High-precision velocity modeling method for igneous rock and related equipment

Abstract

The invention belongs to the technical field of petroleum geological exploration, and discloses an igneous rock high-precision speed modeling method and related equipment, and the method comprises the steps: building a lithofacies-seismic facies chart; constructing a seismic facies prediction model based on the lithofacies-seismic facies chart, and generating an igneous rock lithofacies prediction body through the seismic facies prediction model; establishing an igneous rock low-frequency model by taking the igneous rock facies predictor as a constraint; carrying out post-stack sparse pulse inversion through the igneous rock low-frequency model to obtain a longitudinal wave impedance attribute body, and converting the longitudinal wave impedance attribute body into a longitudinal wave velocity body, namely an igneous rock velocity body; and fusing the igneous rock velocity body with the velocity of the non-igneous rock development area to obtain an igneous rock high-precision velocity model. According to the method, the Ordovician fracture and fracture-cavity reservoir identification capability is obviously improved, and effective data support can be provided for well location argumentation and development schemes.

Description

Technical Field

[0001] This invention belongs to the field of petroleum geological exploration technology, specifically to a high-precision velocity modeling method for igneous rocks and related equipment. Background Technology

[0002] Carbonate reservoirs in a certain area account for about one-third of the basin's total oil and gas resources, and are crucial for the production and stability of the oilfield. The Ordovician carbonate reservoirs in the central part of this region are mainly controlled by late-stage diagenetic fracturing and karstification, forming cavernous and pore-filled reservoirs. These reservoirs exhibit a "beaded" reflection pattern on seismic maps, demonstrating strong heterogeneity. Detailed characterization and development of these reservoirs require high-precision seismic imaging data. However, the central part of this region also features extensive Silurian to Permian igneous rocks, especially the numerous "crater-type" and "mountain-type" craters formed by concentrated volcanic eruptions during the Permian depositional period. These craters exhibit chaotic reflection patterns on seismic profiles. The igneous rock facies are complex, varying in size and exhibiting dramatic lateral velocity variations, making velocity field modeling for migration processing extremely challenging.

[0003] Currently, igneous rock modeling mainly employs two methods: 1) Based on geological understanding, an igneous rock velocity model is established using VSP (Voltage Seismic Projection) and sonic logging velocity constraints. The drawback is significant: VSP and sonic logging velocities only represent the velocity at that specific well location, ignoring the large lateral variations in igneous rock velocity, with the velocity error increasing the further away from the well. 2) While grid tomography velocity modeling offers some improvement over the first method, the significant lithological variations within igneous rocks lead to large velocity variations, resulting in chaotic reflected waves, discontinuous phase axes, low signal-to-noise ratio, and a limited number of effective residual samples. This reduces the reliability of tomographic inversion and makes it difficult to accurately characterize igneous rock velocity variations. The velocity models established using these two methods have low accuracy, resulting in poor continuity of the underlying strata after seismic data migration imaging, especially in the Ordovician strata where the signal-to-noise ratio and resolution are low. Imaging of internal faults and fracture-vuggy reservoirs within the Ordovician strata is also poor, with inaccurate spatial positioning, limiting the detailed description and development of oil reservoirs. Summary of the Invention

[0004] In order to overcome the defects of the existing technology, the purpose of this invention is to provide a high-precision velocity modeling method and related equipment for igneous rocks, so as to solve the technical problems of poor imaging effect and inaccurate spatial position in the existing technology.

[0005] This invention is achieved through the following technical solution: In a first aspect, the present invention provides a high-precision velocity modeling method for igneous rocks, comprising the following steps: Establish lithofacies-seismic facies plates; A seismic facies prediction model is constructed based on the lithofacies-seismic facies map, and igneous lithofacies prediction bodies are generated through the seismic facies prediction model. A low-frequency model of igneous rocks was established using igneous lithofacies prediction bodies as constraints. The longitudinal wave impedance property volume is obtained by performing post-stack sparse pulse inversion using a low-frequency model of igneous rocks, and then the longitudinal wave impedance property volume is transformed into a longitudinal wave velocity volume, i.e., an igneous rock velocity volume. By fusing the velocity volume of igneous rocks with the velocity of non-igneous rock development areas, a high-precision velocity model of igneous rocks is obtained.

[0006] Preferably, in the step of establishing a lithofacies-seismic facies map, the drilled data is classified into igneous lithofacies types, and a lithofacies-seismic facies map is established based on the classified igneous lithofacies types through well-seismic calibration. The specific process is as follows: Analysis of cuttings and logging data from drilled wells in the target igneous development area was conducted to classify the lithofacies types of the igneous rocks. Based on the pre-stack depth migration data, the seismic reflection characteristics corresponding to different igneous lithofacies types were obtained through fine well seismic calibration. A lithofacies-seismic facies chart was established based on the seismic reflection characteristics corresponding to different igneous rock lithofacies types.

[0007] Preferably, in the step of constructing a seismic facies prediction model based on a lithofacies-seismic facies map, the specific process of generating igneous lithofacies prediction bodies using the seismic facies prediction model is as follows: Based on the lithofacies-seismic facies map, multiple three-dimensional volume models corresponding to different lithofacies were constructed to obtain multiple lithofacies models; Based on multiple lithofacies models and the corresponding seismic facies data in the lithofacies-seismic facies chart, iterative training is performed to obtain a seismic facies prediction model; The seismic facies prediction model takes three-dimensional post-stack seismic data as input and outputs igneous lithofacies prediction bodies.

[0008] Preferably, in the step of establishing a low-frequency model of igneous rocks using the predicted igneous lithofacies body as a constraint, the predicted igneous lithofacies body is calibrated to the range of the low-frequency component of the wave impedance statistically obtained from well logging. The calibrated properties of the predicted igneous lithofacies body are then used as constraints to establish the low-frequency model of igneous rocks. The specific process is as follows: Well-seismic fine calibration is carried out based on well logging data and seismic data. Based on well-seismic calibration and seismic interpretation of the stratigraphic horizon, a layered attribute model is established by interpolation along the stratigraphic horizon. By performing spectral analysis on seismic data from igneous rock development areas, a threshold value for missing low-frequency components is obtained. The threshold value is then used to perform low-pass filtering on the impedance curves of standard wells, and the range of values ​​for the low-frequency components of the impedance curves is statistically analyzed. The numerical calibration method was used to calibrate the predicted igneous lithofacies body to the range of the low-frequency component of the wave impedance curve in well logging statistics. By merging the layered attribute model and the calibrated igneous lithofacies prediction body, a low-frequency model is obtained.

[0009] Preferably, in the step of obtaining the P-wave impedance attribute volume through post-stack sparse pulse inversion using an igneous low-frequency model and converting the P-wave impedance attribute volume into the P-wave velocity volume, i.e., the igneous velocity volume, a linear equation in one variable is obtained through impedance and velocity intersection analysis, and the P-wave impedance attribute volume is converted into the P-wave velocity volume based on the linear equation in one variable.

[0010] Preferably, when fusing the velocity body of igneous rocks with the velocity of non-igneous rock development areas, the fusion boundary is smoothed to obtain a high-precision velocity model of igneous rocks.

[0011] Furthermore, the smoothing effect employs Gaussian filtering, median filtering, and / or edge-preserving filtering.

[0012] Secondly, the present invention also provides a high-precision velocity modeling system for igneous rocks, based on the aforementioned high-precision velocity modeling method for igneous rocks, comprising: The initialization unit is configured as follows: Used to create lithofacies-seismic facies maps; The prediction unit is configured as follows: Used to construct a seismic facies prediction model based on lithofacies-seismic facies chart, and to generate igneous lithofacies prediction bodies through the seismic facies prediction model; The model building unit is configured as follows: Used to establish low-frequency models of igneous rocks with igneous lithofacies prediction bodies as constraints; The conversion unit is configured as follows: It is used to obtain the P-wave impedance property volume by performing post-stack sparse pulse inversion through the low-frequency model of igneous rocks, and to transform the P-wave impedance property volume into the P-wave velocity volume, i.e. the igneous rock velocity volume. The fused output unit is configured as follows: This is used to fuse the velocity volume of igneous rocks with the velocity of non-igneous rock development areas to obtain a high-precision velocity model of igneous rocks.

[0013] Thirdly, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the high-precision velocity modeling method for igneous rocks as described above.

[0014] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of the high-precision velocity modeling method for igneous rocks as described above.

[0015] Fifthly, the present invention also provides a computer program product, including computer instructions that instruct a computing device to perform operations corresponding to the high-precision velocity modeling method for igneous rocks described above.

[0016] Compared with the prior art, the present invention has the following beneficial technical effects: This invention provides a high-precision velocity modeling method and related equipment for igneous rocks. The method includes the following steps: establishing a lithofacies-seismic facies map; constructing a seismic facies prediction model based on the lithofacies-seismic facies map, and generating an igneous lithofacies prediction body through the seismic facies prediction model; establishing an igneous low-frequency model with the igneous lithofacies prediction body as a constraint; performing post-stack sparse pulse inversion through the igneous low-frequency model to obtain a P-wave impedance attribute body, and converting the P-wave impedance attribute body into a P-wave velocity body, i.e., an igneous velocity body; fusing the igneous velocity body with the velocity of non-igneous rock development areas to obtain a high-precision velocity model for igneous rocks. This application significantly improves the ability to identify Ordovician fractures and fracture-vuggy reservoirs by establishing a high-precision velocity model that better matches the distribution of igneous lithofacies, with clear fracture characteristics and accurate beaded morphology, which can provide effective data support for well location demonstration and development plan.

[0017] Furthermore, by meticulously classifying igneous rock facies types and establishing a lithofacies-seismic facies chart in conjunction with well-seismic calibration, the accuracy of identifying subsurface igneous rock facies was significantly improved. This method enables seismic data to more accurately reflect the complex lithofacies characteristics of igneous rocks, providing a solid foundation for subsequent velocity modeling.

[0018] Furthermore, the seismic facies prediction model was used to output the predicted igneous rock facies volume, and a low-frequency model of igneous rocks was further established by calibrating to the range of low-frequency components of wave impedance from well logging statistics. This step effectively constrained the construction of the igneous rock velocity model, making the high-precision velocity model of igneous rocks more consistent with the actual underground igneous rock facies distribution, thereby improving the accuracy of the velocity model.

[0019] Furthermore, the high-precision igneous rock velocity model in this invention significantly improves the ability to identify fracture and fracture-vuggy reservoirs under complex geological conditions such as the Ordovician system. Clear fracture characteristics and accurate beaded morphology provide strong evidence for geological interpretation, helping to more accurately assess oil and gas reservoir distribution and reservoir quality.

[0020] Furthermore, based on a high-precision velocity model, this invention enables more scientific well location justification and development plans. Accurate underground velocity structures help predict oil and gas migration paths, assess development risks, and optimize extraction strategies, thereby improving the development efficiency and economic benefits of oil and gas fields. Attached Figure Description

[0021] Figure 1A flowchart of a high-precision velocity modeling method for igneous rocks according to an embodiment of the present invention is shown; Figure 2 This invention illustrates a seismic facies diagram of igneous rocks according to an embodiment of the present invention. Figure 3 A schematic diagram of the seismic facies prediction model according to an embodiment of the present invention is shown; Figure 4(a) shows a seismic profile of igneous rock seismic facies according to an embodiment of the present invention; Figure 4(b) shows a seismic profile of igneous rock seismic facies according to an embodiment of the present invention; Figure 5 This figure shows an inverted P-wave velocity profile of an igneous rock development zone according to an embodiment of the present invention. Figure 6 A comparison diagram of the VSP velocity and inverted P-wave velocity of the verification well in an embodiment of the present invention is shown; Figure 7 This figure shows a comparison of pre-stack depth migration velocities before and after fusion of igneous rock inversion velocities according to an embodiment of the present invention. Figure 8 The diagram shows a comparison of the pre-stack depth migration profiles before and after the fusion of igneous rock inversion velocities according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] The present invention will now be described in further detail with reference to the accompanying drawings: Figure 1 A flowchart illustrating a high-precision velocity modeling method for igneous rocks according to an embodiment of this disclosure is shown, such as... Figure 1 As shown, it includes the following steps: Step S1: Create a lithofacies-seismic facies diagram; Preferably, in the embodiment of this disclosure, in the step of establishing a lithofacies-seismic facies map, the drilled data is classified into igneous lithofacies types, and a lithofacies-seismic facies map is established based on the classified igneous lithofacies types through well-seismic calibration. The specific process is as follows: Analysis of cuttings and logging data from drilled wells in the target igneous development area was conducted to classify the lithofacies types of the igneous rocks. Based on the pre-stack depth migration data, the seismic reflection characteristics corresponding to different igneous lithofacies types were obtained through fine well seismic calibration. Based on the seismic reflection characteristics corresponding to different igneous rock facies types, lithofacies-seismic facies maps were established, such as... Figure 2 As shown.

[0024] Specifically, in some embodiments, the rock cuttings analysis process is as follows: Collect cuttings samples from the drilled wells, ensuring that the samples cover the entire target igneous development area; The rock fragments were subjected to detailed lithological identification through microscopic observation, mineral composition analysis and other means. Igneous rocks are classified into different lithofacies types based on their lithological characteristics, such as mineral composition, structure, and texture.

[0025] The well logging data analysis process is as follows: Well logging data, such as curves of natural gamma, resistivity, and sonic transit time, are used to interpret and identify the physical properties of igneous rocks, such as porosity, permeability, and density. Establish the correspondence between igneous rock facies and well logging response to assist in facies classification.

[0026] The process of obtaining the pre-stack depth migration data involves ensuring the quality of the pre-stack seismic data, including preprocessing steps such as denoising, amplitude recovery, and static correction. Pre-stack depth migration technology is applied to convert the seismic data from the time domain to the depth domain, improving imaging accuracy.

[0027] The fine well-seismic calibration refers to: creating a synthetic seismic record based on well logging data, simulating the response of different lithofacies in the seismic record, comparing the synthetic record with the actual seismic record, and achieving precise matching between well and seismic data through fine adjustments.

[0028] This embodiment utilizes a comprehensive analysis of drilled cuttings and well logging data to more accurately classify the lithofacies types of igneous rocks. Cuttings analysis provides direct physical and chemical characteristics of the rocks, while well logging data reflects their physical properties; combining the two significantly improves the accuracy of lithofacies classification. Through fine-grained well-seismic calibration, the lithofacies information in the well logging data is correlated with the seismic reflection characteristics in the seismic data, establishing a reliable lithofacies-seismic facies relationship. This relationship provides crucial information for subsequent seismic interpretation and velocity modeling, enabling seismic data to more accurately reflect the lithofacies distribution of subsurface igneous rocks. Based on lithofacies-seismic facies maps, seismic data can be interpreted more precisely, improving the quality of seismic imaging. Especially in igneous development areas, due to the significant differences in physical properties between igneous rocks and surrounding rocks, traditional seismic interpretation methods often struggle to accurately identify the boundaries and internal structure of igneous rocks. However, lithofacies-seismic facies maps can more clearly display the lithofacies distribution and seismic response characteristics of igneous rocks, thereby improving the accuracy and reliability of seismic imaging.

[0029] Step S2: Construct a seismic facies prediction model based on the lithofacies-seismic facies map, and generate igneous lithofacies prediction bodies through the seismic facies prediction model; Preferably, in this embodiment of the disclosure, the specific process of constructing a seismic facies prediction model based on a lithofacies-seismic facies map, and generating igneous lithofacies prediction bodies by the seismic facies prediction model, is as follows: Based on the lithofacies-seismic facies chart, multiple three-dimensional volume models corresponding to different lithofacies were constructed to obtain multiple lithofacies models; Based on multiple lithofacies models and the corresponding seismic facies data in the lithofacies-seismic facies chart, iterative training is performed to obtain a seismic facies prediction model; The seismic facies prediction model takes three-dimensional post-stack seismic data as input and outputs predicted igneous lithofacies bodies, such as... Figure 3 As shown in Figure 4, where Figure 4(a) is an east-west seismic profile in the superimposed seismic profile of igneous rocks, and Figure 4(b) is a north-south seismic profile in the superimposed seismic profile of igneous rocks.

[0030] In this embodiment, by constructing multiple three-dimensional volume models corresponding to different igneous rocks and iteratively training these models and seismic facies data from the lithofacies-seismic facies map, a more accurate seismic facies prediction model can be trained. This model can more accurately capture information related to igneous lithofacies in seismic data, thereby improving the accuracy of igneous lithofacies prediction. The seismic facies prediction model takes three-dimensional post-stack seismic data as input and outputs igneous lithofacies prediction volumes, realizing three-dimensional visualization of igneous lithofacies. This helps researchers understand the spatial distribution characteristics of igneous rocks more intuitively, providing strong support for geological interpretation and reservoir prediction. Simultaneously, three-dimensional data facilitates more complex spatial analyses, such as volume calculation and morphological analysis. Accurate igneous lithofacies prediction helps reduce exploration risks. In oil and gas exploration, the development of igneous rocks often has a significant impact on the formation and distribution of oil and gas reservoirs. By predicting the lithofacies distribution of igneous rocks, the potential and risks of oil and gas reservoirs can be assessed more accurately, providing more reliable data support for exploration decisions.

[0031] Step S3: Establish a low-frequency model of igneous rocks using the predicted lithofacies bodies as constraints; Preferably, in the embodiments of this disclosure, in the step of establishing a low-frequency model of igneous rocks using the predicted igneous lithofacies body as a constraint, the predicted igneous lithofacies body is calibrated to the range of the low-frequency component of the wave impedance statistically obtained from well logging, and the calibrated properties of the predicted igneous lithofacies body are used as constraints to establish the low-frequency model of igneous rocks. The specific process is as follows: Well-seismic fine calibration is carried out based on well logging data and seismic data. Based on well-seismic calibration and seismic interpretation of the stratigraphic horizon, a layered attribute model is established by interpolation along the stratigraphic horizon. By performing spectral analysis on seismic data from igneous rock development areas, a threshold value for missing low-frequency components is obtained. The threshold value is then used to perform low-pass filtering on the impedance curves of standard wells, and the range of values ​​for the low-frequency components of the impedance curves is statistically analyzed. The numerical calibration method was used to calibrate the predicted igneous lithofacies body to the range of the low-frequency component of the wave impedance curve in well logging statistics. By merging the layered attribute model and the calibrated igneous lithofacies prediction body, a low-frequency model is obtained.

[0032] In this embodiment, the low-frequency model is calibrated to the range of the low-frequency component of the wave impedance in well logging statistics, ensuring consistency between the low-frequency model and the actual geological conditions. This calibration process reduces model bias caused by data differences or interpretation errors, improving the accuracy and reliability of the low-frequency model. This process integrates the advantages of well logging and seismic data. Well logging data provides high-precision rock physical parameters, while seismic data has broad spatial coverage. By combining the two, the high precision of well logging data is retained, while the spatial continuity of seismic data is utilized, making the low-frequency model more comprehensive and accurate. Seismic data has rich information in the high-frequency band, but often lacks information in the low-frequency band. By utilizing the low-frequency component information in well logging data to supplement and enhance the seismic data, the low-frequency model can include more information about the subsurface geological structure, improving the resolution and depth of seismic imaging.

[0033] Step S4: Obtain the P-wave impedance property volume by performing post-stack sparse pulse inversion using the igneous rock low-frequency model, and convert the P-wave impedance property volume into the P-wave velocity volume, i.e., the igneous rock velocity volume. Preferably, in this embodiment of the disclosure, in the step of obtaining the P-wave impedance attribute volume through post-stack sparse pulse inversion using an igneous low-frequency model, and converting the P-wave impedance attribute volume into the P-wave velocity volume, i.e., the igneous velocity volume, impedance and velocity convergence analysis is used to obtain a linear equation in one variable relating the P-wave impedance and P-wave velocity. Based on this linear equation, the P-wave impedance attribute volume is converted into the P-wave velocity volume, as follows: Figure 5 As shown, the longitudinal wave velocity volume can accurately reflect the spatial variation of igneous rock velocity.

[0034] In some embodiments, in geophysics, impedance typically refers to acoustic impedance or seismic impedance. The relationship between impedance and velocity is an important aspect of understanding the physical properties of subsurface media. Impedance Z is usually defined as density. ρ With speed v The product of, i.e. Z = ρ ⋅ v .

[0035] However, under certain circumstances, if we only focus on the relationship between the P-wave impedance and the P-wave velocity, and assume that the density is constant or does not vary much within a certain region, then we can simplify this relationship.

[0036] Under constant density, impedance is proportional to velocity, that is: Z = ρ ⋅ v； Due to density ρ It is a constant, which we can express as k (A positive real number), therefore: Z = k ⋅ v The aforementioned linear equation in one variable was obtained, where k is a proportionality constant that depends on the density of the medium.

[0037] However, in practical applications, density may not be completely constant. But if the density does not vary much within a certain geological layer or study area, we can approximate the above relationship and estimate the value of k using experimental data or well logging data.

[0038] Crossover analysis is a graphical method used to extract the relationship between two variables from experimental data. In some embodiments, the crossover analysis process is as follows: Acquire P-wave impedance and P-wave velocity data for a series of subsurface media samples; Plot a scatter plot with longitudinal wave velocity as the x-axis and longitudinal wave impedance as the y-axis. If the points in the scatter plot are roughly linearly distributed, then the least squares method or other linear regression methods can be used to fit a straight line, and the slope of this line is the estimated value of k.

[0039] In this embodiment, a low-frequency igneous rock model is used as a constraint, and post-stack sparse pulse inversion can more accurately invert the P-wave impedance property volume of the subsurface medium. Furthermore, utilizing the physical relationship between impedance and velocity, the impedance property volume is transformed into a P-wave velocity volume through intersection analysis. This process not only preserves the geological information in the impedance property volume but also improves the accuracy of the velocity model. The P-wave velocity volume, as a direct representation of igneous rock velocity, can accurately reflect the spatial variation of igneous rock velocity. This is of great significance for understanding the physical characteristics of igneous rocks, predicting reservoir distribution, and assessing oil and gas resource potential. An accurate igneous rock velocity volume provides important geological evidence for exploration and development. Based on this velocity volume, exploration and development strategies can be further optimized, such as determining well location layout, adjusting drilling parameters, and optimizing production plans, thereby improving exploration and development efficiency and success rate.

[0040] In this embodiment, igneous rock development zones typically exhibit complex geological conditions and significant velocity variations. Constructing accurate igneous rock velocity volumes allows for a better understanding of subsurface geological structures, providing strong technical support for exploration and development under complex geological conditions. Velocity models are a crucial component of seismic interpretation. Accurate igneous rock velocity volumes not only improve the precision of seismic interpretation but also enhance its reliability. Based on these velocity volumes, features such as subsurface geological structures, fault systems, and fluid distribution can be identified more accurately.

[0041] Step S5: Fuse the velocity volume of igneous rocks with the velocity of non-igneous rock development areas to obtain a high-precision velocity model of igneous rocks.

[0042] Preferably, in the embodiments of this disclosure, when fusing the velocity body of igneous rocks with the velocity of non-igneous rock development areas, the fusion boundary is smoothed to obtain a high-precision velocity model of igneous rocks.

[0043] Furthermore, the smoothing enhancement employs Gaussian filtering, median filtering, and / or edge-preserving filtering. It should be noted that Gaussian blurring refers to applying Gaussian blur to pixels or voxels near the boundary to reduce sharp boundary effects. Median filtering is used to remove noise while maintaining edge sharpness. Edge-preserving filtering, through specific algorithms or filters, smooths the image region while preserving or enhancing the edge portions of the image. Its basic principle is to distinguish between edge and non-edge regions in the image and apply different processing strategies to these regions.

[0044] In this embodiment, Gaussian blurring is particularly suitable for processing pixels or voxels near boundaries. By applying a Gaussian function to smooth the image, it can effectively reduce ringing effects or artifacts caused by overly sharp boundaries of the velocity model, making the boundary transitions of the velocity model more natural, thereby improving the overall image quality. Median filtering is a non-linear smoothing technique, particularly suitable for removing random noise from images or data. By selecting the median value within a local region as the output value, it can effectively suppress noise interference while preserving edge information. This is significant for improving the data reliability of the velocity model, as reduced noise means a corresponding reduction in velocity estimation error. Edge-preserving filtering is an advanced technique in image processing that can smooth non-edge areas of an image while preserving or even enhancing the features of edge areas. This is especially important for geological interpretation, as the boundaries of geological structures (such as faults, lithological contact zones, etc.) often correspond to the edges of the velocity model. Edge-preserving filtering can more clearly display these geological boundaries, improving the accuracy and reliability of geological interpretation.

[0045] In this embodiment, under complex geological conditions such as igneous rocks, the accuracy of the velocity model is crucial to the quality of seismic imaging. By fusing igneous rock inversion velocities and performing smoothing modifications, the imaging effect of seismic data can be significantly improved. The fused velocity model makes the imaging of the underlying strata of the igneous rock section clearer, the overall structural morphology more reasonable, and the beaded imaging improved. This helps to more accurately identify subsurface geological structures and provides more reliable data support for oil and gas exploration and development.

[0046] This embodiment employs automated smoothing techniques, such as Gaussian filtering, median filtering, and edge-preserving filtering, which significantly reduces the need for manual intervention and improves data processing efficiency. This is of great significance for large-scale data processing and rapid response to exploration needs.

[0047] In some embodiments, morphological operations, such as dilation and erosion, may also be included to adjust the shape and width of the boundary to achieve a smoothing effect; and gradient domain smoothing, which refers to operating within the domain of the velocity gradient to reduce the magnitude of the velocity change while maintaining the direction of the velocity change. It should also be noted that those skilled in the art can adjust the smoothing parameters according to the smoothing effect, and may perform multiple iterations until a satisfactory smoothness is achieved.

[0048] This disclosure also provides a method for verifying the high-precision velocity model of igneous rocks. Multiple wells within the study area that acquire VSP data are selected as verification wells. The inverted P-wave velocity curves at the verification well locations are compared with the VSP P-wave velocity curves. Figure 7 As shown in the figure, the high degree of agreement between the two indicates that the retrieved P-wave velocity has high accuracy and meets the requirements for pre-stack depth migration velocity modeling. Specifically, as... Figure 7 As shown, Figure 7 The attached figure on the left shows the pre-stack depth migration velocity before fusion of igneous rock inversion velocities. Figure 7 The attached figure on the right shows the pre-stack depth migration velocity after the fusion of igneous inversion velocities. It can be seen from the two figures that the fused igneous velocity has better accuracy and the internal structure and boundaries are clearer. Figure 8 This invention discloses a comparison diagram of pre-stack depth migration profiles before and after fusion of igneous rock inversion velocities, wherein the profiles are located in the present invention. Figure 8 The attached image on the left shows a profile of the pre-stack depth migration processing results before fusion of igneous rock inversion velocities, located in... Figure 8 The attached figure on the right is a profile of the pre-stack depth migration result after the fusion of igneous rock inversion velocities. As can be seen from the figure, the imaging of the underlying strata of the igneous rock section is significantly improved, the overall structural morphology is reasonable, and the beaded imaging is significantly improved.

[0049] This disclosure also provides a high-precision velocity modeling system for igneous rocks, including: The initialization unit is configured as follows: Used to create lithofacies-seismic facies maps; The prediction unit is configured as follows: Used to construct a seismic facies prediction model based on lithofacies-seismic facies chart, and to generate igneous lithofacies prediction bodies through the seismic facies prediction model; The model building unit is configured as follows: Used to establish low-frequency models of igneous rocks with igneous lithofacies prediction bodies as constraints; The conversion unit is configured as follows: It is used to obtain the P-wave impedance property volume by performing post-stack sparse pulse inversion through the low-frequency model of igneous rocks, and to transform the P-wave impedance property volume into the P-wave velocity volume, i.e. the igneous rock velocity volume. The fusion output unit is configured as follows: This is used to fuse the velocity volume of igneous rocks with the velocity of non-igneous rock development areas to obtain a high-precision velocity model of igneous rocks.

[0050] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of the high-precision velocity modeling method for igneous rocks.

[0051] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the high-precision velocity modeling method for igneous rocks described in the above embodiments.

[0052] In another embodiment of the present invention, the present invention also provides a computer program product, including computer instructions, which instruct a computing device to perform operations corresponding to the automobile production process management method described above.

[0053] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0054] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0055] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0056] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0057] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0058] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A high-precision velocity modeling method for igneous rocks, characterized in that, Includes the following steps: Establish lithofacies-seismic facies plates; A seismic facies prediction model is constructed based on the lithofacies-seismic facies map, and igneous lithofacies prediction bodies are generated through the seismic facies prediction model. A low-frequency model of igneous rocks was established using igneous lithofacies prediction bodies as constraints. The longitudinal wave impedance property volume is obtained by performing post-stack sparse pulse inversion using a low-frequency model of igneous rocks, and then the longitudinal wave impedance property volume is transformed into a longitudinal wave velocity volume, i.e., an igneous rock velocity volume. By fusing the velocity volume of igneous rocks with the velocity of non-igneous rock development areas, a high-precision velocity model of igneous rocks is obtained.

2. The high-precision velocity modeling method for igneous rocks according to claim 1, characterized in that, In the step of establishing a lithofacies-seismic facies map, the drilled data is classified into igneous lithofacies types, and a lithofacies-seismic facies map is established based on the classified igneous lithofacies types through well-seismic calibration. The specific process is as follows: Analysis of cuttings and logging data from drilled wells in the target igneous development area was conducted to classify the lithofacies types of the igneous rocks. Based on the pre-stack depth migration data, the seismic reflection characteristics corresponding to different igneous lithofacies types were obtained through fine well seismic calibration. A lithofacies-seismic facies chart was established based on the seismic reflection characteristics corresponding to different igneous rock lithofacies types.

3. The high-precision velocity modeling method for igneous rocks according to claim 1, characterized in that, The specific process of constructing a seismic facies prediction model based on a lithofacies-seismic facies map, and generating igneous lithofacies prediction bodies by the seismic facies prediction model, is as follows: Based on the lithofacies-seismic facies chart, multiple three-dimensional volume models corresponding to different lithofacies were constructed to obtain multiple lithofacies models; Based on multiple lithofacies models and the corresponding seismic facies data in the lithofacies-seismic facies chart, iterative training is performed to obtain a seismic facies prediction model; The seismic facies prediction model takes three-dimensional post-stack seismic data as input and outputs igneous lithofacies prediction bodies.

4. The high-precision velocity modeling method for igneous rocks according to claim 1, characterized in that, In the step of establishing a low-frequency model of igneous rocks using igneous lithofacies prediction bodies as constraints, the igneous lithofacies prediction bodies are calibrated to the range of the low-frequency components of wave impedance statistically obtained from well logging. The calibrated properties of the igneous lithofacies prediction bodies are then used as constraints to establish the low-frequency model of igneous rocks. The specific process is as follows: Well-seismic fine calibration is carried out based on well logging data and seismic data. Based on well-seismic calibration and seismic interpretation of the stratigraphic horizon, a layered attribute model is established by interpolation along the stratigraphic horizon. By performing spectral analysis on seismic data from igneous rock development areas, a threshold value for missing low-frequency components is obtained. The threshold value is then used to perform low-pass filtering on the impedance curves of standard wells, and the range of values ​​for the low-frequency components of the impedance curves is statistically analyzed. The numerical calibration method was used to calibrate the predicted igneous lithofacies body to the range of the low-frequency component of the wave impedance curve in well logging statistics. By merging the layered attribute model and the calibrated igneous lithofacies prediction body, a low-frequency model is obtained.

5. The high-precision velocity modeling method for igneous rocks according to claim 1, characterized in that, In the step of obtaining the P-wave impedance attribute volume through post-stack sparse pulse inversion using a low-frequency model of igneous rocks, and transforming the P-wave impedance attribute volume into the P-wave velocity volume, i.e., the igneous rock velocity volume, a linear equation in one variable is obtained through impedance and velocity intersection analysis. Based on the linear equation in one variable, the P-wave impedance attribute volume is transformed into the P-wave velocity volume.

6. The high-precision velocity modeling method for igneous rocks according to claim 1, characterized in that, When fusing the velocity body of igneous rocks with the velocity of non-igneous rock development areas, the fusion boundary is smoothed to obtain a high-precision velocity model of igneous rocks.

7. The high-precision velocity modeling method for igneous rocks according to claim 6, characterized in that, The smoothing effect employs Gaussian filtering, median filtering, and / or edge-preserving filtering.

8. A high-precision velocity modeling system for igneous rocks, characterized in that, The high-precision velocity modeling method for igneous rocks based on any one of claims 1-7 includes: The initialization unit is configured as follows: Used to create lithofacies-seismic facies maps; The prediction unit is configured as follows: Used to construct a seismic facies prediction model based on lithofacies-seismic facies chart, and to generate igneous lithofacies prediction bodies through the seismic facies prediction model; The model building unit is configured as follows: Used to establish low-frequency models of igneous rocks with igneous lithofacies prediction bodies as constraints; The conversion unit is configured as follows: It is used to obtain the P-wave impedance property volume by performing post-stack sparse pulse inversion through the low-frequency model of igneous rocks, and to transform the P-wave impedance property volume into the P-wave velocity volume, i.e. the igneous rock velocity volume. The fusion output unit is configured as follows: This is used to fuse the velocity volume of igneous rocks with the velocity of non-igneous rock development areas to obtain a high-precision velocity model of igneous rocks.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the high-precision velocity modeling method for igneous rocks as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the high-precision velocity modeling method for igneous rocks as described in any one of claims 1-7.

11. A computer program product comprising computer instructions, characterized in that, The computer instructions instruct the computing device to perform the operations corresponding to the high-precision velocity modeling method for igneous rocks as described in any one of claims 1-7.

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