Alignment methods, devices, electronic equipment, and storage media for well logging curves

Abstract

This invention discloses a method, apparatus, electronic device, and storage medium for aligning well logging curves. The method includes: determining a target well logging curve type for multi-well formation comparison within a target area; determining a well logging curve to be processed corresponding to at least two target wells based on the target well logging curve type; determining a target feature curve based on the well logging curve to be processed; determining a center feature curve of a center well corresponding to the target area based on the target feature curve; determining an offset depth based on the center feature curve and the target feature curve; and performing depth alignment of the target feature curve based on the offset depth. Based on the above technical solution, by processing the well logging curves to obtain corresponding feature curves, determining the offset depth based on the feature curves, and performing depth alignment based on the offset depth, the technical effect of improving the processing accuracy and efficiency of multi-well depth alignment is achieved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, electronic device, and storage medium for aligning well logging curves. Background Technology

[0002] Multi-well stratigraphic correlation is an important basis for natural gas and oil exploration. By comprehensively analyzing and comparing the geological profiles of single wells, the same sedimentary strata among multiple wells can be identified, thereby connecting the geological profiles of each well on a plane. This allows us to understand the changes in stratigraphic sequence, lithology, lithofacies and stratigraphic thickness, as well as analyze the distribution and connectivity of reservoirs in the vertical and horizontal directions.

[0003] However, existing multi-well stratigraphic correlation is mainly carried out manually, which is greatly affected by human factors and has low correlation efficiency. Furthermore, the manual processing method is time-consuming and requires high personnel skills, making the implementation of multi-well stratigraphic correlation too difficult. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for aligning well logging curves, in order to solve the problem that existing formation depth curve analysis methods rely on manual operation and have low analysis efficiency.

[0005] According to one aspect of the present invention, a method for aligning well logging curves is provided, the method comprising:

[0006] Determine the target logging curve type for multi-well formation comparison within the target area, and determine the logging curves to be processed corresponding to at least two target wells based on the target logging curve type;

[0007] Based on the logging curve to be processed, a target characteristic curve is determined, and based on the target characteristic curve, a center characteristic curve of the center well corresponding to the target region is determined;

[0008] The offset depth is determined based on the central feature curve and the target feature curve, and the target feature curve is depth-aligned based on the offset depth.

[0009] According to another aspect of the present invention, a well logging curve alignment device is provided, the device comprising:

[0010] The unprocessed curve determination module is used to determine the target logging curve type for multi-well formation comparison within the target area, and to determine the unprocessed logging curves corresponding to at least two target wells based on the target logging curve type.

[0011] The feature curve determination module is used to determine a target feature curve based on the logging curve to be processed, and to determine the center feature curve of the center well corresponding to the target area based on the target feature curve.

[0012] The depth alignment module is used to determine the offset depth based on the center feature curve and the target feature curve, and to perform depth alignment on the target feature curve based on the offset depth.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the well logging curve alignment method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the well logging curve alignment method according to any embodiment of the present invention.

[0018] The technical solution of this invention involves determining the target logging curve type for multi-well formation comparison within a target area, determining the logging curves to be processed corresponding to at least two target wells based on the target logging curve type, then determining the target feature curve based on the logging curves to be processed, determining the center feature curve of the center well corresponding to the target area based on the target feature curve, finally determining the offset depth based on the center feature curve and the target feature curve, and performing depth alignment on the target feature curve based on the offset depth. Based on the above technical solution, by processing the logging curves to obtain corresponding feature curves, determining the offset depth based on the feature curves, and performing depth alignment based on the offset depth, the technical effect of improving the processing accuracy and efficiency of multi-well depth alignment is achieved.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a method for aligning logging curves according to an embodiment of the present invention.

[0022] Figure 2 This is a flowchart of a well logging curve alignment method provided in an embodiment of the present invention;

[0023] Figure 3 This is a structural block diagram of a logging curve alignment device provided in an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Example 1

[0028] Figure 1This is a flowchart illustrating a well logging curve alignment method provided in an embodiment of the present invention. This embodiment is applicable to situations where a characteristic curve corresponding to each well logging curve is determined based on the target well logging curves used for multi-well formation comparison in a target area, and then the offset depth is determined based on the characteristic curves, and the characteristic curves are depth aligned based on the offset depth. This method can be executed by a well logging curve alignment device, which can be implemented in hardware and / or software. The well logging curve alignment device can be configured in an electronic device, which can be a server, terminal device, etc.

[0029] like Figure 1 As shown, the method includes:

[0030] S110. Determine the target logging curve type for multi-well formation comparison within the target area, and determine the logging curves to be processed corresponding to at least two target wells based on the target logging curve type.

[0031] The target area can be the exploration area where multi-well depth alignment analysis is required. The target logging curve type can be understood as the logging curve type used for multi-well formation correlation within the target area. The logging curve to be processed can be the original logging curve that needs to be processed.

[0032] Specifically, the target logging curve type for multi-well formation comparison in the target area is determined. Based on the target logging curve type, logging curves to be processed in at least two wells within the area are determined. For example, the logging curve type associated with the target area can be determined by personnel based on the quality of the logging curves. It is understood that various logging curves can be obtained during the logging process, such as natural gamma logging curves, resistivity logging curves, density logging curves, etc. Therefore, it is necessary to determine the target logging curve type for multi-well formation comparison in the target area, and then obtain the logging curves in multiple wells within the target area that match the target logging curve type, and use them as logging curves to be processed.

[0033] It should be noted that determining the logging curves to be processed for at least two wells in the target area based on the target logging curve type includes: determining the target well corresponding to the target area, obtaining the corresponding logging curve in the target well based on the target logging curve type, and using it as the logging curve to be processed.

[0034] For example, the target well is a mine located within the target area. After determining the target logging curve type, the logging curve corresponding to the target well can be retrieved from the database storing logging curves based on the target curve type, and this curve can be used as the logging curve to be processed. It is understood that since there are at least two mines within the target area, the number of corresponding logging curves to be processed is at least two.

[0035] S120. Determine the target feature curve based on the logging curve to be processed, and determine the center feature curve of the center well corresponding to the target area based on the target feature curve.

[0036] The target characteristic curve can be understood as a curve obtained after processing the logging curve. The characteristic curve reflects the numerical and variational characteristics of the current logging curve. The central well can be the well with the highest correlation coefficient within the target area, and can be understood as the most representative well within the target area. The central characteristic curve can be a characteristic curve used for depth alignment corresponding to the central well within the target area.

[0037] Specifically, a target feature curve is determined based on the logging curve to be processed, and a feature curve of the central well is determined based on the target feature curve. For example, feature extraction can be performed on the logging curve to be processed, and a target feature curve corresponding to the current logging curve can be determined based on the extracted numerical features and variation features. It can be understood that the numerical features can be features such as the maximum value, minimum value, and maximum difference corresponding to the logging curve, and the variation features can be features such as the slope corresponding to the logging curve.

[0038] Based on the above technical solution, the step of determining the target feature curve based on the logging curve to be processed includes: determining the geometric mean and standard deviation corresponding to the current logging curve to be processed, and standardizing the logging curve to be processed based on the geometric mean and the standard deviation; obtaining the extreme value of the standardized curve, and normalizing the standardized curve based on the extreme value of the curve to determine the logging curve to be applied; and determining the target feature curve based on the logging curve to be applied.

[0039] The extreme values ​​of a curve can be understood as the maximum and minimum values ​​corresponding to the current curve.

[0040] Specifically, the geometric mean and standard deviation corresponding to the current logging curve to be processed are determined, and the logging curve to be processed is standardized based on the geometric mean and standard deviation. After standardization, the extreme values ​​of the standardized curve are obtained, and the standardized curve is normalized based on the extreme values ​​to determine the logging curve to be applied. Then, the target feature curve is determined based on the logging curve to be applied. For example, the logging curve to be processed can be standardized according to a standardization formula, where the standardization formula is: R * (i)=(R(i)-R a ) / σ; where R * (i) represents the curve value of the standardized curve at the i-th depth sampling point; R(i) represents the curve value of the original curve at the i-th depth sampling point; R aσ is the geometric mean of the original curve over the entire depth measurement interval; σ is the standard deviation of the original curve over the entire depth measurement interval. The standardized curve is then normalized using the following formula: Among them, R N (i) represents the curve value of the standardized curve at the i-th depth sampling point; This represents the minimum value of the curve across the entire depth measurement range. This represents the maximum value of the curve across the entire depth measurement range.

[0041] Based on the above technical solution, the step of determining the target feature curve based on the logging curve to be applied includes: obtaining the maximum difference between adjacent sampling depths of the logging curve to be applied; determining the feature curve to be processed based on the maximum difference between adjacent sampling depths; resampling the feature curve to be processed based on a preset slope threshold to determine the feature curve to be applied; and determining the target feature curve based on the feature curve to be applied.

[0042] The maximum difference between adjacent sampling depths can be understood as the maximum difference between two adjacent sampling points. The feature curve to be processed can be a feature curve corresponding to the logging curve established based on the maximum difference between adjacent sampling depths. The preset slope threshold can be a pre-set slope value used for resampling. The feature curve to be applied can be understood as the feature curve obtained through resampling.

[0043] Specifically, the maximum difference in adjacent sampling depths of the logging curve to be applied is obtained. Based on this maximum difference, a feature curve to be processed is determined. Then, the feature curve to be processed is resampled based on a preset slope threshold to determine the feature curve to be applied. Finally, the target feature curve is determined based on the feature curve to be applied. For example, after obtaining the maximum difference in adjacent sampling depths, a feature curve to be processed corresponding to the logging curve can be constructed. The specific method for establishing this curve is as follows: S(i)=[1+(R N (i)-R N (i-1)) / max(ΔR N )]·R N (i), where S(i) is the characteristic curve value of the preprocessed curve at the i-th depth sampling point; ΔR N R represents the maximum difference between adjacent sampling depths of the preprocessed curve. N (i) represents the curve value of the standardized curve at the i-th depth sampling point. For the constructed feature curve to be processed, starting from the second depth sampling point, take one depth sampling point before and after the current sampling depth, and connect them sequentially to form two line segments M1M2 and M2M3. Calculate the slopes of M1M2 and M2M3 respectively. Among them, K M1M2 KM2M3 Let M1 and M2 be the slopes of line segments M1 and M2 respectively; D(X1) and S(X1) be the sampling depth and feature curve value of point M1 respectively; D(X2) and S(X2) be the sampling depth and feature curve value of point M2 respectively; and D(X3) and S(X3) be the sampling depth and feature curve value of point M3 respectively. Then, according to the preset slope threshold ε, when the difference between the slopes of line segments M1 and M2 is less than ε, the common point M2 of the two line segments is removed; otherwise, point M2 is retained.

[0044] Based on the above technical solution, the step of determining the target feature curve based on the feature curve to be applied includes: determining the local distance corresponding to each of the feature curves to be applied based on depth sampling points, and determining the distance matrix corresponding to each feature curve to be applied based on the local distance; and determining the target feature curve based on the distance matrix and the feature curve to be applied.

[0045] In this context, a depth sampling point can be understood as a curve point on the feature curve corresponding to a sampling depth. Local distance can be the distance between sampling points at the same depth on two curves.

[0046] Specifically, the local distances corresponding to each of the applied feature curves are determined based on depth sampling points, and a distance matrix corresponding to each applied feature curve is determined based on the local distances. Then, the target feature curve is determined based on the distance matrix and the applied feature curve. For example, after determining the applied feature curves corresponding to each well based on the logging curves to be processed, the matching relationship between two wells is determined based on the applied feature curves. Assuming the applied feature curves of the two wells are Q1(i)|i=1,2,...m and Q2(j)|j=1,2,...n respectively, the local distances between each depth sampling point of the two curves are calculated, resulting in an m×n distance matrix D. The (i,j)th element of the distance matrix D... i,j Represented as: D i,j =|Q1(i)-Q2(j)|.

[0047] Based on the above technical solution, the step of determining the target feature curve based on the distance matrix and the feature curve to be applied includes: determining the minimum cumulative distance corresponding to the feature curve to be applied based on the distance matrix, and determining the path correspondence corresponding to the minimum cumulative distance; resampling the feature curve to be applied based on the path correspondence to determine the target feature curve.

[0048] The minimum cumulative distance can be understood as the minimum sum of local distances between elements determined by the distance matrix. The path correspondence can be the correspondence between the depth sampling points corresponding to the minimum cumulative distance.

[0049] Specifically, based on the distance matrix, the minimum cumulative distance corresponding to the feature curve to be applied is determined, and the path correspondence corresponding to the minimum cumulative distance is determined. Then, based on the path correspondence, the feature curve to be applied is resampled to determine the target feature curve. For example, after determining the distance matrix, a regularized path W is used to represent a correspondence between feature curves Q1 and Q2. The specific form of W can be expressed as: in,, represents the depth sampling point number in characteristic curves Q1 and Q2 respectively; p represents the length of the regularized path W, max(m,n)≤p<m+n-1; In Q1, the first... The depth sampling point and the first in Q2 Each depth sampling point corresponds to a specific depth. Furthermore, in matrix D, the path S(Q1,Q2) with the minimum cumulative local distance from the element at index (0,0) to the element at index (m,n) is calculated, which can be expressed as: Where, r i,j This represents the sum of local distances along the path from the element at index (0,0) to the element at index (i,j) in the distance matrix D. Due to the constraint of path continuity, to reach point (i,j), one can only start from point (i-1,j-1), point (i-1,j), or point (i,j-1). min(r) i-1,j-1 ,r i-1,j ,r i,j-1 The expression indicates that the point with the smallest cumulative distance among the three points above is selected as the starting point. Solving for the path corresponding to the smallest cumulative distance yields the correspondence between the depth sampling points of Q1 and Q2. After resampling the feature curves Q1 and Q2 according to this correspondence, two new curves are obtained: in, and It is a curve with the same length (length p) after Q1 and Q2 are remapped.

[0050] Based on the above technical solution, the step of determining the center feature curve of the center well corresponding to the target region based on the target feature curve includes: determining the correlation coefficient corresponding to each of the target feature curves; determining the sum of the correlation coefficients corresponding to each target feature curve based on the correlation coefficients; and taking the target feature curve with the largest sum of the correlation coefficients as the center feature curve of the center well.

[0051] The correlation coefficient can be understood as a measure of the degree of correlation between two characteristic curves. The sum of correlation coefficients can be the sum of the correlation coefficients between the current target characteristic curve and other characteristic curves.

[0052] Specifically, the correlation coefficients corresponding to each of the target feature curves are determined. Based on these correlation coefficients, a sum of correlation coefficients corresponding to each target feature curve is determined, and the target feature curve with the largest sum of correlation coefficients is taken as the center feature curve of the center well. It should be noted that, to make the multi-well depth alignment method more efficiently match the formation characteristics within the target area, the correlation coefficients between the current target feature curve and other target feature curves can be calculated, thereby obtaining the sum of correlation coefficients corresponding to the current target feature curve. The well with the largest sum of correlation coefficients within the target area is then taken as the center well, and the target feature curve corresponding to the center well is taken as the center feature curve for depth alignment. For example, after determining the target feature curves, the correlation coefficients between each feature curve can be determined to... and For example, and The correlation coefficient can be expressed as:

[0053] Where r is and The correlation coefficient; For the i-th depth sampling point Curve value; for The geometric mean of the curve; For the i-th depth sampling point Curve value; for The geometric mean of the curves. The above calculation is performed pairwise on the characteristic curves of N wells within the study area, yielding N×(N-1) / 2 curve pairs and their corresponding correlation coefficients. The correlation coefficients of the same well with all other wells are accumulated, and the well with the largest sum is designated as the center well, with its well number assigned as 1. The other N-1 wells are labeled 2, 3, ..., N according to their correlation coefficients with the center well's characteristic curve, from largest to smallest. The curve obtained by resampling the center well and the second well after dynamic time-normalization is denoted as... The curves obtained by resampling the remaining wells and the central well after dynamic time warping in their respective wells are denoted as follows:

[0054] S130. Determine the offset depth based on the center feature curve and the target feature curve, and perform depth alignment on the target feature curve based on the offset depth.

[0055] The offset depth can be an offset value used to adjust the depth values ​​of each depth sampling point of the feature curve.

[0056] Specifically, the offset depth is determined based on the central feature curve and the target feature curve, and the target feature curve is depth aligned based on the offset depth. For example, after determining the offset depth based on the central feature curve and the target feature curve, the offset depth can be applied to other target feature curves to complete the depth alignment within the region. Alternatively, the offset depth can be directly added to the feature curve sampling depth.

[0057] Based on the above technical solution, determining the offset depth based on the central feature curve and the target feature curve includes: determining the Euclidean distance between the central feature curve and each of the target feature curves; determining the inversion objective function based on the Euclidean distance, the central feature curve and each of the target feature curves, and determining the offset depth based on the inversion objective function.

[0058] The inversion objective function can be understood as an inversion function constructed based on the relationship between the depth sampling points and the offset depth of each well.

[0059] Specifically, the Euclidean distance between the central feature curve and each of the target feature curves is determined. Then, an inversion objective function is determined based on the Euclidean distance, the central feature curve, and each of the target feature curves, and the offset depth is determined based on the inversion objective function. For example, using... The curve depth is used as the final multi-well alignment depth. Calculation and Based on the Euclidean distance, an inversion objective function for the offset depth of each sampling point in other wells is established, as shown below: Where, E is the residual of the inversion objective function; p is... Number of sampling points; Index j (i) is The i-th depth sampling point is The corresponding depth sampling point; u j (i) is The previous index j The offset depth of (i) depth sampling points; For the i-th depth sampling point Curve value; For the index j (i)+u j (i) depth sampling points Curve value. The offset depth u of each depth sampling point in all wells except the central well, when the objective function reaches its minimum value. j (i)| j＝2～N,i＝1～p ,right By applying the corresponding offset depth to each depth sampling point, depth alignment of multi-well curves within the region can be achieved.

[0060] The technical solution of this invention involves determining the target logging curve type for multi-well formation comparison within a target area, determining the logging curves to be processed corresponding to at least two target wells based on the target logging curve type, then determining the target feature curve based on the logging curves to be processed, determining the center feature curve of the center well corresponding to the target area based on the target feature curve, finally determining the offset depth based on the center feature curve and the target feature curve, and performing depth alignment on the target feature curve based on the offset depth. Based on the above technical solution, by processing the logging curves to obtain corresponding feature curves, determining the offset depth based on the feature curves, and performing depth alignment based on the offset depth, the technical effect of improving the processing accuracy and efficiency of multi-well depth alignment is achieved.

[0061] Example 2

[0062] Figure 2 This is a flowchart illustrating a method for aligning logging curves according to an embodiment of the present invention. This embodiment further optimizes the above-described method for aligning logging curves. Specific implementation details can be found in the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0063] like Figure 2 The method described in this embodiment of the invention includes:

[0064] Determine the characteristic curve to be applied: Specifically, collect logging data from the wells to be compared within the region, select logging curves for multi-well formation comparison, and perform preprocessing such as standardization and normalization on the selected curves. For example, collect logging curve data from N wells (N≥2) within the study area, select logging curves for formation comparison, and perform preprocessing such as standardization and normalization on the selected logging curves. The logging curve standardization method is shown below: R * (i)=(R(i)-R a ) / σ; where R * (i) represents the curve value of the standardized curve at the i-th depth sampling point; R(i) represents the curve value of the original curve at the i-th depth sampling point; R a σ is the geometric mean of the original curve over the entire depth measurement interval; σ is the standard deviation of the original curve over the entire depth measurement interval. The standardized curve is then normalized as follows: Among them, R N (i) represents the curve value of the standardized curve at the i-th depth sampling point; This represents the minimum value of the curve across the entire depth measurement range. This represents the maximum value of the curve across the entire depth measurement range.

[0065] Determine the target characteristic curve and the center characteristic curve of the central well: Specifically, for the preprocessed logging curves, based on the numerical characteristics and depth-dependent variations of the curves, construct a multi-well formation correlation characteristic curve. The specific construction method is as follows: S(i)=[1+(R N (i)-R N (i-1)) / max(ΔR N )]·R N (i); where S(i) is the characteristic curve value of the preprocessed curve at the i-th depth sampling point; ΔR N This represents the maximum difference between adjacent sampling depths of the preprocessed curve. For the generated feature curve, starting from the second depth sampling point, take one depth sampling point before and after the current sampling depth, and connect them sequentially to form two line segments M1M2 and M2M3. Calculate the slopes of M1M2 and M2M3 respectively. Among them, K M1M2 K M2M3 Let X1 and X2 be the slopes of line segments M1M2 and M2M3, respectively; D(X1) and S(X1) be the sampling depth and characteristic curve value of point M1, respectively; D(X2) and S(X2) be the sampling depth and characteristic curve value of point M2, respectively; and D(X3) and S(X3) be the sampling depth and characteristic curve value of point M3, respectively. Based on a preset slope threshold ε, when the difference between the slopes of line segments M1M2 and M2M3 is less than ε, the common point M2 of the two line segments is removed; otherwise, point M2 is retained. The curve obtained by resampling the characteristic curve S according to the above method is denoted as Q. Then, the dynamic time warping method is used to calculate the pairwise depth matching relationship between all wells in the region. The specific calculation method is as follows: Assume that the characteristic curves of the two wells are Q1(i)|i=1,2,...m and Q2(j)|j=1,2,...n, respectively. Calculate the local distance between the sampling points of each depth of the two curves to obtain an m×n distance matrix D. The (i,j)th element of the distance matrix D is... i,j Represented as: D i,j =|Q1(i)-Q2(j)|; A regularized path W is used to represent a correspondence between the characteristic curves Q1 and Q2. The specific form of W can be expressed as: in, These represent the depth sampling point indices in characteristic curves Q1 and Q2, respectively; p represents the length of the regularized path W, where max(m,n)≤p<m+n-1; where, In Q1, the first... The depth sampling point and the first in Q2 Each depth sampling point corresponds to a specific depth. In matrix D, the path S(Q1,Q2) with the minimum cumulative local distance from the element at index (0,0) to the element at index (m,n) is calculated, which can be expressed as: Where, r i,j This represents the sum of local distances along the path from the element at index (0,0) to the element at index (i,j) in the distance matrix D. Due to the constraint of path continuity, to reach point (i,j), one can only start from point (i-1,j-1), point (i-1,j), or point (i,j-1). min(r) i-1,j-1 ,r i-1,j ,r i,j-1 This indicates that the point with the smallest cumulative distance among the three points above is selected as the starting point. Solving for the path corresponding to the smallest cumulative distance yields the correspondence between the depth sampling points of Q1 and Q2. After resampling the feature curves Q1 and Q2 according to the above correspondence, two new curves are obtained: in, and It is a curve with the same length (length p) after Q1 and Q2 are remapped. and The correlation coefficient can be expressed as: Where r is and The correlation coefficient; For the i-th depth sampling point Curve value; for The geometric mean of the curve; For the i-th depth sampling point Curve value; for The geometric mean of the curves. The above calculation is performed pairwise on the characteristic curves of N wells within the study area, yielding N×(N-1) / 2 curve pairs and their corresponding correlation coefficients. The correlation coefficients of the same well with all other wells are accumulated, and the well with the largest sum is designated as the center well, with its well number assigned as 1. The other N-1 wells are labeled 2, 3, ..., N according to their correlation coefficients with the center well's characteristic curve, from largest to smallest. The curve obtained by resampling the center well and the second well after dynamic time-normalization is denoted as... The curves obtained by resampling the remaining wells and the central well after dynamic time warping in their respective wells are denoted as follows:

[0066] Determine the offset depth and perform depth alignment: Specifically, to The curve depth is used as the final multi-well alignment depth. Calculation and Based on the Euclidean distance, an inversion objective function for the offset depth of each sampling point in other wells is established, as shown below: Where E is the residual of the inversion objective function; p is Number of sampling points; Index j (i) is The i-th depth sampling point is The corresponding depth sampling point; u j (i) is The previous index j The offset depth of (i) depth sampling points; For the i-th depth sampling point Curve value; For the index j (i)+u j (i) depth sampling points Curve value. The offset depth u of each depth sampling point in all wells except the central well, when the objective function reaches its minimum value. j (i)| j＝2～N,i＝1～p ,right By applying the corresponding offset depth to each depth sampling point, depth alignment of multi-well curves within the region can be achieved.

[0067] The technical solution of this invention involves determining the target logging curve type for multi-well formation comparison within a target area, determining the logging curves to be processed corresponding to at least two target wells based on the target logging curve type, then determining the target feature curve based on the logging curves to be processed, determining the center feature curve of the center well corresponding to the target area based on the target feature curve, finally determining the offset depth based on the center feature curve and the target feature curve, and performing depth alignment on the target feature curve based on the offset depth. Based on the above technical solution, by processing the logging curves to obtain corresponding feature curves, determining the offset depth based on the feature curves, and performing depth alignment based on the offset depth, the technical effect of improving the processing accuracy and efficiency of multi-well depth alignment is achieved.

[0068] Example 3

[0069] Figure 3 This is a structural block diagram of a well logging curve alignment device provided in an embodiment of the present invention. Figure 3 As shown, the device includes: a curve determination module 310, a feature curve determination module 320, and a depth alignment module 330.

[0070] The unprocessed curve determination module 310 is used to determine the target logging curve type for multi-well formation comparison in the target area, and to determine the unprocessed logging curves corresponding to at least two target wells based on the target logging curve type.

[0071] The feature curve determination module 320 is used to determine a target feature curve based on the logging curve to be processed, and to determine the center feature curve of the center well corresponding to the target area based on the target feature curve.

[0072] The depth alignment module 330 is used to determine the offset depth based on the center feature curve and the target feature curve, and to perform depth alignment on the target feature curve based on the offset depth.

[0073] Based on the above technical solution, the feature curve determination module is used to determine the geometric mean and standard deviation corresponding to the current logging curve to be processed, and to standardize the logging curve to be processed based on the geometric mean and standard deviation; to obtain the extreme value of the standardized curve, and to normalize the standardized curve based on the extreme value of the curve to determine the logging curve to be applied; and to determine the target feature curve based on the logging curve to be applied.

[0074] Based on the above technical solution, the feature curve determination module is used to obtain the maximum difference between adjacent sampling depths of the well logging curve to be applied, determine the feature curve to be processed based on the maximum difference between adjacent sampling depths; resample the feature curve to be processed based on a preset slope threshold to determine the feature curve to be applied; and determine the target feature curve based on the feature curve to be applied.

[0075] Based on the above technical solution, the feature curve determination module is used to determine the local distance corresponding to each of the feature curves to be applied based on the depth sampling points, and to determine the distance matrix corresponding to each feature curve to be applied based on the local distance; and to determine the target feature curve based on the distance matrix and the feature curve to be applied.

[0076] Based on the above technical solution, the feature curve determination module is used to determine the minimum cumulative distance corresponding to the feature curve to be applied based on the distance matrix, and to determine the path correspondence corresponding to the minimum cumulative distance; and to resample the feature curve to be applied based on the path correspondence to determine the target feature curve.

[0077] Based on the above technical solution, the feature curve determination module is used to determine the correlation coefficient corresponding to each of the target feature curves, and to determine the cumulative sum of the correlation coefficients corresponding to each target feature curve based on the correlation coefficients;

[0078] The target feature curve with the largest sum of the correlation coefficients is taken as the center feature curve of the center well.

[0079] Based on the above technical solution, the depth alignment module is used to determine the Euclidean distance between the central feature curve and each of the target feature curves; determine the inversion objective function based on the Euclidean distance, the central feature curve and each of the target feature curves, and determine the offset depth based on the inversion objective function.

[0080] The technical solution of this invention involves determining the target logging curve type for multi-well formation comparison within a target area, determining the logging curves to be processed corresponding to at least two target wells based on the target logging curve type, then determining the target feature curve based on the logging curves to be processed, determining the center feature curve of the center well corresponding to the target area based on the target feature curve, finally determining the offset depth based on the center feature curve and the target feature curve, and performing depth alignment on the target feature curve based on the offset depth. Based on the above technical solution, by processing the logging curves to obtain corresponding feature curves, determining the offset depth based on the feature curves, and performing depth alignment based on the offset depth, the technical effect of improving the processing accuracy and efficiency of multi-well depth alignment is achieved.

[0081] The logging curve alignment device provided in this embodiment of the invention can execute the logging curve alignment method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0082] Example 4

[0083] Figure 4 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0084] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0085] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0086] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the alignment method for well logging curves.

[0087] In some embodiments, the logging curve alignment method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the logging curve alignment method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the logging curve alignment method by any other suitable means (e.g., by means of firmware).

[0088] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0089] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0090] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0091] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0092] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0093] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0094] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0095] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for aligning well logging curves, characterized in that, include: Determine the target logging curve type for multi-well formation comparison within the target area, and determine the logging curves to be processed corresponding to at least two target wells based on the target logging curve type; Based on the logging curve to be processed, a target characteristic curve is determined, and based on the target characteristic curve, a center characteristic curve of the center well corresponding to the target region is determined; The offset depth is determined based on the center feature curve and the target feature curve, and the target feature curve is depth-aligned based on the offset depth. The step of determining the target feature curve based on the logging curve to be processed includes: preprocessing the logging curve to be processed to determine the logging curve to be applied, and determining the target feature curve based on the logging curve to be applied, wherein the preprocessing includes standardization and normalization; The process of determining the target characteristic curve based on the logging curve to be applied includes: Obtain the maximum difference between adjacent sampling depths of the well logging curve to be applied, and determine the feature curve to be processed based on the maximum difference between adjacent sampling depths; The feature curve to be processed is resampled based on a preset slope threshold to determine the feature curve to be applied. The target feature curve is determined based on the feature curve to be applied; Determining the target feature curve based on the feature curve to be applied includes: Based on the depth sampling points, determine the local distance corresponding to each of the feature curves to be applied, and determine the distance matrix corresponding to each feature curve to be applied based on the local distance; The target feature curve is determined based on the distance matrix and the feature curve to be applied; Determining the target feature curve based on the distance matrix and the feature curve to be applied includes: Based on the distance matrix, determine the minimum cumulative distance corresponding to the feature curve to be applied, and determine the path correspondence corresponding to the minimum cumulative distance; The target feature curve is determined by resampling the feature curve to be applied based on the path correspondence. The step of determining the center feature curve of the center well corresponding to the target region based on the target feature curve includes: Determine the correlation coefficient corresponding to each of the target feature curves, and determine the cumulative sum of the correlation coefficients corresponding to each target feature curve based on the correlation coefficients; The target feature curve with the largest sum of the correlation coefficients is taken as the center feature curve of the center well.

2. The method according to claim 1, characterized in that, The step of determining the target characteristic curve based on the logging curve to be processed includes: Determine the geometric mean and standard deviation corresponding to the current logging curve to be processed, and standardize the logging curve to be processed based on the geometric mean and the standard deviation; Obtain the extreme values ​​of the standardized curve, and normalize the standardized curve based on the extreme values ​​to determine the logging curve to be applied. The target characteristic curve is determined based on the well logging curve to be applied.

3. The method according to claim 1, characterized in that, Determining the offset depth based on the central feature curve and the target feature curve includes: Determine the Euclidean distance between the central feature curve and each of the target feature curves; The inversion objective function is determined based on the Euclidean distance, the central feature curve, and each of the target feature curves, and the offset depth is determined based on the inversion objective function.

4. A well logging curve alignment device, characterized in that, include: The unprocessed curve determination module is used to determine the target logging curve type for multi-well formation comparison within the target area, and to determine the unprocessed logging curves corresponding to at least two target wells based on the target logging curve type. The feature curve determination module is used to determine a target feature curve based on the logging curve to be processed, and to determine the center feature curve of the center well corresponding to the target area based on the target feature curve. A depth alignment module is used to determine the offset depth based on the center feature curve and the target feature curve, and to perform depth alignment on the target feature curve based on the offset depth; The feature curve determination module is used to preprocess the logging curve to be processed to determine the logging curve to be applied, and to determine the target feature curve based on the logging curve to be applied, wherein the preprocessing includes standardization and normalization. The feature curve determination module is used to obtain the maximum difference in adjacent sampling depths of the well logging curve to be applied, determine the feature curve to be processed based on the maximum difference in adjacent sampling depths, resample the feature curve to be processed based on a preset slope threshold, determine the feature curve to be applied, and determine the target feature curve based on the feature curve to be applied. The feature curve determination module is used to determine the local distance corresponding to each of the feature curves to be applied based on depth sampling points, and to determine the distance matrix corresponding to each feature curve to be applied based on the local distance; and to determine the target feature curve based on the distance matrix and the feature curves to be applied. The feature curve determination module is used to determine the minimum cumulative distance corresponding to the feature curve to be applied based on the distance matrix, and to determine the path correspondence corresponding to the minimum cumulative distance; and to resample the feature curve to be applied based on the path correspondence to determine the target feature curve. The feature curve determination module is used to determine the correlation coefficient corresponding to each of the target feature curves, and to determine the cumulative sum of the correlation coefficients corresponding to each target feature curve based on the correlation coefficients; the target feature curve with the largest cumulative sum of the correlation coefficients is taken as the center feature curve of the center well.

5. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the alignment method of the logging curves according to any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the alignment method for logging curves according to any one of claims 1-3.

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