A method and system for seismic prediction of weak amplitude delta reservoirs

Abstract

This invention discloses a seismic prediction method for weak-amplitude deltaic reservoirs, comprising the following steps: conducting seismic facies characteristic analysis of typical profiles to determine the seismic response characteristics of deltaic sandstone and lacustrine mudstone; conducting rock physical analysis to determine the P-wave impedance distribution range of sandstone and mudstone; performing relative impedance inversion on seismic data; extracting chaotic attributes from seismic data; performing low-pass filtering on the extracted chaotic attributes; performing structure-guided filtering on the low-pass filtered data; expanding the structure-guided filtering data to the range of P-wave impedance to complete the low-frequency model construction; fusing the constructed low-frequency model with the relative impedance inversion results to obtain the final inversion result; and using the final inversion result to conduct a comprehensive analysis of dominant reservoirs. This invention solves the problem of inaccurate prediction of weak-amplitude deltaic reservoirs in existing technologies by constructing attribute-controlled low-frequency models and performing frequency-division fusion inversion.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas exploration technology, and more specifically, it relates to a seismic prediction method and system for weak-amplitude deltaic reservoirs during the exploration stage. Background Technology

[0002] Recent decades of oil and gas field exploration have shown that many oil and gas fields worldwide are closely related to deltaic sediments. Numerous large and super-large oil and gas fields have been discovered within deltaic sedimentary systems. In my country, the Daqing, Changqing, and Shengli oilfields are all examples of lacustrine deltaic deposits. According to petrological principles, shallow sandstone in sedimentary basins exhibits lower P-wave impedance compared to mudstone, and deltaic facies often display strong amplitude seismic reflections. As burial depth increases, deltaic sandstone reservoirs gradually transition from having similar P-wave impedance to mudstone to exhibiting higher P-wave impedance relative to mudstone. At greater burial depths, deltaic reservoirs often exhibit weak amplitude seismic reflections. In contrast, lacustrine environments, due to their deep waters, rapid burial rates, and the large thickness, wide distribution, and stable deposition of mudstone, experience disturbances in the mudstone properties caused by water level fluctuations, often resulting in continuous strong reflections.

[0003] Conventional methods for predicting sandstone reservoirs in weak-amplitude deltas often rely on attribute extraction and inversion. Attribute extraction methods can characterize the planar distribution trends of sandstone bodies based on the different seismic reflection characteristics of deltaic sandstone and lacustrine mudstone, but they often fail to accurately identify the development locations of sandstone and mudstone on the cross-section. Inversion methods, based on relative impedance profiles, obtain the distribution trends of sandstone and mudstone by adding a low-frequency model. On the relative impedance profile, the distribution characteristics of sandstone and mudstone can be identified at the delta location. However, for lacustrine strata dominated by mudstone, the relative impedance inversion profile can also identify relatively high and low impedances, and the difference between these high and low values ​​is even greater, which can cause confusion in the interpretation of sandstone and mudstone, leading to inversion profile characteristics that are inconsistent with geological understanding. Meanwhile, due to the lack of low-frequency components in seismic data, to supplement these components, a layer-constrained well logging interpolation low-frequency model is often constructed. This method is greatly affected by the strata and is prone to "bull's-eye" phenomena around the well data. While local profiles may conform to geological understanding, the 3D volume inversion results often do not reflect objective reality. In summary, relative impedance inversion results cannot fully describe the distribution characteristics of large-scale thick geological bodies. Furthermore, conventional well logging interpolation low-frequency model construction methods cannot describe the objective distribution of sandstone and mudstone, and conventional methods cannot effectively characterize the differences between lacustrine and deltaic facies.

[0004] Overall, there is currently limited research in the industry on geophysical prediction of weak amplitude deltaic reservoirs, and there is no mature geophysical method that can predict the distribution of weak amplitude deltaic reservoirs in low-well areas during the exploration phase. Summary of the Invention

[0005] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention provides a seismic prediction method for weak-amplitude deltaic reservoirs. It aims to solve the problem of inaccurate prediction of weak-amplitude deltaic reservoirs in the prior art by constructing an attribute-controlled low-frequency model and performing frequency-division fusion inversion. The final reservoir prediction results provide technical support for comprehensive target evaluation and well location deployment.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a seismic prediction method for weak-amplitude deltaic reservoirs, comprising the following steps:

[0008] Based on the drilling data of the study area, typical profile seismic facies characteristics analysis is carried out to determine the seismic response characteristics of deltaic sandstone and lacustrine mudstone. If the deltaic sandstone has poorer continuity and weaker amplitude than the lacustrine mudstone, proceed to the next step; otherwise, the method ends.

[0009] Based on the drilling data of the study area, rock physical analysis is carried out to determine the P-wave impedance distribution range of deltaic sandstone and lacustrine mudstone. If the P-wave impedance of deltaic sandstone is higher than that of lacustrine mudstone, proceed to the next step; otherwise, the method ends.

[0010] Relative impedance inversion is performed on seismic data to obtain the relative impedance inversion results;

[0011] Chaotic attribute extraction is performed on seismic data;

[0012] The extracted chaotic properties are subjected to low-pass filtering.

[0013] A guided filter is constructed to process the chaotic attribute data after low-pass filtering.

[0014] The chaotic attribute data after structural guided filtering was extended to the range of longitudinal wave impedance distribution of deltaic sandstone and lacustrine mudstone to complete the construction of the low-frequency model.

[0015] The constructed low-frequency model and the relative wave impedance inversion results are then subjected to frequency division and fusion inversion to obtain the final phase control inversion results.

[0016] A comprehensive analysis of dominant reservoirs is conducted using phase-controlled inversion results, and the distribution of dominant reservoirs is predicted by capturing profiles and planes.

[0017] Preferably, the rock physical analysis specifically includes:

[0018] Well logging data for the study area were collected, including P-wave velocity, density, and mud content curves. P-wave impedance was calculated by multiplying P-wave velocity and density, and a cross plot of P-wave impedance and depth was created. The distribution range of P-wave impedance for all lithologies was analyzed, and the distribution range of P-wave impedance for deltaic sandstone and lacustrine mudstone, as well as the P-wave impedance threshold values ​​for deltaic sandstone and lacustrine mudstone were obtained.

[0019] Preferably, the relative wave impedance inversion needs to be carried out by combining well seismic calibration, constrained sparse pulse inversion, and 90° phase shift, specifically:

[0020] Well-seismic calibration is performed to determine the polarity of the wavelet: if the positive reflection coefficient corresponds to a positive polarity wavelet, a -90° phase shift is performed; if the positive reflection coefficient corresponds to a negative polarity wavelet, a 90° phase shift is performed.

[0021] Perform constrained sparse pulse inversion to determine the numerical range of the relative wave impedance results. After constrained sparse pulse inversion, check the numerical range of the high-pass filtered longitudinal wave impedance inversion results.

[0022] Based on the obtained phase shift result, it is scaled proportionally by multiplying it by a certain coefficient to achieve the numerical range of the relative wave impedance result determined by the high-pass filtering of the constrained sparse pulse inversion result.

[0023] As a preferred option: the chaotic property is based on the discontinuity detection method of gradient structure tensor. This method uses gradient structure tensor to describe the dip angle and azimuth of geological bodies and uses spectral decomposition of gradient tensor matrix to describe the structural features of seismic data.

[0024] Gradient structure tensor M ρ Defined as:

[0025]

[0026] Using the isotropic Gaussian kernel function G ρ Smoothly average the components of the gradient vector, where G ρ The standard deviation is ρ, and its form is:

[0027]

[0028] In the formula, U represents the original seismic data; U σ The data is the smoothed seismic data, where σ is the noise scaling factor; G σ σ is an isotropic Gaussian kernel with a scale parameter of σ; x, y, z are the spatial orientations;

[0029] For gradient structure tensor M ρ Perform matrix eigenvalue decomposition:

[0030]

[0031] In the formula, λ1≥λ2≥λ3≥0 represents M. ρ The three non-negative eigenvalues ​​are v1, v2, and v3, which are the corresponding eigenvectors. Based on this, the chaotic attribute m is constructed. chaos as follows:

[0032]

[0033] Chaotic properties based on gradient structure tensors can effectively reflect the regularity of local construction: m chaos →1, corresponding to a transverse discontinuity structure; m chaos →0 corresponds to a region with irregular and chaotic reflections; m chaos →-1 corresponds to a regular layered structure.

[0034] Preferably, the low-pass filtering is implemented using a trapezoidal filter, with the upper limit of the trapezoidal filter based on the low-frequency cutoff value of the seismic data.

[0035] As a preferred option: the structure-guided filtering described above uses the dip angle and azimuth angle of the strata to perform directional filtering along the strata. It has the functions of directional filtering, edge detection, and edge protection. It only smooths the information parallel to the seismic phase axis, and does not smooth the information perpendicular to the seismic phase axis. If a lateral discontinuity of the seismic phase axis is found, it will not be smoothed.

[0036] As a preferred option, the specific method for constructing a low-frequency model is as follows:

[0037] Based on the P-wave impedance distribution range of all lithologies, the chaotic attribute data after low-pass filtering is proportionally amplified, with the amplification ratio being 1 / 2 of the maximum value in the P-wave impedance distribution range, to obtain chaotic attribute data a.

[0038] Assuming the P-wave impedance threshold value of the deltaic sandstone and lacustrine mudstone is determined to be b, referring to the data distribution histogram, the magnified chaotic attribute data a is stretched or compressed to obtain chaotic attribute data c. It is ensured that the position of the highest frequency distribution of chaotic attribute data c is the threshold value b, and the maximum and minimum values ​​of chaotic attribute data c are basically consistent with the maximum and minimum values ​​of the P-wave impedance distribution range.

[0039] The final result is a low-frequency model within the range of longitudinal wave impedance distribution in the rock physical analysis results.

[0040] As a preferred embodiment, the frequency division fusion inversion method involves adding the low-frequency model with the phase impedance inversion result of the step phase to obtain the phase control inversion result after supplementing the low-frequency model.

[0041] Secondly, the present invention provides a seismic prediction system for weak-amplitude deltaic reservoirs, comprising:

[0042] The first processing unit is used to conduct seismic facies characteristic analysis of typical profiles based on the well data already drilled in the study area, and to determine the seismic response characteristics of deltaic sandstone and lacustrine mudstone.

[0043] The second processing unit is used to conduct rock physical analysis based on the well data already drilled in the study area to determine the range of longitudinal wave impedance distribution of deltaic sandstone and lacustrine mudstone.

[0044] The third processing unit is used to perform relative acoustic impedance inversion on seismic data and obtain the relative acoustic impedance inversion results.

[0045] The fourth processing unit is used to extract chaotic attributes from seismic data;

[0046] The fifth processing unit is used to perform low-pass filtering on the extracted chaotic properties;

[0047] The sixth processing unit is used to construct a guided filter for the chaotic attribute data after low-pass filtering;

[0048] The seventh processing unit is used to extend the chaotic attribute data after the structure-guided filtering process to the P-wave impedance distribution range of deltaic sandstone and lacustrine mudstone, and complete the low-frequency model construction.

[0049] The eighth processing unit is used to perform frequency division and fusion inversion on the constructed low-frequency model and the relative wave impedance inversion results to obtain the final phase control inversion results.

[0050] The ninth processing unit is used to conduct comprehensive analysis of dominant reservoirs using phase-controlled inversion results, and predicts the distribution of dominant reservoirs by capturing profiles and planes.

[0051] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the steps of the seismic prediction method for weak amplitude deltaic reservoirs described in the first aspect of the present invention.

[0052] The present invention has the following advantages due to the adoption of the above technical solutions:

[0053] 1. The seismic prediction method for weak amplitude deltaic reservoirs provided by this invention can supplement conventional inversion methods by constructing attribute phase-controlled low-frequency models and using frequency-division fusion inversion methods. Under the guidance of geological understanding, the distribution characteristics of sand bodies and mudstones in the three-dimensional space of the target area are obtained, and the prediction results are consistent with geological understanding.

[0054] 2. This invention, through its detailed characterization, can provide effective technical support for the comprehensive evaluation of targets in low-well areas and the deployment of well locations during the exploration phase. Attached Figure Description

[0055] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings:

[0056] Figure 1 This is a flowchart of a seismic prediction method for weak-amplitude deltaic reservoirs provided in an embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this 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 this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0058] The seismic prediction method for weak-amplitude deltaic reservoirs provided by this invention includes the following steps: conducting seismic facies characteristic analysis of typical profiles to determine the seismic response characteristics of deltaic sandstone and lacustrine mudstone; conducting rock physical analysis to determine the P-wave impedance distribution range of sandstone and mudstone; performing relative impedance inversion on seismic data; extracting chaotic attributes from seismic data; performing low-pass filtering on the extracted chaotic attributes; performing structure-guided filtering on the low-pass filtered data; expanding the structure-guided filtering data to the range of P-wave impedance to complete the low-frequency model construction; fusing the constructed low-frequency model with the relative impedance inversion results to obtain the final inversion result; and using the final inversion result to conduct a comprehensive analysis of dominant reservoirs. This invention solves the problem of inaccurate prediction of weak-amplitude deltaic reservoirs in existing technologies by constructing attribute-controlled low-frequency models and performing frequency-division fusion inversion.

[0059] The seismic prediction method and system for weak-amplitude deltaic reservoirs provided in the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0060] Example 1:

[0061] Please see Figure 1 This embodiment provides a seismic prediction method for weak-amplitude deltaic reservoirs, comprising the following steps:

[0062] S100. Based on the drilling data of the study area, conduct seismic facies characteristic analysis of typical profiles to determine the seismic response characteristics of deltaic sandstone and lacustrine mudstone. If the deltaic sandstone has poorer continuity and weaker amplitude than the lacustrine mudstone, proceed to step S200; otherwise, the method ends.

[0063] According to geological understanding, due to the different sedimentary environments of lacustrine and deltaic facies zones, lacustrine mudstones often exhibit continuous strong reflection characteristics, while deltaic sandstones buried deeper often exhibit poor continuous medium-to-weak amplitude reflection characteristics.

[0064] S200. Conduct rock physical analysis based on the drilling data of the study area to determine the P-wave impedance distribution range of deltaic sandstone and lacustrine mudstone. If the P-wave impedance of deltaic sandstone is higher than that of lacustrine mudstone, proceed to step S300; otherwise, the method ends.

[0065] Specifically, rock physical analysis includes:

[0066] Well logging data for the study area were collected, including P-wave velocity, density, and mud content curves. P-wave impedance was calculated by multiplying P-wave velocity and density, and a cross plot of P-wave impedance and depth was created. The distribution range of P-wave impedance for all lithologies was analyzed, and the distribution range of P-wave impedance for deltaic sandstone and lacustrine mudstone, as well as the P-wave impedance threshold values ​​for deltaic sandstone and lacustrine mudstone were obtained.

[0067] S300. Perform relative wave impedance inversion on seismic data.

[0068] Among them, relative wave impedance inversion needs to be carried out by combining well-seismic calibration, constrained sparse pulse inversion, and 90° phase shift, specifically as follows:

[0069] S301. Perform well-seismic calibration to determine the polarity of the wavelet: if the positive reflection coefficient corresponds to a positive polarity wavelet, then perform a -90° phase shift; if the positive reflection coefficient corresponds to a negative polarity wavelet, then perform a 90° phase shift.

[0070] S302. Perform constrained sparse pulse inversion (constrained sparse pulse inversion is a commonly used inversion method in industry), determine the numerical range of the relative wave impedance results, and after constrained sparse pulse inversion, check the numerical range of the high-pass filtered P-wave impedance inversion results. The high-pass filtering is based on the low-frequency cutoff value of the seismic data. For example, if the low-frequency cutoff value is 6Hz, then check the inversion results above 6Hz. This numerical range can provide a reference for the -90° or 90° phase shift results in step S301.

[0071] S303. Based on the phase shift result obtained in step S301, the phase shift result is scaled proportionally by multiplying it by a certain coefficient to reach the numerical range of the relative wave impedance result determined by the high-pass filtering of the constrained sparse pulse inversion result in step S302.

[0072] S400. Extract chaotic attributes from seismic data.

[0073] Among them, the chaotic attribute is a discontinuity detection method based on gradient structure tensor (GST). This method uses gradient structure tensor to describe the dip and azimuth of geological bodies and uses spectral decomposition of gradient tensor matrix to describe the structural characteristics of seismic data. It can effectively identify the inconsistencies in energy changes and dip within the analysis time window.

[0074] Gradient structure tensor M ρ Defined as:

[0075]

[0076] Using the isotropic Gaussian kernel function G ρ Smoothly average the components of the gradient vector, where G ρ The standard deviation is ρ, and its form is:

[0077]

[0078] In the formula, U represents the original seismic data; U σ The data is the smoothed seismic data, where σ is the noise scaling factor; G σ σ is an isotropic Gaussian kernel with a scale parameter of σ; x, y, and z are the spatial orientations.

[0079] For gradient structure tensor M ρ Perform matrix eigenvalue decomposition:

[0080]

[0081] In the formula, λ1≥λ2≥λ3≥0 represents M. ρ The three non-negative eigenvalues ​​are v1, v2, and v3, which are the corresponding eigenvectors. Based on this, the chaotic attribute m is constructed. chaos as follows:

[0082]

[0083] Chaotic properties based on gradient structure tensors can effectively reflect the regularity of local construction: m chaos →1, corresponding to a transverse discontinuity structure; m chaos →0 corresponds to a region with irregular and chaotic reflections; m chaos →-1 corresponds to a regular layered structure. For deltaic sandstone and lacustrine mudstone, deep deltaic sandstone often exhibits weak amplitude difference continuous reflection characteristics, while lacustrine mudstone often exhibits continuous strong reflection characteristics. The differences between the two sedimentary facies can be effectively described by the chaotic properties based on the gradient structure tensor.

[0084] S500. Perform low-pass filtering on the extracted chaotic attributes.

[0085] Low-pass filtering is achieved through a trapezoidal filter. The upper limit of the trapezoidal filter is based on the low-frequency cutoff value of the seismic data. After analyzing the seismic data spectrum, if the frequency band of the seismic data is 6-50Hz, then low-pass filtering is used to obtain chaotic attribute data below 6Hz.

[0086] S600. Construct a guided filter for the chaotic attribute data after low-pass filtering.

[0087] Structure-guided filtering utilizes the dip and azimuth angles of strata to perform directional filtering along the strata. It features directional filtering, edge detection, and edge protection. It smooths only information parallel to the seismic phase axis, without smoothing information perpendicular to it. If a lateral discontinuity in the seismic phase axis is detected, no smoothing is applied. Structure-guided filtering follows the continuity of the seismic phase axis, resulting in filtering results that are more geologically accurate.

[0088] S700. Extend the chaotic attribute data after the construction-guided filtering process to the P-wave impedance distribution range of deltaic sandstone and lacustrine mudstone in step S200 to complete the low-frequency model construction. The specific method is as follows:

[0089] S701. Based on the P-wave impedance distribution range of all lithologies in step S200, the chaotic attribute data after low-pass filtering is proportionally amplified, with the amplification ratio being 1 / 2 of the maximum value in the P-wave impedance distribution range, to obtain chaotic attribute data a.

[0090] S702. Assuming that the longitudinal wave impedance threshold value of the deltaic sandstone and lacustrine mudstone determined in step S200 is b, refer to the data distribution histogram, stretch or compress the magnified chaotic attribute data a to obtain chaotic attribute data c, ensuring that the position of the highest frequency distribution of the chaotic attribute data c is the threshold value b, and that the maximum and minimum values ​​of the chaotic attribute data c are basically consistent with the maximum and minimum values ​​of the longitudinal wave impedance distribution range in step S200.

[0091] S703. Finally, a low-frequency model within the range of longitudinal wave impedance distribution in the rock physics analysis results is obtained.

[0092] S800. The constructed low-frequency model and the relative wave impedance inversion results are combined and inverted by frequency division to obtain the final phased inversion results.

[0093] The frequency division fusion inversion method involves adding the low-frequency model in step S700 with the relative wave impedance inversion result in step S300 to obtain the phase control inversion result after supplementing the low-frequency model.

[0094] S900. Conduct comprehensive analysis of dominant reservoirs using phase-controlled inversion results, and predict the distribution of dominant reservoirs by capturing profiles and planes.

[0095] Example 2:

[0096] The above-described embodiment 1 provides a seismic prediction method for weak-amplitude deltaic reservoirs. Correspondingly, this embodiment provides a seismic prediction system for weak-amplitude deltaic reservoirs. The seismic prediction system provided in this embodiment can implement the seismic prediction method of embodiment 1. This seismic prediction system can be implemented through software, hardware, or a combination of both. For example, the seismic prediction system may include integrated or separate functional modules or units to perform the corresponding steps in the methods of embodiment 1. Since the seismic prediction system for weak-amplitude deltaic reservoirs in this embodiment is basically similar to the method embodiment, the description process of this embodiment is relatively simple. Relevant details can be found in the description of embodiment 1. The seismic prediction system for weak-amplitude deltaic reservoirs in this embodiment is merely illustrative.

[0097] The seismic prediction system for weak-amplitude deltaic reservoirs provided in this embodiment includes:

[0098] The first processing unit is used to conduct seismic facies characteristic analysis of typical profiles based on the well data already drilled in the study area, and to determine the seismic response characteristics of deltaic sandstone and lacustrine mudstone.

[0099] The second processing unit is used to conduct rock physical analysis based on the well data already drilled in the study area to determine the range of longitudinal wave impedance distribution of deltaic sandstone and lacustrine mudstone.

[0100] The third processing unit is used to perform relative acoustic impedance inversion on seismic data and obtain the relative acoustic impedance inversion results.

[0101] The fourth processing unit is used to extract chaotic attributes from seismic data;

[0102] The fifth processing unit is used to perform low-pass filtering on the extracted chaotic properties;

[0103] The sixth processing unit is used to construct a guided filter for the chaotic attribute data after low-pass filtering;

[0104] The seventh processing unit is used to extend the chaotic attribute data after structure-guided filtering to the P-wave impedance distribution range of all lithologies, and complete the low-frequency model construction.

[0105] The eighth processing unit is used to perform frequency division and fusion inversion on the constructed low-frequency model and the relative wave impedance inversion results to obtain the final phase control inversion results.

[0106] The ninth processing unit is used to conduct comprehensive analysis of dominant reservoirs using phase-controlled inversion results, and predicts the distribution of dominant reservoirs by capturing profiles and planes.

[0107] Example 3:

[0108] This embodiment provides a processing device for implementing the seismic prediction method for weak-amplitude deltaic reservoirs provided in Embodiment 1. The processing device can be a client-side processing device, such as a mobile phone, laptop, tablet computer, or desktop computer, to execute the method of Embodiment 1.

[0109] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to enable communication between them. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the seismic prediction method for weak-amplitude deltaic reservoirs provided in Embodiment 1.

[0110] Preferably, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0111] Preferably, the processor can be any type of general-purpose processor such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation herein.

[0112] Example 4:

[0113] The seismic prediction method for weak-amplitude deltaic reservoirs in Embodiment 1 can be specifically implemented as a computer program product. The computer program product may include a computer-readable storage medium on which computer-readable program instructions for executing the method described in Embodiment 1 are loaded.

[0114] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application 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 or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A seismic prediction method for weak-amplitude deltaic reservoirs, characterized in that, Includes the following steps: Based on the drilling data of the study area, typical profile seismic facies characteristics analysis is carried out to determine the seismic response characteristics of deltaic sandstone and lacustrine mudstone. If the deltaic sandstone has poorer continuity and weaker amplitude than the lacustrine mudstone, proceed to the next step; otherwise, the method ends. Based on the drilling data of the study area, rock physical analysis is carried out to determine the P-wave impedance distribution range of deltaic sandstone and lacustrine mudstone. If the P-wave impedance of deltaic sandstone is higher than that of lacustrine mudstone, proceed to the next step; otherwise, the method ends. Relative impedance inversion is performed on seismic data to obtain the relative impedance inversion results; Chaotic attribute extraction is performed on seismic data; The extracted chaotic properties are subjected to low-pass filtering. A guided filter is constructed to process the chaotic attribute data after low-pass filtering. The chaotic attribute data after guided filtering were extended to the P-wave impedance distribution range of deltaic sandstone and lacustrine mudstone to complete the low-frequency model construction; the specific method for constructing the low-frequency model is as follows: Based on the P-wave impedance distribution range of all lithologies, the chaotic attribute data after low-pass filtering is proportionally amplified, with the amplification ratio being 1 / 2 of the maximum value in the P-wave impedance distribution range, to obtain chaotic attribute data a. Assuming the P-wave impedance threshold value of the deltaic sandstone and lacustrine mudstone is determined to be b, referring to the data distribution histogram, the magnified chaotic attribute data a is stretched or compressed to obtain chaotic attribute data c. It is ensured that the position of the highest frequency distribution of chaotic attribute data c is the threshold value b, and the maximum and minimum values ​​of chaotic attribute data c are basically consistent with the maximum and minimum values ​​of the P-wave impedance distribution range. The final result is a low-frequency model within the range of longitudinal wave impedance distribution in the rock physical analysis results; The constructed low-frequency model and the relative wave impedance inversion results are then subjected to frequency division and fusion inversion to obtain the final phase control inversion results. A comprehensive analysis of dominant reservoirs is conducted using phase-controlled inversion results, and the distribution of dominant reservoirs is predicted by capturing profiles and planes.

2. The earthquake prediction method according to claim 1, characterized in that, The rock physical analysis specifically includes: Well logging data for the study area were collected, including P-wave velocity, density, and mud content curves. P-wave impedance was calculated by multiplying P-wave velocity and density, and a cross plot of P-wave impedance and depth was created. The distribution range of P-wave impedance for all lithologies was analyzed, and the distribution range of P-wave impedance for deltaic sandstone and lacustrine mudstone, as well as the P-wave impedance threshold values ​​for deltaic sandstone and lacustrine mudstone were obtained.

3. The earthquake prediction method according to claim 2, characterized in that, The relative wave impedance inversion described above requires combining well-seismic calibration, constrained sparse pulse inversion, and a 90° phase shift, specifically as follows: Well-seismic calibration is performed to determine the polarity of the wavelet: if the positive reflection coefficient corresponds to a positive polarity wavelet, a -90° phase shift is performed; if the positive reflection coefficient corresponds to a negative polarity wavelet, a 90° phase shift is performed. Perform constrained sparse pulse inversion to determine the numerical range of the relative wave impedance results. After constrained sparse pulse inversion, check the numerical range of the high-pass filtered longitudinal wave impedance inversion results. Based on the obtained phase shift result, it is scaled proportionally by multiplying it by a certain coefficient to achieve the numerical range of the relative wave impedance result determined by the high-pass filtering of the constrained sparse pulse inversion result.

4. The earthquake prediction method according to claim 3, characterized in that, The chaotic property described is based on the discontinuity detection method of gradient structure tensor. This method uses gradient structure tensor to describe the dip angle and azimuth of geological bodies and uses spectral decomposition of gradient tensor matrix to describe the structural features of seismic data. Gradient structure tensor Defined as: Using isotropic Gaussian kernel function Smoothly average the components of the gradient vector, where Standard deviation is Its form is: In the formula, U represents the original seismic data; The smoothed seismic data, where This is the noise scaling factor; The scale parameter is An isotropic Gaussian kernel; x , y , z For spatial orientation; Gradient structure tensor Perform matrix eigenvalue decomposition: In the formula, for ; , , The corresponding feature vectors are used to construct chaotic attributes. as follows: -1 Chaotic properties based on gradient structure tensors can effectively reflect the regularity of local construction: This corresponds to a transverse discontinuity structure; This corresponds to a region with irregular and chaotic reflections; This corresponds to a regular layered structure.

5. The earthquake prediction method according to claim 4, characterized in that, The low-pass filtering is implemented using a trapezoidal filter, with the upper limit of the trapezoidal filter based on the low-frequency cutoff value of the seismic data.

6. The earthquake prediction method according to claim 5, characterized in that, The structure-guided filtering described above uses the dip angle and azimuth angle of the strata to perform directional filtering along the strata. It has the functions of directional filtering, edge detection, and edge protection. It only smooths the information parallel to the seismic phase axis, and does not smooth the information perpendicular to the seismic phase axis. If a lateral discontinuity of the seismic phase axis is found, no smoothing will be performed.

7. The earthquake prediction method according to claim 6, characterized in that, The frequency division fusion inversion method involves adding the low-frequency model with the phase impedance inversion result of the step phase to obtain the phase control inversion result after supplementing the low-frequency model.

8. A seismic prediction system for weak-amplitude deltaic reservoirs, characterized in that, include: The first processing unit is used to conduct seismic facies characteristic analysis of typical profiles based on the well data already drilled in the study area, and to determine the seismic response characteristics of deltaic sandstone and lacustrine mudstone. The second processing unit is used to conduct rock physical analysis based on the well data already drilled in the study area to determine the range of longitudinal wave impedance distribution of deltaic sandstone and lacustrine mudstone. The third processing unit is used to perform relative wave impedance inversion on seismic data and obtain the relative wave impedance inversion results. The fourth processing unit is used to extract chaotic attributes from seismic data; The fifth processing unit is used to perform low-pass filtering on the extracted chaotic properties; The sixth processing unit is used to construct a guided filter for the chaotic attribute data after low-pass filtering; The seventh processing unit is used to extend the chaotic attribute data after structurally guided filtering to the P-wave impedance distribution range of deltaic sandstone and lacustrine mudstone, completing the low-frequency model construction; the specific method for constructing the low-frequency model is as follows: Based on the P-wave impedance distribution range of all lithologies, the chaotic attribute data after low-pass filtering is proportionally amplified, with the amplification ratio being 1 / 2 of the maximum value in the P-wave impedance distribution range, to obtain chaotic attribute data a. Assuming the P-wave impedance threshold value of the deltaic sandstone and lacustrine mudstone is determined to be b, referring to the data distribution histogram, the magnified chaotic attribute data a is stretched or compressed to obtain chaotic attribute data c. It is ensured that the position of the highest frequency distribution of chaotic attribute data c is the threshold value b, and the maximum and minimum values ​​of chaotic attribute data c are basically consistent with the maximum and minimum values ​​of the P-wave impedance distribution range. The final result is a low-frequency model within the range of longitudinal wave impedance distribution in the rock physical analysis results; The eighth processing unit is used to perform frequency division and fusion inversion on the constructed low-frequency model and the relative wave impedance inversion results to obtain the final phase control inversion results. The ninth processing unit is used to conduct comprehensive analysis of dominant reservoirs using phase-controlled inversion results, and predicts the distribution of dominant reservoirs by capturing profiles and planes.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the seismic prediction method for weak-amplitude deltaic reservoirs as described in any one of claims 1-7.

Images