Electromagnetic imaging method and device for formation while drilling

Abstract

The invention discloses a while-drilling stratum electromagnetic imaging method and device. The method comprises the steps that an inversion stratum model of while-drilling stratum electromagnetic imaging is determined; constructing an objective function with a regularization item for the inversion stratigraphic model, and calculating the gradient of the objective function with the regularization item to obtain a gradient function with the regularization item; utilizing a pre-gradient matrix obtained by pre-training of a gradient function, iteratively updating stratum parameters of the inversion stratum model according to the number of inversion iterations and the pre-gradient matrix, determining stratum parameters for constructing an inversion graph of the current stratum based on a target function, and obtaining the inversion graph of the current stratum; the pre-gradient matrix corresponds to the number of inversion iterations. The inversion precision and the global convergence capability are improved by combining the inversion with the stratum model of machine learning. The pre-gradient matrix obtained through pre-training is directly applied to inversion, the inversion speed and real-time performance are remarkably improved, and the inversion accuracy of complex stratum parameters is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of geophysical exploration technology, and in particular to a method and device for electromagnetic imaging of formations while drilling. Background Art

[0002] With the increasing scarcity of oil and gas resources, the importance of accurately exploring underground formation characteristics has become increasingly prominent. Traditional geophysical measurement methods have problems such as instability and lack of accuracy under complex formation conditions. Especially in the process of logging while drilling, due to the anisotropy and nonlinear characteristics of the formation, the analysis of the inductive sensing well response becomes more and more complicated. The existing inversion algorithms often rely on the initial model and are prone to fall into the local optimal solution, resulting in uncertainty in the electromagnetic imaging inversion results. Therefore, a more accurate method of electromagnetic imaging of formations while drilling is needed. Summary of the invention

[0003] In view of the above problems, embodiments of the present invention are proposed to provide a method and device for electromagnetic imaging of formations while drilling that overcome the above problems or at least partially solve the above problems.

[0004] According to one aspect of an embodiment of the present invention, a method for electromagnetic imaging of formations while drilling is provided, the method comprising: Determine the inversion formation model for electromagnetic imaging of formations while drilling; An objective function with a regularization term is constructed for the inversion formation model, and the gradient of the objective function with the regularization term is calculated to obtain a gradient function with the regularization term; The pre-gradient matrix obtained by pre-training the gradient function is used to iteratively update the formation parameters of the inversion formation model according to the pre-gradient matrix according to the number of inversion iterations, and the formation parameters for constructing the inversion map of the current formation are determined based on the objective function to obtain the inversion map of the current formation; the pre-gradient matrix corresponds to the number of inversion iterations.

[0005] According to another aspect of an embodiment of the present invention, there is provided a formation electromagnetic imaging device while drilling, comprising: A formation model module suitable for determining the inversion formation model of electromagnetic imaging of formations while drilling; A gradient module is suitable for constructing an objective function with a regularization term for an inverted formation model, and calculating the gradient of the objective function with the regularization term to obtain a gradient function with the regularization term; The iterative inversion module is suitable for using the pre-gradient matrix obtained by pre-training the gradient function, iteratively updating the formation parameters of the inversion formation model according to the pre-gradient matrix according to the number of inversion iterations, and determining the formation parameters for constructing the inversion map of the current formation based on the objective function to obtain the inversion map of the current formation; the pre-gradient matrix corresponds to the number of inversion iterations.

[0006] According to another aspect of an embodiment of the present invention, there is provided a computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned method for electromagnetic imaging of formations while drilling.

[0007] According to another aspect of the embodiments of the present invention, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction enables a processor to execute operations corresponding to the above-mentioned method for electromagnetic imaging of formations while drilling.

[0008] According to another aspect of an embodiment of the present invention, a computer program product is provided, comprising at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the above-mentioned method for electromagnetic imaging of formations while drilling.

[0009] The method and device for electromagnetic imaging of formations while drilling provided by the embodiments of the present invention improve the inversion accuracy and global convergence capability by combining inversion with the inversion formation model of machine learning. Dynamic regularization can make the regularization parameters adaptively adjusted during the iteration process, thereby enhancing the stability and accuracy of the inversion results. The pre-gradient matrix obtained by pre-training is directly applied in the inversion, which significantly improves the inversion speed and real-time performance, and improves the inversion accuracy of complex formation parameters.

[0010] The above description is only an overview of the technical solution of the embodiment of the present invention. In order to more clearly understand the technical means of the embodiment of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present invention more obvious and easy to understand, the specific implementation method of the embodiment of the present invention is specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the embodiments of the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A flow chart of a method for electromagnetic imaging of formations while drilling according to an embodiment of the present invention is shown; Figure 2 A schematic diagram of a seven-layer complex stratigraphic model is shown; Figure 3 A schematic diagram of various parameters of a seven-layer complex stratum model is shown; Figure 4 The schematic diagram of the inversion diagram of the current stratum obtained by the seven-layer complex stratum model is shown; Figure 5 A schematic structural diagram of a formation electromagnetic imaging device while drilling according to an embodiment of the present invention is shown; Figure 6 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0012] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0013] Figure 1 FIG. 4 is a flow chart showing a method for electromagnetic imaging of formations while drilling according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps: Step S101, determining an inversion formation model of electromagnetic imaging of formations while drilling.

[0014] In order to improve the inversion accuracy, inversion methods such as those based on machine learning and deep learning can be used. Although machine learning and the like have strong global optimization capabilities, they lack physical interpretation and the generalizability of the model, which makes them limited in practical applications. Based on the above problems, this embodiment combines machine learning with electromagnetic wave measurement technology and gradient optimization during inversion, which can quickly and accurately invert the electrical parameters of each underground layer and provide high-resolution stratigraphic imaging results, thereby effectively overcoming the defects in traditional inversion technology, improving the reconstruction capability of the inverted stratigraphic model, and providing more reliable data support for oil and gas exploration and geological engineering.

[0015] When performing LWD inversion, building an accurate inversion formation model is the basis of inversion. According to formation parameters, such as the anisotropy and complexity of formation resistivity, different inversion formation models can be used to describe the electrical characteristics of the formation. For example, the isotropic model is suitable for situations where the electrical parameters (such as resistivity and conductivity) of the formation in all directions change relatively little, which can simplify the calculation process. The anisotropic model is suitable for complex formations, where resistivity and conductivity vary with direction. The anisotropic model can be closer to the actual situation of the formation. It depends on the specific implementation situation and is not limited here.

[0016] Step S102, constructing an objective function with a regularization term for the inversion formation model, and calculating the gradient of the objective function with the regularization term to obtain a gradient function with the regularization term.

[0017] The objective function is used to measure the gap between the logging data and the model prediction data. The smaller the objective function value, the higher the matching degree between the model and the actual situation. The objective function of the inversion formation model is constructed based on the logging data and the model prediction data, as shown below:

[0018] Among them, θ is the formation parameter to be inverted, y i is the ith actual logging data, and f(θ) is the predicted value of the inverted formation model.

[0019] Furthermore, when constructing the objective function of the inversion formation model, the logging data of the well to be inverted is collected based on logging instruments, such as electromagnetic logging equipment while drilling, and the logging data includes electrical parameters such as resistivity and conductivity. The logging data can be preprocessed first, and the objective function can be constructed based on the preprocessed logging data, which can provide more accurate formation parameters. Preprocessing includes data denoising and data compensation. During the data acquisition process, the original logging data may be affected by environmental noise and equipment errors, resulting in the emergence of random noise. Data denoising uses denoising algorithms (such as wavelet denoising, average correction algorithm, etc.) to process the data to eliminate these interferences, improve the signal-to-noise ratio of the data, and ensure the smoothness and reliability of the data. Data compensation aims to correct the logging data so that its response values ​​at different depths remain relatively uniform. Since different logging instruments may have different electromagnetic wave response characteristics at different depths, the non-uniformity can be eliminated through compensation processing, such as using interpolation algorithms and statistical correction methods to smooth the logging data to ensure that the measurement values ​​​​at each level can accurately reflect the electrical characteristics of the underground formation. The above is an example, and the specific settings are based on the implementation situation and are not limited here.

[0020] In order to prevent the inversion formation model from overfitting, this embodiment introduces the regularization term λ||θ|| 2 , we get the objective function with regularization term , as shown below:

[0021] λ||θ|| 2 It is a regularization term that can suppress excessive fluctuations in formation parameters, including the regularization coefficient λ and the formation parameter θ. The regularization coefficient λ can control the weight of the regularization term.

[0022] In order to better adapt to different inversion stages, the regularization parameter λ is dynamically adjusted. According to the preset initial regularization parameter, it decays in sequence according to the number of inversion iterations. The decay is determined by the decay factor, and the adaptive adjustment is performed as follows:

[0023] Among them, λ0 is the preset initial regularization parameter, β is the attenuation factor, and k is the number of inversion iterations. In the early stage of inversion, a larger regularization parameter λ is used to suppress the overfitting of the inversion formation model. As the inversion iteration proceeds, the regularization parameter λ is reduced so that the inversion formation model gradually fits the complex characteristics of the actual formation.

[0024] For the objective function with regularization term, its gradient is calculated to obtain the gradient function with regularization term, as shown below:

[0025]

[0026] Among them, ▽J(θ) is the error gradient of the objective function. For the regularization term, L2 regularization can be used, λ||θ|| 2 =2θ. Substituting the error gradient and regularization term into the gradient function, we get the following:

[0027] Step S103, using the pre-gradient matrix obtained by pre-training the gradient function, iteratively updates the formation parameters of the inversion formation model according to the pre-gradient matrix according to the number of inversion iterations, and determines the formation parameters for constructing the inversion map of the current formation based on the objective function to obtain the inversion map of the current formation.

[0028] According to the gradient function, the formation parameters are updated by adaptive gradient optimization using the gradient descent method as shown below:

[0029] Where k is the number of inversion iterations, α is the preset learning rate, and the update step size can be adjusted according to the implementation situation. The direction of change of the formation parameters can be controlled according to the gradient function, so as to adjust the formation parameters in the next inversion iteration.

[0030] Substituting the gradient function into the above gradient descent method, the formula for updating the formation parameters is as follows:

[0031] In the corresponding gradient function, the first term is the error gradient, which adjusts the inversion formation model to converge to the actual observed data; the second term is the regularization gradient, which smoothes the changes in the formation parameters of the inversion formation model.

[0032] In order to facilitate the rapid realization of online real-time inversion, the pre-gradient matrix can be obtained by pre-training with the gradient function in advance, that is, the pre-gradient matrix can be obtained in advance by offline training. When performing online real-time inversion, the pre-gradient matrix can be directly used to quickly update the formation parameters in the iterative inversion process, which significantly improves the inversion speed and real-time performance.

[0033] The specific process of pre-training to obtain the pre-gradient matrix is ​​as follows: construct a training sample set based on prior data, determine the initial inversion parameters from it, train the inversion formation model based on the training sample set, and obtain the pre-training matrix of each inversion iteration number in the training process. The pre-training matrix corresponds to the number of inversion iterations and is determined according to the gradient function. If the number of inversion iterations is 7, then 7 pre-training matrices are obtained, corresponding to each number of inversion iterations. After obtaining the pre-gradient matrix, for any inversion iteration number, the formation parameters of the next inversion iteration number can be obtained according to the difference between the formation parameters of the current inversion iteration number and the pre-gradient matrix corresponding to the current inversion iteration number, so as to be substituted into the inversion formation model for inversion. Here, the prior data includes the initial value range of the formation parameters of the inversion formation model determined according to the well logging data of the neighboring wells collected. If the well logging data of the neighboring wells shows that the formation resistivity interval is 1-10 ohm meters, the initial value range of the formation parameters can be selected to start inversion within the range of 1-10. As the inversion progresses, the range can also be adjusted according to the inversion results of each iteration to make the inversion results more accurate.

[0034] According to the formation parameters corresponding to each inversion iteration number, the value of the objective function of each inversion iteration number can be calculated respectively, and the values ​​of the objective function of each inversion iteration number can be compared to determine the formation parameter of the inversion iteration number corresponding to the minimum value of the objective function as the formation parameter for constructing the inversion map of the current formation, that is, according to the objective function, the formation parameter with the smallest error with the actual logging data is determined as the formation parameter for constructing the inversion map of the current formation. After determining the formation parameters, the inversion map of the current formation can be constructed according to the formation parameters, such as constructing the inversion map of the current formation according to the formation parameters of different depths, etc., which will not be explained in detail here.

[0035] Furthermore, during the inversion iteration process, the formation parameter range can be adjusted according to the inversion iteration results. For example, the initial resistivity parameter range is set to [1, 10] ohm-meter according to the logging data. After the inversion is iterated several times, such as k times, the formation parameter mean and the formation parameter standard deviation are calculated based on the formation parameters of multiple inversion iterations.

[0036]

[0037] in, is the mean value of formation parameters, and σ is the standard deviation of formation parameters. According to the mean value of formation parameters, the concentration trend of formation parameters can be found. For example, if the mean value of formation parameters is 6 ohm-meter, it means that the range of formation parameters is closer to 6 ohm-meter. The smaller the standard deviation of formation parameters, the lower the dispersion of formation parameters.

[0038] The range of formation parameters can be updated according to the formation parameter mean and formation parameter standard deviation, and the parameter interval [θ min ,θ max ] Further update:

[0039] Among them, Δ is the range offset determined by the standard deviation of the formation parameters, which is set according to the implementation situation and is not limited here. If Δ=2, a new value range is obtained, θ min =6-2=4,θ max =6+2=8, the updated formation parameter value range is [4, 8]. When continuing the inversion, you can use the updated formation parameter value range, such as Take the value from the updated formation parameter range [4, 8] and calculate it according to the pre-training matrix , continue to invert the inversion formation model. By adjusting the range of formation parameters, the inversion accuracy can be effectively improved, the amount of calculation can be reduced, and the convergence can be accelerated, making the inversion results closer to the actual formation parameters.

[0040] Furthermore, for the pre-gradient matrix, the dimension of the pre-gradient matrix obtained by pre-training according to the prior data will be relatively large, such as including dimensions such as 1-10, 1-100, etc. If it is directly used for the inversion result of the logging while drilling of the current formation, it will not be accurate enough. However, with the help of the pre-gradient matrix obtained by pre-training, the range of the pre-gradient matrix can be determined according to the logging data of the logging while drilling, and the inversion can be further refined based on the range of the pre-gradient matrix to obtain a more accurate inversion map. In addition, with the help of the pre-trained pre-gradient matrix, the inversion time can be greatly saved and the inversion efficiency can be improved.

[0041] Furthermore, the amount of data in logging while drilling is huge and complex, and the inversion process requires efficient real-time processing. In order to improve the inversion speed, the present embodiment adopts parallel computing technology for inversion, and parallel computing includes data block processing, GPU acceleration, distributed computing, and a hybrid parallel architecture integrating various technologies. Data block processing can divide large-scale logging data and inversion formation models into multiple subtasks according to depth or attributes, and assign them to different computing nodes or threads for parallel processing. Each data block is calculated as an independent task, and the results are finally summarized, thereby significantly shortening the calculation time while ensuring the inversion accuracy. The pressure of a single calculation can be effectively reduced through a fine-grained block method, and the overall efficiency can be improved. On this basis, GPU acceleration further optimizes the calculation speed through its high parallel processing capability. Running tasks on the GPU can greatly shorten the processing time. The GPU can use its multi-threaded architecture to process different data blocks in parallel, and improve computing efficiency through technologies such as data parallelism and shared memory, which is particularly suitable for high-density data processing requirements in deep formation inversion. For larger-scale inversion tasks, distributed computing can be used to assign inversion tasks to multiple computers for parallel processing. Each node completes the data portion it is responsible for, and then aggregates the results to form a complete inversion model. Distributed computing adopts high-speed connection and dynamic allocation mechanisms in data transmission and task scheduling to ensure load balancing and full utilization of computing resources, which can not only significantly improve the inversion speed, but also enhance the stability of the inversion. Combining the advantages of the above-mentioned data block processing, GPU acceleration and distributed computing, the hybrid parallel architecture can further improve the inversion efficiency, optimize multi-level task allocation, asynchronous task management and fault-tolerant mechanisms, and can simultaneously utilize multi-node and GPU parallel computing to accelerate the inversion process and complete it quickly. It also has high fault tolerance, making large-scale inversion under complex formation conditions more efficient and reliable.

[0042] Optionally, this embodiment may further include the following steps: Step S104, for the curve data of any coordinate in the inversion map of the current formation, the curve data is smoothed according to the second-order differences in the horizontal and vertical directions of the coordinates and a preset smoothing factor to obtain the inversion map of the current formation after smoothing.

[0043] Different formation parameters can be obtained according to the inversion iteration. For example, the resistivity of the seven-layer complex inversion formation model is assigned as follows from top to bottom: 8 ohm-meter, 3 ohm-meter, 35 ohm-meter, 15 ohm-meter, 25 ohm-meter, 2 ohm-meter, and 60 ohm-meter. The thickness of each layer is set to be different, and multiple formation dips are randomly set to establish an inversion formation model with different fluctuations. Figure 2 As shown in the figure, the red line is the trajectory designed for measurement while drilling, which can be adjusted according to the real-time detection situation during actual logging. The parameters used in the inversion formation model are as follows: Figure 3As shown in the figure, the dip angle, azimuth, vertical depth, X-axis offset, Y-axis offset, broken line angle, slope rate and other data corresponding to different depths are obtained by forward calculation to obtain the relevant logging curve response, and the curve data is inverted by adaptive gradient optimization, and the inversion effect is optimized to obtain the final inversion map of the current formation.

[0044] Furthermore, the smoothness of the inversion map of the current formation has a great influence on the accuracy of formation feature recognition. However, the inversion map of the current formation generated during the inversion process may be affected by the logging curve data, resulting in jagged and uneven distribution of color blocks. This problem is mainly caused by the spatial discontinuity of the logging data or the noise caused by the heterogeneity and anisotropy of the formation.

[0045] In order to eliminate problems such as jagged color blocks that appear in the inversion map of the current formation, this embodiment performs smoothing based on the inversion result, such as using high-order finite difference smoothing to reduce the discreteness and unnecessary fluctuations of the image, thereby making the inversion map of the current formation smoother and more coherent while retaining important boundary information.

[0046] Specifically, for the curve data of any coordinate in the inversion diagram of the current formation, the curve data is smoothed according to the second-order differences in the horizontal and vertical directions of the coordinates and a preset smoothing factor, such as finite difference smoothing, as shown below:

[0047] in, is the curve data of the coordinates (x, y) in the inversion diagram, For The curve data of the coordinates (x, y) after smoothing, λ is a preset smoothing factor, which is used to control the intensity of smoothing and is set according to the implementation situation and is not limited here. are the second-order differences in the horizontal and vertical directions respectively. During the smoothing process, the above finite difference smoothing process can be repeated several times to gradually reduce the discreteness in the inversion map of the current formation and make the inversion result smoother, such as Figure 4 As shown in the figure, the red line is the trajectory designed for measurement while drilling, which can be adjusted according to the real-time detection situation during actual logging. Smoothing can mainly act on local areas and will not have a significant impact on important formation boundaries, thereby ensuring the practicality of the inversion results. Finite difference smoothing is simple to calculate, can quickly complete the smoothing operation, and can be seamlessly integrated with the previous inversion process. Through finite difference smoothing, the smoothness of the inversion map of the current formation is improved, further enhancing the coherence and readability of the formation characteristics.

[0048] According to the method for electromagnetic imaging of formations while drilling provided by an embodiment of the present invention, by combining inversion with a formation model of machine learning, the inversion accuracy and global convergence ability are improved. Dynamic regularization can make the regularization parameters adaptively adjusted during the iteration process, thereby enhancing the stability and accuracy of the inversion results. The pre-gradient matrix obtained by pre-training is directly applied in the inversion, which significantly improves the inversion speed and real-time performance, and improves the inversion accuracy of complex formation parameters.

[0049] Figure 5 FIG. 2 shows a schematic diagram of the structure of a formation electromagnetic imaging device while drilling provided by an embodiment of the present invention. Figure 5 As shown, the device comprises: A formation model module 510, adapted to determine an inversion formation model of electromagnetic imaging of formations while drilling; A gradient module 520, adapted to construct an objective function with a regularization term for the inversion formation model, and calculate the gradient of the objective function with the regularization term to obtain a gradient function with the regularization term; The iterative inversion module 530 is suitable for using the pre-gradient matrix obtained by pre-training the gradient function, iteratively updating the formation parameters of the inversion formation model according to the pre-gradient matrix according to the number of inversion iterations, and determining the formation parameters for constructing the inversion map of the current formation based on the objective function to obtain the inversion map of the current formation; the pre-gradient matrix corresponds to the number of inversion iterations.

[0050] Optionally, the gradient module 520 is further adapted to: Acquire the logging data of the well to be inverted, and pre-process the logging data of the well to be inverted; the pre-processing includes data noise reduction and data compensation; The objective function of the inversion formation model is constructed based on the preprocessed logging data and model prediction data; A regularization term is added to the objective function to obtain an objective function with a regularization term; wherein the regularization term includes a regularization coefficient and a formation parameter; the regularization coefficient decays in sequence according to the preset initial regularization parameter and the number of inversion iterations, and the decay is determined according to the decay factor; Calculate the gradient of the objective function with the regularization term and obtain the gradient function with the regularization term.

[0051] Optionally, the iterative inversion module 530 is further adapted to: Construct a training sample set based on prior data; Initial inversion parameters are determined, and the inversion formation model is trained according to the training sample set to obtain a pre-training matrix for each inversion iteration number in the training process; wherein the pre-training matrix corresponds to the inversion iteration number and is determined according to a gradient function.

[0052] Optionally, the iterative inversion module 530 is further adapted to: Collect logging data of adjacent wells; Determine the initial value range of the formation parameters of the inversion formation model based on the logging data of adjacent wells; preprocessing includes data noise reduction and data compensation; For any inversion iteration number, the formation parameters of the next inversion iteration number are obtained according to the difference between the formation parameters of the current inversion iteration number and the pre-gradient matrix corresponding to the current inversion iteration number, and are substituted into the inversion formation model for inversion; According to the formation parameters corresponding to each inversion iteration number, the value of the objective function of each inversion iteration number is calculated, and the formation parameters of the inversion iteration number corresponding to the minimum value of the objective function are determined as the formation parameters for constructing the inversion map of the current formation.

[0053] Optionally, the device also includes: a range update module 540, suitable for calculating the formation parameter mean and the formation parameter standard deviation based on the formation parameters of multiple inversion iterations; updating the value range of the formation parameters according to the formation parameter mean and the formation parameter standard deviation; and inverting the inversion formation model according to the updated value range of the formation parameters.

[0054] Optionally, the device also includes: a smoothing module 550, which is suitable for smoothing the curve data of any coordinate in the inversion map of the current formation according to the second-order differences in the horizontal and vertical directions of the coordinates and a preset smoothing factor to obtain the inversion map of the current formation after smoothing.

[0055] The description of each module above refers to the corresponding description in the method embodiment and will not be repeated here.

[0056] An embodiment of the present invention further provides a non-volatile computer storage medium, which stores at least one executable instruction, and the executable instruction can execute operations corresponding to the while-drilling formation electromagnetic imaging method in any of the above method embodiments.

[0057] An embodiment of the present application provides a computer program product, which includes at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to perform operations corresponding to the while-drilling formation electromagnetic imaging method in any of the above-mentioned method embodiments.

[0058] Figure 6 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown, and the specific implementation of the embodiment of the present invention does not limit the specific implementation of the computing device.

[0059] like Figure 6 As shown, the computing device may include: a processor 602 , a communication interface 604 , a memory 606 , and a communication bus 608 .

[0060] in: The processor 602 , the communication interface 604 , and the memory 606 communicate with each other via a communication bus 608 .

[0061] The communication interface 604 is used to communicate with other devices such as clients or other servers.

[0062] The processor 602 is used to execute the program 610, and specifically can execute the relevant steps in the above-mentioned embodiment of the method for electromagnetic imaging of formations while drilling.

[0063] Specifically, the program 610 may include program codes, which include computer operation instructions.

[0064] The processor 602 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0065] The memory 606 is used to store the program 610. The memory 606 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0066] The program 610 can be specifically used to enable the processor 602 to execute the while-drilling formation electromagnetic imaging method in any of the above-mentioned method embodiments. The specific implementation of each step in the program 610 can refer to the corresponding descriptions in the corresponding steps and units in the above-mentioned while-drilling formation electromagnetic imaging embodiment, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described equipment and modules can refer to the corresponding process description in the above-mentioned method embodiment, which will not be repeated here.

[0067] The algorithm or display provided herein is not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the embodiment of the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the embodiment of the present invention described herein, and the description of the above specific language is to disclose the preferred implementation of the embodiment of the present invention.

[0068] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0069] Similarly, it should be understood that in order to streamline the embodiments of the present invention and to aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following intention: that the claimed embodiments of the present invention require more features than the features explicitly recited in each claim. More specifically, as reflected in the claims below, the inventive aspects lie in less than all the features of the single embodiment disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present invention.

[0070] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into a plurality of submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device so disclosed may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0071] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0072] The various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The embodiments of the present invention may also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program for implementing an embodiment of the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0073] It should be noted that the above embodiments illustrate rather than limit the present invention, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim listing a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A method for electromagnetic imaging of formations while drilling, characterized in that: Methods include: Determine the inversion formation model for electromagnetic imaging of formations while drilling; Constructing an objective function with a regularization term for the inversion formation model, and calculating the gradient of the objective function with the regularization term to obtain a gradient function with the regularization term; The pre-gradient matrix obtained by pre-training the gradient function is used to iteratively update the formation parameters of the inversion formation model according to the pre-gradient matrix according to the number of inversion iterations, and the formation parameters for constructing the inversion map of the current formation are determined based on the objective function to obtain the inversion map of the current formation; the pre-gradient matrix corresponds to the number of inversion iterations.

2. The method according to claim 1, characterized in that The step of constructing an objective function with a regularization term for the inversion formation model, and calculating the gradient of the objective function with the regularization term to obtain the gradient function with the regularization term further comprises: Acquire the logging data of the well to be inverted, and pre-process the logging data of the well to be inverted; the pre-processing includes data noise reduction and data compensation; Constructing the objective function of the inversion formation model based on the preprocessed logging data and the model prediction data; A regularization term is added to the objective function to obtain an objective function with a regularization term; wherein the regularization term includes a regularization coefficient and a formation parameter; the regularization coefficient decays in sequence according to the preset initial regularization parameter and the number of inversion iterations, and the attenuation is determined according to an attenuation factor; The gradient of the objective function with the regularization term is calculated to obtain a gradient function with the regularization term.

3. The method according to claim 1, characterized in that The pre-gradient matrix obtained by pre-training the gradient function is specifically: Construct a training sample set based on prior data; Determine initial inversion parameters, train the inversion formation model according to the training sample set, and obtain a pre-training matrix for each inversion iteration number in the training process; wherein the pre-training matrix corresponds to the inversion iteration number and is determined according to the gradient function.

4. The method according to claim 1, characterized in that: The method further comprises: iteratively updating the formation parameters of the inversion formation model according to the pre-gradient matrix obtained by pre-training the gradient function according to the number of inversion iterations, and determining the formation parameters of the inversion map of the current formation based on the objective function. Collect logging data of adjacent wells; Determining the initial value range of the formation parameters of the inversion formation model according to the logging data of the adjacent wells; For any inversion iteration number, the formation parameters of the next inversion iteration number are obtained according to the difference between the formation parameters of the current inversion iteration number and the pre-gradient matrix corresponding to the current inversion iteration number, so as to substitute them into the inversion formation model for inversion; According to the formation parameters corresponding to each inversion iteration number, the value of the objective function for each inversion iteration number is calculated, and the formation parameters for the inversion iteration number corresponding to the minimum value of the objective function are determined as the formation parameters for constructing the inversion map of the current formation.

5. The method according to claim 4, characterized in that The method further comprises: According to the formation parameters of multiple inversion iterations, the formation parameter mean and formation parameter standard deviation are calculated; Updating the value range of the formation parameter according to the formation parameter mean and the formation parameter standard deviation; The inversion formation model is inverted according to the updated value range of the formation parameter.

6. The method according to claim 1, characterized in that The method further comprises: For the curve data of any coordinate in the inversion map of the current formation, the curve data is smoothed according to the second-order differences in the horizontal and vertical directions of the coordinates and a preset smoothing factor to obtain the inversion map of the current formation after smoothing.

7. A formation electromagnetic imaging device while drilling, characterized in that: The device includes: A formation model module suitable for determining the inversion formation model of electromagnetic imaging of formations while drilling; A gradient module, adapted to construct an objective function with a regularization term for the inversion formation model, and calculate the gradient of the objective function with the regularization term to obtain a gradient function with the regularization term; The iterative inversion module is suitable for using the pre-gradient matrix obtained by pre-training the gradient function, iteratively updating the formation parameters of the inversion formation model according to the pre-gradient matrix according to the number of inversion iterations, and determining the formation parameters for constructing the inversion map of the current formation based on the objective function to obtain the inversion map of the current formation; the pre-gradient matrix corresponds to the number of inversion iterations.

8. A computing device, characterized in that include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the method for electromagnetic imaging of formations while drilling as described in any one of claims 1-6.

9. A computer storage medium, characterized in that The storage medium stores at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the method for electromagnetic imaging of formations while drilling as described in any one of claims 1-6.

10. A computer program product, characterized in that The method comprises at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the method for electromagnetic imaging of formations while drilling according to any one of claims 1 to 6.

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