Shale reservoir fracture prediction method and system based on three-dimensional full waveform inversion and medium

Abstract

The present application provides a shale reservoir fracture prediction method and system based on three-dimensional full waveform inversion, and a medium, relating to the technical field of data analysis, comprising: arranging a multi-channel seismic exploration instrument in a shale reservoir area, collecting full waveform seismic data, including longitudinal waves, transverse waves and reflected waves; performing three-dimensional full waveform inversion based on logging data and geological structure maps, and full waveform seismic data to generate a shale reservoir attribute model; constructing a multi-scale inversion strategy, performing fracture sensitivity analysis and fracture morphology dynamic prediction to obtain reservoir fracture distribution characteristics and reservoir fracture morphology characteristics; performing discrete fracture point connection and fracture network reconstruction to construct a reservoir three-dimensional fracture network model and perform fracture prediction visual display. The present application solves the technical problem that the prior art can only provide inference based on limited data, lacks comprehensive capture of complex fracture networks, and cannot comprehensively and accurately describe the distribution and morphology of fractures, affecting reservoir analysis and production decision-making.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically to a method, system, and medium for predicting fractures in shale reservoirs using three-dimensional full waveform inversion. Background Technology

[0002] Shale reservoir fractures directly affect the permeability, reservoir space, and flowability of oil and gas. Therefore, accurately predicting the distribution and morphological characteristics of fractures is crucial for improving the effectiveness and efficiency of exploration and development. Existing shale reservoir fracture prediction methods typically rely on traditional seismic data inversion or analyze fractures solely based on well logging data. These methods often provide inferences based on limited data and lack a comprehensive capture of complex fracture networks. Due to limited inversion accuracy, traditional methods cannot comprehensively and accurately describe the distribution and morphology of fractures. Especially in complex geological environments, the existence and distribution of fractures may be influenced by multiple factors, and traditional methods often struggle to cope with this complexity. These issues pose significant challenges to traditional methods in predicting fractures in complex geological environments, affecting the effectiveness of reservoir analysis and development decisions. Summary of the Invention

[0003] This application provides a method, system, and medium for predicting fractures in shale reservoirs using three-dimensional full waveform inversion. It aims to address the technical problem that existing technologies can only provide inferences based on limited data, lack comprehensive capture of complex fracture networks, and cannot fully and accurately describe the distribution and morphology of fractures, thus affecting reservoir analysis and mining decisions.

[0004] The first aspect disclosed in this application provides a method for predicting fractures in shale reservoirs using three-dimensional full-waveform inversion. The method includes: deploying multi-channel seismic exploration instruments in a shale reservoir area; acquiring full-waveform seismic data in the shale reservoir area using the multi-channel seismic exploration instruments; the waveform signals of the full-waveform seismic data including P-waves, S-waves, and reflected waves; performing three-dimensional full-waveform inversion based on well logging data and geological structure maps of the shale reservoir area, and the full-waveform seismic data, to generate a shale reservoir attribute model; constructing a multi-scale inversion strategy; performing fracture sensitivity analysis and dynamic prediction of fracture morphology based on the shale reservoir attribute model according to the multi-scale inversion strategy, to obtain reservoir fracture distribution characteristics and reservoir fracture morphology characteristics; connecting discrete fracture points and reconstructing the fracture network based on the reservoir fracture distribution characteristics and reservoir fracture morphology characteristics to construct a three-dimensional fracture network model of the reservoir; and visualizing fracture predictions using the three-dimensional fracture network model of the reservoir.

[0005] The second aspect of this application discloses a three-dimensional full-waveform inversion shale reservoir fracture prediction system. This system is used in the aforementioned three-dimensional full-waveform inversion shale reservoir fracture prediction method. The system includes: a seismic data acquisition module for deploying multi-channel seismic exploration instruments in the shale reservoir area to acquire full-waveform seismic data, the waveform signals of which include P-waves, S-waves, and reflected waves; a full-waveform inversion module for performing three-dimensional full-waveform inversion based on well logging data and geological structure maps of the shale reservoir area, and the full-waveform seismic data, to generate a shale reservoir attribute model; a dynamic prediction module for constructing a multi-scale inversion strategy, performing fracture sensitivity analysis and dynamic fracture morphology prediction based on the shale reservoir attribute model according to the multi-scale inversion strategy, to obtain reservoir fracture distribution characteristics and reservoir fracture morphology characteristics; and a visualization module for connecting discrete fracture points and reconstructing the fracture network based on the reservoir fracture distribution characteristics and reservoir fracture morphology characteristics, constructing a three-dimensional reservoir fracture network model, and visualizing the fracture prediction using the three-dimensional reservoir fracture network model.

[0006] The third aspect disclosed in this application provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the three-dimensional full waveform inversion shale reservoir fracture prediction method in the first aspect.

[0007] One or more technical solutions provided in this application have at least the following beneficial effects:

[0008] By deploying multi-channel seismic exploration instruments in shale reservoir areas, it is possible to efficiently acquire full-waveform seismic data, including P-waves, S-waves, and reflected waves. This full-waveform data acquisition provides multi-dimensional subsurface information, which is more comprehensive and accurate than traditional single-waveform acquisition methods. Based on multiple data sources, including well logging data, geological structure maps, and full-waveform seismic data, a shale reservoir attribute model is generated through three-dimensional full-waveform inversion. This inversion can simulate the propagation process of subsurface seismic waves, accurately reflect the complexity of the reservoir, and provide basic data for fracture prediction. Through a multi-scale inversion strategy, fracture characteristics can be analyzed at different scales, including low-frequency, mid-frequency, and high-frequency, thus providing a comprehensive and detailed analysis. This process reveals the macroscopic and microscopic characteristics of reservoir fractures, identifying which geological features significantly influence fracture formation and development, thus providing a scientific basis for subsequent reservoir analysis and exploitation strategies. By connecting discrete fracture points and reconstructing fracture networks based on reservoir fracture distribution and morphology characteristics, this process effectively captures the spatial connectivity and fracture segment distribution within the reservoir, providing fundamental data for subsequent fracture evolution and exploitation plans. The constructed three-dimensional fracture network model provides a visualized three-dimensional structural view for overall reservoir analysis. This model can display fracture characteristics in detail, providing strong support for further development decisions and optimization strategies.

[0009] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0010] Figure 1 A schematic diagram of the three-dimensional full waveform inversion method for predicting fractures in shale reservoirs provided in this application embodiment.

[0011] Figure 2 A schematic diagram of the structure of the shale reservoir fracture prediction system for three-dimensional full waveform inversion provided in the embodiments of this application.

[0012] Figure labeling: Seismic data acquisition module 10, full waveform inversion module 20, dynamic prediction module 30, visualization module 40. Detailed Implementation

[0013] This application provides a method, system, and medium for predicting fractures in shale reservoirs using three-dimensional full waveform inversion. It solves the technical problem that existing technologies can only provide inferences based on limited data, lack comprehensive capture of complex fracture networks, and cannot fully and accurately describe the distribution and morphology of fractures, thus affecting reservoir analysis and mining decisions.

[0014] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0015] Example 1, as Figure 1 As shown in the embodiments of this application, a method for predicting fractures in shale reservoirs using three-dimensional full waveform inversion is provided. The method includes:

[0016] Multi-channel seismic exploration instruments are deployed in the shale reservoir area to collect full-waveform seismic data. The waveform signals of the full-waveform seismic data include P-waves, S-waves, and reflected waves.

[0017] Multichannel seismic exploration instruments are devices used to record the propagation of seismic waves during seismic exploration. Composed of multiple sensors, they can simultaneously receive seismic signals from multiple locations, ensuring comprehensive coverage of shale reservoir areas. Full-waveform seismic data refers to complete data recorded by multichannel seismic exploration instruments, containing all types of seismic waves. Among these, P-waves are compression waves; during seismic propagation, the vibration direction of rock particles is the same as the wave propagation direction. P-waves propagate relatively quickly within the rock strata and are usually the first wave to reach the detector. S-waves are shear waves; the vibration direction of rock particles is perpendicular to the wave propagation direction. S-waves propagate more slowly within the rock strata and suffer greater propagation loss compared to P-waves. When seismic waves encounter rock strata of different densities, reflection occurs. The reflected waves carry information about the rock strata structure and physical properties, making them crucial data for analyzing underground reservoir structures.

[0018] Based on the well logging data and geological structure map of the shale reservoir area, as well as the full-waveform seismic data, a three-dimensional full-waveform inversion is performed to generate a shale reservoir attribute model.

[0019] Well logging data refers to information about underground rock formations obtained through well logging equipment, including physical parameters such as lithology, porosity, density, and wave velocity. This data is collected through downhole sensors or detection equipment and provides a basis for accurate reservoir description. Geological structure maps reflect the geometric morphology, bedding, faults, folds, and other geological structures of underground rock formations, providing necessary information for the spatial distribution and structure of reservoirs.

[0020] Full waveform inversion is a seismic waveform-based inversion technique used to deduce the properties of subsurface media from full waveform seismic data. Compared to traditional inversion methods, full waveform inversion uses complete waveform information for modeling, providing a more accurate description of subsurface structures. Three-dimensional inversion indicates that the inversion process analyzes changes in the subsurface media in three-dimensional space, allowing for a more accurate depiction of reservoir spatial distribution. The three-dimensional full waveform inversion process is based on collected well logging data, geological structure maps, and full waveform seismic data. By simulating the propagation of seismic waves underground, it inversely calculates the physical properties of each subsurface layer. After completing the three-dimensional full waveform inversion, a shale reservoir property model containing detailed geological and physical attributes is obtained. This model accurately describes the physical characteristics of shale reservoirs, such as elastic wave velocity, porosity, and fracture distribution.

[0021] A multi-scale inversion strategy is constructed, and fracture sensitivity analysis and dynamic prediction of fracture morphology are performed based on the shale reservoir attribute model according to the multi-scale inversion strategy to obtain reservoir fracture distribution characteristics and reservoir fracture morphology characteristics.

[0022] Multi-scale inversion strategy refers to analyzing reservoir property changes at different scales, especially analyzing fracture sensitivity at different scales. Each scale represents different physical phenomena or fracture morphology, and detailed analysis is carried out from macroscopic to microscopic levels. Inversion at different scales can reveal different characteristics of the reservoir and indicate the generation and distribution patterns of reservoir fractures.

[0023] Fracture sensitivity analysis refers to simulating the impact of different fracture properties on reservoir physical properties, assessing the influence of fractures on reservoir performance at different scales, such as permeability and porosity. Through shale reservoir property models, it analyzes the impact of fractures on the reservoir, especially on fluid flow and pressure distribution, and identifies key influencing factors of fractures. Dynamic prediction of fracture morphology focuses on simulating the evolution and changes of fractures over time or under different operating conditions. This prediction is used to determine the fracture development trend of the reservoir during the exploitation process and further predict the possibility of future fracture propagation. Through dynamic inversion, time and different operating conditions are used as variables to simulate how fractures change with different conditions.

[0024] By analyzing the inversion results at different scales, the distribution characteristics of reservoir fractures are obtained, including information such as the number, location, and size of fractures. Through dynamic prediction of fracture morphology, the morphological characteristics of reservoir fractures are obtained, indicating the morphological changes of fractures under different conditions, such as the depth, width, and extension direction of fractures.

[0025] Based on the reservoir fracture distribution characteristics and reservoir fracture morphology characteristics, discrete fracture points are connected and fracture network is reconstructed to build a three-dimensional reservoir fracture network model, and fracture prediction is visualized through the three-dimensional reservoir fracture network model.

[0026] Discrete fracture points refer to the location and state of each fracture node found in the reservoir. Connecting discrete fracture points and reconstructing the fracture network involves connecting these discrete fracture points according to their spatial relationships to form a fracture network. This method describes how fractures in the reservoir are spatially interconnected. A three-dimensional fracture network model of the reservoir combines the spatial characteristics and physical properties of the fractures to construct a complete three-dimensional fracture network. This model can present the spatial distribution, connectivity, and relationship of fractures with other geological features. Fracture prediction visualization refers to displaying the three-dimensional fracture network model of the reservoir to users in a graphical way, helping them intuitively understand the distribution and morphology of fractures. The visualization not only includes the spatial distribution of fractures but also shows the impact of fractures on other reservoir properties (such as permeability and fluid conductivity), providing intuitive information support for development and optimization strategies.

[0027] Furthermore, the generation of the shale reservoir property model includes:

[0028] A set of key logging parameters is extracted from the logging data of the shale reservoir area; a signal filter is initialized based on the signal characteristics of the full-waveform seismic data, and the full-waveform seismic data is filtered using the signal filter to obtain usable full-waveform seismic data; the usable full-waveform seismic data is then aligned and corrected for accuracy according to the time series to obtain standard full-waveform seismic data; based on the set of key logging parameters, the geological structure map, and the standard full-waveform seismic data, a three-dimensional full-waveform inversion is performed to generate a shale reservoir attribute model.

[0029] Based on well logging data, a set of key logging parameters is extracted, including: porosity, which describes the amount of voids in the rock formation and affects the storage capacity of oil and gas reservoirs; density, which describes the physical density of the rock formation and helps identify lithology and porosity characteristics; and wave velocity, which describes the propagation speed of P-waves and S-waves, is closely related to the elastic properties of the rock formation, and can provide information about lithology and fractures. Extracting these key logging parameter sets helps to more accurately depict reservoir properties in subsequent inversion analysis, ensuring the accuracy of three-dimensional full-waveform inversion.

[0030] Signal filters are used to filter high-frequency noise or low-frequency interference in seismic data. Based on the signal characteristics of the full-waveform seismic data, the signal filter is initialized. This process involves matching appropriate filter types and parameters, such as frequency response and filtering range, to effectively remove noise from the seismic data. Filtering involves processing the full-waveform seismic data using signal filters to remove noise and retain useful seismic signals. The filtered signal clearly reflects the physical characteristics of the subsurface reservoir. The filtered data is usable full-waveform seismic data, containing signals such as P-waves, S-waves, and reflected waves. After noise suppression, it is more suitable for subsequent inversion analysis.

[0031] Waveform alignment refers to the time synchronization processing of seismic signals from different measurement points or at different times to ensure the temporal consistency of seismic waveforms. This process eliminates inconsistencies caused by factors such as acquisition time differences, enabling correct alignment of various signals. Accuracy correction involves refining waveform data to improve its accuracy. This includes processing such as spatial positioning, amplitude correction, and noise correction. Through correction, the quality of full-waveform seismic data is ensured, eliminating deviations caused by equipment errors, seismic wave propagation, and other factors. After waveform alignment and accuracy correction are completed, standard full-waveform seismic data is obtained, providing high-quality data input for subsequent three-dimensional full-waveform inversion.

[0032] The key logging parameter set provides physical property data of the reservoir, and the geological structure map provides spatial distribution information of the reservoir. Combining these data with full-waveform seismic data can construct a more comprehensive shale reservoir property model.

[0033] Furthermore, the step of generating a shale reservoir attribute model by performing three-dimensional full-waveform inversion based on the set of key logging parameters, the geological structure map, and the standard full-waveform seismic data includes:

[0034] The geological structure map is converted into a three-dimensional geological grid model according to a preset grid precision; the model is initialized based on the three-dimensional geological grid model and the set of key logging parameters to obtain an initial reservoir attribute model; the initial reservoir attribute model is verified by forward modeling and the model parameters are adjusted using the standard full-waveform seismic data to obtain a basic reservoir attribute model; an inversion objective function is constructed, and the basic reservoir attribute model is inverted using the inversion objective function to generate a shale reservoir attribute model.

[0035] In three-dimensional space, the geological features of a geological structure map are divided into small grid cells. The size of each grid cell is determined by a preset grid precision, which is set according to specific needs. Higher grid precision results in smaller grid cells and more precise model details. Based on the divided grid cells, information such as layers, faults, and folds in the geological structure map is transformed into a three-dimensional geological grid model. Each grid cell represents a small part of the reservoir, providing a detailed description of its geological characteristics and physical properties.

[0036] The geological 3D grid model provides the basis for the spatial distribution of the reservoir, and the set of key logging parameters provides the physical properties of each part of the reservoir. By combining these two, and through the model initialization process, each grid cell is assigned corresponding physical properties according to the geological structure. The resulting initial reservoir model reflects the basic characteristics of the reservoir.

[0037] Forward modeling verification refers to simulating the propagation process of seismic waves based on an initial reservoir property model. Through forward modeling verification, the propagation of seismic waves within this initial model is simulated, generating simulated seismic data. Then, the error between the simulated seismic data and standard full-waveform seismic data is calculated, and the parameters of the initial reservoir property model are adjusted. Through this iterative optimization, the accuracy of the model is gradually improved. The goal of model parameter adjustment is to make the simulation results as close as possible to actual standard full-waveform seismic data, thereby improving the model's reliability. After forward modeling verification and model parameter adjustment, a basic reservoir property model is finally obtained. This model has been validated and can accurately reflect the physical characteristics of the reservoir, providing a more accurate foundation for subsequent inversion processes.

[0038] The inversion objective function is used to measure model error, typically the difference between simulated seismic wave data and actual measured data. It is defined by minimizing the sum of squared errors or other statistical analysis methods. Three-dimensional full-waveform inversion optimizes the inversion objective function, progressively adjusting the parameters of the basic reservoir attribute model to minimize the difference between simulated and actual seismic data. The inversion process is iterative; each iteration updates the model parameters until the minimum error condition is met. After the inversion is complete, the final shale reservoir attribute model is generated.

[0039] Furthermore, obtaining the initial reservoir property model includes:

[0040] Well trajectory correction is performed on the set of key logging parameters using the geological 3D mesh model to obtain a usable set of key logging parameters. Outlier cleaning and radial basis function interpolation are then performed on the usable set of key logging parameters to obtain a standard set of key logging parameters. Based on the stratigraphic interfaces and fault lines of the geological 3D mesh model, a 3D constraint surface is constructed. The standard set of key logging parameters is then smoothed using directional weighting according to the 3D constraint surface to obtain a smoothed set of key logging parameters. Finally, ultra-low frequency components are extracted and high frequency components are superimposed on the smoothed set of key logging parameters to obtain the initial reservoir attribute model.

[0041] Well trajectory correction refers to adjusting the depth or location errors in the set of key logging parameters based on a geological 3D grid model. For example, the set of key logging parameters may contain spatial errors due to deviations in logging tools or inaccurate well paths. The correction process ensures that the logging data can be accurately matched with the geological 3D grid model. After correction, the resulting set of usable key logging parameters is more consistent with the actual subsurface conditions, providing accurate data input for subsequent attribute model construction.

[0042] Outliers refer to values ​​in the set of available logging critical parameters that significantly deviate from the normal range. These values ​​may be caused by measurement errors, equipment malfunctions, or human error. Detecting, identifying, and removing these irregular outliers from the available logging critical parameter set leads to more accurate data. Radial basis function interpolation is a commonly used interpolation method suitable for spatial data. It can be used to estimate missing values ​​in the set of available logging critical parameters, especially when logging data is sparse or discontinuous. Through interpolation, a complete and consistent standard set of logging critical parameters is obtained, making all data spatially uniform and smooth, reducing errors caused by missing or incomplete data.

[0043] The three-dimensional constraint surface is a surface constructed based on information such as stratigraphic interfaces and fault lines in a geological three-dimensional grid model. It is used to limit the range of variation of model parameters and ensure the smooth transition and reasonable change of reservoir properties. In this way, the range of variation of well logging data can be defined in space, ensuring that the data is interpolated and smoothed under the premise of conforming to the geological structure.

[0044] Directional weighted smoothing refers to a data smoothing process in which different directional characteristics in space, including geological structures such as interlayer variations and faults, are assigned different weights to the directionality of the standard logging key parameter set. This ensures that logging data in fracture, fault, and other areas are appropriately smoothed. This process allows the smoothed data to maintain the overall trend while also reflecting geological layers, faults, and other geological features. After weighted smoothing, the resulting smoothed logging key parameter set more closely matches the actual physical properties of the reservoir and accurately reflects the geological structure and reservoir characteristics.

[0045] Ultra-low frequency (ULF) components refer to signals with long periods and low frequencies found in the key parameter set of smooth logging. These ULF components typically represent the macroscopic physical properties of the reservoir, such as the overall properties of the rock formation, porosity, and density. Signal processing techniques are used to extract ULF components from the key parameter set of smooth logging to obtain the basic physical characteristics of the reservoir. High-frequency (HF) components represent the detailed features of the reservoir, such as fractures, porosity, and minor inhomogeneities. HF components reflect the fine structure of the reservoir and can provide more local information. Superimposing HF components onto ULF components allows the reservoir property model to reflect both macroscopic characteristics and capture microscopic structures. Through the extraction of ULF components and the superposition of HF components, the resulting initial reservoir property model has a complete physical property description, simultaneously presenting the overall characteristics and detailed structure of the reservoir.

[0046] Furthermore, the generation of the shale reservoir property model includes:

[0047] The inversion objective function is used to perform multi-scale inversion and gradient calculation on the basic reservoir attribute model to obtain a multi-scale model gradient field. Based on the multi-scale model gradient field, a model parameter optimization threshold is determined. Based on the inversion objective function, global iterative optimization is performed within the model parameter optimization threshold until a preset termination condition is met to obtain the target model parameters. Based on the target model parameters, the basic reservoir attribute model is updated and corrected to generate the shale reservoir attribute model.

[0048] Multi-scale inversion optimizes the basic reservoir attribute model at different scales (e.g., low-frequency, mid-frequency, and high-frequency) to ensure that the model can reasonably fit actual data at all levels, from macroscopic to microscopic. Inversion at different scales allows for better capture of the overall characteristics and local features of the reservoir, thereby improving inversion accuracy. In this process, the response of data at different frequencies is inverted; for example, the low-frequency component mainly reflects the macroscopic structure of the reservoir, while the high-frequency component is related to the microstructure (e.g., fractures and pores). Gradient calculation, by calculating the rate of change of model parameters, indicates the direction of model adjustment. The resulting multi-scale model gradient field provides gradient information at each scale, representing the optimization direction of the model at different scale levels and providing an important basis for model updates.

[0049] The model parameter optimization threshold refers to the range of model parameter changes allowed during the optimization process. Based on the magnitude and direction of the gradient field of the multi-scale model, a reasonable model parameter optimization threshold is set to ensure that the parameter adjustment is not too large or too small, thereby affecting the convergence speed and accuracy of the model.

[0050] Global iterative optimization refers to performing multiple iterative calculations within the entire model parameter optimization threshold, gradually adjusting the model parameters to minimize the inversion objective function. In this way, a set of model parameters that minimizes the difference between simulated and actual data is found. In each iteration, the model parameters are fine-tuned based on the inversion objective function value obtained in the previous step, while ensuring that the adjustment range is within a reasonable range to avoid instability or erroneous results caused by over-adjustment. A preset termination condition means stopping iteration when a certain error limit is reached, or when the number of iterations reaches a preset upper limit. For example, when the change in the inversion objective function is less than a certain threshold, indicating that the model has converged, or when the maximum number of iterations is reached, optimization stops. After multiple iterative optimizations, a final set of target model parameters is obtained, which are the model parameters that minimize the inversion objective function.

[0051] Based on the obtained target model parameters, they are applied to the basic reservoir attribute model for parameter update and correction. The updated model can more accurately reflect the actual physical properties of the reservoir. After parameter update and correction, the shale reservoir attribute model is finally generated.

[0052] Furthermore, obtaining the reservoir fracture distribution characteristics and reservoir fracture morphology characteristics includes:

[0053] Based on the multi-scale inversion strategy, low-frequency scale thresholds, mid-frequency scale thresholds, and high-frequency scale thresholds are determined, and multi-scale sensitive parameters are determined based on these thresholds. These multi-scale sensitive parameters are then sequentially introduced into the shale reservoir attribute model to perform fracture sensitivity testing and analysis and output distribution characteristics, thereby obtaining reservoir fracture distribution characteristics. Finally, multi-scale inversion and dynamic morphological prediction are performed based on the shale reservoir attribute model using reflected wave travel time data to obtain reservoir fracture morphological characteristics.

[0054] Low-frequency scales reflect the macroscopic properties of reservoirs, involving large-scale geological structural features such as major strata and porosity variations. Low-frequency scale thresholds are used to distinguish the range of variation of these macroscopic properties; for example, low-frequency scale thresholds involve changes in large-scale physical properties such as elastic wave velocity and density. Mid-frequency scales mainly focus on mid-scale features in reservoirs, such as the formation of fracture networks and the presence of faults. Mid-frequency scale thresholds are used to identify more significant fracture distributions and other mid-scale structures in reservoirs. High-frequency scales reflect minute features in reservoirs, such as microfractures and minute changes in porosity. High-frequency scale thresholds are used to detect subtle changes in reservoirs, especially the distribution of microfractures and fine pores. Multi-scale sensitive parameters are obtained based on low-frequency, mid-frequency, and high-frequency scale thresholds through sensitivity analysis of parameters such as fractures and porosity at each scale, and are used to identify fracture characteristics at different scales and their impact on the reservoir.

[0055] In shale reservoir property models, the multi-scale sensitive parameters obtained in the previous step are introduced into the model. This allows identification of how fracture distribution and behavior are influenced by physical properties at different scales. Fracture sensitivity testing aims to determine which parameters (such as porosity and density) have a significant impact on fracture formation, distribution, and propagation. By changing the values ​​of these parameters and recording the changes in fractures, the most critical parameters can be identified. Through fracture sensitivity testing, the sensitivity of fractures in the reservoir is assessed, thereby determining which geological features have the greatest impact on fracture behavior. After fracture sensitivity analysis, reservoir fracture distribution characteristics are output, including the distribution pattern, number, and location of fractures in the reservoir. These characteristics reflect the spatial distribution and properties of fractures, providing necessary data support for further reservoir analysis and exploitation strategy development.

[0056] Travel time data refers to the time required for reflected waves to return from the formation interface. It is used to estimate the physical properties of subsurface reservoirs and provides effective spatial information on fracture distribution and morphology. Multi-scale inversion is used in this process to identify and predict fracture morphological changes. By performing inversions at different scales, the distribution and morphological characteristics of fractures in the reservoir can be better captured. During the inversion process, model parameters are adjusted based on the travel time data. Dynamic morphological prediction aims to simulate the evolution of fracture morphology over time or under different operating conditions. For example, during hydraulic fracturing, the morphology and propagation of fractures may change. Dynamic morphological prediction can predict the trend of fracture changes under different conditions. Combining the dynamic behavior of fractures with shale reservoir property models provides predictions of fracture morphological evolution. Finally, the reservoir fracture morphological characteristics are output, including fracture width, length, and propagation direction. This information is used for further resource assessment and exploitation decisions.

[0057] Furthermore, obtaining reservoir fracture morphology characteristics includes:

[0058] According to the multi-scale inversion strategy, the multi-scale inversion target is determined; based on the shale reservoir attribute model, multi-scale inversion is performed according to the multi-scale inversion target to obtain multi-scale fracture morphology characteristics; combined with the reflected wave travel time data, the multi-scale fracture morphology characteristics are updated by time series inversion and dynamic morphology prediction to obtain the reservoir fracture morphology characteristics.

[0059] Multi-scale inversion strategies aim to analyze and optimize reservoir fracture characteristics at different scale levels. Low-frequency scales primarily reflect the macroscopic features of the reservoir, mid-frequency scales focus on medium-scale variations in fracture distribution, and high-frequency scales emphasize detailed fracture features. Based on the impact of different scales on reservoir fractures, inversion objectives are set for different scales. These objectives guide the optimization direction during the inversion process, ensuring that the model comprehensively reflects fracture characteristics from multiple levels. For example, low-frequency inversion objectives optimize the general morphology and distribution of the reservoir; mid-frequency objectives focus on a rough estimate of fracture distribution; and high-frequency objectives primarily focus on detailed fracture features and microstructure.

[0060] Based on a shale reservoir property model, inversions were performed at three scales: low-frequency, mid-frequency, and high-frequency. At each scale, the parameters of the reservoir property model were optimized according to the set inversion objectives to ensure that the simulated data matched the actual observation data. For example, low-frequency inversion was used to determine the macroscopic characteristics of the reservoir, such as the main geological strata, lithological changes, and the general trend of fracture distribution. The inversion objective was to ensure the accuracy of reservoir characteristics over a large area while avoiding excessive focus on details. Mid-frequency inversion focused on the formation and expansion of the fracture network in the reservoir, especially the distribution pattern and scale of fractures. This inversion helped identify the existence and development patterns of fractures. High-frequency inversion focused on the details of reservoir fractures, especially the subtle differences in microfractures and fracture fault characteristics. High-frequency inversion captured the detailed structure of the reservoir through minute fluctuations and changes in reflected waves. After multi-scale inversion, multi-scale fracture morphological characteristics were obtained, including fracture distribution, size, direction, and density. Through the inversion results at different scales, a comprehensive understanding of the macroscopic to microscopic structural characteristics of fractures could be obtained.

[0061] By combining reflected wave travel time data with multi-scale fracture morphology characteristics, the spatial accuracy of the fracture model is verified. The time-series data of reflected waves provides a spatiotemporal reference for dynamic fracture prediction. Time-series inversion updates refer to the dynamic correction of fracture morphology characteristics based on reflected wave travel time data. Through time-series inversion updates, the changing trends of fractures at different time points or under different operating conditions can be predicted, providing a reference for reservoir development and subsequent monitoring. Dynamic morphology prediction simulates the changes of fractures at different times or under different operating conditions using reflected wave travel time data and multi-scale inversion results. This dynamic morphology prediction can determine how fractures change over time, such as fracture propagation and development during hydraulic fracturing. Ultimately, accurate reservoir fracture morphology characteristics are output.

[0062] Furthermore, the construction of the reservoir three-dimensional fracture network model includes:

[0063] Spatial neighborhood identification is performed on the distribution and morphological characteristics of the reservoir fractures to obtain a spatial feature set of the reservoir fractures; similarity screening and discrete fracture point connection are performed on the spatial feature set of the reservoir fractures to obtain a set of shale reservoir fracture segments; surface fitting and three-dimensional reconstruction are performed on the set of shale reservoir fracture segments to construct the three-dimensional fracture network model of the reservoir.

[0064] The distribution characteristics of reservoir fractures reflect their spatial location within the reservoir, while the morphological characteristics of fractures describe their size, shape, and structure. Spatial neighborhood identification refers to identifying the connections between fractures through spatial relationships. In reservoirs, fractures typically do not exist in isolation; they may exhibit certain spatial arrangements. Spatial neighborhood identification determines the relative positional relationships between fractures, such as the distance and direction between adjacent fractures. After spatial neighborhood identification, a spatial feature set of reservoir fractures is obtained, which includes information such as the spatial distribution and interrelationships of fractures.

[0065] Similarity screening identifies fractures with similar orientations based on their strike direction (i.e., the direction of fracture propagation). This is because in natural reservoirs, fractures often follow specific directions or tectonic stress lines. By screening for fractures with similar orientations, they can be grouped into different fracture groups or segments. Discrete fracture point connection connects spatially adjacent or similarly oriented fracture points to form fracture segments. Discrete fracture points refer to independent fracture locations, usually inferred from reflected wave data or seismic waveforms. By connecting these fracture points, the connectivity of fracture segments and the topology of the fracture network can be identified. After strike similarity screening and discrete fracture point connection, a set of shale reservoir fracture segments is obtained. This set describes the spatial distribution and interconnections of various fracture segments in the reservoir, providing fundamental data for subsequent 3D fracture network reconstruction.

[0066] Surface fitting refers to the smooth fitting of a set of fracture segments in a shale reservoir to a mathematical model in three-dimensional space. Based on the spatial location and distribution patterns of fractures within the set of fracture segments, a surface or curve model representing the fracture network morphology is fitted. The aim is to smooth the transitions in the fracture network while preserving the spatial characteristics of the fracture segments, making it more natural and continuous in the geological model. Three-dimensional reconstruction transforms the surface-fitted fracture data into a structural model in three-dimensional space, generating an accurate three-dimensional fracture network model of the reservoir. This model displays the spatial distribution, connectivity, and topological structure of fractures within the reservoir, providing precise three-dimensional spatial information for further reservoir analysis, exploitation optimization, and resource assessment.

[0067] In summary, the three-dimensional full-waveform inversion method for predicting fractures in shale reservoirs provided in this application has the following technical advantages:

[0068] By deploying multi-channel seismic exploration instruments in shale reservoir areas, it is possible to efficiently acquire full-waveform seismic data, including P-waves, S-waves, and reflected waves. This full-waveform data acquisition provides multi-dimensional subsurface information, which is more comprehensive and accurate than traditional single-waveform acquisition methods. Based on multiple data sources, including well logging data, geological structure maps, and full-waveform seismic data, a shale reservoir attribute model is generated through three-dimensional full-waveform inversion. This inversion can simulate the propagation process of subsurface seismic waves, accurately reflect the complexity of the reservoir, and provide basic data for fracture prediction. Through a multi-scale inversion strategy, fracture characteristics can be analyzed at different scales, including low-frequency, mid-frequency, and high-frequency, thus providing a comprehensive and detailed analysis. This process reveals the macroscopic and microscopic characteristics of reservoir fractures, identifying which geological features significantly influence fracture formation and development, thus providing a scientific basis for subsequent reservoir analysis and exploitation strategies. By connecting discrete fracture points and reconstructing fracture networks based on reservoir fracture distribution and morphology characteristics, this process effectively captures the spatial connectivity and fracture segment distribution within the reservoir, providing fundamental data for subsequent fracture evolution and exploitation plans. The constructed three-dimensional fracture network model provides a visualized three-dimensional structural view for overall reservoir analysis. This model can display fracture characteristics in detail, providing strong support for further development decisions and optimization strategies.

[0069] Example 2, based on the same inventive concept as the shale reservoir fracture prediction method using three-dimensional full waveform inversion in the aforementioned examples, such as... Figure 2 As shown in the embodiment of this application, a three-dimensional full-waveform inversion shale reservoir fracture prediction system is provided, the system comprising:

[0070] The seismic data acquisition module 10 is used to deploy multi-channel seismic exploration instruments in the shale reservoir area to acquire full-waveform seismic data, including P-wave, S-wave, and reflected waves. The full-waveform inversion module 20 is used to perform three-dimensional full-waveform inversion based on well logging data and geological structure maps of the shale reservoir area, as well as the full-waveform seismic data, to generate a shale reservoir attribute model. The dynamic prediction module 30 is used to construct a multi-scale inversion strategy, and according to the multi-scale inversion strategy, perform fracture sensitivity analysis and dynamic prediction of fracture morphology based on the shale reservoir attribute model to obtain reservoir fracture distribution characteristics and reservoir fracture morphology characteristics. The visualization module 40 is used to connect discrete fracture points and reconstruct the fracture network based on the reservoir fracture distribution characteristics and reservoir fracture morphology characteristics to construct a three-dimensional fracture network model of the reservoir, and to visualize the fracture prediction using the three-dimensional fracture network model of the reservoir.

[0071] Furthermore, the full waveform inversion module 20 is used to perform the following steps: extracting the set of key logging parameters obtained from the logging data of the shale reservoir area; initializing a signal filter based on the signal characteristics of the full waveform seismic data, and using the signal filter to filter the full waveform seismic data to obtain usable full waveform seismic data; aligning and correcting the usable full waveform seismic data according to the time series to obtain standard full waveform seismic data; and performing three-dimensional full waveform inversion based on the set of key logging parameters, the geological structure map, and the standard full waveform seismic data to generate a shale reservoir attribute model.

[0072] Furthermore, the full waveform inversion module 20 is used to perform the following operation steps:

[0073] The geological structure map is converted into a three-dimensional geological grid model according to a preset grid precision; the model is initialized based on the three-dimensional geological grid model and the set of key logging parameters to obtain an initial reservoir attribute model; the initial reservoir attribute model is verified by forward modeling and the model parameters are adjusted using the standard full-waveform seismic data to obtain a basic reservoir attribute model; an inversion objective function is constructed, and the basic reservoir attribute model is inverted using the inversion objective function to generate a shale reservoir attribute model.

[0074] Furthermore, the full waveform inversion module 20 is used to perform the following operation steps:

[0075] Well trajectory correction is performed on the set of key logging parameters using the geological 3D mesh model to obtain a usable set of key logging parameters. Outlier cleaning and radial basis function interpolation are then performed on the usable set of key logging parameters to obtain a standard set of key logging parameters. Based on the stratigraphic interfaces and fault lines of the geological 3D mesh model, a 3D constraint surface is constructed. The standard set of key logging parameters is then smoothed using directional weighting according to the 3D constraint surface to obtain a smoothed set of key logging parameters. Finally, ultra-low frequency components are extracted and high frequency components are superimposed on the smoothed set of key logging parameters to obtain the initial reservoir attribute model.

[0076] Furthermore, the full waveform inversion module 20 is used to perform the following operation steps:

[0077] The inversion objective function is used to perform multi-scale inversion and gradient calculation on the basic reservoir attribute model to obtain a multi-scale model gradient field. Based on the multi-scale model gradient field, a model parameter optimization threshold is determined. Based on the inversion objective function, global iterative optimization is performed within the model parameter optimization threshold until a preset termination condition is met to obtain the target model parameters. Based on the target model parameters, the basic reservoir attribute model is updated and corrected to generate the shale reservoir attribute model.

[0078] Furthermore, the dynamic prediction module 30 is used to perform the following operation steps:

[0079] Based on the multi-scale inversion strategy, low-frequency scale thresholds, mid-frequency scale thresholds, and high-frequency scale thresholds are determined, and multi-scale sensitive parameters are determined based on these thresholds. These multi-scale sensitive parameters are then sequentially introduced into the shale reservoir attribute model to perform fracture sensitivity testing and analysis and output distribution characteristics, thereby obtaining reservoir fracture distribution characteristics. Finally, multi-scale inversion and dynamic morphological prediction are performed based on the shale reservoir attribute model using reflected wave travel time data to obtain reservoir fracture morphological characteristics.

[0080] Furthermore, the dynamic prediction module 30 is used to perform the following operation steps:

[0081] According to the multi-scale inversion strategy, the multi-scale inversion target is determined; based on the shale reservoir attribute model, multi-scale inversion is performed according to the multi-scale inversion target to obtain multi-scale fracture morphology characteristics; combined with the reflected wave travel time data, the multi-scale fracture morphology characteristics are updated by time series inversion and dynamic morphology prediction to obtain the reservoir fracture morphology characteristics.

[0082] Furthermore, the visualization module 40 is used to perform the following operation steps:

[0083] Spatial neighborhood identification is performed on the distribution and morphological characteristics of the reservoir fractures to obtain a spatial feature set of the reservoir fractures; similarity screening and discrete fracture point connection are performed on the spatial feature set of the reservoir fractures to obtain a set of shale reservoir fracture segments; surface fitting and three-dimensional reconstruction are performed on the set of shale reservoir fracture segments to construct the three-dimensional fracture network model of the reservoir.

[0084] Through the foregoing detailed description of the three-dimensional full waveform inversion method for predicting fractures in shale reservoirs, those skilled in the art can clearly understand the three-dimensional full waveform inversion shale reservoir fracture prediction system in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to the method section.

[0085] Example 3 provides a storage medium on which a computer program is stored, which, when executed by a processor, implements any step of Example 1.

[0086] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0087] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting fractures in shale reservoirs using three-dimensional full-waveform inversion, characterized in that, The method includes: Multi-channel seismic exploration instruments are deployed in the shale reservoir area to collect full-waveform seismic data in the shale reservoir area. The waveform signals of the full-waveform seismic data include P-waves, S-waves, and reflected waves. Based on the well logging data and geological structure map of the shale reservoir area, as well as the full-waveform seismic data, a three-dimensional full-waveform inversion is performed to generate a shale reservoir attribute model. A multi-scale inversion strategy is constructed, and fracture sensitivity analysis and dynamic prediction of fracture morphology are performed based on the shale reservoir attribute model according to the multi-scale inversion strategy to obtain reservoir fracture distribution characteristics and reservoir fracture morphology characteristics. Based on the reservoir fracture distribution characteristics and reservoir fracture morphology characteristics, discrete fracture points are connected and fracture network is reconstructed to build a three-dimensional reservoir fracture network model, and fracture prediction is visualized through the three-dimensional reservoir fracture network model. The generated shale reservoir property model includes: Extract the set of key logging parameters from the logging data obtained in the shale reservoir area; Based on the signal characteristics of the full-waveform seismic data, a signal filter is initialized, and the full-waveform seismic data is filtered using the signal filter to obtain usable full-waveform seismic data. The available full-waveform seismic data is aligned and corrected for accuracy according to the time series to obtain standard full-waveform seismic data. Based on the aforementioned set of key logging parameters and geological structure map, as well as the aforementioned standard full-waveform seismic data, a three-dimensional full-waveform inversion is performed to generate a shale reservoir attribute model. The process of generating a shale reservoir attribute model based on the set of key logging parameters, geological structure map, and standard full-waveform seismic data includes: The geological structure map is converted into a three-dimensional geological grid model according to the preset grid precision. The model is initialized based on the geological three-dimensional grid model and the set of key logging parameters to obtain an initial reservoir property model. The initial reservoir attribute model was verified and its parameters were adjusted by forward modeling using the standard full-waveform seismic data to obtain the basic reservoir attribute model. Construct an inversion objective function, and use the inversion objective function to perform three-dimensional full waveform inversion on the basic reservoir attribute model to generate a shale reservoir attribute model; The process of obtaining the initial reservoir property model includes: By combining the geological three-dimensional grid model, well trajectory correction is performed on the set of key logging parameters to obtain a usable set of key logging parameters; The available set of key logging parameters is cleaned of outliers and interpolated using radial basis functions to obtain a standard set of key logging parameters. Based on the stratigraphic interfaces and fault lines of the geological 3D grid model, a 3D constraint surface is constructed. The standard logging key parameter set is then smoothed by directional weighting according to the 3D constraint surface to obtain a smoothed logging key parameter set. The initial reservoir property model is obtained by extracting ultra-low frequency components and superimposing high frequency components on the set of key parameters of smooth logging. The obtained reservoir fracture distribution characteristics and reservoir fracture morphology characteristics include: According to the multi-scale inversion strategy, low-frequency scale threshold, mid-frequency scale threshold and high-frequency scale threshold are determined, and multi-scale sensitive parameters are determined based on the low-frequency scale threshold, mid-frequency scale threshold and high-frequency scale threshold; The multi-scale sensitive parameters are sequentially introduced into the shale reservoir property model to perform fracture sensitivity testing and analysis and output distribution characteristics, thereby obtaining the reservoir fracture distribution characteristics. By combining the travel time data of reflected waves with the shale reservoir property model, multi-scale inversion and dynamic morphological prediction are performed to obtain the morphological characteristics of reservoir fractures. The acquisition of reservoir fracture morphology features includes: Based on the aforementioned multi-scale inversion strategy, the multi-scale inversion objective is determined; Based on the shale reservoir property model, multi-scale inversion is performed according to the multi-scale inversion objective to obtain multi-scale fracture morphology characteristics; By combining the travel time data of the reflected waves, the morphological characteristics of the multi-scale fractures are updated by time series inversion and dynamic prediction to obtain the morphological characteristics of the reservoir fractures.

2. The method for predicting fractures in shale reservoirs using three-dimensional full-waveform inversion as described in claim 1, characterized in that, The generated shale reservoir property model includes: The inversion objective function is used to perform multi-scale inversion and gradient calculation on the basic reservoir attribute model to obtain the multi-scale model gradient field; Based on the gradient field of the multi-scale model, determine the optimization threshold for the model parameters; Based on the inversion objective function, global iterative optimization is performed within the model parameter optimization threshold until a preset termination condition is met to obtain the target model parameters; The basic reservoir attribute model is updated and corrected based on the target model parameters to generate the shale reservoir attribute model.

3. The method for predicting fractures in shale reservoirs using three-dimensional full-waveform inversion as described in claim 1, characterized in that, The construction of the reservoir three-dimensional fracture network model includes: Spatial neighborhood identification is performed on the reservoir fracture distribution characteristics and reservoir fracture morphology characteristics to obtain a reservoir fracture spatial feature set; The spatial feature set of reservoir fractures is screened by orientation similarity and discrete fracture points are connected to obtain a set of shale reservoir fracture segments. Based on the set of fracture segments in the shale reservoir, surface fitting and three-dimensional reconstruction are performed to construct the three-dimensional fracture network model of the reservoir.

4. A three-dimensional full-waveform inversion shale reservoir fracture prediction system, characterized in that, The system is used to implement the three-dimensional full-waveform inversion method for predicting fractures in shale reservoirs according to any one of claims 1-3, the system comprising: The seismic data acquisition module is used to deploy multi-channel seismic exploration instruments in shale reservoir areas and acquire full-waveform seismic data in shale reservoir areas through the multi-channel seismic exploration instruments. The waveform signals of the full-waveform seismic data include P-waves, S-waves and reflected waves. The full waveform inversion module is used to perform three-dimensional full waveform inversion based on the well logging data and geological structure map of the shale reservoir area, as well as the full waveform seismic data, to generate a shale reservoir attribute model. The dynamic prediction module is used to construct a multi-scale inversion strategy, and perform fracture sensitivity analysis and dynamic prediction of fracture morphology based on the shale reservoir attribute model according to the multi-scale inversion strategy to obtain reservoir fracture distribution characteristics and reservoir fracture morphology characteristics. The visualization module is used to connect discrete fracture points and reconstruct the fracture network based on the reservoir fracture distribution characteristics and reservoir fracture morphology characteristics, construct a three-dimensional fracture network model of the reservoir, and perform fracture prediction visualization display through the three-dimensional fracture network model of the reservoir.

5. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the three-dimensional full waveform inversion method for predicting fractures in shale reservoirs according to any one of claims 1 to 3.

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