A method for monitoring formation compressive strength while drilling by integrating logging and logging.

Abstract

This invention discloses a method for monitoring formation compressive strength while drilling by integrating logging and cuttings logging data, comprising: (I) establishing a drill bit compressive strength prediction model while drilling based on engineering logging information of the target area to obtain the compressive strength while drilling based on logging information; (II) calculating the logging compressive strength based on logging information, and comparing the logging compressive strength with the compressive strength while drilling based on logging information to determine the inversion correction coefficients for compressive strength in different lithologies; (III) using Pearson correlation analysis to determine the characteristic minerals and elements of the lithology, establishing a mineral and element multiple regression equation for the inversion correction coefficients of rock compressive strength while drilling, and using the inversion compressive strength and the multiple regression equation for the compressive strength correction coefficients while drilling to monitor the compressive strength value of the drilled formation in real time. This invention can correct the interpretation results of rock compressive strength while drilling in real time through cuttings logging, mineral diffraction logging, and element logging information, thereby improving the accuracy of compressive strength prediction while drilling.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas logging technology, specifically relating to a method for monitoring formation compressive strength while drilling that integrates logging and logging. Background Technology

[0002] Currently, deep formations present complex rock properties, frequently leading to problems such as low mechanical drilling speed and frequent complex accidents during drilling operations. This necessitates real-time and effective methods for monitoring rock compressive strength while drilling to provide guidance for improving drilling speed and efficiency. Both well logging and well logging information have their advantages and disadvantages in reflecting compressive strength. Well logging information is stable and highly accurate, but it requires logging after drilling completion, resulting in poor real-time performance. Well logging information is acquired while drilling, offering good real-time performance, but its stability and accuracy are reduced due to variations in construction conditions and the wellbore environment. Summary of the Invention

[0003] This invention is proposed to solve the problems existing in the prior art, and its purpose is to provide a method for monitoring formation compressive strength while drilling by integrating logging and surveying.

[0004] This invention is achieved through the following technical solution:

[0005] A method for monitoring formation compressive strength while drilling by integrating logging and logging, comprising the following steps:

[0006] (I) Establish a drilling compressive strength prediction model based on the target area engineering logging information to obtain the drilling compressive strength based on the logging information. This includes the following steps:

[0007] (Ⅰ-ⅰ) Record the data of the drill bit entering the well;

[0008] The drill bit data includes drill string assembly, drill bit type, drill bit size, and drill bit newness.

[0009] (I-II) Collecting Engineering Parameters

[0010] The engineering parameters were collected using a well site tool parameter acquisition instrument.

[0011] The engineering parameters include drilling pressure, rotational speed, torque, drilling time, mud density, inlet and outlet discharge rates, and riser pressure, all collected during drilling.

[0012] (Ⅰ-ⅲ) Remove initial drilling bottom hole shape data and abnormal well section data;

[0013] The initial drilling bottom hole shape data and abnormal well section data were removed using a data processor.

[0014] The initial drilling bottom hole shape data refers to the data before the peak value of the parameters that fluctuate drastically during the initial rock breaking process of the new drill bit. The reason for the exclusion is that during the initial rock breaking process of the new drill bit, the contact, deformation and shearing process between the drill bit teeth and the formation is unstable and changes greatly. The drilling pressure, torque and mechanical specific energy parameters will fluctuate drastically and cannot effectively reflect the formation characteristics. After gradually reaching the peak value, they fall and eventually tend to stabilize. Therefore, the data before the peak value is excluded.

[0015] The abnormal well section data includes abnormal drilling data, empty data, and well depth jump data; abnormal drilling data such as tripping in and out of the hole, empty data, and well depth jump data are removed, and only the normal drilling section data is retained;

[0016] (I-IV) Establish a dynamic prediction model for drill bit wear coefficient based on drill bit type and calculate drill bit wear coefficient;

[0017] When the drill bit type is a PDC drill bit, the expression for the dynamic prediction model of the drill bit wear coefficient is:

[0018]

[0019] In the formula: t df A is the wear coefficient of the PDC drill bit, dimensionless; f Formation abrasiveness, dimensionless, determined by rock abrasiveness tests; WOB is drilling pressure, in kN; RPM is rotational speed, in r / min; h d denoted as dimensionless wear of PDC drill bit teeth, and denoted as the ratio of tooth wear height Δh to cutting tooth radius dc / 2; a, b, and c are coefficients.

[0020] When the drill bit type is a roller cone drill bit, the expression for the dynamic prediction model of the drill bit wear coefficient is:

[0021]

[0022] In the formula: t cf A is the wear coefficient of the roller cone drill bit, dimensionless; f Formation abrasiveness, dimensionless, determined by rock abrasiveness tests; RPM is rotational speed, in r / min; WOB is drilling pressure, in kN; h c The dimensionless wear of the tooth of the roller cone drill bit is 0 when the new drill bit is worn out and 1 when the cut is completely worn out; Q1, Q2, D1, D2, and C1 are coefficients.

[0023] (I-V) Establish a drilling compressive strength prediction model based on drill bit type to obtain the drilling compressive strength based on logging information;

[0024] When the drill bit type is a PDC drill bit, a prediction model for the compressive strength of the PDC drill bit while drilling is established based on the rock breaking mechanism of the PDC drill bit.

[0025] The expression for the PDC drill bit's compressive strength prediction model while drilling is as follows:

[0026]

[0027] Where: CCS sz The compressive strength during drilling is expressed in MPa (N). c Total number of cutting teeth, dimensionless; WOB is drill pressure, in kN; RPM is rotational speed, in r / min; D b Drill bit diameter (mm); ROP (Rotation Rate of Pi) (m / h); θ (Back Angle) (°); α (Side Angle) (°); h d The dimensionless wear of the PDC drill bit teeth is the ratio of the tooth wear height Δh to the cutting tooth radius dc / 2; μ f is the rock friction coefficient, which is dimensionless; a, e, g, and K are all coefficients, which are dimensionless.

[0028] When the drill bit type is a roller cone drill bit, based on the rolling rock breaking mechanism of the roller cone drill bit, a prediction model for the compressive strength of the roller cone drill bit while drilling is established based on the interaction mechanism between the roller cone drill bit and the rock.

[0029] The expression for the prediction model of the compressive strength of the roller cone bit during drilling is as follows:

[0030]

[0031] Where: CCS sz The value of WOB is the compressive strength while drilling, in MPa; the value of WOB is the drilling pressure, in kN; n t The number of teeth in contact with the bottom rock is dimensionless; l is the tooth penetration length in mm; ROP is the mechanical drilling rate in m / h; D b ψ is the drill bit diameter in mm; ψ is the cutting angle in °; n t m is the number of teeth in contact with the bottom rock, dimensionless; m is the number of teeth engaged per revolution, dimensionless; RPM is the rotational speed, in r / min; h c The wear of the roller cone drill teeth is dimensionless, 0 for a new drill bit and 1 when the cut is completely worn; a, e, g, K, C1, and C2 are coefficients, dimensionless; w is the tooth cut width, in mm.

[0032] (II) The logging compressive strength is calculated based on the logging information, and the logging compressive strength is compared with the drilling compressive strength based on the logging information to determine the drilling inversion correction coefficient for compressive strength of different lithologies. The specific steps include:

[0033] (II-i) Extract P-wave time difference data based on geophysical well logging information;

[0034] (II-II) Calculate the logging compressive strength based on the empirical formula of P-wave time difference-compressive strength;

[0035] The empirical formula for longitudinal wave time difference-compressive strength is as follows:

[0036] CCS DT =k·DT a

[0037] Where: CCS DT DT is the logging compressive strength, in MPa; k is the P-wave transit time, in µs / ft; k and a are empirical formula coefficients, determined by rock mechanics experiments.

[0038] (Ⅱ-ⅲ) Combine the logging compressive strength and the drilling compressive strength based on logging information to calculate the rock compressive strength correction coefficient based on logging fusion;

[0039] The formula for calculating the rock compressive strength correction coefficient is as follows:

[0040] α=CCS DT / CCS sz

[0041] In the formula: α is the rock compressive strength correction coefficient, which is dimensionless; CCS DT The well logging compressive strength is expressed in MPa; CCS sz The compressive strength during drilling is based on well logging information, and the unit is MPa.

[0042] (II-IV) Based on cuttings logging information and combined with the rock compressive strength correction coefficient based on logging fusion, determine the rock compressive strength correction coefficient corresponding to different lithologies.

[0043] The formula for calculating the rock compressive strength correction coefficient corresponding to different lithologies is as follows:

[0044]

[0045] In the formula: α ri α is the compressive strength correction coefficient for a given lithology, dimensionless; i represents a lithology, selected from any one of the following lithologies: fine sandstone, mudstone, conglomerate, or siltstone; in is the rock compressive strength correction coefficient, dimensionless; n is the number of data belonging to this lithology in the rock compressive strength correction coefficient;

[0046] (III) Using Pearson correlation analysis, the characteristic minerals and elements of the lithology are determined, and a multiple regression equation for the minerals and elements is established for the rock compressive strength inversion correction coefficient obtained in steps (II-III). Using the inversion of compressive strength and the multiple regression equation of compressive strength correction coefficient, the compressive strength value of the drilled formation is monitored in real time, providing decision support for the optimization of drilling construction parameters and the adaptability of drill bits.

[0047] Specifically, the following steps are included:

[0048] (Ⅲ-ⅰ) Mineral composition and elemental composition were collected using a drilling XRD mineral diffraction instrument and an XRF elemental composition analyzer;

[0049] (III-ⅱ) Using a data processor, extract rock compressive strength correction coefficient data according to the depth of mineral and element data;

[0050] (Ⅲ-ⅲ) Calculate the correlation values ​​between mineral components and elemental components and the rock compressive strength correction coefficient, and screen out mineral components and elemental components with an absolute value of correlation greater than 0.3;

[0051] The formula for calculating the correlation value between the mineral composition and elemental composition and the rock compressive strength correction coefficient is as follows:

[0052]

[0053] In the formula: r is the correlation value of the mineral and elemental composition with the rock compressive strength correction coefficient; ω is the content of the mineral and elemental composition, %; α r The rock compressive strength correction factor calculated in step (II-IV) is dimensionless; This is the arithmetic mean of the content of mineral components and elemental components, expressed as a percentage. This is the arithmetic mean of the rock compressive strength correction coefficients, and it is dimensionless.

[0054] (III-IV) Establish a multiple regression model between the mineral and elemental components with high correlation values ​​selected in step (III-III) and the rock compressive strength correction coefficient;

[0055] The expression for the multiple regression model of the mineral composition and elemental composition with the rock compressive strength correction coefficient is as follows:

[0056] α mra =a·ω 石英 +b·ω 白云石 +c·ω 粘土矿物 +…+l·ω Si +m·ω Al +n·ω K +…

[0057] In the formula: α mraω is the rock compressive strength correction factor, dimensionless; i , represents the content of mineral / elemental components, in %; i represents the mineral or elemental component, i is selected from quartz, feldspar, calcite, dolomite, clay minerals, etc., as well as Si, Al, K, Fe, Ca, etc.; a, b, c, l, m, n are the coefficients of each parameter in the multiple regression equation.

[0058] (Ⅲ-ⅴ) Based on the rock compressive strength correction coefficient and the compressive strength while drilling based on logging information, calculate the compressive strength of the rock encountered in the formation in real time.

[0059] The formula for calculating the compressive strength of the rock encountered during drilling in real time is as follows:

[0060] CCS=α·CCS sz

[0061] In the formula: CCS is the corrected compressive strength of the rock encountered during drilling, in MPa; CCS sz The value is the compressive strength during drilling, in MPa; α is calculated by step (Ⅱ-ⅳ) or step (Ⅲ-ⅳ). When the target well only has lithological information, the value calculated by step (Ⅱ-iv) is selected. When the target well contains XRD mineral diffraction logging or XRF element logging information, the value calculated by step (Ⅲ-iv) is selected.

[0062] The beneficial effects of this invention are:

[0063] This invention provides a method for monitoring formation compressive strength while drilling by integrating logging and well logging data from actual well sites. By combining logging and well logging data from actual well sites, an algorithm is used to establish a regional rock compressive strength correction coefficient calculation model, thereby achieving the integration of logging and well logging to improve prediction accuracy and forming a high-precision real-time monitoring method for rock compressive strength while drilling. Attached Figure Description

[0064] Figure 1 This is a flowchart of the method of the present invention;

[0065] Figure 2 This is a field application example of the evaluation of rock compressive strength during drilling, as described in Embodiment 1 of the present invention.

[0066] For those skilled in the art, other related figures can be obtained from the above figures without any creative effort. Detailed Implementation

[0067] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0068] Example 1

[0069] like Figure 1 As shown, a method for monitoring formation compressive strength while drilling that integrates logging and surveying includes the following steps:

[0070] Step 1: Record the drilling tool assembly, drill bit type, size, and newness, as shown in Table 1 below.

[0071] Table 1: Drill Component Assembly, Drill Bit Type, Size, and Newness Data for Well Entry

[0072]

[0073] Step 2: Use a well site engineering parameter acquisition instrument to monitor parameters such as drilling pressure, rotation speed, torque, drilling time, mud density, inlet and outlet discharge, and riser pressure while drilling.

[0074] Step 3: Use the data cleaner to remove initial drilling bottom profile data and abnormal well section data.

[0075] Step 3-1: During the initial rock breaking process of the new drill bit, the mechanical specific energy will fluctuate violently due to the unstable contact, deformation and shearing process between the drill bit teeth and the formation. It will gradually reach a peak and then fall, eventually stabilizing. Data before the peak value will be discarded.

[0076] Step 3-2: Remove abnormal drilling data such as tripping in and out of the hole, stopping drilling, empty data, and sudden changes in well depth, and retain only the data of the normal drilling section.

[0077] Step 4: Establish a dynamic prediction model for drill bit wear coefficient based on drill bit type, and calculate the drill bit wear coefficient;

[0078] When the drill bit type is a PDC drill bit, the dynamic prediction model for the PDC drill bit wear coefficient is as follows:

[0079]

[0080] In the formula: t df A is the wear coefficient of the PDC drill bit, dimensionless; f Formation abrasiveness, dimensionless, determined by rock abrasiveness tests; WOB is drilling pressure, in kN; RPM is rotational speed, in r / min; h d denoted as dimensionless wear of PDC drill bit teeth, and denoted as the ratio of tooth wear height Δh to cutting tooth radius dc / 2; a, b, and c are coefficients.

[0081] When the drill bit type is a roller cone drill bit, the dynamic prediction of the roller cone drill bit wear coefficient is as follows:

[0082]

[0083] In the formula: t cf A is the wear coefficient of the roller cone drill bit, dimensionless;f Formation abrasiveness, dimensionless, determined by rock abrasiveness tests; RPM is rotational speed, in r / min; WOB is drilling pressure, in kN; h c The dimensionless wear of the tooth of the roller cone drill bit is 0 when the new drill bit is worn out and 1 when the cut is completely worn out; Q1, Q2, D1, D2, and C1 are coefficients.

[0084] Step 5: Establish a prediction model for the compressive strength of the drill bit while drilling based on the drill bit type. When the drill bit type is a PDC drill bit, based on the rock breaking mechanism of the PDC drill bit, establish a real-time prediction method for the compressive strength of the PDC drill bit based on the interaction mechanism between the PDC drill bit and the rock.

[0085]

[0086] Where: CCS sz The value of Drilling Pressure is MPa; N is the total number of cutting teeth, dimensionless; WOB is the drilling pressure, kN; RPM is the rotational speed, r / min; D b Drill bit diameter (mm); ROP (Rotation Rate of Pi) (m / h); θ (Back Angle) (°); α (Side Angle) (°); h d The dimensionless wear of the PDC drill bit teeth is the ratio of the tooth wear height Δh to the cutting tooth radius dc / 2; μ f Let be the rock friction coefficient, which is dimensionless; a, c, g, and K are all coefficients, which are dimensionless.

[0087] When the drill bit type is a roller cone drill bit, a real-time prediction method for the compressive strength of the roller cone drill bit is established based on the roller cone drill bit rolling rock breaking mechanism and the interaction mechanism between the roller cone drill bit and the rock.

[0088]

[0089] Where: CCS sz The value of WOB is the compressive strength while drilling, in MPa; the value of WOB is the drilling pressure, in kN; n t The number of teeth in contact with the bottom rock is dimensionless; l is the tooth penetration length in mm; ROP is the mechanical drilling rate in m / h; D b ψ is the drill bit diameter in mm; ψ is the cutting angle in °; n t m is the number of teeth in contact with the bottom rock, dimensionless; m is the number of teeth engaged per revolution, dimensionless; RPM is the rotational speed, in r / min; h c denoted as dimensionless wear of the tooth of the roller cone drill bit, 0 for a new drill bit and 1 when the cut is completely worn; a, e, g, K, C1, and C2 are coefficients, dimensionless; w is the tooth cut width, mm.

[0090] Step 6: Extract P-wave time difference data based on geophysical logging information.

[0091] Step 7: Calculate the logging compressive strength based on the empirical formula for P-wave transit time versus compressive strength. The empirical formula for P-wave transit time versus compressive strength in Block BZ, determined by rock mechanics experiments, is as follows:

[0092] CCS DT =1.1908×10 7 ·DT -3

[0093] Where: CCS DT —Logging compressive strength, MPa, mm; DT—P-wave time difference, us / ft.

[0094] Step 8: Combine the logging compressive strength and the drilling compressive strength to calculate the rock compressive strength correction coefficient, as shown in Table 2 below.

[0095] Table 2: Correction Coefficients for Depth, Logging Compressive Strength, Drilling Compressive Strength, and Rock Compressive Strength

[0096] Depth (m) Well logging compressive strength (MPa) Drilling compressive strength (MPa) Rock compressive strength correction factor 2600 17.098284 16.916527 1.0107443 2605 17.576021 17.628468 0.9970248 2610 17.03254 17.850092 0.954199 2615 18.411227 16.821668 1.0944947 2620 19.144075 16.543161 1.1572199 2625 18.299655 15.821477 1.1566338 2630 18.733709 16.808988 1.1145055 2635 19.937205 16.267488 1.225586 2640 21.113239 15.537022 1.3588987 2645 33.945326 15.072247 2.2521743

[0097] Step 9: Based on lithological logging information and the rock compressive strength correction coefficient calculated by logging fusion, determine the rock compressive strength correction coefficient corresponding to different lithologies.

[0098] Step 10: Collect mineral and elemental compositions using a drilling XRD mineral diffraction instrument and an XRF elemental composition analyzer.

[0099] Step 11: Use the data processor to extract the rock compressive strength correction coefficient data according to the mineral & element data depth.

[0100] Step 12: Screen mineral and elemental components whose absolute values ​​of correlation with the rock compressive strength correction coefficient are greater than 0.3, as shown in Table 3 below.

[0101] Table 3: Correlation Values ​​of Mineral & Elemental Composition and Rock Compressive Strength Correction Coefficient

[0102]

[0103] Step 13: Establish a multiple regression model for the rock compressive strength correction coefficient based on mineral and element information.

[0104] α mra =a·ω 石英 +b·ω 白云石 +c·ω 粘土矿物 +l·ω Si +m·ω Al +n·ωK

[0105] In the formula: α mra ω is the rock compressive strength correction factor, dimensionless; i , representing the content of mineral and elemental components, in %; i = mineral components (quartz, dolomite, clay minerals) and elemental components (Si, Al, K); a, b, c, l, m, n are the coefficients of the parameters in the multiple regression equation;

[0106] Step 14: Calculate the compressive strength of the rock encountered in the formation in real time based on the rock compressive strength correction coefficient and the compressive strength while drilling.

[0107] CCS = α mra ·CCS sz

[0108] In the formula: CCS is the corrected compressive strength of the rock encountered during drilling, in MPa; CCS sz α represents the compressive strength during drilling, in MPa. mra is the rock compressive strength correction coefficient, which is dimensionless.

[0109] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method for monitoring formation compressive strength while drilling using a combination of logging and logging, characterized in that: Includes the following steps: (I) Establish a drilling compressive strength prediction model based on the target area engineering logging information to obtain the drilling compressive strength based on the logging information; (II) The logging compressive strength is calculated based on the logging information, and the logging compressive strength is compared with the drilling compressive strength based on the logging information to determine the correction coefficient for compressive strength of different lithologies; (III) Using Pearson correlation analysis, calculate the correlation values ​​between mineral components and elemental components and rock compressive strength correction coefficient, screen out mineral components and elemental components with absolute values ​​of correlation greater than 0.3, determine lithological characteristic minerals and elements, establish a multiple regression equation on rock compressive strength correction coefficient and minerals and elements, and calculate the compressive strength of rock encountered in formation in real time based on rock compressive strength correction coefficient and drilling compressive strength, wherein the rock compressive strength correction coefficient is obtained through the multiple regression equation in step (II) or step (III).

2. The method for monitoring formation compressive strength while drilling using logging-data fusion as described in claim 1, characterized in that: Step (I) specifically includes the following steps: (Ⅰ-ⅰ) Record the data of the drill bit entering the well; (Ⅰ-ⅱ) Collect engineering parameters; (Ⅰ-ⅲ) Remove initial drilling bottom hole shape data and abnormal well section data; (I-IV) Establish a dynamic prediction model for drill bit wear coefficient based on drill bit type and calculate drill bit wear coefficient; (I-V) Establish a drilling compressive strength prediction model based on the drill bit type to obtain the drilling compressive strength based on logging information.

3. The method for monitoring formation compressive strength while drilling using logging-data fusion as described in claim 2, characterized in that: The drill bit data includes drill string assembly, drill bit type, drill bit size, and drill bit newness; the engineering parameters are collected by a well site tool parameter acquisition instrument; the engineering parameters include drilling pressure, rotational speed, torque, drilling time, mud density, inlet and outlet displacement, and standpipe pressure collected during drilling.

4. The method for monitoring formation compressive strength while drilling using logging-data fusion as described in claim 2, characterized in that: The initial drilling bottom hole shape data and abnormal well section data are removed using a data processor; the abnormal well section data includes abnormal drilling data, empty data, and well depth jump data.

5. The method for monitoring formation compressive strength while drilling using logging-data fusion as described in claim 2, characterized in that: When the drill bit type is a PDC drill bit, the expression for the dynamic prediction model of the drill bit wear coefficient is: In the formula: t df A is the wear coefficient of the PDC drill bit, dimensionless; f Formation abrasiveness, dimensionless, determined by rock abrasiveness tests; WOB is drilling pressure, in kN; RPM is rotational speed, in r / min; h d denoted as dimensionless wear of PDC drill bit teeth, and denoted as the ratio of tooth wear height to cutting tooth radius; a, b, and c are coefficients. When the drill bit type is a roller cone drill bit, the expression for the dynamic prediction model of the drill bit wear coefficient is: In the formula: t cf A is the wear coefficient of the roller cone drill bit, dimensionless; f Formation abrasiveness, dimensionless, determined by rock abrasiveness tests; RPM is rotational speed, in r / min; WOB is drilling pressure, in kN; h c The dimensionless wear of the tooth of the roller cone drill bit is 0 when the new drill bit is worn and 1 when all the tooth height is worn; Q1, Q2, D1, D2, and C1 are coefficients.

6. The method for monitoring formation compressive strength while drilling using logging-data fusion as described in claim 2, characterized in that: When the drill bit type is a PDC drill bit, a prediction model for the compressive strength of the PDC drill bit while drilling is established based on the rock breaking mechanism of the PDC drill bit. The expression for the PDC drill bit's compressive strength prediction model while drilling is as follows: Where: CCS sz The compressive strength during drilling is expressed in MPa (N). c Total number of cutting teeth, dimensionless; WOB is drill pressure, in kN; RPM is rotational speed, in r / min; D b θ is the drill bit diameter in mm; ROP is the mechanical drilling speed in m / h; θ is the backslope angle in °; φ is the side rotation angle in °; h d denoted as μ, representing the dimensionless wear of the PDC drill bit teeth, and denoted as μ, representing the ratio of tooth wear height to cutting tooth radius. f is the rock friction coefficient, which is dimensionless; a, e, g, and K are all coefficients, which are dimensionless. When the drill bit type is a roller cone drill bit, a prediction model for the compressive strength of the roller cone drill bit during drilling is established based on the roller cone drill bit rolling rock breaking mechanism and the interaction mechanism between the roller cone drill bit and the rock. The expression for the prediction model of the compressive strength of the roller cone bit during drilling is as follows: Where: CCS sz The value of WOB is the compressive strength while drilling, in MPa; the value of WOB is the drilling pressure, in kN; n t The number of teeth in contact with the bottom rock is dimensionless; l is the tooth penetration length in mm; ROP is the mechanical drilling rate in m / h; D b ψ is the drill bit diameter in mm; ψ is the cutting angle in °; n t m is the number of teeth in contact with the bottom rock, dimensionless; m is the number of teeth engaged per revolution, dimensionless; RPM is the rotational speed, in r / min; h c denoted as dimensionless wear of the roller cone drill bit teeth, 0 for a new drill bit and 1 when all tooth heights are worn; a, e, g, K, C1, and C2 are coefficients, dimensionless; w is the tooth infeed width, in mm.

7. The method for monitoring formation compressive strength while drilling using logging-data fusion according to claim 1, characterized in that: Step (II) specifically includes the following steps: (II-i) Extract P-wave time difference data based on geophysical well logging information; (II-II) Calculate the logging compressive strength based on the empirical formula of P-wave time difference-compressive strength; The empirical formula for longitudinal wave travel time versus compressive strength is as follows: CCS DT =k·DT a Where: CCS DT DT is the logging compressive strength, in MPa; k is the P-wave transit time, in µs / ft; k and a are empirical formula coefficients, determined by rock mechanics experiments. (Ⅱ-ⅲ) Combine well logging compressive strength and drilling compressive strength based on logging information to calculate the rock compressive strength correction coefficient based on well logging fusion; The formula for calculating the rock compressive strength correction coefficient is as follows: α=CCS DT / CCS sz In the formula: α is the rock compressive strength correction coefficient, which is dimensionless; CCS DT The well logging compressive strength is expressed in MPa; CCS sz The compressive strength during drilling is based on well logging information, and the unit is MPa. (II-IV) Based on cuttings logging information, combined with the rock compressive strength correction coefficient based on logging fusion, determine the rock compressive strength correction coefficient corresponding to different lithologies; The formula for calculating the rock compressive strength correction coefficient corresponding to different lithologies is as follows: In the formula: α ri α is the rock compressive strength correction coefficient corresponding to a certain lithology, where i represents the lithology, selected from any one of fine sandstone, mudstone, conglomerate, or siltstone; in The rock compressive strength correction coefficient calculated in step (Ⅱ-ⅲ) is dimensionless; n is the number of data belonging to this lithology in the rock compressive strength correction coefficient.

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