Paleokarst landform classification method and device

Abstract

The present invention provides a method and apparatus for classifying palaeokarst landforms. The method for classifying palaeokarst landforms includes: generating a slope data volume based on the elevation data volume of the palaeokarst landforms; generating a profile curvature data volume based on the elevation data volume; generating a valley data volume based on the elevation data volume; and classifying the karst palaeogeomorphology of the target work area according to the slope data volume, the profile curvature data volume and the valley data volume. According to the present invention, a more reasonable and refined classification result of the palaeokarst landforms can be obtained. Based on this classification result, the reservoir development characteristics and the differences in gas well production of different micro-geomorphic units can be analyzed, so as to provide guidance for reservoir prediction and well location deployment.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field exploration and development, especially the technical field of fine geomorphic analysis in the exploration and development stage of oil and gas fields, and specifically relates to a method and device for classifying paleokarst landforms. Background Art

[0002] During the geological history period, karst landforms control the movement and distribution of groundwater, thus affecting the transformation of sediments and the final reservoir development characteristics. For example, in karst landforms with a larger slope, groundwater mainly flows vertically, and it is easy to form vertical and oblique dissolution holes and fractures; while in gentle slope areas, the groundwater flow slows down and mainly flows laterally, and it is easy to form horizontal dissolution holes and fractures. This difference affects the prediction of reservoirs and favorable traps during the exploration period, and further affects the classification of reservoir types and the design of development well patterns during the development period. Therefore, the restoration of paleokarst landforms has always been an important part of the research on the main controlling factors of reservoirs and the prediction of reservoir distribution. However, currently in the oil and gas field, for the classification of paleokarst landforms, those skilled in the art only macroscopically divide karst highlands, karst slopes and karst basins, which can provide regional geological understanding in the early stage of exploration or evaluation, but cannot meet the needs of studying the reservoir development characteristics of different geomorphic units in the late stage of evaluation or the development stage, that is, it is necessary to refine the characteristics of early karst landform zoning and carry out research on karst micro-geomorphic units.

[0003] In addition, some domestic scholars have quantitatively characterized karst geomorphic units by defining indicators such as the slope and height of geomorphic units, and carried out research on the division of karst micro-geomorphic units, achieving good results. For example, Cao Jianwen et al. divided the Ordovician weathered crust landforms in the Tarim Basin into 10 types of tertiary landforms such as peak cluster depressions, hill peak depressions, and karst troughs; Jin Mindong divided the Gaoshiti-Moxi area in the Sichuan Basin into 3 geomorphic units such as karst platforms, slopes and superimposed slopes, etc., but usually only single parameters are selected for classification, such as relative elevation, the parameters are relatively single, and there are problems such as unsystematic classification and difficult popularization and application.

[0004] To sum up, the current research on paleokarst landforms is mainly qualitative seismic horizon flattening or quantitative description and division based on thickness and elevation, lacking a quantitative classification and division method for geomorphic units that comprehensively considers multiple parameters. Summary of the Invention

[0005] Aiming at the problems in the prior art, the method and device for classifying paleokarst landforms provided by the present invention can more reasonably classify the paleokarst landforms of the target block, and based on the classification results of the paleokarst landforms, the reservoir development characteristics and the differences in gas well production of different micro-geomorphic units can be analyzed, so as to provide guidance for reservoir prediction and well location deployment.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for classifying palaeokarst landforms, including:

[0008] Generating a slope data volume based on the elevation data volume of the palaeokarst landform;

[0009] Generating a profile curvature data volume based on the elevation data volume;

[0010] Generating a gully data volume based on the elevation data volume;

[0011] Classifying the karst palaeogeomorphology of the target work area according to the slope data volume, the profile curvature data volume and the gully data volume.

[0012] In one embodiment, the method for classifying palaeokarst landforms further includes:

[0013] Restoring the palaeokarst landform of the target layer by using the impression method to generate the elevation data volume.

[0014] In one embodiment, restoring the palaeokarst landform of the target layer by using the impression method to generate the elevation data volume includes:

[0015] Based on the stratigraphic correlation data of the target work area, determining the overlying marker bed of the target layer according to the regional stability, the characteristics of individual wells, the degree of filling and complementing, the distance from the target layer, and the seismic traceability;

[0016] Restoring the palaeokarst landform of the target layer according to the overlying marker bed to generate the elevation data volume.

[0017] In one embodiment, the generating the gully data volume based on the elevation data volume includes:

[0018] Extracting the gully data volume from the elevation data volume by using a gully extraction algorithm.

[0019] In one embodiment, the classifying the karst palaeogeomorphology of the target work area according to the slope data volume, the profile curvature data volume and the gully data volume includes:

[0020] Classifying the karst palaeogeomorphology according to a preset profile curvature threshold, a preset slope threshold and a preset elevation threshold.

[0021] In a second aspect, the present invention provides a device for classifying palaeokarst landforms, the device including:

[0022] A slope data volume generating unit, configured to generate a slope data volume based on the elevation data volume of the palaeokarst landform;

[0023] A curvature data volume generating unit, configured to generate a profile curvature data volume based on the elevation data volume;

[0024] A gully data volume generation unit, configured to generate a gully data volume according to the elevation data volume;

[0025] An ancient landform classification unit, configured to classify the karst ancient landform of the target work area according to the slope data volume, the profile curvature data volume, and the gully data volume.

[0026] In one embodiment, the karst ancient landform classification device further includes:

[0027] An elevation data volume generation unit, configured to use the impression method to restore the karst ancient landform of the target layer to generate the elevation data volume.

[0028] In one embodiment, the elevation data volume generation unit includes:

[0029] A marker bed determination module, configured to determine the overlying marker bed of the target layer based on the stratigraphic correlation data of the target work area according to regional stability, single well characteristics, filling and leveling degree, distance from the target layer, and seismic traceability;

[0030] An ancient landform restoration module, configured to restore the karst ancient landform of the target layer according to the overlying marker bed to generate the elevation data volume.

[0031] In one embodiment, the gully data volume generation unit is specifically configured to extract the gully data volume from the elevation data volume by using a gully extraction algorithm.

[0032] In one embodiment, the ancient landform classification unit is specifically configured to classify the karst ancient landform according to a preset profile curvature threshold, a preset slope threshold, and a preset elevation threshold.

[0033] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the karst ancient landform classification method are implemented.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the karst ancient landform classification method are implemented.

[0035] As can be seen from the above description, for the paleokarst landform classification method and device provided by the embodiments of the present invention, first, a slope data volume is generated based on the elevation data volume of the paleokarst landform; a profile curvature data volume is generated based on the elevation data volume; then, a gully data volume is generated based on the elevation data volume; finally, the karst paleogeomorphology of the target work area is classified according to the slope data volume, the profile curvature data volume, and the gully data volume. According to the present invention, a more reasonable and refined division result of the paleokarst landform can be obtained. Based on this division result, the reservoir development characteristics and gas well production differences of different micro-geomorphic units can be analyzed, so as to provide guidance for reservoir prediction and well location deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0037] Figure 1 Flow schematic of the paleokarst landform classification method in the embodiments of the present invention Figure One ;

[0038] Figure 2 Flow schematic of the paleokarst landform classification method in the embodiments of the present invention Figure Two ;

[0039] Figure 3 Flow schematic of step 500 in the embodiments of the present invention;

[0040] Figure 4 Flow schematic of step 300 in the embodiments of the present invention;

[0041] Figure 5 Flow schematic of step 400 in the embodiments of the present invention;

[0042] Figure 6 Flow schematic of the paleokarst landform classification method in the specific application example of the present invention;

[0043] Figure 7 Cross-sectional comparison diagram of overlying strata in the specific application example of the present invention;

[0044] Figure 8 Schematic diagram of the profile curvature data volume in the specific application example of the present invention;

[0045] Figure 9 Schematic diagram of the gully data volume in the specific application example of the present invention;

[0046] Figure 10Quantitative classification criteria for micro-geomorphology in specific application examples of the present invention;

[0047] Figure 11 Structural schematic of the paleokarst landform classification device in the embodiment of the present invention Figure One ;

[0048] Figure 12 Structural schematic of the paleokarst landform classification device in the embodiment of the present invention Figure Two ;

[0049] Figure 13 Structural schematic diagram of the elevation data volume generation unit in the embodiment of the present invention;

[0050] Figure 14 Structural schematic diagram of the electronic device in the embodiment of the present invention. Detailed implementation manners

[0051] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0052] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned accompanying drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0054] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the accompanying drawings and describe the application in detail with reference to the embodiments.

[0055] An embodiment of the present invention provides a specific implementation method for classifying paleokarst landforms. Refer to Figure 1 , and the method specifically includes the following content:

[0056] Step 100: Generate a slope data volume based on the elevation data volume of the paleokarst landform.

[0057] It can be understood that the paleokarst landform in step 100 refers to the karst formed in the geological history period, which is now buried deep underground. Generally, it refers to carbonate rock karst landforms, including limestone landforms and dolomite landforms, that is, various oil and gas fields where carbonate rock karst reservoirs are developed.

[0058] Step 200: Generate a profile curvature data volume based on the elevation data volume.

[0059] Specifically, calculate the profile curvature values of each grid of the elevation data volume to obtain the profile curvature data volume.

[0060] Step 300: Generate a valley data volume based on the elevation data volume.

[0061] Preferably, determine the elevation limit and the planar length-width ratio limit. Within the range less than the elevation limit, combine the results of the elevation data volume and the valley data volume, circle the area with a length-width ratio greater than the limit, and divide it into valleys.

[0062] Step 400: Classify the karst paleogeomorphology of the target work area according to the slope data volume, the profile curvature data volume, and the valley data volume.

[0063] Specifically, comprehensively consider parameters such as elevation, profile curvature, slope, and length-width ratio, and divide the karst paleogeomorphology of the study area into 8 types of paleokarst micro-geomorphic units, namely steep slopes, slope shoulders, slope feet, residual hills, slope tops, slope bottoms, depressions, and valleys.

[0064] As can be seen from the above description, the method for classifying paleokarst landforms provided by the embodiment of the present invention first generates a slope data volume based on the elevation data volume of the paleokarst landform; generates a profile curvature data volume based on the elevation data volume; then, generates a valley data volume based on the elevation data volume; and finally classifies the karst paleogeomorphology of the target work area according to the slope data volume, the profile curvature data volume, and the valley data volume. According to the present invention, a more reasonable and refined division result of the paleokarst landform can be obtained. Based on this division result, the reservoir development characteristics and gas well production differences of different micro-geomorphic units can be analyzed, so as to provide guidance for reservoir prediction and well location deployment.

[0065] In one embodiment, refer to Figure 2 , the method for classifying paleokarst landforms further includes:

[0066] Step 500: Using the impression method, restore the paleokarst landform of the target layer to generate the elevation data volume.

[0067] It can be understood that the technical principle of the impression method is to assume that the original thickness of each stratigraphic unit remains unchanged, and regard the stratigraphic interface when the erosion of the landform to be restored ends and the deposition of the overlying strata begins as an isochronous surface. Utilize the "mirror image" relationship between the overlying strata and the paleogeomorphology to restore the morphology of the paleogeomorphology through the thickness of the overlying strata. The elevation data volume here is to digitally simulate the ground topography through limited topographic elevation data (i.e., the digital expression of the surface morphology of the terrain). It is a solid ground model representing the ground elevation in the form of an ordered numerical array, and is a branch of the Digital Terrain Model (DTM for short). Various other topographic characteristic values can be derived from this.

[0068] In one embodiment, referring to Figure 3 , Step 500 further includes:

[0069] Step 501: Based on the stratigraphic correlation data of the target work area, determine the overlying marker bed of the target layer according to regional stability, single well characteristics, filling and complementing degree, distance from the target layer, and seismic traceability;

[0070] The marker bed refers to a layer or a group of rock layers with obvious characteristics that can be used as stratigraphic correlation markers. The marker bed should have obvious fossil and lithological characteristics, stable horizons, wide distribution ranges, and be easy to identify.

[0071] Step 502: Restore the paleokarst landform of the target layer according to the overlying marker bed to generate the elevation data volume.

[0072] In one embodiment, referring to Figure 4 , Step 300 further includes:

[0073] Step 301: Use the gully extraction algorithm to extract the gully data volume from the elevation data volume.

[0074] Preferably, the gully data volume can be extracted based on the raster digital elevation model, which is generally divided into three types. The first method is to classify each individual pixel (or the connected pixels) based on local surface characteristics, which is called the local method; the second method is to calculate more global surface information and establish a consistent surface feature structure to obtain the characteristic landform, which is called the global method; the third method is to combine the above two methods.

[0075] The local method traverses the DEM using a small window to find concave or "V"-shaped areas, and the cells at the bottom of these areas are considered the locations of valleys. The local method usually has classification errors and cannot obtain a complete valley network in most cases. Corresponding processing is required to connect them, and trimming and refinement may also be needed to produce a reasonable valley morphology.

[0076] The global method aggregates many grid cells into large geomorphic structural features based on global information such as valley lines, divide lines, and catchment areas. This method is based on the overland flow model of surface runoff and simulates the flow of surface runoff on the ground to generate a water system. This method mainly determines the flow direction according to the maximum slope between the DEM grid cell and its eight adjacent cells. Then, the upstream catchment area of each cell is calculated. Next, a catchment area threshold is determined, and the cells not lower than this threshold are marked as components of the water system. This method is simple and directly generates continuous flow segments.

[0077] In one embodiment, referring to Figure 5 , step 400 further includes:

[0078] Step 401: Classify the paleokarst geomorphology according to a preset profile curvature threshold, a preset slope threshold, and a preset elevation threshold.

[0079] Preferably, for the slope data volume, according to the actual research area situation, determine the maximum and minimum limits of the slope, and divide the area greater than the maximum limit into steep slopes; combining the slope data volume and the profile curvature data volume, within the area between the maximum and minimum limits of the slope, with a profile curvature of 0 as the boundary, divide the area greater than 0 into slope shoulders and the area less than 0 into slope feet; combining the elevation data volume and the slope data volume, within the area less than the minimum limit of the slope, determine 3 elevation boundaries according to the elevation size, divide the area greater than the maximum boundary into residual hills, divide the area less than the minimum boundary into depressions, and divide the slope top and slope bottom in order from high to low in between; based on the above classification technology, determine the elevation boundary and the planar length-width ratio boundary, within the area less than this elevation boundary, combine the results of the elevation data volume and the valley data volume, and delineate the area greater than this length-width ratio boundary and divide it into valleys.

[0080] As can be seen from the above description, the embodiment of the present invention provides a method for classifying paleokarst landforms. Aiming at the problem in the background technology that there is a lack of a comprehensive multi-parameter and quantitative division method for paleokarst micro-landforms, referring to Ruhe's classic slope position classification system in geomorphology, 4 parameters of slope, profile curvature, relative elevation, and length-width ratio are selected, and the quantitative division criteria for each parameter are determined. Combining with the automatic valley extraction algorithm, 8 types of paleokarst micro-landform units are divided, thus realizing the comprehensive multi-parameter and quantitative division of micro-landform units, making the division of paleokarst micro-landform units more systematic and reasonable, and having a large applicable range.

[0081] To further illustrate the present solution, the present invention takes the paleokarst landform of a certain block in the Sichuan Basin as an example to provide a specific application example of the paleokarst landform classification method. The specific application example specifically includes the following contents. See Figure 6 .

[0082] S1: Divide and compare the overlying strata of all wells in the whole area.

[0083] Specifically, conduct research on the overlying strata of the target layer for the wells with relatively rich single-well data in the study area. Select the wells with relatively complete logging curve series and obvious curve characteristics as typical wells. Combine well data with seismic data to clarify the development characteristics of the overlying strata interface of the target layer, establish the stratigraphic division standard of the typical wells, and then take the typical wells as the center to establish cross-sections in the north-south and east-west directions. Conduct stratigraphic division and comparison for the wells passing through the cross-sections. After that, establish well-connected profiles perpendicular to the cross-sections at each well point on the cross-sections, and gradually complete the stratigraphic division and comparison of all wells in the whole area, such as Figure 7 .

[0084] S2: Determine the marker surface.

[0085] First, establish the selection criteria for the overlying marker surface, including regional stability, single-well characteristics, filling and complementing degree, distance from the target layer, and seismic traceability. Comprehensively consider the above five aspects of indicators to optimize the overlying marker surface for the restoration of the paleokarst landform of the target layer. Among them, the overlying marker surface must complete the filling and complementing of the target layer, have good traceability on the seismic profile, and good regional stability. It should be noted that the closer the overlying marker surface is to the target layer, the better.

[0086] S3: Trace and obtain the time-domain seismic layer data of the overlying marker surface and the target layer surface.

[0087] For the overlying marker surface optimized in step S2, trace and obtain the time-domain seismic layer data of the overlying marker surface and the target layer surface in the 3D seismic data volume.

[0088] S4: Determine the overlying marker surface and the target layer surface in the depth domain.

[0089] Perform time-depth conversion on the time-domain stratigraphic layer data to obtain the overlying marker surface and the target layer surface in the depth domain.

[0090] S5: Generate the elevation data volume of the paleokarst landform of the target layer surface.

[0091] Specifically, use the impression method to carry out the restoration of the paleokarst landform of the target layer surface, so as to obtain the elevation data volume of the paleokarst landform of the target layer surface. The elevation data volume consists of several grids on the plane, and each grid has a separate geomorphic elevation value.

[0092] S6: Generate a slope data volume.

[0093] For the elevation data volume, calculate the slope values of each grid in this data volume to obtain the slope data volume.

[0094] S7: Generate a profile curvature data volume.

[0095] For the elevation data volume, calculate the profile curvature values of each grid in this data volume to obtain the profile curvature data volume. Each grid in the profile curvature data volume consists of 0 and 1, such as Figure 8 .

[0096] S8: Classify the palaeokarst landforms.

[0097] Preferably, for the slope data volume, according to the actual situation of the study area, determine the maximum slope limit and the minimum slope limit. The maximum limit is set to 6.4°, and the minimum limit is set to 1°. Divide the grid area with a slope greater than 6.4° into steep slopes. Combining the slope data volume and the profile curvature data volume, within the grid area where the slope is greater than 1° and less than 6.4°, taking the profile curvature of 0 as the boundary, divide the area greater than 0 into slope shoulders, and divide the area less than 0 into slope feet.

[0098] Combining the elevation data volume and the slope data volume, within the grid area where the slope is less than 1°, according to the elevation size and combining the actual situation of the study area, determine 3 elevation boundaries, which are 400m, 380m, and 350m respectively. Divide the grid area with an elevation greater than 400m into residual hills, divide the grid area with an elevation less than 350m into depressions, divide the area with an elevation between 380m and 400m into slope tops, and divide the area with an elevation between 350m and 380m into slope bottoms.

[0099] Load the elevation data volume into the ArcGIS software, and use the functions built in ArcGIS to extract valleys from the elevation data volume to obtain the valley data volume, such as Figure 9 .

[0100] Then, determine the elevation boundary and the planar length-width ratio boundary. Define the elevation top boundary of the valley as 380m and the planar length-width ratio bottom boundary as 2. Within the grid area where the elevation is less than 380m, combined with the extraction result of the ArcGIS valley data volume, delineate the area with a length-width ratio greater than 2 and divide it into valleys.

[0101] Therefore, by comprehensively considering parameters such as elevation, profile curvature, slope, and length-width ratio, the study area can be quantitatively divided into 8 types of palaeokarst micro-geomorphic units, namely steep slopes, slope shoulders, slope feet, residual hills, slope tops, slope bottoms, depressions, and valleys. See Figure 10。Through this quantitative division method considering multiple parameters, relatively fine micro-geomorphic units can be obtained. The residual hills, depressions, slope tops, and bottom planes of slopes are in the shape of low aspect ratio circles or irregular shapes, while the slope shoulders, steep slopes, slope feet, and valleys are in strip shapes. Residual hills and slope tops are mainly developed on the east side of the study area, and steep slopes are developed on the west side. There are more than 10 valleys of varying scales in the study area, with depths ranging from 10 to 20 m and lengths from 5 to 15 km. Large depressions are developed in the southeast, with an area of about 32 km 2 , and the depth is about 25 m. Based on the classification results of these micro-geomorphic units, the reservoir development characteristics and gas well production differences of different micro-geomorphic units can be analyzed, thus providing guidance for reservoir prediction and well location deployment.

[0102] Based on the same inventive concept, the embodiment of the present application also provides a paleokarst landform classification device, which can be used to implement the method described in the above embodiment, as described in the following embodiment. Since the principle of the paleokarst landform classification device for solving problems is similar to that of the paleokarst landform classification method, the implementation of the paleokarst landform classification device can refer to the implementation of the paleokarst landform classification method, and the repeated parts will not be elaborated. Hereinafter, the term "unit" or "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0103] The embodiment of the present invention provides a specific implementation manner of a paleokarst landform classification device capable of implementing the paleokarst landform classification method, see Figure 11 , and the paleokarst landform classification device specifically includes the following:

[0104] The slope data volume generation unit 10 is used to generate a slope data volume according to the elevation data volume of the paleokarst landform;

[0105] The curvature data volume generation unit 20 is used to generate a profile curvature data volume according to the elevation data volume;

[0106] The valley data volume generation unit 30 is used to generate a valley data volume according to the elevation data volume;

[0107] The paleogeomorphology classification unit 40 is used to classify the karst paleogeomorphology of the target work area according to the slope data volume, the profile curvature data volume, and the valley data volume.

[0108] In one embodiment, see Figure 12 , the paleokarst landform classification device further includes:

[0109] The elevation data volume generation unit 50 is used to restore the karst paleogeomorphology of the target layer by using the impression method to generate the elevation data volume.

[0110] In one embodiment, seeFigure 13 , the elevation data volume generation unit 50 includes:

[0111] A marker bed determination module 501, configured to determine the overlying marker bed of the target layer based on the formation correlation data of the target work area, according to regional stability, single well characteristics, filling and complementing degree, distance from the target layer, and seismic traceability;

[0112] An ancient landform restoration module 502, configured to restore the ancient karst landform of the target layer according to the overlying marker bed to generate the elevation data volume.

[0113] In one embodiment, the gully data volume generation unit is specifically configured to extract the gully data volume from the elevation data volume by using a gully extraction algorithm.

[0114] In one embodiment, the ancient landform classification unit is specifically configured to classify the karst ancient landform according to a preset profile curvature threshold, a preset slope threshold, and a preset elevation threshold.

[0115] As can be seen from the above description, the ancient karst landform classification device provided by the embodiments of the present invention first generates a slope data volume according to the elevation data volume of the ancient karst landform; generates a profile curvature data volume according to the elevation data volume; then, generates a gully data volume according to the elevation data volume; and finally classifies the karst ancient landform of the target work area according to the slope data volume, the profile curvature data volume, and the gully data volume. According to the present invention, a more reasonable and refined division result of the ancient karst landform can be obtained. Based on this division result, the reservoir development characteristics and gas well production differences of different micro-landform units can be analyzed, so as to provide guidance for reservoir prediction and well location deployment.

[0116] The devices, modules or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is an electronic device. Specifically, the electronic device can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0117] In a typical example, the electronic device specifically includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the above dynamic data embedding method based on the front-end framework are implemented. The steps include:

[0118] Step 100: Generate a slope data volume according to the elevation data volume of the ancient karst landform;

[0119] Step 200: Generate a profile curvature data volume according to the elevation data volume;

[0120] Step 300: Generate a gully data volume according to the elevation data volume;

[0121] Step 400: Classify the karst paleogeomorphology of the target work area according to the slope data volume, the profile curvature data volume, and the gully data volume.

[0122] The following refers to Figure 14 , which shows a schematic structural diagram of an electronic device 600 suitable for implementing the embodiments of the present application.

[0123] As Figure 14 shown, the electronic device 600 includes a central processing unit (CPU) 601, which can perform various appropriate tasks and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage section 608 into the random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The input / output (I / O) interface 605 is also connected to the bus 604.

[0124] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed so that a computer program read from it can be installed in the storage section 608 as needed.

[0125] Specifically, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned dynamic buried point method based on a front-end framework is implemented.

[0126] In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 609, and / or installed from the removable medium 611.

[0127] A computer-readable medium includes permanent and non-permanent, removable and non-removable media that can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media do not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0128] For the convenience of description, when describing the above devices, they are divided into various units according to their functions and described separately. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0129] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the functions specified in one process Figure One one process or multiple processes and / or blocks Figure One the functions specified in one block or multiple blocks.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure One one process or multiple processes and / or blocks Figure One the functions specified in one block or multiple blocks.

[0131] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0132] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for classifying palaeokarst landforms, characterized in that, Including: Generating a slope data volume based on the elevation data volume of the palaeokarst landform; Generating a profile curvature data volume based on the elevation data volume; Generating a gully data volume based on the elevation data volume; Classifying the palaeokarst landform of the target work area according to the slope data volume, the profile curvature data volume and the gully data volume; wherein, the basis parameters for classifying the palaeokarst landform of the target work area include: slope, profile curvature, relative elevation and aspect ratio, and the palaeokarst landform of the study area is divided into 8 types of palaeokarst micro-landform units, namely steep slope, slope shoulder, slope foot, residual hill, slope top, slope bottom, depression, gully. Specifically: For the slope data volume, according to the actual situation of the study area, determine the maximum slope limit and the minimum slope limit, and divide the area greater than the maximum limit into steep slopes; combining the slope data volume and the profile curvature data volume, within the area between the maximum slope limit and the minimum slope limit, taking the profile curvature of 0 as the boundary, divide the area greater than 0 into slope shoulders and the area less than 0 into slope feet; combining the elevation data volume and the slope data volume, within the area less than the minimum slope limit, according to the elevation size, determine 3 elevation boundaries, divide the area greater than the maximum boundary into residual hills, divide the area less than the minimum boundary into depressions, and divide the slope top and slope bottom in descending order in between; on the basis of the above classification, determine the elevation boundary and the planar aspect ratio boundary, within the area less than the elevation boundary, combine the results of the elevation data volume and the gully data volume, and delineate the area greater than the aspect ratio boundary and divide it into gullies; wherein, the maximum boundary is set to 6.4°, the minimum boundary is set to 1°, the 3 elevation boundaries are 400m, 380m, 350m respectively, and the aspect ratio boundary is 2; The palaeokarst landform classification method further includes: Using the impression method to restore the palaeokarst landform of the target layer to generate the elevation data volume. Specifically: based on the stratigraphic correlation data of the target work area, determine the overlying marker bed of the target layer according to regional stability, single well characteristics, filling and complementing degree, distance from the target layer and seismic traceability; determine the overlying stratigraphic interface characteristics of the target layer and divide and correlate the overlying strata; according to the pre-set selection criteria for the overlying marker surface, determine the overlying marker surface for the restoration of the palaeokarst landform of the target layer; trace the time-domain seismic horizon data of the overlying marker surface and the target layer; perform time-depth conversion on the time-domain seismic horizon data of the overlying marker surface and the target layer to obtain the depth-domain horizon data; carry out the restoration of the palaeokarst landform according to the depth-domain horizon data to obtain the elevation data volume corresponding to the palaeokarst landform of the target layer.

2. The paleokarst landform classification method according to claim 1, wherein The generating the gully data volume according to the elevation data volume includes: Extracting the gully data volume from the elevation data volume by using a gully extraction algorithm.

3. An apparatus for classifying palaeokarst landforms, characterized in that, Including: A slope data volume generating unit for generating a slope data volume based on the elevation data volume of the palaeokarst landform; A curvature data volume generating unit for generating a profile curvature data volume based on the elevation data volume; A gully data volume generating unit for generating a gully data volume based on the elevation data volume; Paleogeomorphic classification units are used to classify the karst paleogeomorphology of the target work area based on the slope data volume, profile curvature data volume, and gully data volume. Among them, the basis parameters for classifying the karst paleogeomorphology of the target work area include slope, profile curvature, relative elevation, and aspect ratio. The karst paleogeomorphology of the study area is divided into 8 types of paleokarst micro-geomorphic units, namely steep slopes, slope shoulders, slope feet, residual hills, slope tops, slope bottoms, depressions, and gullies. Specifically: For the slope data volume, according to the actual situation of the study area, determine the maximum and minimum slope limits, and divide the area greater than the maximum limit into steep slopes. Combining the slope data volume and the profile curvature data volume, within the area between the maximum and minimum slope limits, taking the profile curvature of 0 as the boundary, divide the area greater than 0 into slope shoulders and the area less than 0 into slope feet. Combining the elevation data volume and the slope data volume, within the area less than the minimum slope limit, determine 3 elevation limits according to the elevation. Divide the area greater than the maximum limit into residual hills and the area less than the minimum limit into depressions, and divide the slope top and slope bottom in order from high to low in between. On the basis of the above classification, determine the elevation limit and the planar aspect ratio limit. Within the area less than the elevation limit, combine the results of the elevation data volume and the gully data volume, and delineate the area greater than the aspect ratio limit and divide it into gullies. Among them, the maximum limit is set at 6.4°, the minimum limit is set at 1°, the 3 elevation limits are 400m, 380m, and 350m respectively, and the aspect ratio limit is 2. The described karst paleogeomorphic classification device further includes: An elevation data volume generation unit for restoring the karst paleogeomorphology of the target layer by using the impression method to generate the elevation data volume. Specifically: Based on the stratigraphic correlation data of the target work area, determine the overlying marker layer of the target layer according to regional stability, single-well characteristics, filling and leveling degree, distance from the target layer, and seismic traceability; determine the characteristics of the overlying stratigraphic interface of the target layer and divide and correlate the overlying strata; determine the overlying marker surface for restoring the karst paleogeomorphology of the target layer according to the pre-set selection criteria for the overlying marker surface; trace the time-domain seismic layer data of the overlying marker surface and the target layer; perform time-depth conversion on the time-domain seismic layer data of the overlying marker surface and the target layer to obtain the depth-domain layer data; carry out karst paleogeomorphology restoration based on the depth-domain layer data to obtain the elevation data volume corresponding to the karst paleogeomorphology of the target layer.

4. The paleokarst landform classification device according to claim 3, characterized in that, The gully data volume generation unit is specifically used to extract the gully data volume from the elevation data volume by using a gully extraction algorithm.

5. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the karst paleogeomorphic classification method according to any one of claims 1 to 2.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the karst paleogeomorphic classification method according to any one of claims 1 to 2.

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