A sandwich identification method and apparatus
By constructing a logging curve index matrix and weighting the data, combined with the morphological characteristics of logging curve units, quantitative identification of interlayers was achieved, solving the problems of high cost and low efficiency in existing technologies, and improving identification efficiency and accuracy.
Patent Information
- Application Number
- CN202211608590.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-12-14
- Publication Date
- 2025-11-11
- Estimated Expiration
- 2042-12-14
AI Technical Summary
Current technologies for interlayer identification mainly rely on qualitative methods, which are costly and inefficient, require researchers to have extensive geological experience, and are difficult to achieve efficient quantitative identification.
By constructing an index matrix of sampling points in the well logging curve, obtaining the weight of each index data, performing weighted processing, and using information entropy and index difference degree to determine the probability of interlayer development, combined with the morphological characteristics of the well logging curve unit, quantitative identification of interlayers is achieved.
It improves the efficiency of interlayer identification, reduces the requirements for researchers' geological experience, enables accurate understanding and characterization of interlayers, and supports the analysis of oil-water migration patterns in oil reservoirs and the formulation of development plans.
Smart Images
Figure CN116025344B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of oil reservoir resource development technology, and in particular to a method and apparatus for interlayer identification. Background Technology
[0002] Interlayers are non-reservoir or abnormally low-permeability reservoirs that appear locally in a normal reservoir context. The development and distribution of interlayers have a significant impact on reservoir fluid flow. Accurately understanding and characterizing the distribution of reservoir interlayers is of great significance for analyzing the oil-water migration patterns in oil reservoirs and formulating reasonable development plans.
[0003] Currently, the identification of interlayers is mostly qualitative. For example, core calibration logging is used to establish interlayer identification standards for cored wells, and then interlayers in non-cored wells are identified. This method is not only costly and inefficient, but also requires a high level of geological experience from researchers.
[0004] Therefore, how to develop a simple and efficient method for quantitative identification of interlayers has become a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0005] This application provides a method and apparatus for identifying interlayers, which provides a method for quantitatively identifying interlayers and can improve the efficiency of interlayer identification.
[0006] In a first aspect, embodiments of this application provide a mezzanine identification method, including:
[0007] Based on the first index data of M sampling points in the well logging curve, an index matrix is constructed, wherein each sampling point corresponds to N types of the first index data, and the first index data is used to indicate the well logging response characteristics of each sampling point, wherein M and N are positive integers;
[0008] Obtain the weight corresponding to each type of the first indicator data, and perform weighted processing on the indicator matrix according to the weight to obtain a result matrix composed of the second indicator data;
[0009] Based on the maximum and minimum values of each second indicator data in the result matrix and the N second indicator data corresponding to each sampling point, the probability of interlayer development at each sampling point is determined.
[0010] Based on the interlayer development probability of each sampling point, the interlayer development probability of each of the K logging curve units in the logging curve is obtained. Each unit includes P sampling points, where K and P are positive integers and P is less than M.
[0011] If the probability of interlayer development in any of the logging curve units is greater than a preset threshold, then it is determined that there is an interlayer in the logging curve.
[0012] Optionally, obtaining the weight corresponding to each type of the first indicator data includes:
[0013] Normalize each of the first indicator data in the indicator matrix to obtain a standardized matrix;
[0014] Obtain the information entropy and index difference degree corresponding to each indicator data in the standardized matrix. The information entropy is used to indicate the degree of uncertainty of the indicator data, and the index difference degree is used to indicate the importance of the indicator data. The index difference degree is determined based on the information entropy.
[0015] The weight corresponding to each of the first indicator data is determined based on the degree of difference between each indicator data and the indicator.
[0016] Optionally, determining the interlayer development probability of each sampling point based on the maximum and minimum values corresponding to each type of second indicator data in the result matrix and the N types of second indicator data corresponding to each sampling point includes:
[0017] Based on the maximum and minimum values corresponding to each type of second indicator data in the result matrix, the set of maximum values and the set of minimum values of the second indicator data are obtained.
[0018] For any given sampling point, the interlayer development probability of each sampling point is determined based on the N second indicator data of the sampling point, the set of maximum values, and the set of minimum values.
[0019] Optionally, determining the interlayer development probability of each sampling point based on the N second indicator data of the sampling points, the set of maximum values, and the set of minimum values includes:
[0020] Obtain the first distance between the N second indicator data and the set of maximum values, and the second distance between the N second indicator data and the set of minimum values;
[0021] Based on the first distance and the second distance, determine the distance-based relative approximation of the sampling point;
[0022] The relative approximation is used as the interstitial development probability of each sampling point. The higher the relative approximation, the greater the interstitial development probability of the sampling point.
[0023] Optionally, determining the interlayer development probability of each of the K logging curve units in the logging curve based on the interlayer development probability of each sampling point includes:
[0024] For any logging curve unit cell, obtain the range amplitude and standard deviation of P relative approximations corresponding to P sampling points in the logging curve unit cell;
[0025] The probability of interlayer development in the logging curve unit is determined based on the range amplitude, the standard deviation, and the coefficient of variation of the logging curve unit, wherein the coefficient of variation is determined based on the standard deviation.
[0026] Optionally, obtaining the first distance between the N second indicator data and the set of maximum values includes:
[0027] For any second indicator data, the square of the difference between the second indicator data and the maximum value corresponding to the type of the second indicator data is taken as the third distance between the second indicator data and the set of maximum values;
[0028] The first distance is obtained by summing the N third distances and taking their square roots.
[0029] Optionally, obtaining the second distance between the N second indicator data and the set of minimum values includes:
[0030] For any second indicator data, the square of the difference between the second indicator data and the minimum value corresponding to the type of the second indicator data is taken as the fourth distance between the second indicator data and the set of minimum values;
[0031] The second distance is obtained by summing the N fourth distances and taking their square roots.
[0032] Secondly, embodiments of this application provide a mezzanine identification device, comprising:
[0033] The construction module is used to construct an index matrix based on the first index data of M sampling points in the well logging curve, wherein each sampling point corresponds to N types of the first index data, the first index data is used to indicate the well logging response characteristics of each sampling point, and M and N are positive integers;
[0034] The first acquisition module is used to acquire the weight corresponding to each type of the first indicator data, and to perform weighted processing on the indicator matrix according to the weight to obtain a result matrix composed of the second indicator data.
[0035] The first determining module is used to determine the interlayer development probability of each sampling point based on the maximum and minimum values of each type of second indicator data in the result matrix and the N types of second indicator data corresponding to each sampling point.
[0036] The second acquisition module is used to acquire the interlayer development probability of each of the K logging curve unit cells in the logging curve according to the interlayer development probability of each sampling point. Each unit cell includes P sampling points, where K and P are positive integers and P is less than M.
[0037] The second determining module is used to determine that there is an interlayer in the logging curve if the probability of interlayer development in any of the said unit bodies is greater than a preset threshold.
[0038] Optionally, the above-mentioned interlayer identification device can implement the interlayer identification method described in any of the first aspects.
[0039] Thirdly, this application provides an electronic device, including: a memory and a processor;
[0040] The memory is used to store computer instructions; the processor is used to execute the computer instructions stored in the memory to implement the method of any one of the first aspects.
[0041] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the method of any of the first aspects.
[0042] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method of any one of the first aspects.
[0043] The interlayer identification method and apparatus provided in this application construct an index matrix based on first index data from M sampling points in the well logging curve, obtain the weight corresponding to each type of first index data, and perform weighted processing on the index matrix according to the weights to obtain a result matrix composed of second index data. Based on the maximum and minimum values of each type of second index data in the result matrix and N types of second index data corresponding to each sampling point, the interlayer development probability of each sampling point is determined. Based on the interlayer development probability of each sampling point, the development probability of each of the K well logging curve units in the well logging curve is obtained. If the development probability of any well logging curve unit is greater than a preset threshold, an interlayer is determined to exist. Identifying interlayers quantitatively based on the well logging curve can improve the efficiency of interlayer identification. Attached Figure Description
[0044] Figure 1 A flowchart illustrating a mezzanine identification method provided in this application embodiment. Figure 1 ;
[0045] Figure 2 A flowchart illustrating a mezzanine identification method provided in this application embodiment. Figure 2 ;
[0046] Figure 3 This is a schematic diagram of the structure of a mezzanine identification device provided in an embodiment of this application;
[0047] Figure 4 This is a schematic diagram of the structure of a mezzanine identification electronic device provided in an embodiment of this application. Detailed Implementation
[0048] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0049] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect, without limiting their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" do not necessarily imply that they are different.
[0050] It should be noted that, in the embodiments of this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.
[0051] Interlayers are non-reservoir or abnormally low-permeability reservoirs that appear locally in a normal reservoir context. The development and distribution of interlayers in a reservoir have a significant impact on reservoir fluid flow. Accurately understanding and characterizing the distribution of reservoir interlayers is of great significance for analyzing the oil-water migration patterns in oil reservoirs and formulating reasonable development plans.
[0052] Interlayers can be divided into two main types. The first type is "lithological interlayers," which are the most common type in continental oil reservoirs. These can be further subdivided into "muddy interlayers" and "calcareous interlayers." "Muddy interlayers" are formed during early sedimentary processes due to localized anomalies in the sedimentary environment; "calcareous interlayers" are generally formed during later diagenetic processes due to localized anomalies in the diagenetic environment. The second type is "physical interlayers," which are sandstone themselves, but with relatively low permeability compared to the overall reservoir.
[0053] Current research on interlayers is mainly qualitative, focusing on their sedimentary environment and physical properties, but less on quantitative identification. For example, using core calibration logging to establish interlayer identification standards in cored wells, and then identifying interlayers in non-cored wells, is not only costly but also requires a high level of geological experience from researchers.
[0054] In view of this, embodiments of this application provide a method and apparatus for interlayer identification, which identifies interlayers through well logging data. By using quantitative identification, the efficiency of interlayer identification can be improved, and the requirements for researchers' geological experience are relatively low.
[0055] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be implemented independently or in combination with each other. The same or similar concepts or processes may not be described again in some embodiments.
[0056] Figure 1 Flowchart of the mezzanine identification method provided in the embodiments of this application Figure 1 ,like Figure 1 As shown, it includes the following steps:
[0057] S101. Based on the first index data of M sampling points in the logging curve, construct an index matrix, where each sampling point corresponds to N types of first index data. The first index data is used to indicate the logging response characteristics of each sampling point, and M and N are positive integers.
[0058] In this embodiment, the logging curve refers to a continuous curve generated from logging data to reflect the characteristics of the formation at different depths. The sampling point refers to the data collection point selected when logging the formation. The first index data refers to the logging data obtained at each sampling point in the formation, i.e., the logging response characteristics. For example, the logging response characteristics may include properties such as natural gamma, porosity, permeability, and resistivity at that sampling point.
[0059] It is understandable that the logging response characteristics can be specific values. For example, the logging response characteristics of a certain sampling point can be (0.23, 11.18, 33.80, 3.28), where 0.23 is the value of natural gamma, 11.18 is the value of porosity, 33.80 is the value of permeability, and 3.28 is the value of resistivity.
[0060] In this embodiment of the application, the well logging curve may include M sampling points, each sampling point corresponding to N types of first index data, and an M*N order index matrix can be constructed based on the M sampling points and the N types of first index data corresponding to each sampling point.
[0061] For example, the indicator matrix can be as follows:
[0062]
[0063] Where M is the number of sampling points, N is the number of the first index, and x ij This represents the j-th index value of the i-th sample.
[0064] S102. Obtain the weight corresponding to each type of first indicator data, and perform weighted processing on the indicator matrix according to the weight to obtain the result matrix composed of the second indicator data.
[0065] In this embodiment of the application, each first index data can reflect the interlayer development of the sampling point to a certain extent. Different first index data change significantly during interlayer development. For example, the value corresponding to natural gamma increases significantly and abnormally during interlayer development, while the resistivity changes little. Therefore, when identifying interlayers based on multiple different first index data, different weights need to be assigned to different first index data.
[0066] In this embodiment of the application, the weight corresponding to each first indicator data can be determined based on the information entropy of the indicator or based on empirical values, and one indicator data corresponds to one weight.
[0067] In this embodiment of the application, when obtaining the weight corresponding to each type of first indicator data, the indicator matrix can be weighted according to the weight to obtain a result matrix, and each element in the result matrix can be called a second indicator data.
[0068] For example, the resulting matrix can be as follows:
[0069]
[0070] Among them, v i Let be the weight corresponding to the i-th indicator.
[0071] S103. Based on the maximum and minimum values of each second indicator data in the result matrix and the N types of second indicator data corresponding to each sampling point, determine the interlayer development probability of each sampling point.
[0072] In this embodiment of the application, each second indicator data corresponds to a maximum value and a minimum value. The set of maximum values and the set of minimum values corresponding to the second indicator data can be obtained based on the result matrix. If there are N types of indicator data in the result matrix, then both the set of maximum values and the set of minimum values contain N data points.
[0073] In this embodiment, the maximum value can refer to the value of the parameter that is most effective for the target layer. That is, the larger the parameter value, the greater the positive effect on the target layer and the more conducive it is to the positive evaluation of the target. The minimum value corresponding to the maximum value refers to the value of the parameter that is least effective for the target layer. That is, the larger the value, the greater the negative impact on the target layer.
[0074] In this embodiment, the probability of interlayer development at each sampling point can be determined based on the relative distance between the N second indicator data corresponding to each sampling point and its maximum and minimum value sets.
[0075] For example, the N types of second indicator data corresponding to each sampling point are a set of N data, and the maximum value set and the minimum value set are also sets of N data. The relative approximation between the indicator data set and the maximum value set and the minimum value set is used as the development probability of the sampling point.
[0076] S104. Based on the interlayer development probability of each sampling point, obtain the development probability of each of the K logging curve units in the logging curve. Each unit includes P sampling points, where K and P are positive integers and P is less than M.
[0077] In this embodiment of the application, a logging curve unit cell refers to a logging curve segment in which a property shows a significant difference from that of a neighboring curve segment. Such a logging curve segment is called a logging curve unit cell, denoted as:
[0078] C:{X|x0,x1,…,x n}x j ∈[a,b]j=0,1,…,n
[0079] Where, x i Let be the i-th unit cell in the logging curve, where a and b represent the left and right scales of the logging curve unit cell, respectively.
[0080] For any logging curve unit cell, due to the differences in its curve shape, it will exhibit different mathematical and statistical characteristics. The mathematical and statistical analysis of logging curve units mainly includes the unit cell's mean, range, expected value, and standard deviation. Since mathematical statistics are based on probability theory to analyze data characteristics, it is necessary to assume that events occur with equal probability. Therefore, it is necessary to assume that the probability of any measurement point within a logging curve unit cell taking any value is equal.
[0081] Well logging unit cells can effectively reflect local anomalies in the sedimentary environment (such as source characteristics, sedimentation rate, sedimentary location characteristics, and sedimentation mode) and diagenetic environment (diagenetic mode and intensity) during reservoir formation. These anomalies will inevitably cause changes in the well logging unit cells. By dividing the well logging unit cells, changes in the sedimentary environment can be identified, which in turn allows for the identification of interlayers.
[0082] In this embodiment of the application, based on the shape of the logging curve, K logging curve units can be obtained in the logging curve. Each logging curve unit includes P sampling points. For any logging curve unit, the interlayer development probability of the logging curve unit can be determined based on the interlayer development probability of each sampling point, thereby obtaining the interlayer development probability of each logging curve unit in the logging curve.
[0083] For example, for any logging curve unit, the range amplitude and standard deviation between the interlayer development probabilities of each sampling point can be obtained, and the sum of the range amplitude and standard deviation can be used as the interlayer development probability of the logging curve unit.
[0084] In this embodiment of the application, the sampling interval of current logging instruments is generally 0.125m, while the logging curve unit cell requires at least 2 sampling intervals for the curve shape changes to be reflected. Therefore, the lower limit for identifying the thickness of the interlayer using the logging curve unit cell method is 0.25m.
[0085] S105. If the probability of interlayer development in any logging curve unit is greater than a preset threshold, then it is determined that there is an interlayer in the logging curve.
[0086] In this embodiment of the application, the preset threshold can be selected based on the experience of local geological research or by using experimental algorithms.
[0087] If the development probability of any logging curve unit in the acquired logging curve is greater than a preset threshold, then it can be determined that there is an interlayer in the logging curve.
[0088] In this embodiment, the depth of the interlayer in the formation to be measured can be determined based on the position of the logging curve unit in the logging curve, and the type of interlayer can be determined based on the shape of the logging curve unit.
[0089] It is understood that the execution entity in this application embodiment can be a server that implements data processing. The server obtains the logging curve data input by the user, analyzes the logging curve data, and obtains analysis results on whether there is an interlayer.
[0090] This application provides a method for identifying interlayers. It constructs an index matrix based on first index data from M sampling points in a well logging curve, obtains the weight corresponding to each first index data, and weights the index matrix according to the weights to obtain a result matrix composed of second index data. Based on the maximum and minimum values of each second index data in the result matrix and N types of second index data corresponding to each sampling point, it determines the interlayer development probability of each sampling point. Based on the interlayer development probability of each sampling point, it obtains the development probability of K well logging curve units in the well logging curve. If the development probability of any well logging curve unit is greater than a preset threshold, an interlayer is determined to exist. Identifying interlayers quantitatively based on the well logging curve can improve the efficiency of interlayer identification.
[0091] Figure 2 Flowchart of the mezzanine identification method provided in the embodiments of this application Figure 2 ,exist Figure 1Based on the illustrated embodiments, the mezzanine identification method provided in this application will be further described, such as... Figure 2 As shown, it includes the following steps:
[0092] S201. Based on the first index data of M sampling points in the logging curve, construct an index matrix, where each sampling point corresponds to N types of first index data. The first index data is used to indicate the logging response characteristics of each sampling point, and M and N are positive integers.
[0093] In this embodiment of the application, the specific implementation of S201 is the same as... Figure 1 The specific implementation of S101 in the illustrated embodiment is similar and will not be repeated here.
[0094] S202. Normalize the data of each first indicator in the indicator matrix to obtain a standardized matrix.
[0095] In this embodiment of the application, since the first indicator data have different dimensions and large differences, it is necessary to make each data consistent. Therefore, the first indicator data needs to be normalized.
[0096] In this embodiment of the application, the following formula can be used to normalize any first indicator data:
[0097]
[0098] Among them, z ij For x ij The data obtained after normalization.
[0099] In this embodiment of the application, the normalization process is performed on each first indicator data in the indicator matrix to obtain a standardized matrix, as shown below:
[0100]
[0101] S203. Obtain the weight corresponding to each type of first indicator data, and perform weighted processing on the standard matrix according to the weight to obtain the result matrix.
[0102] In this embodiment of the application, the weight corresponding to each first indicator data can be obtained according to the information entropy corresponding to each indicator data in the standardized matrix.
[0103] Specifically, the information entropy and index difference degree corresponding to each indicator data in the standardized matrix are obtained. The information entropy is used to indicate the degree of uncertainty of the indicator data, and the index difference degree is used to indicate the importance of the indicator data. The index difference degree is determined based on the information entropy. The weight corresponding to each first indicator data is determined based on the index difference degree corresponding to each indicator data.
[0104] In this embodiment, entropy is an index reflecting the degree of uncertainty of information in an information system. The larger the value, the more discrete the data information layout, and the stronger the uncertainty. Applied to weighted classification, the more discrete the value layout of the j-th indicator, the higher the importance of the corresponding index.
[0105] For example, the information entropy corresponding to each indicator data can be determined according to the following formula:
[0106]
[0107] The degree of difference for each indicator data can be determined using the following formula:
[0108]
[0109] Among them, the more dispersed the value of the j-th indicator is, the higher the corresponding h... j The larger the value, the more important the j-th index becomes.
[0110] The weight corresponding to each primary indicator data can be determined according to the following formula:
[0111]
[0112] In this embodiment of the application, after obtaining the weight corresponding to each first indicator data, the corresponding weight is multiplied by the standardized matrix to obtain the result matrix.
[0113] For example, the resulting matrix is shown below:
[0114]
[0115] S204. Based on the maximum and minimum values of each second indicator data in the result matrix, obtain the set of maximum values and the set of minimum values of the second indicator data.
[0116] In this embodiment of the application, each second indicator data corresponds to a maximum value and a minimum value. The set of maximum values and the set of minimum values corresponding to the second indicator data can be obtained based on the result matrix. If there are N types of indicator data in the result matrix, then both the set of maximum values and the set of minimum values contain N data points.
[0117] For example, the set of maximum values can be as follows:
[0118] V + =(w1z i1 ... w N z iN )
[0119] The set of minimum values can be represented as follows:
[0120] V - =(w1z i1 ... w N z iN )
[0121] S205. Obtain the first distance between the N second indicator data points of any sampling point and the maximum value set, and the second distance between the N second indicator data points and the minimum value set.
[0122] In this embodiment of the application, the first distance between the N second index data points of any sampling point and the set of maximum values can be obtained in the following way:
[0123] Specifically, for any second indicator data, the square of the difference between the second indicator data and the maximum value corresponding to the type of the second indicator data is taken as the third distance between the second indicator data and the set of maximum values; the N third distances are summed and squared to obtain the first distance.
[0124] That is, the first distance can be determined by the following formula:
[0125]
[0126] In this embodiment of the application, the second distance between the N second index data points of any sampling point and the minimum value set can be obtained in the following way:
[0127] Specifically, for any second indicator data, the square of the difference between the second indicator data and the minimum value corresponding to the type of the second indicator data is taken as the fourth distance between the second indicator data and the set of minimum values; the N fourth distances are summed and squared to obtain the second distance.
[0128] That is, the second distance can be determined by the following formula:
[0129]
[0130] in, This represents the first distance between the N second indicator data points of the i-th sampling point and the set of maximum values. This represents the second distance between the N second index data points of the i-th sampling point and the set of minimum values.
[0131] S206. Based on the first distance and the second distance, determine the distance-based relative approximation of any sampling point.
[0132] In this embodiment of the application, for interlayer identification, the natural gamma (GR) and resistivity (Rt) of any sampling point are benefit-oriented indicators; the larger the indicator value, the higher the probability of interlayer development at that sampling point. Porosity (POR) and permeability (PERM), on the other hand, are cost-oriented indicators; the smaller the indicator value, the higher the probability of interlayer development at that sampling point.
[0133] Once the distance between the index data of a sampling point and its corresponding maximum and minimum values is determined, the relative approximation of the index data of that sampling point based on the distance can be obtained. This relative distance is used as the development probability of the interlayer at that sampling point. The higher the relative approximation, the greater the probability of the interlayer development at the sampling point.
[0134] For example, the relative approximation of any sampling point can be determined by the following formula:
[0135]
[0136] S207. Based on the interlayer development probability of each sampling point, determine the interlayer development probability of each of the K logging curve units in the logging curve.
[0137] In this embodiment of the application, for any logging curve unit, the interlayer development probability of the logging curve unit can be determined based on the interlayer development probability of each sampling point in the logging curve unit.
[0138] Specifically, the range and standard deviation of the P relative approximations corresponding to the P sampling points in the logging curve unit are obtained; the development probability of the interlayer in the logging curve unit is determined based on the range, standard deviation and the coefficient of variation of the logging curve unit, and the coefficient of variation is determined based on the standard deviation.
[0139] For example, the probability of interlayer development in any logging curve unit can be determined by the following formula.
[0140] P = (V K +J) / 3+D / 4
[0141] Where P is the developmental probability, V K denoted as coefficient of variation, J as range, and D as standard deviation.
[0142] The standard deviation of any logging curve unit cell can be determined by the following formula:
[0143]
[0144] The coefficient of variation can be determined by the following formula:
[0145]
[0146] The range can be determined by the following formula:
[0147] J = |Max(X) i )-Min(X i )|
[0148] Among them, X i The relative approximation of the i-th sampling point in the well logging curve unit cell. This represents the average relative approximation of each sampling point within the well logging curve unit.
[0149] S208. If the probability of interlayer development in any logging curve unit is greater than a preset threshold, then it is determined that there is an interlayer in the logging curve.
[0150] In this embodiment of the application, based on a preset threshold ε p It can determine whether interlayers are developed in the well logging curve unit, when P>ε p If the interlayer is well-developed, it can be assumed that the interlayer is well-developed; otherwise, it can be assumed that the interlayer is not well-developed.
[0151] In this embodiment, the depth of the interlayer in the formation to be measured can be determined based on the position of the logging curve unit in the logging curve, and the type of interlayer can be determined based on the shape of the logging curve unit.
[0152] For example, due to the different responses of argillaceous and calcareous interlayers to logging curves, different logging curve units are formed. argillaceous interlayers are mainly reflected in abnormally high values of the natural gamma logging curve unit shape, with finger-like peaks; calcareous interlayers are mainly reflected in abnormal resistivity logging curve units, with convex peaks.
[0153] This application provides a method for identifying interlayers. By analyzing and processing the index data of well logging curves through quantitative identification, the interlayer development probability of each sampling point is obtained, and then the interlayer development probability of each well logging curve unit is obtained, thereby realizing the identification of interlayers. Furthermore, the type of interlayer can be judged based on the morphology of the well logging curve unit, which improves the efficiency of interlayer identification and reduces the requirements for geological experience of researchers.
[0154] Based on the above-described embodiment of the interlayer identification method, this application also provides an interlayer identification device.
[0155] Figure 3 This is a schematic diagram of the structure of the interlayer identification device 30 provided in the embodiments of this application, as shown below. Figure 3 As shown, it includes:
[0156] The construction module 301 is used to construct an index matrix based on the first index data of M sampling points in the logging curve. Each sampling point corresponds to N kinds of first index data, and the first index data is used to indicate the logging response characteristics of each sampling point. M and N are positive integers.
[0157] The first acquisition module 302 is used to acquire the weight corresponding to each type of first indicator data, and to perform weighted processing on the indicator matrix according to the weight to obtain a result matrix composed of second indicator data.
[0158] The first determining module 303 is used to determine the interlayer development probability of each sampling point based on the maximum and minimum values of each second indicator data in the result matrix and the N second indicator data corresponding to each sampling point.
[0159] The second acquisition module 304 is used to acquire the interlayer development probability of each of the K well logging curve units in the well logging curve according to the interlayer development probability of each sampling point. Each unit includes P sampling points, where K and P are positive integers and P is less than M.
[0160] The second determining module 305 is used to determine that there is an interlayer in the logging curve if the probability of interlayer development in any unit is greater than a preset threshold.
[0161] Optionally, the first acquisition module 302 is further configured to normalize each first indicator data in the indicator matrix to obtain a standardized matrix; acquire the information entropy and indicator difference degree corresponding to each indicator data in the standardized matrix, wherein the information entropy is used to indicate the degree of uncertainty of the indicator data, the indicator difference degree is used to indicate the importance of the indicator data, and the indicator difference degree is determined based on the information entropy; and determine the weight corresponding to each first indicator data according to the indicator difference degree corresponding to each indicator data.
[0162] Optionally, the first determining module 303 is further configured to obtain the maximum value set and minimum value set of the second indicator data according to the maximum value and minimum value corresponding to each second indicator data in the result matrix; and for any sampling point, determine the interlayer development probability of each sampling point according to the N second indicator data of the sampling point, the maximum value set and the minimum value set.
[0163] Optionally, the first determining module 303 is further configured to obtain a first distance between the N second indicator data and the set of maximum values and a second distance between the N second indicator data and the set of minimum values; determine the distance-based relative approximation corresponding to the sampling point based on the first distance and the second distance; and use the relative approximation as the mezzanine development probability of each sampling point, wherein the higher the relative approximation, the greater the mezzanine development probability of the sampling point.
[0164] Optionally, the second acquisition module 304 is further configured to, for any logging curve unit, acquire the range amplitude and standard deviation of P relative approximations corresponding to P sampling points in the logging curve unit; and determine the development probability of the interlayer in the logging curve unit based on the range amplitude, the standard deviation and the coefficient of variation of the logging curve unit, wherein the coefficient of variation is determined based on the standard deviation.
[0165] Optionally, the second acquisition module 304 is further configured to, for any second indicator data, take the square of the difference between the second indicator data and the maximum value corresponding to the type of the second indicator data as the third distance between the second indicator data and the set of maximum values; and sum and take the square root of N third distances to obtain the first distance.
[0166] Optionally, the second acquisition module 304 is further configured to, for any second indicator data, take the square of the difference between the second indicator data and the minimum value corresponding to the type of the second indicator data as the fourth distance between the second indicator data and the set of minimum values; sum N of the fourth distances and perform square root processing to obtain the second distance.
[0167] This application provides a mezzanine identification device that can perform... Figure 1 and Figure 2 The technical solution of the layer identification method embodiment shown is similar in principle and technical effect, and will not be described again here.
[0168] Figure 4 This is a schematic diagram of the structure of a mezzanine identification electronic device provided in an embodiment of this application. Figure 4 As shown, the mezzanine identification electronic device 40 provided in this embodiment may include:
[0169] Processor 401.
[0170] Memory 402 is used to store executable instructions for the terminal device.
[0171] The processor is configured to execute the technical solution of the above-described mezzanine identification method embodiment by executing executable instructions. Its implementation principle and technical effect are similar, and will not be described again here.
[0172] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the technical solution of the above-described mezzanine identification method embodiment. Its implementation principle and technical effect are similar, and will not be repeated here.
[0173] In one possible implementation, a computer-readable medium may include random access memory (RAM), read-only memory (ROM), compact discread-only memory (CD-ROM) or other optical disc storage, disk storage or other magnetic storage devices, or any other medium targeted to carry or to store the required program code in the form of instructions or data structures, and accessible by a computer. Furthermore, any connection is appropriately referred to as a computer-readable medium. For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. As used herein, disks and optical discs include optical discs, laser discs, optical discs, Digital Versatile Discs (DVDs), floppy disks, and Blu-ray discs, where disks typically reproduce data magnetically, while optical discs optically reproduce data using lasers. The above combinations should also be included within the scope of computer-readable media.
[0174] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the technical solution of the above-described mezzanine identification method embodiment. Its implementation principle and technical effects are similar and will not be repeated here.
[0175] In the specific implementation of the aforementioned terminal device or server, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.
[0176] Those skilled in the art will understand that all or part of the steps in any of the above method embodiments can be implemented by hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium, and when the program is executed, all or part of the steps in the above method embodiments are performed.
[0177] If the technical solution of this application is implemented in software form and sold or used as a product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the technical solution of this application can be embodied in the form of a software product, which is stored in a storage medium and includes a computer program or several instructions. This computer software product causes a computer device (which may be a personal computer, server, network device, or similar electronic device) to execute all or part of the steps of the method described in the embodiments of this application.
[0178] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.
Claims
1. A method for identifying interlayers, characterized in that, include: Based on the first index data of M sampling points in the well logging curve, an index matrix is constructed, wherein each sampling point corresponds to N types of the first index data, and the first index data is used to indicate the well logging response characteristics of each sampling point, wherein M and N are positive integers; Obtain the weight corresponding to each type of the first indicator data, and perform weighted processing on the indicator matrix according to the weight to obtain a result matrix composed of the second indicator data; Based on the maximum and minimum values corresponding to each second indicator data in the result matrix and the N types of second indicator data corresponding to each sampling point, the mezzanine development probability of each sampling point is determined. Specifically, this includes: obtaining the set of maximum values and the set of minimum values of the second indicator data based on the maximum and minimum values corresponding to each second indicator data in the result matrix; for any sampling point, obtaining a first distance between the N types of second indicator data and the set of maximum values and a second distance between the N types of second indicator data and the set of minimum values; determining the relative approximation degree based on distance for the sampling point based on the first distance and the second distance, wherein the relative approximation degree = first distance / (first distance + second distance); and using the relative approximation degree as the mezzanine development probability of each sampling point, wherein the higher the relative approximation degree, the greater the mezzanine development probability of the sampling point. Based on the interlayer development probability of each sampling point, the interlayer development probability of each of the K logging curve units in the logging curve is obtained. Each unit includes P sampling points, where K and P are positive integers, and P is less than M. The process of obtaining the interlayer development probability of each of the K logging curve units in the logging curve based on the interlayer development probability of each sampling point includes: for any logging curve unit, obtaining the range amplitude and standard deviation of P relative approximations corresponding to the P sampling points in the logging curve unit; determining the interlayer development probability of the logging curve unit based on the range amplitude, the standard deviation, and the coefficient of variation of the logging curve unit, where the coefficient of variation is determined based on the standard deviation. If the probability of interlayer development in any of the logging curve units is greater than a preset threshold, then it is determined that there is an interlayer in the logging curve.
2. The method according to claim 1, characterized in that, The step of obtaining the weight corresponding to each type of the first indicator data includes: Normalize each of the first indicator data in the indicator matrix to obtain a standardized matrix; Obtain the information entropy and index difference degree corresponding to each indicator data in the standardized matrix. The information entropy is used to indicate the degree of uncertainty of the indicator data, and the index difference degree is used to indicate the importance of the indicator data. The index difference degree is determined based on the information entropy. The weight corresponding to each of the first indicator data is determined based on the degree of difference between each indicator data and the indicator.
3. The method according to claim 1, characterized in that, The step of obtaining the first distance between the N types of second indicator data and the set of maximum values includes: For any second indicator data, the square of the difference between the second indicator data and the maximum value corresponding to the type of the second indicator data is taken as the third distance between the second indicator data and the set of maximum values; The first distance is obtained by summing the N third distances and taking their square roots.
4. The method according to claim 1, characterized in that, The step of obtaining the second distance between the N types of second indicator data and the set of minimum values includes: For any second indicator data, the square of the difference between the second indicator data and the minimum value corresponding to the type of the second indicator data is taken as the fourth distance between the second indicator data and the set of minimum values; The second distance is obtained by summing the N fourth distances and taking their square roots.
5. A mezzanine identification device, characterized in that, include: The construction module is used to construct an index matrix based on the first index data of M sampling points in the well logging curve, wherein each sampling point corresponds to N types of the first index data, the first index data is used to indicate the well logging response characteristics of each sampling point, and M and N are positive integers; The first acquisition module is used to acquire the weight corresponding to each type of the first indicator data, and to perform weighted processing on the indicator matrix according to the weight to obtain a result matrix composed of the second indicator data. The first determining module is used to determine the mezzanine development probability of each sampling point based on the maximum and minimum values corresponding to each type of second indicator data in the result matrix and the N types of second indicator data corresponding to each sampling point. Specifically, it includes: obtaining the maximum value set and minimum value set of the second indicator data based on the maximum and minimum values corresponding to each type of second indicator data in the result matrix; for any sampling point, obtaining a first distance between the N types of second indicator data and the maximum value set and a second distance between the N types of second indicator data and the minimum value set; determining the relative approximation degree based on the distance of the sampling point based on the first distance and the second distance, wherein the relative approximation degree = first distance / (first distance + second distance); and using the relative approximation degree as the mezzanine development probability of each sampling point, wherein the higher the relative approximation degree, the greater the mezzanine development probability of the sampling point. The second acquisition module is used to acquire the interlayer development probability of each of the K logging curve units in the logging curve based on the interlayer development probability of each sampling point. Each unit includes P sampling points, where K and P are positive integers, and P is less than M. The acquisition of the interlayer development probability of each of the K logging curve units in the logging curve based on the interlayer development probability of each sampling point includes: for any logging curve unit, acquiring the range amplitude and standard deviation of the P relative approximations corresponding to the P sampling points in the logging curve unit; determining the interlayer development probability of the logging curve unit based on the range amplitude, the standard deviation, and the coefficient of variation of the logging curve unit, where the coefficient of variation is determined based on the standard deviation. The second determining module is used to determine that there is an interlayer in the logging curve if the probability of interlayer development in any of the said unit bodies is greater than a preset threshold.
6. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the method of any one of claims 1-4.
7. A computer-readable storage medium, characterized in that, It stores a computer program, which is executed by a processor to implement the method of any one of claims 1-4.