Logging interpretation result processing method and device, equipment and storage medium
By modifying the candidate interpretation conclusions in the well log interpretation results and inputting the target log prediction model, the problems of inaccurate log interpretation results and inconsistent interpretation results are solved, and the reliability of the log interpretation results are improved.
Patent Information
- Application Number
- CN202411692432.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-25
- Publication Date
- 2025-03-28
AI Technical Summary
The existing logging interpretation results are not accurate enough. After human experts modify the interpretation conclusion, there is an inconsistency between the interpretation conclusion and the reasoning parameters, which affects the reliability of the logging interpretation results.
A method for processing well logging interpretation results is proposed. By obtaining the candidate interpretation conclusions and candidate inference parameters in the well logging interpretation results, if the candidate interpretation conclusion is an incorrect conclusion, it is modified to obtain the target interpretation conclusion and input it into the preset target logging prediction model to obtain the target inference parameters, and finally display the target interpretation conclusions and target inference parameters.
After manually modifying the candidate interpretation conclusion, target reasoning parameters consistent with the target interpretation conclusion can be quickly obtained, which solves the problem of inconsistency between the interpretation conclusion and the reasoning parameters, and improves the reliability of log interpretation results.
Smart Images
Figure CN119721264A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of well logging interpretation technology, and in particular to a method, device, equipment and storage medium for processing well logging interpretation results. Background Art
[0002] Well logging interpretation refers to the use of the correct method to process the parameters obtained from well logging into geological information (well logging interpretation results). Well logging interpretation results include interpretation conclusions and inference parameters. The well logging interpretation results can be obtained by inputting the well logging parameters into the trained prediction model. Due to the need for professional judgment, the difficulty in handling outliers, and the limitations of existing prediction models, well logging interpretation results are often not accurate enough. In related technologies, human experts modify the interpretation conclusions, but only modifying the interpretation conclusions will lead to inconsistencies between the interpretation conclusions and the inference parameters, affecting the reliability of the well logging interpretation results, thereby affecting subsequent geological evaluations and oil and gas reservoir development decisions. Therefore, how to improve the reliability of well logging interpretation results has become a technical problem that needs to be solved urgently. Summary of the invention
[0003] The main purpose of the embodiments of the present application is to provide a method, device, equipment and storage medium for processing well logging interpretation results, aiming to improve the reliability of well logging interpretation results.
[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a method for processing well logging interpretation results, the method comprising:
[0005] Obtaining well logging interpretation results; wherein the well logging interpretation results include candidate interpretation conclusions and candidate inference parameters;
[0006] If the candidate interpretation conclusion is an erroneous conclusion, obtaining a target interpretation conclusion after modifying the candidate interpretation conclusion;
[0007] Inputting the target interpretation conclusion into a preset target logging prediction model to obtain target reasoning parameters;
[0008] The target interpretation conclusion and the target reasoning parameters are displayed.
[0009] In some embodiments, inputting the target interpretation conclusion into a preset target well logging prediction model to obtain target reasoning parameters includes:
[0010] Performing importance evaluation on the plurality of candidate reasoning parameters to obtain importance evaluation data; wherein the importance evaluation data includes a weight of each candidate reasoning parameter;
[0011] Sort each of the candidate reasoning parameters from large to small according to the corresponding weight, and select the candidate reasoning parameters with a preset number of rankings at the top as the selected reasoning parameters;
[0012] The target interpretation conclusion is input into the target well logging prediction model to obtain the target reasoning parameters corresponding to the selected reasoning parameters.
[0013] In some embodiments, after inputting the target interpretation conclusion into the target well logging prediction model to obtain the target reasoning parameter, the method further includes:
[0014] Determine a target filtering mode from a preset parameter filtering table according to the target inference parameter; wherein the parameter filtering table represents a mapping relationship between the target inference parameter and the target filtering mode;
[0015] The target inference parameter is filtered according to the target filtering method to obtain a filtered target inference parameter.
[0016] In some embodiments, obtaining the well logging interpretation result includes:
[0017] Obtain target logging prediction model;
[0018] Acquire at least one corresponding logging parameter according to the target logging prediction model;
[0019] The logging parameters are input into the target logging prediction model to obtain the logging interpretation results.
[0020] In some embodiments, obtaining the target well logging prediction model includes:
[0021] Get model selection instructions;
[0022] In response to the model selection instruction, a model is selected from a preset prediction model set to obtain the target well logging prediction model; wherein the prediction model set includes a plurality of candidate well logging prediction models.
[0023] In some embodiments, before obtaining the model selection instruction, the method further includes:
[0024] Obtain well logging training data;
[0025] Inputting the well logging training data into a preset original well logging prediction model, and outputting an interpretation prediction result;
[0026] Performing loss calculation on the preset explanation verification result and the explanation prediction result through a preset regression loss function to obtain target loss data;
[0027] The model parameters of the original well logging prediction model are adjusted according to the target loss data to obtain the candidate well logging prediction model.
[0028] In some embodiments, after obtaining the well logging interpretation result, the method further includes:
[0029] If the candidate interpretation conclusion is a correct conclusion, the candidate interpretation conclusion and the candidate reasoning parameters are displayed.
[0030] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a well logging interpretation result processing device, the device comprising:
[0031] An acquisition module, used for acquiring well logging interpretation results; wherein the well logging interpretation results include candidate interpretation conclusions and candidate reasoning parameters;
[0032] A modification module, if the candidate interpretation conclusion is an erroneous conclusion, the modification module is used to obtain a target interpretation conclusion after modifying the candidate interpretation conclusion;
[0033] A prediction module, used for inputting the target interpretation conclusion into a preset target logging prediction model to obtain target reasoning parameters;
[0034] A display module is used to display the target interpretation conclusion and the target reasoning parameters.
[0035] To achieve the above objectives, a third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.
[0036] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.
[0037] The method, device, equipment and storage medium for processing well logging interpretation results proposed in the present application obtain well logging interpretation results, wherein the well logging interpretation results include candidate interpretation conclusions and candidate reasoning parameters. If the candidate interpretation conclusion is an erroneous conclusion, a target interpretation conclusion after modifying the candidate interpretation conclusion is obtained. The target interpretation conclusion is input into a preset target well logging prediction model to obtain the target reasoning parameters. The target interpretation conclusion and the target reasoning parameters are displayed. After manually modifying the candidate interpretation conclusion, the modified target interpretation conclusion is input into the target well logging prediction model, and accurate target reasoning parameters can be quickly obtained, which solves the problem that after the interpretation conclusion is manually modified, the interpretation conclusion is inconsistent with the reasoning parameters, which affects the reliability of the well logging interpretation results, and improves the reliability of the well logging interpretation results. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] Figure 1It is an optional flow chart of the method for processing well logging interpretation results provided in the embodiment of the present application;
[0039] Figure 2 yes Figure 1 Flow chart of step S101 in FIG.
[0040] Figure 3 yes Figure 1 Flow chart of step S103 in FIG.
[0041] Figure 4 is a flow chart of a method for processing well logging interpretation results provided by another embodiment of the present application;
[0042] Figure 5 yes Figure 2 Flow chart of step S201 in FIG.
[0043] Figure 6 is a flow chart of a method for processing well logging interpretation results provided by a third embodiment of the present application;
[0044] Figure 7 is a structural schematic diagram of a well logging interpretation result processing device provided in an embodiment of the present application;
[0045] Figure 8 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION
[0046] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0047] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.
[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.
[0049] Due to the need for professional judgment, the difficulty of handling outliers, and the limitations of existing prediction models, well logging interpretation results are often not accurate enough. In related technologies, human experts modify the interpretation conclusions, but modifying the interpretation conclusions alone will lead to inconsistencies between the interpretation conclusions and the inference parameters, affecting the reliability of the well logging interpretation results, and thus affecting subsequent geological evaluation and oil and gas reservoir development decisions.
[0050] Based on this, the embodiments of the present application provide a method, device, equipment and storage medium for processing well logging interpretation results, which is intended to obtain well logging interpretation results. If a human expert determines that a candidate interpretation conclusion is an erroneous conclusion, the human expert modifies the candidate interpretation conclusion to obtain a target interpretation conclusion. The target interpretation conclusion is input into a preset target well logging prediction model, the target reasoning parameters are output, and the target interpretation conclusion and the target reasoning parameters are output and displayed. Since the target reasoning parameters are parameters consistent with the target interpretation conclusion, when the human expert modifies the candidate interpretation conclusion, the target reasoning parameters consistent with the target interpretation conclusion can also be accurately and quickly obtained, thereby improving the reliability of the well logging interpretation results.
[0051] The well logging interpretation result processing method, device, equipment and storage medium provided in the embodiments of the present application are specifically described through the following embodiments. First, the well logging interpretation result processing method in the embodiments of the present application is described.
[0052] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.
[0053] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.
[0054] The logging interpretation result processing method provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the logging interpretation result processing method, etc., but is not limited to the above forms.
[0055] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.
[0056] Please refer to Figure 1 , Figure 1 is an optional flow chart of the method for processing well logging interpretation results provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S104.
[0057] Step S101, obtaining well logging interpretation results; wherein the well logging interpretation results include candidate interpretation conclusions and candidate inference parameters;
[0058] Step S102, if the candidate interpretation conclusion is an erroneous conclusion, obtaining a target interpretation conclusion after modifying the candidate interpretation conclusion;
[0059] Step S103, inputting the target interpretation conclusion into a preset target logging prediction model to obtain target reasoning parameters;
[0060] Step S104, displaying the target interpretation conclusion and target reasoning parameters.
[0061] See also Figure 2 In some embodiments, step S101 may include but is not limited to steps S201 to S203:
[0062] Step S201, obtaining a target well logging prediction model;
[0063] Step S202, obtaining at least one corresponding logging parameter according to the target logging prediction model;
[0064] Step S203, input the logging parameters into the target logging prediction model to obtain the logging interpretation results.
[0065] In step S201 of some embodiments, it is necessary to pre-train multiple candidate well logging prediction models. Different candidate well logging prediction models may obtain different well logging interpretation results. The corresponding candidate well logging prediction model may be selected as the target well logging prediction model according to the required well logging interpretation results. For example, if it is necessary to interpret reservoir characteristics, a candidate well logging prediction model based on petrophysical characteristics may be selected as the target well logging prediction model. If it is necessary to interpret formation characteristics, a candidate well logging prediction model based on formation characteristics may be selected as the target well logging prediction model.
[0066] In step S202 of some embodiments, different candidate well logging prediction models are trained by different well logging parameters. When the well logging interpretation result needs to be predicted by the candidate well logging prediction model, the corresponding well logging parameters need to be input into the candidate well logging prediction model.
[0067] In one example, a mapping table of each candidate well logging prediction model and well logging parameters is saved in advance. After a candidate well logging prediction model is selected as the target well logging prediction model, the corresponding well logging parameters are screened from the mapping table. For example, the well logging parameters corresponding to the candidate well logging prediction model based on rock physical properties include parameters such as acoustic time difference, compensated neutron, density, and natural gamma. The well logging parameters corresponding to the candidate well logging prediction model based on formation characteristics include parameters such as morphological characteristics (such as columnar, bell-shaped, funnel-shaped, etc.), amplitude characteristics, and contact relationships of various logging curves. Therefore, if a candidate well logging prediction model based on rock physical properties is selected as the target well logging prediction model, the acquired well logging parameters include parameters such as acoustic time difference, compensated neutron, density, and natural gamma.
[0068] In step S203 of some embodiments, the logging parameters are input into the target logging prediction model to obtain the logging interpretation results, wherein the logging interpretation results include candidate interpretation conclusions and candidate reasoning parameters. For example, if the target logging prediction model is a candidate logging prediction model based on petrophysical properties, the candidate interpretation conclusion is the interpretation of reservoir properties, and the candidate reasoning parameters are the porosity, permeability and shale content of the reservoir.
[0069] Steps S201 to S203 shown in the embodiment of the present application can obtain well logging interpretation results through the target well logging prediction model and corresponding well logging parameters.
[0070] In step S102 of some embodiments, if the human expert determines that the predicted candidate interpretation conclusion needs to be modified, it means that the candidate interpretation conclusion is an incorrect conclusion. The human expert modifies the candidate interpretation conclusion into a correct target interpretation conclusion, and thus obtains the target interpretation conclusion.
[0071] In steps S103 to S104 of some embodiments, after the human expert modifies the candidate interpretation conclusion into the correct target interpretation conclusion, in order to make the target interpretation conclusion consistent with the candidate reasoning parameters and improve the accuracy and reliability of the well logging interpretation results, the target interpretation conclusion is input into the target well logging prediction model to obtain the target reasoning parameters. Specifically, the target well logging prediction model is a model that outputs the well logging interpretation results. After the target reasoning parameters are consistent with the target interpretation conclusion, the target interpretation conclusion and the target reasoning parameters are output and displayed.
[0072] In one example, when a human expert determines that a candidate explanation conclusion is a correct conclusion, there is no need to modify the candidate explanation conclusion, and the candidate explanation conclusion and candidate reasoning parameters can be directly output and displayed.
[0073] Steps S101 to S104 shown in the embodiment of the present application are performed by obtaining well logging interpretation results, wherein the well logging interpretation results include candidate interpretation conclusions and candidate reasoning parameters. If the candidate interpretation conclusion is an erroneous conclusion, a target interpretation conclusion after modifying the candidate interpretation conclusion is obtained. The target interpretation conclusion is input into a preset target well logging prediction model to obtain target reasoning parameters. The target interpretation conclusion and target reasoning parameters are displayed. After manually modifying the candidate interpretation conclusion, the modified target interpretation conclusion is input into the target well logging prediction model, and accurate target reasoning parameters can be quickly obtained, which solves the problem that after the interpretation conclusion is manually modified, the interpretation conclusion is inconsistent with the reasoning parameters, which affects the reliability of the well logging interpretation results, thereby improving the reliability of the well logging interpretation results.
[0074] See also Figure 3 In some embodiments, step S103 may include but is not limited to steps S301 to S303:
[0075] Step S301, performing importance evaluation on a plurality of candidate inference parameters to obtain importance evaluation data; wherein the importance evaluation data includes a weight of each candidate inference parameter;
[0076] Step S302, sorting each candidate inference parameter from large to small according to the corresponding weight, and selecting a preset number of candidate inference parameters ranked first as selected inference parameters;
[0077] Step S303, inputting the target interpretation conclusion into the target logging prediction model to obtain the target reasoning parameters corresponding to the selected reasoning parameters.
[0078] In step S301 of some embodiments, since there are multiple candidate inference parameters, if the target logging prediction model modifies each candidate inference parameter, the calculation speed will be too slow. Therefore, only important candidate inference parameters need to be modified, so the importance of multiple candidate inference parameters can be evaluated by random forest algorithm to obtain importance evaluation data. The importance evaluation data includes the weight of each candidate inference parameter, and the larger the weight, the higher the importance.
[0079] In step S302 of some embodiments, the user can set the preset number according to different requirements. When the preset number is two, in order to obtain the two candidate reasoning parameters with the largest weight, each candidate reasoning parameter is sorted from large to small according to the corresponding weight, and then the first two candidate reasoning parameters are selected as the selected reasoning parameters, and only the selected reasoning parameters need to be modified.
[0080] In step S303 of some embodiments, after inputting the target interpretation conclusion into the target logging prediction model, it is only necessary to output the target reasoning parameter corresponding to the selected reasoning parameter. In one example, if the selected reasoning parameter is porosity of 15%, the output target reasoning parameter may be porosity of 20%.
[0081] In steps S301 to S303 shown in this embodiment, importance evaluation is performed on multiple candidate reasoning parameters to select selected reasoning parameters with high importance. Only the selected reasoning parameters need to be modified, thereby improving the modification speed.
[0082] See also Figure 4 In some embodiments, after step S103, the logging interpretation result processing method may also include but is not limited to steps S401 to S402:
[0083] Step S401, determining a target filtering mode from a preset parameter filtering table according to a target inference parameter; wherein the parameter filtering table represents a mapping relationship between the target inference parameter and the target filtering mode;
[0084] Step S402: Filter the target inference parameters according to the target filtering method to obtain filtered target inference parameters.
[0085] In step S401 of some embodiments, after obtaining the target reasoning parameters, the target reasoning parameters can also be filtered and smoothed to remove abnormal values, thereby further improving the accuracy of the target reasoning parameters. Specifically, due to the characteristics of the parameters and the required filtering effects, different target reasoning parameters need to adopt different filtering methods. Therefore, a mapping table of target reasoning parameters and target filtering methods is pre-constructed, and the target filtering method is determined from the parameter filtering table according to the target reasoning parameters. The user can also add, modify or delete the parameter filtering table as needed.
[0086] Specifically, for parameters with random interference, an arithmetic average filtering method can be used for filtering. For parameters with periodic interference, a first-order lag filtering method can be used for filtering.
[0087] In step S402 of some embodiments, after determining the target filtering method, the target reasoning parameters are filtered according to the target filtering method to obtain the filtered target reasoning parameters, and then the target interpretation conclusion and the filtered target reasoning parameters are output and displayed.
[0088] In step S401 to step S402 shown in this embodiment, after the corresponding target filtering method is determined, the target inference parameters are filtered according to the target filtering method to further improve the accuracy of the target inference parameters.
[0089] See also Figure 5 In some embodiments, step S201 may include but is not limited to steps S501 to S502:
[0090] Step S501, obtaining a model selection instruction;
[0091] Step S502, in response to the model selection instruction, a model is selected from a preset prediction model set to obtain a target well logging prediction model; wherein the prediction model set includes a plurality of candidate well logging prediction models.
[0092] In step S501 to step S502 shown in this embodiment, the user selects a model from the prediction model set according to the required well logging interpretation result, and then obtains a model selection instruction. In response to the model selection instruction, a candidate well logging prediction model is selected from the preset prediction model set as the target well logging prediction model. The prediction model set includes a plurality of pre-trained candidate well logging prediction models. By selecting the target well logging prediction model, the well logging interpretation result is predicted based on the target well logging prediction model.
[0093] See also Figure 6 In some embodiments, before step S501, the method for processing the well logging interpretation result further includes but is not limited to steps S601 to S604:
[0094] Step S601, obtaining well logging training data;
[0095] Step S602, inputting the well logging training data into a preset original well logging prediction model, and outputting the interpretation prediction results;
[0096] Step S603, performing loss calculation on the preset explanation verification result and the explanation prediction result by using a preset regression loss function to obtain target loss data;
[0097] Step S604: adjusting the model parameters of the original well logging prediction model according to the target loss data to obtain a candidate well logging prediction model.
[0098] In steps S601 and S602 of some embodiments, after the well logging training data is acquired, the well logging training data may be pre-processed and then input into the original well logging prediction model to output the interpretation prediction results.
[0099] In steps S603 to S604 of some embodiments, the interpretation verification result is the interpretation result of manually annotating the well logging training data, and the loss calculation is performed on the interpretation verification result and the interpretation prediction result through the regression loss function to obtain the target loss data. The model parameters of the original well logging prediction model are adjusted according to the target loss data to obtain the candidate well logging prediction model.
[0100] It should be noted that each candidate well logging prediction model is trained in the above manner, but the well logging training data used may be different.
[0101] In steps S601 to S604 shown in this embodiment, each candidate well logging prediction model is pre-trained, and a target well logging prediction model is selected from multiple candidate well logging prediction models, so as to predict a well logging interpretation conclusion based on the target well logging prediction model.
[0102] See also Figure 7 The embodiment of the present application further provides a well logging interpretation result processing device, which can implement the above-mentioned well logging interpretation result processing method, and the device includes:
[0103] The acquisition module 701 is used to acquire the well logging interpretation result; wherein the well logging interpretation result includes candidate interpretation conclusions and candidate reasoning parameters;
[0104] Modification module 702, if the candidate interpretation conclusion is an erroneous conclusion, the modification module is used to obtain a target interpretation conclusion after modifying the candidate interpretation conclusion;
[0105] Prediction module 703, used to input the target interpretation conclusion into a preset target logging prediction model to obtain target reasoning parameters;
[0106] The display module 704 is used to display the target interpretation conclusion and the target reasoning parameters.
[0107] The specific implementation of the well logging interpretation result processing device is substantially the same as the specific implementation of the well logging interpretation result processing method described above, and will not be described in detail herein.
[0108] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned well logging interpretation result processing method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.
[0109] See also Figure 8 , Figure 8 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:
[0110] The processor 801 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;
[0111] The memory 802 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 802, and the processor 801 calls and executes the well logging interpretation result processing method of the embodiment of this application;
[0112] Input / output interface 803, used to implement information input and output;
[0113] The communication interface 804 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);
[0114] A bus 805 that transmits information between the various components of the device (e.g., the processor 801, the memory 802, the input / output interface 803, and the communication interface 804);
[0115] The processor 801 , the memory 802 , the input / output interface 803 and the communication interface 804 are connected to each other in communication within the device via a bus 805 .
[0116] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned well logging interpretation result processing method is implemented.
[0117] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
[0118] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.
[0119] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.
[0120] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0121] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.
[0122] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.
[0123] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.
[0124] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods 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. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0125] The units described above 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.
[0126] In addition, each functional unit in each embodiment of the present application 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0127] If the integrated unit 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 application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.
[0128] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.
Claims
1. A method for processing well logging interpretation results, characterized in that: The method comprises: Obtaining well logging interpretation results; wherein the well logging interpretation results include candidate interpretation conclusions and candidate inference parameters; If the candidate interpretation conclusion is an erroneous conclusion, obtaining a target interpretation conclusion after modifying the candidate interpretation conclusion; Inputting the target interpretation conclusion into a preset target logging prediction model to obtain target reasoning parameters; The target interpretation conclusion and the target reasoning parameters are displayed.
2. The method according to claim 1, characterized in that The target interpretation conclusion is input into a preset target logging prediction model to obtain target reasoning parameters, including: Performing importance evaluation on the plurality of candidate reasoning parameters to obtain importance evaluation data; wherein the importance evaluation data includes a weight of each candidate reasoning parameter; Sort each of the candidate reasoning parameters from large to small according to the corresponding weight, and select the candidate reasoning parameters with a preset number of rankings at the top as the selected reasoning parameters; The target interpretation conclusion is input into the target well logging prediction model to obtain the target reasoning parameters corresponding to the selected reasoning parameters.
3. The method according to claim 1, characterized in that After inputting the target interpretation conclusion into the target well logging prediction model to obtain the target reasoning parameter, the method further includes: Determine a target filtering mode from a preset parameter filtering table according to the target inference parameter; wherein the parameter filtering table represents a mapping relationship between the target inference parameter and the target filtering mode; The target inference parameter is filtered according to the target filtering method to obtain a filtered target inference parameter.
4. The method according to claim 1, characterized in that: The obtaining of the well logging interpretation result comprises: Obtain target well logging prediction model; Acquire at least one corresponding logging parameter according to the target logging prediction model; The logging parameters are input into the target logging prediction model to obtain the logging interpretation results.
5. The method according to claim 4, characterized in that The step of obtaining a target well logging prediction model comprises: Get model selection instructions; In response to the model selection instruction, a model is selected from a preset prediction model set to obtain the target well logging prediction model; wherein the prediction model set includes a plurality of candidate well logging prediction models.
6. The method according to claim 5, characterized in that Before obtaining the model selection instruction, the method further includes: Obtain well logging training data; Inputting the well logging training data into a preset original well logging prediction model, and outputting an interpretation prediction result; Performing loss calculation on the preset explanation verification result and the explanation prediction result through a preset regression loss function to obtain target loss data; The model parameters of the original well logging prediction model are adjusted according to the target loss data to obtain the candidate well logging prediction model.
7. The method according to claim 1, characterized in that After obtaining the well logging interpretation result, the method further includes: If the candidate interpretation conclusion is a correct conclusion, the candidate interpretation conclusion and the candidate reasoning parameters are displayed.
8. A well logging interpretation result processing device, characterized in that: The device comprises: An acquisition module, used for acquiring well logging interpretation results; wherein the well logging interpretation results include candidate interpretation conclusions and candidate reasoning parameters; A modification module, if the candidate interpretation conclusion is an erroneous conclusion, the modification module is used to obtain a target interpretation conclusion after modifying the candidate interpretation conclusion; A prediction module, used for inputting the target interpretation conclusion into a preset target logging prediction model to obtain target reasoning parameters; A display module is used to display the target interpretation conclusion and the target reasoning parameters.
9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method for processing well logging interpretation results according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for processing well logging interpretation results according to any one of claims 1 to 7 is implemented.