Control method, system and equipment for green-electricity-driven oil field drilling and production equipment and storage medium

Abstract

The invention provides a green-electricity-driven oil field drilling and production equipment control method, system and equipment and a storage medium, and the method comprises the steps: carrying out the rolling prediction of the load power of a drilling machine in a future time window based on the mapping correlation characteristics between a load power demand and a stratum structure and the working condition information in an oil field drilling and production process; the transient peak value of the load power of the super capacitor of the drilling machine in the next operation period is obtained; when the transient peak value is greater than a preset compensation threshold value, performing constraint scheduling on the transient load power of the super capacitor by using a power optimization model to obtain a confidence compensation amount of the transient load power in the super capacitor; and when the drilling machine is in an oil field drilling and production transient working condition, an emergency regulation factor of the drilling machine in the transient working condition is determined through the confidence compensation amount and the rated power of the drilling machine, and then power distribution of the super capacitor and diesel power generation in the next control period of the drilling machine is subjected to confidence regulation based on the emergency regulation factor. Based on the scheme, balanced green power distribution under the transient working condition of the drilling machine can be realized.

Description

Technical Field

[0001] The present application relates to the field of oilfield drilling and production technology, and more specifically, to a green electricity-driven oilfield drilling and production equipment control method, system, equipment and storage medium. Background Art

[0002] Oilfield drilling is a critical step in oil extraction. Drilling rigs bore holes deep underground, penetrating rock formations and reaching oil-bearing strata. After drilling is complete, casing and cementing are performed to create a stable wellbore. Subsequently, equipment such as pumping units are used to extract the oil from the ground and transport it to the surface for storage or transportation. Oilfield drilling requires precise technology and equipment and is a crucial means of obtaining oil resources.

[0003] The power system of existing oilfield drilling and production equipment adopts a fixed ratio allocation strategy rather than a dynamic optimization algorithm. The oilfield drilling and production equipment cannot respond to the fluctuations in drilling rig load and changes in green power output in real time, resulting in a large amount of waste of wind and solar power generation resources. When the drilling rig is in a low-load period, the oilfield drilling and production equipment still maintains a fixed output ratio, resulting in an excess of green power and forced power abandonment; under transient conditions, due to the lack of a prediction-based power pre-allocation mechanism, the oilfield drilling and production equipment has a delayed response and has to rely on diesel engines for emergency power replenishment; the existing static scheduling model does not consider the correlation between formation characteristics and load power, resulting in a serious mismatch between power allocation and actual demand, which in turn causes insufficient comprehensive green power utilization of oilfield drilling and production equipment, far below the theoretically absorbable level, and at the same time causes frequent start and stop of diesel engines, significantly increasing operation and maintenance costs and carbon emissions. Therefore, how to achieve balanced distribution of green power under transient conditions of drilling rigs has become a difficult problem facing the industry. Summary of the Invention

[0004] The present application provides a green electricity-driven oilfield drilling and production equipment control method, system, equipment and storage medium, which can achieve balanced distribution of green electricity under transient working conditions of the drilling rig.

[0005] In a first aspect, the present application provides a control method for green-electricity-driven oilfield drilling and production equipment, wherein the oilfield drilling and production equipment includes a supercapacitor and a diesel generator, and the supercapacitor is a power compensation device driven by green electricity. The method includes: Collect the working condition information of the drilling rig during the drilling and production process in the oil field; Extracting the mapping correlation features between the load power demand and the stratum structure during the oilfield drilling process, and performing a rolling prediction of the drilling rig load power within a future time window based on the mapping correlation features, the load change gradient in the operating condition information, and the stratum structure information of the oilfield, to obtain the transient peak value of the load power of the drilling rig's supercapacitor in the next operating cycle; When the transient peak value is greater than a preset compensation threshold, a power optimization model is constructed that includes power balance constraints, energy storage charge constraints, and equipment safety operation constraints, with the goal of minimizing diesel generator intervention and maximizing green power utilization. The power optimization model is used to constrain the transient load power of the supercapacitor and obtain a confidence compensation amount for the transient load power in the supercapacitor; When the drilling rig is in a transient operating condition of oil field drilling, the emergency adjustment factor of the drilling rig under the transient operating condition is determined by the confidence compensation amount and the rated power of the drilling rig, and then the power output of the diesel generator in the next control cycle of the drilling rig is dynamically adjusted based on the emergency adjustment factor, and the supercapacitor is used to compensate for the transient power gap between the green power output and the load demand.

[0006] In some embodiments, extracting the mapping correlation features between load power demand and formation structure during oilfield drilling specifically includes: Obtaining historical drilling data of the oil field within a specified time period, and then extracting multiple load power intervals from the historical drilling data; A convolutional neural network is used to extract the lithologic image features of each oilfield drilling operation from historical drilling data, and then the mutual information value between each lithologic image feature and each load power interval is determined. The mapping correlation characteristics between load power demand and formation structure during oilfield drilling and production are determined through all mutual information values.

[0007] In some embodiments, a rolling prediction of the drilling rig load power in a future time window is performed based on the mapping association feature, the load change gradient in the working condition information, and the stratum structure information of the oil field, to obtain the transient peak value of the load power of the drilling rig's supercapacitor in the next operation cycle, specifically including: Extracting the load change gradient of the drilling rig in the current operation cycle from the output power in the working condition information; Extracting the formation lithology image of the drilling rig in the next operation cycle from the formation structure information of the oil field; Perform rolling mapping on the formation lithology image and the mapping association feature to obtain a load power demand curve of the drilling rig in the next operation cycle; The demand curve is time-series adjusted according to the load change gradient, and then the transient peak value of the load power of the supercapacitor of the drilling rig in the next operation cycle is screened out from the demand curve after time-series adjustment.

[0008] In some embodiments, using the power optimization model to constrain the transient load power of the supercapacitor to obtain the confidence compensation amount of the transient load power in the supercapacitor specifically includes: Obtain the initial weight coefficients of the supercapacitor and diesel generator in the drilling rig and the response time under transient conditions; Using each initial weight coefficient as an adjustment weight in the power optimization model; Using the response time as a constraint in the power optimization model; The transient power demand of the supercapacitor's transient load power is evaluated using a power optimization model with set adjustment weights and constraints, and the confidence compensation amount of the transient load power in the supercapacitor is obtained.

[0009] In some embodiments, determining the emergency adjustment factor of the drilling rig under transient working conditions by using the confidence compensation amount and the rated power of the drilling rig specifically includes: Determine the power gap ratio by using the confidence compensation amount and the rated power of the drilling rig; An emergency adjustment factor of the drilling rig under transient working conditions is determined based on the power gap ratio.

[0010] In some embodiments, dynamically adjusting the power output of the diesel generator in the next control cycle of the drilling rig based on the emergency adjustment factor and using the supercapacitor to compensate for the transient power gap between the green power output and the load demand specifically includes: Obtain the initial weight coefficients of the supercapacitor and diesel generator in the drilling rig; The initial weight coefficients are updated by the emergency adjustment factor to obtain the power distribution weights of the supercapacitor and the diesel generator; The transient power gap between green power output and load demand is dynamically allocated through various power allocation weights to obtain the output power of diesel generators and the compensation amount of supercapacitors.

[0011] In some embodiments, the operating condition information includes drilling pressure, torque, pump pressure, and wind power and photovoltaic output power of the green power supply system and the charge state of the energy storage system.

[0012] In a second aspect, the present application provides a green electricity-driven oilfield drilling and production equipment control system, comprising: The acquisition module is used to collect the working condition information of the drilling rig during the drilling and production process in the oil field; a processing module for extracting mapping correlation features between load power demand and stratum structure during oilfield drilling and production, and performing a rolling prediction of the drilling rig load power within a future time window based on the mapping correlation features, the load change gradient in the operating condition information, and the stratum structure information of the oilfield, to obtain a transient peak value of the load power of the drilling rig's supercapacitor in the next operating cycle; The processing module is further configured to, when the transient peak value is greater than a preset compensation threshold, construct a power optimization model including power balance constraints, energy storage charge constraints, and equipment safety operation constraints with the goal of minimizing diesel generator intervention and maximizing green electricity utilization, and use the power optimization model to constrain the transient load power of the supercapacitor to obtain a confidence compensation amount for the transient load power in the supercapacitor; The execution module is used to determine the emergency adjustment factor of the drilling rig under the transient working condition of the drilling rig in the oil field drilling and production by using the confidence compensation amount and the rated power of the drilling rig, and then dynamically adjust the power output of the diesel generator in the next control cycle of the drilling rig based on the emergency adjustment factor, and use the supercapacitor to compensate for the transient power gap between the green power output and the load demand.

[0013] In a third aspect, the present application provides a computer device comprising at least one processor, a memory, and at least one communication unit, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned green electricity-driven oilfield drilling and production equipment control method.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions or codes. When the instructions or codes are run on a computer, the computer implements the above-mentioned green electricity-driven oilfield drilling and production equipment control method.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The present application provides a green electricity driven oil field drilling equipment control method, system, equipment and storage medium, which collects the working condition information of the drilling rig during the oil field drilling process; extracts the mapping correlation characteristics between the load power demand and the stratum structure during the oil field drilling process, and performs a rolling prediction of the drilling rig load power in the future time window based on the mapping correlation characteristics, the load change gradient in the working condition information and the stratum structure information of the oil field, and obtains the transient peak value of the load power of the supercapacitor of the drilling rig in the next operation cycle; when the transient peak value is greater than the preset compensation threshold, the diesel generator intervention is minimized and the green electricity utilization is maximized. With the target of the oil and gas rate, a power optimization model including power balance constraints, energy storage charge constraints and equipment safe operation constraints is constructed. The transient load power of the supercapacitor is constrained and scheduled using the power optimization model to obtain the confidence compensation amount of the transient load power in the supercapacitor. When the drilling rig is in the transient working condition of oil field drilling, the emergency adjustment factor of the drilling rig under the transient working condition is determined by the confidence compensation amount and the rated power of the drilling rig. Then, based on the emergency adjustment factor, the power output of the diesel generator in the next control cycle of the drilling rig is dynamically adjusted, and the supercapacitor is used to compensate for the transient power gap between the green power output and the load demand.

[0016] It can be seen that in this application, when the drilling rig is in a transient working condition of oil field drilling and production, the emergency adjustment factor of the drilling rig under the transient working condition is determined by the confidence compensation amount and the rated power of the drilling rig, and then the power output of the diesel generator in the next control cycle of the drilling rig is dynamically adjusted based on the emergency adjustment factor, and the supercapacitor is used to compensate for the transient power gap between the green power output and the load demand; first, the transient peak value is determined to obtain the expected maximum power demand of the drilling rig in the future operation cycle, thereby providing a key power compensation decision basis for oil field drilling and production equipment, and can accurately identify the power fluctuation characteristics of the drilling rig under different geological conditions, especially for the power surge under special working conditions such as drilling in hard rock formations, so that the energy storage system can be prepared to respond in advance and quickly start compensation when the actual power demand reaches the predicted peak, thereby effectively avoiding The situation in which the backup power supply is forced to start due to untimely response in the traditional solution, thereby significantly improving the adaptability of oilfield drilling and production equipment to transient working conditions, creating favorable conditions for the continuous and stable power supply of green electricity; then, by determining the confidence compensation amount, the precise power support value that the energy storage system needs to provide can be obtained, thereby realizing the intelligent coordinated allocation of green electricity and diesel power generation, which can not only meet transient needs but also will not affect the stable operation of oilfield drilling and production equipment. The oilfield drilling and production equipment can automatically optimize the green power output ratio according to changes in working conditions, while ensuring the reliability of power supply and greatly improving the level of clean energy absorption. Especially when dealing with sudden load fluctuations, it can significantly reduce the startup frequency of the backup power supply, making the operation of the entire power supply system more stable and efficient, while reducing the consumption of traditional energy; in summary, based on the above solution, the balanced distribution of green electricity under transient working conditions of the drilling rig can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 is an exemplary flow chart of a method for controlling oilfield drilling and production equipment driven by green electricity according to some embodiments of the present application; Figure 2 It is a schematic diagram of the principle of oil pumping by a drilling rig shown in some embodiments of the present application; Figure 3 is a schematic diagram of a process for determining an emergency adjustment factor according to some embodiments of the present application; Figure 4 This is a schematic structural diagram of a green electricity-driven oilfield drilling and production equipment control system according to some embodiments of the present application; Figure 5 It is a structural diagram of a computer device for implementing a green electricity-driven oilfield drilling and production equipment control method according to some embodiments of the present application. DETAILED DESCRIPTION

[0019] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0020] refer to Figure 1 , which is an exemplary flow chart of a method for controlling oilfield drilling and production equipment driven by green electricity according to some embodiments of the present application. The method for controlling oilfield drilling and production equipment driven by green electricity mainly includes the following steps: In step 101, operating condition information of a drilling rig during oilfield drilling is collected.

[0021] It should be noted that, in this application, the operating condition information includes drilling pressure, torque, pump pressure, as well as the wind power and photovoltaic output power of the green electricity power supply system and the charge state of the energy storage system, among which the drilling pressure is the vertical pressure component reflecting the interaction force between the drill bit and the formation, and the drilling pressure can be used to judge the hardness of the current drilled formation; the torque represents the rotational resistance torque of the drill string, and the torque can be used to evaluate the friction characteristics of the formation and the working state of the drill tool; the pump pressure reflects the resistance of the drilling fluid circulation system, and the pump pressure can be used to judge the cleanliness of the wellbore and the permeability of the formation; the wind power output power refers to the actual output value of the current wind power generation unit; the photovoltaic output power refers to the actual output value of the current solar power generation unit; the energy storage charge state refers to the real-time remaining power percentage of the supercapacitor and the battery pack.

[0022] In specific implementation, multi-source data is collected synchronously through a sensor network deployed at key nodes of the drilling rig. Strain gauge drill pressure sensors and torque meters are installed in the drill string system to monitor drill pressure and torque in real time. A pressure transmitter is set at the mud pump outlet to obtain pump pressure. A bidirectional electricity meter is configured at the green power system grid connection point to record wind and solar power output as the wind power and photovoltaic output power of the green power supply system. The energy storage system reports the charge status through the battery management system. All sensor signals are transmitted to the central controller via the industrial bus, with a sampling frequency of no less than 10Hz to ensure the capture of transient process characteristics. The acquisition system uses timestamp alignment and sliding window filtering technology to eliminate the influence of communication delays and signal noise between devices, thereby combining drill pressure, torque, pump pressure, wind power and photovoltaic output power of the green power supply system, and the charge status of the energy storage system as the operating condition information of the drilling rig during oilfield drilling and production.

[0023] In some embodiments, reference Figure 2 The figure is a schematic diagram of the principle of oil extraction using a drilling rig according to some embodiments of the present application. The figure shows a typical drilling rig, which consists of a large beam and a weight suspended from one end of the beam. The other end of the beam is connected to a rod, the lower end of which is connected to a downhole oil pump. When the weight drops due to gravity, it drives the rod and the oil pump downward, thereby pumping up the oil from the ground. When the weight reaches its lowest point, the rod rebounds upward due to the action of a spring or counterweight, driving the oil pump upward, completing a pumping cycle. This mechanical movement is cyclical, continuously drawing oil from the ground to the surface. The figure also shows some auxiliary equipment, such as a motor and control box, used to drive and control the operation of the drilling rig.

[0024] In step 102, the mapping correlation characteristics between the load power demand and the stratum structure during the oil field drilling process are extracted, and a rolling prediction of the drilling rig load power in the future time window is performed based on the mapping correlation characteristics, the load change gradient in the working condition information, and the stratum structure information of the oil field, to obtain the transient peak value of the load power of the drilling rig's supercapacitor in the next operation cycle.

[0025] In some embodiments, extracting the mapping correlation features between load power demand and stratum structure during oilfield drilling and production can be achieved by using the following steps: Obtaining historical drilling data of the oil field within a specified time period, and then extracting multiple load power intervals from the historical drilling data; A convolutional neural network is used to extract the lithologic image features of each oilfield drilling operation from historical drilling data, and then the mutual information value between each lithologic image feature and each load power interval is determined. The mapping correlation characteristics between load power demand and formation structure during oilfield drilling and production are determined through all mutual information values.

[0026] It should be noted that, in this application, mapping association features; historical drilling data includes a time series data set of operating parameters such as drilling pressure, torque, and speed recorded in past drilling operations; load power interval is a power range segmentation used to classify and identify typical operating conditions; formation lithologic image features are digital representations reflecting rock properties; mutual information value is an indicator that quantifies the degree of correlation between formation characteristics and power intervals.

[0027] In specific implementation, first, historical drilling data of oil field drilling within a specified time period is obtained, and then multiple load power intervals are extracted from the historical drilling data. This can be achieved in the following manner: obtaining the formation lithology image and output power curve of each oil field drilling within a specified time period (the default is the most recent week) from the data warehouse of the oil field drilling equipment, so that the set of output power curves of all formation lithology images is used as the historical drilling data; the output power curve is cleaned and standardized to eliminate the abnormal values ​​caused by sensor failure, and the maximum and minimum values ​​in the output power curve after standardization are counted, and the interval from the minimum value to the maximum value is used as the load power interval, so that multiple load power intervals can be obtained after deduplication of all load power intervals; then, a convolutional neural network is used to extract the formation lithology image features of each oil field drilling in the historical drilling data, and then the mutual information value between each formation lithology image feature and each load power interval is determined. This can be achieved in the following manner: for each oil field drilling , obtain the lithologic image of oilfield drilling from historical drilling data, use multi-scale convolution kernel to extract the stratigraphic features of different layers in the stratigraphic lithologic image, and for the core part of the stratigraphic lithologic image, use the attention mechanism to enhance the feature expression ability of the key area to improve the stratigraphic features as the stratigraphic lithologic image features of oilfield drilling. Through the above method, the stratigraphic lithologic image features of each oilfield drilling can be obtained; for each load power interval, the stratigraphic lithologic image features in the load power interval are screened from the historical drilling data, and the mutual information value between the load power interval and the stratigraphic lithologic image features is estimated through Monte Carlo sampling. Through the above method, the mutual information value between each stratigraphic lithologic image feature and each load power interval can be obtained; finally, the mapping correlation feature between the load power demand and the stratigraphic structure during the oilfield drilling process is determined by all mutual information values. This can be achieved in the following way, that is, the set of all mutual information values ​​is used as the mapping correlation feature between the load power demand and the stratigraphic structure during the oilfield drilling process.

[0028] In some embodiments, a rolling prediction of the drilling rig load power in a future time window is performed based on the mapping association feature, the load change gradient in the operating condition information, and the stratum structure information of the oil field to obtain the transient peak value of the load power of the drilling rig's supercapacitor in the next operating cycle. The following steps can be used to achieve this: Extracting the load change gradient of the drilling rig in the current operation cycle from the output power in the working condition information; Extracting the formation lithology image of the drilling rig in the next operation cycle from the formation structure information of the oil field; Perform rolling mapping on the formation lithology image and the mapping association feature to obtain a load power demand curve of the drilling rig in the next operation cycle; The demand curve is time-series adjusted according to the load change gradient, and then the transient peak value of the load power of the supercapacitor of the drilling rig in the next operation cycle is screened out from the demand curve after time-series adjustment.

[0029] It should be noted that in this application, the transient peak reflects the critical moment that may cause an electric shock; the load change gradient represents an indicator of the rate at which the drilling rig power demand changes over time, and the load change gradient can reflect the dynamic characteristics of the equipment's working state; the stratum lithology image is a characteristic expression of geological parameters in the form of a visualized two-dimensional matrix; the load power demand curve is a continuous function curve of the power demand changing over time in the future operation cycle.

[0030] In specific implementation, first, extracting the load change gradient of the drilling rig in the current operation cycle from the output power in the working condition information can be achieved in the following manner, namely: obtaining all the output powers of the drilling rig in the current operation cycle from the working condition information, thereby calculating the average of all gradient values ​​between adjacent output powers as the load change gradient of the drilling rig in the current operation cycle; secondly, extracting the stratum lithology image of the drilling rig in the next operation cycle from the stratum structure information of the oil field can be achieved in the following manner, namely: inputting the stratum structure information that the oil field needs to drill in the next operation cycle into the central console of the oil field drilling and production equipment, thereby generating the stratum lithology image of the drilling rig in the next operation cycle; then, rolling mapping the stratum lithology image with the mapping association feature to obtain the load power demand curve of the drilling rig in the next operation cycle can be achieved in the following manner, namely: dividing the stratum lithology image into multiple segmented strata, and mapping each segmented stratum with the historical mapping association feature The similarity matching (for example, Euclidean distance) of each stratum lithologic image feature in each similarity matching result is performed, so that the load power interval corresponding to the stratum lithologic image feature with the largest similarity matching result is used as the output power interval of the corresponding segmented stratum, and the median values ​​of each output power interval are arranged and connected according to the depth of each segmented stratum as the load power demand curve of the drilling rig in the next operation cycle; finally, the demand curve is time-series adjusted by the load change gradient, and the transient peak value of the load power of the supercapacitor of the drilling rig in the next operation cycle is screened out from the demand curve after time-series adjustment. This can be achieved in the following manner, namely, the load change gradient is used as the maximum change rate in the demand curve, the demand curve is time-series fitted, and the first-order difference of the demand curve after time-series fitting is performed, so as to screen out the power point whose first-order difference value is greater than a preset transient threshold as the transient peak value of the load power of the supercapacitor of the drilling rig in the next operation cycle.

[0031] In step 103, when the transient peak is greater than a preset compensation threshold, a power optimization model including power balance constraints, energy storage charge constraints, and equipment safety operation constraints is constructed with the goal of minimizing diesel generator intervention and maximizing green electricity utilization. The power optimization model is used to constrain the transient load power of the supercapacitor to obtain a confidence compensation amount for the transient load power in the supercapacitor.

[0032] It should be noted that in this application, the power optimization model is a mathematical programming model based on multi-objective optimization. The power optimization model realizes the optimal energy scheduling of the power system of oilfield drilling and production equipment by establishing a multi-objective optimization function and a constraint system. The power optimization model takes minimizing the use of diesel engines and maximizing the utilization of green electricity as the dual objectives, and constructs an optimization problem containing three types of core constraints: the power balance constraint ensures that the total power generation output matches the load demand in real time; the energy storage charge constraint limits the supercapacitor and lithium battery to operate in a safe charging and discharging range; the equipment safe operation constraint stipulates the power adjustment range and climbing rate of each power supply unit; the power optimization model is solved by a mixed integer second-order cone programming method, and nonlinear constraints are processed through relaxation technology and branch and bound algorithm, and finally the optimal power allocation plan for each power source is output. The robust optimization theory is introduced in the solution process to deal with the uncertainty of wind and solar power generation to ensure that the system can still meet the power supply reliability requirements under extreme working conditions.

[0033] In some embodiments, the power optimization model is used to constrain the transient load power of the supercapacitor and obtain the confidence compensation amount of the transient load power in the supercapacitor by using the following steps: Obtain the initial weight coefficients of the supercapacitor and diesel generator in the drilling rig and the response time under transient conditions; Using each initial weight coefficient as an adjustment weight in the power optimization model; Using the response time as a constraint in the power optimization model; The transient power demand of the supercapacitor's transient load power is evaluated using a power optimization model with set adjustment weights and constraints, and the confidence compensation amount of the transient load power in the supercapacitor is obtained.

[0034] In the specific implementation, first, the initial weight coefficients of the supercapacitor and diesel generator in the drilling rig and the response time under transient conditions are obtained, which can be achieved in the following way: the initial weight coefficients of the supercapacitor and diesel generator in the drilling rig and the response time under transient conditions are obtained from the central console of the oil field drilling and production equipment; secondly, each initial weight coefficient is used as the adjustment weight in the power optimization model; then, the response time is used as the constraint condition in the power optimization model; finally, the power optimization model after setting the adjustment weight and constraint condition is used to evaluate the transient power demand of the supercapacitor's transient load power, and the transient load power of the supercapacitor is obtained. Confidence compensation for load power can be achieved using the following approach: a power optimization model with set adjustment weights and constraints enables coordinated control of multiple energy sources through quantized weight coefficients and time constraints. This power optimization model uses initial weight coefficients (green electricity priority and diesel engine penalty) as the fundamental adjustment parameters of the objective function and incorporates transient response time as a hard constraint into the optimization framework. During the solution process, the power optimization model first constructs a constraint system consisting of the power balance equation, the energy storage dynamic equation, and the physical limitations of the equipment. Then, a power optimization model predictive control algorithm with time-varying weight factors is used for rolling optimization. The response time constraint is addressed using the Lagrange multiplier method to ensure that the supercapacitor replenishment calculation meets both the instantaneous power gap compensation requirement (response time ≤ 20ms) and the overall system economic objectives. The output of the power optimization model is used as the confidence compensation for the transient load power in the supercapacitor. The resulting confidence compensation is the optimal solution that balances dynamic response characteristics and long-term operating costs. Its value is equal to the difference between the current predicted power gap and the real-time adjustable output of green electricity. Physical feasibility is ensured through feasibility verification.

[0035] In step 104, when the drilling rig is in a transient operating condition of oil field drilling, the emergency adjustment factor of the drilling rig under the transient operating condition is determined by the confidence compensation amount and the rated power of the drilling rig, and then the power output of the diesel generator in the next control cycle of the drilling rig is dynamically adjusted based on the emergency adjustment factor, and the supercapacitor is used to compensate for the transient power gap between the green power output and the load demand.

[0036] It should be noted that, in this application, transient operating conditions refer to short-term, sudden extreme working conditions encountered by the drilling rig during the drilling and production process in the oil field, which are usually caused by sudden situations such as drilling into hard formations, wellbore abnormalities, or equipment start-up and shutdown. The characteristics of this transient operating condition are: within the millisecond to second time scale, the drilling rig load power has an amplitude exceeding 30% of the rated value. At the same time, it is accompanied by drastic fluctuations in mechanical parameters such as drilling pressure and torque. Transient operating conditions will cause power quality problems such as grid frequency flicker and DC bus voltage drop. Traditional diesel generators cannot effectively support it due to response delays (usually 2-5 seconds).

[0037] In some embodiments, the emergency adjustment factor of the drilling rig under transient working conditions is determined by the confidence compensation amount and the rated power of the drilling rig, referring to Figure 3 The figure is a schematic diagram of a process for determining an emergency adjustment factor in some embodiments of the present application. In this embodiment, the emergency adjustment factor can be determined by the following steps: In step 1041, the power gap ratio is determined by using the confidence compensation amount and the rated power of the drilling rig; In step 1042, an emergency adjustment factor of the drilling rig under transient working conditions is determined based on the power gap ratio.

[0038] It should be noted that, in the present application, the emergency adjustment factor is a dimensionless coefficient reflecting the urgency of the system, and the emergency adjustment factor can be used to dynamically adjust the aggressiveness of the control strategy; in specific implementation, first, the power gap ratio is determined by the confidence compensation amount and the rated power of the drilling rig, which can be achieved in the following way, namely: the ratio of the confidence compensation amount to the rated power of the drilling rig is used as the power gap ratio, and the power gap ratio indicates the relative degree to which the transient power demand exceeds the normal power supply capacity of the system; then, the emergency adjustment factor of the drilling rig under transient working conditions is determined based on the power gap ratio, which can be achieved in the following way, namely: the sum of the power gap ratio and 1 is used as the emergency adjustment factor of the drilling rig under transient working conditions.

[0039] In some embodiments, dynamically adjusting the power output of the diesel generator in the next control cycle of the drilling rig based on the emergency adjustment factor and using supercapacitors to compensate for the transient power gap between the green power output and the load demand can be achieved by the following steps: Obtain the initial weight coefficients of the supercapacitor and diesel generator in the drilling rig; The initial weight coefficients are updated by the emergency adjustment factor to obtain the power distribution weights of the supercapacitor and the diesel generator; The transient power gap between green power output and load demand is dynamically allocated through various power allocation weights to obtain the output power of diesel generators and the compensation amount of supercapacitors.

[0040] In the specific implementation, first, the initial weights of diesel power generation and green electricity of the drilling rig under transient working conditions are obtained from the central console of the oilfield drilling and production equipment; then, the initial weight coefficients are updated through the emergency adjustment factor to obtain the power distribution weights of supercapacitors and diesel power generation. This can be achieved in the following way, namely: the ratio of the initial weight of diesel power generation to the power gap proportion is used as the distribution weight of diesel power generation, and the product of the initial weight of green electricity and the confidence compensation amount is used as the distribution weight of green electricity; finally, the maximum green electricity output of the oilfield drilling and production equipment is obtained from the central console of the oilfield drilling and production equipment, and the load demand of the drilling rig is compared with the power gap proportion. The difference in maximum green power output is used as the transient power gap between the green power output and the load demand. If the transient power gap is less than or equal to 0, no additional compensation is required. If the transient power gap is greater than 0, the product of the power allocation weight of the diesel generator and the transient power gap is used as the output power of the diesel generator, and the product of the power allocation weight of the supercapacitor and the transient power gap is used as the compensation amount of the supercapacitor. The output power of the diesel generator is achieved by adjusting the fuel injection amount, and its response speed is controlled at the second level; the supercapacitor directly compensates for the millisecond-level power gap through the power electronic converter.

[0041] In addition, in another aspect of the present application, in some embodiments, the present application provides a green electricity driven oil field drilling equipment control system, referring to Figure 4 This figure is a schematic structural diagram of a green-electricity-driven oilfield drilling and production equipment control system according to some embodiments of the present application. The green-electricity-driven oilfield drilling and production equipment control system includes: a collection module 201, a processing module 202, and an execution module 203, which are described as follows: The acquisition module 201 in this application is mainly used to collect the working condition information of the drilling rig during the oil field drilling process; Processing module 202, in the present application, is used to extract mapping association features between load power demand and stratum structure during oilfield drilling and production, and perform a rolling prediction of the drilling rig load power within a future time window based on the mapping association features, the load change gradient in the operating condition information, and the stratum structure information of the oilfield, to obtain the transient peak value of the load power of the drilling rig's supercapacitor in the next operating cycle; It should be noted that the processing module 202 is further configured to, when the transient peak value is greater than a preset compensation threshold, construct a power optimization model including power balance constraints, energy storage charge constraints, and equipment safety operation constraints with the goal of minimizing diesel generator intervention and maximizing green power utilization, and use the power optimization model to constrain the transient load power of the supercapacitor to obtain a confidence compensation amount for the transient load power in the supercapacitor; Execution module 203. In this application, execution module 203 is mainly used to determine the emergency adjustment factor of the drilling rig under transient conditions during oil field drilling and production through the confidence compensation amount and the rated power of the drilling rig, and then dynamically adjust the power output of the diesel generator in the next control cycle of the drilling rig based on the emergency adjustment factor, and use supercapacitors to compensate for the transient power gap between the green power output and the load demand.

[0042] The above describes in detail the examples of the green electricity driven oilfield drilling and production equipment control method, system, equipment and storage medium provided by the embodiments of the present application. It can be understood that in order to realize the above functions, the corresponding device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0043] In some embodiments, the present application also provides a computer device, which includes a memory, a processor and a communication unit, the memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device executes the above-mentioned green electricity-driven oilfield drilling and production equipment control method.

[0044] In some embodiments, reference Figure 5 The dotted line in the figure indicates that the unit or module is optional. The figure is a structural diagram of a computer device for implementing a green-electricity-driven oilfield drilling and production equipment control method according to an embodiment of the present application. The green-electricity-driven oilfield drilling and production equipment control method described in the above embodiment can be Figure 5 The computer device shown in the figure is implemented, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device can be a terminal device, a server or a chip.

[0045] The processor 301 may be a general-purpose processor or a dedicated processor. For example, the processor 301 may be a central processing unit (CPU), which may be used to control the computer device, execute software programs, and process data from the software programs. The computer device may also include a communication unit 305 for inputting (receiving) and outputting (transmitting) signals.

[0046] For example, the computer device may be a chip, the communication unit 305 may be an input and / or output circuit of the chip, or the communication unit 305 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other device.

[0047] For another example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0048] The computer device may include one or more memories 302, on which a program 304 is stored. The program 304 can be executed by the processor 301 to generate instructions 303, so that the processor 301 executes the method described in the above method embodiment according to the instructions 303. Optionally, data (such as a target audit model) can also be stored in the memory 302. Optionally, the processor 301 can also read data stored in the memory 302. The data can be stored at the same storage address as the program 304, or at a different storage address from the program 304.

[0049] The processor 301 and the memory 302 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of a terminal device.

[0050] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or software-based instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0051] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0052] For example, in some embodiments, the present application also provides a computer-readable storage medium, which stores instructions or codes. When the instructions or codes are run on a computer, the computer implements the above-mentioned green electricity-driven oilfield drilling and production equipment control method when executing.

[0053] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0054] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A control method for oilfield drilling equipment driven by green electricity, wherein the oilfield drilling equipment comprises a supercapacitor and a diesel generator, wherein the supercapacitor is a power compensation device driven by green electricity, characterized in that: The method comprises the following steps: Collect the working condition information of the drilling rig during the drilling and production process in the oil field; Extracting the mapping correlation features between the load power demand and the stratum structure during the oilfield drilling process, and performing a rolling prediction of the drilling rig load power within a future time window based on the mapping correlation features, the load change gradient in the operating condition information, and the stratum structure information of the oilfield, to obtain the transient peak value of the load power of the drilling rig's supercapacitor in the next operating cycle; When the transient peak value is greater than a preset compensation threshold, a power optimization model is constructed that includes power balance constraints, energy storage charge constraints, and equipment safety operation constraints, with the goal of minimizing diesel generator intervention and maximizing green power utilization. The power optimization model is used to constrain the transient load power of the supercapacitor and obtain a confidence compensation amount for the transient load power in the supercapacitor; When the drilling rig is in a transient operating condition of oil field drilling, the emergency adjustment factor of the drilling rig under the transient operating condition is determined by the confidence compensation amount and the rated power of the drilling rig, and then the power output of the diesel generator in the next control cycle of the drilling rig is dynamically adjusted based on the emergency adjustment factor, and the supercapacitor is used to compensate for the transient power gap between the green power output and the load demand.

2. The method according to claim 1, wherein Extracting the mapping correlation features between load power demand and stratum structure during oilfield drilling and production specifically includes: Obtaining historical drilling data of the oil field within a specified time period, and then extracting multiple load power intervals from the historical drilling data; A convolutional neural network is used to extract the lithologic image features of each oilfield drilling operation from historical drilling data, and then the mutual information value between each lithologic image feature and each load power interval is determined. The mapping correlation characteristics between load power demand and formation structure during oilfield drilling and production are determined through all mutual information values.

3. The method according to claim 1, wherein Based on the mapping association features, the load change gradient in the working condition information, and the stratum structure information of the oil field, a rolling prediction of the drilling rig load power in a future time window is performed to obtain the transient peak value of the load power of the supercapacitor of the drilling rig in the next operation cycle, specifically including: Extracting the load change gradient of the drilling rig in the current operation cycle from the output power in the working condition information; Extracting the formation lithology image of the drilling rig in the next operation cycle from the formation structure information of the oil field; Perform rolling mapping on the formation lithology image and the mapping association feature to obtain a load power demand curve of the drilling rig in the next operation cycle; The demand curve is time-series adjusted according to the load change gradient, and then the transient peak value of the load power of the supercapacitor of the drilling rig in the next operation cycle is screened out from the demand curve after time-series adjustment.

4. The method according to claim 1, wherein The power optimization model is used to constrain the transient load power of the supercapacitor to obtain the confidence compensation amount of the transient load power in the supercapacitor, specifically including: Obtain the initial weight coefficients of the supercapacitor and diesel generator in the drilling rig and the response time under transient conditions; Using each initial weight coefficient as an adjustment weight in the power optimization model; Using the response time as a constraint in the power optimization model; The transient power demand of the supercapacitor's transient load power is evaluated using a power optimization model with set adjustment weights and constraints, and the confidence compensation amount of the transient load power in the supercapacitor is obtained.

5. The method according to claim 1, wherein Determining the emergency adjustment factor of the drilling rig under transient working conditions by using the confidence compensation amount and the rated power of the drilling rig specifically includes: Determine the power gap ratio by using the confidence compensation amount and the rated power of the drilling rig; An emergency adjustment factor of the drilling rig under transient working conditions is determined based on the power gap ratio.

6. The method according to claim 1, wherein Dynamically adjusting the power output of the diesel generator in the next control cycle of the drilling rig based on the emergency adjustment factor and using supercapacitors to compensate for the transient power gap between the green power output and the load demand specifically includes: Obtain the initial weight coefficients of the supercapacitor and diesel generator in the drilling rig; The initial weight coefficients are updated by the emergency adjustment factor to obtain the power distribution weights of the supercapacitor and the diesel generator; The transient power gap between green power output and load demand is dynamically allocated through various power allocation weights to obtain the output power of diesel generators and the compensation amount of supercapacitors.

7. The method according to claim 1, wherein The working condition information includes drilling pressure, torque, pump pressure, as well as wind power and photovoltaic output power of the green power supply system and the charge state of the energy storage system.

8. A green electricity driven oilfield drilling equipment control system, characterized in that: include: The acquisition module is used to collect the working condition information of the drilling rig during the drilling and production process in the oil field; a processing module for extracting mapping correlation features between load power demand and stratum structure during oilfield drilling and production, and performing a rolling prediction of the drilling rig load power within a future time window based on the mapping correlation features, the load change gradient in the operating condition information, and the stratum structure information of the oilfield, to obtain a transient peak value of the load power of the drilling rig's supercapacitor in the next operating cycle; The processing module is further configured to, when the transient peak value is greater than a preset compensation threshold, construct a power optimization model including power balance constraints, energy storage charge constraints, and equipment safety operation constraints with the goal of minimizing diesel generator intervention and maximizing green electricity utilization, and use the power optimization model to constrain the transient load power of the supercapacitor to obtain a confidence compensation amount for the transient load power in the supercapacitor; The execution module is used to determine the emergency adjustment factor of the drilling rig under the transient working condition of the drilling rig in the oil field drilling and production by using the confidence compensation amount and the rated power of the drilling rig, and then dynamically adjust the power output of the diesel generator in the next control cycle of the drilling rig based on the emergency adjustment factor, and use the supercapacitor to compensate for the transient power gap between the green power output and the load demand.

9. A computer device, characterized in that: The computer device includes at least one processor, a memory, and at least one communication unit, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the green electricity-driven oilfield drilling and production equipment control method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions or codes, which, when executed on a computer, enable the computer to implement the green-electricity-driven oilfield drilling equipment control method according to any one of claims 1 to 7.

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