Method and system for establishing rock drillability prediction model based on drilling speed equation

Abstract

The present application belongs to the technical field of oil and gas drilling engineering, and discloses a rock drillability prediction model establishment method and system based on a drilling speed equation. The present application introduces the drilling speed equation as a theoretical basis, and converts the traditional empirical drillability evaluation method into a quantitative calculation model based on physical mechanism. By inversely calculating the rock drillability level value by using historical drilling data, the drillability parameters of different formations can be obtained without relying on indoor core experiments, scientific mapping from downhole measured data to formation lithology characteristics is realized, and the reliability and applicability of the drillability parameters are significantly improved. The present application establishes a drillability level value calculation model on the basis of inverting the drillability level value, so that the rock drillability prediction is transformed from single-point discrete data to a continuous space model.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas drilling engineering technology, specifically relating to a method and system for establishing a rock drillability prediction model based on the drilling rate equation. Background Technology

[0002] Currently, in drilling engineering, even when using the same type of drill bit, the same drill bit structure parameters, and maintaining consistent drilling parameters (such as drilling pressure, rotational speed, and drilling fluid properties), significant differences in the rate of penetration (ROP) can still occur when drilling in different blocks and formations. One of the key reasons for this difference lies in the varying drillability of the formation rocks.

[0003] Rock drillability is a comprehensive indicator that measures the resistance of rock to drilling and breaking, reflecting the ease with which rock can be broken by a drill bit. Generally speaking, as the burial depth increases, the compaction of the formation increases, the rock becomes denser and stronger, and its drillability gradually decreases, resulting in a decrease in rock breaking efficiency and a slowdown in mechanical drilling speed.

[0004] In engineering practice, rock drillability can be obtained through laboratory tests, such as indentation hardness testing and micro-bit testing, or through field statistics or logging data inversion. For example, by combining data such as drilling rate, drilling pressure, torque, rotational speed, and bottom hole pressure with the drilling rate equation, the variation law of the drillability coefficient can be obtained. By establishing a rock drillability profile, engineers can optimize drill bit selection and adjust drilling parameters for different formations, thereby achieving higher rock breaking efficiency and faster drilling speed.

[0005] However, existing technologies for predicting rock drillability rely on extensive laboratory experiments or logging data, making it difficult to accurately and in real-time reflect changes in rock drillability during drilling. Especially in situations lacking sufficient logging data or complex formations, traditional methods suffer from low prediction accuracy and poor real-time performance, leading to a lack of targeted drill bit selection and parameter optimization, thus impacting drilling efficiency. Summary of the Invention

[0006] The purpose of this invention is to overcome the problem that rock drillability is difficult to predict accurately and in real time at the drilling site, which leads to a lack of scientific basis for drill bit selection and drilling parameter optimization, affecting drilling efficiency and rock breaking effect. The invention provides a method and system for establishing a rock drillability prediction model based on the drilling rate equation.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a method for establishing a rock drillability prediction model based on the drilling rate equation, comprising the following steps:

[0009] Obtain historical drilling data for the target area, and then obtain the stratified average well depth and drilling parameters based on the historical drilling data.

[0010] Based on the drilling rate equation and combined with drilling parameters, the rock drillability grade value is predicted in reverse, and a drillability grade value calculation model is established based on the rock drillability grade value.

[0011] Real-time drilling data of the target area is acquired, and the extreme value of the average rock drillability of each layer is calculated using a drillability grade value determination model.

[0012] Linear regression was performed on the extreme values ​​of the average rock drillability of each layer and the average well depth of each layer to obtain the formation drillability gradient model of the target area.

[0013] A further improvement of this invention is that the drilling parameters include actual drilling data of drill bits at different depths and in different formations; wherein, the actual drilling data of drill bits includes mechanical drilling speed data, drilling pressure data, drilling speed data, drilling fluid displacement data, and density data.

[0014] A further improvement of this invention lies in the following method for reverse-predicting rock drillability grade values ​​based on the drilling rate equation and combined with drilling parameters, and for establishing a drillability grade value calculation model based on the rock drillability grade values:

[0015] Obtain drilling parameters, input them into the drilling rate equation, and obtain the predicted rock drillability grade value:

[0016]

[0017]

[0018]

[0019] in, For mechanical drilling speed data, For drilling pressure data, For drilling speed data, For water power, Let Euler's constant be 1. For drilling fluid density, The equivalent density of the formation, For drilling pressure, The diameter of the drill bit. This refers to the drilling fluid discharge rate. The equivalent diameter of the nozzle. This is the drill pressure sensitivity coefficient. This is the speed sensitivity coefficient. For water power sensitivity coefficient, This is the pressure difference sensitivity coefficient.

[0020] A model for determining the drillability grade is established based on the rock drillability grade value:

[0021]

[0022] in, This represents the drillability rating.

[0023] A further improvement of this invention lies in the drilling pressure sensitivity coefficient. Speed ​​sensitivity coefficient Water power sensitivity coefficient Pressure difference sensitivity coefficient With drillability rating The relationship is:

[0024] .

[0025] A further improvement of this invention lies in the following method for obtaining real-time drilling data of the target area and calculating the extreme value of the average rock drillability of each layer using a drillability rating model:

[0026] Acquire real-time drilling data for the target area, divide the real-time drilling data into different formations based on systems and groups, and use a drillability grade value calculation model to calculate the real-time drilling data of different formations to obtain the extreme value of the average rock drillability of each layer.

[0027] Secondly, this invention provides a system for establishing a rock drillability prediction model based on the drilling rate equation, including:

[0028] The data acquisition module is used to acquire historical drilling data for the target area and obtain the stratified average well depth and drilling parameters based on the historical drilling data.

[0029] The model building module is used to predict rock drillability grade values ​​in reverse based on the drilling rate equation and drilling parameters, and to establish a drillability grade value calculation model based on the rock drillability grade values.

[0030] The model solving module is used to acquire real-time drilling data of the target area and calculates the extreme value of the average rock drillability of each layer using the drillability grade value.

[0031] The data calculation module is used to perform linear regression on the extreme values ​​of the average rock drillability of each layer and the average well depth of each layer to obtain the formation drillability gradient model of the target area.

[0032] A further improvement of this invention is that the functionality of the model building module is implemented through the following method:

[0033] Obtain drilling parameters, input them into the drilling rate equation, and obtain the predicted rock drillability grade value:

[0034]

[0035]

[0036]

[0037] in, For mechanical drilling speed data, For drilling pressure data, For drilling speed data, For water power, Let Euler's constant be 1. For drilling fluid density, The equivalent density of the formation, For drilling pressure, The diameter of the drill bit. This refers to the drilling fluid discharge rate. The equivalent diameter of the nozzle. This is the drill pressure sensitivity coefficient. This is the speed sensitivity coefficient. For water power sensitivity coefficient, This is the pressure difference sensitivity coefficient.

[0038] A model for determining the drillability grade is established based on the rock drillability grade value:

[0039]

[0040] in, This represents the drillability rating.

[0041] A further improvement of this invention is that the function of the model solving module is implemented through the following method:

[0042] Acquire real-time drilling data for the target area, divide the real-time drilling data into different formations based on systems and groups, and use a drillability grade value calculation model to calculate the real-time drilling data of different formations to obtain the extreme value of the average rock drillability of each layer.

[0043] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of establishing a method for predicting rock drillability based on the drilling rate equation.

[0044] Fourthly, the present invention provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of a method for establishing a rock drillability prediction model based on a drilling rate equation.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] This invention introduces a drilling rate equation as its theoretical foundation, transforming traditional empirical drillability evaluation methods into a quantitative calculation model based on physical mechanisms. By using historical drilling data to inversely derive rock drillability grades, drillability parameters for different formations can be obtained without relying on laboratory core experiments, achieving a scientific mapping from downhole measured data to formation lithological characteristics, significantly improving the reliability and applicability of drillability parameters. Based on the inverse drillability grade calculation, this invention establishes a drillability grade calculation model, transforming rock drillability prediction from single-point discrete data to a continuous spatial model. This invention can automatically integrate multiple parameter information such as drilling pressure, rotational speed, displacement, and drilling fluid density, comprehensively reflecting the correspondence between changes in mechanical drilling rate and rock drill resistance characteristics during drilling, thereby obtaining drillability distribution results that better conform to geological reality and avoiding misjudgments caused by single-factor analysis in traditional methods. By introducing real-time drilling data to calculate stratified average rock drillability grades, this invention achieves dynamic prediction and updating of drillability. Compared to traditional methods that rely solely on static experimental data, this method can calculate drillability grades in real time during drilling and automatically adjust model parameters as well depth changes, effectively reflecting the dynamic changes in formation hardness. This feature allows drilling engineers to grasp the changing patterns of formation drillability on-site, providing real-time decision-making basis for drill bit selection, drilling pressure control, and rotation speed optimization. This invention establishes a formation drillability gradient model for the target area by performing linear regression on the average rock drillability grade of each layer and the average well depth, achieving a quantitative expression of formation hardness changes with depth. This model can reveal the influence of formation compaction and diagenesis on drillability, providing layered and zoned drillability prediction curves for regional drilling planning, thereby predicting drilling difficulty in advance and reducing drill string wear and unplanned downtime risks. In summary, this invention enables real-time and accurate prediction of rock drillability parameters on-site, overcoming the reliance on laboratory measurements and manual experience in traditional methods. It provides a scientific basis for drill bit selection and drilling parameter optimization, significantly improving drilling efficiency and rock breaking effect, and has good engineering practicality and promotion prospects. Attached Figure Description

[0047] Figure 1 This is a flowchart of the present invention.

[0048] Figure 2 This is a system diagram of the present invention.

[0049] Figure 3 This is a graph showing the variation of drilling parameters and drillability rating values ​​according to the present invention.

[0050] Figure 4 This is a regression curve of the drillability rating and well depth according to the present invention.

[0051] Figure 5 This is a system diagram of Embodiment 4 of the present invention. Detailed Implementation

[0052] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0053] Example 1:

[0054] See Figure 1 A method for establishing a rock drillability prediction model based on the drilling rate equation includes the following steps:

[0055] S1: Obtain historical drilling data for the target area, and obtain the stratified average well depth and drilling parameters based on the historical drilling data.

[0056] S2, based on the drilling rate equation and combined with drilling parameters, reversely predicts the rock drillability grade value, and establishes a drillability grade value calculation model based on the rock drillability grade value.

[0057] S3: Obtain real-time drilling data for the target area and calculate the extreme value of the average rock drillability of the layer using the drillability grade value calculation model.

[0058] S4 is used to perform linear regression on the extreme values ​​of the average rock drillability of each layer and the average well depth of each layer to obtain the formation drillability gradient model of the target area.

[0059] Example 2:

[0060] See Figure 2 A system for predicting rock drillability based on the drilling rate equation is established, including:

[0061] The data acquisition module is used to acquire historical drilling data for the target area and obtain the stratified average well depth and drilling parameters based on the historical drilling data.

[0062] The model building module is used to predict rock drillability grade values ​​in reverse based on the drilling rate equation and drilling parameters, and to establish a drillability grade value calculation model based on the rock drillability grade values.

[0063] The model solving module is used to acquire real-time drilling data of the target area and calculates the extreme value of the average rock drillability of each layer using the drillability grade value.

[0064] The data calculation module is used to perform linear regression on the extreme values ​​of the average rock drillability of each layer and the average well depth of each layer to obtain the formation drillability gradient model of the target area.

[0065] Example 3:

[0066] This embodiment takes a certain drilling as an example, collects and organizes actual drilling data, and uses actual drilling data of drill bits at different depths and in different formations, namely mechanical drilling speed, drilling pressure, rotation speed, drilling fluid discharge and density.

[0067] Using the general drilling rate equation, the rock drillability grade is predicted in reverse, and a formula for calculating the drillability grade is established:

[0068]

[0069]

[0070]

[0071] in, For mechanical drilling speed data, For drilling pressure data, For drilling speed data, For water power, Let Euler's constant be 1. For drilling fluid density, The equivalent density of the formation, For drilling pressure, The diameter of the drill bit. This refers to the drilling fluid discharge rate. The equivalent diameter of the nozzle. This is the drill pressure sensitivity coefficient. This is the speed sensitivity coefficient. For water power sensitivity coefficient, Pressure difference sensitivity coefficient; Drilling pressure sensitivity coefficient Speed ​​sensitivity coefficient Water power sensitivity coefficient Pressure difference sensitivity coefficient With drillability rating The relationship is:

[0072] .

[0073] In the above formula, except for the drillability grade value All other parameters are actual drilling parameters. A model for determining the drillability grade is established based on the rock drillability grade value:

[0074]

[0075] in, This represents the drillability rating.

[0076] Considering the limited sample data, different strata were divided based on system and group: Quaternary, Neogene, and Paleogene as Group 1; Triassic as Group 2; Permian Leping Series as Group 3; Permian Yangxin and Chuanshan Series as Group 4; and Carboniferous and Ordovician as Group 5. Drillability values ​​for the corresponding strata were calculated. The calculated drillability grades for strata at different depths were summarized, and outliers were removed. Linear regression was performed on the drillability grades and well depths according to step S3, ultimately establishing a regional drillability gradient formula model, as shown in Table 1.

[0077] Table 1. Formation Drillability Gradient Formula Prediction Model

[0078]

[0079] Where H represents the average depth, and R indicates that the Triassic and Permian samples fit well. Table 1 shows that the linear regression results for each stratigraphic group exhibit clear stratification characteristics, with significant differences in drillability gradients corresponding to strata from different geological ages.

[0080] Group 1 (Q, N, E): The linear regression formula is... Correlation coefficient This indicates that the lithology of the shallow Quaternary, Neogene, and Paleogene strata varies considerably, and drillability is strongly influenced by local sedimentary characteristics and the degree of weathering, while well depth has a relatively small impact on drillability.

[0081] Group 2 (Triassic): The formula is as follows , The fitting effect is significant, indicating that the lithology inside the Triassic strata is relatively stable and there is a good linear relationship between well depth and drillability.

[0082] Group 3 (Permian Löping System): The formula is as follows , This indicates that drillability increases linearly with depth, reflecting the trend of enhanced rock compaction and diagenesis.

[0083] Group 4 (Carboniferous and Ordovician): The formula is as follows , This indicates that the drillability gradient changes relatively smoothly in ancient and hard strata, and the model is also well applicable to this deep stratum.

[0084] In summary, the Triassic and Permian samples showed the highest correlation, indicating that the model has a high degree of reliability in fitting the mid-to-deep layers and can reflect the overall trend of formation hardness increasing with depth. The shallow Quaternary and Neogene samples showed relatively low correlation due to strong weathering and heterogeneity, but still generally conformed to the drillability law.

[0085] This example uses specific data from the block to predict the drillability rating of the rock strata at each level, further verifying the accuracy of the model.

[0086] Table 2 shows the drillability grade values ​​corresponding to specific formations in a drilling block, obtained from the formation drillability gradient formula prediction model.

[0087] Table 2. Statistical table of drillability ratings for each layer in the block.

[0088]

[0089] It can be seen that the drillability rating value The overall trend is upward with increasing well depth, reflecting the increased rock hardness and drilling difficulty, which is consistent with the formation compaction law. Table 2 shows the data for each layer in the block, revealing the drillability rating values... The drillability rating increases with average well depth, meaning that the deeper the formation and the denser the lithology, the higher the drillability rating. This pattern is consistent with formation compaction and reflects the gradual change in formation hardness.

[0090] Shallow layers (Quaternary to Paleogene, 0–700 m): lithology mainly consists of loose sand, silt, and mudstone. Between 2.3 and 2.7, it belongs to soft to medium strata, with low drilling resistance.

[0091] Middle layer (Permian to Heshanggou Formation, approximately 1000–2100 m): Between 3.8 and 6.3, the formation is of medium hardness, leading to increased drill bit wear.

[0092] Deep layers (Liujiagou Formation to Taiyuan Formation, 2500–3500 m): The depth ranges from 7 to 9.5, indicating that the formation is medium to hard to hard, which means that the formation's cementation increases with depth and the drilling speed decreases significantly.

[0093] This trend confirms that the model can well reflect the linear relationship between formation hardness and depth, and can serve as a quantitative indicator of lithological changes during drilling.

[0094] The predicted values ​​of drillability ratings for different formation depths are compared with the calculated values ​​obtained from the drillability rating formula. The error is small, indicating that the accuracy of the prediction model is high, as shown in Table 3.

[0095] Table 3 Comparison of Predicted and Calculated Values ​​of Rock Drillability

[0096]

[0097] Table 3 shows that the error range between the predicted values ​​and the measured calculated values ​​is 0.96%–6.96%, with the error of most sample points controlled within ±3%, indicating that the model has high prediction accuracy within the region.

[0098] The error is lowest when the well depth variation is small (in the range of 2384–2388 m), which is only about 1%, indicating that the model can reflect the drillability variation of local continuous formations well.

[0099] When there are abrupt changes in strata or many lithological interlayers (such as 2381 m, 2389 m), the error increases slightly to about 6%, mainly due to nonlinear strata abrupt changes and sample dispersion.

[0100] Overall, the predicted results are in high agreement with the calculated values, demonstrating that the model has strong robustness and generalization ability.

[0101] Comprehensive experimental and verification results show that the formation drillability gradient prediction model can effectively describe the quantitative relationship between well depth and rock drillability. The model in this embodiment has a smaller prediction error and higher accuracy than traditional empirical estimation methods. This embodiment has good physical significance and geological interpretability. This embodiment can serve as an important technical means for drilling engineering design, formation mechanics analysis, and regional lithological correlation. Therefore, the model in this embodiment not only provides a reliable theoretical basis for well drillability evaluation but also provides a practical reference for drilling parameter optimization and engineering risk control.

[0102] Example 4:

[0103] See Figure 5 The present invention also provides an electronic device 100 for establishing a rock drillability prediction model based on the drilling rate equation; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0104] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the rock drillability prediction model establishment method based on the drilling rate equation described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0105] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0106] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for establishing a rock drillability prediction model based on the drilling rate equation, and the processor 102 can execute the multiple instructions to achieve this.

[0107] Obtain historical drilling data for the target area, and then obtain the stratified average well depth and drilling parameters based on the historical drilling data.

[0108] Based on the drilling rate equation and combined with drilling parameters, the rock drillability grade value is predicted in reverse, and a drillability grade value calculation model is established based on the rock drillability grade value.

[0109] Real-time drilling data of the target area is acquired, and the extreme value of the average rock drillability of each layer is calculated using a drillability grade value determination model.

[0110] Linear regression was performed on the extreme values ​​of the average rock drillability of each layer and the average well depth of each layer to obtain the formation drillability gradient model of the target area.

[0111] Example 5:

[0112] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0113] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0114] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0115] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0116] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for establishing a rock drillability prediction model based on the drilling rate equation, characterized in that, Includes the following steps: Obtain historical drilling data for the target area, and obtain the stratified average well depth and drilling parameters based on the historical drilling data; Based on the drilling rate equation and combined with drilling parameters, the rock drillability grade is predicted in reverse, and a drillability grade calculation model is established based on the rock drillability grade. The specific method is as follows: Obtain drilling parameters, input them into the drilling rate equation, and obtain the predicted rock drillability grade value: in, For mechanical drilling speed data, For drilling pressure data, For drilling speed data, For water power, Let Euler's constant be 1. For drilling fluid density, The equivalent density of the formation, For drilling pressure, The diameter of the drill bit. This refers to the drilling fluid discharge rate. The equivalent diameter of the nozzle. This is the drill pressure sensitivity coefficient. This is the speed sensitivity coefficient. For water power sensitivity coefficient, This is the differential pressure sensitivity coefficient; A model for determining the drillability grade is established based on the rock drillability grade value: in, This represents the drillability rating. Real-time drilling data of the target area is acquired, and the extreme value of the average rock drillability of each layer is calculated using a drillability grade value determination model. Linear regression was performed on the extreme values ​​of the average rock drillability of each layer and the average well depth of each layer to obtain the formation drillability gradient model of the target area.

2. The method for establishing a rock drillability prediction model based on the drilling rate equation according to claim 1, characterized in that, Drilling parameters include actual drill bit data at different depths and in different formations; among which, actual drill bit data includes mechanical drilling speed data, drilling pressure data, drilling speed data, drilling fluid displacement data, and density data.

3. The method for establishing a rock drillability prediction model based on the drilling rate equation according to claim 1, characterized in that, Drilling pressure sensitivity coefficient Speed ​​sensitivity coefficient Water power sensitivity coefficient Pressure difference sensitivity coefficient With drillability rating The relationship is: 。 4. The method for establishing a rock drillability prediction model based on the drilling rate equation according to claim 1, characterized in that, The specific method for obtaining real-time drilling data of the target area and calculating the extreme value of the average rock drillability of each layer using the drillability rating model is as follows: Acquire real-time drilling data for the target area, divide the real-time drilling data into different formations based on systems and groups, and use a drillability grade value calculation model to calculate the real-time drilling data of different formations to obtain the extreme value of the average rock drillability of each layer.

5. A system for establishing a rock drillability prediction model based on the drilling rate equation, characterized in that, include: The data acquisition module is used to acquire historical drilling data for the target area and obtain the stratified average well depth and drilling parameters based on the historical drilling data. The model building module is used to predict rock drillability grades in reverse based on the drilling rate equation and drilling parameters, and to build a drillability grade calculation model based on the rock drillability grade. The specific method is as follows: Obtain drilling parameters, input them into the drilling rate equation, and obtain the predicted rock drillability grade value: in, For mechanical drilling speed data, For drilling pressure data, For drilling speed data, For water power, Let Euler's constant be 1. For drilling fluid density, The equivalent density of the formation, For drilling pressure, The diameter of the drill bit. This refers to the drilling fluid discharge rate. The equivalent diameter of the nozzle. This is the drill pressure sensitivity coefficient. This is the speed sensitivity coefficient. For water power sensitivity coefficient, This is the differential pressure sensitivity coefficient; A model for determining the drillability grade is established based on the rock drillability grade value: in, This represents the drillability rating. The model solving module is used to acquire real-time drilling data of the target area and use the drillability grade value to calculate the extreme value of the average rock drillability of the layer. The data calculation module is used to perform linear regression on the extreme values ​​of the average rock drillability of each layer and the average well depth of each layer to obtain the formation drillability gradient model of the target area.

6. The rock drillability prediction model establishment system based on the drilling rate equation according to claim 5, characterized in that, The functionality of the model solver module is implemented through the following methods: Acquire real-time drilling data for the target area, divide the real-time drilling data into different formations based on systems and groups, and use a drillability grade value calculation model to calculate the real-time drilling data of different formations to obtain the extreme value of the average rock drillability of each layer.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for establishing a rock drillability prediction model based on the drilling rate equation as described in any one of claims 1 to 4.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for establishing a rock drillability prediction model based on the drilling rate equation as described in any one of claims 1 to 4.

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