Method, device and equipment for identifying tuff and storage medium

Abstract

The invention provides a tuff identification method, device and equipment and a storage medium, and belongs to the technical field of geophysical exploration. The method comprises the following steps: based on logging curve characteristics of various lithologies in volcanic rocks, performing classification processing on logging data of a target area; generating an elastic parameter curve corresponding to each lithology based on the elastic parameters of each lithology in the classified logging data; selecting elastic parameters of at least two dimensions based on the elastic parameter curve to construct lithology identification factors; and identifying the tuff from the seismic data of the target area by using the lithology identification factor, and determining the distribution range of the tuff. Different lithologic characteristics and distribution rules of volcanic rocks are determined through well logging and earthquake rock physical characteristic analysis, the influence of tuff on reservoir prediction is reduced, sensitive factors for identification of the tuff are constructed on the basis of rock physical analysis, the multiplicity of solutions of volcanic rock prediction is reduced, and the lithologic characteristics of the volcanic rocks and the earthquake prediction precision of the reservoir can be improved.

Description

Technical Field

[0001] The present application relates to the field of geophysical exploration technology, and in particular to a method, device, equipment and storage medium for identifying tuff. Background Art

[0002] The lithology of volcanic strata is complex and diverse, and the reservoirs are extremely heterogeneous. The identification and prediction of volcanic lithology and reservoirs have always been difficult problems in the field of geophysical exploration. The specific difficulties are manifested in the multi-solution and uncertainty of volcanic rock prediction, which greatly affects volcanic oil and gas exploration and development and well deployment.

[0003] Tuff, sedimentary tuff, andesite and other special lithologies are developed in the intermediate-basic volcanic rock formations of underwater eruptions. Since some sedimentary tuffs have similar characteristics to volcanic rock reservoirs, such as low density and low longitudinal wave impedance, the use of density and impedance parameters to predict volcanic rock reservoirs is interfered by the special lithology of sedimentary tuffs. However, there are relatively few studies on the identification and prediction methods of special lithology of sedimentary tuffs in the field. The multi-solution problem of conventional methods is prominent, and the problems of volcanic rock lithology and reservoir distribution are difficult to solve, and the prediction accuracy is low. Summary of the invention

[0004] The present application provides a method, device, equipment and storage medium for identifying tuffite, which can improve the accuracy of seismic prediction of volcanic rock lithology and reservoir. The technical solution is as follows:

[0005] On the one hand, the embodiment of the present application provides a method for predicting a tight sandstone fracture reservoir, comprising:

[0006] Based on the logging curve characteristics of various lithologies in volcanic rocks, the logging data of the target area are classified and processed;

[0007] Based on the elastic parameters of various lithologies in the classified logging data, generating elastic parameter curves corresponding to various lithologies, wherein the elastic parameter curves are used to characterize the corresponding relationship between lithology types and elastic parameter values;

[0008] Selecting elastic parameters of at least two dimensions based on the elastic parameter curve to construct a lithology identification factor, wherein the lithology identification factor of tuff and the lithology identification factors of other types of lithology have different value ranges;

[0009] The lithology identification factor is used to identify the sedimentary tuff from the seismic data of the target area, and the distribution range of the sedimentary tuff is determined.

[0010] On the other hand, an embodiment of the present application provides a prediction device for a tight sandstone fracture reservoir, comprising:

[0011] A processing module is used to classify and process the logging data of the target area based on the logging curve characteristics of various lithologies in volcanic rocks;

[0012] A generation module, for generating elastic parameter curves corresponding to various lithologies based on the elastic parameters of various lithologies in the classified logging data, wherein the elastic parameter curves are used to characterize the corresponding relationship between lithology types and elastic parameter values;

[0013] A construction module, used for selecting elastic parameters of at least two dimensions based on the elastic parameter curve to construct a lithology identification factor, wherein the lithology identification factor of tuff and the lithology identification factors of other types of lithology have different value ranges;

[0014] The identification module is used to identify the sedimentary tuff from the seismic data of the target area by using the lithology identification factor, and determine the distribution range of the sedimentary tuff.

[0015] On the other hand, an embodiment of the present application provides an electronic device, which includes a memory and a processor; a computer program is stored in the memory, and when the computer program is executed by the processor, the method described in the above aspects is implemented.

[0016] On the other hand, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is loaded and executed by a processor to implement the method as described in the above aspects.

[0017] The technical solution provided by this application includes at least the following beneficial effects:

[0018] The identification method, device, equipment and storage medium of sedimentary tuff provided in the present application clarify the different lithological characteristics and distribution patterns of volcanic rocks through well logging and seismic rock physical characteristics analysis. In order to reduce the impact of sedimentary tuff on reservoir prediction, a sensitive factor for sedimentary tuff identification is constructed based on rock physical analysis, and the distribution of sedimentary tuff is predicted in combination with seismic data, which reduces the ambiguity of volcanic rock prediction and can improve the seismic prediction accuracy of volcanic rock lithology and reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments.

[0020] Figure 1 is a flow chart of a method for identifying tuff provided by an exemplary embodiment of the present application;

[0021] Figure 2 is a flow chart of a method for identifying tuff provided by another exemplary embodiment of the present application;

[0022] Figure 3 It is a schematic diagram of the division of different lithologies of volcanic rocks provided by an exemplary embodiment of the present application;

[0023] Figure 4 is a schematic diagram of a two-dimensional elastic parameter curve provided by an exemplary embodiment of the present application;

[0024] Figure 5 is a lithology identification factor inversion profile provided by an exemplary embodiment of the present application;

[0025] Figure 6 is a schematic diagram of tuff distribution prediction provided by an exemplary embodiment of the present application;

[0026] Figure 7 is a flow chart of a method for identifying tuff provided by another exemplary embodiment of the present application;

[0027] Figure 8 is a structural block diagram of a device for identifying tuff provided by an exemplary embodiment of the present application;

[0028] Fig. 9 It is a schematic diagram of the structure of an electronic device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0030] Embodiment 1

[0031] Please refer to Figure 1 , which shows a flow chart of a method for identifying tuff provided by an exemplary embodiment of the present application. The method comprises the following steps:

[0032] Step 101 , based on the logging curve characteristics of various lithologies in volcanic rocks, the logging data of the target area are classified and processed.

[0033] The logging data of volcanic rocks usually contain both electrical characteristic data and rock physical characteristic data (i.e. elastic parameters). Since tuff has special electrical characteristics, i.e. low resistivity and high shale content, the logging data can be classified based on the electrical characteristic data, and the data points in the logging data can be divided into data points corresponding to tuff and data points corresponding to other types of lithology, and then the analysis and tuff prediction can be carried out based on the divided rock physical characteristic data. Among them, the logging curve characteristics include at least one of resistivity and shale content.

[0034] Schematically, the logging data contains parameters a, b, c and d corresponding to each sample point, where parameters a and b are electrical characteristic data. The equipment divides the sample points into tuff sample points and other lithology sample points based on the electrical characteristic data, so that parameters c and d corresponding to tuff and parameters c and d corresponding to other lithologies can be obtained, and then lithology analysis and distribution prediction can be performed based on parameters c and d.

[0035] Step 102: Based on the elastic parameters of various lithologies in the classified logging data, elastic parameter curves corresponding to various lithologies are generated. The elastic parameter curves are used to characterize the corresponding relationship between lithology types and elastic parameter values.

[0036] The elastic parameter curve refers to a curve that characterizes the correspondence between lithology types and elastic parameter values, where the coordinates correspond to the elastic parameters, and the identification type of each data point corresponds to the lithology type. For example, different lithology types are identified by data points of different shapes and / or colors. The device combines elastic parameters in pairs to generate a two-dimensional elastic parameter curve corresponding to each elastic parameter combination.

[0037] Step 103: Select elastic parameters of at least two dimensions based on the elastic parameter curve to construct a lithology identification factor.

[0038] Among them, the lithology identification factor of sedimentary tuff corresponds to a different value range from the lithology identification factors of other types of lithology.

[0039] Different elastic parameters have different degrees of distinguishing lithology. Therefore, lithology analysis based on each elastic parameter will have multiple solutions and inaccurate prediction of tuff distribution.

[0040] In a possible implementation, the electronic device constructs a lithology identification factor using at least two elastic parameters based on elastic parameter curves corresponding to various elastic parameter combinations. The lithology identification factor is an expression composed of the at least two selected elastic parameters.

[0041] The principle for selecting elastic parameters for constructing lithology factors is to make the distribution area of ​​tuff data points different from that of other types of lithology (or the overlap of data points is less than a threshold).

[0042] In another possible implementation, the electronic device generates a lithology identification factor based on the received parameter selection operation, that is, the electronic device displays elastic parameter curves corresponding to all parameter combinations, and the technician manually selects the parameter combination corresponding to the curve with the highest lithology differentiation.

[0043] Step 104: using the lithology identification factor to identify the tuff from the seismic data of the target area, and determining the distribution range of the tuff.

[0044] Since the lithology identification factor of sedimentary tuff has a different value range from that of the lithology identification factors corresponding to other lithologies, the equipment can obtain the lithology factor data body of sedimentary tuff through the lithology identification factor and the seismic data of the target area, so as to predict the distribution range of sedimentary tuff.

[0045] In summary, the identification method of sedimentary tuff provided in the present application clarifies the different lithological characteristics and distribution patterns of volcanic rocks through well logging and seismic rock physical characteristics analysis. In order to reduce the impact of sedimentary tuff on reservoir prediction, a sensitive factor for sedimentary tuff identification is constructed based on rock physical analysis, and the distribution of sedimentary tuff is predicted in combination with seismic data, which reduces the ambiguity of volcanic rock prediction and can improve the seismic prediction accuracy of volcanic rock lithology and reservoirs.

[0046] Embodiment 2

[0047] Please refer to Figure 2 , which shows a flow chart of a method for identifying tuffite provided by another exemplary embodiment of the present application. The method comprises the following steps:

[0048] Step 201 , based on the logging curve characteristics of various lithologies in volcanic rocks, the logging data of the target area are classified and processed.

[0049] In a possible implementation, the logging curve characteristics include at least one of resistivity, shale content and porosity. The electronic device divides the sample points based on the logging curve characteristics of different lithologies, generates logging curves corresponding to various lithologies, and then uses the logging curves as color codes to classify other logging data. Step 201 specifically includes the following steps 201a to 201c:

[0050] Step 201a: determine the sample points in the logging data whose resistivity is less than or equal to the resistivity threshold and whose shale content is greater than the shale content threshold as sample point data of tuff.

[0051] Step 201b: determine the sample points in the remaining logging data whose shale content is less than or equal to the shale content threshold and whose porosity is less than or equal to the porosity threshold as sample point data of tight tuff.

[0052] Step 201c: determine the sample point data with porosity greater than the porosity threshold in the remaining logging data as the sample point data of the reservoir.

[0053] Sedimentary tuff has the characteristics of low resistivity and high mud content. Through experiments, it is determined that the range of values ​​of its resistivity and mud content is different from that of other volcanic rock lithology. Therefore, the sample points of sedimentary tuff can be first divided based on the values ​​of resistivity and mud content. Furthermore, dense tuff has a special mud content and porosity range, and the reservoir has a special porosity range, so the sample point data of dense tuff and reservoir can be further divided.

[0054] Indicatively, the resistivity threshold is 40 ohm·m, the mud content threshold is 40%, and the porosity threshold is 4%. The lithology definition result after division should be consistent with the actual lithology data of the well logging, or the accuracy should reach the accuracy threshold. Figure 3 A schematic diagram showing the results of dividing the different lithologies of volcanic rocks in a certain area.

[0055] Based on the classified logging data, the elastic parameters corresponding to different lithologies can be obtained, and then curves can be drawn based on the elastic parameters to analyze and predict the distribution of tuff.

[0056] Step 202: Combine candidate elastic parameters in pairs to determine elastic parameter pairs.

[0057] In a possible implementation, the elastic parameters include at least two of longitudinal wave impedance, shear wave impedance, longitudinal / straight wave velocity ratio, bulk modulus, Young's modulus and Lame constant.

[0058] In a possible implementation, the electronic device screens at least two elastic parameters to construct a new parameter as a lithology identification factor, so the electronic device first determines a possible parameter combination constituting the lithology identification factor based on the elastic parameters, that is, an elastic parameter pair.

[0059] Step 203 , generating a two-dimensional parameter curve graph corresponding to each pair of elastic parameters, wherein data points of different lithologies in the two-dimensional parameter curve graph correspond to different identification styles.

[0060] Figure 4 A two-dimensional parameter curve corresponding to longitudinal wave impedance and density is shown, wherein the horizontal axis represents the value of longitudinal wave impedance and the vertical axis represents the value of density. The electronic device displays the data points of the corresponding lithology through different styles of identification, and the technicians can directly observe the distribution of various lithology data points. The identification styles of the data points of different lithologies are different, and specifically, at least one of the color and shape of the data points is different.

[0061] Step 204: constructing a lithology identification factor based on the elastic parameter pair with the lowest curve overlap, where the curve overlap is the distribution overlap between the data points of tuff and the data points of other types of lithology.

[0062] The lowest curve overlap means that this pair of elastic parameter combination has the highest discrimination for lithology, so the electronic device selects the elastic parameter pair with the lowest curve overlap to construct the lithology identification factor. Figure 4 The longitudinal wave impedance-density two-dimensional parameter curve shown is the curve with the lowest curve overlap determined by the electronic device. It can be seen that the data points of tuff are obviously distributed in different areas from the data points of the other two lithologies. If the elastic parameter pair with a higher overlap is selected, it will be impossible to distinguish tuff from other lithologies.

[0063] Optionally, the electronic device determines the elastic parameter pair with the lowest curve overlap by calculating the overlap of the areas where different types of lithology data points are located in each two-dimensional curve graph (for example, the ratio of the area of ​​the intersection area to the total area, or the ratio of the number of data points in the communication area to the total number of data points), or directly displays all the two-dimensional curve graphs and determines the elastic parameter pair with the lowest curve overlap when receiving an image selection operation.

[0064] Specifically, step 204 includes the following steps 204a to 204b:

[0065] Step 204a, determining the lithology boundary line of the elastic parameter pair with the lowest curve overlap, the lithology boundary line is a straight line used to separate the area where the sedimentary tuff data points are located from the area where the other types of lithology data points are located in the two-dimensional parameter curve.

[0066] Step 204b, performing coordinate rotation based on the lithology dividing line, and constructing a tuff identification factor based on the rotated coordinate axis, wherein one of the rotated coordinate axes is parallel to the lithology dividing line.

[0067] Usually, the areas where the data points of sedimentary tuff and other lithologies are located are not strictly divided according to the horizontal or vertical direction, for example Figure 4 In the two-dimensional curve graph shown in FIG. 1 , the boundary between the data points of tuff and the other two lithologies corresponds to a slant line. Therefore, in order to facilitate subsequent calculations, the electronic device first constructs the tuff identification factor L1 by rotating the coordinate axis. Figure 4 As shown, by rotating the coordinate axes in the two-dimensional curve graph on the left so that the y-axis is parallel to the dividing line, the tuff identification factor L1 is constructed based on the new coordinate system.

[0068] The electronic device determines the lithology boundary line of the elastic parameter pair with the lowest curve overlap, and the boundary line can be automatically generated by the electronic device or generated based on the boundary line drawing operation.

[0069] In a possible implementation, the elastic parameter pair constituting the lithology identification factor is longitudinal wave impedance and density, and the expression of the lithology identification factor includes:

[0070] L1=1.59+0.00008*AI-DEN

[0071] Where L1 is the lithology identification factor, AI is the longitudinal wave impedance, and DEN is the density.

[0072] Step 205: determining an identification factor data volume corresponding to the lithology identification factor based on the pre-stack inversion data volume of the seismic data.

[0073] Prestack seismic inversion is mainly based on the prestack simultaneous inversion method of the prestack common depth point (CDP) gathers. Figure 4 In the example, the electronic equipment can obtain the longitudinal wave impedance, transverse wave impedance and density parameters through pre-stack simultaneous inversion. These three parameters can be used to obtain the corresponding lithology factor parameter data body through mathematical calculation, that is, the identification factor data body, to achieve the prediction of sedimentary tuff, such as Figure 5 Compared with the post-stack P-wave impedance inversion prediction, the multi-solution of seismic prediction is reduced. Further research on volcanic reservoir prediction is carried out on the basis of lithology prediction, which can reduce the interference of sedimentary tuff on the reservoir and improve the accuracy of volcanic reservoir prediction.

[0074] Step 206: Calculate the thickness of the tuff in the target layer section of the target area and the plane distribution range of the tuff based on the identification factor data body corresponding to the tuff.

[0075] The inverted L1 identification factor data volume is used to calculate the thickness of the target layer tuff and predict the plane distribution range of the tuff, such as Figure 6 The electronic equipment uses the lithology factor results of rock physics analysis and prestack inversion to ultimately determine the distribution range of sedimentary tuff and realize the lithology prediction of sedimentary tuff in volcanic rocks.

[0076] In the embodiments of the present application, based on the logging rock physics analysis, a variety of seismic elastic parameters are integrated to construct a lithology sensitivity factor, thereby improving the accuracy of tuff identification; the lithology factor is obtained through pre-stack simultaneous inversion calculation, the distribution of tuff is predicted, and the multi-solution problem of conventional parameter prediction is reduced; the lithology prediction of tuff reduces the interference factor of tuff on the prediction of volcanic reservoirs, and improves the subsequent volcanic reservoir prediction accuracy. This method can be applied to similar complex lithology and reservoir prediction and other aspects.

[0077] Embodiment 3

[0078] In combination with the above embodiments, Figure 7 As shown, the method for identifying tuff provided in the embodiment of the present application comprises the following steps:

[0079] Step 1: According to the volcanic rock deposition characteristics and well logging curve characteristics in the study area, the electrical characteristics of the volcanic rock sedimentary tuff are determined, and the volcanic rock lithology curve is generated using the well logging curve;

[0080] Step 2: Using the dipole shear wave logging data, calculate various elastic parameter curves, including P-wave impedance, S-wave impedance, P-wave velocity ratio, bulk modulus, Young's modulus and Lame constant, etc., cross-analyze the elastic parameter characteristics of tuff, and construct the sensitive lithology identification factor and corresponding threshold value range of tuff through multiple elastic parameters;

[0081] Step 3: Obtain P- and S-wave impedance and density through pre-stack seismic inversion, and calculate the corresponding sensitivity factor data volume;

[0082] Step 4: Using the lithology factor results of rock physics analysis and prestack inversion, the distribution range of sedimentary tuff is finally determined to achieve the lithology prediction of sedimentary tuff in volcanic rocks.

[0083] The method for constructing multiple elastic parameters in step 2 mainly adopts the intersection analysis of two different elastic parameters. The lithology curve in step 1 is used as a color code to analyze the distribution range and interval of elastic parameters corresponding to different lithologies. The two elastic parameters with obvious distinguishing effect are selected, and the coordinate rotation method is used to construct new elastic parameters. The new elastic parameters are combined parameters formed by the mathematical combination of these two elastic parameters, which have better distinguishing effect than single parameters.

[0084] The prestack seismic inversion in step three is mainly realized based on the prestack simultaneous inversion method of the prestack CRP gathers. The P-wave impedance, S-wave impedance and density parameters can be obtained through the prestack simultaneous inversion. The corresponding lithology factor parameter data body can be obtained by mathematical calculation using these three parameters to realize the prediction of sedimentary tuff. Compared with the post-stack P-wave impedance inversion prediction, the multi-solution of seismic prediction is reduced. Further research on volcanic reservoir prediction is carried out on the basis of lithology prediction, which can reduce the interference of sedimentary tuff on the reservoir and improve the accuracy of volcanic reservoir prediction.

[0085] Embodiment 4

[0086] Please refer to Figure 8 , which shows a structural block diagram of a device for identifying tuffite provided by an exemplary embodiment of the present application, the device comprising:

[0087] Processing module 801, for classifying and processing the well logging data of the target area based on the well logging curve characteristics of various lithologies in the volcanic rock;

[0088] A generating module 802 is used to generate elastic parameter curves corresponding to various lithologies based on the elastic parameters of various lithologies in the classified logging data, wherein the elastic parameter curves are used to characterize the corresponding relationship between lithology types and elastic parameter values;

[0089] A construction module 803 is used to select elastic parameters of at least two dimensions based on the elastic parameter curve to construct a lithology identification factor, wherein the lithology identification factor of tuff and the lithology identification factors of other types of lithology have different value ranges;

[0090] The identification module 804 is used to identify the sedimentary tuff from the seismic data of the target area by using the lithology identification factor, and determine the distribution range of the sedimentary tuff.

[0091] Optionally, the generating module 802 is further used to:

[0092] Combining candidate elastic parameters in pairs to determine elastic parameter pairs;

[0093] Generate a two-dimensional parameter curve graph corresponding to each elastic parameter pair, wherein the data points of different lithologies in the two-dimensional parameter curve graph have different identification styles;

[0094] The building block 803 is further used for:

[0095] The lithology identification factor is constructed based on the elastic parameter pair with the lowest curve overlap, where the curve overlap is the distribution overlap between the data points of the tuff and the data points of other types of lithology.

[0096] Optionally, the construction module 803 is further used to:

[0097] Determine the lithology boundary line of the elastic parameter pair with the lowest curve overlap, wherein the lithology boundary line is a straight line used to separate the area where the tuff data points are located from the area where other types of lithology data points are located in the two-dimensional parameter curve graph;

[0098] Coordinate rotation is performed based on the lithology dividing line, and the tuff identification factor is constructed based on the rotated coordinate axis, wherein one of the rotated coordinate axes is parallel to the lithology dividing line.

[0099] Optionally, the logging curve characteristics include at least one of resistivity, mud content and porosity, and the elastic parameters include at least two of longitudinal wave impedance, shear wave impedance, longitudinal to shear wave velocity ratio, bulk modulus, Young's modulus and Lame constant.

[0100] Optionally, the processing module 801 is further used to:

[0101] Determine the sample points in the logging data whose resistivity is less than or equal to the resistivity threshold and whose shale content is greater than the shale content threshold as the sample point data of the sedimentary tuff;

[0102] Determine the sample points in the remaining logging data whose shale content is less than or equal to the shale content threshold and whose porosity is less than or equal to the porosity threshold as sample point data of tight tuff;

[0103] The sample point data with porosity greater than the porosity threshold in the remaining logging data are determined as the sample point data of the reservoir.

[0104] Optionally, the identification module 804 is further configured to:

[0105] Determine the identification factor data volume corresponding to the lithology identification factor based on the prestack inversion data volume of the seismic data;

[0106] The thickness of the tuff in the target layer section of the target area and the plane distribution range of the tuff are calculated based on the identification factor data body corresponding to the tuff.

[0107] Embodiment 5

[0108] An embodiment of the present application provides an electronic device; Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Fig. 9 As shown, the electronic device 900 includes: a processor 901, at least one communication bus 902, a user interface 903, at least one external communication interface 904, and a memory 905. The communication bus 902 is configured to realize connection and communication between these components. The user interface 903 may include a display screen, and the external communication interface 904 may include a standard wired interface and a wireless interface. The processor 901 is configured to execute the program of the identification method of tuff stored in the memory to implement the steps in the method provided in the above embodiment.

[0109] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. The computer program is loaded and executed by a processor to implement the method described in the above embodiment.

[0110] The embodiment of the present application further provides a computer program product, which runs on a processor of a computer device, so that the computer device executes the method described in the above embodiment.

[0111] It should be noted here that the description of the above storage medium, electronic device, and remote control embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0112] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0113] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, object or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, object or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, object or device including the element.

[0114] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0115] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0116] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0117] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read Only Memory), disks or optical disks, etc. Various media that can store program codes.

[0118] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0119] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for identifying tuff, characterized in that: include: Based on the logging curve characteristics of various lithologies in volcanic rocks, the logging data of the target area are classified and processed; Based on the elastic parameters of various lithologies in the classified logging data, generating elastic parameter curves corresponding to various lithologies, wherein the elastic parameter curves are used to characterize the corresponding relationship between lithology types and elastic parameter values; Selecting elastic parameters of at least two dimensions based on the elastic parameter curve to construct a lithology identification factor, wherein the lithology identification factor of tuff and the lithology identification factors of other types of lithology have different value ranges; The lithology identification factor is used to identify the sedimentary tuff from the seismic data of the target area, and the distribution range of the sedimentary tuff is determined.

2. The method according to claim 1, characterized in that The elastic parameter curves corresponding to various lithologies are generated based on the elastic parameters of various lithologies in the classified logging data, including: Combining candidate elastic parameters in pairs to determine elastic parameter pairs; Generate a two-dimensional parameter curve graph corresponding to each elastic parameter pair, wherein the data points of different lithologies in the two-dimensional parameter curve graph have different identification styles; The step of selecting elastic parameters of at least two dimensions based on the elastic parameter curve to construct a lithology identification factor includes: The lithology identification factor is constructed based on the elastic parameter pair with the lowest curve overlap, where the curve overlap is the distribution overlap between the data points of the tuff and the data points of other types of lithology.

3. The method according to claim 2, characterized in that The step of constructing the lithology identification factor based on the elastic parameter pair with the lowest curve overlap includes: Determine the lithology boundary line of the elastic parameter pair with the lowest curve overlap, wherein the lithology boundary line is a straight line used to separate the area where the tuff data points are located from the area where other types of lithology data points are located in the two-dimensional parameter curve graph; Coordinate rotation is performed based on the lithology dividing line, and the tuff identification factor is constructed based on the rotated coordinate axis, wherein one of the rotated coordinate axes is parallel to the lithology dividing line.

4. The method according to claim 3, characterized in that The elastic parameter pair constituting the lithology identification factor is longitudinal wave impedance and density. The expression of the lithology identification factor includes: L1=1.59+0.00008*AI-DEN Wherein, L1 is the lithology identification factor, AI is the longitudinal wave impedance, and DEN is the density.

5. The method according to any one of claims 1 to 4, characterized in that: The logging curve characteristics include at least one of resistivity, mud content and porosity, and the elastic parameters include at least two of longitudinal wave impedance, shear wave impedance, longitudinal and shear wave velocity ratio, bulk modulus, Young's modulus and Lame constant.

6. The method according to claim 5, characterized in that The well logging data of the target area are classified and processed based on the well logging curve characteristics of various lithologies in the volcanic rock, including: Determine the sample points in the logging data whose resistivity is less than or equal to the resistivity threshold and whose shale content is greater than the shale content threshold as the sample point data of the sedimentary tuff; Determine the sample points in the remaining logging data whose shale content is less than or equal to the shale content threshold and whose porosity is less than or equal to the porosity threshold as sample point data of tight tuff; The sample point data with porosity greater than the porosity threshold in the remaining logging data are determined as the sample point data of the reservoir.

7. The method according to any one of claims 1 to 4, characterized in that: The step of identifying the sedimentary tuff from the seismic data of the target area using the lithology identification factor and determining the distribution range of the sedimentary tuff includes: Determine the identification factor data volume corresponding to the lithology identification factor based on the prestack inversion data volume of the seismic data; The thickness of the tuff in the target layer section of the target area and the plane distribution range of the tuff are calculated based on the identification factor data body corresponding to the tuff.

8. A device for identifying tuff, characterized in that: include: A processing module is used to classify and process the logging data of the target area based on the logging curve characteristics of various lithologies in volcanic rocks; A generation module, for generating elastic parameter curves corresponding to various lithologies based on the elastic parameters of various lithologies in the classified logging data, wherein the elastic parameter curves are used to characterize the corresponding relationship between lithology types and elastic parameter values; A construction module, used for selecting elastic parameters of at least two dimensions based on the elastic parameter curve to construct a lithology identification factor, wherein the lithology identification factor of tuff and the lithology identification factors of other types of lithology have different value ranges; The identification module is used to identify the sedimentary tuff from the seismic data of the target area by using the lithology identification factor, and determine the distribution range of the sedimentary tuff.

9. An electronic device, characterized in that: The method comprises a memory and a processor; a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored, and the computer program is loaded and executed by a processor to implement the method according to any one of claims 1 to 7.

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