An oilfield ignition intelligent control system and method based on multi-source data

Abstract

The present application relates to the field of oilfield ignition intelligent control, more particularly to an oilfield ignition intelligent control system and method based on multi-source data, comprising a multi-source data acquisition module for acquiring multi-source data and storing the same in an oilfield ignition database; an ignition condition analysis module for calling data in the oilfield ignition database, training a model to generate a control strategy containing gas supply, ignition energy and combustion air ratio, and storing the control strategy in an ignition scheme library according to scenarios; an ignition scheme generation module for calling the ignition scheme library, screening out an adapted ignition scheme and marking the priority of the scheme; and an ignition execution scheduling module for displaying the matched ignition scheme to field operators to ensure the standardization of ignition operation; the present application provides precise data support for the whole process of oilfield ignition by integrating four types of multi-source data, effectively solves the problem of extensive strategy caused by scattered data in traditional ignition, and thus improves the safety and energy efficiency of ignition operation.

Description

An intelligent control system and method for oilfield ignition based on multi-source data Technical Field

[0001] This invention relates to the field of intelligent control of oilfield ignition, and more specifically to an intelligent control system and method for oilfield ignition based on multi-source data. Background Technology

[0002] In oilfield ignition operations, with the expansion of mining scale and the increasing complexity of the operating environment, traditional ignition control modes are gradually becoming inadequate to meet actual needs and have many prominent problems. Currently, ignition operations involve a variety of monitoring devices, and the data formats of flame status, gas parameters, etc., output by different devices are inconsistent and lack system integration. Operators need to spend a lot of time manually summarizing and analyzing the data, which is not only inefficient but also often results in data errors and omissions, affecting the scientific nature of the ignition plan.

[0003] Meanwhile, due to differences in collection dimensions and measurement standards, multi-source data cannot directly support intelligent model training, hindering the intelligent transformation of the ignition process. In the ignition strategy generation stage, relying on manual experience to formulate parameters such as gas supply and ignition energy is highly subjective and difficult to dynamically adapt to normal and special operating conditions, easily leading to ignition failure or resource waste, and even causing safety risks. When matching solutions, manual screening of suitable strategies from scattered records is time-consuming and prone to incompatibility due to insufficient experience, affecting ignition efficiency and safety. During execution, the communication of solutions relies on verbal or paper records, which is prone to misunderstandings and makes it difficult to ensure standardized operation; managers also cannot monitor the ignition progress in real time, and information lag reduces overall management efficiency.

[0004] Therefore, there is an urgent need for a system that can integrate multi-source operating condition data, unify data standards, intelligently generate control strategies, accurately match ignition schemes, standardize scheduling and execution processes, and achieve full-process information transparency. The multi-source data acquisition and storage, model training and generation strategies, scenario-based scheme matching, standardized execution scheduling, hierarchical early warning and information interaction steps of this invention specifically address the above-mentioned industry pain points and promote the intelligent, standardized, safe and efficient development of oilfield ignition operations. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides an intelligent control system and method for oilfield ignition based on multi-source data, so as to solve the problems existing in the background art.

[0006] The present invention provides the following technical solution: The present invention provides an intelligent control system for oilfield ignition based on multi-source data, including: a multi-source data acquisition module, used to acquire multi-source data under normal production and special working conditions, and store it in an oilfield ignition database;

[0007] The ignition condition analysis module is used to call data from the oilfield ignition database, use the ignition safety index as a constraint, train the model to generate control strategies that include gas supply, ignition energy and combustion air ratio, and store the control strategies in the ignition scheme library according to the scenario.

[0008] The ignition scheme generation module is used to capture the operating condition triggering conditions, call the ignition scheme library, filter out suitable ignition schemes, and mark the scheme priority.

[0009] The ignition execution scheduling module is used to display the matched ignition scheme to the on-site operators to ensure the standardization of ignition operation;

[0010] The intelligent ignition execution module is used to perform ignition operations, calculate execution deviations in real time, and determine model calculation abnormalities. When the deviation exceeds the threshold or a model calculation abnormality occurs, a graded early warning signal is triggered, and the early warning information is pushed to the ignition execution scheduling module simultaneously.

[0011] The information interaction module is used to communicate between the operation terminal and the monitoring terminal, allowing managers to view the ignition process status in real time.

[0012] The oilfield ignition database is used to store four types of multi-source data with scene tags collected by the multi-source data acquisition module;

[0013] The ignition scheme library is used to store control strategies generated by the ignition condition analysis module, including gas supply, ignition energy, and combustion air ratio, according to different scenarios.

[0014] The technical effects and advantages of this invention are as follows:

[0015] 1. This invention integrates four types of multi-source data—flame status, gas parameters, environmental interference, and equipment status—to construct an information system covering key dimensions of ignition safety, providing precise data support for the entire oilfield ignition process. Based on this, a fusion algorithm is used to train a model to generate control strategies. Combined with ignition safety index constraints, gas supply, ignition energy, and combustion air ratio are dynamically calculated. This effectively solves the problem of coarse strategies caused by fragmented data and experience-based decision-making in traditional ignition systems, reduces the risk of ignition failure and safety hazards caused by improper parameter adaptation, and improves the safety and energy efficiency of ignition operations.

[0016] 2. The ignition scheme generation module of this invention intelligently selects suitable strategies from the scheme library and marks their priorities based on the operating condition triggering conditions. The ignition execution scheduling module displays the schemes in a standardized form and returns the parameters. By constructing a scenario-based scheme library and standardized operating procedures, the inefficiency and bias of manual scheme selection are avoided, and the problem of non-standard operation caused by unclear information transmission in traditional execution is solved. This ensures the consistency and reliability of ignition operation under different operating conditions and improves the efficiency and quality stability of ignition operations.

[0017] 3. The information interaction module of this invention enables information exchange between the operation terminal and the monitoring terminal, allowing management personnel to monitor the ignition process status in real time; the intelligent ignition execution module calculates deviations and anomalies in real time and issues tiered warnings. Through full-process data interaction and intelligent early warning mechanisms, the information barriers between on-site and remote control in ignition operations are broken down, solving the problems of difficult progress tracking and delayed anomaly response in traditional models. This achieves transparent and intelligent control of the entire ignition operation process, helping oilfields optimize their ignition operation management system and enhance operational safety and efficiency. Attached Figure Description

[0018] Figure 1 is a schematic diagram of the structure of the present invention.

[0019] Figure 2 is a flowchart of the steps of the present invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The intelligent control system and method for oilfield ignition based on multi-source data involved in the present invention are not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Referring to Figure 1, this invention provides an intelligent oilfield ignition control system based on multi-source data, comprising:

[0022] The multi-source data acquisition module is used to collect multi-source data in both normal production and special operating conditions, and store it in the oilfield ignition database.

[0023] The ignition condition analysis module is used to call data from the oilfield ignition database, use the ignition safety index as a constraint, train the model to generate control strategies that include gas supply, ignition energy and combustion air ratio, and store the control strategies in the ignition scheme library according to the scenario.

[0024] The ignition scheme generation module is used to capture the operating condition triggering conditions, call the ignition scheme library, filter out suitable ignition schemes, and mark the scheme priority.

[0025] The ignition execution scheduling module is used to display the matched ignition scheme to the on-site operators to ensure the standardization of ignition operation;

[0026] The intelligent ignition execution module is used to perform ignition operations, calculate execution deviations in real time, and determine model calculation abnormalities. When the deviation exceeds the threshold or a model calculation abnormality occurs, a graded early warning signal is triggered, and the early warning information is pushed to the ignition execution scheduling module simultaneously.

[0027] The information interaction module is used to communicate between the operation terminal and the monitoring terminal, allowing managers to view the ignition process status in real time.

[0028] The oilfield ignition database is used to store four types of multi-source data with scene tags collected by the multi-source data acquisition module;

[0029] The ignition scheme library is used to store control strategies generated by the ignition condition analysis module, including gas supply, ignition energy, and combustion air ratio, according to different scenarios.

[0030] Referring to Figure 2, the specific implementation of the present invention includes the following steps:

[0031] S1: The multi-source data acquisition module simultaneously collects four types of multi-source data under normal production and special working conditions, and stores them in the oilfield ignition database with scene tags.

[0032] It needs to be explained that conventional production refers to a stable operating state in which the oilfield ignition operation area is free from adverse weather conditions, the gas supply pressure fluctuation is controlled within ±5% of the rated pressure, and there are no abnormal warnings for equipment operation. Special operating conditions refer to unconventional operating states that may affect ignition safety, such as instantaneous wind speeds exceeding 8m / s and severe thunderstorms, or impurity content in the gas exceeding 3%, temperature of key equipment components exceeding safety thresholds, and the presence of gas leak warnings.

[0033] The four types of multi-source data include: flame status data, covering flame height, flame core offset angle, and temperature field distribution; gas parameter data, including the volume percentage of each component in the gas, real-time flow rate, and supply pressure; environmental interference data, involving wind speed, wind direction, air humidity, and atmospheric pressure in the work area; and equipment status data, including igniter output power, electrode remaining life, gas regulating valve opening degree, and response speed.

[0034] The data acquisition methods are as follows: a combination of multiple types of sensors is deployed in the oilfield ignition operation area. The flame status is collected in collaboration with an infrared thermal imager and a high-definition industrial camera, both of which are triggered synchronously. The infrared thermal imager collects the flame temperature field distribution, while the high-definition industrial camera collects the flame morphology video stream. After analysis, the flame height and flame core offset angle are output. Gas parameters are monitored synchronously using a laser gas analyzer, a smart flow meter, and a pressure transmitter to measure the volume percentage of components such as methane and ethane, real-time flow rate, and supply pressure. Environmental interference data is obtained in real time from a weather station. Equipment status is collected through IoT sensors such as temperature, pressure, and vibration sensors. All sensor data is standardized and encoded before being transmitted to the oilfield ignition database via industrial Ethernet, ensuring that the data is stored synchronously with scene tags and timestamps.

[0035] S2: The ignition condition analysis module calls database data and uses the ignition safety index as a constraint to train the model to generate control strategies that include gas supply, ignition energy, and combustion air ratio, which are then stored in the ignition scheme library according to the scenario.

[0036] It needs to be explained that the ignition safety index calculation process is as follows: A four-dimensional evaluation model is constructed based on four types of multi-source data. The flame safety score is calculated by extracting three core indicators from the flame state data: flame height stability, flame core offset angle compliance, and temperature field distribution uniformity. Each indicator is weighted according to its impact on flame safety, and then the results are obtained through weighted summation. The gas safety score is calculated using the component volume ratio suitability, real-time flow stability, and supply pressure compliance rate from the gas parameter data as key evaluation items. Weights are assigned to each parameter based on their importance in gas safety assurance, and then the results are obtained through weighted summation. The environmental safety score is calculated by selecting the wind speed influence coefficient, wind direction suitability, air humidity tolerance, and atmospheric pressure stability from the environmental interference data as evaluation dimensions, and then assessing the impact of each dimension on environmental safety. The weights are summed to obtain the final result. The equipment safety score is calculated by extracting indicators such as igniter output power stability, electrode remaining life, regulating valve opening accuracy, and response speed compliance rate from the equipment status data. These indicators are weighted according to their actual impact on the safe operation of the equipment, and the equipment safety score is determined through weighted calculation. The ignition safety index is calculated using the formula: ignition safety index = t1 × flame safety score + t2 × gas safety score + t3 × environmental safety score + t4 × equipment safety score, where t1, t2, t3, and t4 are the weights of each safety score, satisfying t1 + t2 + t3 + t4 = 1. The weight values ​​are determined based on historical safety accidents and increase as the corresponding safety score decreases. The ignition safety index ranges from 0 to 100. When the ignition safety index is ≥ 80, it is considered to meet the ignition safety conditions.

[0037] It needs to be explained that the training model steps are as follows:

[0038] A1. Extract historical data with scene labels from the oilfield ignition database, classify them into normal working conditions and special working conditions, and divide each type of data into training set, validation set and test set in a 7:2:1 ratio. Standardize parameters such as flame height and flow rate.

[0039] A2. The framework is built using an attention mechanism + GRU model. The attention mechanism layer focuses on key influencing factors such as the proportion of gas components and wind speed, while the GRU layer learns the changing patterns of equipment state parameters over time.

[0040] A3. With an ignition safety index ≥ 80 as a constraint, set the objective function and iteratively optimize the model weights using gradient descent until the training set fitting error is ≤ 3%;

[0041] It should be explained that the objective function is: with an ignition safety index ≥ 80 as a hard constraint, minimizing the sum of squared deviations between the control parameters predicted by the model and the actual safe ignition parameters in the historical best operating condition, as shown in the formula: n is the training sample size. The representative model predicts the gas supply for the i-th sample group. This represents the actual gas supply value under the historical best operating conditions in the i-th sample group. The representative model predicts the ignition energy for the i-th sample group. This represents the actual ignition energy value of the historical best operating condition in the i-th sample group. The representative model predicts the combustion air ratio for the i-th sample group. The combustion air ratio represents the historical best operating condition value in the i-th sample group.

[0042] A4. Use the validation set to test the output stability of the model in different scenarios. When the strategy deviation under normal working conditions is ≤2% and the strategy deviation under special working conditions is ≤4%, the model output stability verification is completed and the next evaluation is carried out. If the standard is not met, return to step A2 to adjust the model framework and retrain until the stability requirements are met.

[0043] A5. Evaluate the model's generalization ability using the test set: Input test set data. If the model's strategy deviation is ≤2.5% under normal operating conditions and ≤4.5% under special operating conditions, and the ignition safety index prediction accuracy is ≥92%, then the model training is confirmed to be complete. If it does not meet the standard, return to step A2 to adjust the model parameters and re-iterate the training.

[0044] The gas supply calculation process is as follows:

[0045] a1. Calculate the basic gas flow demand Qbase: Based on the target flame height Htarget, the temperature field distribution uniformity Utemp requirement and the current atmospheric pressure Patm, and combined with the weighted average calorific value parameter Cgas of the gas components, calculate the basic gas flow demand Qbase through a thermodynamic model: Qbase=f(Htarget, Utemp, Patm, Cgas).

[0046] It should be explained that Qbase's calculation uses a weighted linear combination form, with the specific formula as follows:

[0047] Qbase=m1×f1(Htarget)+m2×f2(Utemp)+m3×f3(Patm)+m4×f4(Cgas),

[0048] m1, m2, m3 and m4 are the weighting coefficients of the target flame height Htarget, the temperature field distribution uniformity Utemp requirement, the current atmospheric pressure Patm and the weighted average calorific value parameter Cgas of the gas components, respectively, satisfying m1+m2+m3+m4=1. The weighting is determined based on historical safety accident data and energy efficiency experiments, giving priority to ensuring the influence of flame height and temperature field uniformity.

[0049] It needs further explanation that f1(Htarget) = k1 × Htarget, where k1 = 0.5m 3 / h / m, f1 is typically taken as 2-8m, and the basic flow rate increases by 0.5m for every 1m increase in height. 3 / h; f2(Utemp) = k2 × Utemp, k2 = 0.02m 3 / h / %, f2 is typically set at 80%-100%, and for every 1% increase in uniformity, the base flow rate increases by 0.02m³ / h. 3 / h; f3(Patm) = k3 × Patm, k3 = 0.01m 3 / h / kPa, for every 1kPa increase in pressure, the basic flow rate increases by 0.01m³ / h. 3 / h; f4(Cgas) = ​​k4 / Cgas, k4 = 0.005(m 3 / h)*(MJ / m 3 k4 is the baseline flow coefficient corresponding to unit calorific value, used to quantify the inverse adjustment relationship between calorific value and gas flow rate. For every 1 MJ / m³ increase in calorific value... 3 The more heat a unit volume of gas can provide, the less gas flow is required to achieve the same ignition heat demand.

[0050] It should be explained that in oilfield ignition operations, the gas composition refers to the mixture of gas components that participate in combustion, including combustible gases such as methane and ethane, as well as a small amount of impurities. The composition ratio and calorific value characteristics directly affect ignition safety and energy efficiency.

[0051] a2. Introduce component compatibility correction coefficient Kcomp: Based on gas safety, introduce component compatibility correction coefficient Kcomp. When the component compatibility is ≥95%, Kcomp=1.0. For every 5% decrease in compatibility, Kcomp decreases according to the preset curve.

[0052] Furthermore, to clarify the calculation logic of the preset curve, let the component compatibility be α, where α ∈ [0, 100%], derived from the gas safety score. The specific formula is: α = (Sgas - 30β - 20γ) / 50, where Sgas is the gas safety score, β is the real-time flow stability, calculated by (1 - |actual gas flow - rated gas flow| / rated gas flow) × 100%, the rated gas flow is the flow constraint based on the gas supply pressure fluctuation controlled within ±5% of the rated pressure in conventional production, and γ is the supply pressure compliance rate, calculated by (1 - |ΔPdev| / 100) × 100%, where ΔPdev represents the pressure fluctuation. The component compatibility α represents the degree of matching between the gas component and the ignition requirements, and its correspondence with Kcomp is defined as:

[0053] When 95% > α ≥ 70%, Kcomp = 1.0 - 0.008 × (95 - α);

[0054] When α < 70%, Kcomp = 0.6. Setting 0.6 as the minimum threshold avoids abnormal gas supply caused by excessively low adaptation. Through this preset curve, the quantitative calculation of Kcomp under different adaptation degrees can be realized to ensure the accuracy of gas supply correction.

[0055] a3. Calculate the pressure compensation coefficient Kpress: ∆Pdev represents the pressure fluctuation, and the calculation formula is: ∆Pdev=(Preal-Pnominal)÷Pnominal×100%, where Preal is the real-time pressure and Pnominal is the rated pressure.

[0056] a4. Calculate the final gas supply flow rate Qgas: Qgas = Qbase × Kcomp × Kpress.

[0057] The ignition energy calculation process is as follows:

[0058] b1. Calculate the basic ignition power Pbase: Pbase=g(Cgasb,Vign,Hhum), where Cgasb is the minimum ignition energy requirement corresponding to the gas composition, Vign is the preset target ignition frequency, and Hhum is the current ambient air humidity.

[0059] It should be explained that Pbase is calculated using a weighted linear combination, and the specific calculation formula is as follows:

[0060] Pbase=n1×Cgasb×Vign×(n3 / Hhum'),

[0061] Where n1 is the dimensionless conversion coefficient of the minimum ignition energy Cgasb of the gas component, and n3 is the influence coefficient of normalized air humidity Hhum', which is obtained by converting the normalized air humidity Hhum from the current ambient air humidity Hhum: Hhum'=(1-Hhum) / 0.6, and the unit of n1 is kW / (mJ*s). -1 The standard value is 10. 6 Used to implement from mJ*s -1 The unit conversion to kW is calibrated based on historical ignition safety data. n3 is typically set to 0.5, determined by the weighting of environmental interference data on the impact of ignition energy. Vign is the preset target ignition frequency, measured in seconds. -1 The weight is directly reflected by its own value.

[0062] b2. Introducing the environmental compensation coefficient Kenv: Based on the environmental safety score, an environmental compensation coefficient Kenv is introduced. When the air humidity is within the tolerance range, Kenv = 1.0. When the air humidity exceeds the tolerance range, Kenv will be calculated at a rate of 0.1 for every 10% increase in humidity above the tolerance limit. When the wind speed is >5m / s, Kenv increases exponentially with the increase in wind speed.

[0063] It needs to be explained that, to clarify the exponential relationship between wind speed and Kenv, let the real-time wind speed be v, then the specific formula for Kenv is:

[0064] When v≤5m / s, Kenv=1.0, the wind speed is below the threshold, and no additional compensation is required;

[0065] When 5m / s < v ≤ 12m / s e is the natural constant, approximately 2.718; 0.08 is the attenuation coefficient, determined based on historical experimental data. For every 1 m / s increase in wind speed, the ignition energy compensation increases exponentially. For example, when v = 8 m / s, Kenv ≈ 1.271, and when v = 12 m / s, Kenv ≈ 1.751.

[0066] When v > 12 m / s, Kenv = 2.5, setting the highest threshold to avoid excessive ignition energy due to high wind speed. This exponential function can achieve accurate quantification of Kenv under different wind speeds, taking into account both energy requirements and safety constraints under special operating conditions.

[0067] b3. Calculate the equipment attenuation coefficient Kdev: Kdev=1.0+0.03×(1-Lelectrode÷100)+0.1×(1-Spower), where Lelectrode is the remaining electrode life calculated based on the equipment's operating time, in percentage. Spower is the igniter's output power stability value, ranging from 0 to 1, where 1 represents completely stable power and 0 represents extremely unstable power.

[0068] b4. Calculate the final ignition energy Eign: Eign = Pbase × Kenv × Kdev.

[0069] The calculation process for the combustion air ratio is as follows:

[0070] c1. Calculate the theoretically required airflow: Qairtheory: Qairtheory = Qgas × λ × ρair ÷ ρgas ÷ 0.21, where 0.21 is the oxygen content in the air, λ is the excess air coefficient, adjusted according to combustion efficiency, typically taken as 1.1-1.3, and ρair is the air density, typically taken as 1.293 kg / m³. 3 ρgas is the density of the gas, and the value varies depending on the type of gas. For example, the density of methane gas is 0.717 kg / m³. 3 .

[0071] c2. Introduce the wind field coupling coefficient Kwind: Kwind = Kwinddir × Kwindspeed. Kwinddir is the wind direction adaptability, which is affected by the angle between the natural wind direction being evaluated and the direction of the combustion air outlet. When the angle is ≤30°, Kwinddir = 1.0; when the angle is >30° and ≤60°, Kwinddir = 0.8; when the angle is >60°, Kwinddir = 0.6. Kwindspeed is the wind speed influence coefficient, which is affected by the real-time wind speed in the working area. When the wind speed is ≤4m / s, Kwindspeed = 1.0; when the wind speed is ≥4m / s and ≤8m / s, it is dynamically adjusted according to the wind direction adaptability; when the wind speed is >8m / s, i.e., under special working conditions, Kwindspeed = 2.0.

[0072] c3. Introduce flame feedback coefficient Kflame: Based on the flame safety score, introduce flame feedback coefficient Kflame. When the flame core offset angle exceeds the standard or the temperature field is uneven, Kflame is dynamically adjusted.

[0073] c4. Calculate the final combustion air ratio Qair: Qair = Qairtheory × Kwind × Kflame;

[0074] The control strategy generation process is as follows:

[0075] d1. Input the four types of multi-source data and the current scene label into the trained attention mechanism + GRU model;

[0076] d2. Based on the learned historical data patterns and the current state, combined with the hard constraint of an ignition safety index ≥80, the model outputs three parameters that meet the safety requirements under the current operating conditions: the optimal gas supply Qgas, the ignition energy Eign, and the combustion air ratio Qair, along with the confidence level.

[0077] d3. The gas supply Qgas, ignition energy Eign, and combustion air ratio Qair output by the model, together with the snapshot of the key input data on which it is based, the predicted ignition safety index value, and the scenario label, are packaged into a complete ignition control strategy.

[0078] d4. Before the strategy is released, it is quickly verified using built-in verification rules. After the verification is successful, the control strategy is stored in the ignition solution library in a structured record form containing metadata such as timestamp, scene tag, parameter value, and source model version.

[0079] S3: The ignition scheme generation module captures the operating condition triggering conditions, calls the ignition scheme library to filter suitable schemes through feature matching, and marks the priority;

[0080] It should be explained that the working condition triggering conditions refer to the set of core parameter thresholds that can accurately classify the current working scenario. Specifically, they include: environmental triggering conditions, such as marking a special working condition when the wind speed is >8m / s; gas triggering conditions, such as methane content <85% in the component volume percentage; and equipment triggering conditions, such as electrode remaining life <50%. When any condition is met, the module automatically captures and generates the corresponding scenario label, which serves as the index for calling the solution library.

[0081] Further explanation is needed regarding the use of a weighted Euclidean distance algorithm for feature matching to achieve accurate screening. The specific steps are as follows: Six core feature parameters are extracted from the current operating data: methane percentage in the gas composition, real-time wind speed, target flame height design value, igniter output power, air humidity, and supply pressure, forming a feature vector X = (x1, x2, x3, x4, x5, x6); historical strategies under the same scenario label are retrieved from the ignition scheme library, and corresponding features are extracted to form a comparison vector Y = (y1, y2, y3, y4, y5, y6); weights Wi are assigned according to feature importance, and the weighted Euclidean distance is calculated. Set a distance threshold D≤0.15, and select solutions that meet the conditions as a candidate set to ensure that the selected solutions match the current working conditions with a feature degree of ≥90%. It should be noted that the distance threshold is set based on the statistics of historical adaptation cases.

[0082] It should be explained that all six core characteristic parameters are parameters that can be obtained in real time or preset before ignition. Among them, the target flame height design value is a standard flame height value preset based on the current operational requirements, rather than the actual flame height after ignition.

[0083] It should be noted that the priority labeling rules are based on a three-dimensional evaluation system of safety, energy efficiency, and execution difficulty. The safety dimension is determined by the predicted ignition safety index. An index ≥ 90 points is scored as 3 points, and an index between 80 and 89 points is scored as 2 points. The energy efficiency dimension calculates the theoretical utilization rate of gas. A utilization rate ≥ 95% is scored as 3 points, a utilization rate between 90 and 94% is scored as 2 points, and a utilization rate below 90% is scored as 1 point. The execution difficulty is scored based on the complexity of the equipment. Equipment that does not require additional adjustment of equipment parameters is scored as 3 points, equipment that requires local fine-tuning is scored as 2 points, and equipment that requires multi-equipment coordinated adjustment is scored as 1 point. The final priority is sorted according to the sum of the scores of the three dimensions of safety, energy efficiency, and execution difficulty. Equipment with a score ≥ 8 points is marked as first-level priority and is executed first. Equipment with a score of 6-7 points is second-level priority and is used as an alternative. Equipment with a score of 3-5 points is third-level priority and is only used in emergencies. The priority label is then bound to the plan and pushed to the ignition execution scheduling module.

[0084] S4: The ignition execution scheduling module displays the matching scheme to the on-site operators to ensure standardized ignition operations;

[0085] It should be explained that the matching scheme is presented in the form of a visual step-by-step guide, which includes: presenting the key steps of the ignition operation in a time-series flowchart, such as the sequence and duration requirements of "fan start → gas valve adjustment → ignition trigger", displaying the target parameters of each step in real time using a dynamic data panel, and setting a mandatory confirmation mechanism for operation nodes. That is, after the operator completes the current step, he / she must click to confirm before the system will unlock the next step of guidance. The standardized interface guides the operator to execute according to the preset scheme, avoiding human error.

[0086] Further explanation is needed regarding the logic for transmitting real-time operating parameters: The module collects dynamic data during the operation process at a frequency of 20 seconds per transmission through the communication interface with sensors and execution devices. This includes execution-end parameters: actual gas flow rate, actual igniter voltage, and current fan speed; real-time environmental data: instantaneous wind speed and gas concentration changes during operation; and operation status data: the deviation between the actual completion time of each step and the preset time. After compression and encoding, this data is synchronously transmitted to the oilfield ignition database for historical record archiving and to the intelligent ignition execution module to provide a basis for subsequent deviation calculations, thus achieving full data backtracking of the operation process.

[0087] S5: The intelligent ignition execution module executes the ignition operation according to the ignition scheme, calculates the execution deviation in real time and determines the model calculation abnormality. When the deviation exceeds the threshold or the model calculation abnormality occurs, a graded warning signal is triggered and the warning information is pushed to the ignition execution scheduling module in a synchronous manner.

[0088] It should be explained that the calculation method of execution deviation is based on the target parameters in the ignition scheme. The execution data is compared in real time, and the deviation rate is calculated by using the formula: deviation rate = (actual value - target value) ÷ target value × 100%. The deviations of key parameters are calculated separately, including gas supply deviation, ignition energy deviation, and combustion air ratio deviation. The overall deviation is obtained by combining the deviation rates of each parameter, which is used to evaluate the consistency between the operation and the scheme.

[0089] Further explanation is needed regarding the criteria for determining model operation anomalies, which include the magnitude of sudden changes in model output parameters, the deviation of feature parameters from historical patterns, and convergence anomalies during algorithm iteration. When any of these anomalies is met, the model operation is determined to be abnormal.

[0090] It should be noted that the graded early warning signals are divided into three levels according to their severity: Level 1 warning is applicable to single parameter deviations exceeding the threshold but not affecting overall safety, and the warning form is a yellow prompt on the interface, only notifying the operator to make manual fine adjustments; Level 2 warning is applicable to multiple parameter deviations, mild model anomalies, or initial triggering under special operating conditions with a safety index ≥ 75 points, and the warning form is an orange prompt on the interface with flashing, prompting the operator to implement local parameter compensation strategies; Level 3 warning is applicable to deviations exceeding the standard and a safety index < 70 points, severe model anomalies, or a safety index < 75 points under special operating conditions, and the warning form is a red prompt on the interface with vibration alert, while simultaneously pushing the warning details to the scheduling module and monitoring terminal to ensure that the risk is controllable.

[0091] S6: The information interaction module enables managers to view the ignition process status in real time through information exchange between the operation terminal and the monitoring terminal.

[0092] It should be explained that the information exchange between the operation and monitoring terminals is based on an industrial-grade encrypted communication protocol to ensure the security and real-time performance of data transmission. The monitoring terminal is deployed in a remote control center, presenting the overall ignition operation status through a visual monitoring interface, covering the operating data of each module, historical trend comparisons of key parameters, and real-time push notifications of anomaly warnings. The operation terminal is deployed in the on-site operation area, using an explosion-proof touchscreen as its carrier, mainly displaying details of the currently executed ignition scheme, step-by-step operation guidance, and warning prompts, allowing on-site operators to check the execution progress at any time. The information exchange between the two adopts a two-way synchronization mechanism. The execution feedback from the operation terminal is uploaded to the monitoring terminal in real time, and remote commands from the monitoring terminal can also be sent to the operation terminal in real time, forming a closed-loop interaction. This not only meets the management personnel's need for overall control of the ignition process but also ensures efficient collaboration between on-site operation and remote decision-making.

[0093] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0094] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent control system for oilfield ignition based on multi-source data, characterized in that, include: The multi-source data acquisition module is used to collect multi-source data in both normal production and special operating conditions, and store it in the oilfield ignition database. The ignition condition analysis module is used to call data from the oilfield ignition database, and with the ignition safety index as a constraint, train the model to generate control strategies that include gas supply, ignition energy, and combustion air ratio, and store the control strategies in the ignition scheme library according to the scenario; the control strategy generation process is as follows: d1, input four types of multi-source data and the current scenario label into the trained attention mechanism + GRU model; d2. Based on the learned historical data patterns and the current state, combined with the hard constraint of an ignition safety index ≥80, the model outputs three parameters that meet the safety requirements under the current operating conditions: the optimal gas supply Qgas, the ignition energy Eign, and the combustion air ratio Qair, along with the confidence level. d3. Package the gas supply Qgas value, ignition energy Eign value, and combustion air ratio Qair value output by the model, along with snapshots of key input data on which the calculation is based, predicted ignition safety index values, and scene labels, into a complete ignition control strategy; d4. Before the strategy is released, it is quickly verified using built-in verification rules. After verification, the control strategy is stored in the ignition scheme library in a structured record format containing metadata such as timestamps, scene labels, parameter values, and source model versions; The ignition scheme generation module is used to capture the operating condition triggering conditions, call the ignition scheme library, filter out suitable ignition schemes, and mark the scheme priority; the ignition execution scheduling module is used to display the matched ignition schemes to the on-site operators to ensure the standardization of ignition operation. The intelligent ignition execution module is used to perform ignition operations, calculate execution deviations in real time, and determine model calculation abnormalities. When the deviation exceeds the threshold or a model calculation abnormality occurs, a graded early warning signal is triggered, and the early warning information is pushed to the ignition execution scheduling module simultaneously. The information interaction module is used to communicate between the operation terminal and the monitoring terminal, allowing managers to view the ignition process status in real time. The oilfield ignition database is used to store four types of multi-source data with scene tags collected by the multi-source data acquisition module; The ignition scheme library is used to store control strategies generated by the ignition condition analysis module, including gas supply, ignition energy, and combustion air ratio, according to different scenarios.

2. The intelligent oilfield ignition control system based on multi-source data according to claim 1, characterized in that: The term "routine production" refers to a stable operating state where the oilfield ignition operation area is free from adverse weather conditions, the gas supply pressure fluctuation is controlled within ±5% of the rated pressure, and there are no abnormal warnings for equipment operation. "Special operating conditions" refers to unconventional operating states that may affect ignition safety, such as instantaneous wind speeds exceeding 8 m / s, severe thunderstorms, impurity content in the gas exceeding 3%, temperatures of key equipment components exceeding safety thresholds, or gas leak warnings.

3. The intelligent oilfield ignition control system based on multi-source data according to claim 1, characterized in that: The four types of multi-source data include: flame status data, covering flame height, flame core offset angle, and temperature field distribution; gas parameter data, including the volume percentage of each component in the gas, real-time flow rate, and supply pressure; environmental interference data, involving wind speed, wind direction, air humidity, and atmospheric pressure in the work area; and equipment status data, including igniter output power, electrode remaining life, gas regulating valve opening degree, and response speed.

4. The intelligent oilfield ignition control system based on multi-source data according to claim 1, characterized in that: The ignition safety index calculation process is as follows: A four-dimensional evaluation model is constructed based on four types of multi-source data. Core indicators are extracted from flame, gas, environment, and equipment data respectively. Each indicator is weighted according to the influence weight of the corresponding safety dimension to obtain the flame, gas, environment, and equipment safety scores. Then, the ignition safety index is calculated using the formula: Ignition Safety Index = t1 × Flame Safety Score + t2 × Gas Safety Score + t3 × Environment Safety Score + t4 × Equipment Safety Score, where t1, t2, t3, and t4 are the weights of each safety score, satisfying t1 + t2 + t3 + t4 = 1. The weight values ​​are determined based on historical safety accidents and increase as the corresponding safety score decreases. The ignition safety index ranges from 0 to 100. When the ignition safety index is ≥ 80 points, it is determined that the ignition safety conditions are met.

5. The intelligent oilfield ignition control system based on multi-source data according to claim 1, characterized in that: The gas supply calculation process is as follows: a1. Calculate the basic gas flow demand Qbase; a2. Introduce the component adaptation correction coefficient Kcomp; a3. Calculate the pressure compensation coefficient Kpress; a4. Calculate the final gas supply flow rate Qgas.

6. The intelligent oilfield ignition control system based on multi-source data according to claim 5, characterized in that: The formula for calculating the pressure compensation coefficient Kpress is as follows: ∆Pdev represents the pressure fluctuation, and the calculation formula is: ∆Pdev=(Preal-Pnominal)÷Pnominal×100%, where Preal is the real-time pressure and Pnominal is the rated pressure.

7. The intelligent oilfield ignition control system based on multi-source data according to claim 5, characterized in that: The formula for calculating the final gas supply flow rate Qgas is: Qgas = Qbase × Kcomp × Kpress.

8. A smart control method for oilfield ignition based on multi-source data, characterized in that, The method is applicable to the intelligent oilfield ignition control system based on multi-source data as described in claim 1, specifically... Includes the following steps: S1: The multi-source data acquisition module simultaneously collects four types of multi-source data under both routine production and special operating conditions, and stores them in an oilfield ignition database with scenario tags; S2: The ignition condition analysis module calls the database data, uses the ignition safety index as a constraint, trains the model to generate control strategies including gas supply, ignition energy, and combustion air ratio, and stores them in the ignition scheme library according to the scenario; S3: The ignition scheme generation module captures the operating condition triggering conditions, calls the ignition scheme library, filters suitable schemes through feature matching, and marks the priority; S4: The ignition execution scheduling module displays the matching scheme to the on-site operators to ensure the standardization of ignition operation; S5: The intelligent ignition execution module executes the ignition operation according to the ignition scheme, calculates the execution deviation in real time and judges the model calculation abnormality. When the deviation exceeds the threshold or the model calculation abnormality occurs, a graded warning signal is triggered and the warning information is pushed to the ignition execution scheduling module in a synchronous manner. S6: The information interaction module enables managers to view the ignition process status in real time through information exchange between the operation terminal and the monitoring terminal.

9. The intelligent control method for oilfield ignition based on multi-source data according to claim 8, characterized in that: The feature matching employs a weighted Euclidean distance algorithm to achieve accurate screening. The specific steps are as follows: Six core feature parameters are extracted from the current operating data: methane percentage in the gas composition, real-time wind speed, target flame height design value, igniter output power, air humidity, and supply pressure, forming a feature vector X = (x1, x2, x3, x4, x5, x6); historical strategies under the same scenario tag are retrieved from the ignition scheme library, and corresponding features are extracted to form a comparison vector Y = (y1, y2, y3, y4, y5, y6); weights Wi are assigned according to feature importance, and the weighted Euclidean distance is calculated. Set a distance threshold D≤0.15, filter out the solutions that meet the conditions as a candidate set, and ensure that the selected solutions match the current working conditions with a feature degree of ≥90%. The distance threshold is set based on the statistics of historical adaptation cases.

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