Drilling data feasibility judgment method and device, electronic equipment and storage medium

Abstract

According to the drilling data feasibility judgment method and device, the electronic equipment and the storage medium, the to-be-processed data set of the target well is obtained firstly, the to-be-processed data set comprises the multiple drilling data of the target well, and then the parameter deviation degree between each drilling data and the preset standard data is calculated; according to the method, the multiple parameter deviation degrees of the target well are obtained, when any parameter deviation degree exceeds the preset fitness interval, all the parameter deviation degrees of the target well are input into the formula adaptability evaluation model, and adaptability comprehensive evaluation is started only when the parameter deviation degrees are abnormal, so that unnecessary complex calculation is reduced; the real-time performance of feasibility judgment of the drilling data is improved, the formula adaptability evaluation value of the target well combined with the multiple parameter deviation degrees is obtained, the one-sidedness of single parameter judgment is avoided, the accuracy of feasibility judgment of the drilling data is improved, and according to the comparison result of the formula adaptability evaluation value and the preset threshold value, the accuracy of feasibility judgment of the drilling data is improved. And finally outputting a drilling data feasibility report of the target well.

Description

Technical Field

[0001] This application relates to the field of oil and gas exploration technology, and in particular to a method, apparatus, electronic device and storage medium for determining the feasibility of drilling data. Background Technology

[0002] As oil and gas exploration continues to extend into deeper, ultra-deep, and unconventional reservoirs, the drilling environment is becoming increasingly complex, characterized by high temperatures, high pressures, and variable formation pressures. The feasibility of drilling data is closely related to wellbore stability, drilling speed, and drilling safety.

[0003] In related technologies, drilling data monitoring relies on manual sampling followed by laboratory analysis, which cannot effectively reflect real-time downhole conditions. Furthermore, drilling data analysis cannot be combined with real-time engineering parameters, resulting in insufficient accuracy in drilling data analysis.

[0004] Therefore, improving the real-time performance and accuracy of drilling data feasibility assessment has become an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, and storage medium for determining the feasibility of drilling data, in order to solve the problems of low real-time performance and insufficient accuracy in related technologies for determining the feasibility of drilling data.

[0006] In a first aspect, embodiments of this application provide a method for determining the feasibility of drilling data, including:

[0007] Obtain the dataset to be processed for the target well; the dataset to be processed includes multiple drilling data of the target well; the drilling data can be drilling fluid data, geological information data, or any parameter data from engineering data;

[0008] Calculate the parameter deviation between each drilling data point and the preset standard data to obtain the multiple parameter deviations of the target well;

[0009] If the deviation of any parameter exceeds the preset fitness range, the deviation of all parameters of the target well is input into the formulation fitness evaluation model to obtain the formulation fitness evaluation value of the target well.

[0010] Based on the comparison between the formula adaptability assessment value and the preset threshold, a drilling data feasibility report for the target well is output.

[0011] In one possible implementation, obtaining the dataset to be processed from the target well includes:

[0012] Acquire the unprocessed drilling data and historical drilling data of the target well;

[0013] Based on historical drilling data, the drilling data to be processed is normalized to obtain normalized drilling data.

[0014] Multiple well data that meet the preset judgment conditions are selected from the normalized drilling data to obtain the target well's dataset to be processed.

[0015] In one possible implementation, the drilling data to be processed is normalized based on historical drilling data to obtain normalized drilling data, including:

[0016] Obtain the mean and standard deviation of the historical drilling data corresponding to the drilling data to be processed;

[0017] The normalized drilling data is calculated based on the standard deviation and mean of the drilling data to be processed and the historical drilling data.

[0018] In one possible implementation, after calculating the parameter deviation between each drilling data point and preset standard data to obtain multiple parameter deviations for the target well, the method further includes:

[0019] If the deviation of all parameters of the target well is within the preset fitness range, a drilling data feasibility report for the target well is output; the drilling data feasibility report includes the result of determining the feasibility of the drilling data.

[0020] In one possible implementation, based on a comparison between the formulation suitability assessment value and a preset threshold, a drilling data feasibility report for the target well is output, including:

[0021] When the comparison result shows that the formula adaptability assessment value is less than or equal to the preset threshold, the output drilling data feasibility report includes the determination result of the drilling data feasibility.

[0022] When the comparison result shows that the formula adaptability assessment value is greater than the preset threshold, the output drilling data feasibility report includes the determination result that the drilling data is not feasible and the adjustment information of the drilling data.

[0023] In one possible implementation, the process for determining the formulation suitability assessment model is as follows:

[0024] Calculate the deviation of historical parameters corresponding to historical drilling data;

[0025] Based on historical drilling data and historical parameter deviation, the parameter weights of the formulation adaptability assessment model are optimized and trained until the preset convergence condition is met, thus obtaining a well-trained formulation adaptability assessment model.

[0026] Secondly, embodiments of this application provide a drilling data feasibility determination device, comprising:

[0027] The acquisition module is used to acquire the dataset to be processed for the target well; the dataset to be processed includes drilling data from multiple wells of the target well.

[0028] The calculation module is used to calculate the parameter deviation between each drilling data and the preset standard data, and obtain the multiple parameter deviations of the target well.

[0029] The processing module is used to input all parameter deviations of the target well into the formulation adaptability evaluation model when any parameter deviation exceeds the preset fitness range, so as to obtain the formulation adaptability evaluation value of the target well.

[0030] The output module is used to output a drilling data feasibility report for the target well based on the comparison results between the formula suitability assessment value and the preset threshold.

[0031] In one possible implementation, the acquisition module is specifically used for:

[0032] Acquire the unprocessed drilling data and historical drilling data of the target well;

[0033] Based on historical drilling data, the drilling data to be processed is normalized to obtain normalized drilling data.

[0034] Multiple well data that meet the preset judgment conditions are selected from the normalized drilling data to obtain the target well's dataset to be processed.

[0035] In one possible implementation, the drilling data to be processed is normalized based on historical drilling data to obtain normalized drilling data. The acquisition module is further used for:

[0036] Obtain the mean and standard deviation of the historical drilling data corresponding to the drilling data to be processed;

[0037] The normalized drilling data is calculated based on the standard deviation and mean of the drilling data to be processed and the historical drilling data.

[0038] In one possible implementation, after calculating the parameter deviation between each drilling data point and preset standard data to obtain multiple parameter deviations for the target well, the processing module is further configured to:

[0039] If the deviation of all parameters of the target well is within the preset fitness range, a drilling data feasibility report for the target well is output; the drilling data feasibility report includes the result of determining the feasibility of the drilling data.

[0040] In one possible implementation, the output module is specifically used for:

[0041] When the comparison result shows that the formula adaptability assessment value is less than or equal to the preset threshold, the output drilling data feasibility report includes the determination result of the drilling data feasibility.

[0042] When the comparison result shows that the formula adaptability assessment value is greater than the preset threshold, the output drilling data feasibility report includes the determination result that the drilling data is not feasible and the adjustment information of the drilling data.

[0043] In one possible implementation, the processing module is specifically used for:

[0044] Calculate the deviation of historical parameters corresponding to historical drilling data;

[0045] Based on historical drilling data and historical parameter deviation, the parameter weights of the formulation adaptability assessment model are optimized and trained until the preset convergence condition is met, thus obtaining a well-trained formulation adaptability assessment model.

[0046] Thirdly, embodiments of this application provide an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0047] The memory stores instructions that the computer executes;

[0048] The processor executes computer-executable instructions stored in memory to implement the method as described in the first aspect or any of the above.

[0049] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method described in the first aspect or any of the above-mentioned methods.

[0050] Fifthly, embodiments of this application provide a computer program. The computer program product includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the methods described in the first aspect or any of the above-mentioned methods.

[0051] The drilling data feasibility determination method, apparatus, electronic device, and storage medium provided in this application embodiment first acquire a dataset to be processed for the target well, which includes multiple drilling data of the target well. Then, the parameter deviation between each drilling data and preset standard data is calculated to obtain multiple parameter deviations of the target well. If any parameter deviation exceeds a preset fitness range, all parameter deviations of the target well are input into the formulation fitness assessment model. The comprehensive fitness assessment is only initiated when the parameter deviation is abnormal, reducing unnecessary complex calculations, improving the real-time performance of drilling data feasibility determination, and obtaining a formulation fitness assessment value of the target well that combines multiple parameter deviations. This avoids the one-sidedness of single parameter judgment and improves the accuracy of drilling data feasibility determination. Based on the comparison result of the formulation fitness assessment value and a preset threshold, a drilling data feasibility report of the target well is finally output. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0053] Figure 1 A flowchart illustrating the drilling data feasibility determination method provided in this application embodiment. Figure 1 ;

[0054] Figure 2 A flowchart illustrating the drilling data feasibility determination method provided in this application embodiment. Figure 2 ;

[0055] Figure 3 A flowchart illustrating the drilling data feasibility determination method provided in this application embodiment. Figure 3 ;

[0056] Figure 4 This is a schematic diagram of the drilling data feasibility determination device provided in the embodiments of this application;

[0057] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0058] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0060] Before introducing the embodiments of this application, the application background of the embodiments of this application will be explained first:

[0061] As oil and gas exploration continues to extend into deeper, ultra-deep, and unconventional reservoirs, the drilling environment is becoming increasingly complex, characterized by high temperatures, high pressures, and variable formation pressures. The feasibility of drilling data is closely related to wellbore stability, drilling speed, and drilling safety. Drilling fluid failures, such as decreased viscosity and shear strength, a sudden increase in fluid loss, density deviations, or reduced emulsion stability, can lead to serious accidents such as lost circulation, well kicks, or even blowouts.

[0062] In related technologies, drilling data monitoring relies on manual sampling followed by laboratory analysis, with detection intervals typically exceeding several hours, failing to effectively reflect real-time downhole conditions. Furthermore, some field-based online monitoring systems only set alarm thresholds for single indicators, such as exceeding equivalent circulating density limits or sudden changes in a single rheological parameter. However, the feasibility of drilling data is often the result of multiple coupled factors, making it difficult to identify potential risks in a timely manner through single-indicator monitoring.

[0063] Furthermore, most existing drilling data optimization relies on empirical adjustments and lacks collaborative analysis of geological conditions and engineering parameters. Even when methods such as machine learning are introduced, they often suffer from limitations in the dimensionality of input parameters and fail to integrate full-link information from geological, engineering, and drilling fluid performance data. In addition, the model parameters are highly fixed and lack the ability to dynamically adapt to different operating conditions, resulting in decreased prediction accuracy when applied across wells or blocks.

[0064] Therefore, improving the real-time performance and accuracy of drilling data feasibility assessment has become an urgent technical problem to be solved.

[0065] To address the technical problems existing in related technologies, the inventors of this application propose the following approach: To resolve the issues of low real-time performance and accuracy in drilling data feasibility assessment, a comprehensive dataset integrating drilling fluid data, geological information data, and engineering data is acquired to provide a comprehensive data foundation for drilling data feasibility assessment, improving the accuracy of subsequent feasibility evaluations. The drilling data in the dataset is then processed to obtain the parameter deviation of each data point. If any parameter deviation exceeds a preset fitness range, the system quickly identifies whether a single parameter is abnormal, avoiding complex evaluations of all drilling data directly, reducing unnecessary calculations, and improving real-time performance. The deviations of all parameters for the target well are then input into a formulation suitability assessment model to obtain the formulation suitability assessment value for the target well. This assessment value integrates all parameter deviations, avoiding the bias of judging a single parameter and improving the accuracy of drilling data feasibility assessment. Based on the comparison between the formulation suitability assessment value and a preset threshold, a drilling data feasibility report for the target well is output, achieving a closed loop from data to decision-making and providing a precise basis for drilling fluid formulation adjustments.

[0066] The parts not described in detail are disclosed in the following embodiments.

[0067] The technical solution of this application will now be described in detail through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0068] It is worth noting that the application fields of the methods, devices, electronic equipment and storage media in this application are not limited.

[0069] The subject of this application is an electronic device, which may specifically be a server, terminal device, etc.

[0070] Figure 1 A flowchart illustrating the drilling data feasibility determination method provided in this application embodiment. Figure 1 ,like Figure 1 As shown, the method may include the following steps:

[0071] Step 11: Obtain the dataset to be processed for the target well.

[0072] The dataset to be processed includes drilling data from multiple wells of the target well. The drilling data can be any parameter data from the drilling fluid data, geological information data, and engineering data of the target well.

[0073] In this step, geological information data is acquired in real-time by a fully automated online logging while drilling (LWD) system, which measures formation rock physical parameters during drilling. Engineering data is acquired in real-time by a measurement while drilling (MWD) system. Drilling fluid data is acquired through surface monitoring equipment employing high-precision sensors and data acquisition technology to ensure the accuracy of the collected drilling fluid data. This drilling fluid data provides drilling engineers with real-time monitoring of drilling fluid performance and decision support. By monitoring drilling fluid performance data in real-time, drilling engineers can promptly identify trends in drilling fluid performance and take corresponding adjustments to ensure the safe and efficient conduct of drilling operations.

[0074] For example, geological information data includes information such as natural gamma rays and density. Engineering data includes information such as well inclination, azimuth, and bottom hole pressure. Drilling fluid data includes drilling fluid density, oil-water ratio, demulsification voltage, rheological parameters, pH value, funnel viscosity, chloride ion concentration, solids content, and temperature.

[0075] The geological information data mentioned above can be selected from the geological information data of adjacent wells based on the actual drilling operations, and no restrictions are imposed here.

[0076] Drilling fluid density directly reflects the weight of the drilling fluid and is a key factor in controlling bottomhole pressure and preventing formation fluid intrusion. The oil-to-water ratio reflects the proportion of oil and water in the drilling fluid and has a significant impact on its stability and lubricity. Demulsification voltage is an important indicator of the drilling fluid's resistance to contamination. Rheological parameters describe the flow characteristics of the drilling fluid and are crucial to its rock-carrying capacity and wellbore stability. Rheological parameters include plastic viscosity and consistency coefficient, which determine the flow properties and shear dilution properties of the drilling fluid. pH reflects the acidity or alkalinity of the drilling fluid and has a significant impact on its chemical stability and corrosivity. Funnel viscosity is a simple indicator of drilling fluid flowability and is closely related to its rock-carrying capacity; chloride ion concentration relates to the drilling fluid's conductivity and corrosivity. Solid content affects the flowability and filtration performance of the drilling fluid. Temperature affects the physicochemical properties of the components in the drilling fluid, thus influencing its overall performance. Drilling fluid data determines the flow behavior of drilling fluid between the drill string and the wellbore, as well as its ability to carry and suspend drill cuttings. Real-time monitoring and accurate evaluation of drilling fluid data provide data support for adjusting drilling fluid formulations, ensuring that drilling fluids can adapt to complex and changing geological and engineering conditions.

[0077] Step 12: Calculate the parameter deviation between each drilling data and the preset standard data to obtain the multiple parameter deviations of the target well.

[0078] In this step, parameter deviation The calculation can be expressed by the following formula:

[0079]

[0080] In the formula, For the drilling data at time i, The expected value in the formula adaptability standard can be taken as the average value of the drilling data of adjacent wells; This is a standard for the allowable fluctuation range of drilling data.

[0081] The permissible fluctuation range of drilling data is usually a safety control standard established with reference to drilling data obtained from the drilling site, or it can be determined by combining historical statistical data of similar well sections and taking the mean or standard deviation as the permissible fluctuation range. It can be dynamically adjusted based on real-time drilling conditions such as well depth, formation pressure, and temperature changes.

[0082] Deviation is a key indicator that measures the difference between the current drilling state and the ideal drilling state. The preset standard data is determined based on the average value of characteristic data from neighboring wells.

[0083] Specifically, after step 12, the drilling data feasibility assessment method may also include the following implementation methods:

[0084] If the deviation of all parameters of the target well is within the preset fitness range, output a drilling data feasibility report for the target well.

[0085] The drilling data feasibility report includes the results of determining the feasibility of the drilling data.

[0086] The aforementioned preset fitness range is determined based on the average parameter value of historical drilling data from adjacent wells and the engineering control zone (i.e., the standard fluctuation range of parameters).

[0087] For example, the drilling fluid density is 1.5 g / cm³, the density engineering control zone is 0.2, and the preset adaptability range is 1.5 ± 0.2.

[0088] Step 13: If any parameter deviation exceeds the preset fitness range, input all parameter deviations of the target well into the formulation fitness evaluation model to obtain the formulation fitness evaluation value of the target well.

[0089] In this step, if the deviation of any parameter exceeds the preset fitness range, the formulation fitness assessment value is calculated. That is, the deviation of all parameters of the target well is input into the formulation fitness assessment model for processing to obtain the formulation fitness assessment value of the target well.

[0090] The formulation adaptability assessment model is obtained by inputting historical drilling data from adjacent wells into a neural network model for training.

[0091] Step 14: Based on the comparison results between the formula adaptability assessment value and the preset threshold, output the drilling data feasibility report for the target well.

[0092] In this step, if the formulation suitability assessment value is higher than the preset threshold, the feasibility report determines that the current drilling data is feasible and states that all parameter values ​​in the drilling data are within safe ranges, recommending that the existing drilling data be maintained and continuously monitored. If the formulation suitability assessment value is lower than the preset threshold, the feasibility report determines that it is not feasible. In this case, the feasibility report specifies the parameters that need to be adjusted (such as drilling fluid density, rheological parameters, etc.) and provides the adjustment direction with reference to data from adjacent wells (such as increasing the density from 1.18 g / cm³ to 1.22 g / cm³). The feasibility report can provide on-site engineers with intuitive decision-making basis, ensuring that drilling operations are carried out based on data-driven scientific judgment.

[0093] Specifically, step 14 can include the following implementation methods:

[0094] Step 1: When the comparison result shows that the formula adaptability assessment value is less than or equal to the preset threshold, the output drilling data feasibility report includes the determination result of the drilling data feasibility.

[0095] Under this implementation, if the comparison result between the formula suitability assessment value and the preset threshold is that the formula suitability assessment value is less than or equal to the preset threshold, it indicates that the current drilling data is suitable for the current drilling conditions, and the judgment result that the drilling data is feasible is output.

[0096] Among them, the preset threshold Adaptive calculation can be used, as shown in the following formula:

[0097]

[0098] In the formula, Based on the basic threshold, the statistical distribution of the formula adaptability assessment value (i.e. the preset threshold) S of the adjacent well is calculated based on the samples of well sections with normal drilling conditions in the historical drilling data of the adjacent wells, and the upper limit value under the 95% confidence level is taken as the basic threshold. This is a combined function of well depth, formation pressure, and formation temperature, where... For the depth of the well, For formation pressure, This refers to the formation temperature.

[0099]

[0100] In the formula, For reference well depth, the average well depth of historical drilling data from adjacent wells is generally taken; To reference formation pressure, the formation pressure from historical drilling data of adjacent wells is generally used as the benchmark. For reference temperature, the average bottom hole temperature of historical drilling data from adjacent wells is generally taken. , and These are correction factors for well depth, formation pressure, and formation temperature, respectively, used to reflect the relative importance of different parameters (i.e., any parameter data from the historical drilling data of adjacent wells) in the formulation suitability evaluation.

[0101] The correction coefficient can be determined based on actual drilling needs, or its value can be determined through statistical analysis of historical working data or parameter sensitivity analysis, thereby ensuring that the formula adaptability assessment results based on preset thresholds are consistent with actual downhole working conditions.

[0102] For example, the drilling data for the current well section indicates that the formation pressure and temperature changes are gradual, and the main factor affecting drilling is the well depth. When using adaptive calculation, the correction parameter is the well depth, and the correction terms for formation pressure and temperature are ignored. Specifically, the preset threshold based on well depth correction can be calculated using the following formula:

[0103]

[0104] Step 2: When the comparison result shows that the formula adaptability assessment value is greater than the preset threshold, the output drilling data feasibility report includes the determination result that the drilling data is not feasible and the adjustment information of the drilling data.

[0105] Under this implementation, when Exceeding the preset threshold If the drilling data is deemed unsuitable for the current drilling conditions, the output drilling data feasibility report will include the determination that the drilling data is unsuitable and information on adjusting the drilling data.

[0106] Among them, the adjustment information of drilling data refers to the direction of adjustment of drilling data, which can specifically include adjustments to drilling fluid density, oil-water ratio, demulsification voltage, rheological parameters, pH value and chloride ion concentration.

[0107] For example, when drilling data is not feasible, the adjustment information for drilling data can be analyzed by combining the importance patterns of drilling data and historical drilling data of adjacent wells.

[0108] The drilling data feasibility determination method provided in this application first obtains the target well's dataset, which includes multiple drilling data points for the target well. Then, it calculates the parameter deviation between each drilling data point and preset standard data, obtaining multiple parameter deviations for the target well. If any parameter deviation exceeds a preset fitness range, all parameter deviations for the target well are input into the formulation fitness assessment model. A comprehensive assessment is initiated only when parameters are abnormal, reducing unnecessary complex calculations and improving the real-time performance of the determination. The model then obtains the formulation fitness assessment value for the target well. Based on the comparison between the formulation fitness assessment value and a preset threshold, the model combines multiple parameter deviations, avoiding the bias of judging a single parameter and improving the accuracy of drilling data feasibility determination. Finally, it outputs a drilling data feasibility report for the target well.

[0109] Based on the above embodiments, Figure 2 A flowchart illustrating the drilling data feasibility determination method provided in this application embodiment. Figure 2 ,like Figure 2 As shown, step 11 includes the following steps:

[0110] Step 21: Obtain the drilling data to be processed and historical drilling data of the target well.

[0111] In this step, drilling data to be processed is acquired based on the real-time operating conditions of the target well. The drilling data to be processed includes drilling fluid data, geological information data, and engineering data. The drilling data is acquired in real time or periodically through surface sensors and downhole measurement tools (such as MWD and LWD), and directly reflects the current status of the target well.

[0112] Historical drilling data is selected from the database from completed wells that are highly similar to the target well in terms of geological conditions, engineering parameters, or drilling fluid systems. This includes drilling fluid parameters, formation response information, and records of construction anomalies in the corresponding well sections. By simultaneously acquiring the target well's drilling data to be processed and historical drilling data, a comparison between the current state and historical benchmarks is provided for subsequent calculations of parameter deviation and training of formulation adaptability assessment models, ensuring the relevance and reliability of the assessment results.

[0113] Step 22: Normalize the drilling data to be processed based on historical drilling data to obtain normalized drilling data.

[0114] In this step, the normalization process uses the Z-score standardization method to extract the mean and standard deviation of parameters from the historical drilling data of adjacent wells. Then, the parameter values ​​acquired in real time are normalized. Normalization is performed using the following formula:

[0115]

[0116] In the formula, These are the parameter values ​​in the drilling data after normalization processing. This represents the average value of the parameters corresponding to the drilling data to be processed in the historical drilling data of adjacent wells. This represents the standard deviation of the parameters corresponding to the drilling data to be processed in the historical drilling data of adjacent wells.

[0117] Specifically, step 2 can include the following implementation methods:

[0118] Step 1: Obtain the mean and standard deviation of the historical drilling data corresponding to the drilling data to be processed.

[0119] Under this implementation, historical drilling data corresponding to the drilling data to be processed is obtained from the drilling database. First, the data is cleaned and statistically analyzed. Then, the statistical characteristics such as the mean and standard deviation of each parameter in the historical drilling data are calculated to quantify the parameter fluctuation range under drilling conditions.

[0120] Among them, historical drilling data comes from drilling data of completed wells in adjacent wells in the same block, in the same type of formation, or under similar engineering conditions.

[0121] For example, if the mean of historical drilling fluid density data for a certain block is 1.20 g / cm³ and the standard deviation is 0.03 g / cm³, it means that during normal drilling in that block, the density usually fluctuates around 1.20 g / cm³, and 95% of the normal data will fall within the range of 1.14~1.26 g / cm³ (that is, mean ± 2 times standard deviation).

[0122] Step 2: Calculate the normalized drilling data based on the standard deviation and mean of the drilling data to be processed and the historical drilling data.

[0123] Step 23: Select multiple well data that meet the preset judgment conditions from the normalized drilling data to obtain the target well's dataset to be processed.

[0124] In this step, the preset judgment condition is to determine whether the parameters in the normalized drilling data are abnormal. When the absolute value of the normalized drilling data is greater than a threshold, the normalized drilling data is determined to be an outlier. After removing all outliers, the dataset to be processed for the target well is obtained. This feature set not only includes drilling fluid data and geological information data, but also covers engineering data. This comprehensive consideration enables the feature set to fully and accurately reflect the actual performance of drilling fluid in geological and engineering environments.

[0125] For example, the threshold could be 3 times the standard deviation.

[0126] The drilling data feasibility determination method provided in this application first acquires the drilling data to be processed and historical drilling data of the target well. Based on the historical drilling data, the drilling data to be processed is normalized to obtain normalized drilling data. This eliminates the dimensional differences and systematic biases of different types of drilling data, improves the comparability of drilling data, and provides more accurate basic data for subsequent feasibility determination. Finally, multiple drilling data that meet the preset judgment conditions are selected from the normalized drilling data to obtain the target well's dataset to be processed. This reduces the amount of redundant data processing, speeds up the feasibility determination process, and improves the real-time performance of drilling data feasibility determination.

[0127] Based on the above embodiments, Figure 3 A flowchart illustrating the drilling data feasibility determination method provided in this application embodiment. Figure 3 ,like Figure 3 As shown, the process for determining the formulation suitability assessment model is as follows:

[0128] Step 31: Calculate the deviation of historical parameters corresponding to historical drilling data.

[0129] In this step, historical drilling data is first acquired, then normalized, and finally input into the aforementioned deviation parameter. The calculation formula is used to calculate the deviation of historical parameters.

[0130] Step 32: Based on historical drilling data and historical parameter deviation, optimize and train the parameter weights of the formulation adaptability assessment model until the preset convergence condition is met, and obtain the trained formulation adaptability assessment model.

[0131] In this step, the formulation suitability assessment model can be expressed by the following formula:

[0132]

[0133] In the formula, S is the formula suitability assessment value; Let be the weight of the parameter in the i-th historical drilling data; n is the total number of parameters in the historical drilling data; D i This represents the deviation of historical parameters.

[0134] During training, the weights of each parameter are adjusted through continuous iterative learning. This allows the formulation adaptability assessment value S to more accurately reflect the adaptability of the drilling fluid under current geological and engineering conditions. During training, a backpropagation algorithm is used to gradually optimize the weight values ​​by calculating the weight gradients of each parameter in the loss function until the preset training accuracy or number of iterations is reached, resulting in a well-trained formulation adaptability assessment model.

[0135] For example, a neural network was trained on 50 sets of data from adjacent wells. After 500 iterations, the model converged, and the weights of each parameter were optimized. The neural network structure consisted of one input layer, one hidden layer, and one output layer. The number of nodes in the input layer corresponded to the total number of parameters in the historical drilling data, the number of nodes in the hidden layer was set to 10 based on experience, and the number of nodes in the output layer was 1, corresponding to the formulation suitability evaluation value. During training, mean squared error was used as the loss function, and the parameter weights were adjusted through the backpropagation algorithm until the model's performance on the validation set stabilized. After training, the trained formulation suitability evaluation model was obtained.

[0136] The drilling data feasibility determination method provided in this application first calculates the historical parameter deviation corresponding to the historical drilling data, and then optimizes and trains the parameter weights of the formulation adaptability assessment model based on the historical drilling data and the historical parameter deviation, so that the formulation adaptability assessment model can better fit the influence weights of each parameter in the actual drilling scenario, improve the judgment accuracy of the assessment model, until the preset convergence condition is reached, and the trained formulation adaptability assessment model is obtained.

[0137] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0138] Figure 4 This is a schematic diagram of the drilling data feasibility assessment device provided in an embodiment of this application. Figure 4 As shown, the device includes:

[0139] The acquisition module 41 is used to acquire the dataset to be processed for the target well; the dataset to be processed includes multiple drilling data of the target well;

[0140] The calculation module 42 is used to calculate the parameter deviation between each drilling data and the preset standard data to obtain multiple parameter deviations of the target well.

[0141] The processing module 43 is used to input all parameter deviations of the target well into the formulation adaptability evaluation model when any parameter deviation exceeds the preset fitness range, so as to obtain the formulation adaptability evaluation value of the target well.

[0142] Output module 44 is used to output a drilling data feasibility report for the target well based on the comparison results between the formula adaptability assessment value and the preset threshold.

[0143] In one possible implementation, the acquisition module 41 is specifically used for:

[0144] Acquire the unprocessed drilling data and historical drilling data of the target well;

[0145] Based on historical drilling data, the drilling data to be processed is normalized to obtain normalized drilling data.

[0146] Multiple well data that meet the preset judgment conditions are selected from the normalized drilling data to obtain the target well's dataset to be processed.

[0147] In one possible implementation, the drilling data to be processed is normalized based on historical drilling data to obtain normalized drilling data. The acquisition module 41 is further configured to:

[0148] Obtain the mean and standard deviation of the historical drilling data corresponding to the drilling data to be processed;

[0149] The normalized drilling data is calculated based on the standard deviation and mean of the drilling data to be processed and the historical drilling data.

[0150] In one possible implementation, after calculating the parameter deviation between each drilling data point and preset standard data to obtain multiple parameter deviations for the target well, the processing module 43 is further configured to:

[0151] If the deviation of all parameters of the target well is within the preset fitness range, a drilling data feasibility report for the target well is output; the drilling data feasibility report includes the result of determining the feasibility of the drilling data.

[0152] In one possible implementation, the output module 44 is specifically used for:

[0153] When the comparison result shows that the formula adaptability assessment value is less than or equal to the preset threshold, the output drilling data feasibility report includes the determination result of the drilling data feasibility.

[0154] When the comparison result shows that the formula adaptability assessment value is greater than the preset threshold, the output drilling data feasibility report includes the determination result that the drilling data is not feasible and the adjustment information of the drilling data.

[0155] In one possible implementation, the processing module 43 is specifically used for:

[0156] Calculate the deviation of historical parameters corresponding to historical drilling data;

[0157] Based on historical drilling data and historical parameter deviation, the parameter weights of the formulation adaptability assessment model are optimized and trained until the preset convergence condition is met, thus obtaining a well-trained formulation adaptability assessment model.

[0158] The apparatus provided in this application embodiment can be used to execute the determination method in any of the above embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0159] It should be noted that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented in software via processing element calls, while others are implemented in hardware. Additionally, these modules can be fully or partially integrated together, or implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each of the above modules can be completed through the integrated logic circuits in the hardware of the processor element or through software instructions.

[0160] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application, such as... Figure 5 As shown, the electronic device may include: a processor 51, a memory 52, and computer program instructions stored in the memory 52 and executable on the processor 51. When the processor 51 executes the computer program instructions, it implements the method provided in any of the foregoing embodiments.

[0161] Optionally, the various components of the electronic device can be connected via a system bus.

[0162] The memory 52 can be a separate storage unit or a storage unit integrated into the processor 51. The number of processors 51 can be one or more.

[0163] It should be understood that the processor 51 can be a Central Processing Unit (CPU), or other general-purpose processors 51, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor 51 can be a microprocessor 51, or any conventional processor 51. The steps of the method disclosed in this application can be directly manifested as being executed by the hardware processor 51, or being executed by a combination of hardware and software modules within the processor 51.

[0164] The system bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. Memory 52 may include random access memory (RAM) 52, and may also include non-volatile memory (NVM) 52, such as at least one disk storage device 52.

[0165] All or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable memory 52. ​​When the program is executed, it performs the steps of the above method embodiments; and the aforementioned memory 52 (storage medium) includes: read-only memory 52 (ROM), RAM, flash memory 52, hard disk, solid-state hard disk, magnetic tape, floppy disk, optical disk, and any combination thereof.

[0166] The electronic device provided in this application embodiment can be used to execute the method provided in any of the above method embodiments. Its implementation principle and technical effect are similar, and will not be repeated here.

[0167] This application provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the above-described method.

[0168] The aforementioned computer-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, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0169] Optionally, a readable storage medium can be coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Alternatively, the readable storage medium can be an integral part of the processor. Both the processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components within the device.

[0170] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium, and the at least one processor can implement the above-described method when executing the computer program.

[0171] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for determining the feasibility of drilling data, characterized in that, include: Obtain the dataset to be processed from the target well; The dataset to be processed includes drilling data from multiple wells of the target well; The drilling data can be any parameter data from drilling fluid data, geological information data, and engineering data. The parameter deviation between each of the drilling data and the preset standard data is calculated to obtain multiple parameter deviations of the target well; If any of the parameter deviations exceeds the preset fitness range, all parameter deviations of the target well are input into the formulation fitness evaluation model to obtain the formulation fitness evaluation value of the target well. Based on the comparison between the formula adaptability assessment value and the preset threshold, a drilling data feasibility report for the target well is output.

2. The method according to claim 1, characterized in that, The dataset to be processed for obtaining the target well includes: Obtain the drilling data to be processed and historical drilling data of the target well; The drilling data to be processed is normalized based on the historical drilling data to obtain normalized drilling data. Multiple well data that meet preset judgment conditions are selected from the normalized drilling data to obtain the target well's dataset to be processed.

3. The method according to claim 2, characterized in that, The normalization process performed on the drilling data to be processed based on the historical drilling data to obtain normalized drilling data includes: Obtain the mean and standard deviation of the historical drilling data corresponding to the drilling data to be processed; The normalized drilling data is calculated based on the standard deviation and mean of the drilling data to be processed and the historical drilling data.

4. The method according to claim 2, characterized in that, After calculating the parameter deviation between each of the drilling data and the preset standard data to obtain multiple parameter deviations for the target well, the method further includes: If the deviation of each parameter of the target well is within the preset fitness range, a drilling data feasibility report for the target well is output; the drilling data feasibility report includes a determination result of the feasibility of the drilling data.

5. The method according to claim 1, characterized in that, The step of outputting a drilling data feasibility report for the target well based on the comparison result between the formula adaptability assessment value and a preset threshold includes: When the comparison result is that the formula adaptability assessment value is less than or equal to the preset threshold, the output drilling data feasibility report includes a determination result of the drilling data feasibility; When the comparison result indicates that the formula adaptability assessment value is greater than the preset threshold, the output drilling data feasibility report includes a determination that the drilling data is not feasible and adjustment information for the drilling data.

6. The method according to claim 1, characterized in that, The process for determining the formulation suitability assessment model is as follows: Calculate the deviation of historical parameters corresponding to historical drilling data; Based on the historical drilling data and the historical parameter deviation, the parameter weights of the formulation adaptability evaluation model are optimized and trained until the preset convergence condition is met, thus obtaining the trained formulation adaptability evaluation model.

7. A drilling data feasibility assessment device, characterized in that, include: The acquisition module is used to acquire the dataset to be processed from the target well; The dataset to be processed includes drilling data from multiple wells of the target well; The calculation module is used to calculate the parameter deviation between each of the drilling data and the preset standard data, so as to obtain multiple parameter deviations of the target well; The processing module is used to input all parameter deviations of the target well into the formulation adaptability evaluation model to obtain the formulation adaptability evaluation value of the target well when any of the parameter deviations exceeds the preset fitness range. The output module is used to output a drilling data feasibility report for the target well based on the comparison result between the formula adaptability assessment value and the preset threshold.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in 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, when executed by a processor, are used to implement the method as described in any one of claims 1 to 6.

10. A computer program, characterized in that, The computer program includes a computer program stored in a computer-readable storage medium, which at least one processor can read from the computer-readable storage medium, and which, when executing the computer program, can implement the method described in any one of claims 1 to 6.

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