Drilling operation parameter adaptive decision-making method based on feed damping and mechanical specific energy

Abstract

This invention discloses an adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy, comprising: S1: acquiring feed pressure, rotational speed, torque, and drilling speed as drilling parameters, and performing median filtering using a sliding window to obtain filtered drilling parameters; S2: calculating the feed damping coefficient, determining the range of variation of the feed damping coefficient, and converting the feed damping coefficient into an adaptive adjustment index; S3: calculating the mechanical specific energy, and combining it with the feed damping change rate to determine whether the direction of updating the operation parameters needs to be changed, and adjusting the parameter update flag; S4: deciding the final change value of the feed pressure based on the adaptive adjustment index and the parameter update flag. This invention designs an adaptive adjustment strategy for operation parameters based on the magnitude of the feed damping coefficient, and uses mechanical specific energy as a key indicator for evaluating the quality of operation parameters, achieving optimal drilling parameters under different geological conditions; experimental data shows that the proposed method can effectively reduce mechanical specific energy, achieving low-energy consumption and high-efficiency drilling.

Description

Technical Field

[0001] This invention relates to the field of underground tunnel drilling technology in coal mines, and in particular to an adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy. Background Technology

[0002] Drilling operation parameters refer to the key parameters characterizing the controllable factors in the drilling process, including equipment, tools, mud, and operating conditions. These mainly include feed pressure, rotational speed, and pump flow rate. Drilling operation parameter optimization refers to, under certain conditions, using optimization methods to select reasonable timing combinations of drilling operation parameters based on the influence of different parameter combinations on indicators such as drilling speed, drill bit life, drill bit specific energy, and drilling accident rate. This lays an important research foundation for achieving safe and efficient intelligent control of the drilling process.

[0003] Drilling efficiency and safety are determined by various indicators, including drilling speed, bit life, bit energy, and drilling accident rate. Drilling speed is a key indicator of drilling efficiency, while mechanical energy is a key indicator of drilling conditions. Downhole drilling involves a challenging mechanical environment characterized by high temperature, high pressure, steep formations, and mining disturbances. Poor formation drillability and slow drilling speeds make increasing drilling speed crucial. Improving drilling speed while simultaneously extending bit life, increasing bit energy, and reducing drilling accident rate can further enhance both drilling efficiency and safety.

[0004] Mechanical Specific Energy (MSE) is a physical model established to overcome regional differences and conform to technical performance standards. It represents the mechanical energy required to break a unit volume of rock per unit time under feed pressure and torque crushing action. Since drilling speed is proportional to feed pressure and rotational speed within a certain range, using MSE as a criterion for optimizing operating parameters allows for a better understanding of the current operating parameters' strengths and weaknesses. MSE exhibits a non-linear change with operating parameters. For example, when drilling in a single formation, MSE tends to decrease first and then increase with increasing feed pressure. Therefore, finding the optimal point with the lowest MSE is crucial for optimizing operating parameters. Traditional methods do not consider the hardness of the formation during parameter adjustment; changing the same amount of operating parameters in different formations will result in different changes in MSE. Therefore, this invention utilizes the minimum MSE as an evaluation index for operating parameters and introduces feed damping as an adjustment factor for parameter optimization updates, thereby achieving optimal adaptive drilling in different formation environments. The proposed method provides an approach to adaptive decision-making of drilling operation parameters in underground coal mine tunnels, avoiding over-reliance on human experience and having practical significance for improving the safety and efficiency of underground tunnel drilling. Summary of the Invention

[0005] This invention discloses an adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy. It aims to solve the problem that most parameter optimizations in the prior art do not consider the lithology of the formation and rely too much on human experience. The method utilizes mechanical specific energy theory to achieve optimal drilling.

[0006] To solve the above problems, the technical solution adopted by the present invention includes:

[0007] An adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy includes:

[0008] S1 obtains the feed pressure, rotational speed, torque, and drilling speed as drilling parameters: median filtering is performed using a sliding window to obtain filtered drilling parameters;

[0009] S2 Calculation of Feed Damping Coefficient: The filtered drilling parameters are substituted into the feed damping coefficient calculation formula to calculate the key indicator reflecting the current drilling difficulty of the formation. The difference between the current feed damping coefficient and the feed damping coefficient at the previous moment is obtained to obtain the feed damping change rate. The range of change of the feed damping coefficient is determined, and the feed damping coefficient is converted into an adaptive adjustment index.

[0010] S3 Calculates mechanical specific energy: The filtered drilling parameters are substituted into the mechanical specific energy calculation formula to calculate the key indicators reflecting the quality of the current drilling state, and at the same time, the minimum mechanical specific energy within the sliding window is recorded; the current mechanical specific energy is compared with the minimum mechanical specific energy within the sliding window, and combined with the feed damping change rate, it is determined whether the direction of updating the operation parameters needs to be changed, and the parameter update flag is adjusted.

[0011] S4 updates the flag bit based on the adaptive adjustment index and parameters, and decides the final change value of the feed pressure.

[0012] Optionally, the method of obtaining filtered drilling parameters by median filtering using a sliding window includes:

[0013] Data f (k)=Φ[Data(k),Data(k-1),…,Data(k-n+1)];

[0014] Where Data represents feed pressure, rotational speed, drilling speed, or torque; f (k) represents the filtered data at the k-th sampling time; Data(k) represents the original data at the k-th sampling time; Φ[·] represents the median filtering of the sampled data represented by ·, and the median of the sequence is taken; n represents the size of the sliding window.

[0015] Optionally, the key indicators that reflect the current drilling difficulty of the formation include:

[0016]

[0017] In the formula: k r (k) represents the magnitude of the feed damping coefficient at the k-th sampling time; WOB f (i) represents the magnitude of the filter feed pressure at the i-th sampling time; ROP f (i) represents the filtered drilling speed at the i-th sampling time; n represents the size of the sliding window.

[0018] Optionally, the feed damping change rate is expressed as:

[0019]

[0020] In the formula: δ represents the rate of change of feed damping at the k-th sampling time; k r (k) represents the magnitude of the feed damping coefficient at the k-th sampling time; k r (k-1) represents the magnitude of the feed damping coefficient at the (k-1)th sampling time; |·| represents taking the absolute value.

[0021] Optionally, determining the range of variation of the feed damping coefficient and converting the feed damping coefficient into an adaptive adjustment index includes:

[0022]

[0023] In the formula: θ represents the adaptive adjustment index; k rmax With k rmin These are the upper and lower bounds of the set feed damping coefficient.

[0024] Optionally, the mechanical specific energy is calculated as follows:

[0025]

[0026] In the formula: MSE(k) represents the mechanical specific energy at the k-th sampling time, WOB f (k) represents the magnitude of the filtered feed pressure at the k-th sampling time; RPM f (k) represents the filter rotation speed at the k-th sampling time; TOR f (k) represents the magnitude of the filter torque at the k-th sampling time; ROP f (k) represents the filtered drilling speed at the kth sampling time.

[0027] Optionally, the minimum mechanical specific energy within the sliding window is expressed as follows:

[0028] MSE min (k)=MIN[MSE(k),MSE(k-1),…,MSE(k-n+1)];

[0029] Where: MSEmin (k) represents the minimum mechanical specific energy at the k-th sampling time; MIN(·) means taking the minimum value for the sampled data.

[0030] Optionally, S3 specifically includes:

[0031] S31. Determine whether drilling can begin given the process and operating conditions;

[0032] S32. Determine if the drilling speed exceeds the maximum drilling speed limit; if it does, reduce the feed pressure.

[0033] S33. Determine if the torque exceeds the maximum torque limit; if it does, reduce the feed pressure.

[0034] S34. If neither S32 nor S33 exceeds the upper limit, the feed pressure is adjusted according to the adaptive adjustment index and parameter update flag.

[0035] S35. Based on the feed damping change rate and mechanical specific energy, determine whether the parameter update flag needs to be updated;

[0036] S36. Repeat the above steps to achieve adaptive drilling.

[0037] Optionally, the feed damping flag adjustment is based on a comparison between the current mechanical specific energy and the minimum mechanical specific energy;

[0038] If an adjustment causes the current mechanical specific energy to be greater than 1.1 times the minimum mechanical specific energy, and the feed damping change rate is less than 0.2, then change the parameter update flag, that is, change the positive direction to the negative direction, or the negative direction to the positive direction.

[0039] If the rate of change of feed damping is greater than 0.2, the flag will not be changed.

[0040] Optionally, adjusting the feed pressure includes:

[0041] WOB d (k)=WOB d (k-1)+ΔWOB d (k);

[0042] ΔWOB d (k) = SIGN·θ·ΔWOB;

[0043] In the formula: WOB d (k) represents the expected feed pressure at the k-th sampling time; WOB d (k-1) represents the expected feed pressure at the (k-1)th sampling time; ΔWOB d(k) represents the expected feed pressure increment at the kth sampling time; SIGN represents the flag bit, which is -1 or 1; θ represents the adaptive adjustment index; ΔWOB represents the base increment size, which is a positive constant.

[0044] The present invention has the following beneficial effects:

[0045] This invention can effectively utilize drilling parameters and mechanical energy theory to reveal whether the current parameter update direction is correct, thereby guiding the setting of operating parameters, automatically finding the optimal parameters, and ensuring the safety and efficiency of the drilling process. It is practical and applicable. Attached Figure Description

[0046] The accompanying drawings are provided to further illustrate this disclosure and form part of the specification. They are used in conjunction with the following detailed description and are disclosed in connection with the invention, but do not constitute a limitation thereof. In the drawings:

[0047] Figure 1 This is a flowchart of the intelligent adaptive decision-making process for drilling operation parameters based on feed damping and mechanical specific energy of the present invention.

[0048] Figure 2 This is a diagram of the intelligent adaptive decision-making structure for drilling operation parameters based on feed damping and mechanical specific energy of the present invention.

[0049] Figure 3 This is a flowchart of the intelligent adaptive decision-making process for drilling operation parameters based on feed damping and mechanical specific energy, as described in this invention.

[0050] Figure 4 These are the experimental feed pressure data for this invention;

[0051] Figure 5 These are the experimental torque data for this invention;

[0052] Figure 6 These are the experimental drilling rate data of this invention;

[0053] Figure 7 These are the experimental rotational speed data of this invention;

[0054] Figure 8 These are the mechanical specific energy data of this invention;

[0055] Figure 9 These are the feed damping data of this invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0057] The overall solution of this invention is as follows: First, key data of the drilling process are collected in real time and the data is filtered by median; the feed damping and rate of change are calculated based on the obtained data; the adaptive adjustment index is calculated according to the set upper and lower limits of the feed damping; the mechanical specific energy and the minimum mechanical specific energy within the sliding window are calculated based on the obtained data; the parameter update direction is determined according to the feed damping rate of change and mechanical specific energy; and the operating parameters are adjusted by combining the adaptive adjustment index and the parameter update direction.

[0058] The adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy of the present invention includes the following steps:

[0059] First, key parameters of the drilling process are collected: feed pressure (N), rotation speed (rpm), torque (Nm), and drilling speed (m / min). Based on the time scale and trend of the actual data, an appropriate sliding window size is selected, and the data is filtered by median. The median value within the window is then taken to remove data with abnormal changes.

[0060] Construct a feed damping model for the drilling process, input the filtered data into the model to obtain the current feed damping, and then calculate the difference between the current feed damping and the feed damping at the previous moment to obtain the feed damping change rate.

[0061] Based on the set upper and lower limits of the feed damping, the current feed damping is converted into an adaptive adjustment index;

[0062] A mechanical specific energy model is constructed, and the filtered data is substituted into the model to obtain the current mechanical specific energy. At the same time, the minimum mechanical specific energy within the sliding window is solved.

[0063] Based on the relative error between the current mechanical specific energy and the minimum mechanical specific energy, and combined with the magnitude of the feed damping change rate, determine whether the parameter update direction needs to be changed, and update the parameter update flag.

[0064] The operating parameters are adjusted based on the adaptive adjustment indicators and parameter update flags. If the drilling speed and torque exceed the upper limit, the operating parameters are reduced first.

[0065] refer to Figure 1 , Figure 1 This is a flowchart of the intelligent adaptive decision-making process for drilling operation parameters based on feed damping and mechanical specific energy, according to the present invention. The present invention specifically includes the following steps:

[0066] S1 acquires feed pressure, rotational speed, torque, and drilling speed as drilling parameters: data preprocessing, median filtering of drilling data;

[0067] Drilling parameters mainly include operational parameters and status parameters, with core parameters including feed pressure (N), rotational speed (rpm), torque (Nm), and drilling speed (m / min). Due to the complexity of actual downhole conditions, including strong disturbances and measurement noise, data anomalies are unavoidable, affecting the judgment of the drilling status. Therefore, median filtering is first applied to the data to eliminate abnormally changing drilling parameters.

[0068] The filtered drilling parameters can be expressed as:

[0069] Data f (k)=Φ[Data(k),Data(k-1),…,Data(k-n+1)];

[0070] Data represents four key parameters: feed pressure (WOB), rotational speed (RPM), torque (TOR), and drilling speed (ROP). f (k) represents the filtered data at the k-th sampling time; Data(k) represents the original data at the k-th sampling time; Φ[·] represents median filtering of the sampled data represented by ·, taking the median of the sequence; n represents the size of the sliding window. The choice of the sliding window size mainly depends on the time scale of the core parameter change. Since most underground roadway drilling rigs are fully hydraulic, they can track operation commands relatively quickly, so n is generally taken as 10. When the sampling time is 1 second, the size of the sliding window is 10 seconds.

[0071] S2 calculates the feed damping coefficient:

[0072] Please refer to Figure 2 The parameters required to calculate the feed damping coefficient are the filtered feed pressure and the filtered drilling speed. Feed damping is an indicator reflecting the current difficulty of drilling, i.e., the feed pressure required to increase the unit drilling speed. It is expressed as:

[0073]

[0074] In the formula:

[0075] k r (k) represents the magnitude of the feed damping coefficient at the k-th sampling time; WOB f (i) represents the magnitude of the filter feed pressure at the i-th sampling time; ROP f (i) represents the filtered drilling speed at the i-th sampling time; n represents the size of the sliding window.

[0076] Similarly, a sliding window is used to avoid outliers, with n set to 10. With a sampling time of 1 second, the size of the sliding window is 10 seconds.

[0077] The rate of change of feed damping can be expressed as:

[0078]

[0079] In the formula: δ represents the rate of change of feed damping at the k-th sampling time; k r (k) represents the magnitude of the feed damping coefficient at the k-th sampling time; k r (k-1) represents the magnitude of the feed damping coefficient at the (k-1)th sampling time; |·| represents taking the absolute value.

[0080] Based on the aforementioned feed damping, an adaptive adjustment index can be calculated by combining the upper and lower limits of the feed damping. The adaptive adjustment index determines the magnitude of a single parameter update. Due to different lithologies, the feed damping varies, meaning the feed pressure required to increase the unit drilling speed also differs. Therefore, by combining the adaptive adjustment index, the mechanical energy ratio can be optimized with the fewest possible adjustments. The adaptive adjustment index can be expressed as:

[0081]

[0082] In the formula: θ represents the adaptive adjustment index; k r (k) represents the feed damping; k rmax With k rmin The upper and lower limits of the feed damping are set. The adaptive adjustment index is designed as a continuous value between 1 and 5, i.e., a continuous level division. The magnitude of a single update of the operating parameter can be continuously adjusted between levels 1 and 5 according to the adaptive index. In addition, k rmax With k rmin It can be determined by prior knowledge of drilling in different strata.

[0083] S3 calculates the mechanical specific energy index:

[0084] Mechanical Specific Energy (MSE) is a physical model established to overcome regional differences and conform to technical performance standards. It represents the mechanical energy required to break a unit volume of rock per unit time under feed pressure and torque. Since drilling speed is proportional to rotational speed and feed pressure within a certain range, using MSE as a criterion for optimizing operating parameters allows for a better understanding of the advantages and disadvantages of current operating parameters. MSE exhibits a non-linear change with operating parameters; for example, when drilling in a single formation, MSE tends to decrease first and then increase with increasing feed pressure. Finding the optimal point with the lowest MSE is the goal of operating parameter optimization. (Reference) Figure 2 Mechanical specific energy requires calculation of all four core drilling parameters, as shown below:

[0085]

[0086] In the formula: MSE(k) represents the mechanical specific energy at the k-th sampling time; WOB f (k) represents the magnitude of the filtered feed pressure at the k-th sampling time; RPM f (k) represents the filter rotation speed at the k-th sampling time; TOR f (k) represents the magnitude of the filter torque at the k-th sampling time; ROP f (k) represents the filtered drilling speed at the kth sampling time.

[0087] The minimum mechanical specific energy within the sliding window can be expressed as:

[0088] MSE min (k)=MIN[MSE(k),MSE(k-1),…,MSE(k-n+1)];

[0089] Where: MSE min (k) represents the minimum mechanical specific energy at the k-th sampling time; MIN[·] indicates that the minimum value is taken from the sampled data. n is 10. When the sampling time is 1 second, the size of the sliding window is 10 seconds.

[0090] S4 updates the flags based on adaptive adjustment indicators and parameters, and determines the final change in feed pressure:

[0091] S4.1 Calculate the parameter update flag:

[0092] The parameter update flag indicates the direction of parameter change at the next operation parameter update time, i.e., increase or decrease. The trend of mechanical specific energy change will reflect whether the current operation parameter adjustment is moving towards the optimal parameter combination. If the mechanical specific energy shows a continuous decreasing trend, it indicates that the current parameter optimization direction is correct; otherwise, it is incorrect. (Reference) Figure 3 If, during an adjustment, the current mechanical specific energy exceeds 1.1 times the minimum mechanical specific energy, and the feed damping change rate is less than 0.2, then the parameter update flag is changed—either from positive to negative, or vice versa. Using the feed damping change rate as a condition aims to overcome the influence of formation changes on the mechanical specific energy. If the feed damping change rate is greater than 0.2, the flag is not changed; the change in mechanical specific energy is considered a formation influence, not a factor affecting the parameter adjustment direction.

[0093] The specific rules for flag adjustment are as follows:

[0094] IF: MSE(k) > 1.1MSE min (k) and δ<0.2; THEN: Change the flag bit (SIGN: -1→1 or -1→1). Where SIGN represents the flag bit.

[0095] S4.2 Make operational parameter decisions:

[0096] refer to Figure 3 , Figure 3 This invention provides a flowchart of an intelligent adaptive decision-making process for drilling operation parameters based on feed damping and mechanical specific energy, which determines the final operation parameter decisions.

[0097] In actual drilling operations, in addition to considering mechanical specific energy as an evaluation index for setting operating parameters, there are also constraints from procedures, operating conditions, and state parameters. For example, during procedures such as adding drill pipes, the feed pressure and rotational speed are not issued through the adaptive system; similarly, operating parameters cannot be arbitrarily issued when the drilling rig is in abnormal downhole conditions such as stuck drill or buried drill.

[0098] The constraints on the state parameters mainly include the constraints on drilling speed and torque:

[0099] The maximum drilling speed limit is determined based on the selected drill bit, mud discharge rate, and other parameters to avoid poor slag removal and borehole blockage.

[0100] The maximum torque limit is determined based on factors such as the rated power of the drilling rig and the size of the drill rod, in order to avoid prolonged full-load operation and affecting the lifespan of the drilling rig.

[0101] When the drilling speed and torque exceed the maximum limit, the operating parameters are immediately reduced.

[0102] When the drilling speed and torque are within the maximum limit, the operating parameters are adjusted according to the adaptive adjustment index and parameter update flag.

[0103] The update of the operating parameter feed pressure is shown below:

[0104] WOB d (k)=WOB d (k-1)+ΔWOB d (k);

[0105] ΔWOB d (k) = SIGN·θ·ΔWOB;

[0106] In the formula: WOB d (k) represents the expected feed pressure at the k-th sampling time; WOB d (k-1) represents the expected feed pressure at the (k-1)th sampling time; ΔWOB d (k) represents the expected feed pressure increment at the k-th sampling time; SIGN represents the flag bit, which can be -1 or 1; θ represents the adaptive adjustment index; ΔWOB represents the base increment size, a positive constant;

[0107] Steps S1 to S4 above allow for real-time optimization of operating parameters for underground coal mine drilling rigs, achieving optimal drilling. This invention is applicable to drilling rigs that can directly control feed pressure and rotation speed. Figure 3 Based on the program flow graph, an operation parameter optimization system was established and applied to a laboratory micro-drilling rig. Relevant experimental data are as follows: Figures 4 to 7 The figures show the feed pressure (N), rotational speed (rpm), torque (Nm), and drilling speed (mm / s), respectively. Under the system's action, the feed pressure continuously increases, while the mechanical specific energy continuously decreases, as shown... Figure 8 As shown, this achieves continuous approximation of the optimal point. Since the actual drilled rock sample remained unchanged, the feed damping, except for a rapid decrease in the initial stage (0-100 seconds), did not show significant changes thereafter. Figure 9 As shown.

[0108] The present invention has the following beneficial effects after implementation: it can effectively use drilling parameters and mechanical energy theory to reveal whether the current parameter update direction is correct, thereby guiding the setting of operating parameters, automatically finding the optimal parameters, ensuring the safety and efficiency of the drilling process, and has practicality and applicability.

[0109] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. An adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy, characterized in that, include: S1 obtains the feed pressure, rotational speed, torque, and drilling speed as drilling parameters: median filtering is performed using a sliding window to obtain filtered drilling parameters; S2 Calculation of Feed Damping Coefficient: Substitute the filtered drilling parameters into the feed damping coefficient calculation formula to calculate the key indicator reflecting the current drilling difficulty of the formation. Difference between the current feed damping coefficient and the feed damping coefficient at the previous moment is calculated to obtain the feed damping change rate. Determine the range of variation for the feed damping coefficient and convert the feed damping coefficient into an adaptive adjustment index; S3 calculates mechanical specific energy: The filtered drilling parameters are substituted into the mechanical specific energy calculation formula to calculate the key indicators reflecting the quality of the current drilling status, and at the same time, the minimum value of mechanical specific energy within the sliding window is recorded. Compare the current mechanical specific energy with the minimum mechanical specific energy within the sliding window, and combine this with the feed damping change rate to determine whether the direction of updating the operation parameters needs to be changed, and adjust the parameter update flag. S4 updates the flag bit based on the adaptive adjustment index and parameters, and decides the final change value of the feed pressure.

2. The adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy as described in claim 1, characterized in that, The method of obtaining filtered drilling parameters by median filtering using a sliding window includes: ； in, Data This represents feed pressure, rotational speed, torque, and drilling speed; Data f ( k ) indicates the first k Filtered data for each sampling time; Data ( k ) indicates the first k The original data for each sampling time; Φ[·] indicates that the median of the sequence is obtained by performing median filtering on the sampled data represented by ·; n This indicates the size of the sliding window.

3. The adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy according to claim 1 or 2, characterized in that, The key indicators that the calculation reflects the current drilling difficulty of the formation include: ； In the formula: k r ( k ) represents the first k The magnitude of the feed damping coefficient for each sampling time; WOB f (i) represents the first i The magnitude of the filter feed pressure for each sampling time; ROP f (i) represents the first i The filtered drilling speed for each sampling time; n This indicates the size of the sliding window.

4. The adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy according to claim 1 or 2, characterized in that, The feed damping variation rate is expressed as: ； In the formula: δ Representing the k The rate of change of feed damping over time; k r ( k ) represents the first k The magnitude of the feed damping coefficient for each sampling time; k r ( k -1) represents the first k -1 sampling time represents the magnitude of the feed damping coefficient; |·| represents taking the absolute value.

5. The adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy according to claim 1 or 2, characterized in that, The determination of the range of variation of the feed damping coefficient and the conversion of the feed damping coefficient into an adaptive adjustment index include: ； In the formula: θ This represents an adaptive adjustment indicator; k rmax and k rmin These are the upper and lower bounds of the set feed damping coefficient.

6. The adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy according to claim 1 or 2, characterized in that, The mechanical specific energy is calculated as follows: ； In the formula: MSE ( k ) represents the first k Mechanical specific energy at each sampling time WOB f ( k ) represents the first k The magnitude of the filter feed pressure for each sampling time; RPM f ( k ) represents the first k The filter rotation speed for each sampling time; TOR f ( k ) represents the first k The magnitude of the filtering torque at each sampling time; ROP f ( k ) represents the first k The filtered drilling speed for each sampling time.

7. The adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy according to claim 6, characterized in that, The minimum mechanical specific energy within the sliding window is expressed as follows: ； In the formula: MSE min ( k ) represents the first k The minimum mechanical specific energy for each sampling time; MIN[·] indicates that the minimum value is taken for each sampling data.

8. The adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy according to claim 1 or 2, characterized in that, The S3 specifically includes: S31. Determine whether drilling can begin given the process and operating conditions; S32. Determine if the drilling speed exceeds the maximum drilling speed limit; if it does, reduce the feed pressure. S33. Determine if the torque exceeds the maximum torque limit; if it does, reduce the feed pressure. S34. If neither S32 nor S33 exceeds the upper limit, the feed pressure is adjusted according to the adaptive adjustment index and parameter update flag. S35. Based on the feed damping change rate and mechanical specific energy, determine whether the parameter update flag needs to be updated; S36. Repeat the above steps to achieve adaptive drilling.

9. The adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy according to claim 8, characterized in that, The parameter update flag adjustment is based on a comparison between the current mechanical specific energy and the minimum mechanical specific energy; If an adjustment causes the current mechanical specific energy to be greater than 1.1 times the minimum mechanical specific energy, and the feed damping change rate is less than 0.2, then change the parameter update flag, that is, change the positive direction to the negative direction, or the negative direction to the positive direction. If the rate of change of the feed damping is greater than 0.2, the parameter update flag will not be changed.

10. The adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy according to claim 8, characterized in that, Adjusting the feed pressure includes: ； ； In the formula: WOB d ( k ) represents the first k The expected input pressure for each sampling time; WOB d ( k -1) represents the first k -1 sampling time expected feed pressure; ΔWOB d ( k ) represents the first k The expected feed pressure increment for each sampling time; SIGN represents the flag bit, which can be -1 or 1. θ This represents an adaptive adjustment indicator; ΔWOB It represents the basic increment and is a positive constant.

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