Well-seismic integrated horizontal well geosteering risk evaluation method

Abstract

The invention relates to the technical field of unconventional oil-gas exploration, and discloses a well-seismic integrated horizontal well geosteering risk evaluation method, which comprises the following steps: S1, acquiring three-dimensional seismic data, measurement while drilling data and geological logging data through a well-seismic integrated data acquisition platform; s2, constructing a horizontal well geosteering dynamic model based on multi-source heterogeneous data, and integrating a seismic inversion result and real-time drilling parameters; by establishing a well-seismic data dynamic fusion mechanism and a multi-source risk quantification model, the problem of space-time dislocation of seismic inversion and measurement-while-drilling data is solved, the recognition precision of a fault boundary, a lithologic interface and a pressure abnormal zone is ensured to be matched with drilling position changes in real time, risk misjudgment caused by model updating lag in a traditional method is eliminated, and the accuracy of the method is improved. The accuracy and timeliness of geosteering decision making of the complex structure area are improved; by monitoring well track deviation and geological model prediction deviation in real time, the risks of well wall instability and target deviation are avoided, and the operation safety of the horizontal well in the full life cycle is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of unconventional oil and gas exploration technology, specifically to a method for geological steering risk assessment of horizontal wells that integrates well and seismic testing. Background Technology

[0002] With the rapid development of unconventional oil and gas exploration and development technologies such as shale gas, shale oil, and tight gas, exploration targets are expanding to areas with deeper burial, more complex geological structures, and greater development difficulties. This leads to increased risks in drilling out the target layer during geological steering and in complex downhole engineering. In extreme cases, well-filling and sidetracking may even occur, resulting in wasted investment and poor exploration and development results.

[0003] Currently, during horizontal well geological steering operations, due to the ambiguity of underground geological structures and the dynamic changes in the drilling environment, when using integrated well-seismic technology for risk assessment, the fusion of seismic inversion data and measurement-while-drilling data has a time lag, making it impossible to capture sudden anomalies in formation properties during drilling in real time. When the seismic velocity model update lags behind the actual drill bit position changes, it may lead to spatiotemporal misalignment of fault identification boundaries, lithological interfaces, and pressure predictions in the geological steering model. When this deviation is superimposed on dynamic drilling parameters, it will cause inaccurate risk assessment results such as wellbore instability probability and resource target deviation, and it cannot guarantee the timeliness and accuracy of risk warnings in complex structural areas.

[0004] Therefore, a well-seismic integrated geological steering risk assessment method for horizontal wells is proposed to address the above-mentioned problems. Summary of the Invention

[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides an integrated well-seismic geological steering risk assessment method for horizontal wells, solving the problems mentioned in the background section.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a method for geological steering risk assessment of horizontal wells integrating well and seismic testing, the method comprising the following steps: S1. Acquire 3D seismic data, measurement-while-drilling data, and geological logging data through the integrated well-seismic data acquisition platform; S2. Construct a dynamic model of geological steering for horizontal wells based on multi-source heterogeneous data, and integrate seismic inversion results with real-time drilling parameters; S3. Use intelligent algorithms to identify potential geological risk factors in the horizontal well trajectory, including fault zones, lithological change zones, and high-pressure anomaly zones; S4. Assess the probability and risk level of geological steering deviation based on risk factors, and quantify the risks of wellbore instability, drill string jamming and resource loss. S5. Optimize geological guidance decisions based on risk assessment results, and dynamically adjust drill bit trajectory and drilling parameters; S6. Real-time monitoring of the drilling process, with feedback and correction of model parameters through a closed-loop control system; S7. Generate risk assessment reports and guidance optimization suggestions, and output visual early warning signals; S8. When a high-risk event is detected, the emergency protection mechanism is activated and the drilling strategy is automatically adjusted. S9. Collect historical guidance data and risk event records to establish a risk knowledge base; S10. Dynamically update the geological guidance model and risk assessment threshold based on a multi-objective optimization algorithm.

[0007] Preferably, step S1 includes: S11. Deploy a distributed seismic sensor array to acquire high-resolution three-dimensional seismic data and upload it to the data center in real time via a wireless transmission protocol; S12. The integrated measurement while drilling instrument acquires real-time data on drill bit inclination angle, azimuth angle, rotation speed and drilling pressure, and synchronously records well depth trajectory coordinates; S13. Geological logging data, including lithological composition, porosity, and fluid-bearing characteristics, are collected using a core scanner and gamma-ray detector. S14. Clean and standardize heterogeneous data through a data fusion engine to eliminate noise and format conflicts.

[0008] Preferably, step S2 includes: S21. Apply seismic inversion algorithms to transform seismic data into formation velocity models and lithological distribution maps; S22. Construct a dynamic geological guidance model, couple the seismic velocity field with real-time drilling trajectory data, and generate a three-dimensional visualized guidance path; S23. Train the model to adapt parameters using machine learning algorithms, and optimize the model accuracy based on historical data; S24. Set up a model verification module to periodically compare the deviation between the predicted trajectory and the actual drilling trajectory.

[0009] Preferably, step S3 includes: S31. Apply convolutional neural networks to analyze earthquake attribute volumes and automatically identify fault zone boundaries and fractured areas; S32. Use clustering algorithms to delineate lithological abrupt change zones and label high-risk lithological transition zones based on well logging data; S33. Evaluate high-pressure anomaly zones using pressure gradient calculation models and predict blowout risks by combining real-time drilling fluid density data. S34. Generate a heat map of risk factors and label it in the guidance model as an early warning layer.

[0010] Preferably, step S4 includes: S41. Define a risk level matrix to classify geological risks into three levels: low, medium, and high, with the quantified deviation probability ranging from 0 to 1. S42. Calculate the wellbore instability risk index: ; in This indicates the wellbore instability risk index. Represents rock strength parameters, Indicates drilling fluid pressure. Indicates formation pressure. Represents the rock strength weighting coefficient. This represents the pressure ratio weighting coefficient, and the formula quantifies the collapse probability based on rock mechanics and pressure balance. S43. Assess the risk of drill string jamming, and calculate the likelihood of jamming by combining drill bit wear data and trajectory curvature radius; S44. Quantify resource loss risk by estimating production capacity loss caused by deviation from the target layer encountered during drilling through a reserve model.

[0011] Preferably, step S5 includes: S51. Optimize the drill bit trajectory based on the risk level and generate an obstacle avoidance guidance path using a path planning algorithm; S52. Dynamically adjust drilling parameters, including drilling pressure, rotation speed and drilling fluid flow rate, to match risk mitigation needs; S53. Apply reinforcement learning algorithms to train the decision-making model and output the optimal guidance instructions in real time; S54. Optimization suggestions are displayed through a human-computer interaction interface, supporting manual correction by engineers and automatic execution.

[0012] Preferably, step S6 includes: S61. Deploy a real-time monitoring sensor network to continuously collect drilling vibration, temperature, and pressure data; S62. Construct a closed-loop control system, compare actual drilling parameters with model predictions, and generate deviation feedback signals. S63. Dynamically correct the geological steering model parameters based on feedback signals and update the risk assessment results; S64. Set the data sampling frequency to the second level to ensure that the monitoring response time meets the requirements of high-risk working conditions.

[0013] Preferably, the emergency protection mechanism in step S8 includes: S81. When a high-risk event is detected, the drill bit retraction procedure is automatically activated to control the drill bit to exit the danger zone. S82. Start the drilling fluid parameter adjustment module and inject high-density drilling fluid to stabilize the wellbore pressure; S83. Send an emergency shutdown command through redundant communication links to securely lock the system.

[0014] Preferably, step S9 includes: S91. Store historical guidance trajectory data, risk event records, and response measures; S92. Construct a risk knowledge base and extract high-frequency risk patterns and optimal mitigation strategies through data mining; S93. Regularly update the knowledge base and train the model's generalization ability based on new well data.

[0015] Preferably, step S10 includes: S101. Apply a multi-objective optimization algorithm to simultaneously optimize guidance accuracy, risk minimization, and drilling efficiency; S102. Dynamically update risk assessment thresholds: ; in This indicates the updated risk threshold. Indicates the original risk threshold. Indicates the adaptive learning rate. Represents the actual drilling data vector. This represents the model's predicted data vector. The formula adjusts the threshold based on real-time bias to improve the model's robustness. S103. Improve model robustness through iterative learning mechanisms to adapt to changes in complex geological conditions; S104 outputs optimized guidance parameters and risk control protocols to guide subsequent drilling operations.

[0016] (III) Beneficial Effects Compared with existing technologies, this invention provides an integrated well-seismic geological steering risk assessment method for horizontal wells, which has the following beneficial effects: 1. In this invention, when conducting real-time assessment of geological steering risks in horizontal wells, a dynamic fusion mechanism for well seismic data and a multi-source risk quantification model are established to solve the problem of spatiotemporal misalignment between seismic inversion and measurement-while-drilling data. This ensures that the identification accuracy of fault boundaries, lithological interfaces, and pressure anomaly zones matches the changes in well location in real time, eliminates risk misjudgments caused by model update lag in traditional methods, and improves the accuracy and timeliness of geological steering decisions in complex structural areas.

[0017] 2. In this invention, when performing closed-loop control of drilling trajectory risk, the wellbore trajectory deviation and geological model prediction deviation are monitored in real time. Combined with reinforcement learning decision-making algorithms, obstacle avoidance paths and drilling parameter optimization schemes are automatically generated. This enables the system to trigger drill bit trajectory correction and drilling fluid parameter linkage control in milliseconds when the drill string approaches fault fracture zones and high-pressure abnormal zones, thereby avoiding wellbore instability and target point deviation risks and ensuring the safety of horizontal well operations throughout their entire life cycle.

[0018] 3. In this invention, when conducting multi-dimensional risk collaborative evaluation, a geological risk knowledge base and adaptive early warning threshold are constructed in layers. Heterogeneous indicators such as lithological mutation risk, drill string jamming probability, and resource loss are normalized into a multi-level risk heat map. This supports engineers in dynamically adjusting guidance strategies based on risk levels, enabling graded handling of complex geological scenarios such as thin interbedded layers and reverse fault zones, improving the ability to predict high-risk working conditions and the resource encounter rate throughout the well section. Attached Figure Description

[0019] Figure 1 This is a flowchart of a method for geological steering risk assessment of horizontal wells that integrates well and seismic testing according to the present invention. Detailed Implementation

[0020] 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.

[0021] Specific embodiment: A method for geological steering risk assessment of horizontal wells integrating well and seismic testing, the method includes the following steps: S1. Acquire 3D seismic data, measurement-while-drilling data, and geological logging data through the integrated well-seismic data acquisition platform; S2. Construct a dynamic model of geological steering for horizontal wells based on multi-source heterogeneous data, and integrate seismic inversion results with real-time drilling parameters; S3. Use intelligent algorithms to identify potential geological risk factors in the horizontal well trajectory, including fault zones, lithological change zones, and high-pressure anomaly zones; S4. Assess the probability and risk level of geological steering deviation based on risk factors, and quantify the risks of wellbore instability, drill string jamming and resource loss. S5. Optimize geological guidance decisions based on risk assessment results, and dynamically adjust drill bit trajectory and drilling parameters; S6. Real-time monitoring of the drilling process, with feedback and correction of model parameters through a closed-loop control system; S7. Generate risk assessment reports and guidance optimization suggestions, and output visual early warning signals; S8. When a high-risk event is detected, the emergency protection mechanism is activated and the drilling strategy is automatically adjusted. S9. Collect historical guidance data and risk event records to establish a risk knowledge base; S10. Dynamically update the geological guidance model and risk assessment threshold based on a multi-objective optimization algorithm.

[0022] Step S1 includes: S11. Deploy a distributed seismic sensor array to acquire high-resolution three-dimensional seismic data and upload it to the data center in real time via a wireless transmission protocol; S12. The integrated measurement while drilling instrument acquires real-time data on drill bit inclination angle, azimuth angle, rotation speed and drilling pressure, and synchronously records well depth trajectory coordinates; S13. Geological logging data, including lithological composition, porosity, and fluid-bearing characteristics, are collected using a core scanner and gamma-ray detector. S14. Clean heterogeneous data using the data fusion engine and execute the data quality verification formula: ; in The data quality index ranges from 0-100%. For the first type of data weights, For the completeness of the i-th type of data, This is a correction factor for the number of sensors. Number of effective sensors.

[0023] Step S2 includes: S21. Apply seismic inversion algorithms to transform seismic data into formation velocity models and lithological distribution maps; S22. Construct a dynamic geological guidance model, couple the seismic velocity field with real-time drilling trajectory data, and generate a three-dimensional visualized guidance path; S23. Train the model to adapt parameters, using the convergence condition formula: ; in Let be the model parameters for the t-th iteration. The gradient of the loss function. The convergence threshold, This represents the number of iterations. S24. Set up a model verification module to periodically compare the deviation between the predicted trajectory and the actual drilling trajectory.

[0024] Step S3 includes: S31. Apply convolutional neural networks to analyze earthquake attribute volumes and automatically identify fault zone boundaries and fractured areas; S32. Clustering algorithm is used to divide lithological abrupt change zones and calculate risk density: ; in This represents the number of anomalous lithological samples. The area of ​​the floor level. Vertical resolution; S33. Evaluate high-pressure anomaly zones using pressure gradient calculation models and predict blowout risks by combining real-time drilling fluid density data. S34. Generate a heat map of risk factors and label it in the guidance model as an early warning layer.

[0025] Step S4 includes: S41. Define a risk level matrix to classify geological risks into three levels: low, medium, and high, with the quantified deviation probability ranging from 0 to 1. S42. Calculate the wellbore instability risk index: ; in This indicates the wellbore instability risk index. Represents rock strength parameters, Indicates drilling fluid pressure. Indicates formation pressure. Represents the rock strength weighting coefficient. This represents the pressure ratio weighting coefficient, and the formula quantifies the collapse probability based on rock mechanics and pressure balance. S43. Assess the risk of drill string jamming, and calculate the likelihood of jamming by combining drill bit wear data and trajectory curvature radius; S44. Quantify resource loss risk by estimating production capacity loss caused by deviation from the target layer encountered during drilling through a reserve model.

[0026] Step S5 includes: S51. Optimize the drill bit trajectory based on the risk level and generate an obstacle avoidance guidance path using a path planning algorithm; S52. Dynamically adjust drilling parameters, including drilling pressure, rotation speed and drilling fluid flow rate, to match risk mitigation needs; S53. Apply reinforcement learning algorithms to train the decision-making model and output the optimal guidance instructions in real time; The reward function for reinforcement learning decision-making is: ; in The decision reward value. To account for the distance deviation from the target layer, This is the wellbore instability risk index; The decision-making process takes time. , , These are the weighting coefficients; S54. Optimization suggestions are displayed through a human-computer interaction interface, supporting manual correction by engineers and automatic execution.

[0027] Step S6 includes: S61. Deploy a real-time monitoring sensor network to continuously collect drilling vibration, temperature, and pressure data; S62. Construct a closed-loop control system, compare actual drilling parameters with model predictions, and generate deviation feedback signals. S63. Dynamically correct the geological steering model parameters based on feedback signals and update the risk assessment results; S64. Set the data sampling frequency to the second level to ensure that the monitoring response time meets the requirements of high-risk working conditions.

[0028] The emergency protection mechanism in step S8 includes: S81. When a high-risk event is detected, the drill bit retraction procedure is automatically activated to control the drill bit to exit the danger zone. S82. Start the drilling fluid parameter adjustment module and inject high-density drilling fluid to stabilize the wellbore pressure; S83. Send an emergency shutdown command through redundant communication links to securely lock the system.

[0029] Step S9 includes: S91. Store historical guidance trajectory data, risk event records, and response measures; S92. Construct a risk knowledge base and extract high-frequency risk patterns and optimal mitigation strategies through data mining; The knowledge base update rules are as follows: ; in Prioritize knowledge base updates. The severity level is classified as category i, ranging from level 1 to level 5. For regional geological weight, This is the time decay factor; S93. Regularly update the knowledge base and train the model's generalization ability based on new well data.

[0030] Step S10 includes: S101. Apply a multi-objective optimization algorithm to simultaneously optimize guidance accuracy, risk minimization, and drilling efficiency; S102. Dynamically update risk assessment thresholds: ; in This indicates the updated risk threshold. Indicates the original risk threshold. Indicates the adaptive learning rate. Represents the actual drilling data vector. This represents the model's predicted data vector. The formula adjusts the threshold based on real-time bias to improve the model's robustness. S103. Improve model robustness through iterative learning mechanisms to adapt to changes in complex geological conditions; S104 outputs optimized guidance parameters and risk control protocols to guide subsequent drilling operations.

[0031] The steps of this method are as follows: This method achieves accurate geological risk assessment by constructing a four-dimensional collaborative mechanism of "dynamic fusion, intelligent prediction, closed-loop control, and knowledge iteration." First, a real-time interaction channel for well-seismic data is established: a distributed seismic sensor array and a measurement-while-drilling (MWD) instrument are deployed to synchronously acquire 3D seismic wavefield signals, drill bit trajectory, and formation lithology parameters. A heterogeneous data cleaning engine eliminates acquisition noise and format conflicts, forming a dynamic geological guidance model updated in seconds. This model integrates the velocity field generated by seismic inversion with the spatial coordinates of the drilling trajectory, constructing a 3D risk early warning heat map in complex areas such as reverse fault zones and thin interbedded layers, and marking the spatial coordinates of fault fracture boundaries, lithological abrupt change interfaces, and high-pressure anomaly zones in real time.

[0032] Intelligent risk prediction is implemented based on a dynamic model: Convolutional neural networks are used to analyze seismic attribute anomaly patterns, identifying fault extension trends and lithological contact relationships in un-drilled strata; high-risk lithological transition zones are delineated using clustering algorithms, and the probability of wellbore instability is predicted by combining drilling fluid density change rate; the equivalent resource loss caused by vertical displacement of the target layer is calculated simultaneously, generating a three-level risk matrix of red, yellow, and blue. When the drill bit approaches the preset risk threshold, the reinforcement learning decision module immediately triggers closed-loop control: dynamically planning obstacle-avoidance drilling paths, and adjusting the combination of drill pressure, rotation speed, and drilling fluid flow rate parameters in a coordinated manner, completing drill bit trajectory correction and wellbore pressure balance control within milliseconds to avoid risks such as stuck pipe and blowout.

[0033] Real-time monitoring data throughout the entire process is fed back to the risk knowledge base, which stores trajectory correction records, risk mitigation plans, and actual drilling encounters. A spatiotemporal weighting algorithm is used to extract high-frequency risk patterns and optimal response strategies. The knowledge base periodically drives iterative iterations of the geological steering model parameters, optimizing risk assessment thresholds in thin interbedded areas and drilling safety margins in fault zones. This forms a reinforced cycle of learning while drilling, model optimization, and risk re-prediction, improving the resource encounter rate and overall operational safety of horizontal wells under complex geological conditions.

[0034] 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 a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0035] 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 method for geological steering risk assessment of horizontal wells integrating well and seismic testing, characterized in that: The method includes the following steps: S1. Acquire 3D seismic data, measurement-while-drilling data, and geological logging data through the integrated well-seismic data acquisition platform; S2. Construct a dynamic model of geological steering for horizontal wells based on multi-source heterogeneous data, and integrate seismic inversion results with real-time drilling parameters; S3. Use intelligent algorithms to identify potential geological risk factors in the horizontal well trajectory, including fault zones, lithological change zones, and high-pressure anomaly zones; S4. Assess the probability and risk level of geological steering deviation based on risk factors, and quantify the risks of wellbore instability, drill string jamming and resource loss. S5. Optimize geological guidance decisions based on risk assessment results, and dynamically adjust drill bit trajectory and drilling parameters; S6. Real-time monitoring of the drilling process, with feedback and correction of model parameters through a closed-loop control system; S7. Generate risk assessment reports and guidance optimization suggestions, and output visual early warning signals; S8. When a high-risk event is detected, the emergency protection mechanism is activated and the drilling strategy is automatically adjusted. S9. Collect historical guidance data and risk event records to establish a risk knowledge base; S10. Dynamically update the geological guidance model and risk assessment threshold based on a multi-objective optimization algorithm.

2. The integrated well-seismic geological steering risk assessment method for horizontal wells according to claim 1, characterized in that: Step S1 includes: S11. Deploy a distributed seismic sensor array to acquire high-resolution three-dimensional seismic data and upload it to the data center in real time via a wireless transmission protocol; S12. The integrated measurement while drilling instrument acquires real-time data on drill bit inclination angle, azimuth angle, rotation speed and drilling pressure, and synchronously records well depth trajectory coordinates; S13. Geological logging data, including lithological composition, porosity, and fluid-bearing characteristics, are collected using a core scanner and gamma-ray detector. S14. Clean and standardize heterogeneous data through a data fusion engine to eliminate noise and format conflicts.

3. The integrated well-seismic geological steering risk assessment method for horizontal wells according to claim 1, characterized in that: Step S2 includes: S21. Apply seismic inversion algorithms to transform seismic data into formation velocity models and lithological distribution maps; S22. Construct a dynamic geological guidance model, couple the seismic velocity field with real-time drilling trajectory data, and generate a three-dimensional visualized guidance path; S23. Train the model to adapt parameters using machine learning algorithms, and optimize the model accuracy based on historical data; S24. Set up a model verification module to periodically compare the deviation between the predicted trajectory and the actual drilling trajectory.

4. The integrated well-seismic geological steering risk assessment method for horizontal wells according to claim 1, characterized in that: Step S3 includes: S31. Apply convolutional neural networks to analyze earthquake attribute volumes and automatically identify fault zone boundaries and fractured areas; S32. Use clustering algorithms to delineate lithological abrupt change zones and label high-risk lithological transition zones based on well logging data; S33. Evaluate high-pressure anomaly zones using pressure gradient calculation models and predict blowout risks by combining real-time drilling fluid density data. S34. Generate a heat map of risk factors and label it in the guidance model as an early warning layer.

5. The integrated well-seismic geological steering risk assessment method for horizontal wells according to claim 1, characterized in that: Step S4 includes: S41. Define a risk level matrix to classify geological risks into three levels: low, medium, and high, with the quantified deviation probability ranging from 0 to 1. S42. Calculate the wellbore instability risk index: ； in This indicates the wellbore instability risk index. Represents rock strength parameters, Indicates drilling fluid pressure. Indicates formation pressure. Represents the rock strength weighting coefficient. This represents the pressure ratio weighting coefficient, and the formula quantifies the collapse probability based on rock mechanics and pressure balance. S43. Assess the risk of drill string jamming, and calculate the likelihood of jamming by combining drill bit wear data and trajectory curvature radius; S44. Quantify resource loss risk by estimating production capacity loss caused by deviation from the target layer encountered during drilling through a reserve model.

6. The integrated well-seismic geological steering risk assessment method for horizontal wells according to claim 1, characterized in that: Step S5 includes: S51. Optimize the drill bit trajectory based on the risk level and generate an obstacle avoidance guidance path using a path planning algorithm; S52. Dynamically adjust drilling parameters, including drilling pressure, rotation speed and drilling fluid flow rate, to match risk mitigation needs; S53. Apply reinforcement learning algorithms to train the decision-making model and output the optimal guidance instructions in real time; S54. Optimization suggestions are displayed through a human-computer interaction interface, supporting manual correction by engineers and automatic execution.

7. The integrated well-seismic geological steering risk assessment method for horizontal wells according to claim 1, characterized in that: Step S6 includes: S61. Deploy a real-time monitoring sensor network to continuously collect drilling vibration, temperature, and pressure data; S62. Construct a closed-loop control system, compare actual drilling parameters with model predictions, and generate deviation feedback signals. S63. Dynamically correct the geological steering model parameters based on feedback signals and update the risk assessment results; S64. Set the data sampling frequency to the second level to ensure that the monitoring response time meets the requirements of high-risk working conditions.

8. The integrated well-seismic risk assessment method for horizontal wells, as described in claim 1, is characterized in that: The emergency protection mechanism in step S8 includes: S81. When a high-risk event is detected, the drill bit retraction procedure is automatically activated to control the drill bit to exit the danger zone. S82. Start the drilling fluid parameter adjustment module and inject high-density drilling fluid to stabilize the wellbore pressure; S83. Send an emergency shutdown command through redundant communication links to securely lock the system.

9. The integrated well-seismic geological steering risk assessment method for horizontal wells according to claim 1, characterized in that: Step S9 includes: S91. Store historical guidance trajectory data, risk event records, and response measures; S92. Construct a risk knowledge base and extract high-frequency risk patterns and optimal mitigation strategies through data mining; S93. Regularly update the knowledge base and train the model's generalization ability based on new well data.

10. The integrated well-seismic risk assessment method for horizontal wells, as described in claim 1, is characterized in that: Step S10 includes: S101. Apply a multi-objective optimization algorithm to simultaneously optimize guidance accuracy, risk minimization, and drilling efficiency; S102. Dynamically update risk assessment thresholds: ； in This indicates the updated risk threshold. Indicates the original risk threshold. Indicates the adaptive learning rate. Represents the actual drilling data vector. This represents the model's predicted data vector. The formula adjusts the threshold based on real-time bias to improve the model's robustness. S103. Improve model robustness through iterative learning mechanisms to adapt to changes in complex geological conditions; S104 outputs optimized guidance parameters and risk control protocols to guide subsequent drilling operations.

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