Method for automatically recommending drilling parameters during fastest drilling based on micro drilling speed sample

Abstract

The invention discloses a method for automatically recommending drilling parameters during fastest drilling based on a micro drilling speed sample, and belongs to the field of petroleum and natural gas exploration, and the method comprises the following steps: S1, obtaining a drilling sample of the same stratum under drilling of the same column as the micro drilling speed sample; s2, drawing a drilling parameter-micro drilling speed curve in combination with the micro drilling speed sample; s3, taking a part of which the local smoothness is smaller than the overall smoothness as a bit pressure analysis sample, and taking a part of which the local smoothness is greater than the overall smoothness as a jamming risk analysis sample; s4, searching a part of the bit pressure analysis sample with the bit pressure rising and the micro-drilling speed rising first and then being stable as a parameter recommendation sample, and taking the rest part as a drilling speed abnormal sample; and S5, selecting the maximum value of the inlet flow as the recommended inlet flow, selecting the maximum value of the rotating speed as the recommended rotating speed, and selecting the minimum value of the bit pressure as the recommended bit pressure. According to the method, the main control factors influencing the drilling speed in the drilling process are recognized based on the micro drilling speed sample, and the drilling speed increasing effect can be achieved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and in particular to a method for automatically recommending the fastest drilling time drilling parameters based on micro-drilling rate samples. Background Technology

[0002] Rate of drilling (RPM) is one of the most fundamental and important parameters in oil and gas drilling engineering, directly reflecting the efficiency of drilling. RPM is greatly affected by drilling parameters and formation factors. Adjusting drilling parameters to improve RPM is a common method for optimizing drilling parameters. Most current methods study the influence of different combinations of drilling parameters on drilling time and mechanical specific energy objective functions, and use optimization methods or intelligent optimization techniques to achieve optimal drilling parameters. However, there is a large error between the mechanical specific energy calculated based on surface data and the actual downhole specific energy. Mechanistic models have the problem of low prediction accuracy, and machine learning models have the problem of poor solvability.

[0003] Chinese patent document with publication number S3N106321064S1 and publication date of January 11, 2017 discloses a method and apparatus for controlling drilling parameters. The method for controlling drilling parameters includes the following steps: Obtain real-time drilling parameters; Multiple sets of data samples were obtained based on drilling parameters used in the drilling operation. The state of the drill string operation corresponding to the data sample is identified to obtain the state category of the drill string corresponding to the data sample. Specifically, based on the characteristics of the data sample, it is distinguished whether the data sample is in the downhole drill string movement state as sliding drilling or rotary drilling. Based on whether the well depth and drill bit position in the logging data of the data sample are the same, if they are the same, the data sample is classified into state S1, i.e., directional or rotary drilling; if they are not the same, it is classified into state S2, i.e., tripping or reaming. Then, based on the state S1... In the data samples, the rotational speed and torque magnitudes categorize S1 states into rotary drilling and sliding drilling. If the rotational speed is greater than 10 r / m and the torque is greater than 1 kN·m, the drill string state of the data sample is rotary drilling; otherwise, the drill string state of the data sample is sliding drilling. Similarly, in S2 states, the rotational speed and torque magnitudes categorize S2 states into reaming and tripping. If the rotational speed is greater than 10 r / m and the torque is greater than 1 kN·m, the drill string state of the data sample is reaming; otherwise, the drill string state of the data sample is tripping. The drill string energy transfer index of the data sample is obtained based on the drilling parameters, the data sample, and the state category of the drill string corresponding to the data sample; Correlation analysis is performed on the data sample and the drill string energy transfer index of the data sample to obtain the drilling parameters that meet the preset requirements; Drilling parameters that meet the preset requirements are adjusted and controlled according to preset rules.

[0004] The drilling parameter control method disclosed in this patent document optimizes the drill string energy transfer index by calculating it in real time, thereby achieving the optimal control target and improving real-time drilling efficiency. However, it only differentiates operating conditions without analyzing the drilling parameters, failing to identify the main controlling factors affecting the drilling rate, and thus cannot specifically optimize the drilling parameters to improve the drilling rate. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, this invention provides a method for automatically recommending the fastest drilling parameters based on micro-drilling rate samples. Based on micro-drilling rate samples, this invention identifies the main controlling factors affecting the drilling rate during the drilling process and recommends the optimal drilling parameters for the main controlling factors, thereby achieving the effect of increasing drilling speed.

[0006] This invention is achieved through the following technical solution: A method for automatically recommending the fastest drilling time drilling parameters based on micro-drilling rate samples, characterized by the following steps: S1. Based on the drilling samples and logging data, each drilling column is divided, and the formation is divided according to the geological measurement parameters and element logging results. The drilling samples are sliced ​​according to the division results, and the drilling samples of the same formation under the same drilling column are used as micro-drilling rate samples. S2. Based on the time-drilling parameter-micro-drilling rate matrix, and combined with micro-drilling rate samples, plot the drilling parameter-micro-drilling rate curve; S3. Calculate the first derivative of the rotational speed curve and the first derivative of the displacement curve using curve smoothness. Then calculate the standard deviation of the first derivative of the rotational speed curve and the first derivative of the displacement curve within the time interval reference to form local smoothness. Calculate the standard deviation of the first derivative of the rotational speed curve and the first derivative of the displacement curve within the sample time range to form overall smoothness. Take the portion where the local smoothness is less than the overall smoothness as the drill pressure analysis sample for drill pressure analysis, and take the portion where the local smoothness is greater than the overall smoothness as the stuck pipe risk analysis sample for stuck pipe risk analysis. S4. Based on image recognition, find the part of the drilling pressure analysis sample where the drilling pressure rises and the micro-drilling speed first rises and then stabilizes as the parameter recommendation sample, and use the remaining part as the drilling speed anomaly sample for the determination of formation and drill bit newness. S5. In the parameter recommendation sample, select the maximum value of the inlet flow rate as the recommended inlet flow rate, select the maximum value of the rotation speed as the recommended rotation speed, and select the minimum drilling pressure when the drilling parameter-micro-rate curve is stable as the recommended drilling pressure.

[0007] In S1, the drilling sample refers to the time-drilling parameter-micro-drilling rate matrix.

[0008] In S1, dividing each drilling column means, based on the drill string combination recorded in the logging data, finding the time corresponding to the starting and ending well depths of each column when the drilling pressure is greater than 0 in the drilling parameter time series database, and taking the time-drilling parameter-micro-drilling rate matrix within this time period as the same drill bit newness sample.

[0009] In S1, the geological measurement parameters include gamma and resistivity.

[0010] In S1, the logging data includes a drilling parameter time series database, geological parameter measurement results, elemental logging results, and drill string assembly.

[0011] In S1, the elemental logging results refer to the types and contents of elements in the rock cuttings.

[0012] In S1, dividing the formation according to geological measurement parameters and elemental logging results means that in the geological measurement parameters, the gamma measurement value deviates from the average gamma value by no more than 10%, the resistivity measurement value deviates from the average resistivity value by no more than 10%, and the elemental logging results show that the types of elements are consistent and the content deviation is no more than 5%. Then, when the drilling pressure is greater than 0 in the drilling parameter time series database, the time corresponding to the starting depth and ending depth of the well section is found, and the time-drilling parameter-micro-drilling rate matrix within this time period is taken as the same formation sample.

[0013] In S2, the time-drilling parameter-micro-drilling rate matrix refers to obtaining a drilling parameter time series database based on logging data, identifying the longest time span in the drilling parameter time series database when the drilling pressure is greater than 0 and the drill bit position remains unchanged, and using it as the time interval benchmark for calculating the micro-drilling rate. The micro-drilling rate corresponding to each set of drilling parameters is calculated based on the duration of the time interval benchmark, forming a drilling rate sample with time as the recording dimension.

[0014] In S2, the drilling parameter-micro-drilling rate curve includes drilling pressure, rotational speed, inlet flow rate, and micro-drilling rate.

[0015] In S3, the first derivative of the rotational speed curve is calculated using Equation 1; Formula 1; in, The first derivative of the rotational speed curve. The rotational speed is used to uniquely identify the data group with sequence number i. The unique identifier is the rotational speed corresponding to the sequence number i-1. The time in the data group with sequence number i is used to uniquely identify the time. The unique identifier is the time corresponding to sequence number i-1.

[0016] In S3, the first derivative of the displacement curve is calculated using Equation 2; Formula 2; in, The first derivative of the displacement curve. This is the displacement that uniquely identifies the data group with sequence number i. This is to uniquely identify the displacement corresponding to the serial number i-1. The time in the data group with sequence number i is used to uniquely identify the time. The unique identifier is the time corresponding to sequence number i-1.

[0017] In S3, the standard deviation of the first derivative of the rotation speed curve within the time interval reference is calculated using Equation 3; Formula 3; in, This represents the standard deviation of the first derivative of the rotational speed curve over the time interval reference. This is the first derivative of the rotational speed corresponding to the smallest unique identifier within the time interval reference. This is the average of the first derivatives of the speed curves within the time interval reference. This is the first derivative of the rotational speed corresponding to the largest unique identifier within the time interval reference. This serves as the time interval reference.

[0018] In S3, the standard deviation of the first derivative of the displacement curve within the time interval reference is calculated using Equation 4; Equation 4; in, Let be the standard deviation of the first derivative of the displacement curve over the time interval reference. This is the first derivative of the displacement corresponding to the smallest unique identifier within the time interval reference. The average of the first derivatives of the displacement curves within the time interval reference. The first derivative of the displacement corresponding to the largest unique identifier within the time interval reference is given. This serves as the time interval reference.

[0019] In S3, the standard deviation of the first derivative of the rotation speed curve within the sample time range is calculated using Equation 5; Formula 5; in, The first derivative standard deviation of the rotational speed curve within the time range of the micro-drilling speed sample is given. This represents the first derivative of the rotational speed corresponding to the smallest unique identifier within the time range of the micro-drilling speed sample. This represents the average of the first derivatives of the rotational speed curves over the time range of the micro-drilling speed sample. This represents the first derivative of the rotational speed corresponding to the largest unique identifier within the time range of the micro-drilling speed sample. The time corresponding to the largest unique identifier sequence number within the time range of the micro-drilling speed sample.

[0020] In S3, the standard deviation of the first derivative of the displacement curve over the sample time range is calculated using Equation 6; Formula 6; in, The first derivative standard deviation of the displacement curve within the sample time range of micro-drilling speed is given. This represents the first derivative of the displacement corresponding to the smallest unique identifier within the time range of the micro-drilling rate sample. This represents the average of the first derivatives of the displacement curves over the time range of the micro-drilling rate sample. This represents the first derivative of the displacement corresponding to the largest unique identifier within the time range of the micro-drilling rate sample. The time corresponding to the largest unique identifier sequence number within the time range of the micro-drilling speed sample.

[0021] In step S4, image recognition specifically includes: S41. Traverse all points on the drilling parameter-micro-drilling rate curve in the drilling pressure analysis sample, connect each interval point to form an interval line, and at the same time draw a perpendicular line through the middle point to the line connecting the interval points to form a perpendicular line, until all points on the drilling parameter-micro-drilling rate curve have been traversed. S42. Obtain the median M of the perpendicular line. Ｐ The average of the vertical lines M Ｑ The median N of the interval line Ｐ and the average number N of the interval lines Ｑ ; S43. Calculate the standard deviation M of the perpendicular line using the median of the perpendicular line. DP The standard deviation M of the perpendiculars is calculated using the mean of the perpendiculars. DQ Take M DP With M DQ The smaller value in is denoted as M. D0 ; Calculate the standard deviation N of the interval line using the median of the interval line. DP The standard deviation N of the interval lines is calculated using the average of the interval lines. DQ Take N DP With N DQ The smaller value in is denoted as N. D0 ; S44. Identify the mutation point and record its unique identifier; S45. Calculate the slope k of the linear regression equation for the micro-drilling rate curve. s The slope k of the regression equation of the drilling pressure curve d The slope k of the linear regression equation for the micro-drilling rate curve is selected. sThe slope k of the regression equation of the drilling pressure curve d The portions that are all greater than 0 are used as the parameter recommendation samples.

[0022] In S1, slicing the drilling sample according to the division result means taking the intersection of the drilling sample, the newness sample of the same drill bit, and the sample of the same formation based on the unique identifier number to obtain the micro-drilling rate sample.

[0023] The unique identifier refers to the sequential numbering of each group of data in the time-drilling parameter-micro-drilling rate matrix.

[0024] The drilling samples include normal drilling samples and abnormal drilling samples.

[0025] The normal drilling samples and abnormal drilling samples refer to the samples obtained by segmenting the micro-drilling rate samples based on the mutation points.

[0026] The normal drilling sample refers to a time-drilling parameter-micro-drilling rate matrix within a time range that is determined to be a normal drilling sample if the time interval between two adjacent abrupt change points is greater than a preset value.

[0027] The abnormal drilling sample refers to a time-drilling parameter-micro-drilling rate matrix within a time range that is determined to be an abnormal drilling sample if the time interval between two adjacent abrupt change points is less than a preset value.

[0028] In S42, the average number M of the perpendicular lines Ｑ Calculated using Equation 7; Formula 7; in, The length of the perpendicular line corresponding to the data group with the unique identifier number 1. The length of the perpendicular line corresponding to the data group with the unique identifier number 2. The length of the perpendicular line corresponding to the data group with the unique identifier number 3. The length of the perpendicular line corresponding to the data group with the unique identifier number n.

[0029] In S42, the average number N of the interval lines Ｑ Calculated using Equation 8; Formula 8; in, The length of the interval line that uniquely identifies the data group with sequence number 1. The length of the interval line that uniquely identifies the data group with sequence number 2. The length of the interval line that uniquely identifies the data group with sequence number 3. The length of the interval line that uniquely identifies the data group with sequence number n.

[0030] In S43, the standard deviation M of the perpendicular is calculated using the median of the perpendicular. DP This refers to calculation using Equation 9; Formula 9.

[0031] In step S43, the standard deviation M of the perpendiculars is calculated using the mean of the perpendiculars. DQ This refers to calculation using formula 10; Formula 10.

[0032] In step S43, the standard deviation N of the interval line is calculated based on the median of the interval line. DP This refers to calculation using Equation 11; Formula 11.

[0033] In step S43, the standard deviation N of the interval lines is calculated using the average number of the interval lines. DQ This refers to calculation using Equation 12; Equation 12.

[0034] In S45, the slope k of the linear regression equation for the micro-drilling rate curve is calculated using Equation 13. s ; Equation 13; in, The micro-drilling speed corresponding to the i-th data group. This represents the number of data sets shared between any two adjacent mutation points. It is the smallest unique identifier among all data sets between any two adjacent mutation points. It is the largest unique identifier among all data sets between any two adjacent mutation points.

[0035] In S44, determining the abrupt change point refers to the point with the unique identifier number i on the drilling parameter-micro-drilling rate curve, if M i / N i Greater than M D0 / N D0 If the mutation point is positive, then the point is considered a mutation point; otherwise, the point is considered a non-mutation point.

[0036] In S45, the slope k of the regression equation for the drilling pressure curve is calculated using Equation 14. d ; Equation 14; in, Let be the drilling pressure corresponding to the i-th data group.

[0037] The micro-drilling rate refers to the speed at which the drill bit advances through the formation within a time interval reference.

[0038] The micro-drilling speed is calculated using formula 15; Formula 15; in, for The micro-drilling speed at any given moment for The position of the drill bit at any given moment. for The position of the drill bit at any given moment. This serves as the time interval reference.

[0039] The beneficial effects of this invention are mainly reflected in the following aspects: 1. Compared with the prior art, the present invention identifies the main controlling factors affecting the drilling speed during the drilling process based on micro-drilling speed samples, and recommends the optimal drilling parameters for the main controlling factors, thereby achieving the effect of increasing drilling speed.

[0040] 2. This invention, based on a time-series database of drilling parameters, constructs a drilling parameter-micro-drilling rate curve by eliminating interference from formation and drill bit newness factors. This curve matches the changes in drilling parameters with the changes in drilling rate, thereby obtaining the optimal drilling parameters for the fastest drilling time and achieving drilling speedup. 3. This invention divides each drilling column based on drilling samples and logging data, and divides the formation based on geological measurement parameters and elemental logging results. Based on the division results, the drilling samples are sliced ​​to obtain drilling samples of the same formation under the same drilling column as micro-drilling rate samples. This can eliminate the interference caused by formation influence and drilling rate changes due to drill bit wear, and obtain the basis for judging whether the parameters are consistent within a certain time range.

[0041] 4. This invention calculates the first derivative of the rotational speed curve and the first derivative of the displacement curve by curve smoothness, and then calculates the standard deviation of the rotational speed curve and the displacement curve within the time interval benchmark to form local smoothness; calculates the standard deviation of the rotational speed curve and the displacement curve within the sample time range to form overall smoothness; takes the part where the local smoothness is less than the overall smoothness as the drill pressure analysis sample for drill pressure analysis, and takes the part where the local smoothness is greater than the overall smoothness as the stuck drill risk analysis sample for stuck drill risk analysis, which can eliminate the interference of micro-drilling speed changes caused by rotational speed and inlet flow rate fluctuations.

[0042] 5. In this invention, S2, the time-drilling parameter-micro-drilling rate matrix refers to the drilling parameter time series database obtained based on logging data, identifying the longest time span in the drilling parameter time series database when the drilling pressure is greater than 0 and the drill bit position remains unchanged, as the time interval benchmark for calculating the micro-drilling rate. This can eliminate the situation where the micro-drilling rate is 0 due to the acquisition frequency being faster than the drill bit position change frequency, ensuring the integrity and validity of the drilling rate sample. 6. This invention can eliminate interference from other factors and abnormal signals, identify the main controlling factors affecting micro-drilling speed, and recommend optimal drilling parameters. Attached Figure Description

[0043] The present invention will now be further described in detail with reference to the accompanying drawings and specific embodiments: Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0044] Example 1 See Figure 1 A method for automatically recommending the fastest drilling time drilling parameters based on micro-drilling rate samples includes the following steps: S1. Based on the drilling samples and logging data, each drilling column is divided, and the formation is divided according to the geological measurement parameters and element logging results. The drilling samples are sliced ​​according to the division results, and the drilling samples of the same formation under the same drilling column are used as micro-drilling rate samples. S2. Based on the time-drilling parameter-micro-drilling rate matrix, and combined with micro-drilling rate samples, plot the drilling parameter-micro-drilling rate curve; S3. Calculate the first derivative of the rotational speed curve and the first derivative of the displacement curve using curve smoothness. Then calculate the standard deviation of the first derivative of the rotational speed curve and the first derivative of the displacement curve within the time interval reference to form local smoothness. Calculate the standard deviation of the first derivative of the rotational speed curve and the first derivative of the displacement curve within the sample time range to form overall smoothness. Take the portion where the local smoothness is less than the overall smoothness as the drill pressure analysis sample for drill pressure analysis, and take the portion where the local smoothness is greater than the overall smoothness as the stuck pipe risk analysis sample for stuck pipe risk analysis. S4. Based on image recognition, find the part of the drilling pressure analysis sample where the drilling pressure rises and the micro-drilling speed first rises and then stabilizes as the parameter recommendation sample, and use the remaining part as the drilling speed anomaly sample for the determination of formation and drill bit newness. S5. In the parameter recommendation sample, select the maximum value of the inlet flow rate as the recommended inlet flow rate, select the maximum value of the rotation speed as the recommended rotation speed, and select the minimum drilling pressure when the drilling parameter-micro-rate curve is stable as the recommended drilling pressure.

[0045] This embodiment is the most basic implementation method. Compared with the prior art, it identifies the main controlling factors affecting the drilling speed based on micro-drilling speed samples, and recommends the optimal drilling parameters for the main controlling factors, thereby achieving the effect of increasing drilling speed.

[0046] Example 2 See Figure 1 A method for automatically recommending the fastest drilling time drilling parameters based on micro-drilling rate samples includes the following steps: S1. Based on the drilling samples and logging data, each drilling column is divided, and the formation is divided according to the geological measurement parameters and element logging results. The drilling samples are sliced ​​according to the division results, and the drilling samples of the same formation under the same drilling column are used as micro-drilling rate samples. S2. Based on the time-drilling parameter-micro-drilling rate matrix, and combined with micro-drilling rate samples, plot the drilling parameter-micro-drilling rate curve; S3. Calculate the first derivative of the rotational speed curve and the first derivative of the displacement curve using curve smoothness. Then calculate the standard deviation of the first derivative of the rotational speed curve and the first derivative of the displacement curve within the time interval reference to form local smoothness. Calculate the standard deviation of the first derivative of the rotational speed curve and the first derivative of the displacement curve within the sample time range to form overall smoothness. Take the portion where the local smoothness is less than the overall smoothness as the drill pressure analysis sample for drill pressure analysis, and take the portion where the local smoothness is greater than the overall smoothness as the stuck pipe risk analysis sample for stuck pipe risk analysis. S4. Based on image recognition, find the part of the drilling pressure analysis sample where the drilling pressure rises and the micro-drilling speed first rises and then stabilizes as the parameter recommendation sample, and use the remaining part as the drilling speed anomaly sample for the determination of formation and drill bit newness. S5. In the parameter recommendation sample, select the maximum value of the inlet flow rate as the recommended inlet flow rate, select the maximum value of the rotation speed as the recommended rotation speed, and select the minimum drilling pressure when the drilling parameter-micro-rate curve is stable as the recommended drilling pressure.

[0047] In S1, the drilling sample refers to the time-drilling parameter-micro-drilling rate matrix.

[0048] In S1, dividing each drilling column means, based on the drill string combination recorded in the logging data, finding the time corresponding to the starting and ending well depths of each column when the drilling pressure is greater than 0 in the drilling parameter time series database, and taking the time-drilling parameter-micro-drilling rate matrix within this time period as the same drill bit newness sample.

[0049] In S1, the geological measurement parameters include gamma and resistivity.

[0050] In S1, the logging data includes a drilling parameter time series database, geological parameter measurement results, elemental logging results, and drill string assembly.

[0051] In S1, the elemental logging results refer to the types and contents of elements in the rock cuttings.

[0052] In S1, dividing the formation according to geological measurement parameters and elemental logging results means that in the geological measurement parameters, the gamma measurement value deviates from the average gamma value by no more than 10%, the resistivity measurement value deviates from the average resistivity value by no more than 10%, and the elemental logging results show that the types of elements are consistent and the content deviation is no more than 5%. Then, when the drilling pressure is greater than 0 in the drilling parameter time series database, the time corresponding to the starting depth and ending depth of the well section is found, and the time-drilling parameter-micro-drilling rate matrix within this time period is taken as the same formation sample.

[0053] This embodiment is a preferred implementation method. Based on the time series database of drilling parameters, by eliminating the interference of formation and drill bit newness factors, a drilling parameter-micro-drilling rate curve is constructed. The changes in drilling parameters are matched with the changes in drilling rate, thereby obtaining the optimal drilling parameters for the fastest drilling time and achieving drilling speed improvement. Example 3 See Figure 1 A method for automatically recommending the fastest drilling time drilling parameters based on micro-drilling rate samples includes the following steps: S1. Based on the drilling samples and logging data, each drilling column is divided, and the formation is divided according to the geological measurement parameters and element logging results. The drilling samples are sliced ​​according to the division results, and the drilling samples of the same formation under the same drilling column are used as micro-drilling rate samples. S2. Based on the time-drilling parameter-micro-drilling rate matrix, and combined with micro-drilling rate samples, plot the drilling parameter-micro-drilling rate curve; S3. Calculate the first derivative of the rotational speed curve and the first derivative of the displacement curve using curve smoothness. Then calculate the standard deviation of the first derivative of the rotational speed curve and the first derivative of the displacement curve within the time interval reference to form local smoothness. Calculate the standard deviation of the first derivative of the rotational speed curve and the first derivative of the displacement curve within the sample time range to form overall smoothness. Take the portion where the local smoothness is less than the overall smoothness as the drill pressure analysis sample for drill pressure analysis, and take the portion where the local smoothness is greater than the overall smoothness as the stuck pipe risk analysis sample for stuck pipe risk analysis. S4. Based on image recognition, find the part of the drilling pressure analysis sample where the drilling pressure rises and the micro-drilling speed first rises and then stabilizes as the parameter recommendation sample, and use the remaining part as the drilling speed anomaly sample for the determination of formation and drill bit newness. S5. In the parameter recommendation sample, select the maximum value of the inlet flow rate as the recommended inlet flow rate, select the maximum value of the rotation speed as the recommended rotation speed, and select the minimum drilling pressure when the drilling parameter-micro-rate curve is stable as the recommended drilling pressure.

[0054] In S1, the drilling sample refers to the time-drilling parameter-micro-drilling rate matrix.

[0055] In S1, dividing each drilling column means, based on the drill string combination recorded in the logging data, finding the time corresponding to the starting and ending well depths of each column when the drilling pressure is greater than 0 in the drilling parameter time series database, and taking the time-drilling parameter-micro-drilling rate matrix within this time period as the same drill bit newness sample.

[0056] In S1, the geological measurement parameters include gamma and resistivity.

[0057] In S1, the logging data includes a drilling parameter time series database, geological parameter measurement results, elemental logging results, and drill string assembly.

[0058] In S1, the elemental logging results refer to the types and contents of elements in the rock cuttings.

[0059] In S1, dividing the formation according to geological measurement parameters and elemental logging results means that in the geological measurement parameters, the gamma measurement value deviates from the average gamma value by no more than 10%, the resistivity measurement value deviates from the average resistivity value by no more than 10%, and the elemental logging results show that the types of elements are consistent and the content deviation is no more than 5%. Then, when the drilling pressure is greater than 0 in the drilling parameter time series database, the time corresponding to the starting depth and ending depth of the well section is found, and the time-drilling parameter-micro-drilling rate matrix within this time period is taken as the same formation sample.

[0060] In S2, the time-drilling parameter-micro-drilling rate matrix refers to obtaining a drilling parameter time series database based on logging data, identifying the longest time span in the drilling parameter time series database when the drilling pressure is greater than 0 and the drill bit position remains unchanged, and using it as the time interval benchmark for calculating the micro-drilling rate. The micro-drilling rate corresponding to each set of drilling parameters is calculated based on the duration of the time interval benchmark, forming a drilling rate sample with time as the recording dimension.

[0061] In S2, the drilling parameter-micro-drilling rate curve includes drilling pressure, rotational speed, inlet flow rate, and micro-drilling rate.

[0062] This embodiment is another preferred implementation. Based on the drilling samples and logging data, each drilling column is divided, and the formation is divided according to the geological measurement parameters and elemental logging results. The drilling samples are sliced ​​according to the division results, and the drilling samples of the same formation under the same drilling column are obtained as micro-drilling rate samples. This can eliminate the interference caused by formation influence and drilling rate changes due to drill bit wear, and obtain the basis for judging whether the parameters are consistent within a certain time range.

[0063] Example 4 See Figure 1 A method for automatically recommending the fastest drilling time drilling parameters based on micro-drilling rate samples includes the following steps: S1. Based on the drilling samples and logging data, each drilling column is divided, and the formation is divided according to the geological measurement parameters and element logging results. The drilling samples are sliced ​​according to the division results, and the drilling samples of the same formation under the same drilling column are used as micro-drilling rate samples. S2. Based on the time-drilling parameter-micro-drilling rate matrix, and combined with micro-drilling rate samples, plot the drilling parameter-micro-drilling rate curve; S3. Calculate the first derivative of the rotational speed curve and the first derivative of the displacement curve using curve smoothness. Then calculate the standard deviation of the first derivative of the rotational speed curve and the first derivative of the displacement curve within the time interval reference to form local smoothness. Calculate the standard deviation of the first derivative of the rotational speed curve and the first derivative of the displacement curve within the sample time range to form overall smoothness. Take the portion where the local smoothness is less than the overall smoothness as the drill pressure analysis sample for drill pressure analysis, and take the portion where the local smoothness is greater than the overall smoothness as the stuck pipe risk analysis sample for stuck pipe risk analysis. S4. Based on image recognition, find the part of the drilling pressure analysis sample where the drilling pressure rises and the micro-drilling speed first rises and then stabilizes as the parameter recommendation sample, and use the remaining part as the drilling speed anomaly sample for the determination of formation and drill bit newness. S5. In the parameter recommendation sample, select the maximum value of the inlet flow rate as the recommended inlet flow rate, select the maximum value of the rotation speed as the recommended rotation speed, and select the minimum drilling pressure when the drilling parameter-micro-rate curve is stable as the recommended drilling pressure.

[0064] In S1, the drilling sample refers to the time-drilling parameter-micro-drilling rate matrix.

[0065] In S1, dividing each drilling column means, based on the drill string combination recorded in the logging data, finding the time corresponding to the starting and ending well depths of each column when the drilling pressure is greater than 0 in the drilling parameter time series database, and taking the time-drilling parameter-micro-drilling rate matrix within this time period as the same drill bit newness sample.

[0066] In S1, the geological measurement parameters include gamma and resistivity.

[0067] In S1, the logging data includes a drilling parameter time series database, geological parameter measurement results, elemental logging results, and drill string assembly.

[0068] In S1, the elemental logging results refer to the types and contents of elements in the rock cuttings.

[0069] In S1, dividing the formation according to geological measurement parameters and elemental logging results means that in the geological measurement parameters, the gamma measurement value deviates from the average gamma value by no more than 10%, the resistivity measurement value deviates from the average resistivity value by no more than 10%, and the elemental logging results show that the types of elements are consistent and the content deviation is no more than 5%. Then, when the drilling pressure is greater than 0 in the drilling parameter time series database, the time corresponding to the starting depth and ending depth of the well section is found, and the time-drilling parameter-micro-drilling rate matrix within this time period is taken as the same formation sample.

[0070] In S2, the time-drilling parameter-micro-drilling rate matrix refers to obtaining a drilling parameter time series database based on logging data, identifying the longest time span in the drilling parameter time series database when the drilling pressure is greater than 0 and the drill bit position remains unchanged, and using it as the time interval benchmark for calculating the micro-drilling rate. The micro-drilling rate corresponding to each set of drilling parameters is calculated based on the duration of the time interval benchmark, forming a drilling rate sample with time as the recording dimension.

[0071] In S2, the drilling parameter-micro-drilling rate curve includes drilling pressure, rotational speed, inlet flow rate, and micro-drilling rate.

[0072] In S3, the first derivative of the rotational speed curve is calculated using Equation 1; Formula 1; in, The first derivative of the rotational speed curve. The rotational speed is used to uniquely identify the data group with sequence number i. The unique identifier is the rotational speed corresponding to the sequence number i-1. The time in the data group with sequence number i is used to uniquely identify the time. The unique identifier is the time corresponding to sequence number i-1.

[0073] In S3, the first derivative of the displacement curve is calculated using Equation 2; Formula 2; in, The first derivative of the displacement curve. This is the displacement that uniquely identifies the data group with sequence number i. This is to uniquely identify the displacement corresponding to the serial number i-1. The time in the data group with sequence number i is used to uniquely identify the time. The unique identifier is the time corresponding to sequence number i-1.

[0074] In S3, the standard deviation of the first derivative of the rotation speed curve within the time interval reference is calculated using Equation 3; Formula 3; in, This represents the standard deviation of the first derivative of the rotational speed curve over the time interval reference. This is the first derivative of the rotational speed corresponding to the smallest unique identifier within the time interval reference. This is the average of the first derivatives of the speed curves within the time interval reference. This is the first derivative of the rotational speed corresponding to the largest unique identifier within the time interval reference. This serves as the time interval reference.

[0075] In S3, the standard deviation of the first derivative of the displacement curve within the time interval reference is calculated using Equation 4; Equation 4; in, Let be the standard deviation of the first derivative of the displacement curve over the time interval reference. This is the first derivative of the displacement corresponding to the smallest unique identifier within the time interval reference. The average of the first derivatives of the displacement curves within the time interval reference. The first derivative of the displacement corresponding to the largest unique identifier within the time interval reference is given. This serves as the time interval reference.

[0076] In S3, the standard deviation of the first derivative of the rotation speed curve within the sample time range is calculated using Equation 5; Formula 5; in, The first derivative standard deviation of the rotational speed curve within the time range of the micro-drilling speed sample is given. This represents the first derivative of the rotational speed corresponding to the smallest unique identifier within the time range of the micro-drilling speed sample. This represents the average of the first derivatives of the rotational speed curves over the time range of the micro-drilling speed sample. This represents the first derivative of the rotational speed corresponding to the largest unique identifier within the time range of the micro-drilling speed sample. The time corresponding to the largest unique identifier sequence number within the time range of the micro-drilling speed sample.

[0077] In S3, the standard deviation of the first derivative of the displacement curve over the sample time range is calculated using Equation 6; Formula 6; in, The first derivative standard deviation of the displacement curve within the sample time range of micro-drilling speed is given. This represents the first derivative of the displacement corresponding to the smallest unique identifier within the time range of the micro-drilling rate sample. This represents the average of the first derivatives of the displacement curves over the time range of the micro-drilling rate sample. This represents the first derivative of the displacement corresponding to the largest unique identifier within the time range of the micro-drilling rate sample. The time corresponding to the largest unique identifier sequence number within the time range of the micro-drilling speed sample.

[0078] This embodiment is another preferred implementation. The first derivative of the rotational speed curve and the first derivative of the displacement curve are calculated by curve smoothness. Then, the standard deviation of the rotational speed curve and the displacement curve within the time interval is calculated to form local smoothness. The standard deviation of the rotational speed curve and the displacement curve within the sample time range is calculated to form overall smoothness. The part where the local smoothness is less than the overall smoothness is taken as the drill pressure analysis sample for drill pressure analysis, and the part where the local smoothness is greater than the overall smoothness is taken as the stuck drill risk analysis sample for stuck drill risk analysis. This can eliminate the interference of micro-drilling speed changes caused by rotational speed and inlet flow rate fluctuations.

[0079] Example 5 See Figure 1 A method for automatically recommending the fastest drilling time drilling parameters based on micro-drilling rate samples includes the following steps: S1. Based on the drilling samples and logging data, each drilling column is divided, and the formation is divided according to the geological measurement parameters and element logging results. The drilling samples are sliced ​​according to the division results, and the drilling samples of the same formation under the same drilling column are used as micro-drilling rate samples. S2. Based on the time-drilling parameter-micro-drilling rate matrix, and combined with micro-drilling rate samples, plot the drilling parameter-micro-drilling rate curve; S3. Calculate the first derivative of the rotational speed curve and the first derivative of the displacement curve using curve smoothness. Then calculate the standard deviation of the first derivative of the rotational speed curve and the first derivative of the displacement curve within the time interval reference to form local smoothness. Calculate the standard deviation of the first derivative of the rotational speed curve and the first derivative of the displacement curve within the sample time range to form overall smoothness. Take the portion where the local smoothness is less than the overall smoothness as the drill pressure analysis sample for drill pressure analysis, and take the portion where the local smoothness is greater than the overall smoothness as the stuck pipe risk analysis sample for stuck pipe risk analysis. S4. Based on image recognition, find the part of the drilling pressure analysis sample where the drilling pressure rises and the micro-drilling speed first rises and then stabilizes as the parameter recommendation sample, and use the remaining part as the drilling speed anomaly sample for the determination of formation and drill bit newness. S5. In the parameter recommendation sample, select the maximum value of the inlet flow rate as the recommended inlet flow rate, select the maximum value of the rotation speed as the recommended rotation speed, and select the minimum drilling pressure when the drilling parameter-micro-rate curve is stable as the recommended drilling pressure.

[0080] In S1, the drilling sample refers to the time-drilling parameter-micro-drilling rate matrix.

[0081] In S1, dividing each drilling column means, based on the drill string combination recorded in the logging data, finding the time corresponding to the starting and ending well depths of each column when the drilling pressure is greater than 0 in the drilling parameter time series database, and taking the time-drilling parameter-micro-drilling rate matrix within this time period as the same drill bit newness sample.

[0082] In S1, the geological measurement parameters include gamma and resistivity.

[0083] In S1, the logging data includes a drilling parameter time series database, geological parameter measurement results, elemental logging results, and drill string assembly.

[0084] In S1, the elemental logging results refer to the types and contents of elements in the rock cuttings.

[0085] In S1, dividing the formation according to geological measurement parameters and elemental logging results means that in the geological measurement parameters, the gamma measurement value deviates from the average gamma value by no more than 10%, the resistivity measurement value deviates from the average resistivity value by no more than 10%, and the elemental logging results show that the types of elements are consistent and the content deviation is no more than 5%. Then, when the drilling pressure is greater than 0 in the drilling parameter time series database, the time corresponding to the starting depth and ending depth of the well section is found, and the time-drilling parameter-micro-drilling rate matrix within this time period is taken as the same formation sample.

[0086] In S2, the time-drilling parameter-micro-drilling rate matrix refers to obtaining a drilling parameter time series database based on logging data, identifying the longest time span in the drilling parameter time series database when the drilling pressure is greater than 0 and the drill bit position remains unchanged, and using it as the time interval benchmark for calculating the micro-drilling rate. The micro-drilling rate corresponding to each set of drilling parameters is calculated based on the duration of the time interval benchmark, forming a drilling rate sample with time as the recording dimension.

[0087] In S2, the drilling parameter-micro-drilling rate curve includes drilling pressure, rotational speed, inlet flow rate, and micro-drilling rate.

[0088] In S3, the first derivative of the rotational speed curve is calculated using Equation 1; Formula 1; in, The first derivative of the rotational speed curve. The rotational speed is used to uniquely identify the data group with sequence number i. The unique identifier is the rotational speed corresponding to the sequence number i-1. The time in the data group with sequence number i is used to uniquely identify the time. The unique identifier is the time corresponding to sequence number i-1.

[0089] In S3, the first derivative of the displacement curve is calculated using Equation 2; Formula 2; in, The first derivative of the displacement curve. This is the displacement that uniquely identifies the data group with sequence number i. This is to uniquely identify the displacement corresponding to the serial number i-1. The time in the data group with sequence number i is used to uniquely identify the time. The unique identifier is the time corresponding to sequence number i-1.

[0090] In S3, the standard deviation of the first derivative of the rotation speed curve within the time interval reference is calculated using Equation 3; Formula 3; in, This represents the standard deviation of the first derivative of the rotational speed curve over the time interval reference. This is the first derivative of the rotational speed corresponding to the smallest unique identifier within the time interval reference. This is the average of the first derivatives of the speed curves within the time interval reference. This is the first derivative of the rotational speed corresponding to the largest unique identifier within the time interval reference. This serves as the time interval reference.

[0091] In S3, the standard deviation of the first derivative of the displacement curve within the time interval reference is calculated using Equation 4; Equation 4; in, Let be the standard deviation of the first derivative of the displacement curve over the time interval reference. This is the first derivative of the displacement corresponding to the smallest unique identifier within the time interval reference. The average of the first derivatives of the displacement curves within the time interval reference. The first derivative of the displacement corresponding to the largest unique identifier within the time interval reference is given. This serves as the time interval reference.

[0092] In S3, the standard deviation of the first derivative of the rotation speed curve within the sample time range is calculated using Equation 5; Formula 5; in, The first derivative standard deviation of the rotational speed curve within the time range of the micro-drilling speed sample is given. This represents the first derivative of the rotational speed corresponding to the smallest unique identifier within the time range of the micro-drilling speed sample. This represents the average of the first derivatives of the rotational speed curves over the time range of the micro-drilling speed sample. This represents the first derivative of the rotational speed corresponding to the largest unique identifier within the time range of the micro-drilling speed sample. The time corresponding to the largest unique identifier sequence number within the time range of the micro-drilling speed sample.

[0093] In S3, the standard deviation of the first derivative of the displacement curve over the sample time range is calculated using Equation 6; Formula 6; in, The first derivative standard deviation of the displacement curve within the sample time range of micro-drilling speed is given. This represents the first derivative of the displacement corresponding to the smallest unique identifier within the time range of the micro-drilling rate sample. This represents the average of the first derivatives of the displacement curves over the time range of the micro-drilling rate sample. This represents the first derivative of the displacement corresponding to the largest unique identifier within the time range of the micro-drilling rate sample. The time corresponding to the largest unique identifier sequence number within the time range of the micro-drilling speed sample.

[0094] In step S4, image recognition specifically includes: S41. Traverse all points on the drilling parameter-micro-drilling rate curve in the drilling pressure analysis sample, connect each interval point to form an interval line, and at the same time draw a perpendicular line through the middle point to the line connecting the interval points to form a perpendicular line, until all points on the drilling parameter-micro-drilling rate curve have been traversed. S42. Obtain the median M of the perpendicular line. Ｐ The average of the vertical lines M Ｑ The median N of the interval line Ｐ and the average number N of the interval lines Ｑ ; S43. Calculate the standard deviation M of the perpendicular line using the median of the perpendicular line. DP The standard deviation M of the perpendiculars is calculated using the mean of the perpendiculars. DQ Take M DP With M DQ The smaller value in is denoted as M. D0 ; Calculate the standard deviation N of the interval line using the median of the interval line. DP The standard deviation N of the interval lines is calculated using the average of the interval lines. DQ Take N DP With N DQ The smaller value in is denoted as N. D0 ; S44. Identify the mutation point and record its unique identifier; S45. Calculate the slope k of the linear regression equation for the micro-drilling rate curve. s The slope k of the regression equation of the drilling pressure curve d The slope k of the linear regression equation for the micro-drilling rate curve is selected. s The slope k of the regression equation of the drilling pressure curve d The portions that are all greater than 0 are used as the parameter recommendation samples.

[0095] This embodiment is another preferred implementation. In S2, the time-drilling parameter-micro-drilling rate matrix refers to the drilling parameter time series database obtained based on logging data. It identifies the longest time span in the drilling parameter time series database when the drilling pressure is greater than 0 and the drill bit position remains unchanged, and uses it as the time interval benchmark for calculating the micro-drilling rate. This can eliminate the situation where the micro-drilling rate is 0 due to the acquisition frequency being faster than the drill bit position change frequency, and ensure the integrity and effectiveness of the drilling rate sample. Example 6 See Figure 1 A method for automatically recommending the fastest drilling time drilling parameters based on micro-drilling rate samples includes the following steps: S1. Based on the drilling samples and logging data, each drilling column is divided, and the formation is divided according to the geological measurement parameters and element logging results. The drilling samples are sliced ​​according to the division results, and the drilling samples of the same formation under the same drilling column are used as micro-drilling rate samples. S2. Based on the time-drilling parameter-micro-drilling rate matrix, and combined with micro-drilling rate samples, plot the drilling parameter-micro-drilling rate curve; S3. Calculate the first derivative of the rotational speed curve and the first derivative of the displacement curve using curve smoothness. Then calculate the standard deviation of the first derivative of the rotational speed curve and the first derivative of the displacement curve within the time interval reference to form local smoothness. Calculate the standard deviation of the first derivative of the rotational speed curve and the first derivative of the displacement curve within the sample time range to form overall smoothness. Take the portion where the local smoothness is less than the overall smoothness as the drill pressure analysis sample for drill pressure analysis, and take the portion where the local smoothness is greater than the overall smoothness as the stuck pipe risk analysis sample for stuck pipe risk analysis. S4. Based on image recognition, find the part of the drilling pressure analysis sample where the drilling pressure rises and the micro-drilling speed first rises and then stabilizes as the parameter recommendation sample, and use the remaining part as the drilling speed anomaly sample for the determination of formation and drill bit newness. S5. In the parameter recommendation sample, select the maximum value of the inlet flow rate as the recommended inlet flow rate, select the maximum value of the rotation speed as the recommended rotation speed, and select the minimum drilling pressure when the drilling parameter-micro-rate curve is stable as the recommended drilling pressure.

[0096] In S1, the drilling sample refers to the time-drilling parameter-micro-drilling rate matrix.

[0097] In S1, dividing each drilling column means, based on the drill string combination recorded in the logging data, finding the time corresponding to the starting and ending well depths of each column when the drilling pressure is greater than 0 in the drilling parameter time series database, and taking the time-drilling parameter-micro-drilling rate matrix within this time period as the same drill bit newness sample.

[0098] In S1, the geological measurement parameters include gamma and resistivity.

[0099] In S1, the logging data includes a drilling parameter time series database, geological parameter measurement results, elemental logging results, and drill string assembly.

[0100] In S1, the elemental logging results refer to the types and contents of elements in the rock cuttings.

[0101] In S1, dividing the formation according to geological measurement parameters and elemental logging results means that in the geological measurement parameters, the gamma measurement value deviates from the average gamma value by no more than 10%, the resistivity measurement value deviates from the average resistivity value by no more than 10%, and the elemental logging results show that the types of elements are consistent and the content deviation is no more than 5%. Then, when the drilling pressure is greater than 0 in the drilling parameter time series database, the time corresponding to the starting depth and ending depth of the well section is found, and the time-drilling parameter-micro-drilling rate matrix within this time period is taken as the same formation sample.

[0102] In S2, the time-drilling parameter-micro-drilling rate matrix refers to obtaining a drilling parameter time series database based on logging data, identifying the longest time span in the drilling parameter time series database when the drilling pressure is greater than 0 and the drill bit position remains unchanged, and using it as the time interval benchmark for calculating the micro-drilling rate. The micro-drilling rate corresponding to each set of drilling parameters is calculated based on the duration of the time interval benchmark, forming a drilling rate sample with time as the recording dimension.

[0103] In S2, the drilling parameter-micro-drilling rate curve includes drilling pressure, rotational speed, inlet flow rate, and micro-drilling rate.

[0104] In S3, the first derivative of the rotational speed curve is calculated using Equation 1; Formula 1; in, The first derivative of the rotational speed curve. The rotational speed is used to uniquely identify the data group with sequence number i. The unique identifier is the rotational speed corresponding to the sequence number i-1. The time in the data group with sequence number i is used to uniquely identify the time. The unique identifier is the time corresponding to sequence number i-1.

[0105] In S3, the first derivative of the displacement curve is calculated using Equation 2; Formula 2; in, The first derivative of the displacement curve. This is the displacement that uniquely identifies the data group with sequence number i. This is to uniquely identify the displacement corresponding to the serial number i-1. The time in the data group with sequence number i is used to uniquely identify the time. The unique identifier is the time corresponding to sequence number i-1.

[0106] In S3, the standard deviation of the first derivative of the rotation speed curve within the time interval reference is calculated using Equation 3; Formula 3; in, This represents the standard deviation of the first derivative of the rotational speed curve over the time interval reference. This is the first derivative of the rotational speed corresponding to the smallest unique identifier within the time interval reference. This is the average of the first derivatives of the speed curves within the time interval reference. This is the first derivative of the rotational speed corresponding to the largest unique identifier within the time interval reference. This serves as the time interval reference.

[0107] In S3, the standard deviation of the first derivative of the displacement curve within the time interval reference is calculated using Equation 4; Equation 4; in, Let be the standard deviation of the first derivative of the displacement curve over the time interval reference. This is the first derivative of the displacement corresponding to the smallest unique identifier within the time interval reference. The average of the first derivatives of the displacement curves within the time interval reference. The first derivative of the displacement corresponding to the largest unique identifier within the time interval reference is given. This serves as the time interval reference.

[0108] In S3, the standard deviation of the first derivative of the rotation speed curve within the sample time range is calculated using Equation 5; Formula 5; in, The first derivative standard deviation of the rotational speed curve within the time range of the micro-drilling speed sample is given. This represents the first derivative of the rotational speed corresponding to the smallest unique identifier within the time range of the micro-drilling speed sample. This represents the average of the first derivatives of the rotational speed curves over the time range of the micro-drilling speed sample. This represents the first derivative of the rotational speed corresponding to the largest unique identifier within the time range of the micro-drilling speed sample. The time corresponding to the largest unique identifier sequence number within the time range of the micro-drilling speed sample.

[0109] In S3, the standard deviation of the first derivative of the displacement curve over the sample time range is calculated using Equation 6; Formula 6; in, The first derivative standard deviation of the displacement curve within the sample time range of micro-drilling speed is given. This represents the first derivative of the displacement corresponding to the smallest unique identifier within the time range of the micro-drilling rate sample. This represents the average of the first derivatives of the displacement curves over the time range of the micro-drilling rate sample. This represents the first derivative of the displacement corresponding to the largest unique identifier within the time range of the micro-drilling rate sample. The time corresponding to the largest unique identifier sequence number within the time range of the micro-drilling speed sample.

[0110] In step S4, image recognition specifically includes: S41. Traverse all points on the drilling parameter-micro-drilling rate curve in the drilling pressure analysis sample, connect each interval point to form an interval line, and at the same time draw a perpendicular line through the middle point to the line connecting the interval points to form a perpendicular line, until all points on the drilling parameter-micro-drilling rate curve have been traversed. S42. Obtain the median M of the perpendicular line. Ｐ The average of the vertical lines M Ｑ The median N of the interval line Ｐ and the average number N of the interval lines Ｑ ; S43. Calculate the standard deviation M of the perpendicular line using the median of the perpendicular line. DP The standard deviation M of the perpendiculars is calculated using the mean of the perpendiculars. DQ Take M DP With M DQ The smaller value in is denoted as M. D0 ; Calculate the standard deviation N of the interval line using the median of the interval line. DP The standard deviation N of the interval lines is calculated using the average of the interval lines. DQ Take N DP With N DQ The smaller value in is denoted as N. D0 ; S44. Identify the mutation point and record its unique identifier; S45. Calculate the slope k of the linear regression equation for the micro-drilling rate curve. s The slope k of the regression equation of the drilling pressure curve d The slope k of the linear regression equation for the micro-drilling rate curve is selected. s The slope k of the regression equation of the drilling pressure curve d The portions that are all greater than 0 are used as the parameter recommendation samples.

[0111] In S1, slicing the drilling sample according to the division result means taking the intersection of the drilling sample, the newness sample of the same drill bit, and the sample of the same formation based on the unique identifier number to obtain the micro-drilling rate sample.

[0112] The unique identifier refers to the sequential numbering of each group of data in the time-drilling parameter-micro-drilling rate matrix.

[0113] The drilling samples include normal drilling samples and abnormal drilling samples.

[0114] The normal drilling samples and abnormal drilling samples refer to the samples obtained by segmenting the micro-drilling rate samples based on the mutation points.

[0115] The normal drilling sample refers to a time-drilling parameter-micro-drilling rate matrix within a time range that is determined to be a normal drilling sample if the time interval between two adjacent abrupt change points is greater than a preset value.

[0116] The abnormal drilling sample refers to a time-drilling parameter-micro-drilling rate matrix within a time range that is determined to be an abnormal drilling sample if the time interval between two adjacent abrupt change points is less than a preset value.

[0117] In S42, the average number M of the perpendicular lines Ｑ Calculated using Equation 7; Formula 7; in, The length of the perpendicular line corresponding to the data group with the unique identifier number 1. The length of the perpendicular line corresponding to the data group with the unique identifier number 2. The length of the perpendicular line corresponding to the data group with the unique identifier number 3. The length of the perpendicular line corresponding to the data group with the unique identifier number n.

[0118] In S42, the average number N of the interval lines Ｑ Calculated using Equation 8; Formula 8; in, The length of the interval line that uniquely identifies the data group with sequence number 1. The length of the interval line that uniquely identifies the data group with sequence number 2. The length of the interval line that uniquely identifies the data group with sequence number 3. The length of the interval line that uniquely identifies the data group with sequence number n.

[0119] In S43, the standard deviation M of the perpendicular is calculated using the median of the perpendicular. DP This refers to calculation using Equation 9; Formula 9.

[0120] In step S43, the standard deviation M of the perpendiculars is calculated using the mean of the perpendiculars. DQ This refers to calculation using formula 10; Formula 10.

[0121] In step S43, the standard deviation N of the interval line is calculated based on the median of the interval line. DP This refers to calculation using Equation 11; Formula 11.

[0122] In step S43, the standard deviation N of the interval lines is calculated using the average number of the interval lines. DQ This refers to calculation using Equation 12; Equation 12.

[0123] In S45, the slope k of the linear regression equation for the micro-drilling rate curve is calculated using Equation 13. s ; Equation 13; in, The micro-drilling speed corresponding to the i-th data group. This represents the number of data sets shared between any two adjacent mutation points. It is the smallest unique identifier among all data sets between any two adjacent mutation points. It is the largest unique identifier among all data sets between any two adjacent mutation points.

[0124] In S44, determining the abrupt change point refers to the point with the unique identifier number i on the drilling parameter-micro-drilling rate curve, if M i / N i Greater than M D0 / N D0 If the mutation point is positive, then the point is considered a mutation point; otherwise, the point is considered a non-mutation point.

[0125] In S45, the slope k of the regression equation for the drilling pressure curve is calculated using Equation 14. d ; Equation 14; in, Let be the drilling pressure corresponding to the i-th data group.

[0126] The micro-drilling rate refers to the speed at which the drill bit advances through the formation within a time interval reference.

[0127] The micro-drilling speed is calculated using formula 15; Formula 15; in, for The micro-drilling speed at any given moment for The position of the drill bit at any given moment. for The position of the drill bit at any given moment. This serves as the time interval reference.

[0128] This embodiment is the optimal implementation method, which can eliminate interference from other factors and abnormal signals, identify the main controlling factors affecting micro-drilling rate, and recommend the optimal drilling parameters. The basic principle of this invention is as follows: Based on micro-drilling rate samples, by eliminating interference from drill bit newness, formation, and abnormal changes in micro-drilling rate, the main controlling factors affecting the speed of micro-drilling are identified, thereby finding the optimal drilling parameters under the fastest micro-drilling rate and achieving drilling parameter optimization.

[0129] Specifically, the process involves: slicing data based on logging data, geological survey parameters, and elemental logging results to obtain drilling samples from the same formation within the same drilling column as micro-drilling rate samples; plotting drilling parameter-micro-drilling rate curves based on the time-drilling parameter-micro-drilling rate matrix and the micro-drilling rate samples; calculating the local and overall smoothness of the rotation speed and flow rate curves, selecting the portion with local smoothness less than overall smoothness as drill pressure analysis samples for further analysis, and the portion with local smoothness greater than overall smoothness as stuck pipe risk analysis samples; using image recognition to identify portions in the drill pressure analysis samples where drill pressure increases while micro-drilling rate first increases and then stabilizes as parameter recommendation samples, and the remaining portions as drill rate anomaly samples for formation and drill bit newness assessment; in the parameter recommendation samples, the maximum inlet flow rate is selected as the recommended inlet flow rate, the maximum rotation speed is selected as the recommended rotation speed, and the minimum drill pressure when the drilling parameter-micro-drilling rate curve is stable is selected as the recommended drill pressure.

Claims

1. A method for automatically recommending the fastest drilling parameter based on micro-drill rate sample, characterized in that, The method comprises the following steps: S1, according to drilling samples and logging data, each column drilling is divided, and according to geological measurement parameters and element logging results, the stratum is divided, the drilling sample is sliced according to the division result, the drilling sample of the same stratum under the same column drilling is obtained as the micro drilling speed sample; S2, based on the time-drilling parameter-micro drilling speed matrix, the drilling parameter-micro drilling speed curve is drawn combined with the micro drilling speed sample; S3, the first derivative of the rotation speed curve and the first derivative of the displacement curve are calculated by the curve smoothness, and the standard deviation of the first derivative of the rotation speed curve and the first derivative of the displacement curve in the time interval reference is calculated, forming the local smoothness; the standard deviation of the first derivative of the rotation speed curve and the first derivative of the displacement curve in the sample time range is calculated, forming the overall smoothness; the part with the local smoothness less than the overall smoothness is taken as the drilling pressure analysis sample for drilling pressure analysis, and the part with the local smoothness greater than the overall smoothness is taken as the sticking risk analysis sample for sticking risk analysis; S4, based on image recognition, the part with the drilling pressure rising and the micro drilling speed rising first and then stable in the drilling pressure analysis sample is taken as the parameter recommendation sample, and the remaining part is taken as the drilling speed abnormal sample for stratum and bit aggressiveness judgment; S5, in the parameter recommendation sample, the maximum value of the inlet flow is taken as the recommended inlet flow, the maximum value of the rotation speed is taken as the recommended rotation speed, and the minimum value of the drilling pressure when the drilling parameter-micro drilling speed curve is stable is taken as the recommended drilling pressure.

2. The method of claim 1, wherein the method is characterized by: In the S1, the drilling sample refers to the time-drilling parameter-micro drilling speed matrix.

3. The method of claim 1, wherein the method is characterized by: In the S1, the division of each column drilling specifically refers to taking the drilling assembly recorded in the logging data as the basis, finding the time corresponding to the starting depth and ending depth of each column when the drilling pressure is greater than 0 in the drilling parameter time sequence database, and taking the time-drilling parameter-micro drilling speed matrix in this time period as the same bit aggressiveness sample.

4. The method of claim 1, wherein the method is characterized by: In the S1, the geological measurement parameters include gamma and resistivity.

5. The method of claim 1, wherein the method is characterized by: In the S1, the logging data includes the drilling parameter time sequence database, the geological parameter measurement result, the element logging result, and the drilling assembly.

6. The method of claim 1, wherein the method is characterized by: In the S1, the element logging result refers to the types and contents of elements in the cuttings.

7. The method of claim 1, wherein the method is characterized by: In the S1, the division of the stratum according to the geological measurement parameters and the element logging result refers to the deviation of the gamma measurement value from the average gamma value is not more than 10%, the deviation of the resistivity measurement value from the average resistivity value is not more than 10%, and the types of elements in the element logging result are consistent, and the content deviation is not more than 5% in the well section, and the time corresponding to the starting depth and ending depth of the well section when the drilling pressure is greater than 0 in the drilling parameter time sequence database is found, and the time-drilling parameter-micro drilling speed matrix in this time period is taken as the same stratum sample.

8. The method of claim 1, wherein the method is characterized by: In the S2, the time-drilling parameter-micro drilling speed matrix refers to obtaining the drilling parameter time sequence database based on the logging data, identifying the longest time span when the drilling pressure is greater than 0 and the bit position remains unchanged in the drilling parameter time sequence database, as the time interval reference for calculating the micro drilling speed, calculating the micro drilling speed corresponding to each group of drilling parameters with the length of the time interval reference, forming the drilling speed sample with time as the recording dimension.

9. The method of claim 1, wherein the method is characterized by: The S2, the drilling parameter-micro drilling speed curve includes the drilling pressure, the rotating speed, the inlet flow and the micro drilling speed.

10. The method of claim 1, wherein the method is characterized by: The S3, the first derivative of the rotating speed curve is calculated by formula 1; Formula 1; wherein is the first derivative of the rotational speed curve, is the rotational speed uniquely identifying the data set with the sequence number i, is the rotational speed uniquely identifying the data set with the sequence number i-1, is the time uniquely identifying the data set with the sequence number i, is the time uniquely identifying the data set with the sequence number i-1.

11. The method of claim 1, wherein the method is characterized by: The S3, the first derivative of the displacement curve is calculated by formula 2; Formula 2; wherein, is the first derivative of the displacement curve, is the unique identifier for the volume of the data set with the sequence number i, is the unique identifier for the volume corresponding to the sequence number i-1, is the unique identifier for the time of the data set with the sequence number i, is the unique identifier for the time corresponding to the sequence number i-1.

12. The method of claim 1, wherein the method is characterized by: The S3, the standard deviation of the first derivative of the rotating speed curve in the time interval reference is calculated by formula 3; Formula 3; wherein, is the standard deviation of the first derivative of the rotational speed curve within the time interval reference, is the first derivative of the rotational speed corresponding to the minimum unique identification number within the time interval reference, is the average of the first derivative of the rotational speed curve within the time interval reference, is the first derivative of the rotational speed corresponding to the maximum unique identification number within the time interval reference, is the time interval reference.

13. The method of claim 1, wherein the method is characterized by: The S3, the standard deviation of the first derivative of the displacement curve in the time interval reference is calculated by formula 4; Equation 4; wherein, is the standard deviation of the first derivative of the displacement curve within the time interval reference, is the first derivative of the displacement corresponding to the minimum unique identification number within the time interval reference, is the average of the first derivative of the displacement curve within the time interval reference, is the first derivative of the displacement corresponding to the maximum unique identification number within the time interval reference, is the time interval reference.

14. The method of claim 1, wherein the method is characterized by: The S3, the standard deviation of the first derivative of the rotating speed curve in the sample time range is calculated by formula 5; Formula 5; wherein, is the standard deviation of the first derivative of the rotational speed curve over the time range of the micro drill speed sample, is the first derivative of the rotational speed corresponding to the smallest unique identification number within the time range of the micro drill speed sample, is the average of the first derivative of the rotational speed curve over the time range of the micro drill speed sample, is the first derivative of the rotational speed corresponding to the largest unique identification number within the time range of the micro drill speed sample, is the time corresponding to the largest unique identification number within the time range of the micro drill speed sample.

15. The method of claim 1, wherein the method is characterized by: The S3, the standard deviation of the first derivative of the displacement curve in the sample time range is calculated by formula 6; Formula 6; wherein, is the standard deviation of the first derivative of the flow rate curve over the time range of the microdrill rate sample, is the first derivative of the flow rate corresponding to the minimum unique identification number over the time range of the microdrill rate sample, is the average of the first derivative of the flow rate curve over the time range of the microdrill rate sample, is the first derivative of the flow rate corresponding to the maximum unique identification number over the time range of the microdrill rate sample, is the time corresponding to the maximum unique identification number over the time range of the microdrill rate sample.

16. The method of claim 1, wherein the method is characterized by: The S4, the image recognition specifically includes: S41, traversing the drilling pressure analysis sample, connecting every interval point to form an interval line, and drawing a vertical line through the intermediate point, forming a perpendicular line, until all points on the drilling parameter-micro drilling speed curve are traversed; S42, acquire median M of perpendicular line Ｐ , average M of perpendicular line Ｑ , median N of interval line Ｐ , average N of interval line Ｑ ; S43, the standard deviation M of the perpendicular line is calculated with the median of the perpendicular line DP , the standard deviation M of the perpendicular line is calculated with the average of the perpendicular line DQ , take M DP , take the smaller value of M DQ and M D0 ; the standard deviation N of the interval line is calculated with the median of the interval line DP , the standard deviation N of the interval line is calculated with the average of the interval line DQ , take N DP , take the smaller value of N DQ and N D0 ; S44, determining the mutation point and recording the unique identifier of the mutation point; S45, calculating the slope k of the linear regression equation of the micro-drill speed curve s and the slope k of the linear regression equation of the drilling pressure curve d , selecting the part where the slope k of the linear regression equation of the micro-drill speed curve s and the slope k of the linear regression equation of the drilling pressure curve d are both greater than 0 as the parameter recommendation sample.

17. The method of claim 1, wherein the method is characterized by: The S1, the slicing of the drilling sample according to the division result refers to taking the intersection of the drilling sample, the same drill bit sample and the same formation sample to obtain the micro drilling speed sample.

18. The method of claim 17, wherein the method is characterized by: The unique identification serial number refers to sequentially numbering each group of data in the time-drilling parameter-micro drilling speed matrix.

19. The method of claim 1, wherein the method is characterized by: The drilling sample includes a normal drilling sample and an abnormal drilling sample.

20. The method of claim 19, wherein the method is based on a micro-drill bit pattern. The normal drilling sample and the abnormal drilling sample refer to the cutting of the micro drilling speed sample according to the mutation point.

21. The method of claim 19, wherein the method is characterized by: The normal drilling sample refers to that if the time interval of two adjacent mutation points is greater than a preset value, the time-drilling parameter-micro drilling speed matrix in the time range is determined as a normal drilling sample.

22. The method of claim 19, wherein the method is based on a micro-drill bit pattern. The abnormal drilling sample refers to that if the time interval of two adjacent mutation points is less than a preset value, the time-drilling parameter-micro drilling speed matrix in the time range is determined as an abnormal drilling sample.

23. The method of claim 16, wherein the method is based on a micro-drill bit pattern. In S42, the average number M of perpendicular lines Ｑ Calculated by Equation 7; Formula 7; wherein, is the length of the perpendicular corresponding to the data group uniquely identified by the number 1, is the length of the perpendicular corresponding to the data group uniquely identified by the number 2, is the length of the perpendicular corresponding to the data group uniquely identified by the number 3, is the length of the perpendicular corresponding to the data group uniquely identified by the number n.

24. The method of claim 16, wherein the method is based on a micro-drill bit pattern. In the S42, the average number N of the spacer lines Ｑ Calculated by Equation 8; Formula 8; wherein, is the length of the space line corresponding to the data group uniquely identified by the serial number 1, is the length of the space line corresponding to the data group uniquely identified by the serial number 2, is the length of the space line corresponding to the data group uniquely identified by the serial number 3, is the length of the space line corresponding to the data group uniquely identified by the serial number n.

25. The method of claim 16, wherein the method is based on a micro-drill bit pattern. In the S43, the standard deviation M of the plumb line is calculated using the median of the plumb line DP is calculated by Equation 9. Formula 9.

26. The method of claim 16, wherein the method is based on a micro-drill bit pattern. The standard deviation M of the plumb lines is calculated as an average of the plumb lines DQ is calculated by formula 10; Formula 10.

27. The method of claim 16, wherein: In the S43, the standard deviation N of the interval lines is calculated using the median of the interval lines DP is calculated by Formula 11; Formula 11.

28. The method of claim 16, wherein the method is based on a micro-drill bit pattern. The standard deviation N of the interval lines is calculated in S43 in terms of the average number of interval lines DQ is calculated by Equation 12. Formula 12.

29. The method of claim 16, wherein the method is based on a micro-drill bit pattern. In the S45, the slope k of the linear regression equation of the micro-drill velocity curve is calculated by Formula 13 s ; Equation 13; wherein, is the micro drill speed corresponding to the i-th data group, is the number of common data groups between any two adjacent mutation points, is the minimum unique identifier in all data groups between any two adjacent mutation points, is the maximum unique identifier in all data groups between any two adjacent mutation points.

30. The method of claim 16, wherein: In the S44, determining the mutation point refers to a point with a unique identification number i on the drilling parameter-micro drilling speed curve. If M i / N i is greater than M D0 / N D0 , the point is determined as a mutation point, otherwise, the point is determined as a non-mutation point.

31. The method of claim 16, wherein the method is based on a micro-drill bit pattern. In the S45, the slope k of the regression equation of the WOB curve is calculated by Equation 14 d ; Equation 14; wherein, is the weight on bit corresponding to the i-th data group.

32. The method of claim 1, wherein the method is based on a micro-drill bit pattern. The micro drilling speed refers to the speed of the drill bit advancing in the formation within the time interval reference.

33. The method of claim 1, wherein: The micro drilling speed is calculated by formula 15; Formula 15; wherein, is the micro-drill speed at the time instant, is the drill bit position at the time instant, is the drill bit position at the time instant, is the time interval reference.

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