Real-time warning method and system for formation fluid invasion based on multiple-source while-drilling parameters

Abstract

This invention primarily relates to the field of drilling engineering technology. To improve the accuracy and real-time performance of formation fluid intrusion early warning, this invention provides a method and system for real-time early warning of formation fluid intrusion based on multi-source drilling parameters. The core of this method is: collecting multi-source drilling parameters, calculating the dynamic characteristics of each parameter, automatically and dynamically adjusting the diagnostic weights of the multi-source parameters based on the real-time drilling conditions, and calculating local risk values ​​for each parameter; simultaneously establishing a formation fluid intrusion feature library, performing feature matching based on the order and combination of the dynamic characteristics of the drilling parameters, and outputting the matching degree with typical fluid intrusion patterns; finally, establishing a fuzzy rule library based on dynamic characteristics, local risk values, and matching degrees to obtain quantified formation fluid intrusion risks, and performing graded fluid intrusion risk early warning based on risk levels, thereby improving the accuracy, real-time performance, and comprehensiveness of formation fluid intrusion early warning.

Description

Technical Field

[0001] This invention relates to the field of drilling engineering technology, and in particular to a method and system for real-time early warning of formation fluid intrusion based on multi-source drilling parameters. Background Technology

[0002] In the field of geological core drilling, as exploration work advances into deeper and higher-energy strata, the geological conditions encountered become increasingly complex. Formation water intrusion is one of the common and challenging problems during drilling. When encountering high-energy or deep strata, due to the high formation pressure, formation water may surge into the borehole at considerable pressure and flow, causing numerous adverse effects on the drilling operation.

[0003] The entire geological drilling industry is constantly pursuing more efficient and safer exploration methods. Formation water intrusion, which affects drilling progress, threatens construction safety, and may damage drilling equipment, has always been a key focus and research area within the industry. Currently, although some progress has been made in drilling technology and equipment, there is still significant room for improvement in the accurate and real-time monitoring and early warning of formation water intrusion.

[0004] Traditionally, the presence of formation water intrusion relies heavily on the experience of operators. This is achieved by observing the return of flushing fluid from the borehole, such as the fluid level in the circulation tank, drilling fluid density, viscosity, sand content, color, and the presence of air bubbles. Additionally, some existing technologies employ monitoring wells around the borehole to indirectly infer formation water intrusion by monitoring changes in parameters like water level and pressure. While this method provides some reference, the cost of deploying monitoring wells is high, and it cannot reflect the actual situation inside the borehole in real time, creating blind spots and making it difficult to detect sudden, localized formation water intrusion events promptly.

[0005] In summary, the shortcomings of existing fluid intrusion early warning technologies include:

[0006] (1) Highly subjective and unable to provide early warning: Relying on operators to observe the return of flushing fluid to judge the intrusion of formation water, different operators have different judgment standards, and it can only be detected when the intrusion of formation water reaches a certain level and the characteristics of flushing fluid return change significantly. It cannot provide early warning, which may lead to the need to take countermeasures only after a large amount of formation water has entered, increasing the difficulty and cost of treatment, and may even cause safety accidents, such as borehole collapse, equipment damage, etc.

[0007] (2) High cost and monitoring blind spots: The method of setting up monitoring wells requires a large additional investment in the construction, equipment installation and maintenance of monitoring wells. At the same time, the number of monitoring wells is limited and cannot fully cover the area around the borehole. Formation water intrusion far from the location of the monitoring wells cannot be detected in time, resulting in monitoring blind spots and making it difficult to ensure the all-round safety of the drilling process;

[0008] (3) Low monitoring accuracy and weak anti-interference capability of single sensor: When using sound wave, resistivity or pressure sensor alone, it is easily affected by geological environment, mechanical disturbance, etc., resulting in a high false alarm rate and inability to accurately identify early intrusion; lack of multi-source data collaborative analysis mechanism: existing technology does not integrate multiple types of sensors, lacks targeted data fusion algorithm, and cannot give full play to the complementary advantages of different sensors. Summary of the Invention

[0009] The technical problem to be solved by the present invention is to provide a method and system for real-time early warning of formation fluid intrusion based on multi-source drilling parameters, with the aim of improving the accuracy and real-time performance of formation fluid intrusion early warning.

[0010] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0011] On one hand, the present invention provides a real-time early warning method for formation fluid intrusion based on multi-source drilling parameters, the method comprising:

[0012] S1: Collect multi-source drilling parameters and calculate the dynamic characteristics of each drilling parameter;

[0013] S2: Based on the predefined state rule base of drilling conditions, dynamically assign diagnostic weights to multi-source drilling parameters, and calculate the local risk value based on the single drilling parameter based on the diagnostic weights and dynamic characteristics.

[0014] S3: Establish a formation fluid invasion feature library. Based on the sequence and combination of dynamic features of drilling parameters, perform feature matching in the formation fluid invasion feature library and output the temporal matching degree with typical fluid invasion patterns.

[0015] S4: Establish a fuzzy rule base, take dynamic features, local risk values ​​and time series matching degree as input, perform fuzzy calculation, output the formation fluid intrusion risk level, and conduct risk warning based on the risk level.

[0016] Furthermore, the multi-source drilling parameters mentioned in S1 include: the acoustic wave velocity of the fluid in the borehole, the resistivity of the drilling fluid, and the pressure at the bottom of the borehole.

[0017] Furthermore, the dynamic characteristics described in S1 include the instantaneous values, changes, and trends of the drilling parameters.

[0018] Furthermore, the preprocessing described in S1 includes: using a Kalman filter algorithm to remove geological noise and mechanical vibration interference; and using data from stable working conditions during drilling for baseline correction.

[0019] Furthermore, the drilling conditions described in S2 include drilling depth, drilling speed, and mud characteristics.

[0020] Furthermore, the method for calculating the local risk value described in S2 is as follows: ,in For drilling parameter category index, This represents the local risk value for the corresponding drilling parameters. For the diagnostic weights of the corresponding drilling parameters, and This is an empirical coefficient. This represents the change in the corresponding drilling parameters. This represents the changing trend of the corresponding drilling parameters.

[0021] Furthermore, S3 includes:

[0022] Based on the dynamic characteristics of multi-source drilling parameters, typical fluid invasion modes are defined, and a formation fluid invasion feature library is established.

[0023] Based on the sequence and combination of the dynamic characteristics of the current multi-source drilling parameters, typical invasion patterns are matched in the formation fluid invasion feature database, and the temporal matching degree is output.

[0024] Furthermore, S4 employs the Mamdani inference method and uses the centroid method for defuzzification, ultimately outputting an accurate comprehensive risk index and confidence level.

[0025] Furthermore, the risk warning based on risk level described in S4 includes:

[0026] If the comprehensive risk index is less than the first preset value, a level one warning will be issued, and only the data will be updated.

[0027] If the comprehensive risk index is greater than or equal to the first preset value but less than the second preset value, a level-two warning will be issued to increase the frequency of data collection and warnings.

[0028] If the comprehensive risk index is greater than or equal to the third preset value and the intrusion probability is equal to or equal to the fourth preset value, a level 3 warning will be issued. Based on the level 2 warning, the data collection and warning frequency will be further increased, the fluid intrusion parameters will be estimated, and a warning plan will be automatically generated.

[0029] On the other hand, the present invention also provides a real-time formation fluid intrusion early warning system based on multi-source information fusion, the system comprising: a drilling measurement module, a data transmission module, a local risk calculation module, a time series matching degree calculation module, a risk level calculation module, and a risk early warning module;

[0030] The measurement while drilling module is used to collect multi-source drilling parameters and drilling conditions;

[0031] The data transmission module is used to transmit the multi-source drilling parameters collected by the measurement-while-drilling module to the data processing module.

[0032] The local risk calculation module is used to calculate the dynamic characteristics of each drilling parameter and calculate the risk level based on the dynamic characteristics.

[0033] The time-series matching degree calculation module is used to perform feature matching in the established formation fluid invasion feature library according to the sequence and combination of dynamic characteristics of drilling parameters, and output the time-series matching degree with typical fluid invasion modes.

[0034] The risk level calculation module is used to perform fuzzy calculation based on the established fuzzy rule base, taking dynamic features, local risk values ​​and temporal matching degree as inputs, and outputting the formation fluid intrusion risk level.

[0035] The risk warning module is used to provide graded warnings based on risk levels.

[0036] The beneficial effects of this invention are:

[0037] High timeliness: It collects multi-source drilling parameters while drilling, and can issue an early warning in a very short time once there are signs of formation fluid intrusion. Compared with relying on manual observation of flushing fluid return or later analysis of geophysical data, it greatly advances the warning time and buys more time to take countermeasures.

[0038] High accuracy: By comprehensively utilizing multi-source fusion judgment methods such as acoustic wave measurement, resistivity testing and bottom hole pressure monitoring, and combining data analysis with drilling conditions, it can more accurately determine whether formation water has invaded, the location of the invasion and the flow rate, etc., avoiding the subjective and multiple interpretation problems of the judgment in the existing technology, and improving the reliability of the early warning.

[0039] Cost-effective: It eliminates the need for numerous additional monitoring wells; comprehensive monitoring can be achieved simply by installing downhole sensing sensors on the drill pipe, reducing monitoring costs. Furthermore, the ability to promptly detect and address formation water intrusion prevents serious consequences such as equipment damage and borehole abandonment caused by massive formation water intrusion, minimizing economic losses and improving the overall efficiency of drilling operations.

[0040] Comprehensive monitoring: As drilling penetrates deeper into the formation, the system can monitor the conditions at different locations within the borehole in real time, eliminating blind spots. Compared to monitoring wells that can only monitor local areas, this provides a more comprehensive guarantee for the safety of the drilling process. Attached Figure Description

[0041] Figure 1 This is a flowchart of the real-time early warning method for formation fluid intrusion based on multi-source drilling parameters described in this invention.

[0042] Figure 2 This is a schematic diagram of the sensor-integrated downhole probe unit in the measurement while drilling module.

[0043] Figure 3 A schematic diagram of the dynamic weight allocation logic;

[0044] Figure 4 This is a schematic diagram of the real-time early warning system for formation fluid intrusion based on multi-source drilling parameters as described in this invention. Detailed Implementation

[0045] Traditional fluid intrusion early warning systems suffer from drawbacks such as high subjectivity, high cost, blind spots, and weak anti-interference capabilities.

[0046] This invention proposes a real-time early warning method and system for formation fluid invasion based on multi-source drilling parameters. The core of this method is as follows: During drilling, multi-source drilling parameters are collected, and the instantaneous values, changes, and trends of each parameter are calculated. The weights of these parameters in diagnostic analysis are automatically and dynamically adjusted according to the current drilling conditions, and local risk values ​​based on single drilling parameters are calculated. Simultaneously, a formation fluid invasion feature library is established. Based on the sequence and combination of the instantaneous values, changes, and trends of the drilling parameters, feature matching is performed in the database, outputting the temporal matching degree with typical fluid invasion patterns. Finally, a fuzzy rule base is established based on the instantaneous values, changes, trends, local risk values, and matching degree to obtain quantified formation fluid invasion risk, and graded fluid invasion risk early warning is performed based on the risk level.

[0047] The technical solutions in this embodiment 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.

[0048] Figure 1 A flowchart illustrating a real-time early warning method for formation fluid intrusion based on multi-source drilling parameters is shown. Please refer to [link / reference]. Figure 1 The method includes the following steps:

[0049] S1: Real-time acquisition of multi-source drilling parameter data streams. After data preprocessing, the instantaneous value, change amount, and change trend of each drilling parameter are calculated.

[0050] In this embodiment, the multi-source drilling parameters include, but are not limited to, borehole fluid wave velocity, drilling fluid resistivity, and bottom hole fluid pressure.

[0051] The method for obtaining the fluid pressure inside the borehole is as follows: Figure 2 As shown, an acoustic measurement sensor, including a wave transmitter and a sound wave receiver, is embedded in the outer wall of a near-bit measurement-while-drilling (MWD) sub. The transmission frequency of the acoustic measurement sensor is adjustable from 100kHz to 500kHz, measuring the fluid velocity within the borehole by transmitting and receiving acoustic signals. Based on the principle that sound waves propagate at different speeds in different media, when formation water invades, the composition of the fluid within the borehole changes, and the sound wave propagation speed changes accordingly. By monitoring the changes in the acoustic wave velocity in real time and comparing it with a pre-set drilling fluid reference wave velocity, a preliminary determination can be made as to whether formation water has invaded and the extent of the invasion.

[0052] The method for obtaining drilling fluid resistivity is as follows: Figure 2 As shown, a resistivity sensor is embedded in a measurement-while-drilling (MWD) sub, with four electrodes evenly arranged: transmitting electrodes A and B, and receiving electrodes M and N. The electrodes penetrate the sub's outer shell and directly contact the drilling fluid, with waterproof sealing at the openings. The drilling fluid resistivity is calculated by real-time monitoring of the potential difference between the electrodes.

[0053] The method for obtaining the bottom pressure of the hole is as follows: Figure 2 As shown, a pressure sensor is installed at the bottom of the drill pipe near the drill bit to record changes in the bottom hole fluid pressure in real time. A piezoelectric pressure sensing element is used, with a measurement range of 0~50MPa and a response time ≤10ms, enabling rapid capture of pressure fluctuations caused by formation water intrusion.

[0054] In this embodiment, the preprocessing of the collected multi-source drilling parameters includes: (1) using the Kalman filter algorithm to remove geological noise and mechanical vibration interference in the collected data; (2) using data from stable working conditions such as drilling stop and circulation to dynamically correct sensor zero drift and gain drift.

[0055] For the preprocessed data, the instantaneous values, changes and trends of each drilling parameter are calculated in real time within a set sliding time window.

[0056] S2: Calculate the local risk value based on the instantaneous value, change amount, and change trend of a single multi-source drilling parameter.

[0057] Signals based on different physical principles (acoustic, electrical, mechanical) are quantified into a comparable index of anomaly degree. Furthermore, in different formations (such as salt-gypsum layers and fracture zones), the sensitivity and anti-interference capabilities of different sensors vary. Therefore, the local risk value based on a single multi-source drilling parameter is dynamically calculated using dynamic weights to reflect the differences in the local risk contribution of each drilling parameter.

[0058] Dynamic risk weighting of individual drilling parameters, such as Figure 3As shown, real-time drilling condition information is acquired, including but not limited to current depth, mechanical drilling rate, and drilling fluid density. Using a pre-defined rule base, the current state is categorized into one of several predefined conditions (e.g., "Normal Drilling in Sandstone," "Encountering Mudstone," "Drilling in Fractured Sections," "Drilling in Salt-Gypsum Layers," "Tug-in / Tug-out Conditions," etc.). Dynamic diagnostic weights are assigned to multi-source drilling parameters based on a built-in condition-weight mapping table. For example, in "Fractured Sections," pressure signals are most sensitive, so a pressure risk weight of Wp=0.6, a sonic risk weight of Wv=0.3, and a resistivity risk weight of Wr=0.1 are set; in "Salt-Gypsum Layers," resistivity signals are greatly affected by drilling fluid salinity, so a resistivity risk weight of Wr=0.1, a sonic risk weight of Wv=0.4, and a pressure risk weight of Wp=0.5 are set.

[0059] Local risk contribution calculation: Calculate the local risk value of a single drilling parameter. For example, the risk value of acoustic wave sensors: ,in and These are empirical coefficients. Calculations are performed similarly. and This value reflects the degree of anomaly indicated by data from a single sensor.

[0060] S3: Establish a formation fluid invasion feature library. Based on the instantaneous values, changes, and trends of drilling parameters, perform feature matching in the formation fluid invasion feature library and output the matching degree with typical fluid invasion patterns.

[0061] Because the instantaneous values, changes, and trends of drilling parameters under different intrusion states exhibit their own characteristics, for example: Early micro-surge characteristics: The pressure change ΔP shows a small negative jump (e.g., -0.3%), followed by a stable negative trend in the wave velocity change ΔV, while the absolute negative value of the resistivity change trend dR / dt increases significantly. Rapid intrusion characteristics: The pressure change ΔP and resistivity change ΔR undergo large negative jumps almost synchronously (e.g., the pressure change ΔP decreases by more than or equal to 2%, and the resistivity change rate ΔR decreases by more than or equal to 10%), and the wave velocity change trend dV / dt is also significantly negative. Interference characteristic example: Only the resistivity change ΔR changes, while the pressure P and wave velocity V are stably matched, indicating "formation temperature change interference". Therefore, by continuously monitoring the instantaneous values, changes, and trends of multi-source drilling parameters in sequence and combination, real-time matching is performed in a predefined formation fluid invasion feature library to reflect the similarity between the current data pattern and typical invasion patterns. The output result is the time series matching degree Tm, which uses a value between 0 and 1 to intuitively measure the degree of conformity between the time series data to be detected and the reference value. The closer to 1, the better the conformity; the closer to 0, the greater the deviation between the current running sequence and the normal mode, and there may be an anomaly.

[0062] S4: Establish a fuzzy rule base based on dynamic features, local risk values ​​and matching degree, output the risk of formation fluid intrusion, and conduct graded risk warning based on risk level.

[0063] The instantaneous values, changes, trends, local risk values, and time series matching degrees of the drilling parameters obtained from the above steps are all precise numerical values. Fuzzy subsets convert these numerical values ​​into judgment class descriptions such as "negative large, negative small, zero, positive large" and "high, medium, low". That is, the instantaneous values, changes, trends, local risk values, and time series matching degrees of the drilling parameters are used as input variables, and their fuzzy subsets are defined as "negative large (NB)", "negative small (NS)", "zero (ZO)", "positive large (PB)", "positive small (PS)" and membership functions, respectively.

[0064] Membership function: used to determine the extent to which a precise value belongs to a certain fuzzy subset. Example: pressure change ΔP=-9, possibly 90% belongs to NB and 10% belongs to NS.

[0065] Fuzzy rule base: Stores a large number of judgment rules based on expert knowledge, i.e., a risk assessment manual. The rule base contains dozens or even hundreds of similar rules, covering various combinations of indicators. For example, IF (Wr is Low AND Rr is High) AND (other sensors have low risk) THEN Overall risk is low (possibly interference) AND intrusion probability is low; IF ΔP is NB AND dR / dt is NB AND Tm is High THEN Overall risk is very high AND intrusion probability is very high.

[0066] Reasoning and Defuzzification: From "fuzzy conclusions" back to "precise numerical values", the Mamdani reasoning method is used, and the centroid method is used for defuzzification. Finally, the precise comprehensive risk index S(0-100) (the comprehensive risk index is used to explain the severity of fluid intrusion) and intrusion probability Pr(0-1) (the intrusion probability is used to explain the confidence level of fluid intrusion risk) are output.

[0067] Based on the comprehensive risk index S and the intrusion probability Pr, a tiered early warning threshold is set, specifically:

[0068] Level 1 Warning (Green / Monitoring Status): When S < 40, the system interface displays all data normally, and there are no active alarms.

[0069] Level 2 Warning (Yellow / Warning Status): When 40≤S<70, a flashing yellow indicator and a warning message indicating increased risk will be displayed prominently on the ground monitoring interface. A feedback command will be automatically sent to the data processing and analysis module to activate "Enhanced Monitoring Mode," which shortens the data sampling and analysis cycle to half of the normal range, improving monitoring sensitivity. Guidance suggestions, such as "Prepare additional weight-bearing materials," can be provided.

[0070] Level 3 Warning (Red / Alert Status): When S≥70 or Pr≥0.7, the highest level audible and visual alarm is triggered. A full-screen red alarm window pops up on the monitoring interface, and a data snapshot of at least 10 minutes prior to the incident is automatically recorded. Emergency recommendations are automatically generated and displayed, such as immediate drilling cessation, maintaining circulation, and preparing for well shut-in procedures. The system's estimated invasion parameters are displayed synchronously, such as invasion flow rate (cubic meters / hour), cumulative invasion volume, and predicted formation pressure (equivalent density).

[0071] like Figure 4 As shown, the present invention also provides a real-time formation fluid intrusion early warning system based on multi-source information fusion. The system includes a drilling measurement module, a data transmission module, a local risk calculation module, a time series matching degree calculation module, a risk level calculation module, and a risk early warning module.

[0072] The measurement while drilling module is used to collect multi-source drilling parameters and drilling conditions;

[0073] The data transmission module is used to transmit the multi-source drilling parameters collected by the measurement-while-drilling module to the data processing module.

[0074] The local risk calculation module is used to calculate the dynamic characteristics of each drilling parameter and calculate the risk level based on the dynamic characteristics.

[0075] The time-series matching degree calculation module is used to perform feature matching in the established formation fluid invasion feature library according to the sequence and combination of dynamic characteristics of drilling parameters, and output the time-series matching degree with typical fluid invasion modes.

[0076] The risk level calculation module is used to perform fuzzy calculation based on the established fuzzy rule base, taking dynamic features, local risk values ​​and temporal matching degree as inputs, and outputting the formation fluid intrusion risk level.

[0077] The risk warning module is used to provide graded warnings based on risk levels.

Claims

1. A real-time early warning method for formation fluid intrusion based on multi-source drilling parameters, characterized in that, The method includes: S1: Collect multi-source drilling parameters and calculate the dynamic characteristics of each drilling parameter; S2: Based on the predefined state rule base of drilling conditions, dynamically assign diagnostic weights to multi-source drilling parameters, and calculate the local risk value based on the single drilling parameter based on the diagnostic weights and dynamic characteristics. S3: Establish a formation fluid invasion feature library. Based on the sequence and combination of dynamic features of drilling parameters, perform feature matching in the formation fluid invasion feature library and output the temporal matching degree with typical fluid invasion patterns. S4: Establish a fuzzy rule base, take dynamic features, local risk values ​​and time series matching degree as input, perform fuzzy calculation, output the formation fluid intrusion risk level, and conduct risk warning based on the risk level.

2. The method for real-time early warning of formation fluid intrusion based on multi-source drilling parameters according to claim 1, characterized in that, The multi-source drilling parameters mentioned in S1 include: the acoustic wave velocity of the fluid in the borehole, the resistivity of the drilling fluid, and the pressure at the bottom of the borehole.

3. The method for real-time early warning of formation fluid intrusion based on multi-source drilling parameters according to claim 1, characterized in that, The dynamic characteristics described in S1 include the instantaneous values, changes, and trends of the drilling parameters.

4. The method for real-time early warning of formation fluid intrusion based on multi-source drilling parameters according to any one of claims 1-3, characterized in that, The preprocessing described in S1 includes: using a Kalman filter algorithm to remove geological noise and mechanical vibration interference; and using data from stable working conditions during drilling for baseline correction.

5. The method for real-time early warning of formation fluid intrusion based on multi-source drilling parameters according to claim 1, characterized in that, The drilling conditions described in S2 include drilling depth, drilling speed, and mud characteristics.

6. The method for real-time early warning of formation fluid intrusion based on multi-source drilling parameters according to claim 5, characterized in that, The method for calculating the local risk value described in S2 is as follows: ,in For drilling parameter category index, This represents the local risk value for the corresponding drilling parameters. For the diagnostic weights of the corresponding drilling parameters, and This is an empirical coefficient. This represents the change in the corresponding drilling parameters. This represents the changing trend of the corresponding drilling parameters.

7. The method for real-time early warning of formation fluid intrusion based on multi-source drilling parameters according to claim 1, characterized in that, S3 include: Based on the dynamic characteristics of multi-source drilling parameters, typical fluid invasion modes are defined, and a formation fluid invasion feature library is established. Based on the sequence and combination of the dynamic characteristics of the current multi-source drilling parameters, typical invasion patterns are matched in the formation fluid invasion feature database, and the temporal matching degree is output.

8. The method for real-time early warning of formation fluid intrusion based on multi-source drilling parameters according to claim 7, characterized in that, S4 employs the Mamdani inference method and uses the centroid method for defuzzification, ultimately outputting an accurate comprehensive risk index and intrusion probability.

9. The method for real-time early warning of formation fluid intrusion based on multi-source drilling parameters according to claim 7, characterized in that, The risk warning based on risk level described in S4 includes: If the comprehensive risk index is less than the first preset value, a level one warning will be issued, and only the data will be updated. If the comprehensive risk index is greater than or equal to the first preset value but less than the second preset value, a level-two warning will be issued to increase the frequency of data collection and warnings. If the comprehensive risk index is greater than or equal to the third preset value and the intrusion probability is greater than or equal to the fourth preset value, a level 3 warning will be issued. Based on the level 2 warning, the data collection and warning frequency will be further increased, the fluid intrusion parameters will be estimated, and a warning plan will be automatically generated.

10. A real-time formation fluid intrusion early warning system based on multi-source information fusion, used to implement the real-time formation fluid intrusion early warning method based on multi-source information fusion as described in any one of claims 1-9, characterized in that, The system includes: a measurement while drilling module, a data transmission module, a local risk calculation module, a time series matching degree calculation module, a risk level calculation module, and a risk early warning module; The measurement while drilling module is used to collect multi-source drilling parameters and drilling conditions; The data transmission module is used to transmit the multi-source drilling parameters collected by the measurement-while-drilling module to the data processing module. The local risk calculation module is used to calculate the dynamic characteristics of each drilling parameter and calculate the risk level based on the dynamic characteristics. The time-series matching degree calculation module is used to perform feature matching in the established formation fluid invasion feature library according to the sequence and combination of dynamic characteristics of drilling parameters, and output the time-series matching degree with typical fluid invasion modes. The risk level calculation module is used to perform fuzzy calculation based on the established fuzzy rule base, taking dynamic features, local risk values ​​and temporal matching degree as inputs, and outputting the formation fluid intrusion risk level. The risk warning module is used to provide graded warnings based on risk levels.

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