Resistivity imaging method and device based on remote detection electromagnetic wave logging

Abstract

The invention provides a resistivity imaging method and a resistivity imaging device based on remote detection electromagnetic wave logging, which are used for realizing rapid imaging of a medium around a shaft and providing a basis for drilling guidance and stratum evaluation. Comprising the steps of obtaining a remote detection electromagnetic wave logging response measured value of a target well section; segmenting the response measured value, dispersing the stratum corresponding to the segmented response measured value into an initial grid, and obtaining a corresponding network topology structure; constructing an apparent resistivity model based on the stratum prior information of the target well section; selecting a discrete wave number, and obtaining a wave number domain far exploration electromagnetic wave logging response corresponding to the resistivity grid model; obtaining a posterior error, and judging whether the posterior error is smaller than a first threshold value or not; if yes, obtaining a remote detection electromagnetic wave logging response simulation value based on a wavenumber domain remote detection electromagnetic wave logging response; obtaining a data fitting difference based on the remote detection electromagnetic wave logging response simulation value and the remote detection electromagnetic wave logging response actual measurement value; judging whether the data fitting difference is smaller than a second threshold value or not; and if yes, outputting a network topology structure and a resistivity model.

Description

Technical Field

[0001] The present invention relates to the technical field of geosteering while drilling and formation evaluation in borehole geophysical methods, and particularly to a resistivity imaging method and device based on far-detection electromagnetic logging. Background Art

[0002] Developing oil and gas resources in deep earth and deep sea is an important direction for future energy development. Geosteering while drilling technology is a key technology for drilling horizontal wells and developing deep oil and gas reservoirs, which is crucial for accurately hitting the target layer, avoiding drilling risks in real time, and maximizing the oil and gas productivity of horizontal wells. The far-detection electromagnetic logging that has emerged in recent years is the current geological steering instrument with the farthest detection range, whose detection range can reach dozens of meters, and realizes the full tensor measurement of formation resistivity, and has broad application prospects and market value in the fields of three-dimensional geological steering while drilling, oil-water interface imaging, formation evaluation, and fine reservoir description.

[0003] However, the method of measuring and imaging formation resistivity by far-detection electromagnetic waves involves the complex three-dimensional problem of electromagnetic logging data inversion, and more than 95% of the time in inversion is used for forward modeling. The calculation efficiency of three-dimensional forward modeling cannot meet the requirements of real-time steering. Therefore, the industry generally adopts the technology of reducing the model dimension. For example, under the condition of ignoring the influence of the well, the three-dimensional model is simplified into a one-dimensional layered medium model by using the sliding window technology. However, the far-detection electromagnetic wave instrument has a large detection depth and can detect multiple discontinuous and heterogeneous formation interfaces, such as pinched-out formations, folds, and unconformities, which are difficult to be simplified into one-dimensional problems. At the same time, the existing inversion efficiency of inverting far-detection electromagnetic logging data using two-dimensional algorithms is low and overly dependent on human experience, bringing great risks to geosteering while drilling. Summary of the Invention

[0004] The present invention provides a resistivity imaging method and device based on far-detection electromagnetic logging, which is used to realize the rapid imaging of the medium around the wellbore and provide a basis for drilling steering and formation evaluation, etc.

[0005] In a first aspect, the present invention provides a resistivity imaging method based on far-detection electromagnetic logging, including:

[0006] Step 1, obtaining the measured value of the far-detection electromagnetic logging response of the target well section;

[0007] Step 2, segmenting the measured value of the far-detection electromagnetic logging response, discretizing the formation corresponding to the segmented measured value of the far-detection electromagnetic logging response into an initial grid, and obtaining the corresponding network topology structure;

[0008] Step 3, constructing an apparent resistivity model based on the formation prior information of the target well section;

[0009] Step 4: Select discrete wave numbers and obtain the far-detection electromagnetic logging response in the wave number domain of the corresponding resistivity grid model;

[0010] Step 5: Obtain the posterior error and determine whether the posterior error is less than the first threshold; if the posterior error is less than the first threshold, obtain the simulated value of the far-detection electromagnetic logging response based on the far-detection electromagnetic logging response in the wave number domain;

[0011] Step 6: Obtain the data fitting difference based on the simulated value of the far-detection electromagnetic logging response and the measured value of the far-detection electromagnetic logging response;

[0012] Step 7: Determine whether the data fitting difference is less than the second threshold; if the data fitting difference is less than the second threshold, output the network topology and the resistivity model.

[0013] In one implementation, Step 5 further includes: if the posterior error is greater than the first threshold, locally refine the grid according to the unit error distribution in the posterior error, and repeat Steps 4 - 5 until the posterior error is less than the first threshold to obtain an optimized resistivity model.

[0014] In one implementation, Step 7 further includes: if the data fitting difference is greater than the second threshold, obtain the derivative of the magnetic field or electric field at the receiving coil with respect to the unit resistivity; along the gradient direction, adjust the true resistivity value of the formation, and repeat Steps 4 - 7 until the data fitting difference is less than the second threshold or the number of iterations is greater than the upper limit of the number of iterations.

[0015] In one implementation, before Step 2, it further includes: performing standardization processing on the measured value of the far-detection electromagnetic logging response.

[0016] In one implementation, Step 2 includes: segmenting the measured value of the far-detection electromagnetic logging response, determining the window range of the formation model according to the skin depth, adopting a grid meshing technique to discretize the numerical values of the formation model within the window into an initial grid, and obtaining the corresponding network topology and storing it; the initial grid is a triangular or rectangular unit.

[0017] In one implementation, the obtaining of the posterior error in Step 5 includes: estimating the posterior error of the magnetic field or electric field at the receiving coil through the adjoint equation.

[0018] In one implementation, the prior formation information in Step 5 includes at least one of geological data, seismic data, and adjacent well data.

[0019] In a second aspect, the present invention provides a resistivity imaging device based on far-detection electromagnetic logging, including:

[0020] A numerical acquisition module for acquiring the measured value of the far-detected electromagnetic wave logging response of a target well section;

[0021] A network topology relationship determination module for segmenting the formation corresponding to the measured value of the far-detected electromagnetic wave logging response, discretizing the formation corresponding to the segmented measured value of the far-detected electromagnetic wave logging response into an initial grid, and obtaining a corresponding network topology structure;

[0022] A model construction module for constructing an apparent resistivity model based on the prior formation information of the target well section;

[0023] A response acquisition module for selecting discrete wave numbers and obtaining the far-detected electromagnetic wave logging response in the wave number domain of the corresponding resistivity grid model; obtaining the simulated value of the far-detected electromagnetic wave logging response based on the far-detected electromagnetic wave logging response in the wave number domain;

[0024] A judgment module for obtaining the posterior error and judging whether the posterior error is less than a first threshold; judging whether the data fitting error is less than a second threshold;

[0025] A data processing module for obtaining the simulated value of the far-detected electromagnetic wave logging response based on the far-detected electromagnetic wave logging response in the wave number domain;

[0026] An output module for outputting the network topology structure and the resistivity model.

[0027] In a third aspect, the present application provides an electronic device, including a processor and a memory, where the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method provided in the first aspect above are run.

[0028] In a fourth aspect, the present application provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the method provided in the first aspect above are run.

[0029] It can be seen from the above technical solutions that the present invention has the following advantages:

[0030] The present invention provides a resistivity imaging method and device based on far-detected electromagnetic wave logging, and intends to introduce error estimation and grid automatic encryption technology into the far-detected electromagnetic wave logging data inversion algorithm. When performing data inversion, it will no longer rely on human experience, so as to realize the intelligent processing of the discretization of the medium model around the wellbore. Through further numerical solution, the true resistivity of the medium around the wellbore can be obtained, thereby significantly improving the processing efficiency of far-detected electromagnetic wave logging data, making it possible to achieve real-time two-dimensional resistivity imaging based on far-detected electromagnetic wave logging, and further guiding drilling operations and fine evaluation of reservoirs. Description of the Drawings

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0032] Figure 1 It is a schematic flowchart of a resistivity imaging method based on far-detector electromagnetic logging provided by an embodiment of the present invention;

[0033] Figure 2 It is a schematic flowchart of a resistivity imaging method based on far-detector electromagnetic logging provided by another embodiment of the present invention;

[0034] Figure 3 It is a schematic flowchart of a resistivity imaging method based on far-detector electromagnetic logging provided by another embodiment of the present invention;

[0035] Figure 4 It is a resistivity profile for far-detector electromagnetic logging inversion;

[0036] Figure 5 It is a schematic structural diagram of a resistivity imaging device based on far-detector electromagnetic logging provided by an embodiment of the present invention; Detailed implementation manners

[0037] The embodiments of the present invention provide a resistivity imaging method and device based on far-detector electromagnetic logging, which are used to realize rapid imaging of the medium around the wellbore and provide a basis for drilling guidance and formation evaluation, etc.

[0038] To make the objectives, features, and advantages of the present invention more obvious and understandable, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0039] Example 1:

[0040] This embodiment provides a resistivity imaging method based on far-detector electromagnetic logging. Please refer to Figure 1 , Figure 1 It is a schematic flowchart of a resistivity imaging method based on far-detector electromagnetic logging provided by an embodiment of the present invention, including:

[0041] Step 1: Obtain the measured values of the far-detected electromagnetic logging response for the target well section. Further, after Step 1 and before Step 2, it also includes: normalizing the measured values of the far-detected electromagnetic logging response.

[0042] Optionally, the measured values of the far-detected electromagnetic logging response include: the measured values of the original signals and the synthesized signals formed by transmitting and receiving in different azimuths.

[0043] Step 2: Segment the measured values of the far-detected electromagnetic logging response, discretize the formation corresponding to the segmented measured values of the far-detected electromagnetic logging response into initial grids, and obtain the corresponding network topology.

[0044] Further, this step also includes: using the sliding window technique to segment the measured values of the far-detected electromagnetic logging response for the target well section, determining the window range of the formation model according to the skin depth, adopting the grid meshing technique to discretize the numerical values of the formation model within the window into initial grids, obtaining the corresponding network topology and storing it; the initial grids are triangular or rectangular elements.

[0045] Step 3: Construct an apparent resistivity model based on the prior information of the formation in the target well section and assume homogeneity within the grid cells.

[0046] Optionally, the prior information of the formation includes at least one of geological data, seismic data, and adjacent well data.

[0047] Step 4: Select discrete wave numbers and obtain the far-detected electromagnetic logging response in the wave number domain of the corresponding resistivity grid model.

[0048] Specifically, select discrete wave numbers, and in the wave number domain, a two-dimensional numerical algorithm can be used to calculate the far-detected electromagnetic logging response in the wave number domain of the corresponding resistivity grid model. Among them, the two-dimensional numerical algorithm includes but is not limited to the finite element method and the finite difference method.

[0049] Step 5: Obtain the posterior error and determine whether the posterior error is less than the first threshold; if the posterior error is less than the first threshold, obtain the simulated value of the far-detected electromagnetic logging response based on the far-detected electromagnetic logging response in the wave number domain.

[0050] Specifically, the posterior error of the magnetic field or electric field at the receiving coil can be estimated through the adjoint equation.

[0051] Among them, refer to Figure 2As shown, step 5 further includes: if the posterior error is greater than the first threshold, locally refine the grid according to the cell error distribution in the posterior error, and repeat steps 4 - 5 until the posterior error is less than the first threshold, so as to obtain an optimized resistivity grid model. It can be understood that when the posterior error is greater than the first threshold, locally refine the grid according to the cell error distribution, then repeat steps 4 - 5 on the newly generated grid, calculate the new electromagnetic wave logging response, and terminate the iteration and store the final electromagnetic wave far-detection logging response value until the posterior error is less than the first threshold.

[0052] Step 6, obtain the data fitting difference based on the simulated value of the far-detection electromagnetic wave logging response and the measured value of the far-detection electromagnetic wave logging response.

[0053] Specifically, compare the difference between the simulated value of the far-detection electromagnetic wave logging response and the measured value of the far-detection electromagnetic wave logging response, and calculate the data fitting difference.

[0054] Step 7, determine whether the data fitting difference is less than the second threshold; if the data fitting difference is less than the second threshold, output the network topology and the resistivity model.

[0055] Specifically, refer to Figure 3 As shown, step 7 further includes: if the data fitting difference is greater than the second threshold, obtain the derivative of the magnetic field or electric field at the receiving coil with respect to the cell resistivity; along the gradient direction, adjust the true resistivity value of the formation, and repeat steps 4 - 7 until the data fitting difference is less than the second threshold, or the number of iterations is greater than the upper limit of the number of iterations. After the iteration stops, output the network topology relationship of the formation model and the cell resistivity. Optionally, output the simplified two-dimensional true resistivity profile of the formation.

[0056] Specifically, calculate the derivative of the magnetic field or electric field at the receiving coil with respect to the cell resistivity. Optionally, the method for calculating the derivative of the magnetic field or electric field at the receiving coil with respect to the cell resistivity includes but is not limited to the adjoint method.

[0057] Example 2:

[0058] This embodiment provides a resistivity imaging method based on far-detection electromagnetic wave logging. The resistivity imaging method based on far-detection electromagnetic wave logging includes:

[0059] Due to geological activities such as uplift, there are sedimentary discontinuities at the sedimentary layer interfaces in the formation, or due to erosion, intrusion, etc., the formation undergoes mutations or gradually thins. The lack of accurate imaging of these discontinuous boundaries will bring great risks to drilling geological steering.

[0060] In this embodiment, it is assumed that the formation is a stratified sedimentary formation, that is, it is assumed that the formation only changes in the inversion section and remains unchanged in the direction perpendicular to the section. The far-detection electromagnetic logging 2.5D fast imaging algorithm described in the present invention is used to invert and process the far-detection electromagnetic logging data, and the two-dimensional resistivity profile obtained is as Figure 4 shown. Figure 4 The horizontal axis coordinate is the horizontal position of the well, the vertical axis coordinate is the well depth, and the color scale is the resistivity obtained by inversion. The instrument drills from a depth of 300 ft to a depth of 550 ft, and the well deviation angle is 85°. As Figure 4 shown, the grid adaptive 2.5D fast imaging algorithm described in the present invention can determine the resistivity distribution in the two-dimensional simplified formation, and then obtain the image of the formation interface and the resistivity information in each layer. The drilling engineer can further adjust the wellbore trajectory based on this to ensure that the drill bit drills in the target reservoir.

[0061] Example 3:

[0062] This embodiment provides a resistivity imaging device 100 based on far-detection electromagnetic logging. Refer to Figure 5 shown Figure 5 which is a schematic structural diagram of a resistivity imaging device based on far-detection electromagnetic logging provided by an embodiment of the present invention; the resistivity imaging device 100 based on far-detection electromagnetic logging includes: a numerical acquisition module 10, a network topology relationship determination module 20, a model construction module 30, a response acquisition module 40, a judgment module 50, a data processing module 60, and an output module 70.

[0063] The numerical acquisition module 10 is used to acquire the measured value of the far-detection electromagnetic logging response of the target well section;

[0064] The network topology relationship determination module 20 is used to segment the formation corresponding to the measured value of the far-detection electromagnetic logging response, discretize the formation corresponding to the segmented measured value of the far-detection electromagnetic logging response into an initial grid, and obtain the corresponding network topology structure;

[0065] The model construction module 30 is used to construct an apparent resistivity model based on the prior information of the formation of the target well section; optionally, the prior information of the formation includes at least one of geological data, seismic data, and adjacent well data.

[0066] The response acquisition module 40 is used to select discrete wave numbers and obtain the far-detection electromagnetic logging response in the wave number domain of the corresponding resistivity grid model; and obtain the simulated value of the far-detection electromagnetic logging response based on the far-detection electromagnetic logging response in the wave number domain;

[0067] The judgment module 50 is configured to obtain the posterior error and determine whether the posterior error is less than a first threshold; determine whether the data fitting error is less than a second threshold;

[0068] The data processing module 60 is configured to obtain a simulated value of the far-detection electromagnetic wave logging response based on the far-detection electromagnetic wave logging response in the wavenumber domain;

[0069] The output module 70 outputs the network topology structure and the resistivity model.

[0070] Wherein, the resistivity imaging device 100 based on far-detection electromagnetic wave logging further includes an encryption unit 80. If the posterior error is greater than the first threshold, the encryption unit 80 locally encrypts the grid according to the unit error distribution in the posterior error.

[0071] Further, if the data fitting error is greater than the second threshold, the data processing module 60 is further configured to obtain the derivative of the magnetic field or electric field at the receiving coil with respect to the unit resistivity. The resistivity imaging device 100 based on far-detection electromagnetic wave logging further includes a resistivity adjustment unit 90, and the resistivity adjustment unit 90 is configured to adjust the true resistivity value of the formation along the gradient direction.

[0072] Further, the numerical acquisition module 10 is further configured to perform normalization processing on the measured value of the far-detection electromagnetic wave logging response.

[0073] Further, the network topology relationship determination module 20 is further configured to segment the measured value of the far-detection electromagnetic wave logging response by using a sliding window technique, determine the window range of the formation model according to the skin depth, adopt a grid meshing technique, discretize the numerical values of the formation model within the window into initial grids, and obtain and store the corresponding network topology structure; the initial grids are triangular or rectangular units.

[0074] Further, the judgment module 50 is further configured to estimate the posterior error of the magnetic field or electric field at the receiving coil through a dual equation.

[0075] Example 4:

[0076] This embodiment further provides an electronic device. This embodiment provides an electronic device, which may be a mobile phone, a computer, a tablet computer, etc., including a memory and a processor, and a computer program is stored on the memory. When the computer program is executed by the processor, it implements the resistivity imaging method based on far-detection electromagnetic wave logging as described in Embodiment 1 and Embodiment 2. It can be understood that the electronic device may further include an input / output (I / O) interface and a communication component.

[0077] Among them, the processor is used to execute all or part of the steps in the resistivity imaging method based on far-detected electromagnetic wave logging in Embodiment 1 and Embodiment 2. The memory is used to store various types of data, which may include, for example, instructions of any application or method in the electronic device, as well as data related to the application.

[0078] The processor can be implemented by an application specific integrated circuit (ASIC), a digital signal processor (DSP), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is used to execute the resistivity imaging method based on far-detected electromagnetic wave logging in Embodiment 1 and Embodiment 2 above. The resistivity imaging method based on far-detected electromagnetic wave logging includes:

[0079] Step 1, obtaining the measured value of the far-detected electromagnetic wave logging response of the target well section. Further, after Step 1 and before Step 2, it also includes: performing normalization processing on the measured value of the far-detected electromagnetic wave logging response.

[0080] Optionally, the measured value of the far-detected electromagnetic wave logging response includes: the measured values of the original signal and the synthesized signal formed by transmitting and receiving in different directions.

[0081] Step 2, segmenting the measured value of the far-detected electromagnetic wave logging response, discretizing the formation corresponding to the segmented measured value of the far-detected electromagnetic wave logging response into an initial grid, and obtaining the corresponding network topology.

[0082] Further, this step also includes: using the sliding window technique to segment the measured value of the far-detected electromagnetic wave logging response of the target well section, determining the window range of the formation model according to the skin depth, adopting the grid meshing technique, discretizing the numerical values of the formation model within the window into an initial grid, and obtaining the corresponding network topology and storing it; the initial grid is a triangular or rectangular unit.

[0083] Step 3, constructing an apparent resistivity model based on the prior information of the formation of the target well section, and assuming homogeneity within the grid cells.

[0084] Optionally, the prior information of the formation includes at least one of geological data, seismic data, and adjacent well data.

[0085] Step 4: Select discrete wave numbers and obtain the far-detected electromagnetic logging responses in the wave number domain of the corresponding resistivity grid model.

[0086] Specifically, select discrete wave numbers. In the wave number domain, a two-dimensional numerical algorithm can be used to calculate the far-detected electromagnetic logging responses in the wave number domain of the corresponding resistivity grid model. Among them, the two-dimensional numerical algorithm includes but is not limited to the finite element and finite difference algorithms.

[0087] Step 5: Obtain the posterior error and determine whether the posterior error is less than the first threshold; if the posterior error is less than the first threshold, obtain the simulated value of the far-detected electromagnetic logging response based on the far-detected electromagnetic logging responses in the wave number domain.

[0088] Specifically, the posterior error of the magnetic field or electric field at the receiving coil can be estimated through the adjoint equation.

[0089] Among them, Step 5 also includes: if the posterior error is greater than the first threshold, locally refine the grid according to the element error distribution in the posterior error, and repeat Steps 4 - 5 until the posterior error is less than the first threshold to obtain an optimized resistivity model. It can be understood that when the posterior error is greater than the first threshold, locally refine the grid according to the element error distribution, and then repeat Steps 4 - 5 on the newly generated grid to calculate the new electromagnetic logging responses until the posterior error is less than the first threshold, then terminate the iteration and store the final far-detected electromagnetic logging response value.

[0090] Step 6: Obtain the data fitting difference based on the simulated value of the far-detected electromagnetic logging response and the measured value of the far-detected electromagnetic logging response.

[0091] Specifically, compare the difference between the simulated value of the far-detected electromagnetic logging response and the measured value of the far-detected electromagnetic logging response, and calculate the data fitting difference.

[0092] Step 7: Determine whether the data fitting difference is less than the second threshold; if the data fitting difference is less than the second threshold, output the network topology and the resistivity model.

[0093] Specifically, Step 7 also includes: if the data fitting difference is greater than the second threshold, obtain the derivative of the unit resistivity with respect to the magnetic field or electric field at the receiving coil; along the gradient direction, adjust the true resistivity value of the formation, and repeat Steps 4 - 7 until the data fitting difference is less than the second threshold, or the number of iterations is greater than the upper limit of the number of iterations. After the iteration stops, output the network topology relationship of the formation model and the unit resistivity. Optionally, output the simplified two-dimensional true resistivity profile of the formation.

[0094] Specifically, the derivative of the magnetic field or electric field at the receiving coil with respect to the unit resistivity is calculated. Optionally, the method of calculating the derivative of the magnetic field or electric field at the receiving coil with respect to the unit resistivity includes but is not limited to the adjoint method.

[0095] The memory can be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0096] Example 5:

[0097] This embodiment also provides a computer-readable storage medium. The functional units in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. If the functions are implemented as software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium.

[0098] Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling 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 method described in each embodiment of the present invention.

[0099] The aforementioned storage media include: flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, server, APP application store, and other media that can store program check codes, on which a computer program is stored. When the computer program is executed by a processor, the following method steps can be implemented:

[0100] Step 1: Obtain the measured value of the far-detector electromagnetic logging response for the target well section. Further, after Step 1 and before Step 2, it also includes: performing a normalization process on the measured value of the far-detector electromagnetic logging response.

[0101] Optionally, the measured value of the far-detector electromagnetic logging response includes the measured values of the original signal and the synthetic signal formed by transmitting and receiving in different azimuths.

[0102] Step 2: Segment the measured value of the far-detector electromagnetic logging response, discretize the formation corresponding to the segmented measured value of the far-detector electromagnetic logging response into an initial grid, and obtain the corresponding network topology.

[0103] Further, this step also includes: using the sliding window technique to segment the measured value of the far-detector electromagnetic logging response for the target well section, determining the window range of the formation model according to the skin depth, adopting the grid meshing technique, discretizing the numerical values of the formation model within the window into an initial grid, and obtaining the corresponding network topology and storing it; the initial grid is a triangular or rectangular element.

[0104] Step 3: Construct an apparent resistivity model based on the prior information of the formation in the target well section, and assume homogeneity within the grid cells.

[0105] Optionally, the prior information of the formation includes at least one of geological data, seismic data, and adjacent well data.

[0106] Step 4: Select discrete wave numbers and obtain the far-detector electromagnetic logging response in the wave number domain of the corresponding resistivity grid model.

[0107] Specifically, select discrete wave numbers, and in the wave number domain, a two-dimensional numerical algorithm can be used to calculate the far-detector electromagnetic logging response in the wave number domain of the corresponding resistivity grid model. Among them, the two-dimensional numerical algorithm includes but is not limited to the finite element method and the finite difference method.

[0108] Step 5: Obtain the posterior error and determine whether the posterior error is less than the first threshold; if the posterior error is less than the first threshold, obtain the simulated value of the far-detector electromagnetic logging response based on the far-detector electromagnetic logging response in the wave number domain.

[0109] Specifically, the posterior error of the magnetic field or electric field at the receiving coil can be estimated through the adjoint equation.

[0110] Among them, step 5 further includes: if the posterior error is greater than the first threshold, local refinement of the grid is performed according to the cell error distribution in the posterior error, and steps 4 to 5 are repeated until the posterior error is less than the first threshold to obtain an optimized resistivity model. It can be understood that when the posterior error is greater than the first threshold, local refinement of the grid is performed according to the cell error distribution, and then steps 4 to 5 are repeated on the newly generated grid to calculate the new electromagnetic wave logging response until it satisfies that the posterior error is less than the first threshold, at which point the iteration is terminated and the final electromagnetic wave long-spacing logging response value is stored.

[0111] Step 6, obtaining a data fitting difference based on the simulated value of the long-spacing electromagnetic wave logging response and the measured value of the long-spacing electromagnetic wave logging response.

[0112] Specifically, the difference between the simulated value of the long-spacing electromagnetic wave logging response and the measured value of the long-spacing electromagnetic wave logging response is compared, and the data fitting difference is calculated.

[0113] Step 7, determining whether the data fitting difference is less than a second threshold; if the data fitting difference is less than the second threshold, output the network topology and the resistivity model.

[0114] Specifically, step 7 further includes: if the data fitting difference is greater than the second threshold, obtaining the derivative of the magnetic field or electric field at the receiving coil with respect to the cell resistivity; adjusting the true resistivity value of the formation along the gradient direction, and repeating steps 4 to 7 until the data fitting difference is less than the second threshold, or the number of iterations is greater than the upper limit of the number of iterations. After the iteration stops, output the network topology relationship of the formation model and the cell resistivity. Optionally, output a simplified two-dimensional true resistivity profile of the formation.

[0115] Specifically, calculate the derivative of the magnetic field or electric field at the receiving coil with respect to the cell resistivity. Optionally, the method for calculating the derivative of the magnetic field or electric field at the receiving coil with respect to the cell resistivity includes, but is not limited to, the adjoint method.

[0116] Those of ordinary skill in the art can realize that the units and algorithm steps of each example 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. Professional technicians 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 invention. 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 repeated here.

[0117] In several embodiments provided by 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. In actual implementation, there may be other division methods. 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 coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical, or other forms. The units described as separate components may or may not be physically separated. 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.

[0118] The basic principles of the present application have been described above in combination with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are only examples and not limitations. It cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present application. In addition, the above-disclosed specific details are only for the purposes of illustration and facilitating understanding, rather than limitations. The above details do not limit the present application to necessarily adopt the above specific details for implementation.

[0119] The block diagrams of the devices, apparatuses, equipment, and systems involved in the present application are only illustrative examples and do not intend to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any way.

[0120] It should also be noted that in the devices, equipment, and methods of the present application, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present application.

[0121] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but to the broadest scope consistent with the principles and novel features disclosed herein.

[0122] In the description of the present application, the meaning of "a plurality of" is at least two, such as two, three, etc., unless otherwise specifically defined. In the embodiments of the present application, all directional indications (such as up, down, left, right, front, back, top, bottom...) are only used to explain the relative positional relationship and movement of components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products or devices.

[0123] In addition, the mention of "embodiment" in this document means that the specific features, structures or characteristics described in connection with the embodiment may be included in at least one embodiment of the present application. The appearance of this phrase at various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0124] As mentioned above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, and all should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims. The above is only a preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A resistivity imaging method based on far-detection electromagnetic wave logging, characterized in that, Including: Step 1: Obtain the measured value of the far-detected electromagnetic logging response in the target well section; Step 2: Segment the measured value of the far-detected electromagnetic logging response, discretize the formation corresponding to the segmented measured value of the far-detected electromagnetic logging response into an initial grid, and obtain the corresponding network topology; Step 3: Construct an apparent resistivity model based on the prior formation information of the target well section; Step 4: Select discrete wavenumbers and obtain the wavenumber-domain far-detected electromagnetic logging response of the corresponding resistivity grid model; Step 5: Obtain the posterior error and determine whether the posterior error is less than the first threshold; If the posterior error is less than the first threshold, obtain the simulated value of the far-detected electromagnetic logging response based on the wavenumber-domain far-detected electromagnetic logging response; Step 6: Obtain the data fitting error based on the simulated value of the far-detected electromagnetic logging response and the measured value of the far-detected electromagnetic logging response; Step 7: Determine whether the data fitting error is less than the second threshold; If the data fitting error is less than the second threshold, output the network topology and the resistivity model.

2. The resistivity imaging method based on far-detection electromagnetic wave logging according to claim 1, characterized in that Step 5 further includes: If the posterior error is greater than the first threshold, locally refine the grid according to the unit error distribution in the posterior error, and repeat Steps 4 - 5 until the posterior error is less than the first threshold to obtain an optimized resistivity model.

3. The resistivity imaging method based on far-detection electromagnetic wave logging according to claim 1, characterized in that Step 7 further includes: If the data fitting error is greater than the second threshold, obtain the derivative of the magnetic field or electric field at the receiving coil with respect to the unit resistivity; along the gradient direction, adjust the true resistivity value of the formation, and repeat Steps 4 - 7 until the data fitting error is less than the second threshold or the number of iterations is greater than the upper limit of the number of iterations.

4. The resistivity imaging method based on far-detection electromagnetic wave logging according to claim 1, wherein Before Step 2, it further includes: Standardize the measured value of the far-detected electromagnetic logging response.

5. The resistivity imaging method based on far-detection electromagnetic wave logging according to claim 1, characterized in that, Step 2 includes: Segment the measured value of the far-detected electromagnetic logging response using the sliding window technique, determine the window range of the formation model according to the skin depth, adopt the grid meshing technique, discretize the numerical values of the formation model within the window into an initial grid, and obtain the corresponding network topology and store it; the initial grid is a triangular or rectangular unit.

6. The resistivity imaging method based on far-detected electromagnetic wave logging according to claim 1, characterized in that, In Step 5, the obtaining of the posterior error includes: Estimate the posterior error of the magnetic field or electric field at the receiving coil through the adjoint equation.

7. The resistivity imaging method based on far-detection electromagnetic wave logging according to claim 1, characterized in that The prior formation information in Step 3 includes at least one of geological data, seismic data, and adjacent well data.

8. A resistivity imaging device based on a far-detection electromagnetic wave logging, characterized in that, Including: A numerical acquisition module for obtaining the measured value of the far-detected electromagnetic logging response in the target well section; A network topology relationship determination module for segmenting the formation corresponding to the measured value of the far-detected electromagnetic logging response, discretizing the formation corresponding to the segmented measured value of the far-detected electromagnetic logging response into an initial grid, and obtaining the corresponding network topology; A model construction module for constructing an apparent resistivity model based on the prior formation information of the target well section; A response acquisition module for selecting discrete wavenumbers and obtaining the wavenumber-domain far-detected electromagnetic logging response of the corresponding resistivity grid model; obtaining the simulated value of the far-detected electromagnetic logging response based on the wavenumber-domain far-detected electromagnetic logging response; A data processing module, configured to obtain a data fitting difference based on the far-detected electromagnetic logging simulation response and the measured value of the far-detected electromagnetic logging response; A judgment module, configured to obtain a posterior error, and judge whether the posterior error is less than a first threshold; judge whether the data fitting difference is less than a second threshold; An output module, configured to output the network topology structure and the resistivity model.

9. An electronic device, characterized in that, It includes a processor and a memory, and the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the method according to any one of claims 1-7 is run.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the method according to any one of claims 1-7 is run.

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