Method and equipment for optimizing drilling parameters and storage medium

Abstract

The invention provides a method and equipment for optimizing drilling parameters and a storage medium, and relates to the technical field of oil-gas exploration. The method comprises the steps that a speed increasing target is determined according to drilling speed information of a current drilling stratum, and a plurality of drilling parameter optimization combinations are determined according to a target mechanical drilling speed prediction model, a target torque prediction model and drilling parameter constraint conditions; determining a plurality of decision-making indexes according to the predicted mechanical specific energy information and the to-be-optimized drilling parameters, and determining a target decision-making index according to the current drilling stratum; target decision index information of each drilling parameter optimization combination is determined, and a target drilling parameter optimization combination is determined according to the multiple pieces of target decision index information. According to the method provided by the invention, the technical effect of accurately optimizing the drilling parameters when the drilling speed is increased is realized.

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] Complex oil and gas in "two deep and one non-deep" is an important successor field for oil and gas resource exploration and development, and is also a major challenge faced by drilling engineering. In view of the extremely hard and difficult deep formations, extremely poor drillability, complex drilling geological environment, and increasing uncertainties, higher requirements are placed on the precise control and real-time optimization of drilling parameters. In recent years, with the rapid development of artificial intelligence technology, it has been widely used to solve the problem of recommending drilling parameter optimization solutions during drilling, thereby improving drilling efficiency and achieving efficient and economical drilling.

[0003] However, during the drilling process, we are faced with different complex geological conditions, and the mapping relationship between the drilling response parameters (mechanical drilling rate, torque and mechanical specific energy) and the on-site real-time drilling engineering parameters is dynamically changed, which makes it difficult for the optimization results of the drilling parameters to conform to the actual physical process of drilling, and it is impossible to provide a scientific and reasonable drilling parameter optimization solution for on-site engineers. In the prior art, when drilling deep formations, the underground drilling efficiency problems caused by severe wear of the drill bit are less 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 effects, making them difficult to actually apply to the drilling site. Therefore, the prior art has the technical problems of poor accuracy and low optimization of the drilling speed-up parameters when drilling speeds up. Summary of the invention

[0004] The present application provides a method, device and storage medium for optimizing drilling parameters, so as to solve the technical problems in the prior art of low drilling parameter prediction accuracy and poor controllability of optimization 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 according to the drilling speed information of the current drilling formation, and determine multiple drilling parameter optimization combinations according to the target mechanical drilling speed prediction model, the target torque prediction model and the drilling parameter constraint conditions;

[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 according to the target mechanical drilling speed prediction model, the target torque prediction model and the drilling parameter constraint conditions. The target mechanical drilling speed prediction model and the target torque prediction model need to be established, including:

[0010] Collect multiple logging parameters during the drilling process, perform data processing on each logging parameter, and determine the training logging parameters;

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

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

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

[0014] Optionally, after determining the target mechanical drilling speed prediction model and the target torque prediction model, the method further 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] Determine predicted mechanical specific energy information based on predicted drilling speed information and 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 pressure on bit parameter, a rotary table speed parameter and an outlet flow parameter.

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

[0019] Determine the important optimized drilling parameters according to the weight information of each drilling parameter to be optimized, determine the normalized mechanical specific energy information according to the predicted mechanical specific energy information, and determine the drilling parameter importance decision index according to 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 specific performance information.

[0022] Optionally, after determining the target mechanical drilling speed 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 yes, then update the logging parameters;

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

[0026] Optionally, after determining to update the mechanical drilling speed prediction model and the torque prediction model, the method further includes:

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

[0028] The optimized combination of updated drilling parameters is determined based on the updated mechanical drilling speed prediction model and the 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 the speed-up target according to the drilling speed information of the current drilling formation, and determines multiple drilling parameter optimization combinations according to the target mechanical drilling speed prediction model, the target torque prediction model and the drilling parameter constraint conditions;

[0031] The second processing module determines a plurality of decision indicators according to the predicted mechanical specific energy information and the drilling parameters to be optimized, and determines a target decision indicator according to 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 according to the multiple target decision indicator information.

[0033] Optionally, the first processing module is further used for:

[0034] Collect multiple logging parameters during the drilling process, perform data processing on each logging parameter, and determine the training logging parameters;

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

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

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

[0038] Optionally, the first processing module is further used for:

[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] Determine predicted mechanical specific energy information based on predicted drilling speed information and 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 pressure on bit parameter, a rotary table speed parameter and an outlet flow parameter.

[0042] Optionally, the second processing module is further used for:

[0043] Determine the important optimized drilling parameters according to the weight information of each drilling parameter to be optimized, determine the normalized mechanical specific energy information according to the predicted mechanical specific energy information, and determine the drilling parameter importance decision index according to 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 specific performance information.

[0046] Optionally, the first processing module is further used for:

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

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

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

[0050] Optionally, the first processing module and the third processing module are further used for:

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

[0052] The optimized combination of updated drilling parameters is determined based on the updated mechanical drilling speed prediction model and the 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 connected to the processor in communication, comprising:

[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 of the first aspect.

[0057] In a fifth aspect, a computer program product comprises 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 according to the drilling speed information of the current drilling formation, and determine multiple drilling parameter optimization combinations according to the target mechanical drilling speed prediction model, the target torque prediction model and the drilling parameter constraint conditions; determine multiple decision indicators according to the predicted mechanical specific energy information and the drilling parameters to be optimized, and determine the target decision indicator according to the current drilling formation; determine the target decision indicator information of each drilling parameter optimization combination, and determine the target drilling parameter optimization combination according to the multiple target decision indicator information, thereby laying a 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 according to 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 speeding up drilling. 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 a device for optimizing drilling parameters provided in an embodiment of the present application;

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

[0065] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0066] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0067] In drilling operations, due to the diverse and complex geological structures encountered, the key performance indicators in the drilling process and the real-time drilling engineering parameters are dynamically mapped. This feature makes it difficult to accurately match the optimization results of drilling parameters with 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 prior art, 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 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 insignificant speed-up effect. Therefore, the prior art 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 according to the drilling speed information of the current drilling formation, and determine multiple drilling parameter optimization combinations according to the target mechanical drilling speed prediction model, the target torque prediction model and the drilling parameter constraint conditions; determine multiple decision indicators according to the predicted mechanical specific energy information and the drilling parameters to be optimized, and determine the target decision indicator according to the current drilling formation; determine the target decision indicator information of each drilling parameter optimization combination, and determine the target drilling parameter optimization combination according to the multiple target decision indicator information, thereby laying a 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 according to 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 speeding up drilling.

[0069] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. 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 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 according to the drilling speed information of the current drilling formation, and determining a plurality of drilling parameter optimization combinations according to a target mechanical drilling speed prediction model, a target torque prediction model and drilling parameter constraint conditions;

[0072] In this embodiment, the speed-up target refers to determining a new drilling speed according to preset conditions based on the drilling speed of the current 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 drilling speed of the current 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, and 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 drilling speed of the current 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 60m / h, and continue to set a new speed-up target after the drilling speed reaches 60m / h, that is, the speed-up target is to increase the drilling speed to 70m / h; the drilling parameter constraints are to set certain constraints for the drilling parameters to be optimized, so that when determining the optimal combination of drilling parameters, a more reasonable parameter optimization combination can be determined; 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, where the optimization algorithm refers to the optimization of the relevant performance of the algorithm, such as time complexity, space complexity and correctness, to enhance the algorithm's ability to handle problems. Optimization algorithms are widely used in many fields, such as machine learning, economic forecasting, and engineering design. Common optimization algorithm methods include but are not limited to the following: gradient descent method, conjugate gradient algorithm, genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm and swarm intelligence optimization algorithm.

[0073] S102, determining a plurality of decision indicators according to the predicted mechanical specific energy information and the drilling parameters to be optimized, and determining a target decision indicator according to 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 according to 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 through the actual situation of the current drilling formation, so that the determined target drilling parameter optimization combination is more in line with the actual situation of drilling and adapts to the drilling process, thereby improving the accuracy of the drilling parameters when the drilling speed target is achieved.

[0075] S103, determining target decision indicator information of each drilling parameter optimization combination, and determining a target drilling parameter optimization combination according to 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 the target decision indicator is determined. 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 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 multiple target decision indicator information. In this way, a new speed-up target is determined according to the preset conditions based on the drilling speed of the current 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 optimal combinations of drilling parameters 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 Method flow for optimizing drilling parameters provided in the embodiment of the present application Figure 2 This embodiment is in Figure 1 Based on the embodiment, the method for optimizing drilling parameters is described in detail, wherein the speed-up target is determined by step S201, the target mechanical drilling speed prediction model and the target torque prediction model are determined by step S202, the target decision index is determined by steps S203-S204, the target drilling parameter optimization combination is determined by step S205, the target mechanical drilling speed prediction model and the target torque prediction model are updated, and the updated speed-up target is achieved by 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-up target according to drilling speed information of a current drilling formation;

[0080] In this embodiment, the speed-up target can be determined according to a preset percentage, and the preset percentage can increase in arithmetic or geometric progression. At the same time, the reference drilling speed can be changed in real time, and can also be the initial drilling speed information; for example: the current drilling speed is 50m / h, and the speed is increased by 15% based on the current drilling speed, that is, the speed-up target is to increase the drilling speed to 57.5m / h. When the speed-up target is reset next time, the speed is increased by 20% based on the drilling speed of 57.5m / h, that is, the speed-up target is to increase the drilling speed to 69m / h; the current drilling speed is 50m / h, and the speed is increased by 12% based on the current drilling speed, that is, the speed-up target is to increase the drilling speed to 56m / h. When the speed-up target is reset next time, the speed is increased by 24% based on the drilling speed of 50m / h, that is, the speed-up target is to increase the drilling speed to 62m / h. The method of determining the speed-up target can be comprehensively determined according to the current geological conditions of the ore layer and the optimization 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; training a mechanical drilling speed prediction model and a torque prediction model according to the training logging parameters; verifying the mechanical drilling speed prediction model and the torque prediction model according to multiple real-time logging parameters, and determining evaluation index information of the mechanical drilling speed prediction model and the torque prediction model respectively; judging whether the evaluation index information of the mechanical drilling speed prediction model and the evaluation index information of the torque prediction model meet the first preset condition respectively, and if so, determining a target mechanical drilling speed 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 method includes but is not limited to 3σ outlier screening, linear regression interpolation filling and sliding filtering, and the shock anomaly and noise in the drilling are screened out, so as to improve the quality and accuracy of the data sample. 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 dimension effects of different parameters are eliminated, and the elimination method includes but is not limited to the maximum and minimum data normalization method and the standardization method, and then the collected real-time parameter data is updated to the training logging parameters, and the mechanical drilling speed is predicted according to 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 result; further, the trained model is tested and verified, the evaluation index information is determined by the prediction data and the training data, and the index evaluation information is judged, and 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 is not limited to the root mean square error, the mean square error, the mean absolute error and the correlation coefficient.

[0083] S203, respectively determining predicted drilling speed information and predicted torque information according to the target mechanical drilling speed prediction model and the target torque prediction model; determining predicted mechanical specific energy information according to the predicted drilling speed information and the predicted torque information; determining weight information of each drilling parameter to be optimized in the current drilling formation; determining multiple drilling parameter optimization combinations according to the target mechanical drilling speed prediction model, the target torque prediction model and the drilling parameter constraint conditions;

[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 drilling pressure parameter, the rotary table speed parameter and the outlet flow parameter. In the trained target mechanical drilling speed prediction model, the drill bit drilling pressure parameter, the rotary table speed parameter and the outlet flow 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, and provide a data basis for the optimization of the drilling parameters. Further, 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 drilling pressure parameter of the drill bit; Rpm is the rotary speed parameter; Q is the outlet flow parameter; Wob min Wob is the minimum value of the drill bit pressure parameter; max The maximum value of the drill bit pressure parameter; Rpm min is 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 setting of the values ​​in the constraints of the drill bit pressure on bit parameters, rotary table speed parameters and outlet flow rate parameters needs to be adjusted according to the actual drilling conditions, rock properties, drilling equipment and drilling speed-up targets.

[0089] S204, 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; determining target decision indicator information for each drilling parameter optimization combination, and determining a target drilling parameter optimization combination based on multiple target decision indicator information;

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

[0091] Optionally, important optimized drilling parameters are determined according to the weight information of each drilling parameter to be optimized, normalized mechanical specific energy information is determined according to the predicted mechanical specific energy information, and drilling parameter importance decision indicators are determined according to the important optimized drilling parameters and the normalized mechanical specific energy information; specifically, according to the weight information of each drilling parameter to be optimized in the current drilling formation determined in step S203, the important optimized drilling parameters, i.e., the parameters with the first importance in weight ranking, are 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] Further, the drilling parameter importance decision index is determined according to the important optimized drilling parameters and the normalized mechanical specific energy information. Taking the important optimized drilling parameter as the outlet flow 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 parameter to be optimized and the outlet flow parameter under the current drilling conditions.

[0097] Optionally, a change range decision index is determined according to 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 change range 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 as follows:

[0098]

[0099] min(α·E bit +β·P dis )

[0100] Among them, Wob dis Rpm is the Euclidean distance between the bit weight on bit parameter to be optimized and the 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 of the minimum mechanical specific energy under the condition of meeting the speed increase target. The decision index formula is as follows:

[0103] min E bit

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

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

[0106] S205, determining whether the drilling process satisfies the second preset condition; if so, determining to update the logging parameters; updating the target mechanical drilling speed prediction model and the target torque prediction model according to the updated drilling parameters, and determining to update the mechanical drilling speed prediction model and the 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 at the time of initial drilling to the preset depth are obtained, and the target mechanical drilling speed prediction model and the target torque prediction model are trained again with this part of the drilling parameters, and then the model is updated to determine the updated mechanical drilling speed prediction model and the updated torque prediction model; for example, the preset condition is that when the speed-up target is reached, the drilling parameters are collected again to update the model; or when the drilling depth reaches a set fixed interval, multiple drilling parameters are collected when drilling to the current drilling depth. For example, the fixed interval of the drilling depth is 30m. Every time 30m of drilling is completed, the drilling parameters are collected to update the target model for training; optionally, the preset condition can also be set to determine whether the prediction accuracy of the target mechanical drilling speed prediction model and the target torque prediction model is less than or equal to a preset threshold. If so, the target mechanical drilling speed prediction model and the target torque prediction model are updated, and the updating method includes but is not limited to online updating and fine-tuning updating. By using the target mechanical drilling speed 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 according to the drilling speed information of the drilling formation corresponding to the drilling depth; and determining an updated drilling parameter optimization combination according to the updated mechanical drilling speed prediction model and the updated torque prediction model.

[0109] In this embodiment, a new speed-up target is determined according to the drilling speed information of the drilling formation. Further, multiple drilling parameter optimization combinations are determined according to the updated mechanical drilling speed prediction model, the updated torque prediction model and the drilling parameter constraints; multiple decision indicators are determined according to the updated predicted mechanical specific energy information and the drilling parameters to be optimized, and the target decision indicator is determined according to the current drilling formation; the target decision indicator information of each drilling parameter optimization combination is determined, and the target drilling parameter optimization combination is determined according to the multiple target decision indicator information. By setting a new speed-up target and determining the updated drilling parameter optimization combination according to the updated mechanical drilling speed prediction model and the updated torque prediction model, it is possible to dynamically adjust the drilling parameters according to the actual geological conditions of the drilling, avoiding the situation where the drilling parameters are fixed after the speed-up target is achieved, while ignoring the inadaptability of the drilling parameters in the deep formation. At the same time, the speed-up target is continuously adjusted according to the drilling information of the current drilling, and a controllable form of step-by-step speed-up is achieved, meeting the on-site step-by-step controllable intelligent speed-up requirements.

[0110] Figure 3 A schematic diagram of a method for optimizing drilling parameters provided in an embodiment of the present application, such as Figure 3As shown, the method for optimizing drilling parameters provided in 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 according to the target mechanical drilling speed prediction model and the target torque prediction model; the speed-up target is determined according to the drilling speed information of the current drilling, a plurality of drilling parameter optimization combinations are determined according to the optimization algorithm, a plurality of decision indicators are determined according to the predicted mechanical specific energy information and the drilling parameters to be optimized, and the target decision indicator is determined according to 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 according to the plurality of 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. The method dynamically updates the model method to enhance the generalization and accuracy of the model in different wells and different formations, 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. The application can effectively solve the random and unstable problems existing in the 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 according to 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 the training logging parameters; trains a mechanical drilling speed prediction model and a torque prediction model according to the training logging parameters; verifies the mechanical drilling speed prediction model and the torque prediction model according to multiple real-time logging parameters, and determines evaluation index information of the mechanical drilling speed prediction model and the torque prediction model respectively; judges whether the evaluation index information of the mechanical drilling speed prediction model and the evaluation index information of the torque prediction model meet the first preset condition respectively, and if so, determines the target mechanical drilling speed prediction model and the target torque prediction model; determines the predicted drilling speed information and the predicted torque information according to the target mechanical drilling speed prediction model and the target torque prediction model respectively; determines the predicted mechanical specific energy information according to the predicted drilling speed information and the predicted torque information; determines each The invention relates to a method for optimizing the drilling parameters to be optimized, and a method for optimizing the drilling parameters to be optimized comprising: determining a weight information of a drilling parameter to be optimized in the current drilling formation; determining multiple optimization combinations of drilling parameters according to a target mechanical drilling speed prediction model, a target torque prediction model and drilling parameter constraints; determining multiple decision indicators according to the predicted mechanical specific energy information and the drilling parameters to be optimized, and determining the target decision indicator according to the current drilling formation; determining the target decision indicator information of each drilling parameter optimization combination, and determining the target drilling parameter optimization combination according to the multiple target decision indicator information; judging whether the drilling process meets the second preset condition; if so, determining to update the logging parameters; updating the target mechanical drilling speed prediction model and the target torque prediction model according to the updated drilling parameters, and determining the updated mechanical drilling speed prediction model and the updated torque prediction model; determining the updated speed-up target according to the drilling speed information of the drilling formation corresponding to the drilling depth; determining the updated drilling parameter optimization combination according to the updated mechanical drilling speed prediction model and the updated torque prediction model.The method for optimizing drilling parameters provided in the present application establishes a target mechanical drilling speed prediction model and a target torque prediction model, and fully considers the diversity of the intelligent model sample space according to different drilling formation conditions to achieve the global stability of the prediction model. Furthermore, the mechanical specific energy information is predicted by the target mechanical drilling speed prediction model and the target torque prediction model. By setting the speed-up target, the optimization algorithm is used to construct the optimal combination of drilling parameters that meets the speed-up target. Then, the target decision index is determined according to the drilling parameters to be optimized and the mechanical specific energy, and 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. At the same time, as the drilling process proceeds, more training logging parameters are collected, so as to update the target mechanical drilling speed prediction model and the target torque prediction model to improve the accuracy of the prediction model. Further, a new speed-up target is set according to the current drilling speed information, and the updated drilling parameter optimization combination is determined according to the updated mechanical drilling speed prediction model and the updated torque prediction model, so as to achieve the speed-up at different stages of the drilling process, achieve the dynamic optimization of the drilling parameters in the staged speed-up process, and achieve the technical effect of improving the accuracy and optimization of the 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 apparatus 400 for optimizing drilling parameters provided in an embodiment of the present application includes: 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 according to the drilling speed information of the current drilling formation, and determines a plurality of drilling parameter optimization combinations according to 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 according to the predicted mechanical specific energy information and the drilling parameters to be optimized, and determines a target decision indicator according to the current drilling formation;

[0115] The third processing module 403 determines target decision indicator information of each drilling parameter optimization combination, and determines the target drilling parameter optimization combination according to 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 the drilling process, perform data processing on each logging parameter, and determine the training logging parameters;

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

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

[0120] It is determined whether the evaluation index information of the mechanical drilling speed prediction model and the evaluation index information of the torque prediction model meet the first preset condition respectively. If so, a target mechanical drilling speed 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] Determine predicted mechanical specific energy information based on predicted drilling speed information and 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 pressure on bit parameter, a rotary table speed parameter and an outlet flow parameter.

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

[0126] Determine the important optimized drilling parameters according to the weight information of each drilling parameter to be optimized, determine the normalized mechanical specific energy information according to the predicted mechanical specific energy information, and determine the drilling parameter importance decision index according to 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 specific 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 yes, then update the logging parameters;

[0132] The target mechanical drilling speed prediction model and the target torque prediction model are updated respectively according to the updated drilling parameters to determine the updated mechanical drilling speed 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 according to the drilling speed information of the drilling formation corresponding to the drilling depth;

[0135] The optimized combination of updated drilling parameters is determined based on the updated mechanical drilling speed prediction model and the updated torque prediction model.

[0136] The present application provides a device for optimizing drilling parameters, which determines a speed-up target according to 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 the training logging parameters; trains a mechanical drilling speed prediction model and a torque prediction model according to the training logging parameters; verifies the mechanical drilling speed prediction model and the torque prediction model according to multiple real-time logging parameters, and determines evaluation index information of the mechanical drilling speed prediction model and the torque prediction model respectively; judges whether the evaluation index information of the mechanical drilling speed prediction model and the evaluation index information of the torque prediction model meet the first preset condition respectively, and if so, determines the target mechanical drilling speed prediction model and the target torque prediction model; determines the predicted drilling speed information and the predicted torque information according to the target mechanical drilling speed prediction model and the target torque prediction model respectively; determines the predicted mechanical specific energy information according to the predicted drilling speed information and the predicted torque information; determines each The invention relates to a method for optimizing the drilling parameters to be optimized, and a method for optimizing the drilling parameters to be optimized comprising: determining a weight information of a drilling parameter to be optimized in the current drilling formation; determining multiple optimization combinations of drilling parameters according to a target mechanical drilling speed prediction model, a target torque prediction model and drilling parameter constraints; determining multiple decision indicators according to the predicted mechanical specific energy information and the drilling parameters to be optimized, and determining the target decision indicator according to the current drilling formation; determining the target decision indicator information of each drilling parameter optimization combination, and determining the target drilling parameter optimization combination according to the multiple target decision indicator information; judging whether the drilling process meets the second preset condition; if so, determining to update the logging parameters; updating the target mechanical drilling speed prediction model and the target torque prediction model according to the updated drilling parameters, and determining the updated mechanical drilling speed prediction model and the updated torque prediction model; determining the updated speed-up target according to the drilling speed information of the drilling formation corresponding to the drilling depth; determining the updated drilling parameter optimization combination according to the updated mechanical drilling speed prediction model and the updated torque prediction model.The method for optimizing drilling parameters provided in the present application establishes a target mechanical drilling speed prediction model and a target torque prediction model, and fully considers the diversity of the intelligent model sample space according to different drilling formation conditions to achieve the global stability of the prediction model. Furthermore, the mechanical specific energy information is predicted by the target mechanical drilling speed prediction model and the target torque prediction model. By setting the speed-up target, the optimization algorithm is used to construct the optimal combination of drilling parameters that meets the speed-up target. Then, the target decision index is determined according to the drilling parameters to be optimized and the mechanical specific energy, and 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. At the same time, as the drilling process proceeds, more training logging parameters are collected, so as to update the target mechanical drilling speed prediction model and the target torque prediction model to improve the accuracy of the prediction model. Further, a new speed-up target is set according to the current drilling speed information, and the updated drilling parameter optimization combination is determined according to the updated mechanical drilling speed prediction model and the updated torque prediction model, so as to achieve the speed-up at different stages of the drilling process, achieve the dynamic optimization of the drilling parameters in the staged speed-up process, and achieve the technical effect of improving the accuracy and optimization of the drilling parameters.

[0137] Figure 5 This is a hardware schematic 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 at least one processor 501 executes the above method.

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

[0140] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or 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 invention may be directly implemented as being executed by a hardware processor, or may be executed 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 (Non-volatile Memory, NVM), such as at least one disk memory.

[0142] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or 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 storage 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 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 a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: 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 only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

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

[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, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment 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, etc. Various media that can store program codes.

[0151] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0152] Finally, it should be noted that those skilled in the art will readily conceive of 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, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for optimizing drilling parameters, characterized in that: include: Determine the speed-up target according to the drilling speed information of the current drilling formation, and determine multiple drilling parameter optimization combinations according to the target mechanical drilling speed prediction model, the target torque prediction model and the drilling parameter constraint conditions; Determine a plurality of decision indicators according to the predicted mechanical specific energy information and the drilling parameters to be optimized, and determine a target decision indicator according to the current drilling formation; 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: The method of determining a plurality of drilling parameter optimization combinations according to the target mechanical drilling speed prediction model, the target torque prediction model and the drilling parameter constraint conditions requires establishing the target mechanical drilling speed prediction model and the target torque prediction model, including: Collect multiple logging parameters during the drilling process, perform data processing on each logging parameter, and determine the training logging parameters; The mechanical drilling speed prediction model and the torque prediction model are trained respectively according to the training logging parameters; Verifying the mechanical drilling speed prediction model and the torque prediction model respectively according to the multiple real-time logging parameters, and determining evaluation index information of the mechanical drilling speed prediction model and the torque prediction model respectively; It is determined whether the evaluation index information of the mechanical drilling speed prediction model and the evaluation index information of the torque prediction model meet the first preset condition respectively. If so, a target mechanical drilling speed 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 drilling speed prediction model and the target torque prediction model, the method includes: Determine predicted drilling speed information and predicted torque information according to the target mechanical drilling speed prediction model and the target torque prediction model respectively; Determine the predicted mechanical specific energy information according to the predicted drilling speed information and the predicted torque information; 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 parameter.

4. The method according to claim 3, characterized in that The method of determining multiple decision indicators based on the predicted mechanical specific energy information and the drilling parameters to be optimized includes: Determine important optimized drilling parameters according to weight information of each drilling parameter to be optimized, determine normalized mechanical specific energy information according to the predicted mechanical specific energy information, and determine drilling parameter importance decision indicators according to the important optimized drilling parameters and the normalized mechanical specific energy information; Determine a change range decision indicator according to 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 performance information.

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

6. The method according to claim 5, characterized in that After determining to update the mechanical drilling speed prediction model and updating the torque prediction model, the method includes: Determine and update the speed-up target according to the drilling speed information of the drilling formation corresponding to the drilling depth; An updated drilling parameter optimization combination is determined according to 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 the speed-up target according to the drilling speed information of the current drilling formation, and determines multiple drilling parameter optimization combinations according to the target mechanical drilling speed prediction model, the target torque prediction model and the drilling parameter constraint conditions; A second processing module determines a plurality of decision indicators according to the predicted mechanical specific energy information and the drilling parameters to be optimized, and determines a target decision indicator according to the current drilling formation; The third processing module determines target decision indicator information of each drilling parameter optimization combination, and determines the target drilling parameter optimization combination according to 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 a 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.

Images