
One task can only be completed once at most, and each maintenance person can only schedule one task at most in each time period. Whereas tasks with priorities 1 and 2 have to be scheduled.
Ensuring that each maintainer returns to the maintenance center station in the noon rest time, and continuously completing the rest tasks after the rest time. Equation (17) ensures that each serviceman will be assigned a period of rest, j=1 indicating a return to the service station rest, equations (18) and (19) determining the start and end times of the rest time. Since equations (18) and (19) are nonlinear polynomials that are product forms of discrete and continuous variables, the ideas provided with reference to You and Grossmann can be linearized into equations (20) - (24) and equations (25) - (29) in order, where AXS1 ijn represents auxiliary variable 2, AXS2 ijn represents auxiliary variable 3, bxs1 ijn represents auxiliary variable 4, and bxs2 ijn represents auxiliary variable 5.
AXS1i1n≤XSi1nH (21)
AXS2i1n≤(1-XSi1n)H (22)
AXS1i1n≥0 (23)
AXS2i1n≥0 (24)
BXS2i1n≤(1-XSi1n)H (26)
BXS1i1n≥0 (27)
BXS2i1n≥0 (28)
BXS1i1n+BXS2i1n＝Tfin (29)
Where Tf in denotes the end time of service technician i at event point n, tf in-1 denotes the end time of service technician i at event point n-1, ts in denotes the start time of service technician i at event point n, i represents a technician, n represents an event, H represents a scheduling period, and P represents a maintenance technician set; XS ijn =1 indicates that serviceman i is a maintenance task j at event point N, otherwise 0, XS ijn+1 =1 indicates that serviceman i is a maintenance task j at event point n+1, otherwise 0, N 1 is a state transition set, N 2 is an execution task set, t ij denotes the time for repair technician i to complete repair task j, T jj′ denotes the shortest path time required for the transition from repair task j to repair task j ', X ijj′n =1 denotes the transition of repair technician i from repair task j to repair task j' at event point n, otherwise 0, j=1 denotes a special task, i.e. the service technician returns to the service station for rest, j 'denotes a service task different from j, K denotes the set of tasks to be serviced, K 1 denotes a task with priority 1, K 2 denotes a task with priority 2X ijj′n+1 =1 denotes a switchover of service technician i from service task j to service task j' at event point n+1, n max denotes the maximum event point, X ijj′n-1 =1 denotes switching from maintenance task j to maintenance task j 'at event point n-1, otherwise 0, xs ij′n =1 denotes that maintenance technician i does maintenance task j' at event point n, Otherwise, xs ij′n-1 =1 indicates that maintenance technician i is doing maintenance task j' at event point n-1, otherwise, 0, xs i1n =1 indicates that maintenance technician i is returning to maintenance station at event point n, otherwise, 0, bl indicates interrupt task start time, and BU indicates interrupt task end time.
The above formulas (20) - (29) linearize a nonlinear polynomial in the form of the product of a discrete variable and a continuous variable, and in particular, can linearize an equation of the product of a 0-1 variable and a continuous variable.
2. Next, the above step 901 is described.
In specific implementation, after the oil and gas well maintenance task scheduling optimization model based on continuous time expression is established, in actual work, an optimal maintenance task scheduling strategy can be determined by using the model, and the step 901 obtains oil and gas well maintenance task scheduling basic data of a day to be scheduled, and the implementation of the step can be specifically referred to the detailed step of the step 1 when the model is established.
3. Next, for ease of understanding, the above steps 902 and 903 are described together.
In specific implementation, the oil and gas well maintenance task scheduling basic data of the day to be scheduled obtained in the step 901 is input into the established oil and gas well maintenance task scheduling optimization model based on continuous time expression, so that an optimal maintenance task scheduling strategy can be obtained, namely, in the step 3 in fig. 8: solving the scheduling model with a solver, step 4 in fig. 8: and outputting a scheduling result graph (fig. 4 to 7). And finally, obtaining an optimal maintenance task scheduling strategy, and further, carrying out oil-gas well maintenance task scheduling treatment according to the optimal maintenance task scheduling strategy.
The continuous time maintenance task scheduling optimization model has the beneficial effects that under the limitation of considering various constraint conditions such as the transition time between task conversion, the maintenance skill of maintenance personnel, the priority of maintenance tasks, the noon break time of maintenance personnel and the like, the goal of minimizing the time required for completing all tasks can be still realized. The maintenance tasks are distributed and completed within the scheduling time range, and maintenance staff execute the tasks according to the priority order of the maintenance tasks, so that the working time of the maintenance staff is accurate to minutes. The scheduling decision obtained by solving the maintenance task scheduling optimization model based on continuous time expression is effective and feasible, and under the condition of a certain task quantity, the solving time and the solving precision of the model meet the requirements of practical application.
In order to facilitate understanding of how the present invention may be practiced, an example will be described in detail.
In the embodiment of the invention, a solver is adopted to solve the proposed model, and finally, the optimal solution is resolved to give a scheduling decision. The test environment is a personal computer (memory: 8.00GB RAM, CPU: intel Core i5-3230M2.6GHz, operating system: 64-bitWindows 7).
The simulation test of the invention takes maintenance task scheduling of a certain oil extraction production area of a certain oil and gas field as a background, and the field is provided with a maintenance central station, 10 maintenance technicians and 100 oil and gas wells. According to the actual scene requirement, the working time of maintenance personnel is 8 hours, the maintenance personnel rest for two hours in the noon, the relevant information of the task to be maintained and the maintenance personnel before working is known, the information is obtained by upper layer management software, and the maintenance scheduling decision is given through computer simulation solving. The scheduling period is set to 10 hours (i.e., 600 minutes). The operation steps of the invention are shown in the flow chart of fig. 8, the data used is scheduling data of a task on a certain day, the scheduling data are divided into four examples with gradually increased scale, and tasks to be maintained and maintenance staff in the examples are shown in Gantt charts of fig. 4 to 7. Table 1 gives the parameters of the objective function for each example, table 2 is the solution scale for the four examples, the model other solution input data are as follows table 3, table 4, table 5, table 6 and table 7, and the maintenance task allocation decisions are as follows table 8. Table 8 shows maintenance task allocation decisions (scheduling strategies) for each instance, the start time, end time, and maintenance time for each task corresponding to the information in the Gantt chart.
Fig. 4 to 7 are gatte diagrams of the respective example maintenance task allocation cases. The vertical axis represents the number of the maintenance technician, the horizontal axis represents the scheduled time, a black rectangular box (which can be filled with other shapes, such as a circle, etc.) represents the maintenance task that the maintenance personnel is performing, the time on the rectangular box represents the time taken to perform the task, and a white rectangular box (which can be filled with other shapes, such as a triangle, etc.) represents the travel time on the rectangular box for the maintenance personnel to transfer from one task to another. The Gantt chart intuitively reflects the sequence of maintenance tasks and the starting and ending time of each task of each maintenance technician, and gives scheduling decisions of the problems.
TABLE 1 target function reference values
Table 2 example solution Scale
TABLE 3 configuration information related to maintenance tasks
TABLE 4 example 2 configuration information related to maintenance tasks
TABLE 5 example 3 configuration information related to maintenance tasks
TABLE 6 configuration information related to example 4 maintenance tasks
TABLE 7 configuration information about maintenance personnel
Table 8 maintenance task allocation decisions
In summary, the embodiment of the invention provides an oil and gas well maintenance task scheduling processing scheme, in which a maintenance task scheduling optimization model based on continuous time expression, multiple skill levels and multiple task priorities is pre-established, and the model is oriented to specific oil and gas well maintenance task scheduling optimization, and is used for solving the problems of personnel allocation of multiple types of maintenance tasks and scientific and reasonable maintenance path planning. Constraint conditions such as transition time of task switching, maintenance skills of maintenance personnel, priority of maintenance tasks, noon break of maintenance personnel and the like are fully considered, personnel allocation of the maintenance tasks with multi-task priority and scientific and reasonable maintenance path planning are taken as targets, and finally, the maintenance task scheduling optimization problem is described as a mixed integer linear planning model. The invention is beneficial to improving the scientific rationality and efficiency of maintenance task management, reducing the operation management cost, especially reducing the unplanned fault maintenance period, and further reducing the production stopping loss.
The embodiment of the invention also provides an oil and gas well maintenance task scheduling processing device, which is described in the following embodiment. Because the principle of the device for solving the problems is similar to that of the oil and gas well maintenance task scheduling processing method, the implementation of the device can be referred to the implementation of the oil and gas well maintenance task scheduling processing method, and the repetition is not repeated.
Fig. 10 is a schematic structural diagram of an apparatus for scheduling maintenance tasks of an oil and gas well according to an embodiment of the present invention, as shown in fig. 10, the apparatus includes:
the acquisition unit 01 is used for acquiring the oil and gas well maintenance task scheduling basic data;
The determining unit 02 is used for inputting the basic data of the scheduling tasks into a pre-established oil-gas well maintenance task scheduling optimization model based on continuous time expression, so as to obtain an optimal maintenance task scheduling strategy; the oil and gas well maintenance task scheduling optimization model based on continuous time expression is a maintenance task scheduling optimization model based on multiple oil and gas well sites, multiple maintenance skill levels and multiple task priorities of continuous time expression;
and the processing unit 03 is used for carrying out oil and gas well maintenance task scheduling processing according to the optimal maintenance task scheduling strategy.
In one embodiment, the oil and gas well maintenance task scheduling processing device may further include: the establishing unit is used for establishing an oil and gas well maintenance task scheduling optimization model based on continuous time expression according to the following method:
describing and processing preset problems in the dispatching optimization of the maintenance tasks of the oil and gas well;
giving premise assumption features to all preset problems of description processing;
According to the premise assumption characteristics and the number of event points set for the preset type of event, establishing an objective function with the minimum path, the shortest total task time and the maximum number of completed tasks;
Obtaining a constraint condition expression of the objective function according to the initial position of the maintenance personnel, the time consumption of the maintenance task, the priority of the maintenance task and the skill level requirement of the maintenance personnel;
And obtaining the oil and gas well maintenance task scheduling optimization model based on continuous time expression according to the objective function and the constraint condition expression.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the oil and gas well maintenance task scheduling processing method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium which stores a computer program for executing the oil and gas well maintenance task scheduling processing method.
In the embodiment of the invention, compared with the technical proposal that the oil-gas well maintenance task scheduling processing is unreasonable, the efficiency is low and the cost of the scheduling processing is high in the prior art, the oil-gas well maintenance task scheduling processing scheme is characterized by comprising the following steps: acquiring oil and gas well maintenance task scheduling basic data; inputting maintenance task scheduling basic data into a pre-established oil and gas well maintenance task scheduling optimization model based on continuous time expression, and obtaining an optimal maintenance task scheduling strategy; and carrying out oil and gas well maintenance task scheduling treatment according to the optimal maintenance task scheduling strategy. The pre-established oil and gas well maintenance task scheduling optimization model is a maintenance task scheduling optimization model based on continuous time expression, namely a multi-oil and gas well site, a multi-maintenance skill level and a multi-task priority, an optimal maintenance task scheduling strategy can be obtained through the oil and gas well maintenance task scheduling optimization model, oil and gas well maintenance task scheduling processing is carried out according to the optimal maintenance task scheduling strategy, the rationality and the efficiency of the oil and gas well maintenance task scheduling processing can be improved, and the cost of the oil and gas well maintenance task scheduling processing is reduced.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (5)
1. The oil and gas well maintenance task scheduling processing method is characterized by comprising the following steps of:
acquiring oil and gas well maintenance task scheduling basic data, which comprises the following steps: acquiring data of a day scheduling task and a day attendant and basic attribute data related to the day scheduling task and the day attendant;
Inputting the scheduling task basic data into a pre-established oil and gas well maintenance task scheduling optimization model based on continuous time expression, and obtaining an optimal maintenance task scheduling strategy; the oil and gas well maintenance task scheduling optimization model based on continuous time expression is a maintenance task scheduling optimization model based on multiple oil and gas well sites, multiple maintenance skill levels and multiple task priorities of continuous time expression;
according to the optimal maintenance task scheduling strategy, carrying out oil and gas well maintenance task scheduling treatment;
The oil and gas well maintenance task scheduling processing method further comprises the following steps of establishing an oil and gas well maintenance task scheduling optimization model based on continuous time expression according to the following method: describing and processing preset problems in the dispatching optimization of the maintenance tasks of the oil and gas well; giving premise assumption features to all preset problems of description processing; according to the premise assumption characteristics and the number of event points set for the preset type of event, establishing an objective function with the minimum path, the shortest total task time and the maximum number of completed tasks; obtaining a constraint condition expression of the objective function according to the initial position of the maintenance personnel, the time consumption of the maintenance task, the priority of the maintenance task and the skill level requirement of the maintenance personnel; obtaining the oil and gas well maintenance task scheduling optimization model based on continuous time expression according to the objective function and the constraint condition expression;
The constraint expression includes:
The problem satisfies a time sequence constraint, and for each serviceman, the starting time of the next event point must lag behind the ending time of the previous event point, and neither exceeds the scheduling period range; the specific expression form comprises:
the problem satisfies event point duration constraints, determining a length of time for each time period, the length of time being determined by the home maintenance personnel and the maintenance tasks being processed; the specific expression form comprises:
The problem task allocation constraint meets the requirement that one task can only be completed once at most, and each time period of each maintenance personnel can only be provided with one task at most; the specific expression form comprises:
The task allocation of the problem must meet the urgent tasks and important tasks must be scheduled; the specific expression form comprises:
The event point-event segment matching constraint of the problem satisfies that the task in progress and the task in conversion alternately appear on a time axis; the specific expression form comprises:
the problem interrupt task constraint meets the requirement that each maintainer returns to a maintenance center station in noon time, and the rest time is followed by the completion of the rest task; the specific expression form comprises:
where Tf in denotes the end time of service technician i at event point n, tf in-1 denotes the end time of service technician i at event point n-1, ts in denotes the start time of service technician i at event point n, i represents a technician, n represents an event, H represents a scheduling period, and P represents a maintenance technician set; XS ijn =1 indicates that serviceman i is a maintenance task j at event point N, otherwise 0, XS ijn+1 =1 indicates that serviceman i is a maintenance task j at event point n+1, otherwise 0, N 1 is a state transition set, N 2 is an execution task set, T ij denotes the time for repair technician i to complete repair task j, T jj￠ denotes the shortest path time required for the transition from repair task j to repair task j ', X ijj′n =1 denotes the transition of repair technician i from repair task j to repair task j' at event point n, otherwise 0, j=1 denotes a special task, i.e. the service technician returns to the service station for rest, j 'denotes a service task different from j, K denotes the set of tasks to be serviced, K 1 denotes a task with priority 1, K 2 denotes a task with priority 2X ijj′n+1 =1 denotes a switchover of service technician i from service task j to service task j' at event point n+1, n max denotes the maximum event point, X ijj′n-1 =1 denotes switching from maintenance task j to maintenance task j 'at event point n-1, otherwise 0, xs ij′n =1 denotes that maintenance technician i does maintenance task j' at event point n, Otherwise, 0, xs ij′n-1 =1 indicates that the maintenance technician i is performing maintenance task j' at event point n-1, otherwise, 0, xs i1n =1 indicates that the maintenance technician i is returning to the maintenance station at event point n, otherwise, 0, bl indicates the start time of the interrupt task, and BU indicates the end time of the interrupt task;
The objective function is:
Wherein M1 is a cost coefficient for determining the total journey travel time; m2 is a time normalization coefficient of priority penalty; m3 is a task amount normalization time coefficient; m4 is a time normalization coefficient for ensuring that maintenance work is ended within a preset time range when the task amount is smaller than a preset value, tf in represents the ending time of maintenance technician i at event point N, ts in represents the starting time of maintenance technician i at event point N, XS ijn =1 represents that maintenance technician i is doing maintenance task j at event point N, otherwise, N 1 is a state transition set, and N 2 is an execution task set.
2. The method for scheduling and processing maintenance tasks of an oil and gas well according to claim 1, wherein the precondition hypothesis features comprise:
The problem only considers whether the maintenance task is within the repair capability of a maintenance technician, and does not consider whether needed equipment and devices are in shortage;
The problem goes to the position where the maintenance task is located, uses the same transportation means and runs at a constant speed, and determines the running time by combining the actual running condition;
grouping the problem maintenance personnel according to skill level, distributing maintenance tasks for a study object by using maintenance groups, and regarding the grouped maintenance groups as single maintenance personnel;
in the implementation process of maintenance tasks, each maintenance personnel independently completes the maintenance tasks allocated to each maintenance personnel, the maintenance tasks are mutually independent, and the situation of mutual support does not exist;
Each maintainer of the problem executes the maintenance task on the same day according to the scheduling decision result, and the midway adjustment condition and the emergency of the task are not considered;
the problem maintenance tasks are non-preemptive tasks, and all maintenance tasks have no front-back dependency relationship and are independent;
The well where the problem maintenance task is located, the time for each maintenance technician to complete the maintenance task, the importance priority of the task, the skill level requirement of the maintenance task and the related information of the skill level of the maintenance personnel are all known;
Each maintenance group of the problem starts from a maintenance station, and after the maintenance task in the morning is completed, the maintenance station needs to be returned to rest in the noon, and after the maintenance task in the afternoon is completed, the maintenance group returns to the maintenance station;
the problem is that a plurality of maintenance tasks exist in the same well, the importance of the tasks is divided by priority, and the priority of the tasks of the same maintenance well is not unique;
the start time and end time of the problem maintenance task can occur at any point on the time axis, as determined by the results of the oil and gas well maintenance task scheduling optimization model solution, and the length of time is determined by the assigned skill level of the maintenance personnel.
3. An oil and gas well maintenance task scheduling processing device, which is characterized by comprising:
the acquisition unit is used for acquiring the oil and gas well maintenance task scheduling basic data and comprises the following steps: acquiring data of a day scheduling task and a day attendant and basic attribute data related to the day scheduling task and the day attendant;
The determining unit is used for inputting the basic data of the scheduling tasks into a pre-established oil and gas well maintenance task scheduling optimization model based on continuous time expression, so as to obtain an optimal maintenance task scheduling strategy; the oil and gas well maintenance task scheduling optimization model based on continuous time expression is a maintenance task scheduling optimization model based on multiple oil and gas well sites, multiple maintenance skill levels and multiple task priorities of continuous time expression;
the processing unit is used for carrying out oil and gas well maintenance task scheduling processing according to the optimal maintenance task scheduling strategy;
the oil-gas well maintenance task scheduling processing device also comprises: the establishing unit is used for establishing an oil and gas well maintenance task scheduling optimization model based on continuous time expression according to the following method: describing and processing preset problems in the dispatching optimization of the maintenance tasks of the oil and gas well; giving premise assumption features to all preset problems of description processing; according to the premise assumption characteristics and the number of event points set for the preset type of event, establishing an objective function with the minimum path, the shortest total task time and the maximum number of completed tasks; obtaining a constraint condition expression of the objective function according to the initial position of the maintenance personnel, the time consumption of the maintenance task, the priority of the maintenance task and the skill level requirement of the maintenance personnel; obtaining the oil and gas well maintenance task scheduling optimization model based on continuous time expression according to the objective function and the constraint condition expression;
The constraint expression includes:
The problem satisfies a time sequence constraint, and for each serviceman, the starting time of the next event point must lag behind the ending time of the previous event point, and neither exceeds the scheduling period range; the specific expression form comprises:
the problem satisfies event point duration constraints, determining a length of time for each time period, the length of time being determined by the home maintenance personnel and the maintenance tasks being processed; the specific expression form comprises:
The problem task allocation constraint meets the requirement that one task can only be completed once at most, and each time period of each maintenance personnel can only be provided with one task at most; the specific expression form comprises:
The task allocation of the problem must meet the urgent tasks and important tasks must be scheduled; the specific expression form comprises:
The event point-event segment matching constraint of the problem satisfies that the task in progress and the task in conversion alternately appear on a time axis; the specific expression form comprises:
the problem interrupt task constraint meets the requirement that each maintainer returns to a maintenance center station in noon time, and the rest time is followed by the completion of the rest task; the specific expression form comprises:
where Tf in denotes the end time of service technician i at event point n, tf in-1 denotes the end time of service technician i at event point n-1, ts in denotes the start time of service technician i at event point n, i represents a technician, n represents an event, H represents a scheduling period, and P represents a maintenance technician set; XS ijn =1 indicates that serviceman i is a maintenance task j at event point N, otherwise 0, XS ijn+1 =1 indicates that serviceman i is a maintenance task j at event point n+1, otherwise 0, N 1 is a state transition set, N 2 is an execution task set, t ij denotes the time for repair technician i to complete repair task j, T jj′ denotes the shortest path time required for the transition from repair task j to repair task j ', X ijj′n =1 denotes the transition of repair technician i from repair task j to repair task j' at event point n, otherwise 0, j=1 denotes a special task, i.e. the service technician returns to the service station for rest, j 'denotes a service task different from j, K denotes the set of tasks to be serviced, K 1 denotes a task with priority 1, K 2 denotes a task with priority 2X ijj′n+1 =1 denotes a switchover of service technician i from service task j to service task j' at event point n+1, n max denotes the maximum event point, X ijj′n-1 =1 denotes switching from maintenance task j to maintenance task j 'at event point n-1, otherwise 0, xs ij′n =1 denotes that maintenance technician i does maintenance task j' at event point n, Otherwise, 0, xs ij′n-1 =1 indicates that the maintenance technician i is performing maintenance task j' at event point n-1, otherwise, 0, xs i1n =1 indicates that the maintenance technician i is returning to the maintenance station at event point n, otherwise, 0, bl indicates the start time of the interrupt task, and BU indicates the end time of the interrupt task;
The objective function is:
Wherein M1 is a cost coefficient for determining the total journey travel time; m2 is a time normalization coefficient of priority penalty; m3 is a task amount normalization time coefficient; m4 is a time normalization coefficient for ensuring that maintenance work is ended within a preset time range when the task amount is smaller than a preset value, tf in represents the ending time of maintenance technician i at event point N, ts in represents the starting time of maintenance technician i at event point N, XS ijn =1 represents that maintenance technician i is doing maintenance task j at event point N, otherwise, N 1 is a state transition set, and N 2 is an execution task set.
4. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 2 when executing the computer program.
5. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program for executing the method of any one of claims 1 to 2.
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