Method, device and storage medium for optimizing drilling parameters

Abstract

The present application provides a method, device and storage medium for optimizing drilling parameters, which relate to the field of oil and gas exploration technology. The method includes: determining a speed-up target based on the drilling speed information of the current drilling formation, determining multiple drilling parameter optimization combinations based on the target mechanical drilling speed prediction model, the target torque prediction model and the drilling parameter constraints; determining multiple decision indicators based on the predicted mechanical specific energy information and the drilling parameters to be optimized, and determining the target decision indicator based on the current drilling formation; determining the target decision indicator information of each drilling parameter optimization combination, and determining the target drilling parameter optimization combination based on the multiple target decision indicator information. The method of the present application achieves the technical effect of accurately optimizing drilling parameters when increasing drilling speed.

Description

Technical Field

[0001] The present application relates to the field of oil and gas exploration, and in particular to a method, device, and storage medium for optimizing drilling parameters. Background Art

[0002] The complex oil and gas industry in the "two deep and one non-deep" region is a key area of ​​exploration and development for oil and gas resources, and presents a significant challenge for drilling engineers. The extremely hard and difficult deep formations, poor drillability, complex geological environments, and increasing uncertainties all place higher demands on the precise control and real-time optimization of drilling parameters. In recent years, with the rapid development of artificial intelligence (AI) technology, it has been widely used to recommend drilling parameter optimization solutions during the drilling process, thereby improving drilling efficiency and achieving efficient and economical drilling.

[0003] However, during the drilling process, faced with different complex geological conditions, the mapping relationship between the drilling response parameters (mechanical penetration rate, torque and mechanical specific energy) and the on-site real-time drilling engineering parameters is dynamically changing, which makes it difficult for the optimization results of the drilling parameters to conform to the actual drilling physical process, and it is impossible to provide on-site engineers with a scientific and reasonable drilling parameter optimization solution. In the existing technology, when drilling deep formations, the underground drilling efficiency problem caused by severe wear of the drill bit is rarely considered; on the other hand, the existing drilling parameter optimization methods are mostly random optimization of parameters, which leads to large fluctuations in drilling parameters and poor speed-up effect, making it difficult to actually apply them to the drilling site. Therefore, the existing technology has the technical problems of poor accuracy and low optimization of drilling speed-up parameters when drilling speed-up. Summary of the Invention

[0004] The present application provides a method, device and storage medium for optimizing drilling parameters, which are used to solve the technical problems in the prior art of low drilling parameter prediction accuracy and poor controllability of optimized parameters when drilling speed is increased.

[0005] In a first aspect, the present application provides a method for optimizing drilling parameters, the method comprising:

[0006] Determine the speed-up target based on the current drilling formation's drilling speed information, and determine multiple drilling parameter optimization combinations based on the target mechanical drilling speed prediction model, target torque prediction model, and drilling parameter constraints;

[0007] Determine multiple decision indicators based on the predicted mechanical specific energy information and the drilling parameters to be optimized, and determine the target decision indicator based on the current drilling formation;

[0008] Determine target decision indicator information for each drilling parameter optimization combination, and determine the target drilling parameter optimization combination based on multiple target decision indicator information.

[0009] Optionally, multiple drilling parameter optimization combinations are determined based on the target mechanical penetration rate prediction model, the target torque prediction model, and the drilling parameter constraints. The target mechanical penetration rate prediction model and the target torque prediction model need to be established, including:

[0010] Collect multiple logging parameters during drilling, process data for each logging parameter, and determine training logging parameters;

[0011] The mechanical drilling speed prediction model and torque prediction model are trained respectively according to the training logging parameters;

[0012] The mechanical drilling rate prediction model and the torque prediction model are respectively verified according to multiple real-time logging parameters, and evaluation index information of the mechanical drilling rate prediction model and the torque prediction model are respectively determined;

[0013] It is determined whether the evaluation index information of the mechanical penetration rate prediction model and the evaluation index information of the torque prediction model meet the first preset condition respectively. If so, a target mechanical penetration rate prediction model and a target torque prediction model are determined.

[0014] Optionally, after determining the target mechanical penetration rate prediction model and the target torque prediction model, the method includes:

[0015] Determine predicted drilling speed information and predicted torque information according to a target mechanical drilling speed prediction model and a target torque prediction model respectively;

[0016] Determining predicted mechanical specific energy information based on the predicted drilling speed information and the predicted torque information;

[0017] Determine the weight information of each drilling parameter to be optimized in the current drilling formation; wherein the drilling parameters to be optimized include a drill bit weight on bit parameter, a rotary table speed parameter, and an outlet flow rate parameter.

[0018] Optionally, multiple decision indicators are determined based on the predicted mechanical specific energy information and the drilling parameters to be optimized, including:

[0019] Determining important optimized drilling parameters based on weight information of each drilling parameter to be optimized, determining normalized mechanical specific energy information based on predicted mechanical specific energy information, and determining drilling parameter importance decision indicators based on the important optimized drilling parameters and the normalized mechanical specific energy information;

[0020] Determine the change range decision indicator based on the predicted mechanical specific energy information and the drilling parameters to be optimized;

[0021] The minimum mechanical performance decision index is determined based on the predicted mechanical performance information.

[0022] Optionally, after determining the target mechanical penetration rate prediction model and the target torque prediction model, the method further includes:

[0023] determining whether the drilling process satisfies a second preset condition;

[0024] If so, then update the logging parameters;

[0025] The target mechanical penetration rate prediction model and the target torque prediction model are updated respectively according to the updated drilling parameters to determine the updated mechanical penetration rate prediction model and the updated torque prediction model.

[0026] Optionally, after determining to update the mechanical penetration rate prediction model and the torque prediction model, the method includes:

[0027] Determine and update the speed-up target based on the drilling speed information of the drilling formation corresponding to the drilling depth;

[0028] An optimized combination of updated drilling parameters is determined based on an updated mechanical penetration rate prediction model and an updated torque prediction model.

[0029] In a second aspect of the present application, a device for optimizing drilling parameters is provided, comprising:

[0030] The first processing module determines a speed-up target based on the drilling speed information of the current drilling formation, and determines a plurality of drilling parameter optimization combinations based on a target mechanical drilling speed prediction model, a target torque prediction model, and drilling parameter constraints;

[0031] The second processing module determines multiple decision indicators based on the predicted mechanical specific energy information and the drilling parameters to be optimized, and determines the target decision indicator based on the current drilling formation;

[0032] The third processing module determines target decision indicator information of each drilling parameter optimization combination, and determines the target drilling parameter optimization combination based on the multiple target decision indicator information.

[0033] Optionally, the first processing module is further configured to:

[0034] Collect multiple logging parameters during drilling, process data for each logging parameter, and determine training logging parameters;

[0035] The mechanical drilling speed prediction model and torque prediction model are trained respectively according to the training logging parameters;

[0036] The mechanical drilling rate prediction model and the torque prediction model are respectively verified according to multiple real-time logging parameters, and evaluation index information of the mechanical drilling rate prediction model and the torque prediction model are respectively determined;

[0037] It is determined whether the evaluation index information of the mechanical penetration rate prediction model and the evaluation index information of the torque prediction model meet the first preset condition respectively. If so, a target mechanical penetration rate prediction model and a target torque prediction model are determined.

[0038] Optionally, the first processing module is further configured to:

[0039] Determine predicted drilling speed information and predicted torque information according to a target mechanical drilling speed prediction model and a target torque prediction model respectively;

[0040] Determining predicted mechanical specific energy information based on the predicted drilling speed information and the predicted torque information;

[0041] Determine the weight information of each drilling parameter to be optimized in the current drilling formation; wherein the drilling parameters to be optimized include a drill bit weight on bit parameter, a rotary table speed parameter, and an outlet flow rate parameter.

[0042] Optionally, the second processing module is further configured to:

[0043] Determining important optimized drilling parameters based on weight information of each drilling parameter to be optimized, determining normalized mechanical specific energy information based on predicted mechanical specific energy information, and determining drilling parameter importance decision indicators based on the important optimized drilling parameters and the normalized mechanical specific energy information;

[0044] Determine the change range decision indicator based on the predicted mechanical specific energy information and the drilling parameters to be optimized;

[0045] The minimum mechanical performance decision index is determined based on the predicted mechanical performance information.

[0046] Optionally, the first processing module is further configured to:

[0047] determining whether the drilling process satisfies a second preset condition;

[0048] If so, then update the logging parameters;

[0049] The target mechanical penetration rate prediction model and the target torque prediction model are updated respectively according to the updated drilling parameters to determine the updated mechanical penetration rate prediction model and the updated torque prediction model.

[0050] Optionally, the first processing module and the third processing module are further configured to:

[0051] Determine and update the speed-up target based on the drilling speed information of the drilling formation corresponding to the drilling depth;

[0052] An optimized combination of updated drilling parameters is determined based on an updated mechanical penetration rate prediction model and an updated torque prediction model.

[0053] In a third aspect, the present application provides a device for optimizing drilling parameters, comprising: a processor, and a memory in communication with the processor, including:

[0054] Memory stores computer-executable instructions;

[0055] The processor executes the computer-executable instructions stored in the memory to implement the method for optimizing drilling parameters of the first aspect.

[0056] In a fourth aspect, a computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for optimizing drilling parameters in the first aspect.

[0057] In a fifth aspect, a computer program product includes a computer program, which implements the traffic signal control method of the first aspect when executed by a processor.

[0058] The present application provides a method, device and storage medium for optimizing drilling parameters, which determine a speed-up target based on the drilling speed information of the current drilling formation, and determine multiple drilling parameter optimization combinations based on the target mechanical drilling speed prediction model, the target torque prediction model and the drilling parameter constraints; determine multiple decision indicators based on the predicted mechanical specific energy information and the drilling parameters to be optimized, and determine the target decision indicator based on the current drilling formation; determine the target decision indicator information of each drilling parameter optimization combination, and determine the target drilling parameter optimization combination based on the multiple target decision indicator information, thereby laying the foundation for the optimization of drilling parameters through the target mechanical drilling speed prediction model and the target torque prediction model, and determining the target drilling parameter optimization combination based on the decision indicators determined according to different drilling formation conditions, thereby achieving the current speed-up target, and achieving the technical effect of improving the accuracy of drilling parameters when drilling speed is increased. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0060] Figure 1 The process of the method for optimizing drilling parameters provided in the embodiment of the present application Figure 1 ;

[0061] Figure 2 The process of the method for optimizing drilling parameters provided in the embodiment of the present application Figure 2 ;

[0062] Figure 3 A schematic diagram of a method for optimizing drilling parameters provided in an embodiment of the present application;

[0063] Figure 4 A schematic diagram of the structure of the device for optimizing drilling parameters provided in an embodiment of the present application;

[0064] Figure 5 A hardware structure diagram of the equipment for optimizing drilling parameters provided in an embodiment of the present application.

[0065] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0066] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0067] During drilling operations, due to the diverse and complex geological structures encountered, the correlation between the key performance indicators in the drilling process and the real-time drilling engineering parameters presents a dynamic mapping. This characteristic makes it difficult for the optimization results of drilling parameters to accurately match the actual physical dynamics of drilling, which in turn limits the provision of accurate and scientific drilling parameter optimization solutions for field engineers. Although there are drilling speed-up technologies based on optimization algorithms in the existing technology, these methods often ignore the significant reduction in drilling efficiency caused by severe wear of the drill bit due to long-term operation when dealing with deep drilling. At the same time, the current drilling parameter optimization strategies mostly focus on the random adjustment of parameters, which not only leads to large fluctuations in drilling parameters, but also makes it difficult to be widely used in actual drilling operations due to the lack of obvious speed-up results. Therefore, the existing technology has the technical problems of poor accuracy and low optimization of drilling parameters when speeding up drilling.

[0068] The present application provides a method, device and storage medium for optimizing drilling parameters, which determine a speed-up target based on the drilling speed information of the current drilling formation, and determine multiple drilling parameter optimization combinations based on the target mechanical drilling speed prediction model, the target torque prediction model and the drilling parameter constraints; determine multiple decision indicators based on the predicted mechanical specific energy information and the drilling parameters to be optimized, and determine the target decision indicator based on the current drilling formation; determine the target decision indicator information of each drilling parameter optimization combination, and determine the target drilling parameter optimization combination based on the multiple target decision indicator information, thereby laying the foundation for the optimization of drilling parameters through the target mechanical drilling speed prediction model and the target torque prediction model, and determining the target drilling parameter optimization combination based on the decision indicators determined according to different drilling formation conditions, thereby achieving the current speed-up target, and achieving the technical effect of improving the accuracy of drilling parameters when drilling speed is increased.

[0069] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0070] Figure 1 The method flow for optimizing drilling parameters provided in the embodiment of the present application Figure 1 .like Figure 1 As shown, the method for optimizing drilling parameters provided in this embodiment includes:

[0071] S101, determining a speed-up target based on the drilling rate information of the current drilling formation, and determining a plurality of drilling parameter optimization combinations based on a target mechanical drilling rate prediction model, a target torque prediction model, and drilling parameter constraints;

[0072] In this embodiment, the speed-up target refers to determining a new drilling speed according to preset conditions based on the current drilling speed of the drilling formation, and the preset conditions include but are not limited to increasing the drilling speed by a preset percentage and increasing the drilling speed by an arithmetic difference; for example, the current drilling speed of the drilling formation is 50m / h, and the drilling speed is increased by a preset percentage, which is a 10% increase under the current drilling speed. The speed-up target is to increase the drilling speed to 55m / h. After the drilling speed reaches 55m / h, a new speed-up target is set. The speed can be increased by 30% based on the drilling speed of 50m / h, that is, the speed-up target is to increase the drilling speed to 65m / h; furthermore, the current drilling speed of the drilling formation is 50m / h, and the drilling speed is increased by an arithmetic difference, with an increase of 10m / h each time, that is, the speed-up target is Increase the drilling speed to 60 m / h. Once the drilling speed reaches 60 m / h, set a new speed increase target, i.e., increase the drilling speed to 70 m / h. Drilling parameter constraints set constraints on the drilling parameters to be optimized, allowing for a more reasonable optimal parameter combination to be determined. Based on the target mechanical drilling speed prediction model, target torque prediction model, and drilling parameter constraints, and with the set speed increase target as the optimization objective, an optimization algorithm is used to screen multiple optimal drilling parameter combinations that meet the criteria. An optimization algorithm is a method for optimizing algorithm performance, such as time complexity, space complexity, and correctness, to enhance the algorithm's problem-solving capabilities. Optimization algorithms are widely used in various fields, such as machine learning, economic forecasting, and engineering design. Common optimization algorithms include, but are not limited to, gradient descent, conjugate gradient algorithm, genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, and swarm intelligence optimization algorithm.

[0073] S102, determining multiple decision indicators based on the predicted mechanical specific energy information and the drilling parameters to be optimized, and determining a target decision indicator based on the current drilling formation;

[0074] In this embodiment, the drilling speed predicted by the target mechanical drilling speed prediction model and the torque predicted by the target torque prediction model are determined according to the predicted mechanical specific energy information based on the mechanical specific energy calculation model. The mechanical specific energy calculation model is a model that can determine the mechanical specific energy through the drilling speed and torque, including but not limited to the Teale model, the Teale correction model and the Pessier model; multiple decision indicators are determined based on the predicted mechanical specific energy information and the drilling parameters to be optimized. The decision indicators represent the degree of importance and emphasis on the drilling parameters. The purpose of setting the decision indicators is to determine the target decision indicators based on the actual situation of the current drilling formation, so that the determined target drilling parameter optimization combination is more in line with the actual drilling situation and adapts to the drilling process, thereby improving the accuracy of the drilling parameters when the drilling speed target is achieved.

[0075] S103: Determine target decision indicator information for each drilling parameter optimization combination, and determine a target drilling parameter optimization combination based on multiple target decision indicator information.

[0076] In this embodiment, in order to identify the optimal drilling parameter combination, the target decision indicator information corresponding to each drilling parameter optimization combination is calculated after determining the target decision indicator. The target drilling parameter optimization combination is determined through comprehensive analysis and evaluation of multiple target decision indicator information, thereby determining the optimal solution for the drilling parameters and achieving the speed-up goal.

[0077] The present application provides a method for optimizing drilling parameters, which determines a speed-up target based on the drilling speed information of the current drilling formation, and determines multiple drilling parameter optimization combinations based on a target mechanical drilling speed prediction model, a target torque prediction model, and drilling parameter constraints; determines multiple decision indicators based on predicted mechanical specific energy information and the drilling parameters to be optimized, and determines a target decision indicator based on the current drilling formation; determines target decision indicator information for each drilling parameter optimization combination, and determines a target drilling parameter optimization combination based on the multiple target decision indicator information. In this way, a new speed-up target is determined according to the preset conditions based on the current drilling speed of the drilling formation. In order to achieve the target drilling speed, based on the target mechanical drilling speed prediction model, the target torque prediction model and the drilling parameter constraints, the set speed-up target is used as the optimization target, and the optimization algorithm is used to screen out multiple drilling parameter optimization combinations that meet the conditions. The target decision indicators that adapt to the current geological conditions are determined from multiple decision indicators according to the actual situation of the current drilling formation. The target drilling parameter optimization combination is determined through comprehensive analysis and evaluation of multiple target decision indicator information. The determined target drilling parameter optimization combination is more in line with the actual drilling situation and adapts to the drilling process. Therefore, this method is suitable for different types of drilling operations and geological conditions, has certain universality and scalability, can achieve the technical effect of improving the accuracy of drilling parameters when drilling to achieve the speed-up target, and provides a new optimization idea for drilling speed-up.

[0078] Figure 2 The method flow for optimizing drilling parameters provided in the embodiment of the present application Figure 2 This embodiment is based on Figure 1 Based on the embodiment, the method for optimizing drilling parameters is described in detail, wherein determining the speed-up target is implemented through step S201, determining the target mechanical penetration rate prediction model and the target torque prediction model is implemented through step S202, determining the target decision index is implemented according to steps S203-S204, determining the target drilling parameter optimization combination is implemented according to step S205, updating the target mechanical penetration rate prediction model and the target torque prediction model, and implementing the updated speed-up target through steps S206-S207, as shown in FIG. Figure 2 As shown, the method for optimizing drilling parameters provided in this embodiment includes:

[0079] S201, determining a speed-increasing target according to drilling speed information of a current drilling formation;

[0080] In this embodiment, the drilling speed target can be determined based on a preset percentage, which can increase in arithmetic or geometric increments. The reference drilling speed can change in real time or can be the initial drilling speed information. For example, if the current drilling speed is 50 m / h and the drilling speed is increased by 15%, the drilling speed target is increased to 57.5 m / h. When the next drilling speed target is reset, the drilling speed is increased by 20% from 57.5 m / h, which means the drilling speed target is increased to 69 m / h. If the current drilling speed is 50 m / h and the drilling speed is increased by 12%, the drilling speed target is increased to 56 m / h. When the next drilling speed target is reset, the drilling speed is increased by 24% from 50 m / h, which means the drilling speed target is increased to 62 m / h. The method for determining the drilling speed target can be comprehensively determined based on the current geological conditions of the ore layer and the optimized combination of drilling parameters.

[0081] S202, collecting multiple logging parameters during the drilling process, performing data processing on each logging parameter, and determining training logging parameters; respectively training a mechanical penetration rate prediction model and a torque prediction model based on the training logging parameters; respectively validating the mechanical penetration rate prediction model and the torque prediction model based on multiple real-time logging parameters, and respectively determining evaluation index information of the mechanical penetration rate prediction model and the torque prediction model; respectively determining whether the evaluation index information of the mechanical penetration rate prediction model and the evaluation index information of the torque prediction model meet a first preset condition, and if so, determining a target mechanical penetration rate prediction model and a target torque prediction model;

[0082] In this embodiment, real-time drilling data during the drilling process is acquired, and logging parameters include but are not limited to well depth, drilling pressure, torque, rotary table speed, mechanical drilling speed, riser pressure, outlet flow, outlet density, hook load and equivalent density; data processing is performed on the drilling parameters, and the data processing methods include but are not limited to 3σ outlier screening, linear regression interpolation filling and sliding filtering, and the shock anomalies and noise in the drilling are screened out, thereby improving the quality and accuracy of the data samples. Specifically, the screening criteria are drilling parameters whose well depth and drill bit position are almost close and whose drilling pressure and rotation speed are not 0; further, the quantitative influence of different parameters is eliminated, and the elimination methods include but are not limited to data maximum and minimum normalization methods and standardization methods, and then the collected real-time parameter data is updated to training logging parameters, and the mechanical drilling speed is predicted based on the training drilling data. The model and the torque prediction model are trained; wherein, the method for training the model includes but is not limited to deep learning method, machine learning method, traditional statistical method, neural network method and integrated learning method, wherein the mechanical drilling speed prediction model and the torque prediction model include an input layer, a hidden layer and an output layer, wherein the input layer receives the training logging parameters, the hidden layer is used to learn the nonlinear relationship in the data, and the output layer is used to output the prediction results; further, the trained model is tested and verified, and the evaluation index information is determined by the prediction data and the training data, and the index evaluation information is judged. If the preset conditions are met, it is determined as the target mechanical drilling speed prediction model and the target torque prediction model. If not, the training continues; specifically, the evaluation index information includes but is not limited to the root mean square error, the mean square error, the mean absolute error and the correlation coefficient.

[0083] S203, determining predicted drilling rate information and predicted torque information based on the target mechanical drilling rate prediction model and the target torque prediction model, respectively; determining predicted mechanical specific energy information based on the predicted drilling rate information and the predicted torque information; determining weight information of each drilling parameter to be optimized in the current drilling formation; and determining multiple optimized combinations of drilling parameters based on the target mechanical drilling rate prediction model, the target torque prediction model, and drilling parameter constraints;

[0084] In this embodiment, the predicted drilling speed information and the predicted torque information can be determined respectively according to the target mechanical drilling speed prediction model and the target torque prediction model. Further, the predicted mechanical specific energy information is determined by the mechanical specific energy calculation model. The drilling parameters to be optimized include the drill bit weight on bit parameter, the rotary table speed parameter and the outlet flow rate parameter. In the trained target mechanical drilling speed prediction model, the drill bit weight on bit parameter, the rotary table speed parameter and the outlet flow rate parameter are respectively extracted from the model by feature attention weight, so as to determine the weight information of each drilling parameter to be optimized in the current drilling formation, providing a data basis for the optimization of the drilling parameters. Furthermore, based on the predicted drilling speed information and the predicted torque information, multiple drilling parameter optimization combinations are determined based on the drilling parameter constraints, wherein the drilling parameter constraints are:

[0085]

[0086] Among them, Wob is the bit weight parameter; Rpm is the rotary speed parameter; Q is the outlet flow parameter; Wob min Wob is the minimum value of the bit weight on bit parameter; max Rpm is the maximum value of the drill bit weight parameter; min The minimum value of the turntable speed parameter; Rpm max is the maximum value of the turntable speed parameter; Q min is the minimum value of the outlet flow parameter; Q min It is the average value of the outlet flow parameter in the current well section.

[0087] Specifically, the reason why the maximum value of the outlet flow parameter is not selected is that in deeper well sections, if the maximum displacement is selected, the equivalent density of the drilling fluid at the bottom of the hole will be too high, and there will be a risk of well leakage.

[0088] Specifically, the values ​​of the constraints of the drill bit weight on bit parameters, rotary table speed parameters and outlet flow rate parameters need to be adjusted according to the actual drilling conditions, rock properties, drilling equipment and drilling speed increase targets.

[0089] S204: Determine multiple decision indicators based on the predicted mechanical specific energy information and the drilling parameters to be optimized, and determine a target decision indicator based on the current drilling formation; determine target decision indicator information for each drilling parameter optimization combination, and determine a target drilling parameter optimization combination based on the multiple target decision indicator information;

[0090] Specifically, multiple decision indicators are determined based on the predicted mechanical specific energy information and the drilling parameters to be optimized, including but not limited to: a drilling parameter importance decision indicator, a variation range decision indicator, and a minimum mechanical performance decision indicator;

[0091] Optionally, important optimized drilling parameters are determined based on the weight information of each drilling parameter to be optimized, normalized mechanical specific energy information is determined based on the predicted mechanical specific energy information, and a drilling parameter importance decision indicator is determined based on the important optimized drilling parameters and the normalized mechanical specific energy information. Specifically, based on the weight information of each drilling parameter to be optimized in the current drilling formation determined in step S203, the important optimized drilling parameter, i.e., the parameter with the first importance in weight ranking, is determined, and at the same time, the predicted mechanical specific energy information is processed according to the following formula to obtain the normalized mechanical specific energy information:

[0092]

[0093] Among them, E bit ' is the normalized mechanical specific energy information; is the average value of the predicted mechanical specific energy information, E i is the currently predicted mechanical specific energy information; σ bit is the variance of mechanical specific energy.

[0094] Furthermore, the drilling parameter importance decision index is determined based on the important optimized drilling parameters and the normalized mechanical specific energy information. Taking the outlet flow rate parameter as an example, the drilling parameter importance decision index in this case is shown in the following formula:

[0095] min(E bit '+λ·Q dis )

[0096] Among them, E bit ' is the normalized mechanical specific energy information; λ is the distance coefficient; Q dis is the Euclidean distance between the outlet flow rate parameter to be optimized and the outlet flow rate parameter under the current drilling conditions.

[0097] Optionally, a change range decision index is determined based on the predicted mechanical specific energy information and the drilling parameters to be optimized. The change range decision index is a decision index that comprehensively considers the mechanical specific energy information and the range of change of the drilling parameters to be optimized, and is obtained by weighted distribution of the calculated predicted mechanical specific energy information and the drilling parameters to be optimized. The calculation formula is shown below:

[0098]

[0099] min(α·E bit +β·P dis )

[0100] Among them, Wob dis Rpm is the Euclidean distance between the drill bit weight on bit parameter to be optimized and the drill bit weight on bit parameter under the current drilling conditions; dis is the Euclidean distance between the rotary table speed parameter to be optimized and the rotary table speed parameter under the current drilling conditions; Qdis is the Euclidean distance between the outlet flow parameter to be optimized and the outlet flow parameter under the current drilling conditions; E bit is the predicted mechanical specific energy information; α is the mechanical specific energy weighting coefficient; β is the parameter distance weighting coefficient.

[0101] Optionally, determining a minimum mechanical performance decision indicator based on the predicted mechanical specific performance information;

[0102] Specifically, the minimum mechanical performance decision index is the decision on the minimum mechanical specific energy while meeting the speed increase target. The decision index formula is shown as follows:

[0103] min E bit

[0104] Among them, E bit To predict mechanical specific energy information.

[0105] Specifically, the target decision-making indicators can be determined based on the current drilling geological conditions, site requirements and drilling experience.

[0106] S205, determining whether the drilling process satisfies a second preset condition; if so, determining to update the logging parameters; updating the target mechanical penetration rate prediction model and the target torque prediction model according to the updated drilling parameters, and determining an updated mechanical penetration rate prediction model and an updated torque prediction model;

[0107] In this embodiment, certain preset conditions are set for the acquisition process. If the preset conditions are met, the drilling parameters when the initial drilling is performed to the preset depth are acquired. The target ROP prediction model and the target torque prediction model are retrained with these drilling parameters, and then the models are updated to determine the updated ROP prediction model and the updated torque prediction model. For example, the preset condition is that after the speed increase target is reached, the drilling parameters are acquired again to update the model. Alternatively, after the drilling depth reaches a set fixed interval, multiple drilling parameters are acquired when drilling to the current drilling depth. For example, the fixed interval of the drilling depth is 30m. Every time 30m is drilled, the drilling parameters are acquired to update the target model. Optionally, the preset condition can also be set to determine whether the prediction accuracy of the target ROP prediction model and the target torque prediction model is less than or equal to a preset threshold. If so, the target ROP prediction model and the target torque prediction model are updated. The updating method includes but is not limited to online updating and fine-tuning updating. By using the target mechanical penetration rate prediction model and the target torque prediction model, the accuracy of the predicted mechanical specific energy information can be improved when optimizing drilling parameters, thereby further improving the controllability and accuracy of drilling parameter optimization.

[0108] S206: determining an updated speed-up target based on the drilling speed information of the drilling formation corresponding to the drilling depth; and determining an updated drilling parameter optimization combination based on the updated mechanical drilling speed prediction model and the updated torque prediction model.

[0109] In this embodiment, a new speed-up target is determined based on the drilling rate information of the drilling formation. Furthermore, multiple drilling parameter optimization combinations are determined based on the updated mechanical drilling rate prediction model, the updated torque prediction model, and drilling parameter constraints. Multiple decision indicators are determined based on the updated predicted mechanical specific energy information and the drilling parameters to be optimized, and a target decision indicator is determined based on the current drilling formation. Target decision indicator information is determined for each drilling parameter optimization combination, and a target drilling parameter optimization combination is determined based on the multiple target decision indicator information. By setting a new speed-up target and determining an updated drilling parameter optimization combination based on the updated mechanical drilling rate prediction model and the updated torque prediction model, the drilling parameters can be dynamically adjusted based on the actual geological conditions of the drilling. This avoids the situation where, after achieving the speed-up target, the drilling parameters are fixed while ignoring the inadaptability of the drilling parameters in deep formations. Furthermore, the speed-up target is continuously adjusted based on the drilling information of the current drilling, achieving a controllable form of step-by-step speed-up, meeting the on-site demand for intelligent, controllable step-by-step speed-up.

[0110] Figure 3 A schematic diagram of a method for optimizing drilling parameters provided in an embodiment of the present application is shown in FIG. Figure 3As shown, the method for optimizing drilling parameters provided by this embodiment: by collecting drilling parameters during the drilling process, a target mechanical drilling speed prediction model and a target torque prediction model are determined, and further, the predicted drilling speed, predicted torque and predicted mechanical specific energy information of the formation to be drilled are predicted based on the target mechanical drilling speed prediction model and the target torque prediction model; the speed-up target is determined based on the drilling speed information of the current drilling, multiple drilling parameter optimization combinations are determined based on the optimization algorithm, multiple decision indicators are determined based on the predicted mechanical specific energy information and the drilling parameters to be optimized, and the target decision indicator is determined based on the current drilling formation; the target decision indicator information of each drilling parameter optimization combination is calculated, the target drilling parameter optimization combination is determined based on the multiple target decision indicator information, the drilling parameter information to be optimized is recommended, the drilling parameters during the drilling process are further collected, the target mechanical drilling speed prediction model and the target torque prediction model are updated, and then when determining the updated speed-up target, the updated mechanical drilling speed prediction model and the updated torque prediction model provide a basis for determining the target drilling parameter optimization combination. This method enhances the generalization and accuracy of the model in different wells and different formations by dynamically updating the model method, establishes a target mechanical drilling speed prediction model and a target torque prediction model, and lays a good foundation for achieving the drilling speed-up target; at the same time, the speed-up target is adjusted according to the current drilling situation, and the smooth and gradual speed-up target is suitable for different types of drilling operations and geological conditions, and has certain universality and scalability. This application can effectively solve the random and unstable problems of existing drilling speed-up, and provides a new optimization idea for drilling speed-up, which has high practical value and broad application prospects.

[0111] The present application provides a method for optimizing drilling parameters, which determines a speed-up target based on the drilling speed information of the current drilling formation; collects multiple logging parameters during the drilling process, performs data processing on each logging parameter, and determines a training logging parameter; trains a mechanical drilling speed prediction model and a torque prediction model based on the training logging parameters; verifies the mechanical drilling speed prediction model and the torque prediction model based on multiple real-time logging parameters, and determines evaluation index information of the mechanical drilling speed prediction model and the torque prediction model; judges whether the evaluation index information of the mechanical drilling speed prediction model and the evaluation index information of the torque prediction model meet a first preset condition, and if so, determines a target mechanical drilling speed prediction model and a target torque prediction model; determines predicted drilling speed information and predicted torque information based on the target mechanical drilling speed prediction model and the target torque prediction model; determines predicted mechanical specific energy information based on the predicted drilling speed information and the predicted torque information; determines each weight information of the drilling parameters to be optimized in the current drilling formation; determine multiple drilling parameter optimization combinations based on the target mechanical drilling speed prediction model, the target torque prediction model and the drilling parameter constraints; determine multiple decision indicators based on the predicted mechanical specific energy information and the drilling parameters to be optimized, and determine the target decision indicator based on the current drilling formation; determine the target decision indicator information of each drilling parameter optimization combination, and determine the target drilling parameter optimization combination based on the multiple target decision indicator information; judge whether the drilling process meets the second preset condition; if so, determine to update the logging parameters; update the target mechanical drilling speed prediction model and the target torque prediction model according to the updated drilling parameters, and determine the updated mechanical drilling speed prediction model and the updated torque prediction model; determine the updated speed-up target based on the drilling speed information of the drilling formation corresponding to the drilling depth; determine the updated drilling parameter optimization combination based on the updated mechanical drilling speed prediction model and the updated torque prediction model.The method for optimizing drilling parameters provided in this application establishes a target ROP prediction model and a target torque prediction model. Based on different drilling formation conditions, the diversity of the intelligent model sample space is fully considered to achieve global stability of the prediction model. Furthermore, mechanical specific energy information is predicted using the target ROP prediction model and the target torque prediction model. A speed-up target is set and an optimization algorithm is used to construct an optimized combination of drilling parameters that meets the speed-up target. A target decision indicator is then determined based on the drilling parameters to be optimized and the mechanical specific energy. The parameter combination with the highest comprehensive score is recommended as the target drilling parameter optimization combination for the formation to be drilled to achieve the current speed-up target. Simultaneously, as the drilling process progresses, more training logging parameters are collected to update the target ROP prediction model and the target torque prediction model, improving the accuracy of the prediction model. A new speed-up target is further set based on the current drilling ROP information. An updated optimized combination of drilling parameters is determined based on the updated ROP prediction model and the updated torque prediction model, thereby achieving speed-up at different stages of the drilling process. This achieves dynamic optimization of drilling parameters during the staged speed-up process, achieving the technical effect of improving the accuracy and optimization of drilling parameters.

[0112] Figure 4 A schematic diagram of a device for optimizing drilling parameters provided in an embodiment of the present application Figure 1 .like Figure 4 As shown, an embodiment of the present application provides a device 400 for optimizing drilling parameters, the device comprising: a first processing module 401, a second processing module 402 and a third processing module 403;

[0113] The first processing module 401 determines a speed-up target based on the drilling speed information of the current drilling formation, and determines a plurality of drilling parameter optimization combinations based on a target mechanical drilling speed prediction model, a target torque prediction model, and drilling parameter constraints;

[0114] The second processing module 402 determines a plurality of decision indicators based on the predicted mechanical specific energy information and the drilling parameters to be optimized, and determines a target decision indicator based on the current drilling formation;

[0115] The third processing module 403 determines target decision indicator information for each drilling parameter optimization combination, and determines a target drilling parameter optimization combination based on the multiple target decision indicator information.

[0116] In a possible implementation, the first processing module 401 is further configured to:

[0117] Collect multiple logging parameters during drilling, process data for each logging parameter, and determine training logging parameters;

[0118] The mechanical drilling speed prediction model and torque prediction model are trained respectively according to the training logging parameters;

[0119] The mechanical drilling rate prediction model and the torque prediction model are respectively verified according to multiple real-time logging parameters, and evaluation index information of the mechanical drilling rate prediction model and the torque prediction model are respectively determined;

[0120] It is determined whether the evaluation index information of the mechanical penetration rate prediction model and the evaluation index information of the torque prediction model meet the first preset condition respectively. If so, a target mechanical penetration rate prediction model and a target torque prediction model are determined.

[0121] In a possible implementation, the first processing module 401 is further configured to:

[0122] Determine predicted drilling speed information and predicted torque information according to a target mechanical drilling speed prediction model and a target torque prediction model respectively;

[0123] Determining predicted mechanical specific energy information based on the predicted drilling speed information and the predicted torque information;

[0124] Determine the weight information of each drilling parameter to be optimized in the current drilling formation; wherein the drilling parameters to be optimized include a drill bit weight on bit parameter, a rotary table speed parameter, and an outlet flow rate parameter.

[0125] In a possible implementation, the second processing module 402 is further configured to:

[0126] Determining important optimized drilling parameters based on weight information of each drilling parameter to be optimized, determining normalized mechanical specific energy information based on predicted mechanical specific energy information, and determining drilling parameter importance decision indicators based on the important optimized drilling parameters and the normalized mechanical specific energy information;

[0127] Determine the change range decision indicator based on the predicted mechanical specific energy information and the drilling parameters to be optimized;

[0128] The minimum mechanical performance decision index is determined based on the predicted mechanical performance information.

[0129] In a possible implementation, the first processing module 401 is further configured to:

[0130] determining whether the drilling process satisfies a second preset condition;

[0131] If so, then update the logging parameters;

[0132] The target mechanical penetration rate prediction model and the target torque prediction model are updated respectively according to the updated drilling parameters to determine the updated mechanical penetration rate prediction model and the updated torque prediction model.

[0133] In a possible implementation, the first processing module 401 and the third processing module 403 are further configured to:

[0134] Determine and update the speed-up target based on the drilling speed information of the drilling formation corresponding to the drilling depth;

[0135] An optimized combination of updated drilling parameters is determined based on an updated mechanical penetration rate prediction model and an updated torque prediction model.

[0136] The present application provides a device for optimizing drilling parameters, which determines a speed-up target based on the drilling speed information of the current drilling formation; collects multiple logging parameters during the drilling process, performs data processing on each logging parameter, and determines a training logging parameter; trains a mechanical drilling speed prediction model and a torque prediction model based on the training logging parameters; verifies the mechanical drilling speed prediction model and the torque prediction model based on multiple real-time logging parameters, and determines evaluation index information of the mechanical drilling speed prediction model and the torque prediction model; judges whether the evaluation index information of the mechanical drilling speed prediction model and the evaluation index information of the torque prediction model meet a first preset condition, and if so, determines a target mechanical drilling speed prediction model and a target torque prediction model; determines predicted drilling speed information and predicted torque information based on the target mechanical drilling speed prediction model and the target torque prediction model; determines predicted mechanical specific energy information based on the predicted drilling speed information and the predicted torque information; determines each weight information of the drilling parameters to be optimized in the current drilling formation; determine multiple drilling parameter optimization combinations based on the target mechanical drilling speed prediction model, the target torque prediction model and the drilling parameter constraints; determine multiple decision indicators based on the predicted mechanical specific energy information and the drilling parameters to be optimized, and determine the target decision indicator based on the current drilling formation; determine the target decision indicator information of each drilling parameter optimization combination, and determine the target drilling parameter optimization combination based on the multiple target decision indicator information; judge whether the drilling process meets the second preset condition; if so, determine to update the logging parameters; update the target mechanical drilling speed prediction model and the target torque prediction model according to the updated drilling parameters, and determine the updated mechanical drilling speed prediction model and the updated torque prediction model; determine the updated speed-up target based on the drilling speed information of the drilling formation corresponding to the drilling depth; determine the updated drilling parameter optimization combination based on the updated mechanical drilling speed prediction model and the updated torque prediction model.The method for optimizing drilling parameters provided in this application establishes a target ROP prediction model and a target torque prediction model. Based on different drilling formation conditions, the diversity of the intelligent model sample space is fully considered to achieve global stability of the prediction model. Furthermore, mechanical specific energy information is predicted using the target ROP prediction model and the target torque prediction model. A speed-up target is set and an optimization algorithm is used to construct an optimized combination of drilling parameters that meets the speed-up target. A target decision indicator is then determined based on the drilling parameters to be optimized and the mechanical specific energy. The parameter combination with the highest comprehensive score is recommended as the target drilling parameter optimization combination for the formation to be drilled to achieve the current speed-up target. Simultaneously, as the drilling process progresses, more training logging parameters are collected to update the target ROP prediction model and the target torque prediction model, improving the accuracy of the prediction model. A new speed-up target is further set based on the current drilling ROP information. An updated optimized combination of drilling parameters is determined based on the updated ROP prediction model and the updated torque prediction model, thereby achieving speed-up at different stages of the drilling process. This achieves dynamic optimization of drilling parameters during the staged speed-up process, achieving the technical effect of improving the accuracy and optimization of drilling parameters.

[0137] Figure 5 This is a hardware diagram of the method and device for optimizing drilling parameters provided in this application. Figure 5 As shown, the electronic device 500 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0138] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that the at least one processor 501 performs the above method.

[0139] The specific implementation process of the processor 501 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0140] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented by a hardware processor or implemented by a combination of hardware and software modules in the processor.

[0141] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory.

[0142] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified into address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0143] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0144] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0145] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0146] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0147] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units, either through an interface, electrical, mechanical, or other means.

[0148] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0149] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0150] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0151] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0152] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A method for optimizing drilling parameters, characterized in that: include: Determine the speed-up target based on the current drilling formation's drilling speed information, and determine multiple drilling parameter optimization combinations based on the target mechanical drilling speed prediction model, target torque prediction model, and drilling parameter constraints; Determining a plurality of decision indicators based on the predicted mechanical specific energy information and the drilling parameters to be optimized, and determining a target decision indicator based on the current drilling formation, wherein the predicted mechanical specific energy information can be determined based on the predicted drilling rate information and the predicted torque information, and the predicted drilling rate information and the predicted torque information can be determined based on the target mechanical drilling rate prediction model and the target torque prediction model, respectively; Determine target decision indicator information for each drilling parameter optimization combination, and determine the target drilling parameter optimization combination based on multiple target decision indicator information.

2. The method according to claim 1, characterized in that Determining multiple drilling parameter optimization combinations based on the target mechanical penetration rate prediction model, the target torque prediction model, and drilling parameter constraints requires establishing the target mechanical penetration rate prediction model and the target torque prediction model, including: Collect multiple logging parameters during drilling, process data for each logging parameter, and determine training logging parameters; Training the mechanical drilling speed prediction model and the torque prediction model respectively according to the training logging parameters; Verifying the mechanical penetration rate prediction model and the torque prediction model respectively according to a plurality of real-time logging parameters, and determining evaluation index information of the mechanical penetration rate prediction model and the torque prediction model respectively; It is determined whether the evaluation index information of the mechanical penetration rate prediction model and the evaluation index information of the torque prediction model meet a first preset condition respectively. If so, a target mechanical penetration rate prediction model and a target torque prediction model are determined.

3. The method according to claim 2, characterized in that After determining the target mechanical penetration rate prediction model and the target torque prediction model, the method includes: Determine the weight information of each drilling parameter to be optimized in the current drilling formation; wherein the drilling parameters to be optimized include a drill bit weight on bit parameter, a rotary table speed parameter, and an outlet flow rate parameter.

4. The method according to claim 3, characterized in that The multiple decision indicators are determined based on the predicted mechanical specific energy information and the drilling parameters to be optimized, including: Determining important optimized drilling parameters based on weight information of each drilling parameter to be optimized, determining normalized mechanical specific energy information based on the predicted mechanical specific energy information, and determining a drilling parameter importance decision index based on the important optimized drilling parameters and the normalized mechanical specific energy information; Determining a change range decision indicator based on the predicted mechanical specific energy information and the drilling parameters to be optimized; A minimum mechanical performance decision index is determined according to the predicted mechanical specific energy information.

5. The method according to claim 4, characterized in that After determining the target mechanical penetration rate prediction model and the target torque prediction model, the method further includes: Determining whether the drilling process satisfies a second preset condition; If so, then update the logging parameters; The target mechanical penetration rate prediction model and the target torque prediction model are dynamically updated according to the updated logging parameters to determine an updated mechanical penetration rate prediction model and an updated torque prediction model.

6. The method according to claim 5, characterized in that After determining and updating the mechanical penetration rate prediction model and the torque prediction model, the method includes: Determine and update the speed-up target based on the drilling speed information of the drilling formation corresponding to the drilling depth; An updated drilling parameter optimization combination is determined based on the updated mechanical penetration rate prediction model and the updated torque prediction model.

7. A device for optimizing drilling parameters, characterized in that: include: The first processing module determines a speed-up target based on the drilling speed information of the current drilling formation, and determines a plurality of drilling parameter optimization combinations based on a target mechanical drilling speed prediction model, a target torque prediction model, and drilling parameter constraints; a second processing module, determining a plurality of decision indicators based on the predicted mechanical specific energy information and the drilling parameters to be optimized, and determining a target decision indicator based on the current drilling formation, wherein the predicted mechanical specific energy information can be determined based on the predicted drilling rate information and the predicted torque information, and the predicted drilling rate information and the predicted torque information can be determined based on the target mechanical drilling rate prediction model and the target torque prediction model, respectively; The third processing module determines target decision indicator information of each drilling parameter optimization combination, and determines the target drilling parameter optimization combination based on the multiple target decision indicator information.

8. A device for optimizing drilling parameters, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method for optimizing drilling parameters according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method for optimizing drilling parameters according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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