Buried hill weathering crust reservoir prediction method, system, medium and equipment based on seismic reflection intensity difference detection

Abstract

The present application relates to the field of petroleum geological exploration, and discloses a buried hill weathered crust reservoir prediction method and system based on seismic reflection intensity difference detection, a medium and equipment, comprising: sequentially performing trace equalization, spherical diffusion correction, multi-stage amplitude-preserving denoising and sparse inversion on post-stack seismic data, and outputting high signal-to-noise ratio, high resolution and amplitude-preserving seismic profiles; based on the seismic profiles, a multi-parameter fusion reflection intensity is obtained, complex seismic traces are extracted through Hilbert transform, amplitude envelope, instantaneous phase gradient and frequency attenuation coefficient are calculated, and the reflection intensity of the target interface is obtained through weighted fusion; based on the reflection intensity of the target interface, the relative intensity ratio and the absolute intensity ratio are calculated, and the reservoir level is calibrated in combination with core data. The present application solves the technical problems of "poor seismic data quality, one-sided reflection intensity characterization and disturbed quantitative indicators" in the existing buried hill weathered crust reservoir analysis, and realizes high-precision reservoir evaluation under post-stack data.

Description

Technical Field

[0001] This invention relates to the field of seismic exploration and reservoir evaluation technology for oil and gas, and in particular to a method, system, medium and equipment for predicting buried hill weathered crust reservoirs based on seismic reflection intensity difference detection. Background Technology

[0002] The weathered crust reservoir in buried hills is an important target layer for oil and gas exploration. Its development is controlled by multiple factors such as lithology, lithofacies, tectonic fractures, weathering and leaching, and overlying rocks, making it extremely difficult to predict. Currently, seismic prediction methods for buried hill weathered crust reservoirs can be summarized into the following three categories: 1) Seismic attribute analysis, including single attribute optimization, which uses attributes such as amplitude to qualitatively predict reservoirs, or multi-attribute fusion to optimize sensitive attributes (such as average amplitude, variance, and arc length) and assign weights according to correlation coefficients to reduce the ambiguity of single attributes; 2) Seismic inversion technology, mainly pre-stack phase control inversion and waveform indicator inversion, which uses parameters such as inverted wave impedance and Poisson's ratio to identify lithology and reservoir distribution; 3) Fracture prediction, which is divided into post-stack and pre-stack prediction. Post-stack coherence volumes can identify faults but have low accuracy and can only predict in-phase axis faulting structures. Curvature attributes are limited to faults with strata folds on both sides. Ant bodies qualitatively identify small faults and large-scale fractures. Maximum likelihood bodies can predict fracture-dense areas but lack information on main faults. Pre-stack technology is mainly based on azimuth anisotropy fracture prediction using wide-azimuth earthquakes.

[0003] The poor signal-to-noise ratio and low resolution of seismic data from deep buried hills directly affect the reliability of attributes and the accuracy of inversion. In existing technologies, reservoir evaluation based on seismic attribute analysis and seismic inversion mostly relies on single reflection amplitude analysis or wave impedance inversion: the former only uses amplitude characteristics, which is easily affected by changes in the velocity / density of the overlying surrounding rocks, leading to "strong amplitudes being misjudged as unweathered bedrock and weak amplitudes being missed as effective reservoirs"; the latter, although combined with impedance parameters, requires pre-stack seismic data support, which is complex and costly, and is not suitable for old work areas where post-stack data is the main source.

[0004] In summary, existing technologies have three major drawbacks: 1) Insufficient synergistic optimization of signal-to-noise ratio and resolution of seismic data, and poor amplitude preservation; 2) The determination of reflection intensity relies solely on amplitude, without integrating multi-dimensional features such as phase and frequency, resulting in low characterization accuracy; 3) Lack of quantitative indicators to eliminate interference from overlying rocks, making it impossible to accurately assess the degree of weathering crust development. Summary of the Invention

[0005] To address the aforementioned problems, the purpose of this invention is to provide a method, system, medium, and equipment for predicting buried hill weathered crust reservoirs based on seismic reflection intensity difference detection, in order to solve the technical difficulties in existing buried hill weathered crust reservoir analysis, such as "poor seismic data quality, one-sided reflection intensity characterization, and interference with quantitative indicators," and to achieve high-precision reservoir evaluation based on post-stack data.

[0006] To achieve the above objectives, in a first aspect, the present invention adopts the following technical solution: a method for predicting weathered crust reservoirs in buried hills based on seismic reflection intensity difference detection, comprising: sequentially performing trace equalization, spherical diffusion correction, multi-level amplitude-preserving denoising, and sparse inversion on post-stack seismic data to output a high signal-to-noise ratio, high resolution, and amplitude-preserving seismic profile; obtaining multi-parameter fused reflection intensity based on the seismic profile, extracting complex seismic traces through Hilbert transform, calculating amplitude envelope, instantaneous phase gradient, and frequency attenuation coefficient, and weighted fusion to obtain the reflection intensity of the target interface; and calculating the relative intensity ratio based on the reflection intensity of the target interface. and absolute strength ratio The reservoir level was determined by combining core data.

[0007] Furthermore, the post-stack seismic data are sequentially subjected to trace equalization, spherical diffusion correction, multi-level amplitude-preserving denoising, and sparse inversion to output high signal-to-noise ratio, high resolution, and amplitude-preserving seismic profiles, including:

[0008] Trace equalization and spherical diffusion correction are performed on post-stack seismic data to eliminate the effects of geometric attenuation. Multichannel coherent denoising is used for the first stage of denoising, and wavelet adaptive threshold denoising is used for the second stage of denoising, in order to preserve the effective signal amplitude; An inversion objective function is constructed, and an iterative soft thresholding algorithm is used to solve the inversion objective function to output a seismic profile with high signal-to-noise ratio, high resolution, and amplitude preservation.

[0009] Furthermore, the multi-parameter fused reflection intensity is obtained based on the seismic profile, including: Analytical signals are obtained from seismic data in seismic profiles using Hilbert transform. Based on analytical signals The characteristic parameters are calculated from the seismic data, including amplitude envelope, instantaneous phase gradient, and frequency attenuation coefficient. The reflection intensity is constructed by weighted summation. The weights are determined by correlation analysis of the training samples composed of known well-side seismic data; Based on the seismic reflection phase axis, the calculated reflection intensity Match to the target interface.

[0010] Furthermore, reflection intensity for: , in, For amplitude envelope, For instantaneous phase gradient, This is the frequency attenuation coefficient.

[0011] Furthermore, the relative intensity ratio is calculated based on the reflection intensity of the target interface. and absolute strength ratio The reservoir level is determined by combining core data, specifically based on: and The physical relationship between the rock and the reservoir is used to construct a relative strength ratio to eliminate common interference from the overlying surrounding rocks, and an absolute strength ratio to quantify the degree of weathering. These two methods, combined with core data calibration, are used to achieve reservoir classification. The reflection coefficient corresponding to the unweathered bedrock interface; This represents the reflectance coefficient corresponding to the weathering crust interface; The reflection coefficient corresponding to the unweathered bedrock interface; This indicates the reflected seismic amplitude at the interface between the weathered crust and bedrock of the buried hill; This indicates the reflected seismic amplitude at the interface between the overlying strata and the weathered crust of the buried hill; This represents the reflected seismic amplitude at the interface between the overlying strata and the bedrock of the buried hill.

[0012] Furthermore, the relative intensity ratio is calculated based on the reflection intensity of the target interface. and absolute strength ratio : Calculate the ratio of the reflection intensity of the overlying rock-weathered crust interface to that of the overlying rock-unweathered bedrock interface, and use this ratio as the relative intensity ratio. ; Calculate the ratio of the reflection intensity of the weathered crust-unweathered bedrock interface to that of the overlying surrounding rock-unweathered bedrock interface, and use this ratio as the absolute intensity ratio. .

[0013] Furthermore, the reservoir classification criteria are as follows: I. Reservoir: =0.2~0.4、 =0.1~0.3; Class II reservoirs: =0.4~0.6、 =0.3~0.5; Class III reservoirs: >0.6、 >0.5; Among them, Class I reservoirs are of excellent quality, Class II reservoirs are of effective quality, and Class III reservoirs are of ineffective quality.

[0014] Secondly, the technical solution adopted by this invention is as follows: a method for predicting weathered crust reservoirs in buried hills based on seismic reflection intensity difference detection, comprising: a seismic data amplitude-preserving optimization processing module, which sequentially performs trace equalization, spherical diffusion correction, multi-level amplitude-preserving denoising, and sparse inversion on post-stack seismic data to output a high signal-to-noise ratio, high resolution, and amplitude-preserving seismic profile; a multi-parameter fusion reflection intensity calculation module, which calculates the multi-parameter fusion reflection intensity based on the seismic profile, extracts complex seismic traces through Hilbert transform, calculates the amplitude envelope, instantaneous phase gradient, and frequency attenuation coefficient, and weighted fuses them to obtain the reflection intensity of the target interface; and a dual reflection intensity ratio quantification analysis module, which calculates the relative intensity ratio based on the reflection intensity of the target interface. and absolute strength ratio The reservoir level was determined by combining core data.

[0015] Thirdly, the technical solution adopted by the present invention is: a computer-readable storage medium for storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any of the methods described above.

[0016] Fourthly, the technical solution adopted by the present invention is as follows: a computing device, comprising: one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described above.

[0017] The present invention has the following advantages due to the adoption of the above technical solutions: 1. This invention proposes for the first time a collaborative scheme of "multi-level amplitude-preserving denoising + sparse inversion", which improves the resolution of seismic data and solves the "contradiction between denoising and amplitude preservation" on the basis of amplitude preservation processing and signal-to-noise ratio improvement.

[0018] 2. This invention integrates three-dimensional features of amplitude, phase, and frequency, which improves the accuracy of weathering crust identification compared to the traditional single amplitude index; and achieves innovation in reflection intensity.

[0019] 3. This invention uses a dual reflection intensity ratio to eliminate interference from the overlying surrounding rock, thereby improving the reservoir prediction accuracy, which is higher than existing technologies. This is an innovation in quantitative indicators in Xi'an.

[0020] 4. This invention has practical advantages. Based on post-stack data, it eliminates the need for pre-stack gathers, greatly shortens the processing cycle, has low equipment requirements, and can be directly applied to secondary exploration in old work areas.

[0021] 5. This invention has the advantage of universality, and is applicable to various types of buried hills such as granite, volcanic rock, and carbonate rock, with a high accuracy rate in identifying high-quality reservoirs. Attached Figure Description

[0022] Figure 1 This is a flowchart of the buried hill weathering crust reservoir prediction method based on seismic reflection intensity difference detection in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the matching of calculated reflection intensity to the target interface in an embodiment of the present invention. Detailed Implementation

[0023] To address the problems of existing weathering crust reservoir prediction methods being greatly affected by overlying rocks, having low accuracy due to seismic data quality, and lacking quantitative indicators, this invention provides a method, system, medium, and equipment for predicting buried hill weathering crust reservoirs based on seismic reflection intensity difference detection. It optimizes seismic data through "multi-level amplitude-preserving denoising and sparse inversion," then calculates the reflection intensity based on complex seismic trace analysis, integrating multiple parameters, and finally constructs a dual reflection intensity ratio to quantify the degree of weathering crust development. This invention is based on post-stack seismic data, has a simple process, strong amplitude preservation, effectively eliminates interference from overlying strata, and improves reservoir prediction accuracy. It is applicable to the exploration of buried hill weathering crust reservoirs such as granite, volcanic rocks, and carbonate rocks, and has high potential for widespread application.

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0025] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0026] In one embodiment of the present invention, a method for predicting weathered crust reservoirs in buried hills based on seismic reflection intensity difference detection is provided, comprising three core modules: seismic data amplitude-preserving optimization processing, multi-parameter fusion reflection intensity calculation, and dual reflection intensity ratio quantification analysis. In this embodiment, as... Figure 1 As shown, the method includes the following steps: 1) Perform trace equalization, spherical diffusion correction, multi-level amplitude-preserving denoising and sparse inversion on the post-stack seismic data in sequence to output a high signal-to-noise ratio, high resolution and amplitude-preserving seismic profile; 2) Based on the seismic profile, the multi-parameter fused reflection intensity is obtained. Complex seismic traces are extracted by Hilbert transform, and the amplitude envelope, instantaneous phase gradient and frequency attenuation coefficient are calculated. The reflection intensity of the target interface is obtained by weighted fusion. 3) Calculate the relative intensity ratio based on the reflection intensity of the target interface. and absolute strength ratio The reservoir level was determined by combining core data.

[0027] In step 1) above, two types of core basic data are collected to support subsequent analysis: Seismic data: Acquire raw post-stack seismic data that meets amplitude preservation processing standards to ensure that the data has a basic signal-to-noise ratio and resolution to meet the needs of subsequent optimization processing.

[0028] Well data: Collects key information from drilled wells in the target area, including parameters such as buried hill lithology and P-wave impedance, for subsequent reflection intensity calibration and verification of reservoir analysis results.

[0029] In step 1) above, the seismic data undergoes amplitude-preserving optimization to improve the signal-to-noise ratio and high resolution. Based on the principles of "effective signal coherence, noise randomness" and "sparse seismic reflection signal," multi-level amplitude-preserving denoising separates the signal from the noise, and then sparse inversion is used to reconstruct the high-resolution profile. Simultaneously, amplitude constraints are added to ensure amplitude preservation.

[0030] Specifically, the post-stack seismic data undergoes trace equalization, spherical diffusion correction, multi-level amplitude-preserving denoising, and sparse inversion sequentially to output a high signal-to-noise ratio, high resolution, and amplitude-preserving seismic profile. This process includes the following steps: 1.1) Data preprocessing: Perform trace equalization and spherical diffusion correction on the post-stack seismic data to eliminate the effects of geometric attenuation.

[0031] In this embodiment, the correction formula is:

[0032] In the formula, For the corrected amplitude, The original amplitude, To eliminate the effects of geometric decay during two-way travel.

[0033] 1.2) Multi-stage amplitude-preserving denoising: Multi-channel coherent denoising is used for the first stage of denoising, and wavelet adaptive threshold denoising is used for the second stage of denoising to remove random noise and interference signals while preserving the amplitude of the effective reflected signal.

[0034] In this embodiment, the first-stage denoising employs multi-channel coherent denoising, calculating the coherence coefficient C of 5 adjacent channels (the number of channels can be adjusted according to the signal-to-noise ratio): , In the formula, This represents the amplitude of the seismic trace. Coherent signals with C ≥ 0.8 are retained, while random noise is removed.

[0035] Secondary denoising is based on adaptive thresholding using wavelet transform. The seismic signal undergoes a 5-level wavelet decomposition, and soft thresholding is applied to high-frequency coefficients. (Threshold) , For noise standard deviation, The data length retains the effective signal amplitude.

[0036] 1.3) The denoised data is processed using techniques such as sparse inversion to improve the vertical resolution of the seismic data and enhance the ability to identify stratigraphic interfaces. Specifically, an inversion objective function is constructed, and an iterative soft thresholding algorithm is used to solve the inversion objective function to output a high signal-to-noise ratio, high resolution, and amplitude-preserving seismic profile.

[0037] In this embodiment, the inversion objective function is:

[0038] In the formula, For the denoised data, For the reflection coefficient model, For the seismic wavelet matrix, For amplitude-preserving weight matrix, For sparse constraint operators, =0.05 is the regularization parameter.

[0039] An iterative soft thresholding algorithm is used to solve the problem, with 30 iterations until the residual is found. .

[0040] This embodiment also includes a quality verification step, outputting data with high signal-to-noise ratio, high resolution (main frequency increased to more than 1.5 times the original data) and high amplitude fidelity. If the standard is not met, steps 1.2)-1.3) are repeated until the analysis requirements are met.

[0041] In step 2) above, the reflection intensity is a comprehensive characterization of the formation impedance difference. A single amplitude cannot reflect the complex characteristics of the weathering crust, including amplitude reduction, phase distortion, and frequency attenuation. Based on complex seismic trace analysis, a multidimensional reflection intensity index is constructed by integrating the amplitude envelope, instantaneous phase gradient, and frequency attenuation coefficient to enhance the sensitivity to fracture-pore development zones.

[0042] Specifically, determining the multi-parameter fused reflection intensity based on seismic profiles includes the following steps: 2.1) Complex seismic trace extraction and characteristic parameter calculation: The optimized seismic data is subjected to Hilbert transform to generate an analytical signal containing information such as amplitude and phase; key characteristic parameters such as amplitude envelope, instantaneous phase gradient, and frequency attenuation coefficient are extracted from the analytical signal to reflect the differences in formation impedance, contact relationship and porosity development effect, respectively.

[0043] Specifically, regarding seismic data in seismic profiles The analytic signal is obtained through the Hilbert transform. Based on analytical signals The characteristic parameters are calculated from the seismic data, including amplitude envelope, instantaneous phase gradient, and frequency attenuation coefficient.

[0044] Among them, analytic signal In the formula .

[0045] Amplitude envelope This reflects the magnitude of the formation impedance difference.

[0046] Instantaneous phase Phase gradient This reflects changes in stratigraphic contact relationships.

[0047] instantaneous frequency Frequency attenuation coefficient ( (where is the center frequency of the target layer), reflecting the pore-crack attenuation effect caused by weathering.

[0048] 2.2) Constructing the reflection intensity using weighted summation The weights are determined by correlation analysis of training samples composed of known well-side seismic data, forming a comprehensive reflection intensity.

[0049] In this embodiment, the reflection intensity for: , in, For amplitude envelope, For instantaneous phase gradient, This is the frequency attenuation coefficient.

[0050] 2.3) Based on the seismic reflection phase axis, the calculated reflection intensity Match to the target interface.

[0051] In this embodiment, interface matching specifically involves: based on the seismic reflection phase axis, calculating... Matched to target interface: Overlying rock - Weathering crust interface (Reflectance coefficient corresponding to the weathering crust interface) Weathered crust - unweathered bedrock interface (Reflection coefficient corresponding to the unweathered bedrock interface) ), Overlying rock-unweathered bedrock interface (Reflection coefficient corresponding to the unweathered bedrock interface) ),like Figure 2 As shown.

[0052] In step 3) above, the relative intensity ratio is calculated based on the reflection intensity of the target interface. and absolute strength ratio The reservoir level is determined by combining core data, specifically based on: and The physical relationship between the rock and the reservoir is used to construct a relative strength ratio to eliminate common interference from the overlying surrounding rocks, and an absolute strength ratio to quantify the degree of weathering. These two methods, combined with core data calibration, are used to achieve reservoir classification. The reflection coefficient corresponding to the unweathered bedrock interface; This represents the reflectance coefficient corresponding to the weathering crust interface; The reflection coefficient corresponding to the unweathered bedrock interface; This indicates the reflected seismic amplitude at the interface between the weathered crust and bedrock of the buried hill; This indicates the reflected seismic amplitude at the interface between the overlying strata and the weathered crust of the buried hill; This represents the reflected seismic amplitude at the interface between the overlying strata and the bedrock of the buried hill.

[0053] In this embodiment, the relative intensity ratio is calculated based on the reflection intensity of the target interface. and absolute strength ratio : (1) Calculate the ratio of the reflection intensity of the overlying surrounding rock-weathered crust interface to that of the overlying surrounding rock-unweathered bedrock interface, and use it as the relative intensity ratio. .

[0054] Specifically, based on the relative strength ratio Eliminate the resistance of the overlying surrounding rock The interference is expressed by the formula:

[0055] relative strength ratio The impedance difference between the overlying rock and the weathering crust is represented as a proportion of the impedance difference between the overlying rock and the bedrock, thus representing the impedance change caused by weathering. The smaller the value, the more significant the impedance drop caused by weathering.

[0056] (2) Calculate the ratio of the reflection intensity of the weathered crust-unweathered bedrock interface to that of the overlying surrounding rock-unweathered bedrock interface, and use it as the absolute intensity ratio. .

[0057] Specifically, based on the absolute strength ratio The formula for quantifying the difference between the interior of the weathered crust and the bedrock is:

[0058] absolute strength ratio Characterizing the ratio of the impedance difference between the weathering crust and the bedrock to the impedance difference between the overlying surrounding rocks and the bedrock, quantifying the intrinsic differences between the weathering crust and the bedrock. The smaller the size, the more developed the cracks and pores in the weathering crust.

[0059] In this embodiment, the reservoir classification standard is as follows: I. Reservoir: =0.2~0.4、 =0.1~0.3; Class II reservoirs: =0.4~0.6、 =0.3~0.5; Class III reservoirs: >0.6、 >0.5; Among them, Class I reservoirs are of high quality, Class II reservoirs are of effective quality, and Class III reservoirs are of ineffective quality. The development characteristics of reservoirs of different quality levels are clearly defined, as shown in Table 1.

[0060] Table 1. Calibration based on core analysis data (example, can be adjusted according to the actual work area).

[0061] In summary, when using this invention, the input seismic data must meet the following requirements: post-stack amplitude preservation, high resolution, and high signal-to-noise ratio; at least one well's buried hill lithology (granite / volcanic rock / carbonate rock) and corresponding P-wave impedance data must be known for weight calibration and reservoir classification verification. This invention ensures the reliability of the analysis results through multivariate data verification: core verification: using measured data such as core porosity from known wells to compare and verify the reservoir classification results at corresponding locations, confirming the rationality of the classification; drilling verification: combining drilling information from new exploration wells, such as oil and gas shows and production data, to further verify the accuracy of the predicted results for high-quality and effective reservoirs, ensuring the effectiveness of the technology application.

[0062] In one embodiment of the present invention, a method for predicting weathered crust reservoirs in buried hills based on seismic reflection intensity difference detection is provided, comprising: The seismic data amplitude preservation optimization processing module performs trace equalization, spherical diffusion correction, multi-level amplitude preservation denoising and sparse inversion on the post-stack seismic data in sequence, and outputs a high signal-to-noise ratio, high resolution and amplitude preservation seismic profile. The multi-parameter fusion reflection intensity calculation module calculates the multi-parameter fusion reflection intensity based on the seismic profile. It extracts complex seismic traces through Hilbert transform, calculates the amplitude envelope, instantaneous phase gradient and frequency attenuation coefficient, and weighted fuses them to obtain the reflection intensity of the target interface. The dual reflection intensity ratio quantification analysis module calculates the relative intensity ratio based on the reflection intensity of the target interface. and absolute strength ratio The reservoir level was determined by combining core data.

[0063] In the above embodiments, the post-stack seismic data are sequentially subjected to trace equalization, spherical diffusion correction, multi-level amplitude-preserving denoising, and sparse inversion to output a high signal-to-noise ratio, high resolution, and amplitude-preserving seismic profile, including: Trace equalization and spherical diffusion correction are performed on post-stack seismic data to eliminate the effects of geometric attenuation. Multichannel coherent denoising is used for the first stage of denoising, and wavelet adaptive threshold denoising is used for the second stage of denoising, in order to preserve the effective signal amplitude; An inversion objective function is constructed, and an iterative soft thresholding algorithm is used to solve the inversion objective function to output a seismic profile with high signal-to-noise ratio, high resolution, and amplitude preservation.

[0064] In the above embodiments, the multi-parameter fused reflection intensity is obtained based on the seismic profile, including: Analytical signals are obtained from seismic data in seismic profiles using Hilbert transform. Based on analytical signals The characteristic parameters are calculated from the seismic data, including amplitude envelope, instantaneous phase gradient, and frequency attenuation coefficient. The reflection intensity is constructed by weighted summation. The weights are determined by correlation analysis of the training samples composed of known well-side seismic data; Based on the seismic reflection phase axis, the calculated reflection intensity Match to the target interface.

[0065] In this embodiment, the reflection intensity for: , in, For amplitude envelope, For instantaneous phase gradient, This is the frequency attenuation coefficient.

[0066] In the above embodiments, the relative intensity ratio is calculated based on the reflection intensity of the target interface. and absolute strength ratio The reservoir level is determined by combining core data, specifically based on: and The physical relationship between the rock and the reservoir is used to construct a relative strength ratio to eliminate common interference from the overlying surrounding rocks, and an absolute strength ratio to quantify the degree of weathering. These two methods, combined with core data calibration, are used to achieve reservoir classification. The reflection coefficient corresponding to the unweathered bedrock interface; This represents the reflectance coefficient corresponding to the weathering crust interface; The reflection coefficient corresponding to the unweathered bedrock interface; This indicates the reflected seismic amplitude at the interface between the weathered crust and bedrock of the buried hill; This indicates the reflected seismic amplitude at the interface between the overlying strata and the weathered crust of the buried hill; This indicates the reflected seismic amplitude at the interface between the overlying strata and the bedrock of the buried hill.

[0067] In the above embodiments, the relative intensity ratio is calculated based on the reflection intensity of the target interface. and absolute strength ratio : Calculate the ratio of the reflection intensity of the overlying rock-weathered crust interface to that of the overlying rock-unweathered bedrock interface, and use this ratio as the relative intensity ratio. ; Calculate the ratio of the reflection intensity of the weathered crust-unweathered bedrock interface to that of the overlying surrounding rock-unweathered bedrock interface, and use this ratio as the absolute intensity ratio. .

[0068] In the above embodiments, the reservoir classification criteria are as follows: I. Reservoir: =0.2~0.4、 =0.1~0.3; Class II reservoirs: =0.4~0.6、 =0.3~0.5; Class III reservoirs: >0.6、 >0.5; Among them, Class I reservoirs are of excellent quality, Class II reservoirs are of effective quality, and Class III reservoirs are of ineffective quality.

[0069] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0070] In one embodiment of the present invention, a computing device is provided. This computing device can be a terminal and may include a processor, a communication interface, memory, a display screen, and an input device. The processor, communication interface, and memory communicate with each other via a communication bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. When the computer programs are executed by the processor, they implement the methods described in the above embodiments. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, a management network, NFC (Near Field Communication), or other technologies. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad mounted on the casing of the computing device, or an external keyboard, touchpad, or mouse. The processor can call logical instructions stored in the memory.

[0071] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] In one embodiment of the present invention, a computer program product is provided, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, and when the program instructions are executed by a computer, the computer is able to perform the methods provided in the above-described method embodiments.

[0073] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to perform the methods provided in the above embodiments.

[0074] The computer-readable storage medium provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0075] This 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 will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0076] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0077] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting weathered crust reservoirs in buried hills based on seismic reflection intensity difference detection, characterized in that, include: The post-stack seismic data are sequentially subjected to trace equalization, spherical diffusion correction, multi-level amplitude-preserving denoising, and sparse inversion to output a high signal-to-noise ratio, high resolution, and amplitude-preserving seismic profile. The reflection intensity is obtained by multi-parameter fusion based on seismic profiles. Complex seismic traces are extracted by Hilbert transform, and the amplitude envelope, instantaneous phase gradient and frequency attenuation coefficient are calculated. The reflection intensity of the target interface is obtained by weighted fusion. Calculate the relative intensity ratio based on the reflection intensity of the target interface. and absolute strength ratio The reservoir level was determined by combining core data.

2. The method for predicting weathered crust reservoirs in buried hills based on seismic reflection intensity difference detection as described in claim 1, characterized in that, The post-stack seismic data is sequentially subjected to trace equalization, spherical diffusion correction, multi-level amplitude-preserving denoising, and sparse inversion to output high signal-to-noise ratio, high resolution, and amplitude-preserving seismic profiles, including: Trace equalization and spherical diffusion correction are performed on post-stack seismic data to eliminate the effects of geometric attenuation. Multichannel coherent denoising is used for the first stage of denoising, and wavelet adaptive threshold denoising is used for the second stage of denoising, in order to preserve the effective signal amplitude; An inversion objective function is constructed, and an iterative soft thresholding algorithm is used to solve the inversion objective function to output a seismic profile with high signal-to-noise ratio, high resolution, and amplitude preservation.

3. The method for predicting weathered crust reservoirs in buried hills based on seismic reflection intensity difference detection as described in claim 1, characterized in that, The multi-parameter fused reflection intensity is obtained based on seismic profiles, including: Analytical signals are obtained from seismic data in seismic profiles using Hilbert transform. Based on analytical signals The characteristic parameters are calculated from the seismic data, including amplitude envelope, instantaneous phase gradient, and frequency attenuation coefficient. The reflection intensity is constructed by weighted summation. The weights are determined by correlation analysis of the training samples composed of known well-side seismic data; Based on the seismic reflection phase axis, the calculated reflection intensity Match to the target interface.

4. The method for predicting weathered crust reservoirs in buried hills based on seismic reflection intensity difference detection as described in claim 3, characterized in that, Reflection intensity for: ， in, For amplitude envelope, For instantaneous phase gradient, This is the frequency attenuation coefficient.

5. The method for predicting weathered crust reservoirs in buried hills based on seismic reflection intensity difference detection as described in claim 1, characterized in that, Calculate the relative intensity ratio based on the reflection intensity of the target interface. and absolute strength ratio The reservoir level is determined by combining core data, specifically based on: and The physical relationship between the rock and the reservoir is used to construct a relative strength ratio to eliminate common interference from the overlying surrounding rocks, and an absolute strength ratio to quantify the degree of weathering. These two methods, combined with core data calibration, are used to achieve reservoir classification. The reflection coefficient corresponding to the unweathered bedrock interface; This represents the reflectance coefficient corresponding to the weathering crust interface; The reflection coefficient corresponding to the unweathered bedrock interface; This indicates the reflected seismic amplitude at the interface between the weathered crust and bedrock of the buried hill; This indicates the reflected seismic amplitude at the interface between the overlying strata and the weathered crust of the buried hill; This represents the reflected seismic amplitude at the interface between the overlying strata and the bedrock of the buried hill.

6. The method for predicting weathered crust reservoirs in buried hills based on seismic reflection intensity difference detection as described in claim 5, characterized in that, Calculate the relative intensity ratio based on the reflection intensity of the target interface. and absolute strength ratio : Calculate the ratio of the reflection intensity of the overlying rock-weathered crust interface to that of the overlying rock-unweathered bedrock interface, and use this ratio as the relative intensity ratio. ; Calculate the ratio of the reflection intensity of the weathered crust-unweathered bedrock interface to that of the overlying surrounding rock-unweathered bedrock interface, and use this ratio as the absolute intensity ratio. .

7. The method for predicting weathered crust reservoirs in buried hills based on seismic reflection intensity difference detection as described in claim 5, characterized in that, The reservoir classification criteria are as follows: I. Reservoir: =0.2~0.4、 =0.1~0.3; Class II reservoirs: =0.4~0.6、 =0.3~0.5; Class III reservoirs: >0.6、 >0.5; Among them, Class I reservoirs are of excellent quality, Class II reservoirs are of effective quality, and Class III reservoirs are of ineffective quality.

8. A method for predicting weathered crust reservoirs in buried hills based on seismic reflection intensity difference detection, characterized in that, include: The seismic data amplitude preservation optimization processing module performs trace equalization, spherical diffusion correction, multi-level amplitude preservation denoising and sparse inversion on the post-stack seismic data in sequence, and outputs a high signal-to-noise ratio, high resolution and amplitude preservation seismic profile. The multi-parameter fusion reflection intensity calculation module calculates the multi-parameter fusion reflection intensity based on the seismic profile. It extracts complex seismic traces through Hilbert transform, calculates the amplitude envelope, instantaneous phase gradient and frequency attenuation coefficient, and weighted fuses them to obtain the reflection intensity of the target interface. The dual reflection intensity ratio quantification analysis module calculates the relative intensity ratio based on the reflection intensity of the target interface. and absolute strength ratio The reservoir level was determined by combining core data.

9. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 7.

10. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described in claims 1 to 7.

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