A method and system for controlling exploration drilling parameters

Abstract

This invention relates to the field of drilling parameter control technology, and in particular to a method and system for controlling exploration drilling parameters. The method includes the following steps: setting a drilling target and a target priority corresponding to the drilling target, wherein the target priority indicates the importance of the drilling target; acquiring various measurement data during the drilling process and processing the various measurement data; identifying the current formation characteristics and the current drilling tool operating status based on the processed various measurement data; adjusting the drilling target and the target priority based on geological information during the drilling process; and dynamically adjusting the drilling target and parameter control strategy based on real-time geological information and drilling status, thereby better adapting to complex formation changes, optimizing the trade-offs between multiple objectives, and improving the adaptability and control accuracy of drilling.

Description

Technical Field

[0001] This invention relates to the field of drilling parameter control, and more particularly to a method and system for controlling exploration drilling parameters. Background Technology

[0002] Exploratory drilling acquires subsurface geological information and resources through drilling, with its core being drilling into the formation to obtain core samples or data. However, complex and variable formations and harsh downhole environments pose challenges to controlling drilling parameters. Traditional methods struggle to cope with subtle variations in formation, uncertainties in measurement data, and conflicts between multiple objectives, impacting efficiency and data quality.

[0003] Exploration drilling operations are highly sensitive to the identification of fine formation features, making adjustments to macroscopic parameters alone insufficient to meet practical needs. For example, in searching for unconventional oil and gas reservoirs, it is not only necessary to drill through the target formation but also to accurately identify minute oil and gas shows, complex fracture systems, and the locations of different fluid interfaces. These fine features can be highly sensitive to disturbances during drilling. If drilling parameters are adjusted too coarsely and fail to precisely match these microscopic geological features, it may lead to the destruction or omission of critical information. However, the harsh downhole environment often causes sensor data to be subject to random interference or missing data, and complex physical relationships exist between different data points. If noise is not effectively removed, the data is not completed, and these relationships are not revealed, it is difficult to accurately determine the formation and drill string conditions, leading to decision-making biases.

[0004] Furthermore, the formation's response to drilling parameters during drilling is nonlinear, hysteretic, and uncertain (e.g., stress release, plastic deformation, microfracture propagation). Simultaneously, the behavior of the drill string in complex wellbores (bending, torsion, vibration) is continuously changing and difficult to predict, influenced by multiple factors. Traditional methods struggle to perceive and predict these complex behaviors in real time. The operational objectives of exploration drilling also increase the complexity of parameter control, as there are inherent conflicts between multiple objectives. Unlike development wells, whose primary purpose is to quickly reach the target formation, exploration wells typically have multiple objectives that need to be balanced. For example, to obtain high-quality core samples, it may be necessary to reduce the drilling rate; however, at the same time, to control project timelines and costs, it is desirable to increase drilling efficiency. Moreover, ensuring the accuracy of the wellbore trajectory, maintaining wellbore stability, efficiently carrying cuttings, and obtaining accurate logging-while-drilling data all involve trade-offs. Finding an optimal balance among these conflicting operational objectives under complex and variable formation conditions places extremely high demands on the control of drilling parameters.

[0005] Given the interplay of various complex factors, including high sensitivity to fine formation features, inherent uncertainties in various downhole measurement data, nonlinear response characteristics of the formation to drilling operations, and the complex and variable nature of drill string operation, traditional experience-based, table-lookup, or simple feedback-based drilling parameter adjustment methods are no longer sufficient to meet the comprehensive requirements of exploration drilling for accuracy, adaptability, multi-objective optimization, and robust control. Especially in exploration scenarios requiring real-time responses to subtle formation changes, processing downhole data with noise and delays, and considering the complex behavior of the formation and drill string, achieving precise, environmentally adaptable, and multi-objective balanced control of drilling parameters to improve drilling efficiency and avoid downhole accidents while ensuring geological data quality, core integrity, and wellbore safety has become a current challenge in the field of exploration drilling parameter control.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method and system for controlling exploration drilling parameters.

[0008] In a first aspect, the present invention provides a method for controlling exploration drilling parameters, the method comprising the following steps:

[0009] Define drilling objectives and corresponding objective priorities, whereby the objective priorities indicate the importance of the drilling objectives;

[0010] Acquire various measurement data during the drilling process and process the various measurement data;

[0011] Based on the processed measurement data, identify the current formation characteristics and the current drilling tool operating status;

[0012] Based on geological information during the drilling process, the drilling objectives and their priorities are adjusted.

[0013] Based on the adjusted target priority, the identified current formation characteristics, and the identified current drilling tool operating status, a control strategy for drilling parameters is determined. The control strategy, based on the adjusted target priority, prioritizes ensuring the quality of geological information acquisition, wellbore safety, and improving drilling efficiency.

[0014] Calculate the set values ​​of the current drilling parameters and send the set values ​​to the drilling equipment;

[0015] The set value is adjusted based on the drilling response.

[0016] The core innovation of this application lies in combining the function of dynamically adjusting drilling targets and target priorities based on geological information during the drilling process with the function of determining control strategies that focus on different targets based on real-time identification of formation characteristics and drill string operating status. This enables adaptive, multi-objective trade-off control of drilling parameters under complex and variable formation conditions, thereby improving drilling efficiency while ensuring the quality of geological information acquisition and wellbore safety.

[0017] Secondly, an exploration drilling parameter control system is provided, the system comprising:

[0018] The target setting module is used to set the drilling target and the target priority corresponding to the drilling target, wherein the target priority indicates the importance of the drilling target;

[0019] The data processing module is used to acquire various measurement data during the drilling process and process the various measurement data.

[0020] The status recognition module is used to identify the current formation characteristics and the current drilling tool operating status based on various processed measurement data.

[0021] The target adjustment module is used to adjust the drilling target and the target priority based on geological information during the drilling process.

[0022] The strategy determination module is used to determine the control strategy for drilling parameters based on the adjusted target priority, the identified current formation characteristics, and the identified current drilling tool operating status. The control strategy focuses on ensuring the quality of geological information acquisition, wellbore safety, and improving drilling efficiency based on the adjusted target priority.

[0023] The parameter calculation module is used to calculate the set values ​​of the current drilling parameters and send the set values ​​to the drilling equipment;

[0024] The parameter correction module is used to correct the set value based on the drilling response.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] By dynamically adjusting target priorities and identifying formation / drill tool status in real time, and determining control strategies that focus on different objectives based on this information, and then making corrections based on the drilling response, this approach solves the problem that traditional parameter control struggles to cope with complex formations, uncertain data, and conflicts among multiple objectives. It has the advantages of being able to dynamically adjust drilling objectives and parameter control strategies based on real-time geological information and drilling status, thereby better adapting to complex formation changes, optimizing the trade-offs between multiple objectives, and improving the adaptability and control accuracy of drilling. Attached Figure Description

[0027] Figure 1 This is a flowchart of the method of the present invention;

[0028] Figure 2 This is a schematic diagram of the system structure of the present invention;

[0029] In the diagram: 201, Target Setting Module; 202, Data Processing Module; 203, Status Recognition Module; 204, Target Adjustment Module; 205, Strategy Determination Module; 206, Parameter Calculation Module; 207, Parameter Correction Module. Detailed Implementation

[0030] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0031] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0032] Traditional exploration drilling parameter control methods struggle to balance the quality of geological information acquisition, wellbore safety, and drilling efficiency when dealing with complex and variable formation conditions and downhole environments. The highly sensitive requirements for identifying subtle formation features, the inherent uncertainties in various downhole measurement data, the nonlinear response of formations to drilling operations, and the complex and variable nature of drill string operation make it difficult for traditional experience-based, table-lookup, or simple feedback-based parameter adjustment methods to achieve precise, adaptive, and multi-objective optimized control. This affects core integrity, the accuracy of measurement-while-drilling data, and wellbore safety, while also limiting the improvement of overall drilling efficiency.

[0033] For example, when exploring unconventional oil and gas reservoirs, it is necessary to accurately identify minute oil and gas shows, complex fracture systems, and the locations of different fluid interfaces within the formation. During drilling, various measurement data acquired by downhole sensors, such as drill pressure, torque, rotational speed, drilling fluid flow rate, downhole vibration, temperature, pressure, formation gamma rays, and resistivity, are subject to random interference and transient gaps. The formation's response to drilling parameters exhibits nonlinearity, hysteresis, and uncertainty. Drill strings undergo various deformations in long-distance, non-linear wells, including bending, torsion, and axial vibration, such as stick-slip vibration. Simultaneously, multiple objectives must be balanced, including obtaining high-quality core samples, controlling project timelines, ensuring wellbore trajectory accuracy, maintaining wellbore stability, efficiently carrying cuttings, and acquiring accurate logging-while-drilling data. These objectives often conflict. In this scenario, traditional parameter adjustment methods struggle to perceive and predict subtle formation changes and complex drill string behavior in real time, failing to find the optimal balance among multiple objectives. This can lead to potential damage to critical geological information, downhole accidents, or reduced drilling efficiency.

[0034] If the aforementioned problems are not addressed, exploration drilling operations will face consequences such as inaccurate or incomplete geological information acquisition, deterioration in core sample quality, wellbore instability or even collapse, increased risk of drill string damage, and overall low drilling efficiency. This will directly affect the accuracy of subsurface resource assessment, increase operating costs and timelines, and even lead to downhole accidents, posing a serious obstacle to the success of exploration operations.

[0035] Therefore, this application is as follows Figure 1 The method for controlling exploration drilling parameters, as shown, includes the following steps:

[0036] S101. Set the drilling target and the target priority corresponding to the drilling target. The target priority indicates the importance of the drilling target.

[0037] S102. Acquire various measurement data during the drilling process and process the various measurement data;

[0038] S103. Based on the processed measurement data, identify the current formation characteristics and the current drilling tool operating status;

[0039] S104. Adjust the drilling target and target priority based on geological information during the drilling process;

[0040] S105. Based on the adjusted target priority, the identified current formation characteristics, and the identified current drilling tool operating status, determine the control strategy for drilling parameters. The control strategy, based on the adjusted target priority, emphasizes ensuring the quality of geological information acquisition, wellbore safety, and improving drilling efficiency.

[0041] S106. Calculate the current drilling parameter settings and send the settings to the drilling equipment;

[0042] S107. Correct the set value based on the drilling response.

[0043] Drilling objectives refer to the specific goals that the drilling operation aims to achieve, such as reaching a specific depth, obtaining core samples, or traversing a specific formation area. These objectives can be defined using preset depth ranges, formation names, or geological event triggering conditions, primarily providing directional guidance for the drilling process. Objective priority indicates the relative importance of different drilling objectives at the current stage. This can be represented using numerical weights, ranking lists, or Boolean flags, primarily for weighing and prioritizing among multiple conflicting objectives. Various measurement data refer to the real-time data acquired by surface or downhole sensors during the drilling process. These can include, but are not limited to, data on drilling pressure, torque, rotational speed, drilling fluid flow rate, downhole vibration, temperature, pressure, gamma rays, and resistivity, primarily for comprehensively understanding the downhole environment and the operating status of the drill string. Formation characteristics refer to the physical, chemical, or geological properties of the formation where the drill bit is currently located. This includes factors such as rock hardness, drillability, porosity, permeability, stress state, and fracture development, primarily to understand the impact of the formation on the drilling process; drill string operating status refers to the dynamic performance of the drill string during downhole operation. This includes axial vibration, torsional vibration, bending vibration, stick-slip condition, drill bit wear, and drill string stress, primarily to assess drill string efficiency and potential risks; geological information refers to formation-related knowledge or events extracted or inferred from measurement data. This includes formation interface identification, lithological changes, abnormal pressure areas, and potential geological risks, primarily to guide the adjustment of drilling objectives and the formulation of strategies; control strategy refers to the set of rules or decision logic for determining how to adjust drilling parameters based on the current state and objective priorities. It can be constructed using rule-based expert systems, fuzzy control, model predictive control, or machine learning algorithms, primarily to balance different objectives and optimize parameter settings; drilling response refers to the feedback performance of the drilling equipment or downhole environment to the current drilling parameter settings. It can include changes in drilling speed, torque fluctuations, downhole vibration modes, wellbore trajectory changes, cuttings morphology, etc., and is mainly used to evaluate the effect of the current parameter settings and make corrections.

[0044] The proposed solution initiates the drilling process by setting initial drilling objectives and prioritizing these objectives to indicate their importance. During drilling, the system continuously acquires various measurement data from both the surface and downhole, performing necessary processing on this raw data, such as noise reduction and calibration, to improve data reliability. Based on the processed measurement data, the system can identify in real time the specific characteristics of the formation where the drill bit is located and the current operating status of the drill string, such as whether abnormal vibrations or wear have occurred. Simultaneously, the system continuously analyzes geological information acquired during drilling, which may indicate impending formation changes or the entry into critical areas. Based on this dynamic geological information, the system can adjust the initially set drilling objectives and their corresponding priorities in a timely manner, ensuring that the control focus matches the actual geological conditions. Subsequently, the system comprehensively considers the adjusted objective priorities, real-time identified formation characteristics, and the drill string's operating status to determine the optimal drilling parameter control strategy. The core of this strategy lies in dynamically prioritizing and balancing several interrelated objectives—ensuring the quality of geological information acquisition, wellbore safety, and improving drilling efficiency—based on the current objective priorities. Once the control strategy is determined, the system calculates the specific setpoints for each drilling parameter at the current moment and sends these setpoints to the drilling equipment for execution. After the parameters are executed, the system monitors the responses generated during the drilling process, such as changes in drilling speed, torque, and vibration. Based on these drilling responses, the system can evaluate the actual effect of the current parameter settings and make necessary corrections to the calculated setpoints, forming a closed-loop adaptive control process that ensures that drilling parameters can be continuously optimized to cope with constantly changing downhole conditions.

[0045] As one embodiment of the present invention, the step of adjusting the drilling target and target priority based on geological information during the drilling process includes:

[0046] Continuous analysis of the changing trends and correlations presented by various measurement data is conducted to obtain geological information during the drilling process;

[0047] Based on geological information, it is predicted that the drill bit will soon enter a geological formation area that requires attention;

[0048] Before the drill bit enters the formation area of ​​interest, the drilling target and target priority are switched in advance based on the prediction results;

[0049] Based on the converted drilling target and target priority, the drilling parameters are gradually adjusted in a controlled manner to ensure that when the drill bit enters the formation area of ​​interest, the drilling parameters have reached a state suitable for the formation area of ​​interest.

[0050] To better understand this solution, some of its technical features are explained below.

[0051] The analysis of trends and correlations among various measurement data involves examining the patterns of change in different types of measurement data (such as drill pressure, torque, rotational speed, downhole vibration, formation gamma, etc.) over time or depth, and exploring the mutual influences or synchronous changes among these data. This can be achieved using techniques such as time series analysis, correlation analysis, and pattern recognition. Geological information acquired during drilling refers to the attribute information about the current and adjacent formations obtained through processing and interpreting downhole measurement data, such as lithology, hardness, porosity, permeability, stress state, and fluid properties. This can be achieved using techniques such as data fusion, geological interpretation models, and machine learning algorithms. The formation areas requiring attention refer to those sections during drilling where specific control strategies are needed due to their geological characteristics potentially impacting drilling safety, efficiency, or geological information acquisition. Examples include high-pressure formations, easily collapsible formations, oil and gas-bearing layers, hard interlayers, and fault zones. These areas can be determined based on pre-set geological models, historical drilling data, or real-time geological information interpretation. Among them, controlled gradual adjustment refers to planning and controlling the adjustment process of drilling parameters (such as drilling pressure, rotation speed, displacement, etc.) so that they change slowly at a predetermined rate, amplitude or curve, rather than instantaneously. It can be achieved by using slope functions, piecewise linear functions, PID control algorithms combined with rate limits, etc.

[0052] This application's solution acquires geological information during drilling by continuously analyzing the changing trends and correlations of various measurement data. This allows the system to gain a more comprehensive and in-depth understanding of the characteristics of the current and adjacent formations. Based on this geological information, the system can predict when the drill bit will enter a formation area of ​​interest, thus obtaining a time window to respond to formation changes. Before predicting that the drill bit will enter the formation area of ​​interest, the system initiates a conversion of the drilling target and target priority in advance, based on the prediction results. This advance conversion mechanism allows sufficient time for a smooth transition in subsequent parameter adjustments. According to the converted drilling target and target priority, the system instructs the drilling parameters to be adjusted gradually in a controlled manner. This controlled gradual adjustment ensures that parameter changes are not too drastic, avoiding impact on the formation and drilling tools. Ultimately, when the drill bit actually enters the formation area of ​​interest, the drilling parameters have already reached a state suitable for that area, thus ensuring a smooth transition in the drilling process, reducing the risk of downhole accidents, and improving the quality of geological information acquisition. Compared to simply making real-time adjustments based on current geological information, this approach introduces a mechanism of prediction and gradual adjustment, making the entire drilling parameter control process more forward-looking and robust, and more effectively addressing the challenges posed by complex geological changes.

[0053] As one embodiment of the present invention, the control strategy for determining drilling parameters, which prioritizes certain aspects among ensuring the quality of geological information acquisition, wellbore safety, and improving drilling efficiency based on adjusted target priorities, includes the following steps:

[0054] Based on the identified current formation characteristics and the identified current drilling tool operating status, assess the potential impact of the current drilling environment on geological information acquisition, wellbore safety, and drilling efficiency;

[0055] Based on the adjusted target priorities and potential impacts, determine the current importance of each drilling objective (including the quality of geological information acquisition, wellbore safety, and improving drilling efficiency);

[0056] Based on their current importance, construct strategy options that reflect the interrelationships between various drilling objectives;

[0057] From the strategy options, select a control strategy for drilling parameters. The control strategy can maximize the satisfaction of the adjusted target priority while reducing the negative impact on other non-primary objectives, thereby balancing the quality of geological information acquisition, wellbore safety, and improved drilling efficiency.

[0058] Assessing the potential impact of the current drilling environment on geological information acquisition, wellbore safety, and drilling efficiency involves predicting the favorable or unfavorable effects of the current or upcoming drilling environment on the integrity of geological information acquisition, the stability of the wellbore structure, and drilling speed and efficiency, based on currently identified formation characteristics (e.g., rock hardness, fracture development, formation pressure, etc.) and drill string operating conditions (e.g., drilling pressure, torque, vibration, drill bit wear, etc.). Specifically, this can be achieved by mapping the current formation and drill string conditions to the potential impact on each drilling objective using a pre-set model or rule base. The aim is to quantify the risks and opportunities of the current environment in achieving each drilling objective. Determining the current importance of each drilling objective involves dynamically adjusting or refining the relative importance of the three objectives—geological information acquisition quality, wellbore safety, and improved drilling efficiency—based on existing adjusted objective priorities and the assessed potential impacts. Specifically, this can be achieved by combining static objective priorities with dynamic potential impacts through weighted calculations, fuzzy logic judgments, or rule-based reasoning to obtain the importance of each objective reflecting the current situation. The aim is to make control decisions more aligned with the needs of the real-time drilling environment. Constructing strategy options that reflect the interrelationships among various drilling objectives refers to generating a series of feasible drilling parameter control schemes. Each scheme considers the inherent connections and trade-offs among the three objectives: geological information acquisition quality, wellbore safety, and drilling efficiency. Specifically, this can be achieved through predefined strategy templates, optimization algorithm-based generation, or rule sets based on expert experience, forming a set of strategies containing different parameter combinations and adjustment logics. The aim is to provide diverse alternatives that consider the interactions of multiple objectives for subsequent strategy selection. Selecting a drilling parameter control strategy from these options involves choosing the most suitable control scheme from the constructed set of strategy options based on the current importance of each drilling objective. Specifically, this can be achieved by evaluating the expected performance of each strategy option at its current importance level, such as using scoring mechanisms, decision trees, or simple rule matching, to select the strategy that maximizes the achievement of the primary objective while minimizing the negative impact on secondary objectives. The goal is to make the optimal parameter control decision.

[0059] This application's approach assesses the potential impact of the current drilling environment on geological information acquisition, wellbore safety, and drilling efficiency based on identified current formation characteristics and drill string operating status, thereby obtaining quantitative information on the risks and opportunities of the current drilling environment for each drilling objective. Based on this, and combining adjusted objective priorities with potential impacts, the current importance of each drilling objective is determined. This combines static objective priorities with dynamic potential impacts, resulting in an objective importance reflecting the current situation. Furthermore, based on the current importance, strategy options reflecting the interrelationships between drilling objectives are constructed, providing diverse alternatives that consider multi-objective interactions for subsequent strategy selection. Finally, a drilling parameter control strategy is selected from the strategy options. This strategy maximizes the satisfaction of adjusted objective priorities while minimizing negative impacts on other non-primary objectives, thus making the optimal parameter control decision. The entire process forms a closed-loop decision-making process, which organically combines the real-time status of the formation and drilling tools, the dynamically adjusted target priorities, and the inherent conflicts between the targets. This enables the dynamic determination of the optimal drilling parameter control strategy based on the complex and ever-changing drilling environment, allowing for a flexible and reasonable emphasis on ensuring the quality of geological information acquisition, wellbore safety, and improving drilling efficiency.

[0060] As one embodiment of the present invention, the step of selecting a control strategy for drilling parameters from the strategy options, wherein the control strategy can maximize the satisfaction of the adjusted target priority while reducing the negative effects on other non-primary objectives, thereby prioritizing the balance between ensuring the quality of geological information acquisition, wellbore safety, and improving drilling efficiency, includes:

[0061] For each control strategy in the strategy options, a performance simulation is performed based on the current drilling status. The performance simulation considers the evolution trend of the identified current formation characteristics and the identified current drill bit operating status in a short period of time, and takes into account the fluctuation range of various measurement data to generate the simulation results.

[0062] Based on the simulation results, a control strategy for drilling parameters is selected. The control strategy can continuously maintain the satisfaction of the adjusted target priority, while reducing the negative impact on other non-primary objectives, thereby balancing the quality of geological information acquisition, wellbore safety, and improved drilling efficiency.

[0063] Among these, performance extrapolation refers to the simulation or prediction of the application effect of a specific control strategy over a future period. This can be achieved through methods such as physical model-based simulation, data-driven predictive models, or hybrid modeling. Its purpose is to evaluate the performance of the control strategy under dynamic and uncertain environments. The short-term evolution trend of identified current formation characteristics and drill string operating conditions refers to the prediction of the direction and rate of change of currently identified formation properties (e.g., hardness, drillability, stress state) and drill string conditions (e.g., vibration mode, torque fluctuation, drill bit wear) within a relatively short future time window. This can be achieved through methods such as time series analysis, trend extrapolation, or pattern recognition based on historical data. Its purpose is to incorporate the dynamics of the environment and tool conditions into the strategy evaluation. The fluctuation range of various measurement data refers to the fluctuation range of various data acquired by downhole sensors (e.g., drill bit wear, drill string ... The quantification of inherent uncertainties or noise levels (such as pressure, torque, rotational speed, and downhole vibration) can be characterized using statistical analysis, error models, or confidence intervals. Its purpose is to assess the impact of data uncertainty on the effectiveness of control strategies. The extrapolation results refer to the output generated during the performance extrapolation process. These can include predicted drilling parameters (such as predicted drilling speed, torque, and vibration levels), predicted drilling effects (such as predicted core quality, wellbore stability, and bit life), or scores reflecting the degree of target satisfaction. Its purpose is to provide a quantitative evaluation basis for the selection of control strategies. Maintaining the satisfaction of adjusted target priorities means that the selected control strategy can maintain a high level of achievement of the main drilling objectives over a future period, while effectively controlling the negative impacts on non-primary objectives. Its purpose is to ensure the stability and robustness of the drilling process.

[0064] The proposed solution performs performance simulations for each control strategy in the strategy options, based on the current drilling status. These simulations consider the identified current formation characteristics and the short-term evolution trend of the current drill string operation, taking into account the fluctuation range of various measurement data. By incorporating short-term dynamic changes in formation and drill string status, as well as the uncertainties of measurement data, into the strategy evaluation process, the simulation results more accurately reflect the potential performance of the control strategy in actual, dynamic environments, avoiding the limitations of decision-making based solely on instantaneous states. Based on these simulation results, a control strategy for drilling parameters is selected. This strategy consistently maintains the satisfaction of adjusted target priorities while minimizing negative impacts on other non-primary objectives. This allows for a balance between ensuring the quality of geological information acquisition, wellbore safety, and improving drilling efficiency. Because the selection is based on predicted performance simulation results that consider future trends and uncertainties, the chosen control strategy is not only optimal at present but also maintains good performance and stability over a future period, effectively addressing the complexity and dynamism of the actual drilling environment.

[0065] As one embodiment of the present invention, the step of correcting the set value based on the drilling response includes:

[0066] Obtain drilling response, which includes measurement data response indicating formation characteristics and measurement data response indicating drill string operating status;

[0067] The analysis examines the indicative characteristics of formation characteristics in relation to formation changes, and the indicative characteristics of drill string operating status in relation to drill string behavior.

[0068] Based on the changing trend of the measurement data response indicating the formation characteristics, determine the effect of its correction direction and correction magnitude on the set value;

[0069] Based on the fluctuations in the response of measurement data indicating the drilling tool's operating status, and combined with the changes in the response of measurement data indicating formation characteristics, it is determined whether the fluctuations indicate the drilling tool's own behavior.

[0070] Based on the judgment results of the effect of the measurement data response to the indicated formation characteristics and the judgment results of the fluctuation of the measurement data response to the indicated drilling tool operating status, the correction direction and correction range of the set value are determined, and the set value is corrected according to the correction direction and correction range.

[0071] Among them, drilling response refers to the feedback data generated by the downhole environment and drill string status when the drilling equipment executes set parameters, which can be realized using various sensor measurements, equipment operating parameters, etc.; measurement data response indicating formation characteristics refers to measurement data that reflects changes in the physical, chemical, or mechanical properties of the formation, which can be realized using logging-while-drilling data, changes in drilling parameters, etc.; measurement data response indicating drill string operating status refers to measurement data that reflects the drill string's own movement, stress, or wear state, which can be realized using downhole vibration data, torque, drilling pressure, rotational speed, downhole temperature and pressure, etc.; and the indicative characteristics of formation changes refer to the correspondence or sensitivity between the measurement data response and actual formation changes. Historical data analysis, geological models, and machine learning models can be used to achieve this; the indicative characteristics of drill string behavior refer to the correspondence or sensitivity between the measured data response and the actual operating state of the drill string, which can be achieved using mechanical models, vibration analysis, and pattern recognition; the trend of change refers to the direction and rate of change of the measured data response over a period of time, which can be achieved using time series analysis, filtering, and curve fitting; fluctuation refers to the non-periodic or periodic fluctuations of the measured data response in the short term, which can be achieved using statistical analysis, spectrum analysis, and anomaly detection; the correction direction refers to the direction of adjustment of the setpoint by increasing or decreasing it; and the correction magnitude refers to the specific numerical value of the adjustment of the setpoint.

[0072] This application's solution acquires drilling responses, including measurement data responses indicating formation characteristics and those indicating drill string operating status, thus providing foundational data for subsequent analysis and judgment. Based on the acquired response data, the indicative characteristics of the measurement data responses indicating formation characteristics to formation changes, and the indicative characteristics of the measurement data responses indicating drill string operating status to drill string behavior, are analyzed. This helps to understand the current drilling status and accurately grasp formation changes and drill string behavior. Furthermore, based on the changing trends of the measurement data responses indicating formation characteristics, the direction and magnitude of their correction to setpoints are determined, enabling parameter adjustments to adapt to formation changes. Simultaneously, based on the fluctuations in the measurement data responses indicating drill string operating status, combined with the changes in the measurement data responses indicating formation characteristics, it is determined whether the fluctuations indicate the drill string's own behavior, which helps to distinguish between interfering factors and the true drill string status. Finally, based on the judgment results of the response to the measurement data indicating formation characteristics and the judgment results of the fluctuation of the response to the measurement data indicating the drilling tool's operating status, the correction direction and correction range of the setpoints are determined. The setpoints are then corrected according to the correction direction and correction range, thereby achieving real-time, dynamic adjustment of the drilling parameter setpoints. This real-time feedback correction mechanism is based on the initial parameter settings made according to the adjusted target priorities, the identified current formation characteristics, and the identified current drilling tool operating status. It compensates for possible deviations in the initial settings, enabling the drilling parameters to more accurately match the rapidly changing formation and drilling tool status, thus better achieving multi-objective trade-offs and improving overall drilling performance.

[0073] As one embodiment of the present invention, the steps of analyzing the indicative characteristics of measurement data responses indicating formation characteristics to formation changes, and the indicative characteristics of measurement data responses indicating drill string operating status to drill string behavior, include:

[0074] By summarizing the response characteristics of measurement data under different formation conditions and drilling tool operating states in drilling data, a response feature library reflecting formation characteristics and drilling tool behavior is constructed.

[0075] Based on the real-time acquired measurement data responses indicating formation characteristics and drilling tool operating status, the data are compared with features in the response feature library to analyze the indicative characteristics of the measurement data responses indicating formation characteristics to formation changes, and the indicative characteristics of the measurement data responses indicating drilling tool operating status to drilling tool behavior.

[0076] The response feature database is updated by combining downhole environmental information obtained during drilling.

[0077] The response feature library refers to a collection storing measurement data response patterns or laws corresponding to different formation conditions and drilling tool operating states. This can be implemented using databases, lookup tables, or machine learning models. Summarizing the measurement data response features under different formation conditions and drilling tool operating states in drilling data refers to extracting, summarizing, and refining typical change patterns, numerical ranges, or interrelationships of measurement data exhibited by specific formation types or specific drilling tool behaviors from historical or experimental drilling data. This can be implemented using statistical analysis, pattern recognition, or expert systems. The response features reflecting the formation characteristics and drilling tool behavior refer to data patterns or indicators extracted from measurement data that can indicate the current formation type, lithological changes, formation stress state, and the current axial movement, rotation, vibration mode, and wellbore contact state of the drilling tool. These can include the mean, variance, frequency components, correlation, and abrupt change points of the measurement data. The comparison of features in the response feature library refers to comparing and matching the real-time acquired measurement data response with the known feature patterns stored in the response feature library to determine which known formation conditions or drill string behavior pattern the current real-time data is closest to. This can be achieved using methods such as similarity calculation, pattern matching algorithms, or classification algorithms. Downhole environmental information refers to auxiliary information related to the actual downhole situation obtained through various means during the drilling process. This can include logging-while-drilling data, cuttings analysis results, drilling fluid performance parameters, wellbore trajectory information, drill bit wear state estimation, etc. Updating the response feature library refers to correcting, supplementing, or adding existing feature patterns in the response feature library based on the real-time acquired measurement data response, comparison results, and downhole environmental information to improve the accuracy and coverage of the feature library, enabling it to better adapt to new situations or changes encountered during actual drilling. This can be achieved using strategies such as online learning, incremental updates, or periodic reconstruction.

[0078] This application's solution provides a systematic reference standard for analyzing measurement data responses by constructing a response feature library reflecting formation characteristics and drill string behavior. Based on historical drilling data, the feature library contains typical measurement data patterns under different formation conditions and drill string operating states, overcoming the problem of lacking effective reference standards. Subsequently, by comparing the real-time acquired measurement data responses with the features in this feature library, the current formation characteristics and drill string operating state can be quickly and accurately identified. This comparison-based analysis method makes the judgment of the indicative characteristics of measurement data more objective and precise, avoiding the uncertainty caused by relying solely on experience or simple threshold judgments. Furthermore, by combining downhole environmental information acquired during drilling, the response feature library is dynamically updated, ensuring its timeliness and accuracy, enabling it to adapt to constantly changing drilling environments and unknown situations, further improving the reliability of the analysis. This feature library-based analysis method provides more precise input for subsequent correction of setpoints based on drilling responses, allowing the correction direction and magnitude to more accurately reflect actual formation changes and drill string behavior, thereby improving the accuracy and reliability of the entire drilling parameter control method.

[0079] As one embodiment of the present invention, the step of determining whether the fluctuations indicate the behavior of the drill string itself, based on the fluctuations in the measurement data response indicating the drilling string's operating status and in conjunction with the changes in the measurement data response indicating formation characteristics, includes:

[0080] Identify specific features presented in the measurement data response that indicates the operating status of the drill string;

[0081] Identify the changing characteristics presented in the measurement data response that indicates formation properties;

[0082] The specific characteristics of the identified measurement data response indicating the drilling tool's operating status are correlated and compared with the changing characteristics of the identified measurement data response indicating formation characteristics to obtain the comparison results;

[0083] Based on the comparison results, determine whether the fluctuations indicate the behavior of the drilling tool itself, and clarify whether the fluctuations originate from changes in the drilling tool's own operating state or the evolution of formation conditions.

[0084] Identifying specific features in the measurement data response indicating the drilling tool's operating status refers to patterns reflecting abnormalities or changes in the drilling tool's operating status, such as periodic fluctuations in torque, sudden drops in drilling pressure, specific frequencies or amplitudes of drilling tool vibration, and abnormal changes in drilling speed. This can be achieved using signal processing techniques, pattern recognition algorithms, or rule-based expert systems. Identifying changing features in the measurement data response indicating formation characteristics refers to patterns reflecting changes in formation properties or states, such as trend changes, abrupt changes, or outliers in gamma rays, resistivity, and sonic transit time during drilling; changes in the type or content of cuttings in drilling fluid return products; and trend changes in drilling pressure or torque under stable drilling conditions. This can be achieved using trend analysis, abrupt change detection, data smoothing, filtering, or geological interpretation models. Correlate specific characteristics of the identified measurement data responses indicating drill string operating status with the changing characteristics of identified measurement data responses indicating formation characteristics. This involves analyzing the synchronicity, correlation, or causal relationship between the specific characteristics of drill string operating status and the changing characteristics of formation characteristics in time, depth, or space. This can be achieved using time series analysis, cross-correlation analysis, machine learning models, or expert rule bases. Obtaining the correlation results refers to the conclusions or quantitative indicators derived from the correlation comparison, such as the correlation coefficient between drill string characteristics and formation characteristics, the time difference of synchronous occurrence, pattern matching degree, or preliminary attribution based on rule judgment. Determining whether fluctuations indicate drill string behavior based on the correlation results, and clarifying whether the fluctuations originate from changes in the drill string's operating status or evolution of formation conditions, involves comprehensively analyzing the correlation results to determine whether the main source of the fluctuations is a problem with the drill string itself or changes in formation conditions. This can be achieved using logical reasoning, probabilistic models, classification algorithms, or decision rules based on expert knowledge.

[0085] This application's solution first analyzes the response of measurement data indicating the drill string's operating status to identify specific characteristics reflecting its behavior. Simultaneously, it analyzes the response of measurement data indicating formation characteristics to identify variations reflecting formation changes. Then, it compares and correlates these two types of identified characteristics to analyze whether there is synchronicity or correlation between them. For example, if periodic fluctuations in drill string torque occur simultaneously with abrupt changes in formation gamma values, it may indicate that the fluctuations are related to formation changes. If the drill string vibration frequency increases but the formation measurement data shows no significant change, it may indicate that the fluctuations mainly originate from the drill string's own behavior. Based on the results of this correlation comparison, it is possible to more accurately determine whether the fluctuations in the current measurement data response are mainly caused by changes in the drill string's own operating status or by the evolution of formation conditions. This more precise judgment allows for a more accurate identification of the source of fluctuations when correcting setpoints based on drilling responses, avoiding misjudging formation-related fluctuations as drill string problems, or vice versa. This allows for more targeted adjustments in direction and magnitude. For example, if fluctuations are caused by formation changes, it may be necessary to adjust the drilling pressure or rotation speed to adapt to the new formation; if fluctuations are caused by drill string stick-slip, it may be necessary to adjust the drilling pressure, rotation speed, or drilling fluid properties to suppress vibrations. This accurate attribution of fluctuations improves the reliability of the entire parameter correction process, thereby enhancing the overall performance of exploration drilling parameter control methods and better balancing the quality of geological information acquisition, wellbore safety, and drilling efficiency.

[0086] As one embodiment of the present invention, for each control strategy in the strategy options, a performance simulation is performed based on the current drilling state. The performance simulation considers the evolution trend of the identified current formation characteristics and the identified current drill bit operating state in a short period of time, and takes into account the fluctuation range of various measurement data. The steps to generate the simulation results include:

[0087] Monitor the drilling data stream, which includes various measurement data as well as information reflecting the current formation characteristics and the current drilling tool operating status;

[0088] Based on the monitored drilling data stream, the current formation characteristics and the current drilling tool operating status are identified and their evolution trends in a short period of time. The fluctuation range of various measurement data is also identified to obtain evolution trend information and fluctuation range information.

[0089] For each control strategy in the strategy options, performance is extrapolated based on the current drilling status and according to evolution trend information and fluctuation range information to generate extrapolation results.

[0090] Among them, the evolution trend refers to the direction or pattern of change of technical characteristics in a short period of time. It can be identified by methods such as time series analysis, trend line fitting, and machine learning model prediction, with the aim of predicting future states. The fluctuation range refers to the range of numerical changes or degree of uncertainty that technical characteristics may occur under certain time or conditions. It can be identified by methods such as statistical analysis, error analysis, and historical data distribution, with the aim of assessing the uncertainty of the data.

[0091] This application's solution continuously monitors the drilling data stream to obtain comprehensive real-time information. Based on this, in-depth analysis of the data stream is performed to identify the changing patterns of current formation characteristics and drill string operating status over short periods, and to quantify the inherent uncertainties of the measurement data. This dynamic and uncertain information is integrated into the performance simulation process. For each candidate control strategy, based on the current drilling status, combined with predicted evolution trends and data fluctuation ranges, the potential effects of the strategy in the short future are simulated. This simulation method not only considers current static conditions but also proactively considers future changes and data uncertainties. In this way, the generated simulation results can more accurately predict the actual performance of different strategies, especially under conditions of rapid changes in formation or drill string status and interference with measurement data. Using these more accurate and reliable simulation results for strategy selection allows the system to more effectively select the control strategy from the available options that best meets the adjusted target priority while minimizing negative effects. This makes it more informed and robust to prioritize between the quality of geological information acquisition, wellbore safety, and drilling efficiency, thereby improving the overall performance of exploration drilling parameter control methods and solving the problem of inaccurate predictions and insufficient robustness caused by relying solely on instantaneous data for extrapolation.

[0092] As one embodiment of the present invention, the steps of identifying the evolution trend of the identified current formation characteristics and the identified current drilling tool operating status within a short period of time, and identifying the fluctuation range of various measurement data, include:

[0093] The monitored drilling data stream is segmented and analyzed using an adjustable time window.

[0094] Adjust the length of the time window based on the rate of data change or formation response characteristics reflected in the drilling data stream;

[0095] Based on the results of segmented analysis using the adjusted time window, the evolution trend of the current formation characteristics and the current drilling tool operating status in a short period of time is identified, and the fluctuation range of various measurement data is identified to obtain evolution trend information and fluctuation range information.

[0096] The adjustable time window refers to the time period used to analyze drilling data streams. Its length can be dynamically changed according to specific conditions. It can be achieved by using adjustment mechanisms based on data statistical characteristics or preset rules. Its purpose is to enable the granularity of data analysis to adapt to the dynamic changes in the drilling process. The formation response characteristics refer to the specific behavioral patterns or data signal characteristics exhibited by the formation when subjected to drilling. It can be achieved by analyzing the changes in drilling pressure, torque, drilling speed, downhole vibration, and formation measurement data over time or depth. Its purpose is to reflect the current formation response characteristics to drilling activities.

[0097] The proposed solution involves segmenting and analyzing the monitored drilling data stream using an adjustable time window. This allows the data analysis to move beyond fixed time intervals and be flexibly adjusted based on actual conditions. By adjusting the time window length according to the rate of data change or formation response characteristics reflected in the drilling data stream, the system can determine the dynamic nature of the current drilling process. For example, when encountering areas of drastic formation change, the data change rate accelerates, and shortening the time window allows for more timely capture of these changes. Conversely, when encountering relatively stable formations, the window can be appropriately extended to reduce noise interference. This adaptive adjustment mechanism of the time window length enables the data analysis to better match the dynamic characteristics of the actual drilling process. Furthermore, by identifying the evolution trends of the current formation characteristics and drill bit operating status within a short period based on the segmented analysis using the adjusted time window, and recognizing the fluctuation ranges of various measurement data, the system obtains evolution trend information and fluctuation range information. Through dynamically adjusted time windows, it can more accurately capture the short-term changing trends of formation characteristics and drill bit operating status, and quantify the fluctuation range of measurement data. This information is crucial for subsequent performance simulations because it provides more accurate input parameters, enabling the simulation results to more realistically reflect the potential risks and optimization potential during actual drilling. Incorporating this more accurate information into the performance simulation process makes the results more reliable, thereby guiding subsequent parameter control decisions and improving the accuracy and robustness of control.

[0098] like Figure 2 The diagram shows an exploration drilling parameter control system, which includes:

[0099] The target setting module 201 is used to set the drilling target and the target priority corresponding to the drilling target. The target priority indicates the importance of the drilling target.

[0100] The data processing module 202 is used to acquire various measurement data during the drilling process and process the various measurement data.

[0101] The status recognition module 203 is used to identify the current formation characteristics and the current drilling tool operating status based on the processed various measurement data;

[0102] The target adjustment module 204 is used to adjust the drilling target and target priority based on geological information during the drilling process.

[0103] The strategy determination module 205 is used to determine the control strategy for drilling parameters based on the adjusted target priority, the identified current formation characteristics, and the identified current drilling tool operating status. The control strategy focuses on ensuring the quality of geological information acquisition, wellbore safety, and improving drilling efficiency based on the adjusted target priority.

[0104] The parameter calculation module 206 is used to calculate the set values ​​of the current drilling parameters and send the set values ​​to the drilling equipment;

[0105] The parameter correction module 207 is used to correct the set value based on the drilling response.

[0106] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for controlling exploration drilling parameters, characterized in that, The method includes the following steps: Define drilling objectives and corresponding objective priorities, whereby the objective priorities indicate the importance of the drilling objectives; Acquire various measurement data during the drilling process and process the various measurement data; Based on the processed measurement data, identify the current formation characteristics and the current drilling tool operating status; Based on geological information during the drilling process, the drilling objectives and their priorities are adjusted. Based on the adjusted target priority, the identified current formation characteristics, and the identified current drilling tool operating status, a control strategy for drilling parameters is determined. The control strategy, based on the adjusted target priority, prioritizes ensuring the quality of geological information acquisition, wellbore safety, and improving drilling efficiency. Calculate the set values ​​of the current drilling parameters and send the set values ​​to the drilling equipment; The set value is corrected based on the drilling response; The step of adjusting the drilling target and the target priority based on geological information during the drilling process includes: Continuous analysis of the changing trends and correlations of the various measurement data is conducted to obtain geological information during the drilling process; Based on the geological information, it is predicted that the drill bit will soon enter a geological formation area that requires attention; Before the drill bit is predicted to enter the formation area of ​​interest, the drilling target and the conversion of the target priority are initiated in advance based on the prediction results. Based on the converted drilling target and the target priority, the drilling parameters are instructed to be adjusted gradually in a controlled manner to ensure that when the drill bit enters the formation area of ​​interest, the drilling parameters have reached a state suitable for the formation area of ​​interest. The control strategy for determining drilling parameters, which prioritizes certain aspects among ensuring the quality of geological information acquisition, wellbore safety, and improving drilling efficiency based on the adjusted target priorities, includes the following steps: Based on the identified current formation characteristics and the identified current drilling tool operating status, assess the potential impact of the current drilling environment on geological information acquisition, wellbore safety, and drilling efficiency; Based on the adjusted target priorities and the potential impacts, determine the current importance of each drilling target; Based on the current importance, construct strategy options that reflect the interrelationships between the drilling objectives; From the strategy options, select a control strategy for drilling parameters that maximizes the satisfaction of the adjusted target priority while minimizing negative impacts on other non-primary objectives, thereby balancing the quality of geological information acquisition, wellbore safety, and improved drilling efficiency.

2. The exploration drilling parameter control method according to claim 1, characterized in that, The step of selecting a control strategy for drilling parameters from the strategy options, wherein the control strategy can maximize the satisfaction of the adjusted target priority while minimizing the negative impact on other non-primary objectives, thereby prioritizing the quality of geological information acquisition, wellbore safety, and improved drilling efficiency, includes: For each control strategy in the strategy options, a performance simulation is performed based on the current drilling status. The performance simulation considers the evolution trend of the identified current formation characteristics and the identified current drill bit operating status in a short period of time, and takes into account the fluctuation range of the various measurement data, in order to generate the simulation result. Based on the simulation results, a control strategy for drilling parameters is selected. This control strategy can continuously maintain the satisfaction of the adjusted target priority while reducing the negative impact on other non-primary objectives, thereby balancing the quality of geological information acquisition, wellbore safety, and improved drilling efficiency.

3. The exploration drilling parameter control method according to claim 1, characterized in that, The step of correcting the set value based on the drilling response includes: The drilling response is obtained, which includes measurement data responses indicating formation characteristics and measurement data responses indicating the operating status of the drill string; The indicative characteristics of the measured data response indicating formation characteristics to formation changes, and the indicative characteristics of the measured data response indicating drill string operating status to drill string behavior are analyzed. Based on the changing trend of the measurement data response indicating the formation characteristics, determine its effect on the correction direction and correction magnitude of the set value; Based on the fluctuations in the measurement data response indicating the drilling tool's operating status, and combined with the changes in the measurement data response indicating formation characteristics, it is determined whether the fluctuations indicate the drilling tool's own behavior. Based on the judgment results of the effect of the measurement data response indicating formation characteristics and the judgment results of the fluctuation of the measurement data response indicating drilling tool operating status, the correction direction and correction range of the set value are determined, and the set value is corrected according to the correction direction and correction range.

4. The exploration drilling parameter control method according to claim 3, characterized in that, The steps of analyzing the indicative characteristics of the measurement data response indicating formation characteristics to formation changes, and the indicative characteristics of the measurement data response indicating drill string operating status to drill string behavior, include: By summarizing the response characteristics of measurement data under different formation conditions and drilling tool operating states in drilling data, a response feature library reflecting formation characteristics and drilling tool behavior is constructed. Based on the real-time acquired measurement data response indicating formation characteristics and the measurement data response indicating drill bit operating status, the data is compared with features in the response feature library to analyze the indicative characteristics of the measurement data response indicating formation characteristics to formation changes and the indicative characteristics of the measurement data response indicating drill bit operating status to drill bit behavior. The response feature library is updated by combining downhole environmental information obtained during the drilling process.

5. The exploration drilling parameter control method according to claim 3, characterized in that, The step of determining whether the fluctuations, based on the fluctuations in the measurement data response indicating the drilling tool's operating status and in conjunction with the changes in the measurement data response indicating formation characteristics, indicate whether the fluctuations indicate the drilling tool's own behavior includes: Identify specific features presented in the measurement data response indicating the drilling tool's operating status; Identify the changing characteristics presented in the measurement data response indicating the formation properties; The specific characteristics of the identified measurement data response indicating the drilling tool's operating status are correlated and compared with the changing characteristics of the identified measurement data response indicating formation characteristics to obtain the comparison results; Based on the comparison results, determine whether the fluctuation indicates the behavior of the drilling tool itself, and clarify whether the fluctuation originates from a change in the drilling tool's own operating state or an evolution of formation conditions.

6. The exploration drilling parameter control method according to claim 2, characterized in that, The step of generating the simulation results for each control strategy in the strategy options, based on the current drilling status, includes: (The simulation considers the identified current formation characteristics and the short-term evolution trend of the identified current drill bit operating status, and takes into account the fluctuation range of the various measurement data.) Monitor the drilling data stream, which includes the various measurement data and information reflecting the current formation characteristics and the current drilling tool operating status; Based on the monitored drilling data stream, the evolution trend of the identified current formation characteristics and the identified current drilling tool operating status within a short period of time is identified, and the fluctuation range of the various measurement data is identified to obtain the evolution trend information and the fluctuation range information; For each control strategy in the strategy options, performance is extrapolated based on the current drilling state and according to the evolution trend information and the fluctuation range information to generate the extrapolation result.

7. The exploration drilling parameter control method according to claim 6, characterized in that, The steps of identifying the evolution trend of the identified current formation characteristics and the identified current drill bit operating status within a short period of time, and identifying the fluctuation range of the various measurement data, include: The monitored drilling data stream is segmented for analysis, and the segmented analysis uses an adjustable time window; The length of the time window is adjusted based on the rate of data change or formation response characteristics reflected in the drilling data stream. Based on the results of segmented analysis using the adjusted time window, the evolution trend of the identified current formation characteristics and the identified current drilling tool operating status within a short period of time is identified, and the fluctuation range of the various measurement data is identified, thereby obtaining the evolution trend information and the fluctuation range information.

8. An exploration drilling parameter control system, used to execute the exploration drilling parameter control method as described in any one of claims 1-7, characterized in that, The system includes: The target setting module is used to set the drilling target and the target priority corresponding to the drilling target, wherein the target priority indicates the importance of the drilling target; The data processing module is used to acquire various measurement data during the drilling process and process the various measurement data. The status recognition module is used to identify the current formation characteristics and the current drilling tool operating status based on various processed measurement data. The target adjustment module is used to adjust the drilling target and the target priority based on geological information during the drilling process. The strategy determination module is used to determine the control strategy for drilling parameters based on the adjusted target priority, the identified current formation characteristics, and the identified current drilling tool operating status. The control strategy focuses on ensuring the quality of geological information acquisition, wellbore safety, and improving drilling efficiency based on the adjusted target priority. The parameter calculation module is used to calculate the set values ​​of the current drilling parameters and send the set values ​​to the drilling equipment; The parameter correction module is used to correct the set value based on the drilling response.

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