Green electricity driven oilfield drilling equipment control method, system, equipment and storage medium

Abstract

This application provides a control method, system, equipment, and storage medium for green-electric-driven oilfield drilling and production equipment. Based on the mapping relationship between load power demand and formation structure, and operating condition information during oilfield drilling and production, the system performs rolling predictions of the drilling rig load power within future time windows to obtain the transient peak load power of the drilling rig's supercapacitor in the next operating cycle. When the transient peak value exceeds a preset compensation threshold, a power optimization model is used to constrain and schedule the transient load power of the supercapacitor, obtaining a confidence compensation amount for the transient load power in the supercapacitor. Under the transient operating conditions of oilfield drilling and production, an emergency adjustment factor for the drilling rig under transient conditions is determined using the confidence compensation amount and the drilling rig's rated power. Based on this emergency adjustment factor, the power allocation between the supercapacitor and diesel generator in the next control cycle of the drilling rig is confidently adjusted. Based on the above scheme, a balanced distribution of green electricity under the transient operating conditions of the drilling rig can be achieved.

Description

Technical Field

[0001] This application relates to the field of oilfield drilling and production technology, and more specifically, to a green electric drive control method, system, equipment, and storage medium for oilfield drilling and production equipment. Background Technology

[0002] Oilfield drilling and production is a crucial step in oil extraction. Drilling rigs drill deep underground, penetrating rock strata to reach oil-bearing formations. After drilling is complete, processes such as casing installation and cementing are used to create a stable wellbore. Subsequently, pumping units and other equipment are used to extract oil from underground and transport it to the surface for storage or transportation. Oilfield drilling and production requires precise technology and equipment support and is an important means of obtaining oil resources.

[0003] The existing power systems of oilfield drilling and production equipment employ a fixed-ratio allocation strategy rather than a dynamic optimization algorithm. This results in the equipment's inability to respond in real time to fluctuations in rig load and changes in green energy output, leading to a significant waste of wind and solar power resources. When the rig is under low load, the equipment maintains a fixed output ratio, causing a surplus of green energy that is then forced to be abandoned. Under transient conditions, the lack of a prediction-based power pre-allocation mechanism causes a lag in the response of the equipment, necessitating reliance on diesel generators for emergency power replenishment. Existing static scheduling models do not consider the correlation between formation characteristics and load power, resulting in a severe mismatch between power allocation and actual demand. Consequently, the overall green energy utilization rate of oilfield drilling and production equipment is insufficient, far below the theoretical absorption capacity. This also leads to frequent start-stop of diesel generators, significantly increasing maintenance costs and carbon emissions. Therefore, achieving a balanced allocation of green energy under transient drilling conditions has become a major challenge for the industry. Summary of the Invention

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

[0005] In a first aspect, this application provides a control method for green electric-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 based on green electric drive. The method includes:

[0006] Collect operating information of drilling rigs during oilfield drilling and production processes;

[0007] Extract the mapping correlation features between load power demand and formation structure during oilfield drilling and production. Based on the mapping correlation features, the load change gradient in the working condition information and the formation structure information of the oilfield, perform rolling prediction of the drilling rig load power in the future time window to obtain the transient peak value of the supercapacitor load power of the drilling rig in the next working cycle.

[0008] When the transient peak value is greater than the preset compensation threshold, a power optimization model is constructed with the goal of minimizing diesel generator intervention and maximizing green electricity utilization. The model includes power balance constraints, energy storage charge constraints, and equipment safe operation constraints. The power optimization model is used to constrain and schedule the transient load power of the supercapacitor to obtain the confidence compensation amount of the transient load power in the supercapacitor.

[0009] When the drilling rig is in the transient operating condition of oilfield drilling and production, 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. Then, based on the emergency adjustment factor, the power output of 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 green power output and load demand.

[0010] In some embodiments, extracting the mapping relationship between load power demand and formation structure during oilfield drilling and production specifically includes:

[0011] Obtain historical drilling and production data of the oilfield within a specified time period, and then extract multiple load power ranges from the historical drilling and production data;

[0012] Convolutional neural networks are used to extract the formation lithology image features of each oilfield drilling and production in historical drilling and production data, and then determine the mutual information value between each formation lithology image feature and each load power range.

[0013] The mapping relationship between load power demand and formation structure during oilfield drilling and production is determined by using all mutual information values.

[0014] In some embodiments, the rolling prediction of drilling rig load power within a future time window is performed based on the mapping correlation features, the load change gradient in the operating condition information, and the formation structure information of the oilfield, to obtain the transient peak value of the drilling rig's supercapacitor load power in the next operating cycle, specifically including:

[0015] Extract the load change gradient of the drilling rig in the current working cycle from the output power in the operating condition information;

[0016] Extract formation lithology images of the drilling rig in the next work cycle from the formation structure information of the oilfield;

[0017] By performing a rolling mapping between the formation lithology image and the mapping association feature, the load power demand curve of the drilling rig in the next working cycle is obtained;

[0018] The demand curve is time-series adjusted by the load change gradient, and then the transient peak value of the supercapacitor load power of the drilling rig in the next working cycle is selected from the time-series adjusted demand curve.

[0019] In some embodiments, 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:

[0020] Obtain the initial weighting coefficients of the supercapacitor and diesel generator in the drilling rig, as well as the response time under transient conditions;

[0021] Each initial weight coefficient is used as an adjustment weight in the power optimization model;

[0022] The response time is used as a constraint in the power optimization model;

[0023] The transient power demand of the supercapacitor 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.

[0024] In some embodiments, determining the emergency adjustment factor of the drilling rig under transient conditions using the confidence compensation amount and the rated power of the drilling rig specifically includes:

[0025] The power deficit percentage is determined by the confidence compensation amount and the rated power of the drilling rig;

[0026] The emergency adjustment factor of the drilling rig under transient conditions is determined based on the power deficit ratio.

[0027] In some embodiments, dynamically adjusting the power output of diesel generators 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 green electricity output and load demand specifically includes:

[0028] Obtain the initial weighting coefficients for the supercapacitor and diesel generator in the drilling rig;

[0029] The power allocation weights of the supercapacitor and diesel generator are obtained by updating each initial weight coefficient through the emergency adjustment factor.

[0030] By dynamically allocating the transient power gap between green electricity output and load demand through various power allocation weights, the output power of diesel generators and the compensation amount of supercapacitors are obtained.

[0031] In some embodiments, the operating condition information includes drilling pressure, torque, pump pressure, as well as the wind power and photovoltaic output power of the green power supply system and the state of charge of the energy storage system.

[0032] Secondly, this application provides a green electric drive oilfield drilling and production equipment control system, comprising:

[0033] The data acquisition module is used to collect operating condition information of the drilling rig during the oilfield drilling and production process;

[0034] The processing module is used to extract the mapping correlation features between load power demand and formation structure during oilfield drilling and production. Based on the mapping correlation features, the load change gradient in the working condition information and the formation structure information of the oilfield, the module performs rolling prediction of the drilling rig load power in the future time window to obtain the transient peak value of the supercapacitor load power of the drilling rig in the next working cycle.

[0035] The processing module is also used to construct a power optimization model that includes power balance constraints, energy storage charge constraints and equipment safety operation constraints when the transient peak value is greater than a preset compensation threshold, with the goal of minimizing diesel generator intervention and maximizing green electricity utilization. The power optimization model is then used to perform constraint scheduling on the transient load power of the supercapacitor to obtain the confidence compensation amount of the transient load power in the supercapacitor.

[0036] The execution module is used to determine the emergency adjustment factor of the drilling rig under transient conditions of drilling and production in the oil field by means of 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.

[0037] Thirdly, this application provides a computer device, which 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 above-described green electric drive oilfield drilling and production equipment control method.

[0038] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the aforementioned green electric drive oilfield drilling and production equipment control method.

[0039] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0040] This application provides a control method, system, equipment, and storage medium for green-electric-driven oilfield drilling and production equipment. The method involves collecting operating condition information of the drilling rig during the oilfield drilling and production process; extracting the mapping correlation features between load power demand and formation structure during the oilfield drilling and production process; and performing rolling predictions of the drilling rig load power within future time windows based on these mapping correlation features, the load change gradient in the operating condition information, and the oilfield formation structure information to obtain the transient peak value of the drilling rig's supercapacitor load power in the next operating cycle. When the transient peak value exceeds a preset compensation threshold, the method minimizes diesel generator intervention and maximizes green electricity utilization. To achieve the target efficiency, a power optimization model is constructed, which includes power balance constraints, energy storage charge constraints, and equipment safe operation constraints. The power optimization model is used to constrain and schedule the transient load power of the supercapacitor, thereby obtaining the confidence compensation amount of the transient load power in the supercapacitor. When the drilling rig is in the transient operating condition of oilfield drilling and production, 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. Then, based on the emergency adjustment factor, the power output of 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 green power output and load demand.

[0041] Therefore, in this application, when the drilling rig is in the transient operating condition of oilfield drilling and production, 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. Then, based on the emergency adjustment factor, the power output of diesel generators in the next control cycle of the drilling rig is dynamically adjusted, and the transient power gap between green energy output and load demand is compensated using supercapacitors. Firstly, determining the transient peak value yields the expected maximum power demand of the drilling rig in future operating cycles, thus providing a crucial power compensation decision basis for oilfield drilling and production equipment. This accurately identifies the power fluctuation characteristics of the drilling rig under different geological conditions, especially providing early warning for sudden power surges under special conditions such as drilling hard rock formations. This allows the energy storage system to prepare in advance and quickly initiate compensation when the actual power demand reaches the predicted peak, thereby effectively avoiding [further issues]. The traditional approach, which forces the backup power supply to be activated due to untimely response, significantly improves the adaptability of oilfield drilling and production equipment to transient conditions, creating favorable conditions for the continuous and stable supply of green electricity. Then, by determining the confidence compensation amount, the precise power support value required by the energy storage system can be obtained, thereby achieving intelligent and coordinated allocation of green electricity and diesel power generation. This satisfies transient demands without affecting the stable operation of oilfield drilling and production equipment. The equipment can automatically optimize the green electricity output ratio according to changes in operating conditions, significantly improving the absorption of clean energy while ensuring power supply reliability. Especially when dealing with sudden load fluctuations, it can significantly reduce the frequency of backup power supply activation, making the entire power supply system operate more smoothly and efficiently, while reducing the consumption of traditional energy. In summary, based on the above approach, a balanced distribution of green electricity can be achieved under transient drilling conditions. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is an exemplary flowchart of a green electric drive oilfield drilling and production equipment control method according to some embodiments of this application;

[0044] Figure 2 This is a schematic diagram illustrating the principle of oil extraction by a drilling rig according to some embodiments of this application;

[0045] Figure 3 This is a flowchart illustrating the process of determining the emergency adjustment factor according to some embodiments of this application;

[0046] Figure 4 This is a schematic diagram of the structure of a green electric drive oilfield drilling and production equipment control system according to some embodiments of this application;

[0047] Figure 5 This is a schematic diagram of the structure of a computer device for implementing a green electric drive oilfield drilling and production equipment control method according to some embodiments of this application. Detailed Implementation

[0048] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] refer to Figure 1 The figure is an exemplary flowchart of a green electric drive oilfield drilling and production equipment control method according to some embodiments of this application. The green electric drive oilfield drilling and production equipment control method mainly includes the following steps:

[0050] In step 101, the operating condition information of the drilling rig during the oilfield drilling and production process is collected.

[0051] It should be noted that, in this application, the operating condition information includes drill pressure, torque, pump pressure, as well as the wind power and photovoltaic output power of the green electricity power supply system and the state of charge of the energy storage system. Drill pressure is the vertical pressure component reflecting the interaction force between the drill bit and the formation, and can be used to determine the hardness of the current drilling formation; torque characterizes the rotational resistance torque of the drill string, and can be used to assess the formation friction characteristics and the working condition of the drill string; pump pressure reflects the resistance of the drilling fluid circulation system, and can be used to determine the cleanliness of the wellbore and the permeability of the formation; wind power output power refers to the actual output value of the current wind power generation unit; photovoltaic output power refers to the actual output value of the current solar power generation unit; and the state of charge of the energy storage refers to the real-time remaining percentage of the supercapacitor and battery pack.

[0052] In practice, multi-source data is synchronously acquired 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; pressure transmitters are installed at the mud pump outlet to obtain pump pressure; bidirectional energy meters are configured at the green energy system grid connection point to record wind and solar power output as the wind and solar power output of the green energy power supply system; the energy storage system reports its state of charge through the battery management system; all sensor signals are transmitted to the central controller via an industrial bus, with a sampling frequency of no less than 10Hz to ensure the capture of transient process characteristics; the acquisition system employs timestamp alignment and sliding window filtering technology to eliminate the impact of communication delays and signal noise between devices, thus providing the drilling pressure, torque, pump pressure, wind and solar power output of the green energy power supply system, and state of charge of the energy storage system as the drilling rig's operating condition information during oilfield drilling and production.

[0053] In some embodiments, reference Figure 2 The figure described herein is a schematic diagram illustrating the principle of oil extraction using a drilling rig according to some embodiments of this application. The figure shows a typical drilling rig consisting of a large crossbeam and a weight suspended from one end of the crossbeam. The other end of the crossbeam is connected to a rod, the lower end of which is connected to an oil pump located underground. When the weight falls due to gravity, it drives the rod and the oil pump downwards, thereby pumping oil up from the ground. When the weight reaches its lowest point, the rod springs upwards due to a spring or counterweight, driving the oil pump upwards, completing one pumping cycle. This mechanical movement is periodic, continuously extracting oil from underground to the surface. The figure also shows auxiliary equipment, such as motors and control boxes, used to drive and control the operation of the drilling rig.

[0054] In step 102, the mapping correlation features between load power demand and formation structure during oilfield drilling and production are extracted. Based on the mapping correlation features, the load change gradient in the working condition information and the formation structure information of the oilfield, the rolling prediction of the drilling rig load power in the future time window is performed to obtain the transient peak value of the load power of the drilling rig's supercapacitor in the next working cycle.

[0055] In some embodiments, the following steps can be used to extract the mapping relationship between load power demand and formation structure during oilfield drilling and production:

[0056] Obtain historical drilling and production data of the oilfield within a specified time period, and then extract multiple load power ranges from the historical drilling and production data;

[0057] Convolutional neural networks are used to extract the formation lithology image features of each oilfield drilling and production in historical drilling and production data, and then determine the mutual information value between each formation lithology image feature and each load power range.

[0058] The mapping relationship between load power demand and formation structure during oilfield drilling and production is determined by using all mutual information values.

[0059] It should be noted that in this application, the mapping correlation features; historical drilling and production data includes a time series dataset of working condition parameters such as drilling pressure, torque, and rotation speed recorded in past drilling and production operations; the load power range is a power range segment used to classify and identify typical working conditions; the formation lithology image features are digital representations reflecting rock properties; and the mutual information value is an indicator that quantifies the degree of correlation between formation features and power range.

[0060] In specific implementation, firstly, historical drilling and production data of the oilfield within a specified time period is obtained. Then, multiple load power intervals can be extracted from this historical data in the following way: The formation lithology images and output power curves for each oilfield drilling and production operation within a specified time period (defaulting to the most recent week) are obtained from the data warehouse of the oilfield drilling and production equipment. The set of output power curves for all formation lithology images is then used as historical drilling and production data. The output power curves are cleaned and standardized to eliminate outliers caused by sensor malfunctions. The maximum and minimum values ​​in the standardized output power curves are then statistically analyzed, and the interval between the minimum and maximum values ​​is used as the load power interval. After removing duplicate load power intervals, multiple load power intervals are obtained. Then, a convolutional neural network is used to extract the formation lithology image features of each oilfield drilling and production operation from the historical data. The mutual information values ​​between each formation lithology image feature and each load power interval can be determined in the following way: For each oilfield drilling and production operation... The process involves obtaining formation lithology images of oilfield drilling and production from historical drilling and production data. Multi-scale convolutional kernels are used to extract formation features at different levels within these images. For the core portion of the formation lithology images, an attention mechanism is employed to enhance the feature representation of key areas, thus refining the formation features as the formation lithology image features for oilfield drilling and production. This method yields the formation lithology image features for each oilfield drilling and production run. For each load power range, formation lithology image features within that range are selected from historical drilling and production data. Monte Carlo sampling is used to estimate the mutual information value between the load power range and the formation lithology image features. This method yields the mutual information value between each formation lithology image feature and each load power range. Finally, the mapping relationship between load power demand and formation structure during oilfield drilling and production is determined using all mutual information values. This can be achieved by using the set of all mutual information values ​​as the mapping relationship between load power demand and formation structure during oilfield drilling and production.

[0061] In some embodiments, the rolling prediction of 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 formation structure information of the oilfield, to obtain the transient peak value of the drilling rig's supercapacitor load power in the next operating cycle, can be achieved through the following steps:

[0062] Extract the load change gradient of the drilling rig in the current working cycle from the output power in the operating condition information;

[0063] Extract formation lithology images of the drilling rig in the next work cycle from the formation structure information of the oilfield;

[0064] By performing a rolling mapping between the formation lithology image and the mapping association feature, the load power demand curve of the drilling rig in the next working cycle is obtained;

[0065] The demand curve is time-series adjusted by the load change gradient, and then the transient peak value of the supercapacitor load power of the drilling rig in the next working cycle is selected from the time-series adjusted demand curve.

[0066] It should be noted that, in this application, the transient peak value reflects the critical moment that may trigger a power surge; the load change gradient represents the rate of change of the drilling rig's power demand over time, and this load change gradient can reflect the dynamic characteristics of the equipment's working state; the stratigraphic lithology image is a characteristic expression of geological parameters in the form of a two-dimensional matrix; and the load power demand curve is a continuous function curve of the power demand over time within the future operating cycle.

[0067] In specific implementation, firstly, extracting the load change gradient of the drilling rig in the current working cycle from the output power in the operating condition information can be achieved by: obtaining all the output power of the drilling rig in the current working cycle from the operating condition information, and then calculating the average of all gradient values ​​between adjacent output power as the load change gradient of the drilling rig in the current working cycle; secondly, extracting the formation lithology image of the drilling rig in the next working cycle from the formation structure information of the oilfield can be achieved by: inputting the formation structure information of the oilfield that needs to be drilled and produced in the next working cycle into the central control console of the oilfield drilling and production equipment, thereby generating the formation lithology image of the drilling rig in the next working cycle; then, performing rolling mapping between the formation lithology image and the mapping association features to obtain the load power demand curve of the drilling rig in the next working cycle can be achieved by: dividing the formation lithology image into multiple segmented formations, and mapping each segmented formation with historical mapping association features. The similarity matching (e.g., Euclidean distance) of the lithological image features of each stratum is performed. The load power interval corresponding to the lithological image feature with the largest similarity matching result is taken as the output power interval of the corresponding stratum segment. The median of each output power interval is arranged and connected according to the depth of each stratum segment to form the load power demand curve of the drilling rig in the next working cycle. Finally, the demand curve is time-series adjusted by the load change gradient. The transient peak value of the supercapacitor load power of the drilling rig in the next working cycle can be screened from the time-series adjusted demand curve. This can be achieved by: taking the load change gradient as the maximum change rate in the demand curve, performing time-series fitting on the demand curve, performing first-order difference on the time-series fitted demand curve, and screening the power point with the first-order difference value greater than the preset transient threshold as the transient peak value of the supercapacitor load power of the drilling rig in the next working cycle.

[0068] In step 103, when the transient peak value is greater than the preset compensation threshold, a power optimization model is constructed with the goal of minimizing diesel generator intervention and maximizing green electricity utilization. This model includes power balance constraints, energy storage charge constraints, and equipment safe operation constraints. The power optimization model is then used to constrain and schedule the transient load power of the supercapacitor to obtain the confidence compensation amount of the transient load power in the supercapacitor.

[0069] It should be noted that the power optimization model in this application is a mathematical programming model based on multi-objective optimization. This power optimization model achieves optimal energy scheduling of the power system of oilfield drilling and production equipment by establishing a multi-objective optimization function and constraint system. The power optimization model takes minimizing diesel engine usage and maximizing green electricity utilization as dual objectives, and constructs an optimization problem containing three types of core constraints: power balance constraints ensure that the total power output matches the load demand in real time; energy storage charge constraints limit the operation of supercapacitors and lithium batteries within the safe charging and discharging range; and equipment safe operation constraints specify the power adjustment range and ramp rate of each power unit. The power optimization model is solved using a mixed integer second-order cone programming method, and nonlinear constraints are handled through relaxation techniques and branch and bound algorithms. Finally, the optimal power allocation scheme of each power source is output. Robust optimization theory is introduced during the solution process to handle the uncertainty of wind and solar power generation, ensuring that the system can still meet the power supply reliability requirements under extreme operating conditions.

[0070] In some embodiments, the confidence compensation amount for the transient load power of the supercapacitor is obtained by constraining the scheduling of the transient load power using the power optimization model, which can be achieved through the following steps:

[0071] Obtain the initial weighting coefficients of the supercapacitor and diesel generator in the drilling rig, as well as the response time under transient conditions;

[0072] Each initial weight coefficient is used as an adjustment weight in the power optimization model;

[0073] The response time is used as a constraint in the power optimization model;

[0074] The transient power demand of the supercapacitor 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.

[0075] In specific implementation, firstly, obtaining the initial weighting coefficients of the supercapacitor and diesel generator in the drilling rig, as well as the response time under transient conditions, can be achieved as follows: The initial weighting coefficients of the supercapacitor and diesel generator in the drilling rig, as well as the response time under transient conditions, are obtained from the central control console of the oilfield drilling and production equipment. Secondly, each initial weighting coefficient is used as an adjustment weight in the power optimization model. Then, the response time is used as a constraint condition in the power optimization model. Finally, the power optimization model with set adjustment weights and constraints is used to evaluate the transient power demand of the supercapacitor's transient load power, thus obtaining the transient power demand of the supercapacitor. The confidence compensation for load power can be achieved as follows: A power optimization model with set adjustment weights and constraints can achieve multi-energy coordinated control through quantified weight coefficients and time constraints. This power optimization model uses initial weight coefficients (green electricity priority weight and diesel engine penalty weight) as the basic adjustment parameters of the objective function, embedding transient response time as a hard constraint into the optimization framework. During the solution process, the power optimization model first constructs a constraint system including power balance equations, energy storage dynamic equations, and equipment physical limitations. Then, a power optimization model predictive control algorithm with time-varying weight factors is used for rolling optimization. The response time constraint is handled using the Lagrange multiplier method to ensure that the supercapacitor's compensation calculation simultaneously meets the instantaneous compensation requirement for the power gap (response time ≤ 20ms) and the overall economic objective of the system. Therefore, the output of the power optimization model is used as the confidence compensation for transient load power in the supercapacitor. The final output 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.

[0076] In step 104, when the drilling rig is in the transient operating condition of oilfield drilling and production, 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. Then, based on the emergency adjustment factor, the power output of diesel generator in the next control cycle of the drilling rig is dynamically adjusted, and the transient power gap between green power output and load demand is compensated by supercapacitor.

[0077] It should be noted that, in this application, transient operating conditions refer to short-term, sudden extreme working conditions encountered by drilling rigs during oilfield drilling and production. These conditions are usually caused by sudden events such as encountering hard formations, wellbore abnormalities, or equipment start-up and shutdown. The characteristics of this transient operating condition are: within a time scale of milliseconds to seconds, the drilling rig load power experiences a momentary peak with an amplitude exceeding 30% of the rated value, accompanied by severe fluctuations in mechanical parameters such as drilling pressure and torque. Transient operating conditions can lead to power quality problems such as grid frequency flicker and DC bus voltage drop, which traditional diesel generators cannot effectively support due to response delays (usually 2-5 seconds).

[0078] In some embodiments, the emergency adjustment factor of the drilling rig under transient conditions is determined by the confidence compensation amount and the rated power of the drilling rig, with reference to... Figure 3 The diagram is a flowchart illustrating the determination of the emergency adjustment factor in some embodiments of this application. In this embodiment, the determination of the emergency adjustment factor can be achieved through the following steps:

[0079] In step 1041, the power deficit percentage is determined by the confidence compensation amount and the rated power of the drilling rig;

[0080] In step 1042, the emergency adjustment factor of the drilling rig under transient conditions is determined based on the power deficit ratio.

[0081] It should be noted that in this application, the emergency adjustment factor is a dimensionless coefficient reflecting the urgency of the system. The emergency adjustment factor can be used to dynamically adjust the aggressiveness of the control strategy. In specific implementation, firstly, the power gap ratio can be determined by the confidence compensation amount and the rated power of the drilling rig in the following way: the ratio of the confidence compensation amount to the rated power of the drilling rig is taken as the power gap ratio, which represents 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 conditions can be determined based on the power gap ratio in the following way: the sum of the power gap ratio and 1 is taken as the emergency adjustment factor of the drilling rig under transient conditions.

[0082] In some embodiments, the power output of diesel generators in the next control cycle of the drilling rig can be dynamically adjusted based on the emergency adjustment factor, and the transient power gap between green electricity output and load demand can be compensated using supercapacitors, which can be achieved through the following steps:

[0083] Obtain the initial weighting coefficients for the supercapacitor and diesel generator in the drilling rig;

[0084] The power allocation weights of the supercapacitor and diesel generator are obtained by updating each initial weight coefficient through the emergency adjustment factor.

[0085] By dynamically allocating the transient power gap between green electricity output and load demand through various power allocation weights, the output power of diesel generators and the compensation amount of supercapacitors are obtained.

[0086] In specific implementation, firstly, the initial weights of diesel power generation and green energy generation under transient conditions are obtained from the central control console of the oilfield drilling and production equipment. Then, the initial weight coefficients are updated using the emergency adjustment factor. The power allocation weights of the supercapacitor and diesel power generation can be achieved as follows: the ratio of the initial weight of diesel power generation to the power deficit ratio is used as the allocation weight of diesel power generation, and the product of the initial weight of green energy and the confidence compensation amount is used as the allocation weight of green energy. Finally, the maximum green energy output of the oilfield drilling and production equipment is obtained from the central control console, and the drilling rig load demand is then compared with... The difference between the maximum green power output and the load demand is used as the transient power gap between green power output and load demand. If the transient power gap is less than or equal to 0, it means that no additional compensation is needed. 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 quantity, and its response speed is controlled on the order of seconds. The supercapacitor directly compensates for the millisecond-level power gap through the power electronic converter.

[0087] In another aspect, in some embodiments, this application provides a green electric drive oilfield drilling and production equipment control system, referencing... Figure 4 The figure is a schematic diagram of the structure of a green electric driven oilfield drilling and production equipment control system according to some embodiments of this application. The green electric driven oilfield drilling and production equipment control system includes: a data acquisition module 201, a processing module 202, and an execution module 203, which are described below:

[0088] The data acquisition module 201 in this application is mainly used to collect the working condition information of the drilling rig during the oilfield drilling and production process;

[0089] Processing module 202, in this application, is used to extract the mapping correlation features between load power demand and formation structure during oilfield drilling and production. Based on the mapping correlation features, the load change gradient in the working condition information and the formation structure information of the oilfield, rolling prediction of the drilling rig load power in the future time window is performed to obtain the transient peak value of the load power of the drilling rig's supercapacitor in the next working cycle.

[0090] It should be noted that the processing module 202 is also used to construct a power optimization model that includes power balance constraints, energy storage charge constraints and equipment safety operation constraints when the transient peak value is greater than the preset compensation threshold, with the goal of minimizing diesel generator intervention and maximizing green electricity utilization. The power optimization model is used to perform constraint scheduling on the transient load power of the supercapacitor to obtain the confidence compensation amount of the transient load power in the supercapacitor.

[0091] The execution module 203 in this application is mainly used to determine the emergency adjustment factor of the drilling rig under the transient working condition of drilling and production in the oil field by means of 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.

[0092] The foregoing has detailed examples of green-electric-driven oilfield drilling and production equipment control methods, systems, devices, and storage media provided in the embodiments of this application. It is understood that the corresponding apparatus, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0093] In some embodiments, this application also provides a computer device, the computer device including a memory, a processor and a communication unit, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device executes the above-described green electric driven oilfield drilling and production equipment control method.

[0094] In some embodiments, reference Figure 5 The dashed lines in the figure indicate that the unit or module is optional. This figure is a structural schematic diagram of a computer device for implementing a green electric drive oilfield drilling and production equipment control method according to an embodiment of this application. The green electric drive oilfield drilling and production equipment control method described in the above embodiments can be achieved through… Figure 5 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device may be a terminal device, a server or a chip.

[0095] Processor 301 can be a general-purpose processor or a special-purpose processor. For example, processor 301 can be a central processing unit (CPU), which can be used to control computer devices, execute software programs, and process data from software programs. The computer device may also include a communication unit 305 for inputting (receiving) and outputting (transmitting) signals.

[0096] For example, the computer device may be a chip, and the communication unit 305 may be the input and / or output circuit of the chip, or the communication unit 305 may be the communication interface of the chip, which may be a component of a terminal device, network device or other device.

[0097] For 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.

[0098] The computer device may include one or more memories 302 storing a program 304. The program 304 can be executed by a processor 301 to generate instructions 303, causing the processor 301 to execute the method described in the above method embodiments according to the instructions 303. Optionally, the memory 302 may also store data (such as a target audit model). Optionally, the processor 301 may also read data stored in the memory 302, which may be stored at the same storage address as the program 304, or it may be stored at a different storage address than the program 304.

[0099] The processor 301 and memory 302 can be configured separately or integrated together, for example, integrated on the system on chip (SOC) of the terminal device.

[0100] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software 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 gate, transistor logic devices, or discrete hardware components.

[0101] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] For example, in some embodiments, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described green electric drive oilfield drilling and production equipment control method.

[0103] 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.

[0104] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A control method for green electric-driven oilfield drilling and production equipment, wherein the oilfield drilling and production equipment includes a supercapacitor and a diesel generator, the supercapacitor being a power compensation device based on green electric drive, characterized in that, The method includes the following steps: Collect operating information of drilling rigs during oilfield drilling and production processes; Extract the mapping correlation features between load power demand and formation structure during oilfield drilling and production. Based on the mapping correlation features, the load change gradient in the working condition information and the formation structure information of the oilfield, perform rolling prediction of the drilling rig load power in the future time window to obtain the transient peak value of the supercapacitor load power of the drilling rig in the next working cycle. When the transient peak value is greater than the preset compensation threshold, a power optimization model is constructed with the goal of minimizing diesel generator intervention and maximizing green electricity utilization. The model includes power balance constraints, energy storage charge constraints, and equipment safe operation constraints. The power optimization model is used to constrain and schedule the transient load power of the supercapacitor 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 oilfield 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. Then, based on the emergency adjustment factor, the power output of 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 green power output and load demand. Specifically, the rolling prediction of 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 formation structure information of the oilfield, yields the transient peak value of the drilling rig's supercapacitor load power in the next operating cycle, including: Extract the load change gradient of the drilling rig in the current working cycle from the output power in the operating condition information; Extract formation lithology images of the drilling rig in the next work cycle from the formation structure information of the oilfield; By performing a rolling mapping between the formation lithology image and the mapping association feature, the load power demand curve of the drilling rig in the next working cycle is obtained; The demand curve is time-series adjusted by the load change gradient, and then the transient peak value of the supercapacitor load power of the drilling rig in the next working cycle is selected from the time-series adjusted demand curve.

2. The method as described in claim 1, characterized in that, The specific features for extracting the mapping relationship between load power demand and formation structure during oilfield drilling and production include: Obtain historical drilling and production data of the oilfield within a specified time period, and then extract multiple load power ranges from the historical drilling and production data; Convolutional neural networks are used to extract the formation lithology image features of each oilfield drilling and production in historical drilling and production data, and then determine the mutual information value between each formation lithology image feature and each load power range. The mapping relationship between load power demand and formation structure during oilfield drilling and production is determined by using all mutual information values.

3. The method as described in claim 1, characterized in that, The power optimization model is used to constrain and schedule the transient load power of the supercapacitor, and the confidence compensation amount for the transient load power in the supercapacitor specifically includes: Obtain the initial weighting coefficients of the supercapacitor and diesel generator in the drilling rig, as well as the response time under transient conditions; Each initial weight coefficient is used as an adjustment weight in the power optimization model; The response time is used as a constraint in the power optimization model; The transient power demand of the supercapacitor 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.

4. The method as described in claim 1, characterized in that, Determining the emergency adjustment factor of the drilling rig under transient conditions using the confidence compensation amount and the rated power of the drilling rig specifically includes: The power deficit percentage is determined by the confidence compensation amount and the rated power of the drilling rig; The emergency adjustment factor of the drilling rig under transient conditions is determined based on the power deficit ratio.

5. The method as described in claim 1, characterized in that, Based on the aforementioned emergency adjustment factor, the power output of diesel generators in the next control cycle of the drilling rig is dynamically adjusted, and the transient power gap between green electricity output and load demand is compensated using supercapacitors, specifically including: Obtain the initial weighting coefficients for the supercapacitor and diesel generator in the drilling rig; The power allocation weights of the supercapacitor and diesel generator are obtained by updating each initial weight coefficient through the emergency adjustment factor. By dynamically allocating the transient power gap between green electricity output and load demand through various power allocation weights, the output power of diesel generators and the compensation amount of supercapacitors are obtained.

6. The method as described in claim 1, characterized in that, The operating condition information includes drilling pressure, torque, pump pressure, as well as the wind power and photovoltaic output power of the green power supply system and the state of charge of the energy storage system.

7. A green electric-driven oilfield drilling and production equipment control system, which uses the method described in any one of claims 1 to 6 to control the oilfield drilling and production equipment, characterized in that, The system includes: The data acquisition module is used to collect operating condition information of the drilling rig during the oilfield drilling and production process; The processing module is used to extract the mapping correlation features between load power demand and formation structure during oilfield drilling and production. Based on the mapping correlation features, the load change gradient in the working condition information and the formation structure information of the oilfield, the module performs rolling prediction of the drilling rig load power in the future time window to obtain the transient peak value of the supercapacitor load power of the drilling rig in the next working cycle. The processing module is also used to construct a power optimization model that includes power balance constraints, energy storage charge constraints and equipment safety operation constraints when the transient peak value is greater than a preset compensation threshold, with the goal of minimizing diesel generator intervention and maximizing green electricity utilization. The power optimization model is then used to perform constraint scheduling on the transient load power of the supercapacitor to obtain the confidence compensation amount of the transient load power in the supercapacitor. The execution module is used to determine the emergency adjustment factor of the drilling rig under transient conditions of drilling and production in the oil field by means of 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.

8. 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 computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device executes the green electric drive oilfield drilling and production equipment control method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or code that, when executed on a computer, cause the computer to implement the green electric drive oilfield drilling and production equipment control method as described in any one of claims 1 to 6.

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