Method and device for predicting favorable zones of sand body development based on composite operation

Abstract

The present invention provides a method and device for predicting favorable zones of sand body development based on composite operations. First, a weight factor data volume is generated according to seismic data; then, a custom gain seismic volume is determined based on the weight factor data volume and the seismic data; thereafter, a planar graph of the root mean square amplitude between layers is generated according to the custom gain seismic volume and seismic interpretation horizon information; and finally, the favorable zones of sand body development are determined according to the planar graph of the root mean square amplitude between layers. Compared with the similar attributes widely used currently, the sand body prediction effect of the present invention is more accurate, the characteristics of the sand body development area are more prominent, it is convenient for visual identification, there is no need to adopt multiple attribute optimizations and pattern recognition, and the work efficiency is greatly improved, providing a favorable reference for determining the distribution of favorable zones of sand body development.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration, and particularly relates to a method and device for predicting favorable zones of sand body development based on composite operations. Background Art

[0002] In the early 1970s, following the advent of color seismic data, seismic attributes were first introduced and their concepts were initially established. After nearly 50 years of development, the definition of seismic attributes has evolved from the single bright spot identification technology before its formal introduction to an effective seismic interpretation technology with advantages such as multi-disciplines, multi-methods, multi-scales, and multi-types. Against the backdrop of the rapid development of technologies such as pre-stack and post-stack reservoir inversion, neural networks, and deep learning, seismic attributes still remain an important technical means for carrying out fine structural interpretation, seismic facies analysis, prediction of reservoir physical property parameters, and oil and gas detection. Currently, there are more than 300 clearly defined seismic attributes, and dozens of commonly used seismic attributes, mainly including amplitude statistics, spectrum statistics, sequence statistics, correlation statistics, and complex trace statistics, etc.

[0003] In the study of clastic rock reservoirs, technologies such as the extraction, analysis and optimization, pattern recognition, and attribute fusion of seismic attributes have been widely applied. Among them, amplitude statistics attributes often show a high degree of correlation in the prediction of the planar distribution characteristics of sand bodies and the analysis and determination of favorable zones of lithology development, and are attribute types widely accepted and often used by scientific researchers. However, the results of a single attribute based on a single seismic parameter often fail to comprehensively characterize geological targets; while for the analysis and optimization and pattern recognition carried out based on multiple attributes, the multiple attributes selected - which not only have differences in algorithm expression and result focus, but also show differences in the degree of agreement with the geological disclosure of completed wells and the scope of significance - lead to an increase in the reconciliation degree of attribute recognition results, a decrease in recognition, and weak indication. Among them, for pattern recognition under sample supervision, even if specific qualitative or quantitative samples are used, the fuzzy processing method is prone to weakening or even mutating the favorable features of the recognition results, and has limited guiding significance in the actual prediction, optimization, and well location drilling of favorable zones of sand bodies, so there are many deficiencies. Summary of the Invention

[0004] In view of the problems in the prior art, the present invention provides the following technical solutions:

[0005] An embodiment of one aspect of the present invention provides a method for predicting favorable zones of sand body development based on composite operations, including:

[0006] Generating a weight factor data volume according to seismic data;

[0007] Determining a self-defined gain seismic volume according to the weight factor data volume and the seismic data;

[0008] Generate a planar map of the root mean square amplitude between layers based on the custom gain seismic volume and seismic interpretation horizon information;

[0009] Determine the favorable zones for sand body development based on the planar map of the root mean square amplitude between layers;

[0010] In a preferred embodiment, the generating of the weight factor data volume based on seismic data includes:

[0011] Statistically analyze the maximum amplitude value and the minimum amplitude value in the seismic data;

[0012] Generate the weight factor data volume based on the maximum amplitude value and the minimum amplitude value;

[0013] In a preferred embodiment, the determining of the custom gain seismic volume based on the weight factor data volume and the seismic data includes:

[0014] Using the weight factor data volume, generate a seismic weight index data volume according to the types of lithologies exposed by the drilled wells in the work area;

[0015] Generate the custom gain seismic volume based on the seismic weight index data volume and the seismic data;

[0016] In a preferred embodiment, the generating of the planar map of the root mean square amplitude between layers based on the custom gain seismic volume and seismic interpretation horizon information includes:

[0017] Using the custom gain seismic volume, with the seismic interpretation horizon information as the time window, substitute into the following root mean square formula:

[0018]

[0019] where RMS(A GAIN ) is the root mean square amplitude between layers, A GAIN is the custom gain seismic volume, N is the number of sample points within the time window, and x i is the amplitude value of the i-th sample point of the custom gain seismic volume within the time window;

[0020] On the other hand, the present invention provides a device for predicting favorable zones for sand body development based on composite operations, including:

[0021] A weight factor data volume generation module, which generates a weight factor data volume based on seismic data;

[0022] A custom gain seismic volume generation module, which determines a custom gain seismic volume based on the weight factor data volume and the seismic data;

[0023] The interlayer root-mean-square amplitude attribute plane map generation module generates an interlayer root-mean-square amplitude attribute plane map according to the custom gain seismic volume and seismic interpretation horizon information;

[0024] The favorable zone determination module for sand body development determines the favorable zone for sand body development according to the interlayer root-mean-square amplitude attribute plane map.

[0025] In a preferred embodiment, the weight factor data volume generation module includes:

[0026] The amplitude value statistics unit statistics the maximum amplitude value and the minimum amplitude value in the seismic data;

[0027] The data volume generation unit generates the weight factor data volume according to the maximum amplitude value and the minimum amplitude value.

[0028] In a preferred embodiment, the custom gain seismic volume generation module includes:

[0029] The seismic weight index data volume generation unit uses the weight factor data volume to generate a seismic weight index data volume according to the lithology types revealed by the completed wells in the work area;

[0030] The custom gain seismic volume generation unit generates the custom gain seismic volume according to the seismic weight index data volume and the seismic data.

[0031] In a preferred embodiment, the interlayer root-mean-square amplitude attribute plane map generation module uses the custom gain seismic volume, takes the seismic interpretation horizon information as a time window, and substitutes it into the following root-mean-square formula:

[0032]

[0033] Where RMS(A GAIN ) is the interlayer root-mean-square amplitude, A GAIN is the custom gain seismic volume, N is the number of samples in the time window, and x i is the amplitude value of the i-th sample of the custom gain seismic volume in the time window.

[0034] Another embodiment of 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 method for predicting a favorable zone for sand body development based on composite operations are implemented.

[0035] Another embodiment of 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 method for predicting a favorable zone for sand body development based on composite operations are implemented.

[0036] As can be seen from the above technical solutions, the present invention provides a method and device for predicting favorable zones of sand body development based on composite operations. First, a weight factor data volume is generated according to seismic data; then, a custom gain seismic volume is determined based on the weight factor data volume and the seismic data; thereafter, a planar map of the root mean square amplitude between layers is generated based on the custom gain seismic volume and seismic interpretation horizon information; finally, the favorable zones of sand body development are determined based on the planar map of the root mean square amplitude between layers. Compared with the similar attributes widely used currently, the sand body prediction effect of the present invention is more accurate, the characteristics of the sand body development area are more prominent, it is convenient for visual identification, there is no need to adopt multiple attribute optimizations and pattern recognition, and the work efficiency is greatly improved, providing a favorable reference for determining the distribution of favorable zones of sand body development. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] 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 use in the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0038] Figure 1 Schematic diagram of the main profile of the weight factor data volume g;

[0039] Figure 2 Schematic diagram of the main profile of the seismic weight index data volume V;

[0040] Figure 3 Custom gain seismic volume A GAIN Schematic diagram of the main profile

[0041] Figure 4 Root mean square RMS(A GAIN ) planar effect diagram of the amplitude between layers obtained based on composite operations;

[0042] Figure 5 Planar effect diagram of the traditional root mean square amplitude attribute;

[0043] Figure 6 It is a schematic flowchart of the method for predicting favorable zones of sand body development based on composite operations in the embodiments of the present invention;

[0044] Figure 7 It is a schematic structural diagram of the device for predicting favorable zones of sand body development based on composite operations in the embodiments of the present invention;

[0045] Figure 8 It is a schematic structural diagram of the electronic device in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] 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. Apparently, 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 creative efforts shall fall within the protection scope of the present invention.

[0047] An embodiment of the present invention provides a method for predicting favorable zones of sand body development based on composite operations. First, a weight factor data volume is generated according to seismic data; then, a custom gain seismic volume is determined according to the weight factor data volume and the seismic data; then, a planar map of the root mean square amplitude attribute between layers is generated according to the custom gain seismic volume and seismic interpretation horizon information; finally, the favorable zones of sand body development are determined according to the planar map of the root mean square amplitude attribute between layers. Compared with the similar attributes widely used currently, the sand body prediction effect of the present invention is more accurate, the characteristics of the sand body development area are more prominent, it is convenient for visual identification, there is no need to adopt multiple attribute optimizations and pattern recognition, and the work efficiency is greatly improved, providing a favorable reference for determining the distribution of favorable zones of sand body development.

[0048] The method for predicting favorable zones of sand body development based on composite operations according to the present invention obtains a seismic weight index data volume by calculating the weight factor data volume, performs gain processing on the original seismic result data, and conducts root mean square amplitude attribute calculation based on the custom gain seismic volume. The details of the sand body development zones ([ Figure 4 ) revealed are richer, the contrast between the favorable and unfavorable zones is distinct, the recognition rate is significantly improved, and it is significantly better than the traditional planar effect of the root mean square amplitude attribute ([ Figure 5 ). The following combines the accompanying drawings to illustrate the application effect of the method for predicting favorable zones of sand body development based on composite operations according to the present invention in the Niuju area of the Liaohe Oilfield.

[0049] Specifically as Figure 6 shown, the method for predicting favorable zones of sand body development based on composite operations of the present invention includes:

[0050] S1: Generate a weight factor data volume according to seismic data.

[0051] In this step, specifically, using high-resolution seismic data A, the maximum amplitude value A max = 6119.81 and the minimum amplitude value A min = -5560.37 are statistically obtained and substituted into the formula:

[0052]

[0053] to obtain the weight factor data volume g ([ Figure 1), where a is the amplitude value of any point within seismic body A.

[0054] Figure 1 It is a schematic diagram of the main profile of the weight factor data volume g. Compared with the original seismic data, its value range has been compressed. Against the background that current seismic interpretation software generally uses 32 bits as the maximum digit limit, if other processes described in the present invention are directly used to calculate and convert the original seismic data, the data range often exceeds the limit, and subsequent calculations such as scaling and squaring cannot be performed, easily resulting in data bad points or inability to calculate. However, through this step of weight factor calculation, the data range can be effectively compressed, reducing the adverse effects of unreasonable strong, very strong reflections and weak, very weak reflections in the resultant seismic data on the identification of favorable sand bodies, making the prediction results more objective. The calculation formula used in this step is the same as the known data normalization formula in the industry, but the purpose is different. The purpose is to reasonably compress the value range of the original seismic data and use the result as one of the factors for subsequent weight calculation.

[0055] S2: Determine a custom gain seismic body based on the weight factor data volume and the seismic data.

[0056] In this step, using the weight factor data volume g, according to the types of lithologies revealed by the completed wells in the work area, the cuttings grain sizes are mainly divided into 4 types, obtaining v max = 4, v min = 1, and substituting into the formula:

[0057] V = (v max - v min ) × g + v min

[0058] That is,

[0059] V = 3g + 1

[0060] Obtain the seismic weight index data volume V( Figure 2 ). Among them, v max and v min should be determined according to the types of lithologies revealed by the completed wells in the study area.

[0061] Figure 2 is a schematic diagram of the main profile of the seismic weight index data volume V. It can be seen from Figure 2 that first, the value obtained by subtracting 1 from the value of the main lithology type revealed by the known completed wells is used as the magnification factor of the weight factor data volume g, and the product result plus 1 is used to obtain the seismic weight index data volume V. The purpose of this embodiment is to hierarchize the weight factor data volume g, thereby realizing the classification of the amplitude value range in the seismic data volume according to geological lithology types.

[0062] S3: Generate a plane map of the root mean square amplitude between layers based on the custom gain seismic volume and seismic interpretation horizon information.

[0063] In this step, the weight index data volume V and high-resolution seismic data A are substituted into the formula:

[0064] A GAIN = A × V 2

[0065] to obtain the custom gain seismic volume A GAIN ( Figure 3 ) where V 2 is the square of the variable value V at any point in the seismic weight index data volume.

[0066] Figure 3 is a schematic diagram of the main profile of the custom gain seismic volume with further gain obtained through this corresponding step. This custom gain seismic volume is superior to the original seismic data and is more suitable for extracting the property of the root mean square amplitude between layers.

[0067] S4: Determine the favorable zone for sand body development based on the plane map of the root mean square amplitude between layers.

[0068] In this step, the custom gain seismic volume A GAIN is used with the seismic interpretation horizon as the time window and substituted into the root mean square formula:

[0069]

[0070] to obtain the plane map of the root mean square RMS(A GAIN ) property of the amplitude between layers obtained through the above composite operation( Figure 4 ).

[0071] where N is the number of sample points within the time window, and x i is the amplitude value of the i-th sample point of the custom gain seismic volume A GAIN within the time window.

[0072] Figure 4 is a schematic diagram of the final map obtained by the present invention, Figure 5 is a plane display map of the root mean square amplitude extracted using the original seismic data. Compared with Figure 5 , Figure 4 has more details and better effects. For example, Figure 5 the characteristics of the attribute results on the east side obtained by the conventional method are not prominent, while the results obtained by the method of the present invention strengthen this part. It is generally considered that the high-value area has a good correspondence with the sand body distribution, and the low-value area corresponds to the mudstone distribution area.

[0073] As can be known from the above description, for the method for predicting favorable zones of sand body development based on composite operation according to the present invention, compared with the similar attributes widely used currently, the sand body prediction effect is more accurate, the characteristics of the sand body development area are more prominent, it is convenient for visual identification, there is no need to adopt multiple attribute optimizations and pattern recognition, the work efficiency is greatly improved, and it provides a favorable reference for determining the distribution of favorable zones of sand body development.

[0074] Based on the same inventive concept, on the other hand, the present invention provides a device for predicting favorable zones of sand body development based on composite operation, as Figure 7 shown, including:

[0075] A weight factor data volume generation module 1, which generates a weight factor data volume according to seismic data;

[0076] A custom gain seismic volume generation module 2, which determines a custom gain seismic volume according to the weight factor data volume and the seismic data;

[0077] An interlayer amplitude root mean square attribute plan generation module 3, which generates an interlayer amplitude root mean square attribute plan according to the custom gain seismic volume and seismic interpretation horizon information;

[0078] A favorable sand body development zone determination module 4, which determines the favorable sand body development zone according to the interlayer amplitude root mean square attribute plan.

[0079] The present invention provides a device for predicting favorable zones of sand body development based on composite operation. First, a weight factor data volume is generated according to seismic data; then, a custom gain seismic volume is determined according to the weight factor data volume and the seismic data; then, an interlayer amplitude root mean square attribute plan is generated according to the custom gain seismic volume and seismic interpretation horizon information; finally, the favorable sand body development zone is determined according to the interlayer amplitude root mean square attribute plan. For the present invention, compared with the similar attributes widely used currently, the sand body prediction effect is more accurate, the characteristics of the sand body development area are more prominent, it is convenient for visual identification, there is no need to adopt multiple attribute optimizations and pattern recognition, the work efficiency is greatly improved, and it provides a favorable reference for determining the distribution of favorable zones of sand body development.

[0080] In a preferred embodiment, the weight factor data volume generation module includes:

[0081] An amplitude value statistics unit, which statistics the maximum amplitude value and the minimum amplitude value in the seismic data;

[0082] A data volume generation unit, which generates the weight factor data volume according to the maximum amplitude value and the minimum amplitude value.

[0083] In a preferred embodiment, the custom gain seismic volume generation module includes:

[0084] An earthquake weight index data volume generation unit generates an earthquake weight index data volume by using a weight factor data volume according to the types of lithologies revealed by the completed wells in the work area.

[0085] A custom gain seismic volume generation unit generates the custom gain seismic volume according to the earthquake weight index data volume and the seismic data.

[0086] In a preferred embodiment, the interlayer amplitude root mean square attribute plane map generation module uses the custom gain seismic volume, takes the seismic interpretation horizon information as a time window, and substitutes it into the following root mean square formula:

[0087]

[0088] where RMS(A GAIN ) is the interlayer amplitude root mean square, A GAIN is the custom gain seismic volume, N is the number of sample points within the time window, and x i is the amplitude value of the i-th sample point of the custom gain seismic volume within the time window.

[0089] An embodiment of the present invention also provides a specific implementation manner of an electronic device capable of implementing all steps in the sand body development favorable zone prediction method based on composite operations in the above embodiment. Refer to Figure 8 , and the electronic device specifically includes the following:

[0090] A processor 601, a memory 602, a communication interface 603, and a bus 604;

[0091] Among them, the processor 601, the memory 602, and the communication interface 603 complete mutual communication through the bus 604; the communication interface 603 is used to realize information transmission between related devices such as the sand body development favorable zone prediction device based on composite operations and user terminals.

[0092] The processor 601 is used to call the computer program in the memory 602, and when the processor executes the computer program, it realizes all steps in the sand body development favorable zone prediction method in the above embodiment.

[0093] An embodiment of the present invention also provides a computer-readable storage medium capable of implementing all steps in the sand body development favorable zone prediction method based on composite operations in the above embodiment. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it realizes all steps in the sand body development favorable zone prediction method in the above embodiment.

[0094] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program type of embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the partial description of the method embodiments.

[0095] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0096] Although the present invention provides method operation steps as described in the embodiments or flowcharts, based on routine or non-creative labor, there can be more or fewer operation steps. The order of steps listed in the embodiments is only one way among the numerous execution orders of steps and does not represent the only execution order. When the actual device or client product is executed, it can be executed in the order of the method shown in the embodiments or the drawings or in parallel (e.g., in an environment of parallel processors or multi-threaded processing).

[0097] The systems, 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 a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, an in-vehicle human-machine interaction device, 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.

[0098] Although the embodiments of this specification provide method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative means. The step sequences listed in the embodiments are only one way among the execution sequences of numerous steps and do not represent the only execution sequence. When the actual device or terminal product is executing, it may be executed in the method sequence shown in the embodiments or the drawings or in parallel (for example, in an environment of parallel processors or multi-threaded processing, or even in a distributed data processing environment). The term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, product or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, product or device. Without further limitation, there is no exclusion of additional identical or equivalent elements in the process, method, product or device comprising the said elements.

[0099] For the convenience of description, the above device is described by dividing it into various modules according to functions. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same or multiple software and / or hardware, or the modules implementing the same function can be realized by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0100] Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, the method steps can be logically programmed to enable the controller to be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. to achieve the same function. Therefore, such a controller can be regarded as a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0101] The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block of the flowchart illustrations and / or block diagrams, and combinations of flows and / or blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing apparatus create means for implementing the functions specified in the flow Figure 1 in one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more blocks.

[0102] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the functions specified in the flow Figure 1 in one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more blocks.

[0103] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow Figure 1 in one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more blocks.

[0104] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0105] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

[0106] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media that can store information 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 cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information accessible by a computing device. As defined herein, a computer-readable medium does not include transitory media such as modulated data signals and carrier waves.

[0107] Those skilled in the art will appreciate that the embodiments of this specification can be provided as a method, system, or computer program product. Therefore, the embodiments of this specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of this specification 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.) that contain computer-usable program code.

[0108] The embodiments of this specification can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The embodiments of this specification can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0109] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple. For related parts, reference can be made to the description of the method embodiments. In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the embodiments of this specification. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0110] The above is only the embodiment of this specification and is not used to limit the embodiments of this specification. For those skilled in the art, various changes and modifications can be made to the embodiments of this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of this specification shall be included in the scope of the claims of the embodiments of this specification.

Claims

1. A method for predicting favorable zones of sand body development based on composite operations, characterized in that, it includes: Generating a weight factor data volume according to seismic data; Determining a custom gain seismic volume according to the weight factor data volume and the seismic data; Generating a planar map of the root mean square amplitude between layers according to the custom gain seismic volume and seismic interpretation horizon information; Determining the favorable zones of sand body development according to the planar map of the root mean square amplitude between layers; Among them, the generating of the weight factor data volume according to seismic data includes: Statistical maximum amplitude value and minimum amplitude value in the seismic data; Calculating and obtaining the weight factor data volume by using the normalization formula according to the maximum amplitude value and the minimum amplitude value; where the normalization formula is: Among them, g represents the weight factor data volume, a represents the amplitude value of any point in the seismic data, and A max represents the maximum amplitude in the seismic data, and A min represents the minimum amplitude in the seismic data; Among them, the determining of the custom gain seismic volume according to the weight factor data volume and the seismic data includes: According to the lithology types revealed by the completed wells in the study area, using the formula V = (v max - v min ) × g + v min calculate the seismic weight index data volume, and calculate the custom gain seismic volume through the formula A GAIN = A × V 2 ; where V represents the seismic weight index data volume, A GAIN represents the custom gain seismic volume, A represents the seismic data, v max and v min are determined according to the lithology types revealed by the completed wells in the study area, v max represents the maximum value of the lithology types revealed by the completed wells in the study area, and v min represents the minimum value of the lithology types revealed by the completed wells in the study area; Among them, the generating of the planar map of the root mean square amplitude between layers according to the custom gain seismic volume and seismic interpretation horizon information includes: Using the custom gain seismic volume, with the seismic interpretation horizon as the time window, substituting into the root mean square formula to obtain the planar map of the root mean square amplitude between layers.

2. The method for predicting favorable zones of sand body development according to claim 1, characterized in that, the root mean square formula is: where RMS(A GAIN ) is the root mean square of the interlayer amplitude, A GAIN is the custom gain seismic volume, N is the number of samples within the time window, and x i is the amplitude value of the i-th sample of the custom gain seismic volume within the time window.

3. A device for predicting favorable zones of sand body development based on composite operations, characterized in that, it includes: A weight factor data volume generation module, which generates a weight factor data volume according to seismic data; A custom gain seismic volume generation module, which determines a custom gain seismic volume according to the weight factor data volume and the seismic data; A planar map generation module of the root mean square amplitude between layers, which generates a planar map of the root mean square amplitude between layers according to the custom gain seismic volume and seismic interpretation horizon information; A favorable zone determination module for sand body development, which determines the favorable zones of sand body development according to the planar map of the root mean square amplitude between layers; Among them, the weight factor data volume generation module is specifically used for: Statistical maximum amplitude value and minimum amplitude value in the seismic data; calculating and obtaining the weight factor data volume by using the normalization formula according to the maximum amplitude value and the minimum amplitude value; where the normalization formula is: Among them, g represents the weight factor data volume, a represents the amplitude value of any point in the seismic data, and A max represents the maximum amplitude in the seismic data, and A min represents the minimum amplitude in the seismic data; Among them, the custom gain seismic volume generation module is specifically used for: According to the lithology types revealed by the completed wells in the study area, the seismic weight index data volume is calculated using the formula V = (v max - v min ) × g + v min , and the custom gain seismic volume is calculated through the formula A GAIN = A × V 2 ; where V represents the seismic weight index data volume, A GAIN represents the custom gain seismic volume, A represents the seismic data, v max and v min are determined according to the lithology types revealed by the completed wells in the study area, v max represents the maximum value of the lithology types revealed by the completed wells in the study area, and v min represents the minimum value of the lithology types revealed by the completed wells in the study area. Among them, the planar map generation module of the root mean square amplitude between layers is specifically used for: Using the custom gain seismic volume, with the seismic interpretation horizon as the time window, substituting into the root mean square formula to obtain the planar map of the root mean square amplitude between layers.

4. The device for predicting favorable zones of sand body development according to claim 3, characterized in that, the root mean square formula is: Among them, RMS(A GAIN ) is the root mean square of the interlayer amplitude, A GAIN is the custom gain seismic volume, N is the number of samples within the time window, and x i is the amplitude value of the i-th sample of the custom gain seismic volume within the time window.

5. An electronic device, including 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 method for predicting favorable zones of sand body development based on composite operations according to claim 1 or 2.

6. A computer-readable storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, it implements the steps of the method for predicting favorable zones of sand body development based on composite operations according to claim 1 or 2.

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