A method and apparatus for correlating formations

Abstract

The application discloses a stratum correlation method and device. The method comprises the following steps: performing continuous wavelet transform on selected well logging curves of a target well to obtain a modulus distribution diagram of the continuous wavelet transform coefficients at each depth point under each set scale; obtaining a modulus maximum line from the modulus distribution diagram; taking the position of the selected well logging curve when the modulus maximum line takes the minimum value of the scale variable as a singular value position; determining a to-be-correlated depth interval of the target well according to the singular value position and a correlation depth interval of a standard well determined in advance; obtaining a first data point sequence of the selected well logging curves in the to-be-correlated depth interval and a second data point sequence of standard well logging curves of the standard well in the correlation depth interval; determining a path between the first data point sequence and the second data point sequence according to a set rule; determining the distance of each path; and obtaining a stratum correlation result of the target well and the standard well according to the path with the shortest distance. The method can realize automatic fine stratum correlation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of processing and interpretation of petroleum geophysical exploration well data, and particularly relates to a stratum correlation method and device. BACKGROUND

[0002] Inter-well stratum correlation in a hydrocarbon reservoir is one of the key steps of reservoir description and reservoir characterization, and the correlation result directly determines the reservoir framework and further controls the spatial distribution of reservoirs in the reservoir, and finally affects the development of the hydrocarbon reservoir. The core work of stratum correlation is mainly completed by geologists according to the characteristics of marker beds, sedimentary cycles and lithological combinations of well logging curves, through the establishment of cross-well sections covering all wells in the oilfield. However, as the oilfield enters the middle and late development stage, the number of drilled wells increases, and the workload of stratum correlation is very heavy. At present, commercial software is used to assist stratum correlation, but its main contribution is limited to the establishment of well database and correlation section, and the core work of single-well stratum division and analysis of inter-well stratum correspondence on cross-well section is mainly completed by geologists through comprehensive analysis of eyes, hands and brains. The disadvantages are: first, it brings great consumption of eyesight, physical strength and mental strength; second, it is highly subjective, and the correlation result is highly dependent on the relevant knowledge and experience of geologists.

[0003] In recent years, some scholars have tried to realize automatic stratum correlation by using methods such as fuzzy mathematics, intra-layer difference and cluster analysis, Walsh transform, well logging signal similarity correlation and neural network, but no obvious success has been achieved that can be applied to industrial practice (Xu Z, Liu YM, Zhou XM, He H, Zhang B, Wu H, Gao J. Automatic stratum correlation experiment based on convolutional neural network algorithm. Bulletin of Petroleum Science, 2019, 01: 1-10).

[0004] In addition, in the process of carrying out stratum correlation based on well logging data, geologists often encounter geological faults, and the above stratum correlation algorithms are helpless for this special situation. SUMMARY

[0005] In view of the above problems, the present application is proposed to provide a stratum correlation method and device which can overcome the above problems or at least partially solve the above problems, and can realize automatic fine stratum correlation.

[0006] In a first aspect, an embodiment of the present application provides a stratum correlation method, comprising:

[0007] Performing continuous wavelet transform on selected well logging curves of a target well to obtain a modulus distribution map of continuous wavelet transform coefficients at each depth point under each set scale, obtaining a modulus maximum line from the modulus distribution map, and taking the position of the well logging curve when the modulus maximum line takes the minimum value of the scale variable as a singular value position;

[0008] determine a depth interval to be compared of the target well according to the singular value position and a predetermined comparison depth interval of the standard well;

[0009] obtain a first data point sequence of the logging curve in the depth interval to be compared and a second data point sequence of the standard logging curve of the standard well in the comparison depth interval, determine paths between the first data point sequence and the second data point sequence according to a set rule, determine distances of each path, and obtain a formation comparison result of the target well and the standard well according to the path with the shortest distance.

[0010] In a second aspect, an embodiment of the present application provides a formation comparison device, which comprises:

[0011] a continuous wavelet transform module, configured to perform continuous wavelet transform on a selected logging curve of a target well, to obtain a modulus distribution map of continuous wavelet transform coefficients at each depth point under each set scale, obtain a modulus maximum line from the modulus distribution map, and take a position of the logging curve when the modulus maximum line takes a minimum value of a scale variable as a singular value position;

[0012] a depth comparison interval determination module, configured to determine a depth interval to be compared of the target well according to the singular value position and a predetermined comparison depth interval of the standard well;

[0013] a formation comparison module, configured to obtain a first data point sequence of the logging curve in the depth interval to be compared and a second data point sequence of the standard logging curve of the standard well in the comparison depth interval, determine paths between the first data point sequence and the second data point sequence according to a set rule, determine distances of each path, and obtain a formation comparison result of the target well and the standard well according to the path with the shortest distance.

[0014] In a third aspect, an embodiment of the present application provides a computer storage medium, which stores computer executable instructions, and the computer executable instructions are executed by a processor to implement the formation comparison method.

[0015] In a fourth aspect, an embodiment of the present application provides a server, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor implements the formation comparison method when executing the program.

[0016] The above technical solution provided by the embodiments of the present application has at least the following beneficial effects:

[0017] (1) The stratum correlation method provided by the embodiment of the present application, through continuous wavelet transform of selected well logging curves of a target well, identifies singular value positions; and then according to the correlation depth interval of a standard well, determines the depth interval to be correlated of the target well, obtains the first data point sequence and the second data point sequence of the two intervals as the objects to be correlated; determines a plurality of paths between the first data point sequence and the second data point sequence, through the calculation of the distance of each path, obtains the shortest path, and thus obtains the stratum correlation result of the target well and the standard well. Through continuous wavelet transform, automatic stratum division is realized, and based on the dynamic time warping algorithm, fine stratum correlation is realized, so as to reduce the workload of geologists and reduce the personal subjectivity in the stratum correlation process.

[0018] (2) The stratum correlation method provided by the embodiment of the present application, uses the stratum correlation result (the path with the shortest distance between the first data point sequence and the second data point sequence) to obtain the depth correspondence curve in the depth interval to be correlated of the target well and the correlation depth interval of the standard well; and determines the position with a slope change greater than a set threshold in the depth correspondence curve as a fault position. On the basis of automatic stratum correlation, automatic identification of the fault is realized.

[0019] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and achieved by means of the structures particularly pointed out in the written description and the appended drawings.

[0020] The technical solutions of the present application will be further described in detail below with the help of the drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, and are used together with the embodiments of the present application to explain the present application, and do not constitute a limitation on the present application. In the drawings:

[0022] Figure 1 It is a flow chart of the stratum correlation method in the embodiment of the present application;

[0023] Figure 2 It is a schematic diagram of the calculation result of the continuous wavelet transform coefficient in the embodiment of the present application;

[0024] Figure 3 It is a schematic diagram of the modulus maximum value line of the continuous wavelet transform coefficient in the embodiment of the present application;

[0025] Figure 4 It is a specific implementation flow chart of path determination in the embodiment of the present application;

[0026] Figure 5 It is a depth correspondence graph between two wells in the embodiment of the present application;

[0027] Figure 6 Figure 1 is a structural schematic diagram of a stratum correlation device according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms without being limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be thoroughly understood and fully conveyed to those skilled in the art.

[0029] It should be understood that the terms used in the present application are merely used to describe particular embodiments and are not intended to limit the present application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. Although preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present application. All documents mentioned in the specification are incorporated by reference to disclose and describe the methods and / or materials related to the documents. In the event of conflict, the contents of the specification control.

[0030] To solve the problem that stratum correlation automation cannot be achieved in the prior art, an embodiment of the present application provides a stratum correlation method and device, which can automatically perform fine stratum correlation. EMBODIMENT

[0031] An embodiment of the present application provides a stratum correlation method, a flowchart of which is shown in Figure 1 Figure 1, and the method comprises the following steps:

[0032] Step S11: performing continuous wavelet transform on selected well logging curves of a target well to obtain a modulus distribution map of continuous wavelet transform coefficients at each depth point under each set scale, obtaining a modulus maximum line from the modulus distribution map, and taking a position of a logging curve at which a scale variable takes a minimum value as a singular value position.

[0033] In some embodiments, the selected well logging curves of the target well can be subjected to continuous wavelet transform by the following formula (1) to obtain a modulus distribution map of continuous wavelet transform coefficients at each depth point under each set scale:

[0034] (1)

[0035] In formula (1), is a continuous wavelet transform coefficient of the well logging curve at a scale s depth x0; is the expression of the well logging curve, x is the well logging depth; is the wavelet transform base function, which can be the first derivative of a Gaussian function; min and x max are the minimum and maximum depths of the well logging curve, respectively.

[0036] In practical applications, the value of s is usually set to a natural number between 1 and 16.

[0037] Referring to FIG. 6, a diagram of the calculation results of the continuous wavelet transform coefficients is shown, Figure 2 where the horizontal axis b represents the discrete number of the well logging curve depth, i.e., the number of the data points in the well logging curve. Since the well logging curve is sampled at equal intervals, the specific depth value of a data point can be known by the discrete number, given the depth value of the starting data point in the well logging curve; the vertical axis a represents the scale variable s. Figure 2 After all the continuous wavelet transform coefficients are calculated, the modulus maximum line thereof is calculated (see FIG. 7); when the noise is too large, noise reduction processing is required before the modulus maximum line is calculated, and the modulus less than a set modulus threshold in the modulus distribution is set to zero; finally, the position of the well logging curve at which the scale variable s takes the minimum value (s = 1) is taken as the singular value position. The singular value position can be directly taken as the geological boundary line.

[0038] Figure 3 In the noise reduction processing process, the greater the modulus threshold is set, the more sufficient the noise reduction processing is, and the fewer the singular values are. The size of the modulus threshold can be flexibly set according to specific needs.

[0039] Step S12: According to the singular value position and the pre-determined comparative depth interval of the standard well, the comparative depth interval of the target well is determined.

[0040] Step S13: The first data point sequence of the well logging curve in the comparative depth interval and the second data point sequence of the standard well in the comparative depth interval are obtained, the paths between the first data point sequence and the second data point sequence are determined according to a set rule, the distances of each path are determined, and the formation comparison result of the target well and the standard well is obtained according to the path with the shortest distance.

[0041] First, the selected well logging curve of the target well and the standard well are normalized. The selected well logging curve of the target well and the standard well are the same type of well logging curve.

[0042] First, the selected well logging curve of the target well and the standard well are normalized. The selected well logging curve of the target well and the standard well are the same type of well logging curve.

[0043] ​According to the well logging sampling interval, obtain the first data point sequence of the selected well logging curve within the depth range to be compared, and the second data point sequence of the standard well logging curve within the depth range to be compared. Optionally, based on the well logging sampling interval, the number of data points in the first data point sequence and the number of data points in the second data point sequence can be reduced by thinning, but the interval between the first data points and the interval between the second data points must be consistent.

[0044] The first data points in the first data point sequence are arranged in order of depth from shallowest to deepest; the second data points in the second data point sequence are also arranged in order of depth from shallowest to deepest. See [link to relevant documentation]. Figure 4 As shown, determining the path between the first data point sequence and the second data point sequence includes the following steps:

[0045] Step S41: Determine the first first data point in the first data point sequence and the first second data point in the second data point sequence as the data pair to be matched.

[0046] Step S42: Determine whether the current data pair simultaneously satisfies the following conditions: the first data point is the last first data point in the first data point sequence, and the second data point is the last second data point in the second data point sequence.

[0047] If step S42 is correct, proceed to step S44; otherwise, proceed to step S43.

[0048] Step S43: Determine the next data pair based on the current data pair.

[0049] Specifically, the first data point in the next data pair remains unchanged, and the second data point is the next adjacent second data point of the second data point in the current data pair; or, the first data point in the next data pair is the next adjacent first data point of the first data point in the current data pair, and the second data point remains unchanged; or, the first data point in the next data pair is the next adjacent first data point of the first data point in the current data pair, and the second data point is the next adjacent second data point of the second data point in the current data pair.

[0050] That is, the currently determined data pair (i) k , j k ) and the next data pair to be determined (i k+1 , j k+1 The following conditions must be met between i and i: k+1 = i k +1, j k+1 = j k ; or i k+1 = i k j k+1 = j k +1; or i k+1 = ik +1, j k+1 = j k +1. Wherein, i is the serial number of the first data point in the first data point sequence, and j is the serial number of the second data point in the second data point sequence.

[0051] After step S43, return to execute step S42 until step S42 judges yes.

[0052] Step S44: form the data pairs into a data pair sequence according to the determined order, as a path between the first data point sequence and the second data point sequence.

[0053] For each path, determine the distance between the two data points of each data pair in the path according to the following formula (2), and determine the sum of the distances between the two data points of each data pair as the distance of the path:

[0054] d ( i , j )=( q i – c j ) 2 (2)

[0055] In formula (2), q i represents the logging value of the i-th first data point in the first data point sequence, c j represents the logging value of the j-th second data point in the second data point sequence, d ( i , j ) represents the distance between the i-th first data point in the first data point sequence and the j-th second data point in the second data point sequence.

[0056] Mark the first data point sequence as Q = q 1, q 2, …, q m , and mark the second data point sequence as C = c 1, c 2, …, c n , and solve the following optimization problem:

[0057]

[0058] Wherein, w is a kind of corresponding relationship between the two groups of data; the above formula requires to calculate the minimum value of the cumulative distance of all possible corresponding relationships, and dynamic programming method can conveniently solve this problem.

[0059] For example, the first data point sequence Q = q 1, q 2, …, q m m = 3 in the first data point sequence C = c 1, c 2, …, c n n = 2 in the second data point sequence, then there are 5 possible corresponding relationships (paths), which are w1 = ((1, 1), (2, 1), (3, 1), (3, 2)), w2 = ((1, 1), (2, 1), (3, 2)), w3 = ((1, 1), (2, 1), (2, 2), (3, 2)), w4 = ((1, 1), (2, 2), (3, 2)), w5 = ((1, 1), (1, 2), (2, 2), (3, 2)). For w2, the cumulative distance is d(w2) = d(1, 1) + d(2, 1) + d(3, 2).

[0060] The stratum correlation method provided by the embodiment of the application comprises the following steps: performing continuous wavelet transform on selected well logging curves of a target well to identify singular value positions; then determining a to-be-correlated depth interval of the target well according to a correlation depth interval of a standard well, obtaining a first data point sequence and a second data point sequence of the two intervals as to-be-correlated objects; determining a plurality of paths between the first data point sequence and the second data point sequence, calculating the distance of each path, obtaining the shortest path, and thus obtaining a stratum correlation result of the target well and the standard well. The automatic stratum division is realized through continuous wavelet transform, the fine stratum correlation is realized based on the dynamic time warping algorithm, and the workload of geologists is reduced and the personal subjectivity in the stratum correlation process is reduced.

[0061] In some embodiments, based on the obtained depth corresponding relationship graph between the two wells, the previous presumption can be further confirmed; or the above method is repeatedly applied to logging data of multiple parameters (≥3) to obtain multiple corresponding relationship graphs, and if the corresponding relationship graphs are approximately consistent, the previous presumption can be accepted.

[0062] In some embodiments, the method can further comprise the following steps: obtaining a depth corresponding relationship curve in the to-be-correlated depth interval of the target well and the correlation depth interval of the standard well according to the shortest path; judging whether there is a position with a slope greater than a set threshold in the depth corresponding relationship curve; if yes, determining that there is a fault at a first matching position of the target well in the position with the slope greater than the set threshold in the depth corresponding relationship curve, or determining that there is a fault at a second matching position of the standard well in the position with the slope greater than the set threshold in the depth corresponding relationship curve.

[0063] By the slope change of the depth correspondence curve, the automatic identification of the fault is realized on the basis of the automatic correlation of the strata.

[0064] Further, if the depth correspondence curve has a position with a slope greater than a set threshold, and whether the target well or the standard well has a fault at the matching position, the position with the slope greater than the set threshold of the depth correspondence curve can be determined to be closer to the coordinate axis corresponding to the target well or the standard well, if closer to the coordinate axis corresponding to the target well, it indicates that the depth value of the target well at the matching position is greater, and it can be further determined that the target well has a fault at the matching position; otherwise, if closer to the coordinate axis corresponding to the standard well, it indicates that the depth value of the standard well at the matching position is greater, and it can be further determined that the standard well has a fault at the matching position.

[0065] Referring to Figure 5 Fig. 1 shows the depth correspondence curve diagram of well Mimosa 4-8 and well Mimosa 4-9, it can be seen that the slope of the depth correspondence curve below 1403m of well Mimosa 4-8 and below 1422m of well Mimosa 4-9 suddenly changes, that is, there is no depth correspondence, and the curve at the sudden change is closer to well Mimosa 4-9, proving that well Mimosa 4-9 has a fault at the corresponding position (1422m).

[0066] The selection standard of the above standard well is that the strata development feature is obvious, and the strata are complete and there is no fault; and the selected standard logging curve is a logging curve with obvious lithology response.

[0067] For a work area with more developed faults, it is difficult to find a standard well without passing through a fault, so a well with a fault can also be selected as a standard well. In the subsequent strata correlation process, if it is determined that the standard well has a fault at the second matching position, the missing layer section of the standard well at the second matching position, and the matching layer section in the selected logging curve of the target well can also be determined; and the matching layer section in the selected logging curve is inserted at the second matching position of the standard logging curve. That is, in the actual strata correlation process, the standard logging curve is constantly improved, and its applicability is gradually expanded to the entire study area.

[0068] In some embodiments, after obtaining the final standard logging curve, the strata correlation steps of each target well can also be re-executed, that is, for each target well, the above steps S12 and S13 are re-executed.

[0069] Based on the inventive concept of the present application, the embodiments of the present application also provide a strata correlation device, the structure of the device is shown in Figure 6 Fig. 2, which comprises:

[0070] a continuous wavelet transform module 61, configured to perform continuous wavelet transform on a selected log curve of a target well to obtain a modulus distribution map of continuous wavelet transform coefficients at each depth point under each set scale, obtain a modulus maximum line from the modulus distribution map, and take a position of the log curve at which a scale variable takes a minimum value as a singular value position;

[0071] a depth contrast interval determination module 62, configured to determine a to-be-contrasted depth interval of the target well according to the singular value position and a contrast depth interval of a standard well;

[0072] a formation contrast module 63, configured to obtain a first data point sequence of the log curve in the to-be-contrasted depth interval and a second data point sequence of a standard log curve of the standard well in the contrast depth interval, determine a path between the first data point sequence and the second data point sequence according to a set rule, determine a distance of each path, and obtain a formation contrast result of the target well and the standard well according to a path with a shortest distance.

[0073] In some embodiments, the continuous wavelet transform module 61 is specifically configured to:

[0074] perform continuous wavelet transform on the selected log curve of the target well by using the following formula (1) to obtain a modulus distribution map of continuous wavelet transform coefficients at each depth point under each set scale:

[0075] (1)

[0076] In the formula (1), is a continuous wavelet transform coefficient of the log curve at a scale s and a depth x0; is an expression of the log curve, and x is a logging depth; is a wavelet transform base function; x min and x max are minimum and maximum depths of the log curve, respectively.

[0077] In some embodiments, after the continuous wavelet transform module 61 obtains the modulus distribution map of the continuous wavelet transform coefficients at each depth point under each set scale, the continuous wavelet transform module 61 is further configured to:

[0078] set a modulus smaller than a set modulus threshold in the modulus distribution map to zero.

[0079] In some embodiments, the first data point in the first data point sequence and the second data point in the second data point sequence are arranged in an order from shallow to deep, and the formation contrast module 63 is specifically configured to:

[0080] The first data point in the first data point sequence and the first second data point in the second data point sequence are determined as a data pair to be matched; it is judged whether the current data pair simultaneously satisfies that the first data point is the last first data point in the first data point sequence and the second data point is the last second data point in the second data point sequence; if not, the next data pair is determined according to the current data pair, the first data point in the next data pair is unchanged, and the second data point is the next second data point adjacent to the second data point in the current data pair; or, the first data point in the next data pair is the next first data point adjacent to the first data point in the current data pair, and the second data point is unchanged; or, the first data point in the next data pair is the next first data point adjacent to the first data point in the current data pair, and the second data point is the next second data point adjacent to the second data point in the current data pair; if yes, the data pairs are arranged in the determined order to form a data pair sequence, which is taken as a path between the first data point sequence and the second data point sequence.

[0081] In some embodiments, the stratum correlation module 63 is specifically configured to:

[0082] For each path, the distance between the two data points of each data pair in the path is determined according to formula (2) as follows:

[0083] d i , j = ( q i - c j ) 2 (2)

[0084] In formula (2), q i represents the logging value of the i th first data point in the first data point sequence, c j represents the logging value of the j th second data point in the second data point sequence, d i , j represents the distance between the i th first data point in the first data point sequence and the j th second data point in the second data point sequence.

[0085] In some embodiments, before the stratum correlation module 63 acquires the first data point sequence of the logging curve in the depth interval to be correlated and the second data point sequence of the standard logging curve of the standard well in the depth interval to be correlated, the stratum correlation module 63 is further configured to:

[0086] perform normalization processing on the logging curve and the standard logging curve of the standard well. ​​

[0087] In some embodiments, the apparatus further comprises a fault identification module 64, configured to:

[0088] obtain a depth correspondence curve between the target well and the standard well according to the shortest distance path; determine whether the depth correspondence curve has a position with a slope greater than a set threshold; if yes, determine that a fault exists at a first matching position of the depth correspondence curve with the slope greater than the set threshold, or a second matching position of the standard well with the slope greater than the set threshold.

[0089] In some embodiments, the apparatus further comprises a standard logging curve updating module 65, configured to, if the fault identification module 64 determines that a fault exists at the second matching position of the standard well:

[0090] determine a missing layer section of the standard well at the second matching position and a matching layer section of the logging curve in the missing layer section; and insert the matching layer section of the logging curve at the second matching position of the standard logging curve.

[0091] In some embodiments, the stratum correlation module 63 is specifically configured to:

[0092] obtain a first data point sequence of the logging curve in the target well in the target depth interval and a second data point sequence of the standard logging curve of the standard well in the depth interval according to a logging sampling interval.

[0093] As to the apparatus in the above embodiments, the specific manners in which the modules perform operations have been described in detail in the embodiments of the method, and thus will not be described in detail here.

[0094] Based on the inventive concept of the present application, the embodiments of the present application further provide a computer storage medium, which stores computer executable instructions, and the computer executable instructions are executed by a processor to implement the above stratum correlation method.

[0095] Based on the inventive concept of the present application, the embodiments of the present application further provide a server, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor executes the program to implement the above stratum correlation method.

[0096] Unless specifically stated otherwise, terms such as processing, computing, calculating, determining, displaying, or the like, can refer to an action or process of one or more processing or computing systems, or similar devices, that manipulate or transform data represented as physical (e.g., electronic) quantities within the systems' registers or memories into other data similarly represented as physical quantities within the systems' memories, registers or other such information storage, transmission or display devices. The terms "information," "data," "instructions," “command,” “signal,” “bit,” “symbol,” and “chip” refer to physical quantities that can be measured, represented, or otherwise manipulated in a processing system.

[0097] It should be understood that the specific order or hierarchy of steps in the processes disclosed are examples of exemplary approaches. Based upon design preferences, it should be understood that the specific order or hierarchy of steps in the processes can be re-arranged while remaining within the scope of the present disclosure.

[0098] Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0099] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor and the storage medium can reside as discrete components in a user terminal.

[0100] For software implementation, the techniques described in this application can be implemented with modules (for example, procedures, functions, and so on) that perform the functions described in this application. The software codes can be stored in memory units and executed by processors. The memory unit can be implemented within the processor or externally to the processor, in which case it can be communicatively coupled to the processor via various means as is well known in the art.

[0101] The above description includes one or more examples of the embodiments. Of course, not all possible combinations of components or methods described above can be described, but one of ordinary skill in the art will recognize that further combinations and permutations of various embodiments are possible. The terms "first," "second," and the like do not imply significance or order other than the order of description of the components.

Claims

1. A stratigraphic correlation method, characterized in that, include: A continuous wavelet transform is performed on the selected logging curve of the target well to obtain the modulus distribution map of the continuous wavelet transform coefficients at each depth point under each set scale. The modulus maximum line is obtained from the modulus distribution map. The position of the logging curve when the scale variable takes the minimum value of the modulus maximum line is taken as the singular value position. Based on the location of the singular value and the pre-determined comparison depth range of the standard well, the comparison depth range of the target well is determined; Obtain the first data point sequence of the logging curve within the depth range to be compared, and the second data point sequence of the standard logging curve of the standard well within the depth range to be compared; determine the first first data point in the first data point sequence and the first second data point in the second data point sequence as a data pair to be matched; determine whether the current data pair simultaneously satisfies that the first data point is the last first data point in the first data point sequence and the second data point is the last second data point in the second data point sequence; if not, determine the next data pair based on the current data pair, where the first data point in the next data pair remains unchanged, and the second data point is the next adjacent second data point of the second data point in the current data pair; Alternatively, the first data point in the next data pair is the next first data point adjacent to the first data point in the current data pair, while the second data point remains unchanged; Alternatively, the first data point in the next data pair is the next first data point adjacent to the first data point in the current data pair, and the second data point is the next second data point adjacent to the second data point in the current data pair; If so, the data pairs are arranged into a data pair sequence in a determined order, which serves as a path between the first data point sequence and the second data point sequence; the distance of each path is determined, and the formation correlation results between the target well and the standard well are obtained based on the path with the shortest distance. The depth correspondence curve between the target well and the standard well is obtained based on the shortest path. It is then determined whether there is a position where the slope of the depth correspondence curve is greater than a set threshold. If so, it is determined that there is a fault at the first matching position of the target well where the slope of the depth correspondence curve is greater than the set threshold, or that there is a fault at the second matching position of the standard well where the slope of the depth correspondence curve is greater than the set threshold. If it is determined that a fault exists at the second matching position of the standard well, the missing segment of the standard well at the second matching position and the matching segment of the missing segment in the logging curve are determined; the matching segment in the logging curve is inserted at the second matching position of the standard logging curve.

2. The method as described in claim 1, characterized in that, The step of performing continuous wavelet transform on the selected logging curve of the target well to obtain the modulus distribution map of the continuous wavelet transform coefficients at each depth point under each set scale specifically includes: The selected logging curves of the target well are subjected to continuous wavelet transform using the following formula (1) to obtain the modulus distribution map of the continuous wavelet transform coefficients at each depth point under each set scale: （1） In formula (1), For well logging curves on scale s Continuous wavelet transform coefficients at depth x0; Here is the expression for the logging curve, where x is the logging depth; x are wavelet transform basis functions; min and x max These represent the minimum and maximum depths of the well logging curve, respectively.

3. The method as described in claim 2, characterized in that, The wavelet transform basis functions are the first derivatives of the Gaussian functions.

4. The method as described in claim 2, characterized in that, After obtaining the modulus distribution map of the continuous wavelet transform coefficients at each depth point under each set scale, the method further includes: Set the moduli in the moduli distribution diagram that are less than the set moduli threshold to zero.

5. The method as described in claim 1, characterized in that, Determining the distance of each path specifically includes: For each path, the distance between the two data points of each data pair in the path is determined according to the following formula (2), and the sum of the distances between the two data points of each data pair is determined as the distance of the path: d ( i , j )=( q i – c j ) 2 （2） In formula (2), q i This represents the logging value of the i-th data point in the first data point sequence. c j This represents the logging value of the j-th second data point in the second data point sequence. d ( i , j ) represents the distance between the i-th first data point in the first data point sequence and the j-th second data point in the second data point sequence.

6. The method as described in claim 5, characterized in that, Before acquiring the first data point sequence of the logging curve within the depth range to be compared, and the second data point sequence of the standard logging curve of the standard well within the depth range to be compared, the method further includes: The logging curves and the standard logging curves of the standard wells are normalized.

7. The method according to any one of claims 1 to 6, characterized in that, The acquisition of the first data point sequence of the logging curve within the depth range to be compared, and the second data point sequence of the standard logging curve of the standard well within the depth range to be compared, specifically includes: According to the logging sampling interval, the first data point sequence of the logging curve within the depth range to be compared and the second data point sequence of the standard logging curve of the standard well within the depth range to be compared are obtained.

8. A stratigraphic correlation device, characterized in that, include: The continuous wavelet transform module is used to perform continuous wavelet transform on the selected logging curve of the target well to obtain the modulus distribution map of the continuous wavelet transform coefficients at each depth point under each set scale. The modulus maximum line is obtained from the modulus distribution map, and the position of the logging curve when the scale variable takes the minimum value of the modulus maximum line is taken as the singular value position. The depth comparison interval determination module is used to determine the comparison depth interval of the target well based on the location of the singular value and the comparison depth interval of a pre-determined standard well. The formation correlation module is used to acquire a first data point sequence of the logging curve within the depth range to be compared, and a second data point sequence of the standard logging curve of the standard well within the depth range to be compared; the first first data point in the first data point sequence and the first second data point in the second data point sequence are identified as data pairs to be matched; it is determined whether the current data pair simultaneously satisfies that the first data point is the last first data point in the first data point sequence and the second data point is the last second data point in the second data point sequence; if not, the next data pair is determined based on the current data pair, the first data point in the next data pair remains unchanged, and the second data point is the next adjacent second data point of the second data point in the current data pair; Alternatively, the first data point in the next data pair is the next first data point adjacent to the first data point in the current data pair, while the second data point remains unchanged; Alternatively, the first data point in the next data pair is the next first data point adjacent to the first data point in the current data pair, and the second data point is the next second data point adjacent to the second data point in the current data pair; If so, the data pairs are arranged into a data pair sequence in a determined order, which serves as a path between the first data point sequence and the second data point sequence. The distance of each path is determined, and the formation correlation results between the target well and the standard well are obtained based on the path with the shortest distance. The fault identification module is used to obtain the depth correspondence curve between the target well's depth interval and the standard well's depth interval based on the shortest path; determine whether there is a position where the slope of the depth correspondence curve is greater than a set threshold; if so, determine that there is a fault at the first matching position of the target well where the slope of the depth correspondence curve is greater than the set threshold, or that there is a fault at the second matching position of the standard well where the slope of the depth correspondence curve is greater than the set threshold; If the fault identification module determines that a fault exists in the standard well at the second matching position, the standard logging curve update module is used to determine the missing segment of the standard well at the second matching position and the matching segment of the missing segment in the logging curve; and insert the matching segment of the logging curve at the second matching position of the standard logging curve.

9. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the stratigraphic correlation method according to any one of claims 1 to 7.

10. A server, characterized in that, include: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor, when executing the program, implements the stratigraphic correlation method according to any one of claims 1 to 7.

Images