Logging method and device based on electromagnetic waves and electronic equipment

Abstract

The invention relates to the technical field of logging while drilling, in particular to a logging method and device based on electromagnetic waves and electronic equipment. In the technical scheme provided by the embodiment of the invention, induced electromotive force excited by a single-frequency electromagnetic wave signal at at least two receiving spiral coils is measured based on a logging instrument, and an amplitude ratio signal and a phase difference signal are respectively obtained through an analysis model constructed based on a neural network according to the induced electromotive force; the amplitude ratio and the phase difference information are subjected to resistivity conversion to obtain horizontal resistivity and vertical resistivity respectively, and formation evaluation is completed based on the horizontal resistivity and the vertical resistivity. According to the embodiment of the invention, the evaluation of the stratum is completely realized by constructing the two indexes.

Description

Technical Field

[0001] The present invention relates to the technical field of logging while drilling, and provides a logging method, device and electronic device based on electromagnetic waves. Background Technique

[0002] Electromagnetic wave resistivity logging while drilling mainly measures the formation resistivity parameters, which is an important part of logging while drilling technology. By recording the amplitude ratio and phase difference signals of the induced electromotive force between two receiving coils, the amplitude ratio resistivity and phase difference resistivity are obtained. However, traditional electromagnetic wave resistivity logging while drilling has many limitations: the instrument detection depth is relatively shallow, the formation upper and lower interfaces cannot be identified, and it is not sensitive to formation electrical anisotropy information. Therefore, higher requirements are also put forward for electromagnetic wave logging while drilling technology in real-time geological steering, reservoir anisotropy evaluation, directional logging interpretation, etc. Summary of the Invention

[0003] In order to solve the above technical problems, the present application provides a logging method, device and electronic device based on electromagnetic waves, and evaluates formation information by constructing two indicators.

[0004] In order to achieve the above object, the technical solutions adopted in the embodiments of the present application are as follows:

[0005] In a first aspect, a logging method based on electromagnetic waves is provided. The method is based on a logging instrument, and the logging instrument includes a transmitting helical coil, a receiving helical coil and a processor arranged around a drill pipe. The method is configured in the processor and includes: measuring the induced electromotive force excited by an electromagnetic wave signal at at least two receiving helical coils based on the logging instrument, and respectively obtaining a plurality of amplitude signals and a plurality of phase difference signals corresponding to four detection modes based on the induced electromotive force; processing the plurality of amplitude signals and the plurality of phase difference signals by an analysis model that converges after training to obtain a formation horizontal resistivity and a formation vertical resistivity; the analysis model is constructed based on a neural network, and a horizontally layered multi-anisotropic formation model is configured in the analysis model; and completing formation evaluation based on the formation horizontal resistivity and the formation vertical resistivity.

[0006] Further, the four detection modes are correspondingly configured with detection performances, and the detection performances include formation boundaries, anisotropy and dip angle, formation resistivity and anisotropy.

[0007] Further, the measuring the induced electromotive force excited by an electromagnetic wave signal at at least two receiving helical coils based on the logging instrument includes: obtaining the magnetic field intensity obtained by the receiving helical coil, and obtaining the induced electromotive force based on the electromagnetic induction law.

[0008] Further, obtaining the magnetic field strength acquired by the receiving helical coil includes: based on the setting directions of the transmitting helical coil and the receiving helical coil, determining the distribution of the magnetic field strength components based on the setting directions and obtaining the corresponding magnetic field strength components, and obtaining the corresponding magnetic field strength based on the magnetic field strength components.

[0009] Further, the induced electromotive force is determined according to the following formula: V = -iωμNHA, where ω is the angular frequency, N is the number of turns of the coil, H is the magnetic field strength, A is the cross-sectional area of the coil, and μ is the magnetic permeability of the formation medium.

[0010] Further, obtaining the multiple amplitude signals and multiple phase difference signals corresponding to four detection modes respectively based on the induced electromotive force includes: based on the induced electromotive force components corresponding to the detection modes, and determining the corresponding amplitude ratio and phase difference based on the induced electromotive force components.

[0011] Further, the analysis model includes a U-NET neural network, including an encoder unit, a decoder unit, and a connection layer configured at the output end of the decoder unit; the encoder includes a one-dimensional convolutional layer and two depthwise separable convolutional layers for extracting response features and converting them into multiple feature vectors; the decoder unit is configured with an upsampling layer and multiple transposed convolutional layers to convert the multiple feature vectors into multiple one-dimensional sequences; the connection layer is used to classify the multiple one-dimensional sequences to obtain the horizontal resistivity of the formation and the vertical resistivity of the formation.

[0012] Further, a horizontally layered multi-anisotropic formation model is configured in the connection layer.

[0013] In a second aspect, a logging device based on electromagnetic waves is provided. The device is based on a logging instrument, and the logging instrument includes a transmitting helical coil, a receiving helical coil, and a processor arranged around a drill pipe, and includes: a data acquisition module for measuring the induced electromotive force excited by electromagnetic wave signals at at least two receiving helical coils based on the logging instrument, and respectively obtaining multiple amplitude signals and multiple phase difference signals corresponding to four detection modes based on the induced electromotive force; a data processing module for processing the multiple amplitude signals and the multiple phase difference signals based on a trained and convergent analysis model to obtain the horizontal resistivity of the formation and the vertical resistivity of the formation; an analysis module for completing formation evaluation based on the horizontal resistivity of the formation and the vertical resistivity of the formation.

[0014] In a third aspect, an electronic device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the logging method described in any one of the above is implemented.

[0015] In the technical solution provided by the embodiments of the present application, based on the logging instrument measuring the induced electromotive force excited by the single-frequency electromagnetic wave signal at at least two of the receiving helical coils, the amplitude ratio and the phase difference signal are respectively obtained according to the induced electromotive force through an analysis model constructed based on a neural network, the horizontal resistivity and the vertical resistivity are respectively obtained from the amplitude ratio and the phase difference information based on resistivity conversion, and the formation evaluation is completed based on the horizontal resistivity and the vertical resistivity. The embodiments of the present application have completely realized the evaluation of the formation by constructing two indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] The methods, systems, and / or programs in the drawings will be further described according to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, where the example numbers represent similar mechanisms in the various views of the drawings.

[0018] Figure 1 It is a schematic flowchart of the logging method provided by the embodiments of the present application.

[0019] Figure 2 It is a schematic structural diagram of the logging device provided by the embodiments of the present application.

[0020] Figure 3 It is a schematic structural diagram of the terminal device provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] In order to better understand the above technical solutions, the technical solutions of the present application will be described in detail below through the drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solutions of the present application, rather than limitations on the technical solutions of the present application. Without conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0022] In the following detailed description, many specific details are set forth by way of example in order to provide a thorough understanding of the relevant teachings. However, it will be apparent to those skilled in the art that the present application can be practiced without these details. In other instances, well-known methods, procedures, systems, components, and / or circuits have been described at a relatively high level without detail in order to avoid unnecessarily obscuring aspects of the present application.

[0023] In this application, flowcharts are used to illustrate the execution processes performed by the system according to the embodiments of this application. It should be clearly understood that the execution processes of the flowcharts may not be executed in sequence. On the contrary, these execution processes may be executed in reverse order or simultaneously. Additionally, at least one other execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.

[0024] Before further elaborating on the embodiments of the present invention, the nouns and terms involved in the embodiments of the present invention are described. The nouns and terms involved in the embodiments of the present invention are subject to the following explanations.

[0025] (1) Responsive to, which is used to represent the conditions or states upon which the executed operations depend. When the dependent conditions or states are met, one or more of the executed operations may be real-time or may have a set delay; without special instructions, there is no restriction on the execution order of the multiple executed operations.

[0026] (2) Based on, which is used to represent the conditions or states upon which the executed operations depend. When the dependent conditions or states are met, one or more of the executed operations may be real-time or may have a set delay; without special instructions, there is no restriction on the execution order of the multiple executed operations.

[0027] In the context of the continuous deepening of oil and gas exploration and development worldwide and the increasing saturation of conventional oil and gas reservoir exploration and development, the exploration of unconventional and offshore oil and gas reservoirs has become crucial. However, the geological conditions of these oil and gas reservoirs are complex, and the exploration and development are difficult. Horizontal well and high-angle well technologies are widely used during drilling, and conventional wireline logging is difficult to meet the requirements of the current exploration and development status. Logging-while-drilling (LWD) technology is widely used because of its unique advantages. In terms of formation evaluation, the data is less affected by invasion, the data is true and reliable, and to a certain extent, it can indicate the anisotropy of the formation; in terms of engineering applications, drilling is safe and efficient, and the data is measured, transmitted, and processed in real time; in terms of geosteering, the drilling trajectory can be controlled in real time, improving the drilling efficiency and the oil and gas encounter rate.

[0028] Therefore, it is of great significance to conduct a comprehensive and in-depth study on LWD technology. As an important part of LWD technology, electromagnetic logging-while-drilling belongs to the category of alternating current logging. It can not only measure the formation resistivity information but also use the azimuth measurement information to detect the formation interface and the oil-water interface. The logging tool measures the induced electromotive force excited by the single-frequency electromagnetic wave signal at two or more receiving coils, and then respectively obtains the amplitude ratio and phase difference signals according to the measured induced electromotive force. After the corresponding resistivity conversion methods, the formation apparent resistivity information (attenuation resistivity Rad, phase difference resistivity Rps) is obtained respectively, and then the formation evaluation is completed.

[0029] However, the above resistivity conversion method is proposed based on isotropic media, and there are certain limitations when applied to anisotropic media. Therefore, the embodiments of the present application provide a logging method based on electromagnetic waves to more accurately describe the electrical anisotropy of the formation.

[0030] Therefore, aiming at the above problems in the background technology, the embodiments of the present application provide a logging method based on electromagnetic waves. This method is carried out based on a logging instrument, and the logging instrument includes a transmitting helical coil and a receiving helical coil arranged around the drill pipe.

[0031] Among them, both the transmitting and receiving antennas are spiral rings, and the spiral rings are placed in the grooves of the drill collar. An alternating current is applied to the transmitting spiral ring, generating an alternating vortex magnetic field (referred to as the primary magnetic field) in the spiral ring. The alternating magnetic field generates an alternating electric field (referred to as the primary electric field) in the surrounding space, and eddy currents (induced eddy currents) are generated in the conductive medium. The magnitude of the eddy current is proportional to the conductivity σ of the surrounding environment. Part of the eddy current flows from the side of the drill collar through the mud and the formation for measuring the conductivity of the side medium; part of it flows along the drill collar to the drill bit and then into the formation in front of the drill bit for measuring the conductivity of the medium in front of the drill bit. Therefore, this instrument has the ability to simultaneously measure the azimuthal resistivity and the resistivity of the drill bit, detect the resistivity of the un-drilled formation in front of the drill bit, and determine the formation lithology and reservoir characteristics.

[0032] After the electromagnetic wave is emitted by the transmitting coil, it experiences a propagation process from the wellbore to the formation and then back to the wellbore. During this process, its energy changes at all times and is finally received by the receiving coil to form an induced electromotive force. Due to the different positions of the two receiving coils, the induced electromotive forces generated are also significantly different. The difference is mainly reflected in the amplitude change and phase shift of the induced electromotive force. Then, according to the induced electromotive forces of different coils, amplitude ratio signals and phase difference signals are obtained. After resistivity conversion respectively, the attenuation resistivity can be obtained from the amplitude ratio signal, and the phase difference resistivity can be obtained from the phase difference signal, thereby reflecting the formation resistivity information. The above is the measurement principle of the electromagnetic wave resistivity logging while drilling. However, this resistivity conversion method is based on isotropic media and cannot reflect the characteristics of the electrical anisotropy of the formation. The electrical anisotropy of the formation is one of the important influencing factors of the electrical logging response, and its accurate solution is very important.

[0033] In the embodiments of the present application, through a signal processing method based on a neural network, resistivity parameters that can reflect the anisotropy of the formation can be obtained, which is convenient for reservoir evaluation; at the same time, electrical anisotropy is an interference factor in interface detection in geosteering and reservoir delineation. Accurately determining anisotropy helps to improve the accuracy of interface detection.

[0034] Refer to Figure 1 , the method includes the following steps:

[0035] Step S11. Measure the induced electromotive forces excited by the electromagnetic wave signals at at least two receiving helical coils based on the logging instrument, and respectively obtain a plurality of amplitude signals and a plurality of phase difference signals corresponding to four detection modes based on the induced electromotive forces.

[0036] In this embodiment, when the azimuthal electromagnetic wave logging-while-drilling instrument is working, nine electromagnetic field component signals will be generated during the formation response process of the instrument pair. Four detection modes are formed by combining different component signals. These four detection modes are respectively: Mode 1: USDA (Ultra-deep Symmetrized Directional Attenuation) and USDP (Ultra-deep Symmetrized Directional Phase-shift). Mode 1 can detect the boundary and adopts a symmetric setting to maximize the sensitivity to the formation boundary; Mode 2: UADA (Ultra-deep Anti-symmetrized Directional Attenuation) and UADP (Ultra-deep Anti-symmetrized Directional Phase-shift). Mode 2 adopts an asymmetric setting to eliminate the boundary effect and obtain the resistivity anisotropy and relative dip value; Mode 3: UHRA (Ultra-deep Harmonic Resistivity Attenuation) and UHRP (Ultra-deep Harmonic Resistivity Phase-shift). Mode 3 measures the formation resistivity value by using the combined xx and zz components; Mode 4: UHAA (Ultra-deep Harmonic Anisotropy Attenuation) and UHAP (Ultra-deep Harmonic Anisotropy Phase-shift). Mode 4 quantifies the anisotropy of the resistivity by using the difference between the xx and yy components.

[0037] Among them, for the four detection modes and the detection performance, for Mode 1, it corresponds to the detection performance of the formation boundary, for Mode 2, it corresponds to the detection performance of anisotropy / dip, for Mode 3, it corresponds to the detection performance of the formation resistivity, and for Mode 4, it corresponds to the detection performance of anisotropy.

[0038] Among them, the magnetic field strength obtained by the receiving spiral coil through the instrument for the induced electromotive force is determined. In the case of the azimuthal electromagnetic wave resistivity logging while drilling instrument, the magnetic field signal strength received by the receiving coil will be affected by the azimuth angle of the instrument. Assume the wellbore coordinates are X′Y′Z′, the formation coordinates are XYZ, and the instrument coordinate system is X″Y″Z″.

[0039] When the magnetic moment of the instrument receiving coil is in the X′Z′ plane position, the azimuth angle of the instrument is 0°, that is, θ = 0°. When the instrument rotates along the z′ axis, the azimuth angle of the instrument will also change continuously, and the induced electromotive force of the magnetic field measured by the receiving coil on the instrument will also change continuously. When calculating the magnetic field strength received by the instrument receiving coil in each direction, the inclination angles of the transmitting coil and the receiving coil need to be considered. This inclination angle is the angle formed by the direction of the coil magnetic moment and the direction of the instrument axis. In this embodiment, the transmitting coil is an axial coil, that is, θ T = 0. In the wellbore coordinates, the magnetic field strength components received by the instrument receiving coil in each direction are:

[0040] H x = H′ xx sinθ T cosθ + H′ yx sinθ T sinθ + H′ zx cosθ T ,

[0041] H y = H′ xy sinθ T cosθ + H′ yy sinθ T sinθ + H′ zy cosθ T ,

[0042] H z = H′ xz sinθ T cosθ + H′ yz sinθ T sinθ + H′ zz cosθ T .

[0043] Combining the above magnetic field strength components to obtain the magnetic field strength at the receiving coil, that is: H R = H x sinθ R cosθ + H y sinθ R sinθ + H z cosθ R, where for the transmitting coil being an axial coil, i.e., θ T = 0, and the instrument operates in the xz plane, then H′ yz = H′ zy = 0. Therefore, the above equation can be updated to: H R = H′ zz cosθ R + H′ zx sinθ R cosθ.

[0044] For the obtained magnetic field intensity mentioned above, and based on the electromagnetic induction law, the induced electromotive force is obtained and expressed by the following formula: V = -iωμN R H R A, where ω is the angular frequency, N is the number of turns of the coil, H is the magnetic field intensity, A is the cross-sectional area of the coil, and μ is the magnetic permeability of the formation medium.

[0045] Among them, for the amplitude ratio (USDA) in Mode 1: The phase difference (USDP) in Mode 1: For the amplitude ratio (UADA) in Mode 2: The phase difference (UADP) in Mode 2: For the amplitude ratio (UHRA) in Mode 3: The phase difference (UHRP) in Mode 3: For the amplitude ratio (UHAA) in Mode 4: The phase difference (UHAP) in Mode 4:

[0046] Step S12. Process the multiple amplitude signals and the multiple phase difference signals based on the trained and converged analysis model to obtain the formation horizontal resistivity and the formation vertical resistivity.

[0047] In this embodiment, multiple amplitude signals and multiple phase difference signals acquired by the current instrument can be determined through step S11. The target signal parameters for logging are the formation horizontal resistivity and the formation vertical resistivity. Among them, in this solution, the above two parameters are determined by using the processing method of a neural network, and a horizontally layered multi - anisotropic formation model is configured in this network. Specifically, for the neural network, a U - NET neural network is adopted, which includes an encoder unit and a decoder unit, and a connection layer is arranged at the output end of the decoder unit. Specifically, the encoder includes a one - dimensional convolutional layer and two depth - separable convolutional layers. The encoder is used to receive the electromagnetic wave logging response data of multiple amplitude ratios and phase differences in the above - mentioned modes 1 to 4, and extract the curve features of the above - mentioned multiple data to convert them into multiple feature vectors. In this embodiment, the decoder is provided with multiple de - convolutional layers. The number of de - convolutional layers is the same as the number of convolutional layers in the encoder, and is connected to the corresponding convolutional layer through an up - sampling layer.

[0048] Among them, for the decoder, through the up - sampling layer and the de - convolutional layer, the multiple feature vectors extracted by the encoder are further compressed and converted into multiple one - dimensional sequences used to describe the formation model, and the multiple one - dimensional sequences are input into the horizontally layered multi - anisotropic formation model in the connection layer. For the formation model in this embodiment, the number of model layers n is less than or equal to 3 layers. Each layer of resistivity is set as R, that is, Rh and Rv, and the anisotropy coefficient is λ (λ 2 = Rv / Rh), and the well deviation angle is randomly taken in the range of 60° to 85°.

[0049] Step S13. Complete formation evaluation based on the formation horizontal resistivity and the formation vertical resistivity.

[0050] In the embodiment of the present application, the result of formation evaluation is mainly whether there is a formation interface ahead, and the judgment criterion is determined based on the signal changes of the horizontal resistivity and the vertical resistivity. When the logging instrument's down - hole angle is θ = 0°, a 5% change in the vertical resistivity measurement signal is used as a pre - judgment that there is a formation interface ahead, and a 10% change in the horizontal resistivity measurement signal is used as a pre - judgment that there is a formation interface ahead; when the logging instrument's down - hole angle is 70° ≤ θ ≤ 80°, a 15% change in the vertical resistivity measurement signal is used as a pre - judgment that there is a formation interface ahead, and a 10% change in the horizontal resistivity measurement signal is used as a pre - judgment that there is a formation interface ahead; when θ is 10° to 70°, a 10% change in the vertical resistivity measurement signal is used as a pre - judgment that there is a formation interface ahead, and a 10% change in the horizontal resistivity measurement signal is used as a pre - judgment that there is a formation interface ahead.

[0051] Refer to Figure 2, for the logging method provided in the embodiments of the present application, there is also provided a logging device 20. The device is based on a logging instrument, and the logging instrument includes a transmitting helical coil, a receiving helical coil, and a processor arranged around the drill pipe, and includes:

[0052] A data acquisition module 21, configured to measure the induced electromotive force excited by an electromagnetic wave signal at at least two receiving helical coils based on the logging instrument, and respectively obtain a plurality of amplitude signals and a plurality of phase difference signals corresponding to four detection modes based on the induced electromotive force;

[0053] A data processing module 22, configured to process the plurality of amplitude signals and the plurality of phase difference signals based on an analysis model that converges after training to obtain a formation horizontal resistivity and a formation vertical resistivity;

[0054] An analysis module 23, configured to complete formation evaluation based on the formation horizontal resistivity and the formation vertical resistivity.

[0055] In the embodiments of the present application, the induced electromotive force excited by a single-frequency electromagnetic wave signal at at least two of the receiving helical coils is measured based on a logging instrument. According to the induced electromotive force, an amplitude ratio and a phase difference signal are respectively obtained through an analysis model constructed based on a neural network. The horizontal resistivity and the vertical resistivity are respectively obtained based on the resistivity conversion of the amplitude ratio and the phase difference information, and formation evaluation is completed based on the horizontal resistivity and the vertical resistivity.

[0056] Referring to Figure 3 , the above method can also be integrated into the provided terminal device 300. Due to relatively large differences that may occur in the device due to configuration or performance, it may include one or more processors 301 and a memory 302. One or more application programs or data may be stored in the memory 302. Among them, the memory 302 may be short-term storage or persistent storage. The application programs stored in the memory 302 may include one or more modules (not shown in the figure), and each module may include a series of computer-executable instructions in the terminal device. Further, the processor 301 may be configured to communicate with the memory 302 and execute a series of computer-executable instructions in the memory 302 on the terminal device. The terminal device may also include one or more power supplies 303, one or more wired / wireless network interfaces 304, one or more input / output interfaces 305, one or more keyboards 306, etc.

[0057] In a specific embodiment, the terminal device includes a memory and one or more programs. One or more of the programs are stored in the memory, and one or more of the programs may include one or more modules. Each module may include a series of computer-executable instructions in the terminal device and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following:

[0058] Based on the induced electromotive force excited by the electromagnetic wave signal measured by the logging instrument at at least two receiving helical coils, and respectively obtaining a plurality of amplitude signals and a plurality of phase difference signals corresponding to four detection modes based on the induced electromotive force;

[0059] Processing the plurality of amplitude signals and the plurality of phase difference signals based on the trained and convergent analysis model to obtain the formation horizontal resistivity and the formation vertical resistivity;

[0060] Completing formation evaluation based on the formation horizontal resistivity and the formation vertical resistivity.

[0061] The following is a specific introduction to the components of the processor:

[0062] Among them, in this embodiment, the processor is an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. For example: one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs).

[0063] Optionally, the processor can execute various functions by running or executing software programs stored in the memory and calling data stored in the memory, such as executing the above Figure 1 shown method.

[0064] In a specific implementation, as an embodiment, the processor may include one or more microprocessors.

[0065] Among them, the memory is used to store the software program for executing the solution of the present application and is controlled by the processor for execution. The specific implementation manner can refer to the above method embodiments and will not be elaborated here.

[0066] Optionally, the memory may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or may also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor or may exist independently and be coupled to the processing unit through the interface circuit of the processor. The embodiments of the present application do not make specific limitations on this.

[0067] It should be noted that the structure of the processor shown in this embodiment does not constitute a limitation on the device. The actual device may include more or fewer components than shown in the figure, or combine some components, or have a different component layout.

[0068] In addition, the technical effects of the processor can refer to the technical effects of the method described in the above method embodiments and will not be elaborated here.

[0069] It should be understood that the processor in the embodiments of the present application may be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0070] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0071] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0072] In this application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0073] It should be understood that in various embodiments of the present application, the order of the serial numbers of the above processes does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0074] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0075] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0076] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0077] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0079] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0080] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims described above.

Claims

1. A logging method based on electromagnetic waves, characterized in that, The method is based on a logging instrument, which includes a transmitting helical coil, a receiving helical coil, and a processor arranged around a drill pipe. The method is configured in the processor and includes: Measuring the induced electromotive force excited by an electromagnetic wave signal at at least two receiving helical coils based on the logging instrument, and respectively obtaining a plurality of amplitude signals and a plurality of phase difference signals corresponding to four detection modes based on the induced electromotive force; Processing the plurality of amplitude signals and the plurality of phase difference signals based on an analysis model that converges after training to obtain the formation horizontal resistivity and the formation vertical resistivity; the analysis model is constructed based on a neural network, and a horizontally layered multi - anisotropic formation model is configured in the analysis model; Completing formation evaluation based on the formation horizontal resistivity and the formation vertical resistivity.

2. The electromagnetic wave-based logging method according to claim 1, wherein The four detection modes are correspondingly configured with detection performances, and the detection performances include formation boundaries, anisotropy and dip angle, formation resistivity, and anisotropy.

3. The logging method based on electromagnetic waves according to claim 1, characterized in that The measuring the induced electromotive force excited by an electromagnetic wave signal at at least two receiving helical coils based on the logging instrument includes: obtaining the magnetic field intensity acquired by the receiving helical coil, and obtaining the induced electromotive force based on the law of electromagnetic induction.

4. The electromagnetic wave-based logging method according to claim 3, wherein The obtaining the magnetic field intensity acquired by the receiving helical coil includes: based on the setting directions of the transmitting helical coil and the receiving helical coil, determining the distribution of the magnetic field intensity components based on the setting directions and obtaining the corresponding magnetic field intensity components, and obtaining the corresponding magnetic field intensity based on the magnetic field intensity components.

5. The electromagnetic wave based logging method according to claim 4, characterized in that, The induced electromotive force is determined according to the following formula: V=-iωμNHA, where ω is the angular frequency, N is the number of turns of the coil, H is the magnetic field intensity, A is the cross - sectional area of the coil, and μ is the magnetic permeability of the formation medium.

6. The electromagnetic wave-based logging method according to claim 5, characterized in that, The respectively obtaining a plurality of amplitude signals and a plurality of phase difference signals corresponding to four detection modes based on the induced electromotive force includes: based on the induced electromotive force components corresponding to the detection mode, and determining the corresponding amplitude ratio and phase difference based on the induced electromotive force components.

7. The electromagnetic wave-based logging method according to claim 1, wherein, The analysis model includes a U - NET neural network, which includes an encoder unit, a decoder unit, and a connection layer arranged at the output end of the decoder unit; the encoder includes a one - dimensional convolutional layer and two depth - separable convolutional layers for extracting response features and converting them into a plurality of feature vectors; the decoder unit is configured with an up - sampling layer and a plurality of transposed convolutional layers to convert the plurality of feature vectors into a plurality of one - dimensional sequences; the connection layer is used to classify the plurality of one - dimensional sequences to obtain the formation horizontal resistivity and the formation vertical resistivity.

8. The electromagnetic wave-based logging method according to claim 7, wherein, A horizontally layered multi - anisotropic formation model is configured in the connection layer.

9. A logging device based on electromagnetic waves, characterized in that, The device is based on a logging instrument, which includes a transmitting helical coil, a receiving helical coil, and a processor arranged around a drill pipe, and includes: A data acquisition module, configured to measure the induced electromotive force excited by an electromagnetic wave signal at at least two receiving helical coils based on the logging instrument, and respectively obtain a plurality of amplitude signals and a plurality of phase difference signals corresponding to four detection modes based on the induced electromotive force; A data processing module, configured to process the plurality of amplitude signals and the plurality of phase difference signals based on an analysis model that converges after training, so as to obtain a formation horizontal resistivity and a formation vertical resistivity; An analysis module, configured to complete formation evaluation based on the formation horizontal resistivity and the formation vertical resistivity.

10. An electronic device, comprising a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the logging method according to any one of claims 1 to 8 is implemented.

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