Method, device and equipment for identifying sedimentary tuff and storage medium

Abstract

The application provides a method, device and equipment for identifying tuff, and a storage medium, and belongs to the technical field of geophysical exploration. The method comprises the following steps: performing classification processing on logging data of a target area based on logging curve characteristics of various lithologies in volcanic rocks; generating elastic parameter curves corresponding to various lithologies based on elastic parameters of various lithologies in the classified logging data; constructing a lithology identification factor based on elastic parameters in at least two dimensions selected from the elastic parameter curves; identifying tuff from seismic data of the target area by using the lithology identification factor, and determining a distribution range of the tuff. Different lithology characteristics and distribution rules of volcanic rocks are determined by logging and seismic rock physical characteristic analysis, the influence of tuff on reservoir prediction is reduced, and the sensitivity factor for identifying tuff is constructed based on rock physical analysis, the multi-solution problem of volcanic rock prediction is reduced, and the seismic prediction accuracy of volcanic rock lithology and reservoirs can be improved.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology, and in particular to a method, apparatus, equipment and storage medium for identifying tuff. Background Technology

[0002] Volcanic rock formations are complex and diverse in lithology, and reservoirs are highly heterogeneous. Identification and prediction of volcanic rock lithology and reservoirs have always been challenging problems in the field of geophysical exploration. The specific difficulties are manifested in the multiple solutions and uncertainties of volcanic rock prediction, which greatly affect the exploration and development of volcanic rock oil and gas and well location deployment.

[0003] Submarine eruptions of intermediate-basic volcanic strata contain tuff, sedimentary tuff, andesite, and other special lithologies. Since some sedimentary tuff shares similar characteristics with volcanic reservoirs, such as low density and low P-wave impedance, density and impedance parameters are used to predict the interference of these special tuff lithologies in volcanic reservoirs. However, current research on methods for identifying and predicting these special tuff lithologies is relatively limited. Conventional methods suffer from significant ambiguity, making it difficult to address issues related to volcanic lithology and reservoir distribution, resulting in low prediction accuracy. Summary of the Invention

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

[0005] On the one hand, embodiments of this application provide a method for predicting tight sandstone fractured reservoirs, including:

[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 well logging data, elastic parameter curves corresponding to various lithologies are generated. The elastic parameter curves are used to characterize the correspondence between lithology type and elastic parameter value.

[0008] Based on the elastic parameter curve, at least two dimensions of elastic parameters are selected to construct lithology identification factors, wherein the lithology identification factors of tuff correspond to different value ranges than the lithology identification factors of other types of lithology.

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

[0010] On the other hand, embodiments of this application provide a device for predicting tight sandstone fractured reservoirs, comprising:

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

[0012] The generation module is used to generate elastic parameter curves corresponding to various lithologies based on the elastic parameters of various lithologies in the classified well logging data. The elastic parameter curves are used to characterize the correspondence between lithology type and elastic parameter value.

[0013] The construction module is used to select at least two dimensions of elastic parameters based on the elastic parameter curve to construct lithology identification factors, wherein the lithology identification factors of tuff correspond to different value ranges than the lithology identification factors of other types of lithology.

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

[0015] On the other hand, embodiments of this application provide an electronic device, which includes a memory and a processor; the memory stores a computer program, which, when executed by the processor, implements the methods described above.

[0016] On the other hand, embodiments of this application provide a computer-readable storage medium storing a computer program that is loaded and executed by a processor to implement the methods described above.

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

[0018] The method, apparatus, equipment, and storage medium for identifying tuff provided in this application clarify the different lithological characteristics and distribution patterns of volcanic rocks through well logging and seismic rock physical characteristic analysis. In order to reduce the impact of tuff on reservoir prediction, a sensitive factor for tuff identification is constructed based on rock physical analysis. The distribution of tuff is predicted by combining seismic data, which reduces the ambiguity of volcanic rock prediction and can improve the accuracy of seismic prediction of volcanic rock lithology and reservoirs. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0020] Figure 1 This is a flowchart of a method for identifying tuff provided in an exemplary embodiment of this application;

[0021] Figure 2 This is a flowchart of a method for identifying tuff provided in another exemplary embodiment of this application;

[0022] Figure 3 This is a schematic diagram illustrating the classification of different lithologies of volcanic rocks provided in an exemplary embodiment of this application;

[0023] Figure 4 This is a schematic diagram of a two-dimensional elastic parameter curve provided in an exemplary embodiment of this application;

[0024] Figure 5 This is an inversion profile of lithology identification factors provided in an exemplary embodiment of this application;

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

[0026] Figure 7 This is a flowchart of a method for identifying tuff provided in another exemplary embodiment of this application;

[0027] Figure 8 This is a structural block diagram of a tuff identification device provided in an exemplary embodiment of this application;

[0028] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0030] Example 1

[0031] Please refer to Figure 1 The diagram illustrates a flowchart of a method for identifying tuff provided in an exemplary embodiment of this application. The method includes 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] Well logging data for volcanic rocks typically includes both electrical and petrophysical characteristics (i.e., elastic parameters). Due to the unique electrical characteristics of tuff, namely low resistivity and high clay content, well logging data can be classified based on these electrical characteristics. This allows for the division of data points into those corresponding to tuff and those corresponding to other lithologies. Furthermore, analysis and prediction of tuff can be performed based on the resulting petrophysical characteristics. Well logging curve characteristics include at least one of resistivity and clay content.

[0034] In a schematic representation, the well logging data includes parameters a, b, c, and d corresponding to each sample point, where parameters a and b are electrical characteristic data. Based on these electrical characteristic data, the equipment classifies the sample points into tuff sample points and other lithological sample points, thereby obtaining parameters c and d corresponding to tuff and other lithologies. Furthermore, lithological analysis and distribution prediction are then performed based on parameters c and d.

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

[0036] An elastic parameter curve is a curve that characterizes the correspondence between lithology and elastic parameter values. The coordinates correspond to the elastic parameters, and the identifier type of each data point corresponds to the lithology; for example, different lithology types use data point identifiers of different shapes and / or colors. The equipment generates a two-dimensional elastic parameter curve for each pair of elastic parameter combinations.

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

[0038] Among them, the lithological identification factor of tuffstone corresponds to a different value range than that of other types of lithology.

[0039] Different elastic parameters have varying degrees of differentiation in lithology. Therefore, lithological analysis based on each elastic parameter alone can lead to multiple solutions and inaccurate prediction of tuff distribution.

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

[0041] The principle for selecting the elastic parameters for constructing lithology factors is to ensure that the distribution areas of data points for tuff are different from those of data points for other types of lithology (or that 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 the elastic parameter curves corresponding to all parameter combinations, and technicians manually select the parameter combination corresponding to the curve with the highest lithology discrimination.

[0043] Step 104: Identify tuff from seismic data of the target area using lithological identification factors and determine the distribution range of tuff.

[0044] Because the lithological identification factor of tuff differs from that of other lithologies, the equipment can calculate the lithological factor data volume of tuff using the lithological identification factor and seismic data of the target area, thereby enabling the prediction of the distribution range of tuff.

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

[0046] Example 2

[0047] Please refer to Figure 2 The diagram illustrates a flowchart of a method for identifying tuff provided in another exemplary embodiment of this application. The method includes 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 one possible implementation, the logging curve characteristics include at least one of resistivity, clay content, and porosity. The electronic device divides the sample points based on the logging curve characteristics of different lithologies, generating logging curves corresponding to each lithology. Then, using the logging curves as color codes, other logging data are classified and processed. Step 201 specifically includes the following steps 201a to 201c:

[0050] Step 201a: The sample points in the well logging data whose resistivity is less than or equal to the resistivity threshold and whose clay content is greater than the clay content threshold are identified as tuff sample points.

[0051] Step 201b: The sample points in the remaining logging data with a clay content less than or equal to the clay content threshold and a porosity less than or equal to the porosity threshold are identified as sample points of dense tuff.

[0052] Step 201c: Identify the sample data points in the remaining logging data whose porosity is greater than the porosity threshold as reservoir sample data.

[0053] Tuff is characterized by low resistivity and high clay content. Experiments have shown that its resistivity and clay content ranges differ from those of other volcanic rocks. Therefore, tuff samples can be initially delineated based on resistivity and clay content values. Furthermore, tight tuff exhibits unique clay content and porosity ranges, while reservoirs have unique porosity ranges. Therefore, sample data for tight tuff and reservoirs can be further delineated.

[0054] For illustration purposes, the resistivity threshold is 40 ohm-meters, the clay content threshold is 40%, and the porosity threshold is 4%. The lithological definition results after classification should be consistent with the actual lithological data from well logging, or the accuracy should reach the accuracy threshold. Figure 3 This diagram illustrates the results of classifying volcanic rocks of different lithologies in a certain region.

[0055] Based on the classified well logging data, the elastic parameters corresponding to different lithologies can be obtained. Then, curves can be plotted based on the elastic parameters, and the distribution of tuff can be analyzed and predicted.

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

[0057] In one possible implementation, the elastic parameters include at least two of the following: longitudinal wave impedance, transverse wave impedance, longitudinal-to-transverse wave velocity ratio, bulk modulus, Young's modulus, and Lamé constant.

[0058] In one possible implementation, the electronic device screens at least two elastic parameters to construct new parameters as lithology identification factors. Therefore, the electronic device first determines possible combinations of parameters that constitute lithology identification factors, i.e., elastic parameter pairs, based on the elastic parameters.

[0059] Step 203: Generate two-dimensional parameter curves corresponding to each elastic parameter pair. The label styles for data points of different lithologies in the two-dimensional parameter curves are different.

[0060] Figure 4 A two-dimensional parameter curve corresponding to longitudinal wave impedance and density is shown, where the horizontal axis represents the value of longitudinal wave impedance and the vertical axis represents the value of density. Electronic equipment displays data points corresponding to different lithologies using different styles of labels, allowing technicians to directly observe the distribution of data points for various lithologies. The label styles for data points differ for different lithologies; specifically, at least one of the data point colors and shapes is different.

[0061] Step 204: Based on the elastic parameter pair with the lowest curve overlap, construct the lithology identification factor. 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 indicates that the combination of elastic parameters has the highest lithological differentiation. Therefore, the electronic device selects the elastic parameter pair with the lowest curve overlap to construct the lithological identification factor. Figure 4 The longitudinal wave impedance-density two-dimensional parameter curve shown is the curve with the lowest overlap determined by the electronic equipment. It can be seen that the data points of tuff are clearly distributed in different regions compared to the data points of the other two lithologies. If an elastic parameter pair with a higher overlap is selected, it will be impossible to distinguish tuff from the other lithologies.

[0063] Optionally, the electronic device can determine the elastic parameter pair with the lowest curve overlap by calculating the overlap of the regions where different types of lithological data points are located in each two-dimensional curve graph (e.g., the proportion of the area of ​​the intersection region to the total area, or the proportion of the number of data points in the intersection region to the total number of data points), or directly display all two-dimensional curve graphs and determine 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: Determine the lithological boundary line of the elastic parameter pair with the lowest curve overlap. The lithological boundary line is a straight line used to separate the area where the tuff data points are located from the areas where other types of lithological data points are located in the two-dimensional parameter curve diagram.

[0066] Step 204b involves rotating the coordinates based on the lithological boundary line and constructing a tuff identification factor based on the rotated coordinate axes, wherein one of the rotated coordinate axes is parallel to the lithological boundary line.

[0067] Data points for tuff and other lithologies are typically not strictly categorized by horizontal or vertical orientation. For example... Figure 4 The two-dimensional curve diagram shown indicates that the boundary between the data points of tuff and the other two lithologies corresponds to a sloping line. Therefore, to facilitate subsequent calculations, the electronic device first constructs the tuff identification factor L1 by rotating the coordinate axes. Figure 4 As shown, by rotating the coordinate axes in the two-dimensional curve graph on the left to make the y-axis parallel to the boundary line, the tuff identification factor L1 is constructed based on the new coordinate system.

[0068] Electronic equipment determines the lithological boundary of the elastic parameter pair with the lowest curve overlap. This boundary can be generated automatically by the electronic equipment or based on a boundary drawing operation.

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

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

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

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

[0073] Pre-stack seismic inversion is mainly implemented based on the pre-stack simultaneous inversion method for the common depth point (CDP) gather. For Figure 4 the example in, the electronic device can obtain the P-wave impedance, S-wave impedance, and density parameters through pre-stack simultaneous inversion. Using these three parameters, the corresponding lithology factor parameter data volume, that is, the identification factor data volume, can be obtained through mathematical calculations to achieve the prediction of tuffaceous siltstone, as Figure 5 shown. Compared with post-stack P-wave impedance inversion prediction, it reduces the multi-solution problem of seismic prediction. Further carrying out the research on volcanic reservoir prediction based on lithology prediction can reduce the interference of tuffaceous siltstone on the reservoir and improve the accuracy of volcanic reservoir prediction.

[0074] Step 206: Calculate the thickness of tuffaceous siltstone and the planar distribution range of tuffaceous siltstone in the target interval of the target area based on the identification factor data volume corresponding to tuffaceous siltstone.

[0075] Calculate the thickness of tuffaceous siltstone in the target interval using the inverted L1 identification factor data volume, and predict the planar distribution range of tuffaceous siltstone, as Figure 6 shown. The electronic device uses the results of petrophysical analysis and pre-stack inversion lithology factors to finally determine the distribution range of tuffaceous siltstone and achieve the prediction of the lithology of tuffaceous siltstone in volcanic rocks.

[0076] In the embodiments of the present application, based on well logging petrophysical analysis, a lithology sensitive factor is constructed by integrating multiple seismic elastic parameters, which improves the accuracy of tuffaceous siltstone identification; the lithology factor is obtained through pre-stack simultaneous inversion calculation to predict the distribution of tuffaceous siltstone and reduce the multi-solution problem of conventional parameter prediction; the lithology prediction of tuffaceous siltstone reduces the interference factors of tuffaceous siltstone on volcanic reservoir prediction and improves the accuracy of subsequent volcanic reservoir prediction. This method can be applied to aspects such as similar complex lithology and reservoir prediction.

[0077] Embodiment III

[0078] Combining the above respective embodiments, as Figure 7 shown, the method for identifying tuffaceous siltstone provided by the embodiments of the present application includes the following steps:

[0079] Step 1: Based on the characteristics of volcanic rock sedimentation and well logging curves in the study area, determine the electrical properties of volcanic tuff and generate volcanic rock lithology curves using the well logging curves;

[0080] Step 2: Using well logging dipole shear wave data, calculate various elastic parameter curves, including P-wave impedance, S-wave impedance, P-wave / S-wave velocity ratio, bulk modulus, Young's modulus, and Lamé constant, and analyze the elastic parameter characteristics of tuff. Construct sensitive lithological identification factors and corresponding threshold value ranges for tuff through multiple elastic parameters.

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

[0082] Step four: Using the results of rock physics analysis and pre-stack inversion of lithological factors, the distribution range of tuff is finally determined, realizing the lithological prediction of tuff in volcanic rocks.

[0083] In step two, the multiple elastic parameter construction methods mainly adopt the intersection analysis of two different elastic parameters. Using the lithology curve in step one as a color mark, the distribution range and interval of elastic parameters corresponding to different lithologies are analyzed. The two elastic parameters with obvious distinguishing effects are selected and new elastic parameters are constructed by using coordinate rotation. The new elastic parameters are combined parameters formed by the mathematical combination of the two elastic parameters, which have a better distinguishing effect than single parameters.

[0084] In step three, pre-stack seismic inversion is mainly based on the pre-stack simultaneous inversion method of pre-stack CRP gathers. Through pre-stack simultaneous inversion, P-wave impedance, S-wave impedance, and density parameters can be obtained. Using these three parameters, the corresponding lithological factor parameter data volume can be obtained through mathematical calculation, thereby enabling the prediction of tuff. Compared with post-stack P-wave impedance inversion prediction, it reduces the ambiguity of seismic prediction. Based on lithological prediction, further research on volcanic reservoir prediction can be carried out, which can reduce the interference of tuff on the reservoir and improve the accuracy of volcanic reservoir prediction.

[0085] Example 4

[0086] Please refer to Figure 8 The diagram illustrates a structural block diagram of a tuff identification device provided in an exemplary embodiment of this application, the device comprising:

[0087] Processing module 801 is used to classify and process logging data of the target area based on the logging curve characteristics of various lithologies in volcanic rocks;

[0088] The generation module 802 is used to generate elastic parameter curves corresponding to various lithologies based on the elastic parameters of various lithologies in the classified well logging data. The elastic parameter curves are used to characterize the correspondence between lithology type and elastic parameter value.

[0089] The construction module 803 is used to select elastic parameters of at least two dimensions based on the elastic parameter curve to construct lithology identification factors, wherein the lithology identification factors of tuff correspond to different value ranges than the lithology identification factors of other types of lithology.

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

[0091] Optionally, the generation module 802 is further configured to:

[0092] The candidate elastic parameters are combined in pairs to determine the elastic parameter pairs;

[0093] Generate two-dimensional parameter curves corresponding to each elastic parameter pair, wherein the labeling style of the data points for different lithologies in the two-dimensional parameter curves is different;

[0094] The building module 803 is also 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 building module 803 is further configured to:

[0097] The lithological boundary line of the elastic parameter pair with the lowest curve overlap is determined. The lithological boundary line is a straight line used to separate the area where the tuff data points are located in the two-dimensional parameter curve graph from the area where other types of lithological data points are located.

[0098] The coordinates are rotated based on the lithological boundary line, and the tuff identification factor is constructed based on the rotated coordinate axes, wherein one of the rotated coordinate axes is parallel to the lithological boundary line.

[0099] Optionally, the logging curve characteristics include at least one of resistivity, clay content, and porosity, and the elastic parameters include at least two of P-wave impedance, S-wave impedance, P-wave to S-wave velocity ratio, bulk modulus, Young's modulus, and Lamé constant.

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

[0101] The sample points in the well logging data whose resistivity is less than or equal to the resistivity threshold and whose clay content is greater than the clay content threshold are identified as the sample point data of the tuff.

[0102] The sample points in the remaining logging data with a clay content less than or equal to the clay content threshold and a porosity less than or equal to the porosity threshold are identified as sample points of dense tuff.

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

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

[0105] Based on the pre-stack inversion data volume of the earthquake data, the identification factor data volume corresponding to the lithology identification factor is determined;

[0106] Based on the identification factor data volume corresponding to the tuff, the thickness of the tuff in the target layer of the target area and the planar distribution range of the tuff are calculated.

[0107] Example 5

[0108] This application provides an electronic device; Figure 9 This is a schematic diagram of the composition structure of the electronic device provided in the embodiments of this application, such as... Figure 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 enable communication between these components. The user interface 903 may include a display screen, and the external communication interface 904 may include standard wired and wireless interfaces. The processor 901 is configured to execute a program stored in the memory for identifying tuff, to implement the steps of the method provided in the above embodiments.

[0109] This application also provides a computer-readable storage medium storing a computer program, which is loaded and executed by a processor to implement the methods described in the above embodiments.

[0110] This application also provides a computer program product that runs on a processor of a computer device, causing the computer device to perform the methods described in the above embodiments.

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

[0112] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; 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 this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0113] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, object, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, object, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, object, or apparatus that includes that element.

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

[0115] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0116] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0117] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0118] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a controller to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0119] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of identifying tuffisites, characterized in that, The method comprises: classifying logging data of a target area based on logging curve characteristics of various types of volcanic rocks; generating elastic parameter curves corresponding to various types of rocks based on elastic parameters of various types of rocks in the classified logging data, the elastic parameter curves being used to represent the corresponding relationship between rock types and elastic parameter values; constructing a rock identification factor based on elastic parameters of at least two dimensions selected from the elastic parameter curves, wherein the rock identification factor of the tuffite is different from rock identification factors of other types of rocks in a corresponding value range; identifying the tuffite from seismic data of the target area by using the rock identification factor, and determining the distribution range of the tuffite; the method of generating elastic parameter curves corresponding to various types of rocks based on elastic parameters of various types of rocks in the classified logging data comprises: determining elastic parameter pairs by combining candidate elastic parameters two by two; generating two-dimensional parameter curve graphs corresponding to each elastic parameter pair, wherein different rock data points in the two-dimensional parameter curve graphs correspond to different identification styles; the method of constructing a rock identification factor based on elastic parameters of at least two dimensions selected from the elastic parameter curves comprises: constructing the rock identification factor based on the elastic parameter pair with the lowest curve overlap degree, wherein the curve overlap degree is the distribution overlap degree of the tuffite data points and other types of rock data points; the method of constructing the rock identification factor based on the elastic parameter pair with the lowest curve overlap degree comprises: determining a rock boundary line of the elastic parameter pair with the lowest curve overlap degree, wherein the rock boundary line is a straight line used to separate the area where the tuffite data points are located from the area where other types of rock data points are located in the two-dimensional parameter curve graph; performing coordinate rotation based on the rock boundary line, and constructing the tuffite identification factor based on the rotated coordinate axes, wherein one of the coordinate axes after rotation is parallel to the rock boundary line.

2. The method of claim 1, wherein, The elastic parameter pair constituting the rock identification factor is the longitudinal wave impedance and the density, and the expression of the rock identification factor comprises: L1=1.59+0.00008*AI-DEN wherein L1 is the rock identification factor, AI is the longitudinal wave impedance, and DEN is the density.

3. The method according to any one of claims 1 to 2, characterized in that, The logging curve characteristics comprise at least one of resistivity, shale content, and porosity, and the elastic parameters comprise at least two of the longitudinal wave impedance, the transverse wave impedance, the ratio of longitudinal wave velocity to transverse wave velocity, the bulk modulus, the Young's modulus, and the Lame constant.

4. The method of claim 3, wherein, The method of classifying logging data of a target area based on logging curve characteristics of various types of volcanic rocks comprises: determining sample data of the tuffite as sample points in the logging data with resistivity less than or equal to a resistivity threshold value and shale content greater than a shale content threshold value; determining sample data of the dense tuffite as sample points in the remaining logging data with shale content less than or equal to the shale content threshold value and porosity less than or equal to a porosity threshold value; determining sample data of the reservoir as sample points in the remaining logging data with porosity greater than the porosity threshold value.

5. The method according to any one of claims 1 to 2, characterized in that, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

6. An apparatus for identifying a tuff according to the method of any one of claims 1 to 5, characterized in that The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

7. An electronic device, comprising: The method comprises the following steps:

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