Stratum structure analysis method and device based on speed change rate curve

Abstract

The invention discloses a stratigraphic structure analysis method and device based on a speed change rate curve, and the method comprises the steps: carrying out the preprocessing of micro-motion data, obtaining the pre-processed micro-motion data, and dividing the pre-processed micro-motion data into a plurality of micro-motion data segments with the consistent time duration; frequency dispersion characteristics of the multiple micro-motion data segments are extracted based on a spatial autocorrelation method, a frequency dispersion curve is generated based on the frequency dispersion characteristics, and a speed curve is generated based on the frequency dispersion curve; performing first-order derivative calculation on the speed curve to obtain a speed conversion rate curve; three initial inversion models are constructed based on the speed transformation rate curve; the three initial inversion models are optimized on the basis of the speed change rate curve, the optimized three initial inversion models are fused to obtain a stratum structure analysis model, stratum structure analysis is performed on the basis of the stratum structure analysis model, and the inversion precision of the inversion model is improved by generating and analyzing the speed change rate curve. And the reliability of a stratigraphic structure analysis result is obviously improved.

Description

Technical Field

[0001] This application relates to the field of geological exploration technology, and more specifically, to a method and device for analyzing formation structure based on a velocity change rate curve. Background Art

[0002] In geological exploration, the microtremor inversion technology is an important means for analyzing formation structure. However, when dealing with complex multi-layer geological conditions, conventional inversion methods often have insufficient layering accuracy and are difficult to accurately capture formation interfaces and subtle changes. Especially in areas with lithological mutations and velocity inversions, the resolution of the formation model is significantly reduced. This technical bottleneck not only restricts the ability to analyze high-precision formation structures in complex geological environments but also has a certain impact on practical applications such as engineering design and disaster assessment.

[0003] Under complex geological conditions, the change in the seismic wave propagation velocity directly reflects the physical properties of the formation. Traditional methods mainly focus on the velocity curve itself, but less on the analysis of the change gradient of velocity with depth. As a result, the accuracy of the inversion model in traditional methods is insufficient, and the reliability of the analysis results of the geological structure is insufficient.

[0004] Therefore, there is an urgent need for a better solution. Summary of the Invention

[0005] The present invention provides a method and device for analyzing formation structure based on a velocity change rate curve to solve the technical problems of insufficient accuracy of the inversion model and insufficient reliability of the analysis results of the geological structure in the prior art. The method includes: Obtain microtremor data, preprocess the microtremor data to obtain preprocessed microtremor data, and divide the preprocessed microtremor data into multiple microtremor data segments with the same time length according to a predetermined time interval; Extract the dispersion characteristics of multiple microtremor data segments based on the spatial autocorrelation method, generate a dispersion curve based on the dispersion characteristics, and generate a velocity curve based on the dispersion curve; Calculate the first derivative of the velocity curve to obtain a velocity transformation rate curve; Construct three initial inversion models based on the velocity transformation rate curve. The three initial inversion models include an initial shallow model, an initial middle model, and an initial deep model; Optimize the three initial inversion models based on the velocity transformation rate curve, fuse the optimized three initial inversion models to obtain a formation structure analysis model, and perform formation structure analysis based on the formation structure analysis model.

[0006] In some specific embodiments, the micro - motion data is pre - processed to obtain pre - processed micro - motion data, and the pre - processed micro - motion data is divided into a plurality of micro - motion data segments with the same length according to a predetermined time interval, specifically: The micro - motion data is denoised through a band - pass filter to obtain denoised micro - motion data; The denoised micro - motion data is normalized and standardized to obtain the pre - processed micro - motion data; The pre - processed micro - motion data is divided into a plurality of micro - motion data segments with a time length of 5 minutes.

[0007] In some specific embodiments, the dispersion characteristics of the plurality of micro - motion data segments are extracted based on the spatial autocorrelation method, a dispersion curve is generated based on the dispersion characteristics, and a velocity curve is generated based on the dispersion curve, specifically: The data characteristics in each frequency band of the plurality of micro - motion data segments are extracted based on the spatial autocorrelation method to obtain multi - band characteristics; A dispersion curve is generated based on the multi - band characteristics; Based on the dispersion curve and through a genetic algorithm for preliminary inversion, the velocity curve is obtained.

[0008] In some specific embodiments, three initial inversion models are constructed based on the velocity transformation rate curve, and the three initial inversion models include an initial shallow - layer model, an initial middle - layer model, and an initial deep - layer model, specifically: The key feature points in the region with significant gradient change are determined through the velocity change rate curve, and the key feature points are used as the initial boundaries of the three initial inversion models; The three initial inversion models are dynamically constrained based on the velocity distribution information in the dispersion curve; Initial parameters are set for the three initial inversion models respectively, and the three initial inversion models are constructed based on the initial parameters and the initial boundaries. The initial parameters include the velocity value, gradient, and boundary shape of the initial inversion model.

[0009] In some specific embodiments, the three initial inversion models are optimized based on the velocity transformation rate curve, specifically: The initial shallow - layer model is used as the boundary condition for the initial middle - layer model to constrain the initial middle - layer model; The initial middle - layer model is used as the boundary condition for the initial deep - layer model to constrain the initial deep - layer model; The region with significant gradient transformation is optimized based on the velocity transformation rate curve; Determine a speed flipping region based on the speed transformation rate curve, and adjust the speed value and boundary position of the speed flipping region by using an adaptive fitting algorithm based on actual drilling data; Globally optimize the speed distribution and gradient of the three initial inversion models by using a genetic algorithm, and optimize the local parameters of the three initial inversion models by using a Monte Carlo method to obtain three optimized initial inversion models.

[0010] In some specific embodiments, fuse the three optimized initial inversion models to obtain a formation structure analysis model, specifically: Assign different weights to the three optimized initial inversion models respectively; Smooth the interlayer transition regions of the three initial inversion models with assigned weights by using a bicubic interpolation algorithm to obtain a fused inversion model; Adjust the abnormal regions in the fused inversion model to obtain an adjusted inversion model, where the abnormal regions include regions with significant gradient changes, lithologic mutations, and buried structure boundary regions; Globally optimize the speed distribution and boundary position of the adjusted inversion model by using the least squares error method to obtain the formation structure analysis model.

[0011] In some specific embodiments, adjusting the abnormal regions in the fused inversion model to obtain an adjusted inversion model includes: Mark the regions with significant gradient changes by using high-density sampling and local fitting techniques, optimize and fit the speed distribution of the regions with significant gradient changes, and verify the boundary positions of the regions with significant gradient changes by combining the actual drilling data; Use an adaptive fitting algorithm to adjust the speed distribution of the lithologic mutation and buried structure boundary regions, and identify the interface positions of the lithologic mutation and buried structure boundary regions through the gradient characteristics in the speed change rate curve.

[0012] Correspondingly, the present invention also proposes a formation structure analysis device based on a speed change rate curve, and the device includes: A preprocessing module, configured to obtain microseismic data, preprocess the microseismic data to obtain preprocessed microseismic data, and divide the preprocessed microseismic data into a plurality of microseismic data segments with the same time length according to a predetermined time interval; A speed curve generation module, configured to extract the dispersion characteristics of a plurality of the microseismic data segments based on a spatial autocorrelation method, generate a dispersion curve based on the dispersion characteristics, and generate a speed curve based on the dispersion curve; A rate of change of speed curve generation module, configured to calculate a first derivative of the speed curve to obtain a rate of change of speed curve; A model construction module, configured to construct three initial inversion models based on the rate of change of speed curve, where the three initial inversion models include an initial shallow model, an initial middle model, and an initial deep model; A model optimization and fusion module, configured to optimize the three initial inversion models based on the rate of change of speed curve, fuse the optimized three initial inversion models to obtain a formation structure analysis model, and perform formation structure analysis based on the formation structure analysis model.

[0013] One embodiment of the present invention further provides a computing device, including: a memory and a processor; the memory is configured to store computer-executable instructions, and the processor is configured to execute the computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the formation structure analysis method based on the rate of change of speed curve as described in any one of the above are implemented.

[0014] One embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps of the formation structure analysis method based on the rate of change of speed curve as described in any one of the above are implemented.

[0015] By applying the above technical solutions, a formation structure analysis method based on the rate of change of speed curve is proposed. The method includes: obtaining microseismic data, preprocessing the microseismic data to obtain preprocessed microseismic data, and dividing the preprocessed microseismic data into a plurality of microseismic data segments with the same time length according to a predetermined time interval; extracting the dispersion characteristics of the plurality of microseismic data segments based on the spatial autocorrelation method, generating a dispersion curve based on the dispersion characteristics, and generating a speed curve based on the dispersion curve; calculating a first derivative of the speed curve to obtain a rate of change of speed curve; constructing three initial inversion models based on the rate of change of speed curve, where the three initial inversion models include an initial shallow model, an initial middle model, and an initial deep model; optimizing the three initial inversion models based on the rate of change of speed curve, fusing the optimized three initial inversion models to obtain a formation structure analysis model, and performing formation structure analysis based on the formation structure analysis model. By generating and analyzing the rate of change of speed curve, the inversion accuracy of the inversion model is improved, and the reliability of the formation structure analysis result is significantly improved. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 is a flowchart of a formation structure analysis method based on a velocity change rate curve provided by an embodiment of the present application; Figure 2 is a schematic structural diagram of a formation structure analysis device based on a velocity change rate curve provided by an embodiment of the present application; Figure 3 is a structural block diagram of a computing device provided by an embodiment of the present application. Detailed implementation manners

[0018] In the following description, many specific details are set forth to facilitate a thorough understanding of this specification. However, this specification can be implemented in many other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of this specification. Therefore, this specification is not limited by the specific implementations disclosed below.

[0019] The terms used in one or more embodiments of this specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of this specification. The singular forms "a" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more of the associated listed items.

[0020] It should be understood that although the terms first, second, etc. may be used in one or more embodiments of this specification to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first can also be called the second, and similarly, the second can also be called the first. Depending on the context, the word "if" as used herein can be interpreted as "when" or "while" or "in response to determining".

[0021] As Figure 1 shown, the present application proposes a formation structure analysis method based on a velocity change rate curve, and the method includes the following steps: Step S101 , acquiring micro-motion data, preprocessing the micro-motion data to obtain preprocessed micro-motion data, and dividing the preprocessed micro-motion data into a plurality of micro-motion data segments with the same time length according to a predetermined time interval.

[0022] In a possible implementation, micro-motion data is acquired, the micro-motion data is pre-processed to obtain pre-processed micro-motion data, and the pre-processed micro-motion data is divided into a plurality of micro-motion data segments of the same length according to a predetermined time interval, specifically: Performing denoising processing on the micro-motion data by using a bandpass filter to obtain denoised micro-motion data; Performing normalization and standardization processing on the denoised micro-motion data to obtain the pre-processed micro-motion data; The preprocessed micro-motion data is divided into a plurality of micro-motion data segments with a time length of 5 minutes.

[0023] In this embodiment, data preprocessing is a basic step in the speed change rate curve analysis, which aims to clean up the noise and inconsistency in the micro-motion data and lay a solid foundation for subsequent analysis.

[0024] In this embodiment, in a complex field environment, micro-motion data is often interfered by low-frequency noise in the surrounding environment, and may even be mixed with high-frequency background noise of the equipment. Therefore, this scheme uses a bandpass filter and sets the frequency band to 1 Hz to 20 Hz to remove useless signals and retain the key frequency band related to the formation. High-pass filtering is used to remove background noise below 1 Hz, while low-pass filtering is used to filter out high-frequency interference above 20 Hz to ensure the clarity and integrity of the target signal.

[0025] In this embodiment, due to differences in observation equipment performance and environmental conditions, data from different nodes may have amplitude deviations. By normalizing the micromotion data, the amplitudes of all data are standardized to a uniform range, thereby ensuring that the micromotion data between different nodes are comparable and consistent.

[0026] In this embodiment, in order to improve the time resolution, the pre-processed micro-motion data is divided into multiple 5-minute time periods. Each segment of data is processed independently to capture the slight differences in time changes and verify the stability of the micro-motion data.

[0027] After preprocessing, the signal-to-noise ratio of micro-motion data is significantly improved, providing high-quality input for subsequent velocity curve generation.

[0028] Step S102 : extracting dispersion characteristics of the plurality of micro-motion data segments based on a spatial autocorrelation method, generating dispersion curves based on the dispersion characteristics, and generating velocity curves based on the dispersion curves.

[0029] In this embodiment, the velocity curve reflects the variation of the seismic wave propagation velocity with depth and is a key link in calculating the velocity change rate curve.

[0030] In a possible implementation manner, the dispersion characteristics of the multiple microtremor data segments are extracted based on the spatial autocorrelation method, a dispersion curve is generated based on the dispersion characteristics, and a velocity curve is generated based on the dispersion curve. Specifically: The data characteristics in each frequency band of the multiple microtremor data segments are extracted based on the spatial autocorrelation method to obtain multi-frequency band characteristics; A dispersion curve is generated based on the multi-frequency band characteristics; Based on the dispersion curve and through a genetic algorithm for preliminary inversion, the velocity curve is obtained.

[0031] In this embodiment, based on the spatial autocorrelation (SPAC) method, the dispersion characteristics of the microtremor signal are extracted. The shallow layer data covers high-frequency information from 10 Hz to 50 Hz, the middle layer data is concentrated in the intermediate frequency range from 5 Hz to 20 Hz, and the deep layer data is mainly distributed in the low-frequency band from 0.5 Hz to 10 Hz. By extracting the characteristics of each frequency band, a complete dispersion curve is formed to reflect the wave velocity characteristics in different depth ranges.

[0032] In this embodiment, using the dispersion curve, a genetic algorithm is used to perform a preliminary inversion of the underground velocity structure. Due to its global optimization ability, the genetic algorithm can quickly converge to the optimal solution in a complex geological environment. The inversion result covers a depth range of 0 meters to 500 meters, and the error is controlled within 2%, providing a reliable velocity curve for subsequent analysis.

[0033] Step S103, by calculating the first derivative of the velocity curve, the velocity change rate curve is obtained.

[0034] In this embodiment, the velocity change rate curve reveals the change gradient of the velocity with depth by calculating the first derivative of the velocity curve, which is the core innovation point of this technology.

[0035] In this embodiment, numerical differentiation is performed on the velocity curve to generate the velocity change rate curve. A significant change in the gradient directly corresponds to a formation interface or a lithology mutation point. For example, at the peak position of the velocity change rate curve, a lithology boundary or a velocity inversion area can usually be identified.

[0036] In this embodiment, by analyzing the slope change of the gradient curve, the position and characteristics of the formation interface are further determined. This process combines the characteristics of the dispersion curve to verify the authenticity of the change point. For the velocity inversion phenomenon, the velocity change rate curve can intuitively reveal its depth and range, providing an important basis for complex geological analysis. The generation of the velocity change rate curve not only improves the layering accuracy but also provides key constraint conditions for the optimization of the subsequent inversion model.

[0037] Step S104: Construct three initial inversion models based on the velocity transformation rate curve. The three initial inversion models include an initial shallow model, an initial middle model, and an initial deep model.

[0038] In a possible implementation manner, constructing three initial inversion models based on the velocity transformation rate curve, where the three initial inversion models include an initial shallow model, an initial middle model, and an initial deep model, specifically: Determine key feature points in the region with significant gradient changes through the velocity change rate curve, and use the key feature points as the initial boundaries of the three initial inversion models; Dynamically constrain the three initial inversion models based on the velocity distribution information in the dispersion curve; Set initial parameters for the three initial inversion models respectively, and construct the three initial inversion models based on the initial parameters and the initial boundaries. The initial parameters include the velocity value, gradient, and boundary form of the initial inversion model.

[0039] In this embodiment, the optimization of the inversion model is the core link to achieve refined analysis of the formation structure and an important step to ensure the accuracy and reliability of the final model. In this technical solution, the optimization of the inversion model is carried out through three main steps: initial model construction, parameter adjustment, and model fusion and optimization, gradually generating a complete formation model with high resolution and multiple layers from details to the whole.

[0040] In this embodiment, constructing the initial inversion model aims to provide an accurate basic framework for the inversion process, ensuring that the model has sufficient constraint conditions and can truly reflect the physical properties of the formation.

[0041] In this embodiment, the velocity change rate curve is used as the core reference basis. By analyzing the region with significant gradient changes, the formation interface and lithology mutation points are accurately identified. For example, in the shallow region, a large gradient change often corresponds to the bottom boundary of the overburden layer, while in the deep region, the steep inflection point of the velocity change rate curve may indicate the top boundary of the bedrock. These feature points are directly used as the initial boundaries of the model, providing strong constraint conditions for the subsequent inversion.

[0042] In this embodiment, in addition to the velocity change rate curve, the initial model also combines the velocity distribution information in the dispersion curve to provide dynamic constraints for each depth level. For example, the shallow model focuses on the velocity distribution accuracy of the overburden layer, while the middle model focuses on capturing structural features and gradient changes.

[0043] In this embodiment, when constructing the model, corresponding velocity values, gradients, and boundary morphologies are set for each depth range. These parameters are adjusted according to the geological characteristics of the target area to ensure the physical consistency and geological rationality of the initial model.

[0044] Step S105: Optimize the three initial inversion models based on the velocity transformation rate curve, fuse the optimized three initial inversion models to obtain a formation structure analysis model, and perform formation structure analysis based on the formation structure analysis model.

[0045] In a possible implementation, optimizing the three initial inversion models based on the velocity transformation rate curve specifically includes: Using the initial shallow model as the boundary condition of the initial middle model to constrain the initial middle model; Using the initial middle model as the boundary condition of the initial deep model to constrain the initial deep model; Optimizing the significantly gradient-transformed regions based on the velocity transformation rate curve; Determining the velocity flip regions based on the velocity transformation rate curve, and using an adaptive fitting algorithm to adjust the velocity values and boundary positions in the velocity flip regions based on actual borehole data; Globally optimizing the velocity distributions and gradients of the three initial inversion models through a genetic algorithm, and optimizing the local parameters of the three initial inversion models using the Monte Carlo method to obtain the optimized three initial inversion models.

[0046] In this embodiment, the model optimization adopts a progressive strategy from shallow to deep. The shallow model serves as the boundary condition for the middle model, while the middle model provides the initial constraint for the deep model. This progressive method can minimize error propagation and ensure the consistency of the overall model.

[0047] In this embodiment, under the guidance of the velocity change rate curve, key optimization is carried out on the regions with significant gradient changes. For example, the regions with large shallow velocity gradients may correspond to formation boundaries, and at these positions, increasing the sampling density and parameter adjustment can significantly improve the model analysis accuracy.

[0048] In this embodiment, in a complex geological environment, the velocity inversion regions are often the difficulties in model optimization. Through a detailed analysis of the gradient curve and combined with actual borehole data, an adaptive fitting algorithm is used for these regions to dynamically adjust the velocity values and boundary positions to ensure the accuracy of the model.

[0049] In this embodiment, the genetic algorithm is used to globally optimize the velocity distribution and gradient, and the Monte Carlo method is combined to further refine the local parameters, so that the model can accurately reflect the formation characteristics while reducing the risk of overfitting.

[0050] In a possible implementation, the three optimized initial inversion models are fused to obtain a formation structure analysis model, specifically: Different weights are assigned to the three optimized initial inversion models respectively; The bicubic interpolation algorithm is used to smooth the interlayer transition regions of the three initial inversion models after weight assignment to obtain a fused inversion model; The abnormal regions in the fused inversion model are adjusted to obtain an adjusted inversion model, and the abnormal regions include regions with significant gradient changes, lithologic mutations, and buried structure boundary regions; The global consistency of the velocity distribution and boundary position of the adjusted inversion model is optimized by the minimum error method to obtain the formation structure analysis model.

[0051] In a possible implementation, adjusting the abnormal regions in the fused inversion model to obtain an adjusted inversion model includes: The regions with significant gradient changes are marked by high-density sampling and local fitting techniques, the velocity distribution of the regions with significant gradient changes is optimized and fitted, and the boundary positions of the regions with significant gradient changes are verified in combination with the actual borehole data; The velocity distribution of the lithologic mutation and buried structure boundary regions is adjusted by an adaptive fitting algorithm, and the interface positions of the lithologic mutation and buried structure boundary regions are identified by the gradient characteristics in the velocity change rate curve.

[0052] In this embodiment, model fusion is an important step in integrating the initial shallow model, initial middle model, and initial deep model, and its purpose is to generate a complete formation model that not only conforms to the actual geological characteristics but also has high resolution and consistency.

[0053] In this embodiment, models at different depths have different signal-to-noise ratios and resolutions. Therefore, during the fusion process, the weight of the shallow model is set to 50%, and the weights of the middle and deep models are 30% and 20% respectively. This weight assignment ensures the sensitivity of the model to details while taking into account the integrity of the deep regions.

[0054] In this embodiment, the bicubic interpolation algorithm is used to smooth the interlayer transition regions during the fusion process, reducing the discontinuity of the boundaries and making the transition of the model from shallow to deep natural and smooth.

[0055] In this embodiment, the local gradient anomalies of the velocity change rate curve usually reflect velocity inversion or lithology mutation regions, and these complex geological structures require fine optimization. Through high-density sampling and local fitting techniques, regions with significant gradient changes are first marked, and then the velocity distribution is optimized and fitted to ensure the analytical accuracy of the inversion points and lithology mutations. The key boundary positions are verified by combining borehole data to further improve the reliability of the model.

[0056] In this embodiment, for the lithology mutation and buried structure boundary regions, an adaptive fitting algorithm is used to adjust the velocity distribution, and the interface positions are accurately identified through gradient features. The optimized model clearly shows the characteristics of the velocity inversion and lithology mutation regions, significantly improving the resolution of the model and providing a solid support for the analysis of complex geological environments.

[0057] In this embodiment, the shallow, middle, and deep models are integrated to ensure the coherence and integrity of the stratigraphic structure. The velocity distribution and boundary positions in the model are globally adjusted by the least error method to solve the deviation problem between models at different depths. Multiscale interpolation techniques are used to smooth the interlayer transition regions, making the model show more natural continuity in the vertical direction while maintaining high horizontal resolution. The optimized model not only has more consistent boundary characteristics but also shows a high degree of coordination between the detailed resolution and the overall structure, providing a reliable support for the stratigraphic analysis in complex geological environments.

[0058] In summary, the conventional microtremor inversion technology has the following deficiencies: 1. Low layering accuracy: For subtle stratigraphic changes, conventional velocity curves are difficult to capture velocity mutation points and lithology interfaces, resulting in insufficient model accuracy.

[0059] 2. Unable to accurately identify velocity inversion phenomena: Under complex multi-layer geological conditions, velocity inversion phenomena often lead to difficulties in interpreting stratigraphic models, and traditional technologies cannot effectively characterize the geological features of these regions.

[0060] 3. Insufficient lithology interface analysis ability: In traditional inversion technologies, the identification of stratigraphic demarcation points relies on empirical judgments and lacks systematic and automated processing procedures.

[0061] Due to the above defects of the conventional inversion technology, the present invention reflects the gradient change of velocity with depth through the velocity change rate curve, revealing the subtle differences in stratigraphic features. The velocity change rate curve is the first derivative of the velocity curve, intuitively reflecting the change amplitude and trend of velocity with depth.

[0062] A significant change in the rate-of-change-of-velocity curve usually corresponds to a formation interface, a velocity inversion region, or a lithology mutation point. Its generation process includes denoising and preprocessing of the original microseismic data, generating a velocity curve through an inversion algorithm, and then calculating the derivative of the velocity curve to obtain the rate-of-change-of-velocity curve. In regions with significant gradient changes, combined with the characteristics of the dispersion curve, the position and characteristics of the formation boundary point can be further refined and analyzed.

[0063] In addition, the rate-of-change-of-velocity curve can also be used to optimize the inversion result. During the construction of the inversion model, the rate-of-change-of-velocity curve provides accurate constraint conditions for the initial model, effectively improving the resolution and reliability of the model.

[0064] Based on the above solutions, the present application has the following advantages: 1. Improve the ability to analyze formation interfaces: Accurately identify velocity mutation points through gradient information and accurately locate lithology interfaces.

[0065] 2. Analyze the velocity inversion phenomenon: Use the slope change of the rate-of-change-of-velocity curve to systematically identify and analyze the geological characteristics of the velocity inversion region.

[0066] 3. Optimize the inversion result: Provide more accurate constraint conditions for the inversion model through the rate-of-change-of-velocity curve and improve the inversion accuracy.

[0067] 4. Provide a high-resolution formation model: Integrate gradient information with the velocity curve to generate a formation structure model with higher resolution and reliability.

[0068] Furthermore, evaluate the formation structure analysis model generated in this solution, and the evaluation results are as follows: 1. The final model shows high resolution and high reliability from the shallow layer to the deep layer. The shallow layer part details the thickness and lithology distribution of the overburden, and the middle and deep layer models accurately reflect the tectonic characteristics and bedrock morphology.

[0069] 2. Compare and analyze the generated model with borehole data and seismic profiles. The verification results show that the interface matching degree of the shallow layer model reaches more than 90%, and the deep layer velocity error is controlled within 1%.

[0070] 3. The optimized model provides a scientific basis for the analysis of formation structures in complex geological environments, and significantly improves the ability to analyze the velocity inversion region, laying a solid foundation for subsequent engineering design and geological hazard assessment.

[0071] 4. Compare and analyze the generated formation model with borehole data to verify the accuracy of the model within different depth ranges. The test results show that the matching degree of shallow layer formation interface identification reaches more than 90%, and the deep layer velocity error is controlled within 1%.

[0072] 5. Quantify the error of the model and evaluate the error distribution in different depth ranges. The results show that the overall error is less than 5%, and the resolution of the model is significantly higher than that of traditional inversion methods.

[0073] In summary, by applying the above technical solutions, a formation structure analysis method based on the velocity change rate curve is proposed. The method includes: obtaining microseismic data, preprocessing the microseismic data to obtain preprocessed microseismic data, and dividing the preprocessed microseismic data into multiple microseismic data segments with the same time length according to a predetermined time interval; extracting the dispersion characteristics of multiple microseismic data segments based on the spatial autocorrelation method, generating a dispersion curve based on the dispersion characteristics, and generating a velocity curve based on the dispersion curve; calculating the first derivative of the velocity curve to obtain a velocity transformation rate curve; constructing three initial inversion models based on the velocity transformation rate curve, the three initial inversion models including an initial shallow layer model, an initial middle layer model, and an initial deep layer model; optimizing the three initial inversion models based on the velocity transformation rate curve, and fusing the optimized three initial inversion models to obtain a formation structure analysis model, and performing formation structure analysis based on the formation structure analysis model. By generating and analyzing the velocity change rate curve, the inversion accuracy of the inversion model is improved, and the reliability of the formation structure analysis results is significantly improved.

[0074] The embodiment of the present application also proposes a formation structure analysis device based on the velocity change rate curve, as Figure 2 shown. The device includes: A preprocessing module 10, configured to obtain various geological information from a target area, perform data preprocessing on the various geological information, and construct a feature vector based on the various geological information after data preprocessing; A velocity curve generation module 20, configured to perform block division on the basis of a density clustering algorithm and the feature vector to obtain a plurality of independent blocks, and optimize the block boundaries of the plurality of independent blocks based on the feature vector to obtain a plurality of optimized independent blocks; A velocity change rate curve generation module 30, configured to construct an independent inversion model based on geological characteristics in each optimized independent block, and perform inversion processing based on the independent inversion model to obtain an inversion result within each optimized independent block; A model construction module 40, configured to perform integration processing on the inversion results within each optimized independent block to obtain an integrated inversion result; A model optimization and fusion module 50, configured to optimize the three initial inversion models based on the velocity transformation rate curve, and fuse the optimized three initial inversion models to obtain a formation structure analysis model, and perform formation structure analysis based on the formation structure analysis model.

[0075] Figure 3 FIG.

[0075] shows a structural block diagram of a computing device 400 provided according to an embodiment of the present specification. The components of the computing device 400 include, but are not limited to, a memory 410 and a processor 420. The processor 420 is connected to the memory 410 via a bus 430, and a database 450 is used to store data.

[0076] The computing device 400 further includes an access device 440, which enables the computing device 400 to communicate via one or more networks 460. Examples of such networks include the Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 440 may include one or more of any type of wired or wireless network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC).

[0077] In an embodiment of the present specification, the above components of the computing device 400 and Figure 3 other components not shown therein may also be connected to each other, e.g., via a bus. It should be understood that Figure 3 the shown structural block diagram of the computing device is merely for illustrative purposes and not a limitation on the scope of the present specification. Those skilled in the art may add or replace other components as needed.

[0078] The computing device 400 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smart phones), wearable computing devices (e.g., smart watches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 400 can also be a mobile or stationary server.

[0079] Among them, the processor 420 is used to execute the following computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the above-mentioned formation structure analysis method based on the rate-of-change-of-speed curve are realized. The above is a schematic solution of a computing device in this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the above-mentioned formation structure analysis method based on the rate-of-change-of-speed curve belong to the same concept. For the details not described in detail in the technical solution of the computing device, reference can be made to the description of the technical solution of the above-mentioned formation structure analysis method based on the rate-of-change-of-speed curve.

[0080] An embodiment of this specification also provides a computer-readable storage medium, which stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps of the above-mentioned formation structure analysis method based on the rate-of-change-of-speed curve are realized.

[0081] The above is a schematic solution of a computer-readable storage medium in this embodiment. It should be noted that the technical solution of this storage medium and the technical solution of the above-mentioned formation structure analysis method based on the rate-of-change-of-speed curve belong to the same concept. For the details not described in detail in the technical solution of the storage medium, reference can be made to the description of the technical solution of the above-mentioned formation structure analysis method based on the rate-of-change-of-speed curve.

[0082] An embodiment of this specification also provides a computer program, wherein when the computer program is executed on a computer, the computer is made to execute the steps of the above-mentioned formation structure analysis method based on the rate-of-change-of-speed curve.

[0083] The above is a schematic solution of a computer program in this embodiment. It should be noted that the technical solution of this computer program and the technical solution of the above-mentioned formation structure analysis method based on the rate-of-change-of-speed curve belong to the same concept. For the details not described in detail in the technical solution of the computer program, reference can be made to the description of the technical solution of the above-mentioned formation structure analysis method based on the rate-of-change-of-speed curve.

[0084] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0085] The computer instructions include computer program code, which may be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0086] It should be noted that for the foregoing method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the embodiments of this specification are not limited by the described order of actions, because according to the embodiments of this specification, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential for the embodiments of this specification.

[0087] In the above embodiments, the descriptions of the various embodiments have their own focuses. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0088] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The alternative embodiments do not exhaust all the details and do not limit the invention to only the specific embodiments described. Obviously, many modifications and variations can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the embodiments of this specification, so that those skilled in the art can understand and utilize this specification well. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A method for analyzing formation structure based on the rate-of-change-of-velocity curve, characterized in that, The method includes: Obtaining microseismic data, preprocessing the microseismic data to obtain preprocessed microseismic data, and dividing the preprocessed microseismic data into multiple microseismic data segments with the same time length according to a predetermined time interval; Extracting the dispersion characteristics of multiple microseismic data segments based on the spatial autocorrelation method, generating a dispersion curve based on the dispersion characteristics, and generating a velocity curve based on the dispersion curve; Calculating the first derivative of the velocity curve to obtain a velocity transformation rate curve; Constructing three initial inversion models based on the velocity transformation rate curve, the three initial inversion models including an initial shallow layer model, an initial middle layer model, and an initial deep layer model; Optimizing the three initial inversion models based on the velocity transformation rate curve, fusing the optimized three initial inversion models to obtain a formation structure analysis model, and performing formation structure analysis based on the formation structure analysis model.

2. The method according to claim 1, wherein Obtaining microseismic data, preprocessing the microseismic data to obtain preprocessed microseismic data, and dividing the preprocessed microseismic data into multiple microseismic data segments with the same length according to a predetermined time interval, specifically: Denosing the microseismic data through a band-pass filter to obtain denoised microseismic data; Performing normalization processing and standardization processing on the denoised microseismic data to obtain the preprocessed microseismic data; Dividing the preprocessed microseismic data into multiple microseismic data segments with a time length of 5 minutes.

3. The method according to claim 2, characterized in that, Extracting the dispersion characteristics of multiple microseismic data segments based on the spatial autocorrelation method, generating a dispersion curve based on the dispersion characteristics, and generating a velocity curve based on the dispersion curve, specifically: Extracting the data characteristics within each frequency band of multiple microseismic data segments based on the spatial autocorrelation method to obtain multi-frequency band characteristics; Generating a dispersion curve based on the multi-frequency band characteristics; Performing preliminary inversion based on the dispersion curve through a genetic algorithm to obtain the velocity curve.

4. The method according to claim 3, wherein Constructing three initial inversion models based on the velocity transformation rate curve, the three initial inversion models including an initial shallow layer model, an initial middle layer model, and an initial deep layer model, specifically: Determining key feature points in the region with significant gradient changes through the velocity change rate curve, and using the key feature points as the initial boundaries of the three initial inversion models; Performing dynamic constraints on the three initial inversion models based on the velocity distribution information in the dispersion curve; Setting initial parameters for the three initial inversion models respectively, and constructing the three initial inversion models based on the initial parameters and the initial boundaries, the initial parameters including the velocity value, gradient, and boundary form of the initial inversion model.

5. The method according to claim 4, characterized in that, Optimizing the three initial inversion models based on the velocity transformation rate curve, specifically: Using the initial shallow layer model as the boundary condition of the initial middle layer model to constrain the initial middle layer model; Using the initial middle layer model as the boundary condition of the initial deep layer model to constrain the initial deep layer model; Optimizing the region with significant gradient transformation based on the velocity transformation rate curve; Determine the velocity flip region based on the velocity transformation rate curve, and adjust the velocity value and boundary position of the velocity flip region by using an adaptive fitting algorithm based on the actual drilling data; Globally optimize the velocity distribution and gradient of the three initial inversion models by using a genetic algorithm, and optimize the local parameters of the three initial inversion models by using a Monte Carlo method to obtain three optimized initial inversion models.

6. The method according to claim 5, wherein Fuse the three optimized initial inversion models to obtain a formation structure analysis model, specifically: Assign different weights to the three optimized initial inversion models respectively; Smooth the interlayer transition regions of the three initial inversion models with assigned weights by using a bicubic interpolation algorithm to obtain a fused inversion model; Adjust the abnormal regions in the fused inversion model to obtain an adjusted inversion model, where the abnormal regions include regions with significant gradient changes, lithologic mutations, and buried structure boundary regions; Globally optimize the velocity distribution and boundary position of the adjusted inversion model by using the minimum error method to obtain the formation structure analysis model.

7. The method according to claim 6, wherein Adjust the abnormal regions in the fused inversion model to obtain an adjusted inversion model, including: Mark the regions with significant gradient changes by using high-density sampling and local fitting techniques, optimize and fit the velocity distribution of the regions with significant gradient changes, and verify the boundary positions of the regions with significant gradient changes by combining the actual drilling data; Adjust the velocity distribution of the lithologic mutation and buried structure boundary regions by using an adaptive fitting algorithm, and identify the interface positions of the lithologic mutation and buried structure boundary regions by using the gradient characteristics in the velocity change rate curve.

8. A formation structure analysis device based on a speed change rate curve, characterized in that, The device includes: A preprocessing module, configured to acquire microseismic data, preprocess the microseismic data to obtain preprocessed microseismic data, and divide the preprocessed microseismic data into multiple microseismic data segments with the same time length according to a predetermined time interval; A velocity curve generation module, configured to extract the dispersion characteristics of multiple microseismic data segments based on a spatial autocorrelation method, generate a dispersion curve based on the dispersion characteristics, and generate a velocity curve based on the dispersion curve; A velocity change rate curve generation module, configured to calculate the first derivative of the velocity curve to obtain a velocity transformation rate curve; A model construction module, configured to construct three initial inversion models based on the velocity transformation rate curve, where the three initial inversion models include an initial shallow layer model, an initial middle layer model, and an initial deep layer model; A model optimization and fusion module, configured to optimize the three initial inversion models based on the velocity transformation rate curve, fuse the three optimized initial inversion models to obtain a formation structure analysis model, and perform formation structure analysis based on the formation structure analysis model.

9. A computing device, characterized in that, Including: A memory and a processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the formation structure analysis method based on the velocity change rate curve according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps of the formation structure analysis method based on the speed change rate curve according to any one of claims 1 to 7 are implemented.

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