A rapid monitoring and identification system and method for groundwater pollution in decommissioned oil well clusters.

Abstract

A rapid monitoring and identification system and method for groundwater pollution in decommissioned oil well clusters is disclosed, belonging to the field of water pollution monitoring technology. To address the problems of existing technologies that can only collect water samples at a single depth and cannot achieve stratified monitoring, and that pollution diffusion information cannot be captured in a timely manner when the pollution plume deviates from the preset well location, this rapid monitoring and identification system for groundwater pollution in decommissioned oil well clusters includes a stratified sampling monitoring module, a dynamic blind spot monitoring module, a background data analysis module, a wireless communication transmission module, and a control terminal. It achieves accurate multi-depth monitoring based on the vertical differentiation characteristics of pollutants in decommissioned oil well clusters, eliminating the problem of missed detections in single-depth sampling. Furthermore, the invention uses a dynamic blind spot monitoring module to adjust the sampling points as the pollution plume migrates, eliminating blind spots in fixed well network monitoring and achieving three-dimensional integrated monitoring without blind spots.

Description

Technical Field

[0001] This invention relates to the field of water pollution monitoring technology, specifically to a rapid monitoring and identification system and method for groundwater pollution in decommissioned oil well clusters. Background Technology

[0002] A large number of oil wells are shut down and decommissioned due to declining production capacity or expiration of their exploitation period, forming large-scale decommissioned oil well clusters. Due to defects in wellbore sealing technology, long-term corrosion and damage of casing, and failure of cement sheath bonding, these oil wells cause leakage of pollutants such as crude oil, petroleum hydrocarbons, salts, heavy metals, and volatile organic compounds from the formation, which in turn pollutes the regional groundwater aquifer. Groundwater pollution in the decommissioned oil well cluster area is characterized by strong concealment, dispersed pollution sources, complex migration paths, and obvious vertical stratification. Once the pollution spreads, it will not only damage the regional groundwater resources, but also cause long-term harm to the surrounding ecological environment and human safety.

[0003] Existing monitoring and identification technologies for groundwater pollution in decommissioned oil well clusters mostly employ a fixed monitoring well combined with single-depth sampling. However, these technologies have several shortcomings in practical applications, including: Firstly, pollutants leaking from decommissioned oil wells exhibit significant vertical differentiation. Light petroleum hydrocarbons and volatile organic compounds tend to accumulate in shallow aquifers, while heavy petroleum hydrocarbons and NAPL-phase pollutants tend to sink to medium-deep aquifers. Current technologies, capable of collecting water samples at a single depth, cannot achieve stratified monitoring, making it highly susceptible to missing pollutant components from different aquifers, resulting in incomplete pollution identification. Secondly, the fixed monitoring well layout creates monitoring blind spots. Decommissioned oil well clusters are often scattered, and groundwater runoff is affected by faults, fissures, and aquitards, resulting in unpredictable pollution plume migration trajectories. The fixed well network cannot dynamically adjust its coverage according to the pollution plume, creating monitoring gaps once the plume deviates from the preset well locations. This prevents timely capture of pollution diffusion information, ultimately leading to monitoring delays, frequent false alarms and missed reports, and failing to meet the practical needs for rapid monitoring and identification of groundwater pollution in decommissioned oil well clusters.

[0004] To address the above issues, a rapid monitoring and identification system and method for groundwater pollution in decommissioned oil well clusters is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a rapid monitoring and identification system and method for groundwater pollution in decommissioned oil well clusters. By using this invention, the problems of existing technologies, which can only collect water samples at a single depth and cannot achieve stratified monitoring, are easily missed in detecting pollution components in different aquifers, resulting in incomplete pollution identification are solved. In addition, this invention also solves the problem of monitoring gaps when pollution plumes deviate from the preset well locations, making it impossible to capture pollution diffusion information in a timely manner, resulting in monitoring lag and frequent false alarms.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a rapid monitoring and identification method for groundwater pollution in decommissioned oil well clusters, comprising the following steps: S1: Conduct preliminary hydrogeological surveys in the area of ​​decommissioned oil well clusters, establish a three-dimensional hydrogeological model, simultaneously complete the preset of stratified sampling parameters and the initial fixed monitoring well network benchmark delineation, and construct a basic monitoring data system; S2: Perform layered monitoring and dynamic blind spot monitoring within the initial fixed monitoring well to achieve compatibility between layered monitoring and dynamic network deployment functions; S3: Initiate routine monitoring, conduct multi-depth synchronous sampling and in-situ detection through layered monitoring in fixed monitoring wells, collect layered monitoring data of basic well network, and form an initial three-dimensional pollution dataset; S4: Based on the real-time three-dimensional pollution dataset, the three-dimensional distribution pattern of pollution plumes in the decommissioned oil well cluster area is inferred by combining groundwater flow field parameters. By comparing the coverage of the initial fixed monitoring well network, the blind spots of planar monitoring and the layers that are missed in vertical monitoring are accurately determined. S5: Dispatch the dynamic blind spot monitoring unit to the identified monitoring blind area, and simultaneously carry out planar blind spot deployment and vertical stratified sampling monitoring, integrate the blind spot data with the fixed well network data, and update the three-dimensional model of the pollution plume; S6: Based on the real-time migration and changes of the pollution plume, dynamically adjust the stratified sampling interval and monitoring point layout to form a closed-loop management and control system, enabling rapid identification and early warning of groundwater pollution in the decommissioned oil well cluster area.

[0007] Furthermore, when establishing a three-dimensional hydrogeological model in S1, it is necessary to collect geological survey reports, drilling logs, wellbore plugging data, and groundwater hydrological data of the decommissioned oil well group. Combined with on-site borehole exploration, the vertical aquifer structure is divided, and the aquitard, casing damage section, and permeability coefficient parameters are marked. At the same time, the core pollution area, transition diffusion area, and peripheral control area are divided according to the distribution of oil wells, groundwater flow direction, and geological fissures to achieve three-dimensional analysis of regional hydrogeology.

[0008] Furthermore, the preset stratified sampling parameters in S1 need to determine the key monitoring depth and stratified sampling interval based on the vertical thickness of the aquifer and the vertical differentiation characteristics of pollutants leaking from decommissioned oil wells. At the same time, unidirectional sealing and crosstalk prevention sampling thresholds are configured to prevent water samples from different depths from mixing.

[0009] Furthermore, the S2 employs a multi-level layered sampling device, with multiple independent sampling chambers arranged longitudinally along the probe. Each sampling chamber is equipped with a dedicated contaminant sensor, depth encoder, and negative pressure suction system. The sampling chamber is fitted with a sealing diaphragm and a one-way check valve, and the exterior is coated with a sand-proof and oil-proof adhesive coating, making it suitable for the groundwater environment with high sediment and high oil content in the area of ​​decommissioned oil well clusters.

[0010] Furthermore, during routine monitoring in S3, a layered monitoring method is adopted to simultaneously sample at preset depth locations. Each depth sensor independently completes pollutant concentration detection and uploads data in real time. After sampling, the pipeline is automatically flushed to remove interfering data and calibrate the regional pollution background value, forming a vertical pollution profile of a single well.

[0011] Furthermore, the three-dimensional inversion and extrapolation of the pollution plume in S4 requires access to real-time layered monitoring data from a fixed well network. Combined with a three-dimensional hydrogeological model and pollutant migration algorithm, the planar extent, migration direction, leading edge position, vertical diffusion depth, and interlayer permeability of the pollution plume can be obtained in real time to generate a visualized three-dimensional pollution plume cloud map.

[0012] Furthermore, when S6 dynamically adjusts the monitoring strategy, if the pollution plume spreads in a plane, it moves the supplementary monitoring points forward and adds temporary monitoring points; if the pollution plume contracts, it cancels redundant supplementary monitoring points; if the pollution plume spreads vertically, it denies the stratified sampling interval and increases the monitoring frequency. At the same time, it initiates emergency intensive monitoring and pushes early warning information for pollution exceeding the standard and cross-layer leakage anomalies.

[0013] This invention also proposes another technical solution: a rapid monitoring and identification system for groundwater pollution in decommissioned oil well clusters, comprising: The system includes a stratified sampling monitoring module, a dynamic blind spot monitoring module, a background data analysis module, a wireless communication transmission module, and a control terminal. The stratified sampling and monitoring module is deployed in a fixed monitoring well to achieve simultaneous multi-depth sampling and in-situ detection of groundwater in the decommissioned oil well cluster area; The dynamic blind spot monitoring module is used to rush to the blind spot of the planar monitoring to carry out temporary supplementary layered monitoring; The hierarchical sampling monitoring module and the dynamic blind spot monitoring module are connected to the background data analysis module through the wireless communication transmission module to realize real-time uploading of monitoring data; The background data analysis module is used to invert and deduce the three-dimensional morphology of pollution plumes, determine monitoring blind spots, integrate and analyze monitoring data, and identify pollution status. The control terminal is electrically connected to the background data analysis module and is used to issue monitoring commands, display monitoring data, and push pollution early warning information.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention integrates vertical stratified synchronous sampling and planar dynamic adaptive network layout technology to achieve multi-depth accurate monitoring of pollutants in decommissioned oil well clusters, eliminating the problem of missed detection in single-depth sampling. In addition, this invention can also adjust the layout of monitoring points with the migration of pollution plumes through dynamic blind spot monitoring modules, eliminating blind spots in fixed well network monitoring and achieving three-dimensional integrated monitoring without dead angles.

[0015] 2. This invention adopts an in-situ real-time detection mode, which eliminates the need for manual sampling and testing, thus shortening the monitoring cycle. Furthermore, the background system can automatically invert the pollution plume and identify blind spots, eliminating the need for manual analysis and enabling rapid identification and early warning of pollution. This is suitable for the prevention and control needs of pollution in decommissioned oil well clusters, where pollution is concealed and spreads rapidly.

[0016] 3. The monitoring equipment of this invention is optimized for the high sediment, high oil content and complex hydrogeological environment of retired oil well clusters. It has anti-clogging, anti-adhesion and anti-interference characteristics. The hardware system is stable and reliable, and the operation and maintenance cost is low. It is suitable for areas with concentrated distribution of various retired oil well clusters.

[0017] 4. This invention constructs a closed-loop mechanism, adopts a dynamic adjustment monitoring strategy, and calibrates equipment parameters in real time to ensure the accuracy of monitoring data and provide reliable data support for the treatment of groundwater pollution in decommissioned oil well clusters. Attached Figure Description

[0018] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a diagram illustrating the method steps of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: As Figure 1 As shown, a rapid monitoring and identification system for groundwater pollution in decommissioned oil well clusters includes: The system includes a stratified sampling monitoring module, a dynamic blind spot monitoring module, a background data analysis module, a wireless communication transmission module, and a control terminal.

[0021] The stratified sampling monitoring module is a modular, multi-level stratified sampling device deployed inside the initial fixed monitoring well. Its main body includes a stainless steel probe, multiple independent sealed sampling chambers, characteristic pollutant sensors, a depth encoder, a variable frequency negative pressure suction system, an automatic flushing pipeline, and a sand-proof and oil-proof coating. The sampling chambers are arranged equidistantly or as needed along the longitudinal direction of the probe and are equipped with petroleum hydrocarbon sensors, conductivity sensors, pH sensors, and volatile organic compound sensors to adapt to the detection needs of pollutants leaking from decommissioned oil wells. The depth encoder enables precise depth positioning, the negative pressure suction system is used to adaptively adjust the suction pressure, the automatic flushing pipeline can avoid residual pollution, and the sand-proof and oil-proof coating can improve the durability of the device in complex groundwater environments. This module can realize simultaneous sampling at multiple depths in a single well and in-situ real-time detection.

[0022] The dynamic blind spot monitoring module includes portable rapid sampling probes, simple temporary monitoring tubes, vehicle-mounted monitoring equipment, and UAV-borne monitoring equipment. All devices integrate stratified sampling functions and miniature sensors, and are characterized by portability, speed, and remote control. They can flexibly go to various planar monitoring blind spots and complete temporary supplementary point stratified monitoring without permanent drilling, solving the problem of monitoring blank areas that cannot be covered by fixed well networks.

[0023] The stratified sampling monitoring module and the dynamic blind spot monitoring module are connected to the back-end data analysis module through the wireless communication transmission module to realize real-time uploading of monitoring data. The wireless communication transmission module adopts LoRa and NB-IoT dual-mode communication, which can adapt to the weak signal scenario in remote areas of decommissioned oil well groups, and realize bidirectional data transmission between the stratified sampling monitoring module, the dynamic blind spot monitoring module and the back-end data analysis module, so as to ensure real-time uploading of monitoring data and accurate issuance of control commands, and ensure stable and reliable data transmission.

[0024] The background data analysis module includes a data preprocessing unit, a pollution plume inversion unit, a blind zone identification unit, and a data fusion unit. The data preprocessing unit is responsible for removing interfering data and calibrating pollution background values. The pollution plume inversion unit can deduce the three-dimensional pollution plume morphology based on hydrogeological models and migration algorithms. The blind zone identification unit compares the well network coverage with the pollution plume distribution to automatically determine monitoring blind zones. The data fusion unit integrates data from fixed wells and blind spot fillers to update the pollution model and achieve rapid analysis and identification of pollution status.

[0025] The control terminal is a touch-screen industrial control all-in-one machine, which is electrically connected to the back-end data analysis module. It has functions such as command issuance, data display, early warning push and parameter setting. Managers can remotely control the monitoring process, view the three-dimensional pollution cloud map and receive pollution early warning through the control terminal to realize intelligent management and control of groundwater pollution monitoring in the decommissioned oil well cluster area.

[0026] Example 2: As Figure 2 As shown, a rapid monitoring and identification method for groundwater pollution in decommissioned oil well clusters includes the following steps: S1: Conduct preliminary hydrogeological surveys of the decommissioned oil well cluster area, establish a three-dimensional hydrogeological model, simultaneously complete the pre-setting of stratified sampling parameters and the initial delineation of the fixed monitoring well network benchmark, and construct a basic monitoring data system, as detailed below: Given the unique characteristics of the decommissioned oil well cluster area, hydrogeological analysis, stratification parameter setting, and initial network delineation were completed simultaneously to avoid redundant exploration and ensure data continuity. First, geological exploration reports from previous years, single-well drilling logs, wellbore sealing acceptance data, groundwater level, and water quality monitoring data were collected for the area. On-site drilling was also conducted, with a total of 12 exploration boreholes drilled to obtain first-hand data on stratigraphy, aquifer depth, aquitard distribution, casing damage location, and groundwater permeability. Based on this data, a three-dimensional hydrogeological model of the decommissioned oil well cluster area was established using a GIS geographic information system, accurately delineating shallow unconfined aquifers with depths ranging from 5m to 20m vertically. The study identified three high-risk sections of casing damage, two groundwater flow direction turning zones, and four aquitard interfaces, covering a medium-depth confined aquifer of 25m-60m and a deep micro-confined aquifer of 65m-100m. Based on the density of decommissioned oil wells, groundwater flow direction, and geological fissure distribution, the study divided the area into a core pollution zone (a densely populated area of ​​decommissioned oil wells, totaling 42 wells), a transitional diffusion zone (within 300m of the core zone), and a peripheral control zone (within 500m of the transitional zone), clearly defining the pollution risk levels of each area.

[0027] Subsequently, stratified sampling parameters were preset. Considering the characteristics of pollutants from decommissioned oil wells, which are mainly petroleum hydrocarbons, salts, and benzene compounds, and based on the vertical differentiation pattern of light components rising and heavy components sinking, the key monitoring depths for each aquifer were determined. Specifically, the monitoring depths for shallow unconfined water were 8m and 15m, respectively; the monitoring depths for medium-level confined water were 30m, 45m, and 60m, respectively; and the monitoring depths for deep micro-confined water were 70m and 90m, respectively. The stratified sampling interval in the conventional area was set to 3m, and the interval in the core contaminated area was increased to 1m. At the same time, a one-way sealed sampling threshold was set, and the water volume in a single sampling chamber was controlled to 500mL to prevent crosstalk and mixing of water samples from adjacent layers, thus avoiding the risk of missed detection at the source.

[0028] Finally, the initial fixed monitoring well network baseline was determined. Based on the three-dimensional hydrogeological model and risk zoning results, 8 fixed monitoring wells were deployed in the core pollution area, 6 fixed monitoring wells in the transition diffusion area, and 4 fixed monitoring wells in the outer control area, for a total of 18 fixed monitoring wells. The well spacing in the core area was controlled at 150m, the well spacing in the transition area was controlled at 300m, and the well spacing in the outer area was controlled at 500m. Six blind spots in the initial well network were marked, including 2 blank areas with excessive well spacing, 3 swampy areas that are difficult for personnel to reach, and 1 hidden seepage channel area. The coordinates, monitoring depths, and blind spot information of all fixed wells were entered into the GIS system to achieve the binding management of point locations and depths.

[0029] S2: Perform stratified monitoring and dynamic blind spot monitoring within the initial fixed monitoring wells to achieve compatibility between stratified monitoring and dynamic network deployment functions, as detailed below: First, a modular multi-stage stratified sampling device was installed in 18 fixed monitoring wells. This device consists of seven independent sealed sampling chambers arranged longitudinally along a stainless steel probe. Each chamber corresponds to a preset monitoring depth and is equipped with petroleum hydrocarbon sensors, conductivity sensors, pH sensors, and volatile organic compound sensors to detect characteristic pollutants from leaking decommissioned oil wells in real time. A high-precision depth encoder is installed at the bottom of the probe, with a positioning error controlled within ±0.1m. A variable frequency negative pressure suction system is also included, which can adaptively adjust the suction pressure according to the water pressure of different aquifers. A one-way check valve and a sealing diaphragm are installed at the sampling chamber outlet to prevent backflow and crosstalk. The outer wall of the device is coated with a polytetrafluoroethylene (PTFE) anti-sand and anti-oil adhesion coating to prevent underground cement sand and crude oil from adhering and clogging the pipeline. After the stratified sampling device for each fixed well is installed, depth positioning calibration and sensor zero-point calibration are performed to ensure sampling accuracy and detection precision.

[0030] Simultaneously, a dynamic blind spot monitoring module was prepared, equipped with 6 sets of portable rapid sampling probes, 10 sets of simple temporary monitoring tubes, 2 vehicle-mounted monitoring devices, and 1 set of UAV-borne monitoring devices. All blind spot monitoring devices are compatible with stratified sampling functions and can be quickly lowered to a preset depth via a winch mechanism to collect water samples from the corresponding strata. Portable probes are suitable for rapid blind spot monitoring in flat areas, vehicle-mounted devices are suitable for mobile monitoring in open areas, and UAV-borne devices are suitable for remote blind spot monitoring in areas that are difficult for personnel to reach, such as swamps and densely populated well clusters, comprehensively covering all types of blind spots marked in the initial well network.

[0031] Finally, system integration and data docking were carried out. The fixed well stratified sampling device, dynamic blind spot monitoring module and back-end data analysis module were connected through LoRa wireless communication module. Then, the data transmission link was debugged to ensure that the data packet loss rate was less than 1%. The sensor accuracy, depth positioning error and sampling flow rate were calibrated, and the automatic flushing pipeline function was tested to ensure that the vertical stratified data and the planar point data were uploaded in real time and uniformly parsed by the back-end, so as to achieve stable operation of the hardware system.

[0032] S3: Initiate routine monitoring by conducting multi-depth synchronous sampling and in-situ detection through stratified monitoring within fixed monitoring wells. Collect stratified monitoring data from the basic well network to form an initial three-dimensional pollution dataset, as detailed below: The control terminal issues monitoring commands, and the stratified sampling device in the fixed well is precisely lowered to the preset monitoring depth via a winch mechanism. The depth encoder provides real-time feedback on the position information. Once in position, the probe is locked, and the variable frequency negative pressure suction system is activated. The seven sampling chambers start sampling simultaneously, with the sampling time for each chamber controlled within 3 minutes to ensure sufficient water sample collection and no sample mixing. After sampling, the sensors corresponding to each sampling chamber independently conduct in-situ detection, collecting real-time data on pollutant concentration, water level, water temperature, and conductivity at each depth. This data is then uploaded to the background data analysis module via a wireless communication module for analysis. After sampling, the pipeline flushing program is automatically activated, with a flushing time of 5 minutes, to remove residual water sample and pollutants from the pipeline and prevent data interference in subsequent monitoring.

[0033] Layered monitoring data from 18 fixed monitoring wells were collected and combined with flow field parameters such as groundwater flow velocity, flow direction, and permeability coefficient. The data was then uploaded to the backend data analysis module for preprocessing to remove abnormal data caused by equipment fluctuations and environmental interference. The background value of groundwater pollution in the area was calibrated, and a three-dimensional monitoring database was established. For each fixed well, monitoring data from different depths were integrated, and a vertical pollution profile of each well was generated to clearly show the distribution of pollution concentration in each aquifer, identify high-pollution vertical layers, and preliminarily understand the basic pollution status of groundwater in the decommissioned oil well cluster area. In this embodiment, the routine monitoring frequency was set to once a day, and the frequency was increased to twice a day in the core pollution area to ensure data real-time performance.

[0034] S4: Based on real-time 3D pollution datasets, and combined with groundwater flow field parameters, the 3D distribution pattern of pollution plumes in the decommissioned oil well cluster area is inferred. By comparing the coverage of the initial fixed monitoring well network, the blind spots in planar monitoring and the undetected vertical layers are accurately determined, as follows: The backend data analysis module accesses real-time layered monitoring data from the fixed well network, combines it with the previously established three-dimensional hydrogeological model, calls the pollutant migration algorithm, and comprehensively considers factors such as groundwater flow field, formation permeability, and pollutant density to invert and deduce the three-dimensional distribution pattern of the pollution plume in the decommissioned oil well cluster area in real time. It obtains the plane range, migration direction, leading edge advance speed, vertical diffusion depth, and interlayer permeability of the pollution plume, and generates a visualized three-dimensional pollution plume cloud map to intuitively display the pollution diffusion trajectory.

[0035] By overlaying and comparing the 3D pollution plume map with the coverage area of ​​the initial fixed monitoring well network, the system automatically identifies two types of monitoring blind zones: one type is planar blind zones, with a total of 4 newly identified blind zones, including 2 areas where the pollution plume front exceeds the coverage area of ​​the fixed well network and 2 blank areas in swamps where no fixed wells have been deployed; the other type is vertically missed layers, identifying high concentrations of petroleum hydrocarbon pollution at a depth of 40m in the middle layer of confined water, which was not included in the initial stratified sampling plan and belongs to the missed detection range. The system automatically marks the coordinates, area, risk level, and recommended monitoring depth of the identified blind zones, forming a blind zone list, and pushes it to the control terminal to provide accurate data support for subsequent blind zone supplementation monitoring.

[0036] S5: Dispatch the dynamic blind spot monitoring unit to the identified monitoring blind area, simultaneously carry out planar blind spot deployment and vertical stratified sampling monitoring, integrate the blind spot data with the fixed well network data, and update the three-dimensional model of the pollution plume, as detailed below: Based on the aforementioned blind spot list, the control terminal dispatches the dynamic blind spot monitoring module to the corresponding locations: for flat, planar blind spots, portable rapid sampling probes are used, and three temporary monitoring points are manually deployed to quickly reach the recommended monitoring depth for stratified sampling monitoring; for difficult-to-reach blind spots such as swamps, unmanned aerial vehicle (UAV)-borne monitoring equipment is used to remotely control water sample collection and in-situ detection; for vertically missed layers, the depth of the fixed well stratified sampling device is adjusted, and a new 40m deep sampling point is added to complete the vertical monitoring data.

[0037] During the blind spot monitoring process, the temporary monitoring points strictly followed the fixed well stratified sampling standards, controlling the sampling interval, water volume, and testing procedures to ensure data consistency. The blind spot data was uploaded to the background data analysis module in real time and integrated with the fixed well network stratified data. This process can eliminate duplicate data, correct errors, update the three-dimensional pollution plume model, and correct the pollution diffusion trajectory. After the blind spot monitoring, the groundwater pollution in the decommissioned oil well group area achieved no blind spots in the plane and no missed detections in the vertical direction. The complete distribution pattern of the pollution plume was accurately captured, and two decommissioned oil wells were identified as the main sources of leakage pollution. The pollution data was complete and reliable.

[0038] S6: Based on the real-time migration and changes of the pollution plume, dynamically adjust the stratified sampling interval and monitoring point layout to form a closed-loop management system, enabling rapid identification and early warning of groundwater pollution in decommissioned oil well clusters, as detailed below: The background data analysis module tracks the dynamics of the pollution plume in real time and adjusts the monitoring plan according to the pollution diffusion trend. Specifically, for the case where the pollution plume is advancing towards the front, two temporary supplementary monitoring points are moved forward and one new temporary monitoring point is added to closely follow the trajectory of the pollution plume's front. For areas where the pollution plume has not shown significant contraction, the existing fixed well network and supplementary monitoring points are retained. For the case of pollution diffusion at a depth of 40m in the middle layer of confined water, the sampling interval at this layer is increased to 1m, and the monitoring frequency is increased to once every 6 hours.

[0039] The system monitors data in real time. If any abnormalities such as excessive pollutant concentration or cross-layer leakage are detected, an emergency encrypted monitoring mode is immediately activated, increasing the monitoring frequency in the core area to once every hour. At the same time, audible and visual warnings are pushed to management personnel through the control terminal, marking the location, depth, and concentration of pollution, providing a rapid basis for pollution control. In addition, a regular operation and maintenance calibration mechanism is established, calibrating the sensor accuracy and depth positioning error every 15 days, maintaining the dynamic blind spot monitoring module monthly, and cleaning pipeline sediment and oil. Based on seasonal groundwater level changes, the sealing status of decommissioned oil wells, and re-inspection results, the stratified sampling plan and monitoring well network layout are updated quarterly to ensure the long-term stable operation of the system and achieve continuous and rapid monitoring and identification of groundwater pollution in the decommissioned oil well cluster area.

[0040] It should be noted that the parameters in the above two embodiments are for better explanation of the embodiments, and the specific parameters should be set according to the actual monitoring conditions.

[0041] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0042] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A rapid monitoring and identification method for groundwater pollution in decommissioned oil well clusters, characterized in that, Includes the following steps: S1: Conduct preliminary hydrogeological surveys in the area of ​​decommissioned oil well clusters, establish a three-dimensional hydrogeological model, simultaneously complete the preset of stratified sampling parameters and the initial fixed monitoring well network benchmark delineation, and construct a basic monitoring data system; S2: Perform layered monitoring and dynamic blind spot monitoring within the initial fixed monitoring well to achieve compatibility between layered monitoring and dynamic network deployment functions; S3: Initiate routine monitoring, conduct multi-depth synchronous sampling and in-situ detection through layered monitoring in fixed monitoring wells, collect layered monitoring data of basic well network, and form an initial three-dimensional pollution dataset; S4: Based on the real-time three-dimensional pollution dataset, the three-dimensional distribution pattern of pollution plumes in the decommissioned oil well cluster area is inferred by combining groundwater flow field parameters. By comparing the coverage of the initial fixed monitoring well network, the blind spots of planar monitoring and the layers that are missed in vertical monitoring are accurately determined. S5: Dispatch the dynamic blind spot monitoring unit to the identified monitoring blind area, and simultaneously carry out planar blind spot deployment and vertical stratified sampling monitoring, integrate the blind spot data with the fixed well network data, and update the three-dimensional model of the pollution plume; S6: Based on the real-time migration and changes of the pollution plume, dynamically adjust the stratified sampling interval and monitoring point layout to form a closed-loop management and control system, enabling rapid identification and early warning of groundwater pollution in the decommissioned oil well cluster area.

2. The rapid monitoring and identification method for groundwater pollution in decommissioned oil well clusters according to claim 1, characterized in that: When establishing a three-dimensional hydrogeological model in S1, it is necessary to collect geological survey reports, drilling logs, wellbore plugging data, and groundwater hydrological data of the decommissioned oil well group. Combined with on-site borehole exploration, the vertical aquifer structure is divided, and the aquitard, casing damage section, and permeability coefficient parameters are marked. At the same time, according to the distribution of oil wells, the direction of groundwater flow, and geological fissures, the core pollution area, the transition diffusion area, and the peripheral control area are divided to achieve three-dimensional analysis of the regional hydrogeology.

3. The rapid monitoring and identification method for groundwater pollution in decommissioned oil well clusters according to claim 2, characterized in that: The preset parameters for stratified sampling in S1 need to be determined based on the vertical thickness of the aquifer and the vertical differentiation characteristics of pollutants leaking from decommissioned oil wells. The key monitoring depth and stratified sampling interval should be determined, and unidirectional sealing and crosstalk prevention sampling thresholds should be configured to prevent water samples from different depths from mixing.

4. The rapid monitoring and identification method for groundwater pollution in decommissioned oil well clusters according to claim 3, characterized in that: The S2 uses a multi-level layered sampling device, with multiple independent sampling chambers arranged longitudinally along the probe. Each sampling chamber is equipped with a dedicated contaminant sensor, depth encoder and negative pressure suction system. The sampling chamber is equipped with a sealing diaphragm and a one-way check valve, and the outside is equipped with a sand-proof and oil-proof adhesion coating, which is suitable for the groundwater environment with high sediment and high oil content in the area of ​​decommissioned oil well clusters.

5. The rapid monitoring and identification method for groundwater pollution in decommissioned oil well clusters according to claim 4, characterized in that: During routine monitoring in S3, a layered monitoring method is used to simultaneously sample at preset depth locations. Each depth sensor independently detects pollutant concentrations and uploads data in real time. After sampling, the pipeline is automatically flushed to remove interfering data and calibrate the regional pollution background value, forming a vertical pollution profile of a single well.

6. The rapid monitoring and identification method for groundwater pollution in decommissioned oil well clusters according to claim 5, characterized in that: In the S4 three-dimensional inversion and extrapolation of the pollution plume, it is necessary to access real-time layered monitoring data from a fixed well network, combine a three-dimensional hydrogeological model and a pollutant migration algorithm, and obtain the plane range, migration direction, front position, vertical diffusion depth and interlayer permeability of the pollution plume in real time to generate a visualized three-dimensional pollution plume cloud map.

7. A rapid monitoring and identification method for groundwater pollution in decommissioned oil well clusters according to claim 6, characterized in that: When S6 dynamically adjusts the monitoring strategy, if the pollution plume spreads in a plane, it moves the supplementary monitoring points forward and adds temporary monitoring points; if the pollution plume contracts, it cancels redundant supplementary monitoring points; if the pollution plume spreads vertically, it denies the stratified sampling interval and increases the monitoring frequency. At the same time, it initiates emergency intensive monitoring and pushes early warning information for pollution exceeding the standard and cross-layer leakage anomalies.

8. A rapid monitoring and identification system for groundwater pollution in decommissioned oil well clusters, applied to the rapid monitoring and identification method for groundwater pollution in decommissioned oil well clusters as described in claims 1-7, characterized in that, include: The system includes a stratified sampling monitoring module, a dynamic blind spot monitoring module, a background data analysis module, a wireless communication transmission module, and a control terminal. The stratified sampling and monitoring module is deployed in a fixed monitoring well to achieve simultaneous multi-depth sampling and in-situ detection of groundwater in the decommissioned oil well cluster area; The dynamic blind spot monitoring module is used to rush to the blind spot of the planar monitoring to carry out temporary supplementary layered monitoring; The hierarchical sampling monitoring module and the dynamic blind spot monitoring module are connected to the background data analysis module through the wireless communication transmission module to realize real-time uploading of monitoring data; The background data analysis module is used to invert and deduce the three-dimensional morphology of pollution plumes, determine monitoring blind spots, integrate and analyze monitoring data, and identify pollution status. The control terminal is electrically connected to the background data analysis module and is used to issue monitoring commands, display monitoring data, and push pollution early warning information.

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