Regional decomposition-based two-dimensional array resistivity inversion method, electronic equipment, storage medium and device

Abstract

The invention discloses a region decomposition-based two-dimensional array resistivity inversion method, electronic equipment, a storage medium and a device. The method comprises the steps that area decomposition is conducted on a large array lateral logging response data set based on the layer interface position of a target area, a plurality of sub-areas are obtained, and every three adjacent stratums form one sub-area; the thickness and the center point position of each stratum are calculated, and then the thickness and the center point position of each sub-region are obtained; constructing a one-dimensional inversion model based on the stratum to perform one-dimensional array resistivity inversion; and constructing a two-dimensional inversion model based on the sub-regions to perform two-dimensional array resistivity inversion, and taking a one-dimensional inversion result as an initial parameter value of the two-dimensional inversion model. The method fully considers the influence of radial mud invasion and longitudinal surrounding rock layers, and strengthens the information constraint of the upper and lower well sections, thereby weakening the influence of surrounding rock, guaranteeing the extraction precision of the real resistivity of the stratum, and achieving the rapid and accurate imaging of the two-dimensional profile of the resistivity of the stratum.

Description

Technical Field

[0001] This invention belongs to the field of petroleum exploration and development technology, and more specifically, relates to a two-dimensional array resistivity inversion method based on region decomposition, electronic equipment, storage medium and device. Background Technology

[0002] Compared to traditional resistivity logging, array lateral logging instruments can provide 4–6 vertically higher resolution apparent resistivity lines, enabling more detailed characterization of radial invasion and vertical resistivity profiles of the reservoir. However, due to post-drilling measurements, the pressure difference between the wellbore and encountered permeable layers can cause radial invasion of drilling mud filtrate, resulting in a complete mismatch between the apparent resistivity information and the actual formation resistivity. This makes it impossible for logging interpreters to make accurate reservoir assessments. Therefore, geophysical inversion techniques are typically used to reconstruct the resistivity profile.

[0003] Currently, array resistivity inversion methods for vertical or deviated well formations mainly rely on one-dimensional point-by-point iterative inversion. However, in actual formations, apparent resistivity is affected not only by lateral mud intrusion but also by the vertical influence of the surrounding rock. The one-dimensional point-by-point iterative inversion method is based on an infinitely thick one-dimensional radial inversion model, which does not consider the influence of the surrounding rock, resulting in limited inversion accuracy and directly affecting the accuracy of subsequent oil and gas reservoir identification.

[0004] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention, and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to propose a two-dimensional array resistivity inversion method, electronic equipment, storage medium and device based on domain decomposition, to constrain the logging information of upper and lower layers, and to consider the influence of surrounding rock in the three-layer inversion model, thereby weakening the influence of surrounding rock, ensuring the extraction accuracy of the true resistivity of the formation, and finally realizing rapid and accurate imaging of the two-dimensional resistivity profile of the formation.

[0006] To achieve the above objectives, this invention proposes a two-dimensional array resistivity inversion method, electronic device, storage medium, and apparatus based on region decomposition.

[0007] According to a first aspect of the present invention, a two-dimensional array resistivity inversion method based on domain decomposition is proposed, comprising:

[0008] Based on the layer interface location of the target area, the large array lateral logging response dataset is decomposed into multiple sub-regions, with each sub-region consisting of three adjacent strata.

[0009] The thickness and center point location of each stratum are calculated, thereby obtaining the thickness and center point location of each sub-region;

[0010] A one-dimensional inversion model is constructed based on the strata, and a one-dimensional array resistivity inversion is performed based on the location of the center point of each stratum and the one-dimensional inversion model.

[0011] A two-dimensional inversion model is constructed based on the sub-regions. A two-dimensional array resistivity inversion is performed based on the center point position of each sub-region and the two-dimensional inversion model. The one-dimensional inversion result is used as the initial parameter value of the two-dimensional inversion model.

[0012] Optionally, both the one-dimensional inversion model and the two-dimensional inversion model are:

[0013]

[0014] Where x is the parameter vector to be inverted in the inversion model, x = (Di, Rxo, Rt) T Di represents the intrusion depth, Rxo represents the resistivity of the intrusion zone, Rt represents the resistivity of the original formation, T represents the transpose of the matrix, S(x) represents the finite element forward modeling response of the array lateral instrument, including five apparent resistivity values ​​from RLA1 to RLA5, d represents the measured data, and x represents the intrusion depth. p The model has a known reference vector, and μ is the regularization parameter.

[0015] Optionally, the update step size of the parameters to be inverted in the one-dimensional inversion model and the two-dimensional inversion model is:

[0016]

[0017] Where Δx is the update step size of the parameters to be inverted, J is the Jacobian matrix, and μ k α is the regularization parameter, I is the identity matrix, α is the iteration parameter, and k is the number of iterations.

[0018] Optionally, the two-dimensional array resistivity inversion based on the center point location of each sub-region and the two-dimensional inversion model includes:

[0019] S1. Substitute the two-dimensional inversion model of the sub-region into the finite element forward modeling and determine whether the L2 norm of the forward modeling result and the measured result meets the accuracy requirements. If yes, proceed to step S4; otherwise, proceed to step S2.

[0020] S2. Set the range of formation intrusion depth and resistivity based on the reservoir prior information, and constrain the range of variation of the parameters to be inverted based on the range of formation intrusion depth and resistivity.

[0021] S3. Based on the range of change and the two-dimensional inversion model, iteratively update the parameters to be inverted, and execute S1 based on the updated parameters to be inverted.

[0022] S4. Output the intrusion depth, intrusion zone resistivity and original formation resistivity of the intermediate layer of the sub-region, and determine whether all sub-regions have completed the inversion. If yes, proceed to step S5. If no, use the intrusion depth, intrusion zone resistivity and original formation resistivity of the intermediate layer as the initial parameter values ​​of the upper layer of the two-dimensional inversion model of the next sub-region and proceed to step S1.

[0023] S5. Calculate the error of the inversion results of all the sub-regions and determine whether the error meets the preset accuracy. If yes, proceed to step S7. If no, increase the number of layer-by-layer scanning inversions.

[0024] S6. Determine whether the current layer-by-layer scanning inversion number is greater than the preset layer-by-layer scanning inversion number. If yes, proceed to step S7. If no, use the current layer-by-layer scanning inversion result as the parameter of the two-dimensional inversion model and proceed to step S1.

[0025] S7. Output the two-dimensional inversion results and perform two-dimensional electrical profile imaging based on the two-dimensional inversion results.

[0026] Optionally, the expression for calculating the L2 norm is:

[0027]

[0028] Among them, S_RLAi m_j and Obs_RLAi m_j The results are the finite element forward modeling result and the measured result, respectively, of the i-th apparent resistivity value at the intermediate measuring point at the center point of the j-th sub-region.

[0029] Optionally, the expression for calculating the error of the inversion results for all the said sub-regions is:

[0030]

[0031] Among them, S_RLAi p and Obs_RLAi p The results are the finite element forward modeling result and the measured result, respectively, of the i-th apparent resistivity value at the intermediate measuring point at the p-th stratum center location.

[0032] Optionally, the two-dimensional electrical profile imaging based on the two-dimensional inversion result includes:

[0033] Based on the two-dimensional inversion results, resistivity values ​​are assigned to each formation, and all resistivities are logarithmically converted to obtain a two-dimensional electrical profile resistivity color map.

[0034] Specifically, the number of strata is determined based on the depth of the intermediate measurement point of the strata, and the corresponding two-dimensional inversion result is determined based on the number of strata; the radial region of the strata with an intrusion depth smaller than that of the two-dimensional inversion result is assigned the intrusion zone resistivity of the two-dimensional inversion result, and the radial region of the strata with an intrusion depth greater than that of the two-dimensional inversion result is assigned the original stratum resistivity of the two-dimensional inversion result.

[0035] According to a second aspect of the present invention, a two-dimensional array resistivity inversion device based on domain decomposition includes:

[0036] The region decomposition module is used to decompose the large array lateral logging response dataset into multiple sub-regions based on the layer interface location of the target region, with each sub-region consisting of three adjacent strata.

[0037] The calculation module is used to calculate the thickness and center point location of each of the strata, thereby obtaining the thickness and center point location of each of the sub-regions;

[0038] The one-dimensional inversion module is used to construct a one-dimensional inversion model based on the strata, and to perform one-dimensional array resistivity inversion based on the location of the center point of each stratum and the one-dimensional inversion model;

[0039] The two-dimensional inversion module is used to construct a two-dimensional inversion model based on the sub-region, perform two-dimensional array resistivity inversion based on the center point position of each sub-region and the two-dimensional inversion model, and use the one-dimensional inversion result as the initial parameter value of the two-dimensional inversion model.

[0040] According to a third aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0041] At least one processor; and,

[0042] A memory communicatively connected to the at least one processor; wherein,

[0043] The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the two-dimensional array resistivity inversion method based on region decomposition as described in any of the first aspects.

[0044] According to a fourth aspect of the invention, a non-transitory computer-readable storage medium is provided, which stores computer instructions for causing a computer to execute the two-dimensional array resistivity inversion method based on region decomposition as described in any of the first aspects.

[0045] The beneficial effects of this invention are as follows: By performing regional decomposition on a large array lateral logging response dataset of the target area, this invention groups three adjacent strata into a sub-region, resulting in multiple sub-regions. Different inversion sub-regions partially cover each other, thus allowing the inversion results of the previous sub-region to constrain the inversion of the next sub-region in a timely manner, achieving constraint on logging information between upper and lower layers and weakening the influence of the surrounding rock. A one-dimensional inversion model is constructed based on the strata, and a two-dimensional inversion model is constructed based on the sub-regions, fully considering the influence of radial mud intrusion and longitudinal surrounding rock layers, ensuring the accuracy of extracting the true resistivity of the formation, and achieving rapid and accurate imaging of the two-dimensional resistivity profile. This invention reduces the uncertainty of manual random assignment by using the one-dimensional inversion results as the initial parameter values ​​of the two-dimensional inversion model, and improves the global optimization capability and the speed of two-dimensional inversion.

[0046] The system of the present invention has other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0047] The above and other objects, features and advantages of the present invention will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0048] Figure 1 A flowchart illustrating the steps of a two-dimensional array resistivity inversion method based on domain decomposition according to the present invention is shown.

[0049] Figure 2 A flowchart illustrating the steps of a two-dimensional array resistivity inversion method based on region decomposition according to Embodiment 2 of the present invention is shown.

[0050] Figure 3 A schematic diagram of dataset region decomposition according to Embodiment 2 of the present invention is shown.

[0051] Figure 4 A schematic diagram of a one-dimensional inversion model according to Embodiment 2 of the present invention is shown.

[0052] Figure 5 A schematic diagram of a two-dimensional inversion model according to Embodiment 2 of the present invention is shown.

[0053] Figure 6 A schematic diagram of the numerical model parameter distribution and array resistivity response according to Embodiment 2 of the present invention is shown.

[0054] Figure 7A schematic diagram of resistivity imaging using a numerical model according to Embodiment 2 of the present invention is shown.

[0055] Figure 8 A schematic diagram of the conventional one-dimensional inversion imaging result according to Embodiment 2 of the present invention is shown.

[0056] Figure 9 A schematic diagram of the two-dimensional inversion imaging results according to Embodiment 2 of the present invention is shown.

[0057] Figure 10 A schematic diagram of the error distribution of the one-dimensional inversion result and the two-dimensional inversion result according to Embodiment 2 of the present invention is shown. Detailed Implementation

[0058] The invention will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0059] like Figure 1 As shown, a two-dimensional array resistivity inversion method based on domain decomposition according to the present invention includes:

[0060] Based on the layer interface location of the target area, the large array lateral logging response dataset is decomposed into multiple sub-regions, with each sub-region consisting of three adjacent strata.

[0061] Calculate the thickness and center point location of each stratum, and then obtain the thickness and center point location of each sub-region;

[0062] A one-dimensional inversion model is constructed based on the strata, and a one-dimensional array resistivity inversion is performed based on the location of the center point of each stratum and the one-dimensional inversion model.

[0063] A two-dimensional inversion model is constructed based on sub-regions. Two-dimensional array resistivity inversion is performed based on the center point location of each sub-region and the two-dimensional inversion model. The one-dimensional inversion results are used as the initial values ​​of the parameters of the two-dimensional inversion model.

[0064] Specifically, this invention determines the layer interface location based on the inflection point of the apparent resistivity curve or gamma, spontaneous potential, and borehole diameter lithology curves of the well logging data of the target area. Based on the layer interface location, the large-scale array lateral logging response dataset is decomposed into multiple sub-regions. That is, the large-scale array lateral logging response dataset is divided into multiple formations based on the layer interface location, with each three adjacent formations forming a sub-region. Specifically, the middle and lower layers of the j-th sub-region are the upper and middle layers of the (j+1)-th sub-region. The thickness and center point location of each formation are calculated based on the layer interface location. The thickness and center point location of each sub-region are then obtained based on the thickness and center point location of each formation. The calculation expression for the thickness and center point location of each formation is: H i =Z i+1 -Z i Z im =(Z i +Z i+1 ) / 2, where Z is the interface depth, i is the formation number, i < M+1, and M is the total number of formations; a one-dimensional inversion model is constructed based on the formations. The well inclination angle information of each formation is obtained by logging according to the formation dip angle. One-dimensional array resistivity inversion is performed based on the logging response and well inclination angle of the center point position in the well inclination angle information of each formation. The parameters to be inverted are the invasion depth Di, the resistivity of the invasion zone Rxo, and the resistivity of the original formation Rt. The one-dimensional inversion results are output and saved. A two-dimensional inversion model is constructed based on sub-regions, that is, each sub-region is a three-layer inversion model. The corresponding logging response and well inclination angle information are obtained according to the center point position of each sub-region and input into the two-dimensional inversion model for two-dimensional array resistivity inversion. The one-dimensional inversion results are used as the initial values ​​of the two-dimensional inversion model parameters. This invention decomposes a large array of lateral logging response datasets of the target area into regions, grouping three adjacent strata into a sub-region, resulting in multiple sub-regions. These sub-regions partially overlap, allowing the inversion results of the previous sub-region to constrain the inversion of the next sub-region, thus achieving constraint on logging information between upper and lower layers and mitigating the influence of the surrounding rock. A one-dimensional inversion model is constructed based on the strata, and a two-dimensional inversion model is constructed based on the sub-regions. This fully considers the radial mud intrusion and the longitudinal influence of the surrounding rock layers, ensuring the accuracy of extracting the true resistivity of the formation and achieving rapid and accurate two-dimensional imaging of the formation resistivity profile. Furthermore, by using the one-dimensional inversion results as the initial parameter values ​​for the two-dimensional inversion model, this invention reduces the uncertainty of manually assigned parameters, improves global optimization capabilities, and increases the speed of two-dimensional inversion.

[0065] In one example, both the one-dimensional inversion model and the two-dimensional inversion model are:

[0066]

[0067] Where x is the parameter vector to be inverted in the inversion model, x = (Di, Rxo, Rt) TDi represents the intrusion depth, Rxo represents the resistivity of the intrusion zone, Rt represents the resistivity of the original formation, T represents the transpose of the matrix, S(x) represents the finite element forward modeling response of the array lateral instrument, including five apparent resistivity values ​​from RLA1 to RLA5, d represents the measured data, and x represents the intrusion depth. p The model has a known reference vector, and μ is the regularization parameter.

[0068] Specifically, both the one-dimensional and two-dimensional inversion models employ the regularized Levenberg-Marquardt algorithm for inversion. The regularized Levenberg-Marquardt algorithm is an optimization algorithm that combines the characteristics of gradient descent and Gauss-Newton methods, improving the algorithm's performance and stability by introducing a regularization term.

[0069] In one example, the update step size of the parameters to be inverted in the one-dimensional inversion model and the two-dimensional inversion model is:

[0070]

[0071] Where Δx is the update step size of the parameters to be inverted, J is the Jacobian matrix, and μ k α is the regularization parameter, I is the identity matrix, α is the iteration parameter, and k is the number of iterations.

[0072] In one example, the two-dimensional array resistivity inversion based on the center point location of each sub-region and the two-dimensional inversion model includes:

[0073] S1. Substitute the two-dimensional inversion model of the sub-region into the finite element forward modeling and determine whether the L2 norm of the forward modeling result and the measured result meets the accuracy requirements. If yes, proceed to step S4; otherwise, proceed to step S2.

[0074] S2. Set the range of formation intrusion depth and resistivity based on the reservoir prior information, and constrain the variation range of the parameters to be inverted based on the range of formation intrusion depth and resistivity.

[0075] S3. Iteratively update the parameters to be inverted based on the range of change and the two-dimensional inversion model, and execute S1 based on the updated parameters to be inverted.

[0076] S4. Output the intrusion depth, resistivity of the intrusion zone, and resistivity of the original formation of the intermediate layer of the sub-region, and determine whether all sub-regions have been inverted. If yes, proceed to step S5. If no, use the intrusion depth, resistivity of the intrusion zone, and resistivity of the original formation of the intermediate layer as the initial parameter values ​​of the upper layer of the two-dimensional inversion model of the next sub-region and proceed to step S1.

[0077] S5. Calculate the error of the inversion results of all sub-regions and determine whether the error meets the preset accuracy. If yes, proceed to step S7. If no, increase the number of layer-by-layer scanning inversions.

[0078] S6. Determine whether the current layer-by-layer scanning inversion count is greater than the preset layer-by-layer scanning inversion count. If yes, proceed to step S7. If no, use the current layer-by-layer scanning inversion result as the parameter of the two-dimensional inversion model and proceed to step S1.

[0079] S7. Output the two-dimensional inversion results and perform two-dimensional electrical profile imaging based on the two-dimensional inversion results.

[0080] In one example, the expression for calculating the L2 norm is:

[0081]

[0082] Among them, S_RLAi m_j and Obs_RLAi m_j The results are the finite element forward modeling result and the measured result, respectively, of the i-th apparent resistivity value at the intermediate measuring point at the center point of the j-th sub-region.

[0083] In one example, the expression for calculating the error of the inversion results for all sub-regions is:

[0084]

[0085] Among them, S_RLAi p and Obs_RLAi p The results are the finite element forward modeling result and the measured result, respectively, of the i-th apparent resistivity value at the intermediate measuring point at the p-th stratum center location.

[0086] In one example, performing two-dimensional electrical profile imaging based on two-dimensional inversion results includes:

[0087] Based on the two-dimensional inversion results, resistivity values ​​are assigned to each formation, and all resistivities are logarithmically converted to obtain a two-dimensional electrical profile resistivity color code map.

[0088] Specifically, the number of strata is determined based on the depth of the intermediate measurement points of the strata, and the corresponding two-dimensional inversion result is determined based on the number of strata; the radial region of the strata with an intrusion depth smaller than the two-dimensional inversion result is assigned the intrusion zone resistivity of the two-dimensional inversion result, and the radial region of the strata with an intrusion depth greater than the two-dimensional inversion result is assigned the original stratum resistivity of the two-dimensional inversion result.

[0089] Specifically, this invention assigns resistivity values ​​to each formation based on the two-dimensional inversion results, and determines the formation's layer number based on the depth of the intermediate measurement point. p-1 <Dep p <Z p Among them, Dep p Z represents the depth of the intermediate measurement point. pZ represents the depth of the lower interface of the stratum where the intermediate measurement point is located. p-1 The depth of the upper interface of the stratum where the intermediate measurement point is located is used to determine the longitudinal position of the stratum where the intermediate measurement point is located. Based on the number of layers, the two-dimensional inversion result corresponding to the sub-region of the stratum as the intermediate layer can be determined. For radial regions in the stratum with an intrusion depth smaller than the intrusion depth of the two-dimensional inversion result, the intrusion zone resistivity of the two-dimensional inversion result is assigned. For radial regions in the stratum with an intrusion depth larger than the intrusion depth of the two-dimensional inversion result, the original stratum resistivity of the two-dimensional inversion result is assigned. This completes the assignment of values ​​to the radial regions of the stratum, that is, the position of the lateral medium is determined. Each stratum is processed in the above manner from top to bottom. All resistivities are logarithmically converted to obtain a two-dimensional electrical resistivity color code map.

[0090] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the invention. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present invention can be combined with each other.

[0091] Example 1

[0092] This embodiment provides a two-dimensional array resistivity inversion method based on domain decomposition, including:

[0093] Based on the layer interface location of the target area, the large array lateral logging response dataset is decomposed into multiple sub-regions, with each sub-region consisting of three adjacent strata.

[0094] Calculate the thickness and center point location of each stratum, and then obtain the thickness and center point location of each sub-region;

[0095] A one-dimensional inversion model is constructed based on the strata, and a one-dimensional array resistivity inversion is performed based on the location of the center point of each stratum and the one-dimensional inversion model.

[0096] A two-dimensional inversion model is constructed based on sub-regions. Two-dimensional array resistivity inversion is performed based on the center point location of each sub-region and the two-dimensional inversion model. The one-dimensional inversion results are used as the initial values ​​of the parameters of the two-dimensional inversion model.

[0097] Both the one-dimensional inversion model and the two-dimensional inversion model are:

[0098]

[0099] Where x is the parameter vector to be inverted in the inversion model, x = (Di, Rxo, Rt) T Di represents the intrusion depth, Rxo represents the resistivity of the intrusion zone, Rt represents the resistivity of the original formation, T represents the transpose of the matrix, S(x) represents the finite element forward modeling response of the array lateral instrument, including five apparent resistivity values ​​from RLA1 to RLA5, d represents the measured data, and x represents the intrusion depth. pThe model has a known reference vector, and μ is the regularization parameter.

[0100] The update step size for the parameters to be inverted in the one-dimensional and two-dimensional inversion models is:

[0101]

[0102] Where Δx is the update step size of the parameters to be inverted, J is the Jacobian matrix, and μ k α is the regularization parameter, I is the identity matrix, α is the iteration parameter, and k is the number of iterations.

[0103] The two-dimensional array resistivity inversion based on the center point location of each sub-region and the two-dimensional inversion model includes:

[0104] S1. Substitute the two-dimensional inversion model of the sub-region into the finite element forward modeling and determine whether the L2 norm of the forward modeling result and the measured result meets the accuracy requirements. If yes, proceed to step S4; otherwise, proceed to step S2.

[0105] S2. Set the range of formation intrusion depth and resistivity based on the reservoir prior information, and constrain the variation range of the parameters to be inverted based on the range of formation intrusion depth and resistivity.

[0106] S3. Iteratively update the parameters to be inverted based on the range of change and the two-dimensional inversion model, and execute S1 based on the updated parameters to be inverted.

[0107] S4. Output the intrusion depth, resistivity of the intrusion zone, and resistivity of the original formation of the intermediate layer of the sub-region, and determine whether all sub-regions have been inverted. If yes, proceed to step S5. If no, use the intrusion depth, resistivity of the intrusion zone, and resistivity of the original formation of the intermediate layer as the initial parameter values ​​of the upper layer of the two-dimensional inversion model of the next sub-region and proceed to step S1.

[0108] S5. Calculate the error of the inversion results of all sub-regions and determine whether the error meets the preset accuracy. If yes, proceed to step S7. If no, increase the number of layer-by-layer scanning inversions.

[0109] S6. Determine whether the current layer-by-layer scanning inversion count is greater than the preset layer-by-layer scanning inversion count. If yes, proceed to step S7. If no, use the current layer-by-layer scanning inversion result as the parameter of the two-dimensional inversion model and proceed to step S1.

[0110] S7. Output the two-dimensional inversion results and perform two-dimensional electrical profile imaging based on the two-dimensional inversion results.

[0111] The expression for calculating the L2 norm is:

[0112]

[0113] Among them, S_RLAi m_j and Obs_RLAi m_j The results are the finite element forward modeling result and the measured result, respectively, of the i-th apparent resistivity value at the intermediate measuring point at the center point of the j-th sub-region.

[0114] The expression for calculating the error of the inversion results for all sub-regions is as follows:

[0115]

[0116] Among them, S_RLAi p and Obs_RLAi p The results are the finite element forward modeling result and the measured result, respectively, of the i-th apparent resistivity value at the intermediate measuring point at the p-th stratum center location.

[0117] Two-dimensional electrical profile imaging based on two-dimensional inversion results includes:

[0118] Based on the two-dimensional inversion results, resistivity values ​​are assigned to each formation, and all resistivities are logarithmically converted to obtain a two-dimensional electrical profile resistivity color code map.

[0119] Specifically, the number of strata is determined based on the depth of the intermediate measurement points of the strata, and the corresponding two-dimensional inversion result is determined based on the number of strata; the radial region of the strata with an intrusion depth smaller than the two-dimensional inversion result is assigned the intrusion zone resistivity of the two-dimensional inversion result, and the radial region of the strata with an intrusion depth greater than the two-dimensional inversion result is assigned the original stratum resistivity of the two-dimensional inversion result.

[0120] Example 2

[0121] like Figure 2 As shown, this embodiment provides a two-dimensional array resistivity inversion method based on domain decomposition, including:

[0122] s1. Input well logging data and determine the location of the layer interface by the inflection point of the apparent resistivity curve or lithological curves such as gamma and spontaneous potential;

[0123] s2. A large array lateral logging response dataset with depth points is decomposed into several sub-regions from top to bottom. Each sub-region consists of three strata, such as... Figure 3 As shown;

[0124] Specifically, based on the interface information obtained from the inflection point of the well logging curve, the dataset is divided into M strata from top to bottom and further divided into N sub-regions. Each sub-region is a three-layer model, where the middle and lower layers of the j-th sub-region are the upper and middle layers of the (j+1)-th sub-region.

[0125] s3. Based on the location of the layer interface, calculate the thickness and center point location of each stratum;

[0126] Specifically, the calculation expressions for the thickness and the center point position of each formation are: H i =Z i+1 -Z i ,Z im =(Z i +Z i+1 ) / 2, where Z is the interface depth, i is the formation number, i<M+1, and M is the total number of formations;

[0127] S4. Obtain the well deviation angle information based on the dipmeter logging, input the logging response of the center point position and the well deviation angle into a one-dimensional inversion model for one-dimensional inversion, and save the one-dimensional inversion result;

[0128] Specifically, first establish a one-dimensional radial invasion inversion model, as Figure 4 shown. The parameters to be inverted are the invasion depth Di, the resistivity of the invaded zone Rxo, and the resistivity of the virgin formation Rt. Then input the apparent resistivity [RLA1, RLA2, RLA3, RLA4, RLA5] of each logging point, and logarithmize it [Log 10 (RLA1), Log 10 (RLA2), Log 10 [[ID=2⑦]](RLA3), Log 10 (RLA4), Log 10 [[ID=三十一]](RLA5)]; Assign initial inversion values to the inversion model. For the nth logging point, Di i [[ID=三十三]]is manually assigned as 0.2m, Rxo i and Rt i are automatically assigned as RLA1 i and RLA5 i respectively; Then use the regularized Levenberg-Marquardt algorithm to invert it; Finally, output the one-dimensional inversion result and save it.

[0129] Among them, the regularized Levenberg-Marquardt inversion algorithm is specifically:

[0130] The cost function C(x) used for inversion is:[[ID=四十八]]

[0131]

[0132] In the above formula, x is the vector of parameters to be inverted in the formation model, x=(Di, Rxo, Rt) T ; The superscript T represents the transpose of the matrix, S(x) is the finite element forward response of the array induction logging instrument, including five apparent resistivity values of RLA1~RLA5; d is the measured data, x p is the known reference vector of the model, and μ is the regularization parameter.

[0133] Setting the derivative of the above equation with respect to x to zero, we get:

[0134]

[0135] In formula (4), Δx is the update step size of the parameters to be inverted, J is the Jacobian matrix, and μ k α is the regularization parameter, I is the identity matrix, and α is the iteration parameter.

[0136] s5. Construct a two-dimensional inversion model, such as Figure 5 As shown, each sub-region is a two-dimensional inversion model of three strata. The well logging response and well inclination information of the center point of the sub-region are input into the two-dimensional inversion model for two-dimensional inversion. The one-dimensional inversion results are used as the initial values ​​of the parameters of the two-dimensional inversion model.

[0137] Specifically, the method for using the one-dimensional inversion result as the initial value of the two-dimensional inversion model parameters is as follows:

[0138] The two-dimensional inversion model represents the j-th subregion composed of three strata, numbered k, m, and l. Each stratum has parameters Di, Rxo, and Rt. For the i-th stratum, the one-dimensional inversion result is 1DInv_Di. i 1DInv_Rxo i and 1DInv_Rt i This is assigned to a two-dimensional model, meaning that each layer of the two-dimensional model has a parameter of 1DInv_Di. k_j 1DInv_Rxo k_j and 1DInv_Rt k_j ;1DInv_Di m_j 1DInv_Rxo m_j and 1DInv_Rt m_j ;1DInv_Di l_j 1DInv_Rxo l_j and 1DInv_Rt l_j The intermediate layer parameters are the parameters to be inverted; the actual logging response value of the input center point is [Obs_RLA1]. m_j Obs_RLA2 m_j Obs_RLA3 m_j Obs_RLA4 m_j Obs_RLA5 m_j ];

[0139] s6. Input the two-dimensional inversion model into the finite element forward modeling to generate simulation data;

[0140] Specifically, the three-layer model parameter 1DInv_Di k_j 1DInv_Rxo k_j and 1DInv_Rt k_j;1DInv_Di m_j 1DInv_Rxo m_j and 1DInv_Rt m_j ;1DInv_Di l_j 1DInv_Rxo l_j and 1DInv_Rt l_j Substituting the values ​​into the finite element forward modeling, five apparent resistivity values ​​[S_RLA1] were obtained. m_j S_RLA2 m_j S_RLA3 m_j S_RLA4 m_j S_RLA5 m_j ].

[0141] s7. Calculate the fitting difference between the simulation results and the measured data, and determine whether the accuracy is met. If yes, proceed to step s11; otherwise, proceed to step s8.

[0142] Specifically, the inversion accuracy is set to 10. -7 The iteration stops when the forward modeling results and the norm 2 of the measured data meet the accuracy requirements. The norm 2 is defined as follows:

[0143]

[0144] In the above formula, S_RLAi m_j and Obs_RLAi m_j The finite element forward modeling results and measured results are respectively the ith apparent resistivity value of the intermediate measuring point at the center point of the j-th sub-region.

[0145] s8. Apply conditional constraints to the parameters to be inverted, mainly the range of variation of the parameters to be inverted;

[0146] Specifically, the parameters to be inverted include the intrusion depth, the resistivity of the intrusion zone, and the resistivity of the original formation. Based on prior reservoir information, the range of formation intrusion depth is set to Di. min ~Di max The resistivity range is R min ~R max Therefore, appropriate constraints are imposed on the inversion parameters to ensure that the parameters of the formation model are within a reasonable range.

[0147] s9. The regularized Levenberg-Marquardt algorithm is used to iteratively update the parameters of the inversion model;

[0148] s10. Substitute the updated parameters into the forward calculation and determine whether the accuracy requirements are met. If yes, proceed to step s11; otherwise, use the updated parameters as the parameters of the two-dimensional model to continue the inversion iteration and proceed to step s6.

[0149] Specifically, the process of using the updated parameters as parameters for the two-dimensional model to continue the inversion iteration is as follows:

[0150] After the p-th iteration update, the parameter is the intrusion depth of the m-th layer, 2DInv_Di. m_j_q , Intrusion resistivity 2DInv_Rxo m_j_q and the original formation resistivity 2DInv_Rt m_j_q Replace the previous iteration model parameter 2DInv_Di with it. m_j_q-1 2DInv_Rxo m_j_q-1 and 2DInv_Rt m_j_q-1 If it's the first inversion iteration, replace 1DInv_Di m_j 1DInv_Rxo m_j and 1DInv_Rt m_j The maximum number of inversion iterations is set to 15; if q > 15, the iteration stops.

[0151] s11. Output the depth of the intermediate layer intrusion and the resistivity of the intrusion zone and the original strata in the sub-region;

[0152] Specifically, to meet accuracy requirements or reach the maximum number of iterations, the output sub-region intermediate layer intrusion depth, intrusion zone, and undisturbed formation resistivity are 2DInv_Di. m_j 2DInv_Rxo m_j and 2DInv_Rt m_j ;

[0153] s12. Process the measured data of the center point of the next sub-region, and use the inversion result of the previous sub-region as the initial value of the model of the next sub-region. Determine whether all sub-regions have been processed. If yes, proceed to step s13; otherwise, proceed to step s5.

[0154] Specifically, the method for processing the measured data of the center point of the next sub-region is as follows:

[0155] First, determine if all sub-regions have been processed, i.e., whether j+1 is greater than N. If so, stop processing; otherwise, set 2DInv_Di. m_j 2DInv_Rxo m_j and 2DInv_Rt m_j Assign it as the top-level parameter of the (j+1)th sub-region, i.e., the k-th level parameter, and continue to invert the (j+1)th sub-region;

[0156] s13. Calculate the error of the inversion results for all sub-regions and determine whether the accuracy requirements are met. If yes, proceed to step s15; otherwise, increase the number of layer-by-layer inversions and proceed to step s14.

[0157] The expression for calculating the error of the inversion results for all sub-regions is as follows:

[0158]

[0159] Among them, S_RLAi p and Obs_RLAi p The results are the finite element forward modeling result and the measured result, respectively, of the i-th apparent resistivity value at the intermediate measuring point at the p-th stratum center location.

[0160] s14. Determine whether the number of layer-by-layer scanning inversions meets the preset number. If yes, proceed to step s15. Otherwise, initialize the sub-region number to 1, use the current layer-by-layer scanning inversion result as the parameter of the two-dimensional inversion model, and proceed to step s5.

[0161] s15. Output and save the two-dimensional inversion results, and perform two-dimensional electrical profile imaging.

[0162] Specifically, output the inversion results for each stratum: 2DInv_Di _p 2DInv_Rxo p and 2DInv_Rt p Where p is 1 to M, the process of performing two-dimensional electrical profile imaging on the inversion results is as follows:

[0163] S151. Based on the inversion results, resistivity values ​​are assigned to each formation, and the radial extension distance x of the formation is set to 3m. If the depth Z of the measurement point at the center of each formation... p-1 <Dep p <Z p For values ​​less than 2DInv_Di _p The radial region imparts resistivity 2DInv_Rxo p Greater than 2DInv_Di _p The radial region imparts resistivity 2DInv_Rt p That is, the depth of the current central measurement point is located between the interfaces of the upper and lower strata (Z is the depth at the interface). This allows us to determine the layer number of the stratum where the central measurement point is located, thus determining the longitudinal position of the stratum where the central measurement point is located. The radial region assignment determines the position of the lateral medium, and thus the two-dimensional electrical profile is drawn. Based on the layer number, we can determine the two-dimensional inversion result corresponding to the sub-region of this stratum as the intermediate layer, and then perform radial region assignment.

[0164] S152. Repeat S151 from top to bottom to process each formation;

[0165] S153. Logarithmize all resistivities to obtain a two-dimensional electrical profile resistivity color code.

[0166] This embodiment uses a six-layer model as an example to illustrate the effectiveness of the two-dimensional array resistivity inversion method based on domain decomposition. First, a six-layer mud-invaded formation model is constructed with a well inclination angle of 0°, a wellbore size of 8 inches, and a mud resistivity of 0.1 Ω·m. The model parameters are distributed as follows: invasion depth ranges from 0 to 0.6 m, the resistivity of the invaded zone ranges from 2 to 10 Ω·m, and the resistivity of the undisturbed formation ranges from 2 to 50 Ω·m. The model parameter distribution, apparent resistivity logging curves, and imaging schematic diagram are shown below. Figure 6 and Figure 7 As shown, the colors in the image represent the resistivity values; the brighter the color, the greater the resistivity value. Figure 8 This is the result of traditional one-dimensional inversion imaging. Figure 9 The image shows the two-dimensional inversion imaging result obtained using the method described in this embodiment. Figure 10 The image shows the error distribution of the one-dimensional inversion result (left) and the two-dimensional inversion result (right), where RE_Rxo 1DInv RE_Rt 1DInv RE_Rxo 2DInv RE_Rt 2DInv The errors are represented by the one-dimensional and two-dimensional inversion results of resistivity in the intrusive zone and the undisturbed formation, respectively. It can be observed that the average relative errors of Rxo and Rt after traditional one-dimensional inversion are 15.72% and 57.55%, respectively, while the average relative errors of Rxo and Rt after two-dimensional inversion are 4.64% and 7.05%, respectively. Using the inversion method of this embodiment, the accuracy of the formation resistivity profile reconstruction is significantly improved, with improvements of 13.15% and 118.96%, respectively, verifying the accuracy and reliability of the two-dimensional array resistivity inversion method based on domain decomposition in this embodiment.

[0167] Example 3

[0168] This embodiment provides a two-dimensional array resistivity inversion device based on domain decomposition, including:

[0169] The region decomposition module is used to decompose the large array lateral logging response dataset into multiple sub-regions based on the layer interface location of the target region, with each sub-region consisting of three adjacent strata.

[0170] The calculation module is used to calculate the thickness and center point location of each stratum, and then obtain the thickness and center point location of each sub-region.

[0171] The one-dimensional inversion module is used to construct a one-dimensional inversion model based on the strata, and to perform one-dimensional array resistivity inversion based on the location of the center point of each stratum and the one-dimensional inversion model.

[0172] The two-dimensional inversion module is used to construct a two-dimensional inversion model based on sub-regions. It performs two-dimensional array resistivity inversion based on the center point location of each sub-region and the two-dimensional inversion model, and uses the one-dimensional inversion results as the initial parameter values ​​of the two-dimensional inversion model.

[0173] Example 4

[0174] This disclosure also provides an electronic device, which includes:

[0175] At least one processor; and,

[0176] A memory communicatively connected to the at least one processor; wherein,

[0177] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the two-dimensional array resistivity inversion method based on region decomposition in Embodiment 1.

[0178] An electronic device according to embodiments of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0179] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0180] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0181] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0182] Example 5

[0183] This disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the two-dimensional array resistivity inversion method based on region decomposition in Embodiment 1.

[0184] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0185] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0186] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A regionally decomposed two-dimensional array resistivity inversion method, characterized in that, The method comprises the following steps: regionally decomposing a large array lateral logging response data set based on the layer interface position of a target region to obtain a plurality of sub-regions, each three adjacent strata being a sub-region; calculating the thickness and center point position of each stratum, and further obtaining the thickness and center point position of each sub-region; constructing a one-dimensional inversion model based on the strata, and performing one-dimensional array resistivity inversion based on the center point position of each stratum and the one-dimensional inversion model; constructing a two-dimensional inversion model based on the sub-regions, performing two-dimensional array resistivity inversion based on the center point position of each sub-region and the two-dimensional inversion model, and taking the one-dimensional inversion result as the initial value of the parameters of the two-dimensional inversion model.

2. The regionally-decomposed, two-dimensional array resistivity inversion method of claim 1, wherein, The one-dimensional inversion model and the two-dimensional inversion model are both: where x is the vector of the parameters to be inverted of the inversion model, x = (Di, Rxo, Rt) T , Di is the invasion depth, Rxo is the invaded resistivity, Rt is the virgin formation resistivity, T denotes the transpose of a matrix, S(x) is the finite element forward response of the array lateral instrument including five apparent resistivity values of RLA1~RLA5, d is the measured data, x p is the known reference vector of the model, and μ is the regularization parameter.

3. The regionally-decomposed, two-dimensional array resistivity inversion method of claim 2, wherein, The update step of the to-be-inverted parameters of the one-dimensional inversion model and the two-dimensional inversion model is: where Δx is the parameter update step, J is the Jacobian matrix, μ k is the regularization parameter, I is the identity matrix, α is the iteration parameter, and k is the iteration number.

4. The regionally-decomposed, two-dimensional array resistivity inversion method of claim 1, wherein, The two-dimensional array resistivity inversion based on the center point position of each sub-region and the two-dimensional inversion model comprises: S1, bringing the two-dimensional inversion model of the sub-region into finite element forward modeling, judging whether the two-norm of the forward modeling result and the measured result meets the accuracy requirement, if yes, executing step S4, and if no, executing step S2; S2, setting the stratum invasion depth range and resistivity range based on the prior information of the reservoir, and constraining the variation range of the to-be-inverted parameters based on the stratum invasion depth range and resistivity range; S3, iteratively updating the to-be-inverted parameters based on the variation range and the two-dimensional inversion model, and executing S1 based on the updated to-be-inverted parameters; S4, outputting the invasion depth, invasion zone resistivity and original stratum resistivity of the intermediate layer of the sub-region, and judging whether all the sub-regions have completed inversion, if yes, executing step S5, and if no, taking the invasion depth, invasion zone resistivity and original stratum resistivity of the intermediate layer as the initial value of the parameters of the upper layer of the two-dimensional inversion model of the next sub-region to execute S1; S5, calculating the error of the inversion result of all the sub-regions, and judging whether the error meets the preset accuracy, if yes, executing step S7, and if no, increasing the number of layer-by-layer scanning inversions; S6, judging whether the current layer-by-layer scanning inversion number is greater than the preset layer-by-layer scanning inversion number, if yes, executing step S7, and if no, taking the current layer-by-layer scanning inversion result as the parameters of the two-dimensional inversion model to execute step S1; S7, outputting the two-dimensional inversion result, and performing two-dimensional electrical property profile imaging based on the two-dimensional inversion result.

5. The regionally-decomposed, two-dimensional array resistivity inversion method of claim 4, wherein, The calculation expression of the two-norm is: Where S_RLAi m_j and Obs_RLAi m_j are the finite element forward modeling results and the measured results of the i-th apparent resistivity value of the intermediate measuring point of the center point of the j-th sub-region, respectively.

6. The regionally-decomposed, two-dimensional array resistivity inversion method of claim 4, wherein, The calculation expression of the error of the inversion result of all the sub-regions is: Where S_RLAi p and Obs_RLAi p are the finite element forward modeling results and the measured results of the i-th apparent resistivity value of the middle measuring point of the p-th formation center point position, respectively.

7. The regionally-decomposed, two-dimensional array resistivity inversion method of claim 4, wherein, The two-dimensional electrical property profile imaging based on the two-dimensional inversion result comprises: assigning resistivity to each stratum based on the two-dimensional inversion result, and logarithmizing all the resistivities to obtain a two-dimensional electrical property profile resistivity color scale map; The number of layers of the formation is determined based on the depth of the intermediate measuring point of the formation, and a two-dimensional inversion result corresponding to the formation is determined based on the number of layers; an invaded zone resistivity of the two-dimensional inversion result is assigned to a radial region in the formation that is less than an invaded depth of the two-dimensional inversion result, and an original formation resistivity of the two-dimensional inversion result is assigned to a radial region in the formation that is greater than the invaded depth of the two-dimensional inversion result.

8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

9. A non-transitory computer-readable storage medium, comprising: The non-transitory computer-readable storage medium stores computer instructions for causing a computer to perform the method of any one of claims 1-7.

10. A device for two-dimensional array resistivity inversion based on domain decomposition, characterized in that, The method comprises: a region decomposition module configured to perform region decomposition on a large array lateral logging response data set based on layer interface positions of a target region to obtain a plurality of sub-regions, each three adjacent formations being a sub-region; a calculation module configured to calculate a thickness and a center point position of each formation, and further obtain a thickness and a center point position of each sub-region; a one-dimensional inversion module configured to construct a one-dimensional inversion model based on the formations, and perform one-dimensional array resistivity inversion based on the center point position of each formation and the one-dimensional inversion model; a two-dimensional inversion module configured to construct a two-dimensional inversion model based on the sub-regions, perform two-dimensional array resistivity inversion based on the center point position of each sub-region and the two-dimensional inversion model, and take the one-dimensional inversion result as a parameter initial value of the two-dimensional inversion model.

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