A remote control and dispatch system suitable for multiple scenarios in oil fields

Abstract

This invention discloses a remote control and scheduling system applicable to multiple scenarios in oilfields, relating to the field of control and scheduling technology. The system includes an instruction generation unit, a decision control unit, an adjustment control unit, and an acquisition and transmission unit. The acquisition and transmission unit periodically collects equipment operating parameters and transmits them to the decision control unit. The decision control unit uses a particle swarm optimization algorithm to complete the initial task allocation, performs secondary allocation based on the equipment operating parameters, predicts the expected production capacity of the equipment within the production time limit based on an efficiency decay model, compares the initial task volume to determine whether adjustment is needed, marks equipment that needs adjustment and whose theoretical target power exceeds the rated upper limit as incremental equipment, and calculates the incremental allocation to be allocated. Through a dynamic coupling adaptation algorithm, the incremental allocation to be allocated is optimized and split into non-incremental equipment with allowable load. The instruction generation unit generates corresponding instructions and transmits them to the adjustment control unit, realizing dynamic and efficient scheduling in multiple scenarios in oilfields.

Description

Technical Field

[0001] This invention relates to the field of control and scheduling technology, specifically to a remote control and scheduling system applicable to multiple scenarios in oil fields. Background Technology

[0002] In actual oilfield production, traditional production task control and scheduling methods have shortcomings: On the one hand, initial task allocation often relies on manual experience or simple equal distribution, making it difficult to achieve coordinated optimization of overall production efficiency and total energy consumption. This can easily lead to uneven equipment load, overload of some equipment, or idle capacity. In addition, when faced with task increments, there is a lack of a scientific secondary allocation mechanism, making it difficult to quickly and reasonably allocate incremental tasks to suitable equipment. This fails to meet the dynamic and efficient scheduling needs of different oilfield production scenarios, ultimately affecting overall production efficiency. On the other hand, the characteristics of equipment operating efficiency decreasing with accumulated operating time are not considered, making it impossible to accurately predict the actual capacity of equipment within the entire production timeframe. This often results in some equipment failing to complete initial tasks on time or consuming excessive energy to complete tasks. Therefore, the existing control and scheduling model can no longer meet the comprehensive requirements of oilfield production for efficiency, energy consumption control, and dynamic adaptability. There is an urgent need for a remote control and scheduling system that can achieve scientific task allocation, accurate capacity prediction, and adaptability to multiple scenarios. Summary of the Invention

[0003] The purpose of this invention is to provide a remote control and scheduling system applicable to multiple scenarios in oil fields. By allocating tasks through multi-objective optimization and dynamic coupling adaptation algorithms, and predicting production capacity through efficiency decay models, the system achieves the goal of dynamic control and scheduling in multiple scenarios in oil fields.

[0004] The technical solution to achieve the objective of this invention is as follows:

[0005] A remote control and dispatch system suitable for multiple scenarios in oil fields includes a control terminal and an oil field controlled terminal. The control terminal includes a command generation unit and a decision control unit, while the oil field controlled terminal includes a regulation control unit and a data acquisition and transmission unit.

[0006] The control unit allocates initial tasks based on the user-input production task quantity and production time limit, generates and sends initial allocation instructions, receives equipment operating parameters, obtains equipment operating efficiency and calculates the expected production capacity within the production time limit by using an efficiency decay model that integrates time decay and load decay terms, compares the initial task quantity to locate equipment to be adjusted, filters and marks incremental equipment based on the difference between the theoretical target power and the rated power limit of the equipment to be adjusted, combines the total incremental amount to be allocated that the incremental equipment cannot complete within the production time limit, and completes the secondary allocation of incremental equipment through a dynamic coupling adaptation algorithm based on the power redundancy, capacity redundancy coefficient and unit energy consumption coefficient of non-incremental equipment, generates theoretical target power and sends secondary allocation instructions.

[0007] The oilfield controlled terminal receives initial allocation instructions and secondary allocation instructions in sequence, controls the corresponding equipment to operate at the corresponding power, collects and processes the equipment operating parameters, and feeds them back to the control terminal.

[0008] Furthermore, predicting the expected capacity of the equipment within the production timeframe based on an efficiency decay model includes the following steps:

[0009] Outliers in the equipment operating parameters are removed using the Raida criterion, and the average of the parameters from the first three sampling periods is used to fill the gaps after removing outliers.

[0010] Considering the long-term operation of the equipment, based on the mechanical wear and load fatigue under different cumulative operating times, an efficiency decay model including time decay term and load decay term is constructed. This model uses the cumulative operating time of the equipment as a variable to reflect the ratio of the current operating efficiency of the equipment to the efficiency in a brand-new state. In order to make the model fit the actual operating law of the equipment, the actual efficiency data of the equipment under different cumulative operating times are collected, and the least squares method is used to fit the efficiency decay coefficient in the model. By minimizing the sum of squared errors between the actual efficiency of the equipment and the efficiency calculated by the model, the decay coefficient that fits the actual state of the equipment is determined, and finally a model that can accurately reflect the efficiency decay law of the equipment is formed.

[0011] The power utilization rate of the equipment is calculated by dividing the current actual operating power of the equipment by its rated operating power when producing the benchmark production capacity, and then combining the current efficiency of the equipment calculated by the efficiency decay model. The actual production capacity of the equipment in the current sampling period is obtained by multiplying the current efficiency by the benchmark production capacity by the power utilization rate.

[0012] Determine the number of sampling periods included in the remaining production time of the equipment, combine the efficiency decay model to predict the actual capacity of the equipment in each remaining sampling period, obtain the cumulative capacity in the remaining production time, and then add the remaining cumulative capacity to the equipment's completed capacity to finally obtain the expected capacity of the equipment in the entire production time.

[0013] Furthermore, when fitting the efficiency decay coefficient using the least squares method, the actual efficiency data of the equipment under different cumulative operating times is collected, substituted into the efficiency decay model formula, and the sum of squared errors between the actual efficiency and the model-calculated efficiency is minimized to generate an efficiency decay coefficient that adapts to the actual operating state of the equipment.

[0014] Furthermore, the decision control unit calculates the theoretical target power for completing the initial task and locates the incremental equipment, including the following steps:

[0015] Based on the initial workload, production time limit, efficiency decay model and benchmark capacity of the equipment, the formula for calculating the theoretical target power is derived by using the equation that the initial workload must be equal to the sum of the actual capacity of each sampling period within the production time limit. This theoretical target power is the minimum operating power required for the equipment to just complete the initial workload.

[0016] The derived theoretical target power is compared with the upper limit of the device's rated power. If the theoretical target power is less than or equal to the upper limit of the rated power, a control command containing the theoretical target power is generated and transmitted to the adjustment control unit via wireless network to adjust the device's operating power to the theoretical target power. If the theoretical target power exceeds the upper limit of the rated power, it is determined that the device cannot complete the initial task by adjusting the power, and it is marked as an incremental device. The upper limit of the device's rated power is recorded.

[0017] Furthermore, when calculating the incremental capacity to be allocated, the maximum expected capacity of the incremental equipment when running at its rated power limit is first calculated. Combining the rated power limit of the incremental equipment, the number of sampling periods included in the remaining production time, the baseline capacity, and the current efficiency, the maximum capacity that the equipment can complete when running at its rated power limit throughout the entire production time limit is predicted. Then, the initial task of the incremental equipment is subtracted from the maximum expected capacity, and the difference is the incremental capacity to be allocated to the equipment. This incremental capacity needs to be split and completed by other non-incremental equipment.

[0018] Furthermore, the initial task quantity is calculated based on the particle swarm optimization algorithm, including the following steps:

[0019] Obtain the total production task and production time limit of the oilfield in the current cycle, as well as the baseline production capacity, energy consumption and rated power limit of each piece of equipment in the previous sampling period;

[0020] Let the initial task quantity of each piece of equipment be the decision variable, and the initial task quantities of all equipment constitute the set of decision variables. Three types of constraints are set: first, the total amount constraint, the sum of the initial task quantities of all equipment must be equal to the total production task of the oilfield as a whole, neither exceeding nor falling short; second, the upper limit constraint of a single piece of equipment, the initial task quantity of a single piece of equipment shall not exceed the maximum production capacity that the equipment can complete within the production time limit; and third, the non-negativity constraint, the initial task quantity of each piece of equipment shall not be negative to avoid logical contradictions. The optimization objectives are divided into two categories: first, minimizing the maximum time consumption of a single piece of equipment to ensure that all equipment completes its tasks as synchronously as possible; and second, minimizing the total energy consumption of all equipment.

[0021] The particles are encoded, and each particle corresponds to a set of initial task allocation schemes. The particle dimension is consistent with the number of devices. Each position value of the particle represents the initial task volume of the corresponding device. The population size is set, and the initial particle positions that meet the constraints are randomly generated. First, the task volume of each device is randomly allocated from 0 to its maximum capacity. Then, the task volume of all devices is adjusted proportionally to ensure that the sum is equal to the total production task. At the same time, the initial particle velocity, learning factor, inertia weight and maximum number of iterations are initialized.

[0022] The optimization algorithm parameters are initialized, the task allocation scheme corresponding to the particles is encoded, the population size is set, the initial particle positions and velocities are randomly generated, and the learning factor, inertia weight and maximum number of iterations are initialized.

[0023] Calculate the dual objective function value for each particle, namely the maximum time consumption and total energy consumption of a single device. Based on the Pareto dominance relationship, filter non-dominated solutions. If both objective values ​​of a particle are better than those of another particle, the former dominates the latter, and the latter is eliminated. All non-dominated solutions are retained to form the Pareto optimal solution set. Next, update the individual optimal position and the global optimal position. Then, update the particle velocity and position according to the preset formula. If the particle position violates the constraint after the update, the task amount exceeding the maximum capacity is truncated to the maximum capacity. The excess part is distributed proportionally to the devices that have not reached the maximum capacity. Then, readjust the task amount of all devices to ensure that the total amount constraint is met. Finally, repeat the above steps until the iteration is completed.

[0024] When the number of iterations reaches the maximum number of iterations, or when the objective function value of the Pareto optimal solution set no longer fluctuates for ten consecutive generations, the iteration stops. Based on the actual needs of the oilfield, the weights of the two types of objectives are preset, and each solution in the Pareto optimal solution set is scored using a linear weighting method. The solution with the highest weighted score is selected as the final initial task allocation scheme.

[0025] Furthermore, when the decision control unit performs secondary incremental allocation, it calculates the current load status of non-incremental equipment, including power redundancy, capacity redundancy coefficient, and unit energy consumption coefficient.

[0026] The space power redundancy that reflects the equipment’s ability to increase its operating power is obtained by subtracting its current actual operating power from the rated power limit of non-incremental equipment. The larger the value, the greater the potential of the equipment to undertake incremental tasks.

[0027] First, calculate the maximum additional capacity that non-incremental equipment can generate when operating at its rated power limit during the remaining production time. Then, calculate the amount of initial tasks that the equipment has not completed. Divide the additional maximum capacity by the amount of initial tasks that have not been completed to obtain the capacity redundancy coefficient. The larger the coefficient, the stronger the equipment's ability to undertake incremental tasks.

[0028] Divide the baseline energy consumption by the baseline capacity to obtain the baseline unit capacity energy consumption of non-incremental equipment. Then divide the baseline unit capacity energy consumption by the current operating efficiency of the equipment to obtain the unit energy consumption coefficient. The smaller the coefficient, the more energy-efficient the equipment is when undertaking incremental tasks.

[0029] Furthermore, the decision control unit uses a dynamic coupling adaptation algorithm to split the incremental allocation to non-incremental devices, including the following steps:

[0030] Add up the unallocated increments of all incremental devices to obtain the total unallocated increment;

[0031] Based on the power redundancy, capacity redundancy coefficient, and unit energy consumption coefficient of non-incremental equipment, incremental secondary allocation is completed through a dynamic coupling adaptation algorithm. First, the power redundancy and unit energy consumption coefficient are normalized by dividing the single equipment load index value by the maximum value of the load index among all non-incremental equipment. Then, according to the oilfield demand, the weight coefficients of the three load indices, namely power redundancy, capacity redundancy coefficient, and unit energy consumption coefficient, are set by expert scoring method. Finally, the dynamic coupling weight is calculated. Equipment with higher dynamic coupling weight is given priority to undertake incremental tasks. The allocation ratio is determined according to the proportion of dynamic coupling weight. The allocation ratio of a single equipment is the dynamic coupling weight of that equipment divided by the sum of the dynamic coupling weights of all non-incremental equipment.

[0032] Calculate the initial allocation increment for each non-incremental device based on the allocation ratio. Add the initial allocation increment to the initial task of the device and determine whether the sum exceeds the maximum capacity of the device within the production time limit. If it does not exceed the limit, determine that the device will bear this initial allocation increment. If it exceeds the limit, calculate the increment of the excess part and redistribute the excess part according to the allocation ratio of other non-incremental devices. Repeat this process until all the increments to be allocated are allocated to non-incremental devices with sufficient load capacity.

[0033] Furthermore, outliers in equipment operating parameters are removed using the Raida criterion. The collected equipment operating parameters are categorized into three types: actual operating power, cumulative operating time, and completed capacity. For each type of parameter, the average value and standard deviation of the parameter are calculated over the most recent consecutive sampling periods. Parameter values ​​that exceed the range of the average value minus three times the standard deviation to the average value plus three times the standard deviation are identified as outliers and removed from the parameter sequence.

[0034] Furthermore, the baseline capacity and baseline energy consumption of each production equipment in the previous sampling period are the actual capacity and total energy consumed by the production equipment under normal production conditions, continuously operating at the designed standard fixed power for a complete sampling period, that is, the time period from the start of parameter acquisition to the end of acquisition.

[0035] Compared with the prior art, the advantages of this invention are:

[0036] 1. Implement initial and secondary task allocation through multi-objective optimization, adapt to equipment capabilities, avoid equipment overload and idleness, and improve efficiency and reduce costs;

[0037] 2. Introduce an efficiency decay model to accurately predict production capacity, dynamically adjust power and locate incremental equipment, and avoid misjudgment of production capacity and energy waste. Attached Figure Description

[0038] Figure 1 A flowchart of a remote control and dispatch system applicable to multiple scenarios in oil fields;

[0039] Figure 2 This is a flowchart of the initial task allocation process in this invention;

[0040] Figure 3 This is a flowchart of the efficiency decay model prediction and incremental device positioning process in this invention;

[0041] Figure 4 This is a flowchart of the secondary incremental allocation process in this invention. Detailed Implementation

[0042] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0043] like Figure 1 As shown, this invention discloses a remote control and scheduling system applicable to multiple scenarios in oil fields, including a control terminal and an oil field controlled terminal:

[0044] The control unit includes an instruction generation unit and a decision control unit. The decision control unit is used to execute initial task allocation and secondary incremental allocation. It generates corresponding instructions through the instruction generation unit and transmits them wirelessly to the adjustment control unit. When performing initial task allocation, the decision control unit obtains the production task and production time limit, and simultaneously obtains the baseline capacity and baseline energy consumption of each production device in the previous sampling period. It then calls the particle swarm optimization algorithm to calculate the initial task load for each production device, aiming to maximize global production efficiency and minimize total energy consumption. The instruction generation unit generates initial allocation instructions and transmits them wirelessly to the corresponding adjustment control unit. During secondary incremental allocation, after receiving the device operating parameters, the decision control unit considers the decrease in device operating efficiency with accumulated time. It uses a capacity prediction method based on an efficiency decay model to dynamically calculate the expected capacity that each device can complete within the entire production time limit and compares it with the initial task load to determine whether each device can independently complete its initial task load. If the expected capacity is greater than or equal to the initial task load... If the initial task quantity is less than the production time limit, the task is deemed complete. If the initial task quantity is less than the production time limit, the task is deemed to require adjustment. For equipment requiring adjustment, the theoretical target power to meet the task requirements is calculated based on the initial task quantity and production time limit, and compared with the rated power limit. If the theoretical target power is less than or equal to the rated power limit, a control command is generated and wirelessly transmitted to the corresponding adjustment control unit to adjust the equipment power to the theoretical target power. If the theoretical target power is greater than the rated power limit, the equipment is marked as an incremental equipment. The maximum expected capacity of the incremental equipment when running at the rated power limit is calculated. The maximum expected capacity is then subtracted from the initial task quantity to obtain the incremental to be allocated. The total amount of all incremental to be allocated is calculated. Based on the power redundancy, capacity redundancy coefficient, and unit energy consumption coefficient of non-incremental equipment, the dynamic coupling weight of each non-incremental equipment is calculated through a dynamic coupling adaptation algorithm. Based on the dynamic coupling weight, the incremental to be allocated is optimized, split, and allocated to non-incremental equipment. The theoretical target power required for each non-incremental equipment to undertake the incremental task is calculated. Finally, a secondary allocation command is generated and wirelessly transmitted to the corresponding adjustment control unit.

[0045] The controlled end of the oilfield includes a regulation and control unit and a data acquisition and transmission unit. The regulation and control unit receives initial allocation instructions and secondary allocation instructions in sequence, and controls the production equipment to operate at a specified power. The data acquisition and transmission unit collects equipment operating parameters through sensors during each sampling period, including actual operating power, cumulative operating time, and completed production capacity, and transmits them to the decision control unit via a wireless network.

[0046] like Figure 2 As shown, further, the decision control unit executes the initial task allocation, and combined with the overall production task volume and production time limit within the current cycle of the oilfield, the optimal initial task allocation for each production equipment is solved using the particle swarm optimization algorithm, and instructions are generated and sent to the control equipment, including the following steps:

[0047] Obtain the total production task of the oilfield in the current cycle. and production time limit Record the total number of all production equipment in the oilfield as follows: single device Collect basic parameters of all production equipment, including individual equipment. During the previous sampling period Benchmark capacity within single device During the previous sampling period Internal benchmark energy consumption Maximum rated power of a single device The rated power limit of a single device is used to constrain the maximum workload of the device and avoid overload. The baseline capacity and baseline energy consumption are based on the production equipment operating at a fixed power under normal production conditions during the previous sampling period. Production capacity and energy consumption during operation;

[0048] Before performing multi-objective optimization, first define the decision variables and constraints. To be assigned to the device The initial task load and the task allocation of all devices constitute a decision variable vector. The objective is to optimize the solution to the requirements, while setting constraints to ensure the feasibility of task allocation. These constraints include total quantity constraints, single-device upper limit constraints, and non-negativity constraints. The expression for the total quantity constraint is shown below:

[0049] ,

[0050] in, To be assigned to the device The initial workload. The expression represents the total production task of the oilfield. It means that the sum of the initial tasks of all equipment must equal the overall production task of the oilfield; it cannot exceed or fall short of the target. The upper limit constraint expression for a single piece of equipment is shown below:

[0051] ,

[0052] in, To be assigned to the device The initial workload. For equipment The maximum production capacity that can be produced within the entire production period. This refers to the number of data collection periods included within the production timeframe. For a single device During the previous sampling period The baseline capacity within the range, with non-negativity constraint expressions as shown below:

[0053] ,

[0054] The purpose of nonnegativity constraints is to ensure that the initial allocation amount cannot be negative when allocating initial workload to all devices, thus avoiding logical contradictions.

[0055] An optimization function is constructed with the dual objectives of maximizing global production efficiency and minimizing total energy consumption. Global production efficiency is defined as the total capacity per unit time. Due to total capacity constraints, the total capacity is fixed within a given production time limit. Therefore, in practical optimization, maximizing global production efficiency is transformed into minimizing the maximum time consumption of a single production device. The global production efficiency function is then... The expression is as follows:

[0056] ,

[0057] in, The initial workload allocated to device 1. For device 1 during the previous sampling period The benchmark capacity within, Similarly, the time taken for device 1 to complete the initial workload is also... The time taken for device 2 to complete the initial workload. For equipment The time taken to complete the initial task is expressed as follows: This expression aims to ensure all devices complete tasks as synchronously as possible, avoiding individual devices slowing down the overall progress, thereby maximizing effective productivity per unit time. Total energy consumption is the sum of the energy consumption of all devices; hence, the total energy consumption function is... The expression is as follows:

[0058] ,

[0059] in, For equipment During the previous sampling period The baseline energy consumption within, For equipment The energy consumption per unit of production capacity will be used to power equipment. initial task volume With equipment Multiply the unit capacity energy consumption to obtain the equipment Complete the initial task The total energy consumption is summed to obtain the total energy consumption of the production equipment. The oilfield operating cost is reduced by minimizing the total energy consumption.

[0060] To initialize the particle swarm optimization (PSO) algorithm parameters, the particles are first encoded, with each particle representing a set of task allocation schemes. The particle dimension is... Consistent with the number of devices, at the particle position middle, Indicates the first Each particle corresponds to a device The initial task allocation amount, setting the population size as Randomly generate initial particle positions, that is, randomly generate initial task assignments that satisfy constraints, including randomly assigning each device a position from 0 to 1. The amount of work within the range, and calculate the current sum and... The difference is used to scale the workload of each device proportionally until the sum equals the difference. ,particle speed This indicates the adjustment range of the initial workload for each device; the initial value is set randomly. Within the range, Values Ten percent, and simultaneously initialize the learning factor. and inertia weight and maximum number of iterations ;

[0061] Iterative optimization is performed to find the optimal task allocation scheme by gradually approaching it through swarm intelligence optimization of particle swarms. Calculate its dual objective function value The goal is to minimize both the maximum time consumption and the total energy consumption, and to evaluate the merits of particles based on the Pareto dominance relation. If both objective values ​​of particle A are better than those of particle B, then A dominates B, and B is eliminated. All non-dominated solutions are retained, forming a Pareto optimal solution set. Individual and global optimal updates are then performed, with the individual optimal solution being the best. Recording particles The optimal position from the initial stage to the current iteration, i.e., the best-performing task allocation scheme in the history of this particle, is the global optimum. To randomly select a solution from the Pareto optimal set as the global guiding particle and avoid the algorithm getting trapped in local optima, particle velocity and position are updated. The particle velocity update formula is shown below:

[0062] ,

[0063] in, For particles In the In the next iteration, the device The speed of task load adjustment For inertial weights, These are learning factors, used to control the acceleration of particles moving towards their individual optimal and global optimal states, respectively. The random number between 0 and 1 is generated randomly in each iteration, making the adjustment direction and amplitude of the particles uncertain and enhancing the diversity of the search. For particles The corresponding device in the individual optimal position The amount of tasks allocated, For particles In the The corresponding device in the next iteration The current task allocation amount, For the globally optimal position, the corresponding device The task allocation and location update formula are as follows:

[0064] ,

[0065] in, For particles In the In the next iteration, the corresponding device The speed at which the workload is adjusted determines the device's performance in the next iteration. How much the workload will increase or decrease, depending on the device The task allocation is adjusted by adding a certain amount to the original task allocation to obtain the updated task allocation. If the updated location violates the constraints, the task allocation exceeding the upper limit is truncated. The excess workload is proportionally allocated to other devices that have not reached their limits, and the total is recalculated. The workload of all devices is scaled proportionally to ensure that the total limit is met. When the number of iterations reaches [a certain threshold], [the process continues]. The iteration stops when the objective function value of the Pareto optimal solution set no longer fluctuates after ten consecutive iterations, or before the maximum number of iterations has been reached.

[0066] The Pareto optimal solution set contains multiple non-dominated solutions, which are not absolutely superior or inferior to each other. One non-dominated solution exhibits high efficiency but slightly higher energy consumption, while another has low energy consumption but slightly lower efficiency. Based on the actual needs of the oilfield—whether the oilfield prioritizes efficiency or energy consumption—weights are preset. A linear weighting method is used to assign these weights to the dual objective function and perform calculations. The solution with the optimal weighted value is selected as the final task allocation scheme. , the elements Corresponding to the first Task allocation decisions for individual devices;

[0067] Will The data is converted into structured instructions, including device ID, task quantity, and start time, and transmitted to the regulation and control units of each device via the oilfield wireless network to complete the initial task allocation.

[0068] like Figure 3 As shown, furthermore, a capacity prediction algorithm based on an efficiency decay model is used to dynamically calculate the expected capacity that each piece of equipment can complete within the entire production timeframe, and finally locate the incremental equipment, including the following steps:

[0069] The acquisition and transmission unit obtains information from each device. During the current sampling period Operating parameters, including actual operating power Cumulative duration Completed production capacity The data was validated, outliers were removed using the Raida criterion, and the average of the previous three periods was used to fill the gaps after the removal.

[0070] To quantify the mechanical wear accumulated over time and the load fatigue caused by accelerated wear due to high-power operation, an efficiency decay model is constructed by combining time decay terms and load decay terms. The expression of the efficiency decay model is as follows:

[0071] ,

[0072] Simplified and merged into:

[0073] ,

[0074] in, For equipment run The current operating efficiency of the equipment after one hour, that is, the ratio of efficiency to its state when it was brand new. , For equipment initial efficiency, To accumulate runtime, The equipment efficiency degradation coefficient represents the rate of degradation caused by operating time. This is the load attenuation coefficient, reflecting the amplification effect of the load on attenuation. The cumulative average power utilization rate, which is the average load intensity of the equipment from startup to the present, is expressed as follows:

[0075] ,

[0076] in, For the first Average operating power during the data collection period The duration of a single data collection period is represented by the numerator, which is the cumulative power consumed, and the denominator is the cumulative power at the rated power.

[0077] The efficiency degradation model was validated by obtaining historical operating data of the equipment, including the average operating power, cumulative duration, and actual efficiency for each data collection period. The actual efficiency can be obtained by dividing the actual output by the theoretical output, while the average load intensity is calculated and fitted using the least squares method. and This minimizes the sum of squared errors between the predicted efficiency and the actual efficiency, i.e. The efficiency attenuation coefficient is obtained by solving. and At this point, the efficiency degradation model closely matches the actual performance of the equipment;

[0078] Based on the current power and efficiency degradation model, the actual capacity of the equipment is determined by first calculating the ratio of the actual operating power of the current production equipment to the power used by the equipment to produce one benchmark unit of capacity. Power utilization rate The calculation formula is as follows:

[0079] ,

[0080] in, For production equipment Actual operating power Given the operating power of the production equipment at its baseline capacity, and combining this with the power utilization rate, baseline capacity, and efficiency degradation, the operating power of the production equipment can be calculated. Actual capacity during a sampling period under the current conditions The calculation formula is as follows:

[0081] ,

[0082] in, For equipment The baseline capacity is obtained by multiplying the baseline capacity, power utilization rate, and current equipment operating efficiency with reaction decay.

[0083] computing devices Remaining production time and calculate the equipment Cumulative capacity for the remaining period The calculation formula is as follows:

[0084] ,

[0085] in, For production equipment Remaining production time, This is the ratio of the remaining production time of the production equipment to the previous sampling period, therefore the equipment... The total expected production capacity within the entire lifecycle to the production deadline is ;

[0086] Production equipment Total projected capacity throughout the entire production cycle within the production timeframe With the initial workload of the device If a comparison is made, Determine if the device can complete the assigned initial workload. If the equipment is identified as needing adjustment, it will be entered into the theoretical target power calculation process.

[0087] Calculation enables the device The theoretical target power that exactly completes the initial task must satisfy the following equation:

[0088] ,

[0089] The theoretical target power can be obtained by solving and deriving the solution. The calculation formula is as follows:

[0090] ,

[0091] The theoretical target power obtained through this calculation formula is for the equipment. Minimum operating power required to complete the initial workload;

[0092] Based on the calculated theoretical target power, it is compared with the upper limit of the rated power of the equipment. If... The instruction generation unit generates instructions and sends them to the corresponding adjustment and control unit in the oilfield to adjust the power to... ,like The device is marked as an incremental device, and a command is generated and sent to the oilfield regulation and control unit to ensure that all incremental devices operate at their rated power limit, and the incremental devices are calculated. The gap between the production capacity operating at the rated power limit and the initial task is recorded as the incremental capacity to be allocated. .

[0093] like Figure 4 As shown, further, based on the incremental power to be allocated and the equipment operating parameters, and using the dynamic coupling adaptation algorithm, the theoretical target power required for each non-incremental device to undertake the incremental task is calculated, including the following steps:

[0094] The total number of incremental units to be allocated is obtained by summing up the incremental units to be allocated for all incremental units. Its expression is as follows:

[0095] ,

[0096] in, This is the set of incremental devices, i.e., the device numbers marked as incremental devices. For incremental equipment The unassigned increments are filtered, and the non-incremental devices in the device operating parameters are obtained. The set of non-incremental devices not marked as increments is then processed. For each non-incremental device Extract operating parameters, which are consistent with the overall device serial number. Distinguish, Replace with non-incremental device serial number And assign values, including the current operating power. Initial task volume Rated power limit Remaining production time Completed production capacity Current efficiency and the previous sampling period Internal benchmark capacity ;

[0097] For each non-incremental device, calculate the power redundancy, capacity redundancy coefficient, and unit energy consumption coefficient. The power redundancy is the difference between the device's current operating power and its rated power limit. This reflects the potential for increased power output and the capacity redundancy factor. The maximum additional production capacity that non-incremental equipment can undertake during the remaining production time when the equipment is operating at its rated power limit is calculated using the following formula:

[0098] ,

[0099] in, This refers to the power utilization rate of non-incremental equipment operating at its rated power limit. Non-incremental equipment The remaining initial workload, Non-incremental equipment Given a fixed power during the previous sampling period, the numerator represents the additional capacity that can be generated in the remaining time under the rated power. The larger the capacity redundancy coefficient, the stronger the ability to undertake incremental tasks. The above formula can be further simplified as follows:

[0100] ,

[0101] Unit energy consumption coefficient The energy consumption per unit of production capacity when undertaking incremental tasks is used to prioritize the selection of energy-saving equipment. The calculation formula is as follows:

[0102] ,

[0103] in, Non-incremental equipment During the previous sampling period The baseline energy consumption within, Non-incremental equipment The energy consumption per unit of production capacity, divided by the energy consumption of non-incremental equipment. Current efficiency Then, the unit energy consumption reflecting the actual decay state is obtained. The smaller the size, the more energy-efficient the equipment is in handling incremental tasks;

[0104] Based on the power redundancy, capacity redundancy coefficient, and unit energy consumption coefficient of non-incremental equipment, a dynamic coupling adaptation algorithm is used to determine the task allocation ratio of each non-incremental equipment. First, for each non-incremental equipment, the comprehensive power redundancy is calculated. Capacity redundancy coefficient and unit energy consumption coefficient Calculate dynamic coupling weight using three load metrics The calculation formula is as follows:

[0105] ,

[0106] in, The weighting coefficients are determined using an expert scoring method based on the actual needs of the oilfield. , These represent the maximum power redundancy and unit energy consumption coefficient among all non-incremental devices, respectively, used to normalize the indicators to the range of 0 to 1, and dynamically couple the weights. The higher the value, the more priority the device has in undertaking incremental tasks. The proportion of incremental tasks undertaken by each non-incremental device is positively correlated with the dynamic coupling weight. Therefore, determining the task allocation ratio is crucial. The calculation formula is as follows:

[0107] ,

[0108] in, To determine the task allocation ratio, Greater than or equal to 0, therefore, non-incremental devices Incremental workload to be undertaken At the same time, the constraints must be met, namely, the incremental task shall not exceed the maximum capacity of the equipment. If the constraints are exceeded, the excess part shall be distributed to other equipment proportionally and recalculated until all constraints are met.

[0109] Based on the assigned incremental tasks, the required power adjustment value is calculated backwards, which is the theoretical target power for the equipment's operation. The calculation formula is as follows:

[0110] ,

[0111] The above formula has been constrained, so the operating power of the equipment after the power adjustment value will not exceed the upper limit of the rated power of the equipment, and relevant instructions can be generated directly.

[0112] For each non-incremental device, generate a data set including the device ID and the incremental task quantity. Theoretical target power The secondary allocation instructions are transmitted to the corresponding device's adjustment and control unit via wireless network to ensure that the device operates at the target power and undertakes incremental tasks.

[0113] This invention discloses a remote control and scheduling system applicable to multiple scenarios in oilfields. The system includes a control terminal and an oilfield controlled terminal. The control terminal includes an instruction generation unit and a decision control unit, while the oilfield controlled terminal includes an adjustment control unit and an acquisition and transmission unit. After receiving the production task quantity and production time limit, the control terminal constructs a dual-objective optimization function that minimizes the maximum time consumption of a single device and minimizes the total energy consumption using a particle swarm optimization algorithm. Combining the total quantity, the upper limit of a single device, and non-negativity constraints, it completes the initial task allocation and sends the initial instruction. The acquisition and transmission unit periodically collects equipment operating parameters and feeds them back to the control terminal. The decision control unit uses the Laida criterion to eliminate outlier parameters and integrates the time decay term with the negative... The efficiency decay model of load attenuation term predicts the expected production capacity of equipment within the production time limit, compares the initial task volume to locate the equipment to be adjusted, calculates the theoretical target power and marks the incremental equipment whose theoretical target power exceeds the rated upper limit and the incremental equipment to be allocated; then, based on the power redundancy, production capacity redundancy coefficient and unit energy consumption coefficient of non-incremental equipment, the incremental equipment to be allocated is optimized and split into non-incremental equipment through dynamic coupling adaptation algorithm. The instruction generation unit generates secondary allocation instructions and transmits them to the adjustment and control unit. The oilfield controlled end controls the operation of equipment according to the instructions, realizing dynamic and efficient scheduling in multiple scenarios of oilfield, and solving the problems of unscientific task allocation, inaccurate production capacity prediction and disordered incremental allocation in traditional scheduling.

[0114] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A remote control scheduling system suitable for oilfield multi-scenarios, characterized in that, The control end and the oilfield controlled end are included: The control end allocates initial task quantity according to the production task quantity and production time limit input by the user, generates and sends initial allocation instructions, receives device operation parameters, obtains device operation efficiency through the efficiency decay model of the fusion of time decay and load decay, calculates the expected production capacity within the production time limit, compares the initial task quantity to locate the device to be adjusted, selects the incremental device based on the difference between the theoretical target power and the upper limit of the rated power of the device to be adjusted, combines the total amount of the incremental task that cannot be completed within the production time limit, calculates the power redundancy, the production capacity redundancy coefficient and the unit energy consumption coefficient of the non-incremental device, completes the secondary allocation of the incremental task through the dynamic coupling algorithm, generates the theoretical target power and sends the secondary allocation instructions; The oilfield controlled end receives the initial allocation instructions and the secondary allocation instructions in turn and controls the corresponding device to operate at the corresponding power, collects and processes the device operation parameters and feeds back to the control end, wherein the oilfield controlled end includes a collection and sending unit; The predicted productivity of the equipment within the production time limit is calculated by combining the efficiency attenuation model, including: acquiring the running parameters of each equipment in the current sampling period , including the actual running power , the cumulative duration , and the completed productivity ; removing abnormal values in the running parameters of the equipment based on the Relyada criterion, and calculating the parameter mean of the previous three sampling periods to fill the vacancy after removing the abnormal values; considering the attenuation characteristics of the equipment running over time, constructing an exponential form of the efficiency attenuation model, combining the historical running data of the equipment, and fitting the efficiency attenuation coefficient by using the least square method to obtain the efficiency attenuation model that fits the actual efficiency attenuation law of the equipment , wherein, is the current equipment running efficiency after the equipment runs for a certain number of hours, that is, the efficiency ratio compared to the brand-new state, , is the initial efficiency of the equipment , is the cumulative running duration, is the equipment efficiency attenuation coefficient, indicating the attenuation rate caused by the running time, is the load attenuation coefficient, reflecting the amplification effect of the load on the attenuation, is the cumulative average power utilization rate; inputting the equipment running duration into the efficiency attenuation model to obtain the current equipment running efficiency, and calculating the power utilization rate of the equipment, multiplying the current equipment running efficiency by the power utilization rate, and then multiplying by the benchmark productivity to obtain the actual productivity of a single sampling period; determining the number of sampling periods contained in the remaining production time of the equipment, combining the actual productivity of a single sampling period and the current equipment running efficiency, calculating the cumulative productivity within the remaining production time of the equipment, and adding the completed productivity of the equipment to obtain the predicted productivity of the equipment within the entire production time limit; The theoretical target power of the device to be adjusted is calculated and the incremental device is located, including: according to the initial task quantity, production time limit, current device operation efficiency and baseline production capacity of the device, based on the equation relationship that the initial task quantity is equal to the actual production capacity accumulated in each sampling period within the production time limit, the theoretical target power is derived; compare the theoretical target power with the upper limit of the rated power of the device, if the theoretical target power is less than or equal to the upper limit of the rated power, generate control instructions to adjust the device operating power to the theoretical target power, if the theoretical target power is greater than the upper limit of the rated power, mark the device as an incremental device; The to-be-allocated increment is split to non-incremental devices through a dynamic coupling algorithm, including: adding the to-be-allocated increments of all devices to obtain the total amount of to-be-allocated increments; based on the power redundancy, production capacity redundancy coefficient and unit energy consumption coefficient of the non-incremental device, the dynamic coupling weight calculation is performed on the three load indexes through the dynamic coupling algorithm, and the task allocation proportion is determined according to the ratio of the dynamic coupling weight to the dynamic coupling weight of all non-incremental devices; add the incremental task to the initial task quantity of the non-incremental device, if the sum exceeds the maximum capacity of the device, allocate the excess part to other non-incremental devices in proportion, and repeat the calculation until the constraints are met.

2. The remote control and dispatch system for multiple scenarios in oilfield of claim 1, wherein, The least square method is used to fit the efficiency decay coefficient, the actual efficiency data of the device under different cumulative running time are collected, the efficiency decay model is substituted, the sum of squares of the error between the actual efficiency and the model calculated efficiency is minimized, and the efficiency decay coefficient suitable for the actual running state of the device is determined.

3. The remote control and dispatch system for multiple scenarios in oilfield of claim 1, wherein, The to-be-allocated increment is calculated, the power utilization rate corresponding to the upper limit of the rated power, the number of sampling periods contained in the production time limit, the baseline production capacity and the current efficiency are combined to calculate the maximum expected production capacity that the incremental device can complete within the entire production time limit when running at the upper limit of the rated power, and then the initial task quantity of the incremental device is subtracted from the maximum expected production capacity to obtain the difference, which is the to-be-allocated increment.

4. The remote control dispatch system for multiple scenarios in oilfield of claim 1, wherein, The initial task quantity is calculated based on the particle swarm algorithm, including the following steps: Get the total amount of the current period production task of the oilfield, the production time limit and the baseline production capacity, energy consumption and rated power upper limit of each device in the previous sampling period of each device; The initial task amount of each production device is defined and constitutes a decision variable vector, total amount constraints, single-device upper limit constraints and non-negative constraints are set, and a dual-objective optimization function for maximizing production efficiency and minimizing energy consumption is constructed; The optimization algorithm parameters are initialized, the particles are coded as corresponding task allocation schemes, the population size is set, the initial particle position and velocity are randomly generated, and the learning factor, inertia weight and maximum iteration number are initialized; The double-objective function values of each particle are calculated, non-dominated solutions are retained to form a Pareto optimal solution set, individual and global optimal solutions are updated, particle velocity and position are updated according to a preset formula, and task amount is adjusted when constraints are violated; When the maximum iteration number is reached or the objective function value no longer fluctuates, the Pareto optimal solution is scored by linear weighting method according to the actual demand and preset weight, and the optimal solution is selected as the initial task amount.

5. The remote control dispatch system for multiple scenarios in oilfield of claim 1, wherein, The power redundancy is the difference between the current operating power and the upper limit of the rated power, the capacity redundancy coefficient is the capacity of the device running at the upper limit of the rated power in the remaining time divided by the uncompleted task amount, and the unit energy consumption coefficient is the ratio of the baseline energy consumption of the non-incremental device to the unit capacity energy consumption, and then the ratio is divided by the current efficiency of the device.

6. The remote control dispatch system for multiple scenarios in oilfield of claim 1, wherein, Based on the Laplace criterion, abnormal values in the device operating parameters are removed, wherein the device operating parameters include actual operating power, cumulative operating time, and completed capacity, the mean and standard deviation of each parameter are calculated, and the parameters corresponding to the standard deviation exceeding the preset multiple of the mean are determined as abnormal values and removed.

7. The remote control dispatch system for multiple scenarios in oilfield of claim 1, wherein, The baseline capacity and baseline energy consumption of each production device in the previous sampling period are the actual capacity generated and the total amount of energy consumed when the production device runs continuously at the designed standard fixed power under normal production conditions.

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