Pollution source risk assessment method and system for oil and gas field environmental protection treatment
By combining IoT sensing terminals, data processing platforms, and AI dynamic risk assessment models, the real-time and accuracy issues of oil and gas field pollution source assessment have been resolved, achieving closed-loop management throughout the entire process and improving the timeliness and scientific rigor of risk assessment.
Patent Information
- Application Number
- CN202610060666.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-16
- Publication Date
- 2026-05-01
AI Technical Summary
Existing technologies are insufficient to achieve real-time, accurate, and predictable assessment of pollution sources in oil and gas fields. Traditional assessment methods rely on static weights and preset scenarios, lack adaptive learning capabilities, and cannot support early warning and dynamic intervention during the event. Insufficient data fusion platforms lead to lagging risk prevention and control.
The system uses IoT sensing terminals to collect multi-source heterogeneous data in real time, cleans and integrates the data through a data processing platform, uses an AI dynamic risk assessment model for self-learning and parameter optimization, and combines an intelligent decision-making module to generate risk warnings and governance suggestions, forming a closed-loop management process.
It enables real-time, accurate assessment and intelligent management of complex dynamic pollution source risks in oil and gas fields, improving the timeliness and scientific rigor of risk assessment, and supporting generalized applications across multiple pollution source types and well site scenarios, significantly outperforming traditional static assessment models.
Smart Images
Figure CN121961234A_ABST
Abstract
Description
Pollution source risk assessment methods and systems for environmental remediation in oil and gas fields Technical Field
[0001] This invention relates to the field of environmental protection and governance technology for oil and gas fields, specifically to a method and system for assessing pollution source risks in environmental protection and governance of oil and gas fields. Background Technology
[0002] In the oil and gas extraction industry, environmental protection and pollution control are always key issues accompanying production activities. The entire process of oil and gas field exploration and development, including drilling, well completion, fracturing, gas production, and routine maintenance, continuously generates diverse pollutants of varying types and properties. These mainly include solid and liquid wastes such as oil-bearing rock cuttings, drilling mud, fracturing flowback fluid, and produced water from the gas field, as well as volatile organic compounds, combustion flue gas, and various types of mechanical noise. These pollution sources are characterized by dispersed generation points, complex compositions, and dynamic changes with different operational stages. Their potential environmental risks involve multiple impacts on soil, groundwater, surface water, and the atmosphere. Traditional environmental management methods largely rely on periodic monitoring, decentralized data recording, and static risk assessments based on fixed scorecards or empirical formulas. These methods struggle to accurately and in real-time depict the dynamic risk situation under the coupled effects of complex geological conditions, variable production conditions, and uncertain environmental factors. This results in lags in risk prevention and control, requires improved resource allocation efficiency, and fails to fully meet the increasingly stringent requirements for comprehensive and refined environmental supervision, as well as the urgent need for enterprises to transform towards green and low-carbon practices.
[0003] Currently, the industry is actively promoting the digital and intelligent transformation of environmental governance. At the data acquisition level, the application of IoT sensors and online monitoring equipment is gradually increasing, enabling the acquisition of real-time concentration and flow information for some pollution sources. Regarding assessment methods, some studies are attempting to introduce multi-indicator comprehensive evaluation systems and are beginning to explore the application of geographic information systems in environmental sensitivity analysis. However, existing technological systems still have significant limitations. On the one hand, the phenomenon of data silos is widespread; multi-source heterogeneous information such as geological data, real-time production data, historical emission data, and environmental monitoring data lacks a unified and efficient fusion platform, making it difficult to form a panoramic and interconnected understanding of pollution sources and their environment. On the other hand, mainstream environmental risk assessment models are mostly based on static weights and preset scenarios, with fixed model parameters that cannot adaptively learn new data patterns and lack the ability to predict risk evolution trends. Essentially, they remain post-event evaluation or periodic review tools, unable to support pre-event warnings and dynamic intervention during events. Summary of the Invention
[0004] The present invention aims to provide a method and system for pollution source risk assessment in oil and gas field environmental governance, which can realize real-time, accurate, predictable assessment and intelligent closed-loop management of complex dynamic pollution source risks in oil and gas fields.
[0005] To achieve the above objectives, the present invention provides the following basic solution.
[0006] Scheme 1, a pollution source risk assessment method for oil and gas field environmental governance, includes a pollution source sensing module, a data processing platform, an AI dynamic risk assessment model, and an intelligent decision-making module. The pollution source sensing module collects multi-source heterogeneous data on solid waste, liquid waste, exhaust gas, and noise pollution sources in real time through IoT sensing terminals deployed at well sites. The data processing platform establishes a communication connection with the pollution source sensing module to clean, standardize, and fuse the received multi-source heterogeneous data to form a structured dataset. The AI dynamic risk assessment model establishes a communication connection with the data processing platform to invoke pre-trained machine learning algorithms based on the structured dataset. The system employs a learning or deep learning algorithm to dynamically calculate the comprehensive risk coefficient of pollution sources and outputs risk assessment results. The AI dynamic risk assessment model performs self-learning and parameter optimization based on updates to the input data. The intelligent decision-making module establishes a communication connection with the AI dynamic risk assessment model to generate and output risk warning information, governance suggestions, or emergency response plans for different pollution sources based on the risk assessment results. The AI dynamic risk assessment model, acting as a central processing unit, drives and coordinates the pollution source perception module, data processing platform, and intelligent decision-making module to achieve closed-loop intelligent management of pollution sources throughout the entire process from identification and assessment to governance decisions.
[0007] Scheme 2, a pollution source risk assessment method for oil and gas field environmental governance, applies the pollution source risk assessment system for oil and gas field environmental governance described in Scheme 1, including the following steps: S1, Data acquisition step: Real-time collection of multi-source heterogeneous data on solid waste, liquid waste, exhaust gas, and noise pollution sources through IoT sensing terminals deployed at oil and gas field well sites; S2, Data processing step: Cleaning, standardizing, and fusing the collected multi-source heterogeneous data to form a structured dataset for risk assessment; S3, Dynamic risk assessment step: Inputting the structured dataset into a pre-trained AI dynamic risk assessment model, wherein the AI dynamic risk assessment model is based on... Using machine learning or deep learning algorithms, the comprehensive risk coefficient of pollution sources is dynamically calculated, and risk assessment results are generated. The AI dynamic risk assessment model performs self-learning and parameter optimization based on updates to the input data. S4, Intelligent Decision Generation Step: Based on the risk assessment results output by the AI dynamic risk assessment model, risk warning information, governance suggestions, or emergency response plans for different pollution sources are automatically generated and output. The dynamic calculation and self-learning optimization of the AI dynamic risk assessment model drive the data collection, data processing, dynamic risk assessment, and intelligent decision generation steps, achieving closed-loop intelligent management of pollution sources from identification and assessment to governance decisions.
[0008] The working principle and advantages of this invention are as follows: The pollution source risk assessment method and system of this invention for environmental governance in oil and gas fields can achieve real-time, accurate, predictable assessment and intelligent closed-loop management of complex dynamic pollution source risks in oil and gas fields. The key point is that this solution, through a unified data fusion platform and standardized interfaces, aggregates and correlates multi-source heterogeneous sensing data, production background information, and environmental spatial data distributed throughout the well site in real time. This enables high-frequency, multi-dimensional data collection of solid, liquid, gas, and noise pollution sources and their related environmental and production parameters, overcoming the shortcomings of limited coverage and delayed data updates in traditional monitoring methods, and providing a panoramic analytical foundation for AI models. Notably, the AI dynamic risk assessment model designed in this solution is not a simple digitization of traditional scoring formulas, but rather a hybrid architecture that integrates parametric calculation and machine learning. This architecture can both inherit the domain knowledge framework to ensure the interpretability of the results and continuously learn the complex nonlinear relationships in the data to achieve real-time calculation and short-term prediction of risk coefficients. It supports generalized applications for multiple pollution source types and multiple well site scenarios, significantly outperforming traditional static scoring models and significantly improving the timeliness and scientific rigor of risk assessment.
[0009] More importantly, this system forms a complete "perception-assessment-decision-feedback" closed loop. The intelligent decision-making module can automatically match the knowledge base to generate highly actionable governance suggestions, optimization paths or emergency plans based on the risk level and attribution analysis output by the model. It can also directly drive management actions through the visualization platform, thereby directly transforming the results of intelligent analysis into concrete productivity that improves environmental governance efficiency and economic benefits. Attached Figure Description
[0010] Figure 1 is a schematic diagram of the system structure of the pollution source risk assessment method and system embodiment of the present invention for environmental protection management of oil and gas fields. Detailed Implementation
[0011] The following detailed description of the specific implementation method is as shown in Figure 1: The pollution source risk assessment method for environmental governance of oil and gas fields includes a pollution source perception module, a data processing platform, an AI dynamic risk assessment model, an intelligent decision-making module, an emergency response subsystem, a waste treatment path optimization subsystem, and an intelligent emission early warning subsystem.
[0012] The pollution source sensing module collects multi-source heterogeneous data on solid waste, liquid waste, exhaust gas and noise pollution sources in real time through IoT sensing terminals deployed at the well site.
[0013] Specifically, in this embodiment, the pollution source sensing module consists of intelligent sensing terminals, data acquisition units (RTU / DTU), and a network deployed at key nodes of the well site. Specifically, it includes: (1) a solid waste pollution source data acquisition unit, including: a weighing and identification unit: explosion-proof electronic belt scales and ultra-high frequency RFID readers are installed at the rock cuttings vibrating screen outlet, screw conveyor, and temporary stockpile entrance. RFID tags are affixed to the rock cuttings bags or transfer boxes. When solid waste passes through, the system automatically records its weight (tons), production time, source well number, and preset classification code. The data is uploaded in real time via industrial Wi-Fi or 4G / 5G network.
[0014] Composition sensing unit: An online near-infrared spectroscopy (NIR) analyzer is installed at the feed inlet of a solid waste treatment line (such as a thermal desorption unit), or samples are periodically sent to a mobile laboratory. The analyzer rapidly estimates the oil content (%) in oil-based rock cuttings based on spectral characteristics, which serves as the pollutant concentration. (Approximate value of petroleum hydrocarbon content, unit: mg / kg). The mobile laboratory accurately determines the content of TPH, PAHs, and heavy metals using standard methods (such as HJ 607), and the test report is automatically entered through the system interface.
[0015] (2) Wastewater pollution source data acquisition unit, including: Online monitoring unit: Multi-parameter online water quality analyzer is installed at key nodes such as drilling fluid circulation tank, flowback fluid collection tank, and inlet and outlet of wastewater treatment device. This instrument integrates a pH sensor, a chemical oxygen demand (COD) online analysis module, a petroleum online analyzer, a turbidity / suspended solids (TSS) sensor, and an electromagnetic flowmeter. For example, a COD analyzer using the "potassium dichromate digestion-photometric method" principle automatically samples and analyzes every 2 hours, outputting... COD value (unit: mg / L). The flow meter records the flow rate of waste liquid generated or treated in real time. ).
[0016] Furthermore, the system interfaces with a third-party laboratory information management system (LIMS) to automatically retrieve full analysis reports (including heavy metal ions, chloride ions, characteristic organic matter, etc.) of periodically submitted water samples for supplementation and calibration of online data.
[0017] (3) Exhaust gas and leakage monitoring unit, including: For organized emission monitoring: Install a continuous emission monitoring system (CEMS) on the chimneys of stationary combustion equipment such as heating furnaces, flares, and generators to monitor in real time. , Particulate matter concentration (unit: (and flue gas flow rate, temperature, and oxygen content).
[0018] For monitoring fugitive emissions and leaks: In areas prone to VOCs leakage, such as tank areas, loading and unloading areas, and wellheads, deploy open-path Fourier transform infrared spectroscopy (OP-FTIR) systems or lidar scanning systems to achieve planar distribution scanning of VOCs concentrations and generate concentration cloud maps. At critical leak points such as valves, flanges, and pump seals, install high-sensitivity laser methane sensors and PID sensors for continuous, targeted monitoring of CH4 and VOCs, with thresholds as low as ppm.
[0019] Smart cameras equipped with both thermal imaging and visible light dual-spectrum sensors are installed in key areas. AI algorithms run via edge computing boxes to analyze video streams in real time, identifying "heat plumes from gas leaks" or "reflective / wet areas on the ground from liquid leaks," and triggering screenshots and alerts.
[0020] (4) Noise pollution source data acquisition unit, including: multi-functional automatic noise monitoring stations installed around the plant boundary, near high noise sources such as fracturing pump units and compressor sheds. These stations are equipped with high-level microphones, data acquisition devices, and GPRS communication modules, enabling continuous measurement and recording of the equivalent continuous A-weighted sound level. Maximum sound level Statistical analysis of sound levels L10 / L50 / L90, and automatic calculation of daytime equivalent sound levels. and nighttime equivalent sound level The data is packaged and uploaded once per hour.
[0021] (5) Environmental and background data access unit, including: accessing data from the well site's self-built meteorological station (wind speed, wind direction, temperature, humidity, air pressure) or gridded data provided by the National Meteorological Administration. It also reads data such as well depth, drilling pressure, displacement, casing pressure, and gas production in real time from the drilling parameter instrument and SCADA system via OPC UA or API interface. Furthermore, it accesses waste transfer manifests, treatment facility operation records (such as incinerator temperature, activated carbon replacement cycle), environmental inspection records, and emergency response documents from the enterprise ERP or environmental management platform for assessing management control level (M) and control efficiency (…). ).
[0022] In addition, the system pre-configures or calls GIS layers such as high-precision digital elevation model (DEM), hydrogeological map, ecological protection red line, and settlement distribution through WMS service to calculate environmental vulnerability (V).
[0023] The pollution source perception module also includes a pollution source intelligent classification unit. This unit dynamically identifies and automatically classifies pollution sources based on operational stage information and pollution source characteristic data obtained from the data processing platform, using a pre-trained AI classification model. Specifically, the unit includes the following steps: constructing a standardized input feature vector based on operational stage information (e.g., first-stage drilling, third-stage fracturing, stable production, etc.) and pollution source characteristic data (multi-source heterogeneous data of solid waste, liquid waste, exhaust gas, and noise pollution sources) obtained from the data processing platform; inputting this standardized input feature vector into a pre-trained multimodal AI classification model; processing the input vector using machine learning algorithms and outputting the probability distribution of the pollution source belonging to each category in a preset multi-level classification system; the preset multi-level classification system includes: Level 1 classification (medium): solid waste, liquid waste, exhaust gas, and noise.
[0024] Secondary classification (source / nature): Solid waste: oil-based rock debris, water-based rock debris, contaminated waste, and domestic waste.
[0025] Waste fluids: drilling fluid, fracturing flowback fluid, gas field water, domestic sewage, and oily rainwater.
[0026] Exhaust gases: fuel combustion flue gas, process fugitive VOCs, test vent gas, and fugitive methane.
[0027] Noise: noise from machinery and equipment, construction pulse noise, and traffic noise.
[0028] Level 3 classification (risk characteristics): high-chlorine waste liquid, high-hydrocarbon solid waste, high-sulfur waste gas, etc. (This level can be directly linked to risk assessment parameters).
[0029] Based on the probability distribution and a preset confidence threshold, the optimal classification label for the pollution source is automatically determined. When the classification confidence is below the threshold or there is classification ambiguity, a manual review process is triggered, and the manual determination result is used as the final classification label. The determined final classification label is output to the data processing platform and the AI dynamic risk assessment model for subsequent risk assessment and differentiated management. For example, once classified as "oil-based rock cuttings," the system automatically associates the pollution source instance. The system uses a preset value for the toxicity coefficient (for petroleum hydrocarbons and heavy metals) and a recommended processing path (such as pyrolysis). Simultaneously, the input feature vector and final classification label from this classification are used as new samples and stored in a classification case library for periodic incremental learning training of the pre-trained multimodal AI classification model.
[0030] The data processing platform establishes a communication connection with the pollution source sensing module to clean, standardize, and fuse the received multi-source heterogeneous data to form a structured dataset.
[0031] Specifically, the data processing platform has a built-in data cleaning rule engine, including: outlier handling: for sensor data, it adopts " The principle or box plot method is used to identify and remove abnormal jump values caused by equipment failure, and then fill them in using linear interpolation or mean of the data before and after the failure.
[0032] Missing value handling: For temporary missing periodic test data (such as laboratory tests), the average of historical data from similar well sites or regression predictions based on production parameters (such as footage) are used to temporarily fill the missing data, and the data is updated after the data arrives.
[0033] Format standardization: Convert data from different systems (such as "mg / L" in real-time monitoring, "ppm" in laboratory reports, and "qualified / unqualified" in management records) into international standard units (such as mg / L, mg / m³) or normalize them to the [0,1] range.
[0034] The fusion process includes: spatiotemporal alignment: all data entries are forcibly bound to "timestamps" and "spatial coordinates (well number, equipment tag number)". The data processing platform uses "well site-time" as an index to associate different types of data (such as the COD value of the flowback fluid of a well, the concurrent production gas volume, and the surrounding wind speed) at the same time and location into a complete record.
[0035] Feature extraction: Calculate derived features from the raw data. For example, extract "daytime equivalent sound level Ld" and "nighttime equivalent sound level Ln" from continuous noise data; calculate the "leakage frequency" of different operational stages from historical leakage records as the initial value of the exposure probability (E); calculate the "average operating load rate of the treatment facility" from the equipment operation log as a reference for control efficiency.
[0036] The cleaned and merged data is organized by "contamination source instance". For example, a "waste liquid" record contains: [Well No.: A1, Time: 2023-10-27 14:00, Type: fracturing flowback fluid, Volume: 50 COD concentration: 1200 mg / L, petroleum hydrocarbons: 85 mg / L, current treatment process: integrated skid-mounted unit, designed treatment efficiency. : 0.95, indicating the vulnerability of groundwater in the area. [0.8 (high), distance to the nearest sensitive water body: 1500m, ...]. This dataset will serve as input to the AI dynamic risk assessment model.
[0037] The data processing platform integrates a geographic information system (GIS) to overlay, analyze, and visualize the spatial location of pollution sources, information on environmentally sensitive areas, and the risk assessment results.
[0038] The AI dynamic risk assessment model establishes a communication connection with the data processing platform to dynamically calculate the comprehensive risk coefficient of pollution sources based on the structured dataset by calling pre-trained machine learning or deep learning algorithms, and outputs the risk assessment results; the AI dynamic risk assessment model performs self-learning and parameter optimization based on the updates of the input data.
[0039] The AI dynamic risk assessment model includes a parameterized calculation sub-model and an AI algorithm sub-model; the parameterized calculation sub-model is based on a formula. Calculate the base risk value.
[0040] Where C represents the pollutant concentration, which is the maximum value or 95th percentile of the current or recent detected concentration.
[0041] E represents the exposure probability, which is dynamically calculated by combining the frequency of historical events and the intensity of current operations (e.g., "under fracturing operations" is given a higher weight).
[0042] V is the environmental vulnerability coefficient, which is assigned a value based on the analysis results of overlaying the location of the pollution source with sensitive areas in the GIS map (e.g., a value of 1 is assigned to areas located upstream of water source protection areas, and 0.5 is assigned to general areas).
[0043] T is the toxicity coefficient. In this embodiment, the pollutant concentration is mapped to a standardized toxicity index (0-1) by querying the built-in pollutant toxicity database (which integrates authoritative data sources such as MSDS and USEP IRIS).
[0044] M represents the management control level coefficient. In this embodiment, a score of 0-1 is obtained based on the compliance rate, inspection completion rate, employee training records, etc., recorded by the enterprise management platform.
[0045] Furthermore, after calculating the basic risk value, a refined calculation is performed using a modified formula based on the specific type of pollution source. Specifically, the parameterized calculation sub-model includes modified calculation units for different types of pollution sources: for solid waste pollution sources, its risk coefficient... Through formula The following corrections were made: This refers to the resource utilization rate; for example, if the oil recovery rate is 90% after the pyrolysis of a batch of rock cuttings, then... .
[0046] For waste liquid pollution sources, the risk factor Through formula The following corrections were made: To improve processing efficiency; for example, calculate (inlet concentration - outlet concentration) / inlet concentration based on online monitoring data. If the integrated unit's COD processing efficiency is 90%, then... .
[0047] For sources of exhaust gas pollution, their risk factor Through formula Make corrections, among which, For pollution control efficiency, such as the VOCs removal rate of incinerators.
[0048] For noise, its risk factor Through formula H represents the influence coefficient of sensitive areas (e.g., higher coefficients at night or near villages). This is the evaluation value for the effectiveness of sound insulation measures.
[0049] Calculated , , , and all intermediate parameters ( (etc.) constitute an eigenvector.
[0050] The AI algorithm sub-model uses the basic risk value and related parameters ( , , , As one of the input features, the algorithm trains and optimizes the weights to predict risk, outputting the final risk level and risk coefficient. Specifically, this involves the following operations: taking the feature vector generated by the parameterized calculation sub-model, along with its contextual data (such as production stage and weather), as input. A pre-trained AI model (e.g., using XGBoost or a neural network) receives this input. The AI model uses historical data from the past 3-5 years, including all cleaned feature data, as input, and uses the occurrence of a real environmental event (and its level) as a label for supervised learning. During the training phase, it learns the complex nonlinear relationship between each parameter and the actual risk consequences (such as whether a pollution event occurs and the event level) from a large amount of historical data (including historical accident cases).
[0051] When the model is running, it fine-tunes the weights of the parameters in the parametric formula. For example, in heavy rain, the model will automatically increase the weights of "environmental vulnerability (V)" and "exposure probability (E)".
[0052] It outputs a final, AI-optimized comprehensive risk coefficient (0-100) and risk level (e.g., 0-30 low, 31-60 medium, 61-85 high, 86-100 extremely high). It also outputs the main risk contributing factors, such as "This high risk is mainly caused by 'excessive COD concentration in waste liquid' and 'decreased efficiency of treatment facilities'."
[0053] All input features, output results, and subsequent occurrences of real-world events (as labels) from this evaluation will be stored as new samples in the historical database. The system will periodically (e.g., weekly) fine-tune the AI model using incremental learning algorithms to continuously evolve its evaluation capabilities.
[0054] The intelligent decision-making module establishes a communication connection with the AI dynamic risk assessment model, and is used to generate and output risk warning information, governance suggestions or emergency response plans for different pollution sources based on the risk assessment results.
[0055] The emergency response subsystem includes a leak sensor network deployed in key areas for real-time monitoring of gas and liquid leak signals; the AI dynamic risk assessment model receives and analyzes the leak signals, and when a leak event is determined to have occurred, it triggers the intelligent decision-making module to initiate the emergency process. The intelligent decision-making module outputs an emergency response plan that includes leak location, risk level, evacuation range, and disposal measures.
[0056] The waste treatment path optimization subsystem is connected to the data processing platform to obtain waste property and quantity data. The AI dynamic risk assessment model evaluates the environmental impact index and economic benefit index of different preset treatment paths. The environmental impact index is based on the life cycle assessment concept and integrates carbon emissions, secondary waste generation, and potential pollution risk data throughout the entire path. The environmental impact index and economic benefit index are used as optimization objectives, with constraints including: upper limit of treatment capacity, regulatory compliance (e.g., oil content must be <2% for landfill), time window, etc. A multi-objective optimization algorithm is used to solve the problem and obtain a set of Pareto optimal solutions. The Pareto optimal solutions are sorted and weighed according to preset decision preferences to obtain the path evaluation result. The intelligent decision-making module outputs the recommended optimal treatment path and resource utilization scheme based on the path evaluation result.
[0057] The intelligent emission early warning subsystem includes online emission monitoring equipment for continuously monitoring the pollutant concentration and flow rate at the emission outlet; the AI dynamic risk assessment model analyzes emission trends and sends an early warning signal to the intelligent decision-making module when the emission data exceeds a preset threshold or is predicted to exceed the standard; the intelligent decision-making module outputs emission control strategy adjustment suggestions or initiates linkage commands for treatment facilities based on the early warning signal.
[0058] Specifically, regarding risk warning information: when the risk level output by the AI dynamic risk assessment model is "medium", the system will turn the pollution source icon yellow on the visualization map of the management platform and automatically send a prompt message to the mobile APP of the relevant responsible personnel.
[0059] When the AI dynamic risk assessment model outputs a risk level of "high" or "extremely high," the system triggers an audible and visual alarm and automatically generates a "High-Risk Warning Notification," which is then pushed to the well site manager, environmental protection department, and superiors via the platform, SMS, and email. The notification automatically includes the risk location, level, cause, and preliminary handling requirements.
[0060] Regarding governance recommendations: The intelligent decision-making module has a pre-set governance measures knowledge base, which links best feasible technologies (BAT) for various risk scenarios. Based on the risk assessment results (especially attribution analysis), specific measures are matched and recommended from the governance measures knowledge base.
[0061] Example (based on the Changning H6 platform): The risk assessment for the backflow liquid is "medium-high", and the main reason is the treatment efficiency. The results did not meet expectations. The intelligent decision-making module may recommend: "Check the operating status of the dosing pump in the coagulation settling tank of the AYY-WT-30Ⅰ integrated skid-mounted unit and optimize the PAC / PAM dosing ratio. It is expected to increase the COD treatment efficiency to over 65%, with an increase in cost of approximately 5 yuan per cubic meter."
[0062] For waste disposal pathway optimization, the AI dynamic risk assessment model calls a specialized multi-objective optimization algorithm to simultaneously evaluate the full life-cycle environmental and direct economic costs of different pathways such as "landfill", "reinjection", "pyrolysis", and "biological treatment", and finally recommends a Pareto optimal solution.
[0063] Regarding the emergency response plan: When the emergency response subsystem confirms a leak and triggers an "extremely high" risk, the intelligent decision-making module is immediately activated.
[0064] By combining sensor signal strength and airflow models, the core area of the leak and its potential impact range are delineated on a map. The most suitable contingency plan (such as a "wellhead natural gas minor leak contingency plan") is then matched from the emergency plan database and pushed to the emergency command center and on-site personnel terminals with a single click. The plan automatically includes a list of necessary emergency supplies (such as leak-sealing tools and oil-absorbing mats), personnel evacuation routes, and contact information for external rescue units. Based on the scale and risk level of the leak, the system recommends initiating the appropriate level of emergency response (such as site-level or company-level) and generates a resource allocation order.
[0065] Furthermore, the output interface of the intelligent decision-making module is connected to a visualized intelligent environmental management platform; the intelligent environmental management platform is used to centrally display pollution source distribution maps, real-time risk heat maps, early warning information lists, governance task work orders and emergency resource scheduling views, and provides cross-terminal access functions; the intelligent environmental management platform also has a model management unit, which is used for users to manage the version of the AI dynamic risk assessment model, backtrack training data, evaluate the effect and manually intervene in parameter tuning.
[0066] The AI dynamic risk assessment model serves as the central processing unit, driving and coordinating the pollution source perception module, data processing platform, and intelligent decision-making module to achieve closed-loop intelligent management of pollution sources throughout the entire process from identification and assessment to governance decisions.
[0067] This embodiment also provides a pollution source risk assessment method for oil and gas field environmental governance. The method utilizes a pollution source risk assessment system for oil and gas field environmental governance as described above, including the following steps: S1, Data acquisition step: Real-time collection of multi-source heterogeneous data on solid waste, liquid waste, exhaust gas, and noise pollution sources via IoT sensing terminals deployed at oil and gas field well sites; S2, Data processing step: Cleaning, standardizing, and fusing the collected multi-source heterogeneous data to form a structured dataset for risk assessment; S3, Dynamic risk assessment step: Inputting the structured dataset into a pre-trained AI dynamic risk assessment model. The model is based on machine learning or deep learning algorithms to dynamically calculate the comprehensive risk coefficient of pollution sources and generate risk assessment results. The AI dynamic risk assessment model performs self-learning and parameter optimization based on updates to the input data. Step S4, intelligent decision generation: Based on the risk assessment results output by the AI dynamic risk assessment model, it automatically generates and outputs risk warning information, governance suggestions, or emergency response plans for different pollution sources. The dynamic calculation and self-learning optimization of the AI dynamic risk assessment model drive the data collection, data processing, dynamic risk assessment, and intelligent decision generation steps, realizing closed-loop intelligent management of pollution sources from identification and assessment to governance decisions.
[0068] This embodiment provides a pollution source risk assessment method and system for environmental governance of oil and gas fields, which can realize real-time, accurate, predictable assessment and intelligent closed-loop management of complex dynamic pollution source risks in oil and gas fields.
[0069] The above descriptions are merely embodiments of the present invention. Commonly known structures and characteristics of the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent.
Claims
1. A pollution source risk assessment system for environmental remediation in oil and gas fields, characterized in that, The system includes a pollution source sensing module, a data processing platform, an AI dynamic risk assessment model, and an intelligent decision-making module. The pollution source sensing module collects multi-source heterogeneous data on solid waste, liquid waste, exhaust gas, and noise pollution sources in real time via IoT sensing terminals deployed at the well site. The data processing platform establishes a communication connection with the pollution source sensing module to clean, standardize, and fuse the received multi-source heterogeneous data, forming a structured dataset. The AI dynamic risk assessment model establishes a communication connection with the data processing platform to dynamically apply pre-trained machine learning or deep learning algorithms based on the structured dataset. The system calculates the comprehensive risk coefficient of pollution sources and outputs the risk assessment results. The AI dynamic risk assessment model performs self-learning and parameter optimization based on the updates of the input data. The intelligent decision-making module establishes a communication connection with the AI dynamic risk assessment model and generates and outputs risk warning information, governance suggestions, or emergency response plans for different pollution sources based on the risk assessment results. The AI dynamic risk assessment model serves as a central processing unit, driving and coordinating the pollution source perception module, data processing platform, and intelligent decision-making module to achieve closed-loop intelligent management of pollution sources from identification and assessment to governance decisions.
2. The pollution source risk assessment system for environmental remediation of oil and gas fields according to claim 1, characterized in that, The AI dynamic risk assessment model includes a parameterized calculation sub-model and an AI algorithm sub-model; the parameterized calculation sub-model is based on a formula. A basic risk value is calculated, where C is the pollutant concentration, E is the exposure probability, V is the environmental vulnerability coefficient, T is the toxicity coefficient, and M is the management and control level coefficient. The AI algorithm sub-model uses the basic risk value and related parameters as one of the input features, optimizes the weights through algorithm training, performs risk prediction, and outputs the final risk level and risk coefficient.
3. The pollution source risk assessment system for environmental remediation of oil and gas fields according to claim 2, characterized in that, The parameterized calculation sub-model includes correction calculation units for different types of pollution sources: for solid waste pollution sources, its risk coefficient... Through formula The following corrections were made: For resource utilization rate; for waste liquid pollution sources, its risk coefficient. Through formula The following corrections were made: For processing efficiency; for waste gas pollution sources, their risk factor. Through formula Make corrections, among which, For pollution control efficiency.
4. The pollution source risk assessment system for environmental remediation of oil and gas fields according to claim 1, characterized in that, The pollution source perception module also includes a pollution source intelligent classification unit; the pollution source intelligent classification unit uses a pre-trained AI classification model to dynamically identify and automatically classify pollution sources based on the operation stage information and pollution source characteristic data obtained from the data processing platform.
5. The pollution source risk assessment system for environmental remediation of oil and gas fields according to claim 1, characterized in that, It also includes an emergency response subsystem; the emergency response subsystem includes a leak sensor network deployed in key areas for real-time monitoring of gas and liquid leak signals; the AI dynamic risk assessment model receives and analyzes the leak signals, and when a leak event is determined to have occurred, it triggers the intelligent decision-making module to start the emergency process, and the intelligent decision-making module outputs an emergency response plan that includes leak location, risk level, evacuation range and disposal measures.
6. The pollution source risk assessment system for environmental remediation of oil and gas fields according to claim 1, characterized in that, It also includes a waste treatment path optimization subsystem; the waste treatment path optimization subsystem is connected to the data processing platform to obtain waste property and quantity data; the AI dynamic risk assessment model evaluates the environmental impact index and economic benefit index of different preset treatment paths to obtain path evaluation results; the intelligent decision-making module outputs recommended optimal treatment path and resource utilization scheme based on the path evaluation results.
7. The pollution source risk assessment system for environmental remediation of oil and gas fields according to claim 1, characterized in that, It also includes an intelligent emission early warning subsystem; the intelligent emission early warning subsystem includes online emission monitoring equipment for continuously monitoring the pollutant concentration and flow rate at the emission outlet; the AI dynamic risk assessment model analyzes emission trends and sends an early warning signal to the intelligent decision-making module when the emission data exceeds a preset threshold or is predicted to exceed the standard; the intelligent decision-making module outputs emission control strategy adjustment suggestions or initiates linkage instructions for treatment facilities based on the early warning signal.
8. The pollution source risk assessment system for environmental remediation of oil and gas fields according to claim 1, characterized in that, The data processing platform integrates a geographic information system, which is used to overlay, analyze, and visualize the spatial location of pollution sources, information on environmentally sensitive areas, and the risk assessment results.
9. The pollution source risk assessment system for environmental remediation of oil and gas fields according to claim 1, characterized in that, The output interface of the intelligent decision-making module is connected to a visualized intelligent environmental management platform. The intelligent environmental management platform is used to centrally display pollution source distribution maps, real-time risk heat maps, early warning information lists, governance task work orders, and emergency resource scheduling views, and provides cross-terminal access functionality. The intelligent environmental management platform also has a model management unit, which allows users to manage the version of the AI dynamic risk assessment model, backtrack training data, evaluate its effectiveness, and manually adjust parameters.
10. A pollution source risk assessment method for environmental remediation in oil and gas fields, characterized in that, The pollution source risk assessment system for oil and gas field environmental governance as described in any one of claims 1-9 is used to conduct pollution source risk assessment, comprising the following steps: S1, data acquisition step: real-time acquisition of multi-source heterogeneous data on solid waste, liquid waste, exhaust gas, and noise pollution sources through IoT sensing terminals deployed at oil and gas field well sites; S2, data processing step: cleaning, standardizing, and fusion processing of the acquired multi-source heterogeneous data to form a structured dataset for risk assessment; S3, dynamic risk assessment step: inputting the structured dataset into a pre-trained AI dynamic risk assessment model, wherein the AI dynamic risk assessment model is based on machine learning or deep learning. The learning algorithm dynamically calculates the comprehensive risk coefficient of pollution sources and generates risk assessment results. The AI dynamic risk assessment model performs self-learning and parameter optimization based on updates to the input data. Step S4, intelligent decision generation: Based on the risk assessment results output by the AI dynamic risk assessment model, it automatically generates and outputs risk warning information, governance suggestions, or emergency response plans for different pollution sources. The dynamic calculation and self-learning optimization of the AI dynamic risk assessment model drive the data acquisition, data processing, dynamic risk assessment, and intelligent decision generation steps, achieving closed-loop intelligent management of pollution sources from identification and assessment to governance decisions.