Laminate type shale oil reservoir seismic dessert prediction method, system, device and medium

Abstract

This invention belongs to the field of seismology technology and discloses a method, system, equipment, and medium for predicting seismic sweet spots in lamellar shale oil reservoirs. The method includes: obtaining a linear fitting relationship between elastic parameters and formation quartz content and porosity based on rock physics and well logging data; establishing an approximate equation for the PP reflection coefficient characterizing porosity, quartz content, and density based on the linear fitting relationship; establishing a least-squares functional of observed and simulated data based on the PP reflection coefficient approximate equation, and obtaining the formation quartz content percentage through inversion iteration; and predicting the seismic sweet spots of lamellar shale oil reservoirs based on the formation quartz content percentage through planar distribution. This invention creatively develops a waveform indication inversion technique based on reservoir sensitivity curve reconstruction, effectively predicting the distribution range of 73 segments of lamellar shale oil reservoirs. It can also predict the quartz content of shale layers, preferentially selecting lamellar shale oil reservoir distribution areas with high quartz content, good reservoir properties, and fracturing capabilities.

Description

Technical Field

[0001] This invention belongs to the field of seismology technology and relates to seismic inversion technology, particularly to a method, system, equipment, and medium for predicting seismic sweet spots in layered shale oil reservoirs. Background Technology

[0002] Shale oil in the Ordos Basin is a typical unconventional source-formed reservoir, classified into three types: interlayered, lamellar, and foliated. Among them, the Chang 71 and Chang 72 members are interlayered shale oil reservoirs, belonging to the upper sweet spot of the Chang 7 member. The Chang 73 member exhibits alternating lamellar and foliated reservoirs, belonging to the lower sweet spot of the Chang 7 member. Lamellar shale oil reservoirs exhibit rapid lateral variation, thin formations, and are difficult to predict seismic activity. Lamellar shale oil reservoirs are the primary target for future development. Currently, the target for horizontal well drilling and reservoir development of lamellar shale oil is high-quartz shale reservoirs, which have good oil reserves and engineering fracturing capabilities. Currently, among the various onshore seismic exploration methods, there is a lack of effective methods for predicting seismic sweet spots in lamellar shale oil reservoirs. Therefore, there is an urgent need to develop seismic prediction methods for high-quartz lamellar shale reservoirs.

[0003] Prior to this, seismic sweet spots in lamellar shale oil reservoirs could be predicted by the change in the delay time of adjacent valleys in the reflection phase axis of the mudstone section of post-stack seismic data. This technique, by calculating the delay time of the half-amplitude points of adjacent valleys, established a linear relationship between reservoir thickness and valley delay time, enabling the prediction of seismic sweet spots in the Chang 73 lamellar shale oil reservoir using seismic data.

[0004] For example, patent document CN117055104A discloses a "Shale Oil Reservoir Prediction Method Based on Seismic Valley Delay Time Analysis," which includes: Step 1: Obtaining typical well and well-side seismic data for the area to be predicted; Step 2: Establishing a shale oil reservoir seismic response model; Step 3: Establishing a relationship between shale oil reservoir thickness and the increase in seismic valley delay time; Step 4: Comparing and interpreting the upper and lower halves of the seismic valley; Step 5: Obtaining the valley delay time; Step 6: Predicting the shale oil reservoir; Step 7: Applying the prediction results from Step 6 to previous shale oil reservoir exploration and subsequent development. The drawback of this method is that it can only identify the extent of lamellar reservoirs, but cannot specifically predict the quartz content of the formation, thus failing to further optimize lamellar shale reservoirs with high quartz content, good reservoir properties, and fracturing capabilities. Summary of the Invention

[0005] The rapid lateral variation and thin strata of lamellar shale oil reservoirs, coupled with the difficulty in forming independent reflection axes on seismic profiles due to differences in elastic parameters between lamellar and surrounding rocks, hinder the precise local delineation of favorable sweet spots in practical applications, thus limiting the development and utilization of the Chang 73 lamellar shale oil reservoir. The purpose of this invention is to provide a method for...

[0006] This invention provides a method, system, equipment, and medium for predicting seismic sweet spots, solving the problem that conventional seismic reservoir prediction methods cannot predict and select high-quartz, well-concentrated, and fracture-resistant layered shale reservoirs. To achieve the above objectives, this invention provides the following technical solution:

[0007] This invention provides a method for predicting seismic sweet spots in lamellar shale oil reservoirs, the method comprising the following steps:

[0008] Based on rock physics and well logging data, a linear fitting relationship between elastic parameters and formation quartz content and porosity was obtained;

[0009] Based on the linear fitting relationship, an approximate equation for the reflectance coefficient of PP, characterized by porosity, quartz content, and density, is established.

[0010] Based on the approximate equation of the PP reflection coefficient, a least squares functional of the observed data and the simulated data is established, and the quartz content of the strata is obtained by inversion and iteration.

[0011] Based on the quartz content of the formation, seismic sweet spots in laminar shale oil reservoirs are predicted by planar distribution.

[0012] Furthermore, the method for obtaining a linear fitting relationship between elastic parameters and formation quartz content and porosity based on rock physics and well logging data includes:

[0013] Using rock physics data and well logging data, cross plots were created to show the relationship between velocity and porosity and quartz content. Based on the cross plot results, linear fitting was performed to derive the relationships between P-wave and S-wave velocities and porosity and quartz content.

[0014] Furthermore, the linear fitting relationship is as follows:

[0015]

[0016] Among them, v P V represents the longitudinal wave velocity. S φ represents the transverse wave velocity, φ represents the porosity, and S represents the quartz content within the volume of a spatial unit, ranging from 0 to 1.

[0017] Furthermore, the establishment of an approximate equation for the PP reflectance coefficient, characterizing porosity, quartz content, and density, based on the aforementioned linear fitting relationship, includes:

[0018] Based on the aforementioned linear fitting relationship, and using the classic Aki-Richards three-parameter linear approximation equation as a foundation, the longitudinal and transverse wave velocities are replaced by quartz content and porosity, and an approximate equation for the PP reflection coefficient characterized by porosity, quartz content, and density is established.

[0019] Furthermore, the approximate equation for the PP reflectance coefficient, characterized by porosity, quartz content, and density, is expressed as follows:

[0020]

[0021] in,

[0022]

[0023] Where R represents the reflection coefficient, θ represents the incident angle, φ represents the porosity, S represents the quartz content within the volume of a spatial unit (ranging from 0 to 1), ρ represents the density, and P1, P2, P3 represent coefficients, v P V represents the longitudinal wave velocity. S Let θ represent the transverse wave velocity, θ represent the incident angle, and Δ represent the disturbance quantity.

[0024] Furthermore, the process of establishing a least-squares functional based on the PP reflection coefficient approximation equation, and obtaining the quartz content ratio of the formation through inversion iteration, includes:

[0025] The approximate equation for the reflection coefficient of PP is transformed into a three-parameter elastic impedance inversion formula;

[0026] Based on the elastic impedance inversion formula, the EI data volume d is obtained by elastic impedance inversion using three seismic angle gathers as the observation data, and then the least squares functional of the observation data and the simulation data is established.

[0027] Based on the least squares functional, the quartz content of the formation is integrated and normalized through inversion iteration to obtain the quartz content ratio of the formation.

[0028] Furthermore, the elastic impedance inversion formula is as follows;

[0029]

[0030] Where EI represents elastic resistance, φ represents porosity, S represents the quartz content in the volume of a spatial unit, ranging from 0 to 1, ρ represents density, P1, P2, and P3 represent coefficients, and θ represents the angle of incidence.

[0031] Furthermore, the least squares functional expression is as follows:

[0032]

[0033] Where J represents the objective functional, m represents the physical property parameter, G represents the forward modeling equation (i.e., equation (2)), and d represents the observation data.

[0034] Furthermore, the expression for the proportion of quartz content in the formation is as follows:

[0035]

[0036] Where χ represents the percentage of quartz content in a certain stratum (maximum value is 1), S represents the quartz content in the volume of a spatial unit, 1 represents a pure quartz segment, h represents the thickness, Δ represents the disturbance amount, top represents the top of the stratum, and bot represents the bottom of the stratum.

[0037] The present invention also provides a seismic sweet spot prediction system for lamellar shale oil reservoirs, the system comprising:

[0038] The linear fitting relationship acquisition module is used to obtain linear fitting relationships between elastic parameters and formation quartz content and porosity based on rock physics and well logging data.

[0039] The PP reflectance coefficient approximation equation establishment module is used to establish an approximate equation for the PP reflectance coefficient based on the linear fitting relationship, which characterizes porosity, quartz content, and density.

[0040] The quartz content percentage acquisition module is used to establish a least-squares functional of the observed data and the simulated data based on the PP reflection coefficient approximation equation, and to obtain the quartz content percentage of the strata through inversion iteration.

[0041] The earthquake sweet spot prediction module is used to predict earthquake sweet spots in laminar shale oil reservoirs based on the quartz content ratio of the formation and through planar distribution.

[0042] The present invention also provides an electronic device, comprising:

[0043] One or more processors;

[0044] Storage device for storing one or more programs;

[0045] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the above-described seismic sweet spot prediction methods for layered shale oil reservoirs.

[0046] The present invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the seismic sweet spot prediction method for layered shale oil reservoirs as described above.

[0047] The technical effects and advantages of this invention are as follows:

[0048] This invention creatively develops a waveform indication inversion technology based on reservoir sensitivity curve reconstruction. Using this technology, this invention can effectively predict the distribution range of the long 73 segment lamellar shale oil reservoir. At the same time, it can also predict the quartz content of the shale layer, and select the distribution area of ​​lamellar shale oil reservoirs with high quartz content, good reservoir properties, and fracturing capability, providing a basis for subsequent horizontal well deployment and development.

[0049] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description and the drawings. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the embodiments or the prior art will be described below.

[0051] The accompanying drawings used in the technical description are briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0052] Figure 1 A flowchart of a seismic sweet spot prediction method for lamellar shale oil reservoirs provided by the present invention;

[0053] Figure 2 A cross-plot of longitudinal wave velocity versus porosity and quartz content provided in an embodiment of the present invention;

[0054] Figure 3 A cross-plot of shear wave velocity versus porosity and quartz content provided in an embodiment of the present invention;

[0055] Figure 4 This is a three-dimensional distribution map of a 73-meter-long sand body in a certain work area provided in an embodiment of the present invention;

[0056] Figure 5 This is a thickness map of source rock in a certain work area provided in an embodiment of the present invention;

[0057] Figure 6 This is a 3D quartz content percentage diagram of a 73-segment long section in a certain work area provided in an embodiment of the present invention;

[0058] Figure 7 A schematic diagram of a seismic sweet spot prediction system for lamellar shale oil reservoirs provided by the present invention;

[0059] Figure 8 This is a schematic diagram of an electronic device provided by the present invention. Detailed Implementation

[0060] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0062] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein.

[0063] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or device that includes a series of steps or sub-modules is not necessarily limited to those steps or sub-modules that are explicitly listed, but may include other steps or sub-modules that are not explicitly listed or that are inherent to such process, method, product, or device.

[0064] To address the shortcomings of existing technologies, this invention discloses a method, system, equipment, and medium for predicting seismic sweet spots in lamellar shale oil reservoirs. Figure 1 A flowchart of a seismic sweet spot prediction method for lamellar shale oil reservoirs provided by this invention is shown below. Figure 1 As shown, the method includes the following steps:

[0065] Step S100: Based on rock physics and well logging data, obtain the linear fitting relationship between elastic parameters and formation quartz content and porosity;

[0066] The Chang 73 lamellar shale oil reservoir in the Ordos Basin is characterized by rapid lateral variations and frequent vertical thin-layer superposition. Seismic analysis consistently treats it as a reservoir box for exploration and prediction. The quartz content in the formation is a crucial indicator for assessing the reservoir's reservoir capacity and fracturing capability. Higher quartz content in the shale formation generally indicates better reservoir capacity and fracturing ability. Therefore, the focus of seismic prediction for lamellar shale oil sweet spots is predicting the quartz content and porosity of the reservoir box.

[0067] The following plots, created using rock physics and well logging data, illustrate the relationship between velocity, porosity, and quartz content. Based on these plots, the linear relationships between P-wave and S-wave velocities and porosity and quartz content can be derived as follows:

[0068]

[0069] Among them, v P V represents the longitudinal wave velocity. S φ represents the transverse wave velocity, φ represents the porosity, and S represents the quartz content within the volume of a spatial unit, ranging from 0 to 1.

[0070] Equation (1) establishes the relationship between elastic parameters (longitudinal and transverse wave velocities) and physical property parameters (porosity and quartz content), which is used to establish the subsequent seismic forward modeling equations for physical property parameters.

[0071] Step S200: Based on the linear fitting relationship in step S100, establish an approximate equation for the PP reflectance coefficient characterizing porosity, quartz content, and density.

[0072] Based on the classic Aki-Richards three-parameter linear approximation equation, by substituting the longitudinal and transverse wave velocities in equation (1) with quartz content and porosity, the approximate equation expressions for the PP reflection coefficient characterized by porosity, quartz content, and density are as follows:

[0073]

[0074] Where R represents the reflection coefficient, θ represents the incident angle, φ represents the porosity, S represents the quartz content within the volume of the spatial unit (ranging from 0 to 1), ρ represents the density, and P1, P2, and P3 represent coefficients, with the following specific forms:

[0075]

[0076] Among them, v P V represents the longitudinal wave velocity. S Let θ represent the transverse wave velocity, θ represent the incident angle, and Δ represent the disturbance quantity.

[0077] Equations (2) and (3) are approximate equations for the PP reflectance coefficient characterized by porosity, quartz content and density. By establishing the forward modeling equations, an inversion strategy can be further implemented.

[0078] Step S300: Based on the approximate equation of the PP reflection coefficient, establish the least squares functional of the observed data and the simulated data, and obtain the quartz content of the strata through inversion and iteration;

[0079] The approximate equation for the PP reflection coefficient in step S200 is transformed into a three-parameter elastic impedance inversion formula as follows;

[0080]

[0081] Where EI represents elastic resistance, φ represents porosity, S represents the quartz content in the volume of a spatial unit, ranging from 0 to 1, ρ represents density, P1, P2, and P3 represent coefficients, and θ represents the angle of incidence.

[0082] In practical applications, elastic impedance inversion is first performed using three seismic angle gathers to obtain the EI data volume d as the observation data. Then, the least squares functional expression for the observation data and the simulation data is established as follows:

[0083]

[0084] Where J represents the objective functional, m represents the physical property parameter, G represents the forward modeling equation (i.e., equation (2)), and d represents the observation data.

[0085] The inversion iteration was implemented using a Newton-type algorithm. Finally, the quartz content of the formation was integrated and normalized to obtain the expression for the proportion of quartz content in the formation, as follows:

[0086]

[0087] Where χ represents the percentage of quartz content in a certain stratum (maximum value is 1), S represents the quartz content in the volume of a spatial unit, 1 represents a pure quartz segment, h represents the thickness, Δ represents the disturbance amount, top represents the top of the stratum, and bot represents the bottom of the stratum.

[0088] Step S400: The quartz content in the shale formation can be calculated by equation (6). Through planar distribution analysis, high quartz content, good reservoir properties, and fracturing ability of the lamellar shale reservoir can be selected as the seismic sweet spot of the lamellar shale oil reservoir.

[0089] Example:

[0090] The technical solution of this invention will be further explained below using seismic data from a certain work area in the Ordos Basin as an example.

[0091] This invention provides a method for predicting seismic sweet spots in lamellar shale oil reservoirs, comprising the following steps:

[0092] Step S100: First, based on rock physics and well logging data, create cross-plots of P-wave and S-wave velocities with porosity and quartz content. Based on the cross-plot results, linear fits can be performed to obtain the relationships between P-wave and S-wave velocities and porosity and quartz content (Equation (1)). Figure 2 This is a cross-plot of longitudinal wave velocity versus porosity and quartz content provided in an embodiment of the present invention. Figure 3 A cross-plot of transverse wave velocity versus porosity and quartz content provided for an embodiment of the present invention.

[0093] Step S200: Based on the results of previous geological surveys, laboratory analysis, and seismic data processing, the fitting relationship between three key parameters—porosity (φ), quartz content (S), and density (ρ)—and the seismic response was determined, namely Equation (2). This equation expresses the intrinsic relationship between seismic properties and reservoir physical properties through a mathematical model, providing a theoretical basis for subsequent reservoir parameter inversion. Based on Equation (2), we designed a process for predicting seismic sweet spots in layered shale oil reservoirs. This process aims to accurately obtain reservoir porosity, quartz content, and density information by inputting specific seismic angle gather data and utilizing advanced inversion techniques, thereby assessing the sweet spot region of the reservoir.

[0094] Step S300: Perform three-parameter inversion and quartz content calculation. First, we collect three key seismic angle gather data, which typically include 0–30°, 30–60°, and 60–90° data volume angle gathers, containing rich seismic wave propagation information, used to invert the three key reservoir parameters: porosity (φ), quartz content (S), and density (ρ). Next, using the objective function established by equation (5), we perform a joint inversion of the three parameters: porosity, quartz content, and density. Equation (5) is a mismatch function based on the least squares optimization algorithm, aiming to minimize the difference between the predicted seismic attributes and the actual seismic data, thereby solving for the reservoir parameter values ​​that best match reality. Among them, the observed seismic data are the actual collected seismic angle gather data, and the predicted seismic data are the simulated values ​​calculated by convolution of equation (1).

[0095] Step S400: Extract the quartz content volume in the underground space. This is an important component of the inversion results, directly reflecting the spatial distribution characteristics of quartz content in the reservoir. Apply equation (6) to process the extracted quartz function data and calculate the proportion of quartz content in a certain stratum. Through the above steps, we have completed the prediction process for seismic sweet spots in laminar shale oil reservoirs, providing important geological basis for subsequent oil and gas exploration and development. Figure 4 The image shows the distribution map of the Chang 73 sand body calculated from 3D seismic data of a work area in Ordos. The black line represents the distribution of the sand body, which is relatively uniform. Figure 5 This is a map showing the thickness of the source rock corresponding to this work area, for comparison. Figure 4 The results of the sand body distribution show that the black circle appears to be...

[0096] The source rocks in the region are significantly thinner, but Figure 4 The thickness of the medium sandstone body does not change significantly. This result indicates that the quartz content is relatively high in the area with the black line due to the decrease in mudstone content, but this phenomenon cannot be observed from the thickness of the sandstone alone. Figure 6This is a 3D quartz content percentage map of section 73 in the work area. The percentage of quartz content in the source rock strata can be directly predicted from this map. This conclusion is used... Figure 4 and Figure 5 Conventional seismic methods require a combination of methods to obtain the desired results. The above examples illustrate the practicality and relevance of this invention. Increased quartz content in source rock formations indicates improved shale oil reservoir potential and formation fracturing capability, providing strong evidence for subsequent shale oil horizontal well development.

[0097] Based on the above method, the present invention also provides a seismic sweet spot prediction system for layered shale oil reservoirs. Figure 7 A schematic diagram of a seismic sweet spot prediction system for lamellar shale oil reservoirs provided by the present invention is shown below. Figure 7 As shown, the system includes:

[0098] The linear fitting relationship acquisition module 201 is used to acquire the linear fitting relationship between elastic parameters and formation quartz content and porosity based on rock physics and well logging data; the PP reflection coefficient approximation equation establishment module 202 is used to establish the PP reflection coefficient approximation equation characterizing porosity, quartz content and density based on the linear fitting relationship; the formation quartz content acquisition module 203 is used to establish the least squares functional of observation data and simulation data based on the PP reflection coefficient approximation equation, and obtain the formation quartz content ratio through inversion iteration; the seismic sweet spot prediction module 204 is used to predict the seismic sweet spot of the lamellar shale oil reservoir based on the formation quartz content ratio through planar distribution.

[0099] Furthermore, the linear fitting relationship acquisition module 201 is specifically used to generate cross-plots of velocity versus porosity and quartz content values ​​using rock physics data and well logging data, and to linearly fit the relationships between P-wave and S-wave velocities and porosity and quartz content based on the cross-plot results.

[0100] Furthermore, the PP reflection coefficient approximation equation establishment module 202 is specifically used to obtain an approximation equation for the PP reflection coefficient characterized by porosity, quartz content, and density, based on the linear fitting relationship and the classic Aki-Richards three-parameter linear approximation equation, by substituting the longitudinal and transverse wave velocities into quartz content and porosity.

[0101] Furthermore, the quartz content percentage acquisition module 203 includes: a transformation unit for converting the PP reflection coefficient approximation equation into a three-parameter elastic impedance inversion formula; an establishment unit for using the elastic impedance inversion formula to perform elastic impedance inversion with three seismic angle gathers to obtain EI data volume d as observation data, and then establishing a least squares functional between the observation data and the simulation data; and a normalization unit for integrating and normalizing the quartz content percentage of the strata based on the least squares functional during the inversion iteration to obtain the quartz content of the strata.

[0102] Based on the same inventive concept, the present invention also provides an electronic device. Figure 8 A schematic diagram of an electronic device provided by the present invention, such as... Figure 8 As shown, the electronic device includes at least one processor 301, at least one communication interface 302, at least one memory 303, and at least one communication bus 304; wherein the processor 301, communication interface 302, and memory 303 communicate with each other through the communication bus 304.

[0103] Memory 303 stores computer programs;

[0104] The processor 301 is used to execute the program stored in the memory 303 to implement the seismic sweet spot prediction method for layered shale oil reservoirs.

[0105] Optionally, the communication interface can be an interface of a communication module, such as the interface of a GSM module; the processor may be a CPU, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The memory may include high-speed RAM and may also include non-volatile memory, such as at least one disk storage device. The memory stores a program, and the processor calls the program stored in the memory to execute some or all of the above-described method embodiments.

[0106] Based on the same inventive concept, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed, implements some or all of the above-described method embodiments. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0107] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting seismic sweet spots in lamellar shale oil reservoirs, characterized in that, The method includes the following steps: Based on rock physics and well logging data, a linear fitting relationship between elastic parameters and formation quartz content and porosity was obtained; Based on the linear fitting relationship, an approximate equation for the reflectance coefficient of PP, characterized by porosity, quartz content, and density, is established. Based on the approximate equation of the PP reflection coefficient, a least squares functional of the observed data and the simulated data is established, and the quartz content of the strata is obtained by inversion and iteration. Based on the quartz content of the formation, seismic sweet spots in laminar shale oil reservoirs are predicted by planar distribution.

2. The method for predicting seismic sweet spots in lamellar shale oil reservoirs according to claim 1, characterized in that, The aforementioned linear fitting formula for obtaining elastic parameters and formation quartz content and porosity based on rock physics and well logging data includes: Using rock physics data and well logging data, cross plots were created to show the relationship between velocity and porosity and quartz content. Based on the cross plot results, linear fitting was performed to derive the relationships between P-wave and S-wave velocities and porosity and quartz content.

3. The method for predicting seismic sweet spots in lamellar shale oil reservoirs according to claim 1, characterized in that, The linear fitting relationship is as follows: Among them, v P V represents the longitudinal wave velocity. S φ represents the transverse wave velocity, φ represents the porosity, and S represents the quartz content within the volume of a spatial unit, ranging from 0 to 1.

4. The method for predicting seismic sweet spots in lamellar shale oil reservoirs according to claim 2, characterized in that, The aforementioned establishment of an approximate equation for the PP reflectance coefficient, characterizing porosity, quartz content, and density based on the linear fitting relationship, includes: Based on the aforementioned linear fitting relationship, and using the classic Aki-Richards three-parameter linear approximation equation as a foundation, the longitudinal and transverse wave velocities are replaced by quartz content and porosity, and an approximate equation for the PP reflection coefficient characterized by porosity, quartz content, and density is established.

5. The method for predicting seismic sweet spots in lamellar shale oil reservoirs according to claim 4, characterized in that, The approximate equation for the PP reflectance coefficient, characterized by porosity, quartz content, and density, is expressed as follows: in, Where R represents the reflection coefficient, θ represents the incident angle, φ represents the porosity, S represents the quartz content within the volume of a spatial unit (ranging from 0 to 1), ρ represents the density, and P1, P2, P3 represent coefficients, v P V represents the longitudinal wave velocity. S Let θ represent the transverse wave velocity, θ represent the incident angle, and Δ represent the disturbance quantity.

6. The method for predicting seismic sweet spots in lamellar shale oil reservoirs according to claim 1, characterized in that, The aforementioned method, based on the approximate equation of the PP reflection coefficient, establishes a least-squares functional of observed and simulated data, and obtains the quartz content ratio of the formation through inversion and iteration, including: The approximate equation for the reflection coefficient of PP is transformed into a three-parameter elastic impedance inversion formula; Based on the elastic impedance inversion formula, the EI data volume d is obtained by elastic impedance inversion using three seismic angle gathers as the observation data, and then the least squares functional of the observation data and the simulation data is established. Based on the least squares functional, the quartz content of the formation is integrated and normalized through inversion iteration to obtain the quartz content ratio of the formation.

7. The method for predicting seismic sweet spots in lamellar shale oil reservoirs according to claim 6, characterized in that, The elastic impedance inversion formula is as follows; Where EI represents elastic resistance, φ represents porosity, S represents the quartz content in the volume of a spatial unit, ranging from 0 to 1, ρ represents density, P1, P2, and P3 represent coefficients, and θ represents the angle of incidence.

8. The method for predicting seismic sweet spots in lamellar shale oil reservoirs according to claim 6, characterized in that, The least squares functional expression is as follows: Where J represents the objective functional, m represents the physical property parameter, G represents the forward modeling equation (i.e., equation (2)), and d represents the observation data.

9. The method for predicting seismic sweet spots in lamellar shale oil reservoirs according to claim 1, characterized in that, The expression for the percentage of quartz content in the formation is as follows: Where χ represents the percentage of quartz content in a certain stratum (maximum value is 1), S represents the quartz content in the volume of a spatial unit, 1 represents a pure quartz segment, h represents the thickness, Δ represents the disturbance amount, top represents the top of the stratum, and bot represents the bottom of the stratum.

10. A seismic sweet spot prediction system for lamellar shale oil reservoirs, characterized in that, The system includes: The linear fitting relationship acquisition module is used to obtain linear fitting relationships between elastic parameters and formation quartz content and porosity based on rock physics and well logging data. The PP reflectance coefficient approximation equation establishment module is used to establish an approximate equation for the PP reflectance coefficient based on the linear fitting relationship, which characterizes porosity, quartz content, and density. The quartz content percentage acquisition module is used to establish a least-squares functional of the observed data and the simulated data based on the PP reflection coefficient approximation equation, and to obtain the quartz content percentage of the strata through inversion iteration. The earthquake sweet spot prediction module is used to predict earthquake sweet spots in laminar shale oil reservoirs based on the quartz content ratio of the formation and through planar distribution.

11. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the seismic sweet spot prediction method for layered shale oil reservoirs as described in any one of claims 1-9.

12. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the seismic sweet spot prediction method for layered shale oil reservoirs as described in any one of claims 1-9.

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