Geological constraint velocity depth modeling method and device based on VSP velocity

Abstract

The invention discloses a geological constraint velocity depth modeling method and device based on VSP velocity. According to the technical scheme, the method comprises the steps that first arrival pickup time, micro-logging data and logging data of a target work area are collected; performing micro-logging constraint step-by-step near-surface velocity inversion based on the first arrival pickup time and the micro-logging data to obtain a near-surface velocity model; establishing an initial velocity model of geological constraint based on VSP velocity based on the logging data; establishing a full-depth TTI anisotropic parameter model; and performing optimization based on the initial velocity model and the full-depth TTI anisotropy parameter model to obtain a geological constraint velocity depth model based on the VSP velocity. According to the technical scheme of the embodiment of the invention, the initial velocity model precision and the velocity depth model precision can be improved, the mountain front zone structure is accurately imaged, and the oil exploration risk is reduced.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, and in particular to a method and apparatus for geologically constrained velocity-depth modeling based on VSP velocity. Background Technology

[0002] The piedmont zone is one of the most important areas for oil and gas exploration in my country. Due to its abundant oil and gas resources, it has become a crucial battleground for finding strategic successors to these resources. However, due to the complex surface and subsurface structures, as well as the influence of low-quality seismic data, piedmont imaging often fails to accurately reflect reality. Furthermore, velocity modeling in the piedmont zone is extremely difficult; data-driven velocity models often suffer from multiple solutions and cannot accurately reflect subsurface velocity changes, resulting in inaccurate imaging that misleads subsequent exploration and development efforts.

[0003] The surface of the piedmont region is undulating and drastically changing. Under non-horizontal surface conditions, the key to accurate imaging of complex structures lies in migration velocity. If the accurate velocity is known, even with a low signal-to-noise ratio in the seismic data, it is possible to accurately image very complex subsurface structures. However, there is currently no velocity modeling method specifically for the piedmont region.

[0004] Therefore, a method is needed to address the problem that conventional seismic data processing techniques struggle to obtain reliable dual-complex seismic imaging data. Summary of the Invention

[0005] This invention provides a geologically constrained velocity-depth modeling method and apparatus based on VSP velocity to solve the problem that conventional seismic data processing techniques are unable to obtain reliable dual-complex seismic imaging data.

[0006] According to one aspect of the present invention, a geologically constrained velocity-depth modeling method based on VSP velocity is provided, comprising:

[0007] Collect initial pickup time, micrologging data, and well logging data for the target work area;

[0008] Based on the initial arrival pickup time and the micrologging data, a micrologging-constrained stepwise near-surface velocity inversion is performed to obtain a near-surface velocity model.

[0009] An initial velocity model based on geological constraints and VSP velocity is established based on the well logging data.

[0010] Establish a full-depth TTI anisotropic parameter model;

[0011] Based on the initial velocity model and the full-depth TTI anisotropic parameter model, an optimization is performed to obtain a geologically constrained velocity-depth model based on VSP velocity.

[0012] Optionally, after collecting the initial arrival time, micrologging data, and well logging data of the target work area, the method further includes one or more of the following steps:

[0013] Check the pickup accuracy at the initial pickup time;

[0014] The micrologging data is interpolated to form a velocity profile, and then superimposed with surface outcrop data to determine whether it conforms to geological and geophysical laws. Micrologging points that do not conform to geological laws are corrected or removed.

[0015] The well logging data is subjected to a well-to-well stratification survey and / or an overall curve check.

[0016] Optionally, the step of performing micro-logging-constrained stepwise near-surface velocity inversion based on the first arrival pickup time and the micro-logging data to obtain a near-surface velocity model includes:

[0017] An initial near-surface velocity model is obtained by interpolating the micro-logging velocity data.

[0018] Using the initial arrival pickup data, the initial arrival pickup time within the first distance range with an offset is selected, and the initial near-surface velocity model is used as a constraint to obtain the first near-surface velocity model to be used by tomographic inversion.

[0019] The initial arrival time of the mid-offset distance within the second distance range is selected as the data input, and the first near-surface velocity model to be used is used as the constraint velocity model input to perform near-surface velocity inversion, thereby obtaining the second near-surface velocity model to be used; wherein, the second distance range is greater than the first distance range;

[0020] The initial arrival time within the third distance range of the mid-offset is selected as the data input. The second near-surface velocity model to be used is used as the constraint velocity model input for near-surface velocity inversion to obtain the near-surface velocity model; wherein, the third distance range is greater than the second distance range.

[0021] Optionally, establishing an initial velocity model based on the geological constraints of VSP velocity using the well logging data includes:

[0022] An equivalent structural model containing near-surface structural information is constructed using time-off domain seismic imaging data and geological outcrop information from the well logging data.

[0023] Using VSP logging data from multiple wells in the logging data, and employing simplified equivalent structural layers as constraints, an initial velocity model based on structural morphology is established.

[0024] Optionally, establishing the full-depth TTI anisotropic parameter model includes:

[0025] A preliminary equivalent regional geological structure model is constructed using geological outcrop information and pre-stack time migration data from the well logging data.

[0026] The geological structure model map is offset to the depth domain using the time domain velocity to obtain the initial depth domain geological structure model;

[0027] The initial depth domain geological structure model is spliced ​​with the near-surface velocity model to obtain the full-depth TTI anisotropic velocity model.

[0028] Optionally, the step of stitching the initial depth-domain geological structure model with the near-surface velocity model to obtain the full-depth TTI anisotropic velocity model includes:

[0029] Based on the near-surface velocity model, the VSP velocity is compared and calibrated to determine the position where the near-surface velocity model and VSP velocity have a higher degree of agreement than a set value, which is then used as the splicing point. A splicing surface is generated based on the splicing point.

[0030] By stitching the initial depth domain geological structure model with the near-surface velocity model through the stitching surface, the full-depth TTI anisotropic velocity model is obtained.

[0031] Optionally, based on the initial velocity model and the full-depth TTI anisotropic parameter model, optimization is performed to obtain a geologically constrained velocity-depth model based on VSP velocity, including:

[0032] An initial isotropic velocity model is obtained by using conventional mesh tomography based on data-driven methods.

[0033] Using the gun first arrival information in the time-domain gather or gun gather data volume, the TTI anisotropic velocity model at the full depth is optimized by using a joint inversion method of refracted and reflected waves.

[0034] Based on the difference between the initial isotropic velocity model and the optimized full-depth TTI anisotropic velocity model, the initial anisotropic parameter Delta0 is calculated using the formula.

[0035] The initial anisotropy parameter Epsilon0 is obtained by assigning values ​​to the initial anisotropy parameter Delta0.

[0036] The initial tilt and azimuth models are established using the equivalent construction model.

[0037] Based on the initial velocity model, the initial dip and azimuth model, Delta0 and Epsilon0, the geologically constrained velocity-depth model based on VSP velocity is obtained through optimization.

[0038] According to another aspect of the present invention, a geologically constrained velocity-depth modeling apparatus based on VSP velocity is provided, comprising:

[0039] The data collection unit is used to collect the initial arrival pickup time, micrologging data, and well logging data of the target work area;

[0040] The first model construction unit is used to perform micro-logging constrained stepwise near-surface velocity inversion based on the first arrival pickup time and the micro-logging data to obtain a near-surface velocity model.

[0041] The second model construction unit establishes an initial velocity model based on the well logging data and geological constraints of VSP velocity.

[0042] The third model building unit establishes a full-depth TTI anisotropic parameter model;

[0043] The model optimization unit is used to optimize the initial velocity model and the full-depth TTI anisotropic parameter model to obtain a geologically constrained velocity-depth model based on VSP velocity.

[0044] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0045] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the geologically constrained velocity-depth modeling method based on VSP velocity according to any embodiment of the present invention.

[0046] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the geologically constrained velocity-depth modeling method based on VSP velocity as described in any embodiment of the present invention.

[0047] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the geologically constrained velocity-depth modeling method based on VSP velocity as described in any embodiment of the present invention.

[0048] The technical solution of this invention includes: collecting first arrival pickup time, micrologging data, and well logging data of the target work area; performing micrologging-constrained stepwise near-surface velocity inversion based on the first arrival pickup time and the micrologging data to obtain a near-surface velocity model; establishing a geologically constrained initial velocity model based on VSP velocity based on the well logging data; establishing a full-depth TTI anisotropic parameter model; and optimizing the initial velocity model and the full-depth TTI anisotropic parameter model to obtain a geologically constrained velocity-depth model based on VSP velocity. The technical solution of this invention can improve the accuracy of the initial velocity model and the velocity-depth model, enabling accurate imaging of the foreland belt structure and reducing the risks of oil exploration.

[0049] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart of a geologically constrained velocity-depth modeling method based on VSP velocity provided in Embodiment 1 of the present invention;

[0052] Figure 2 This is a schematic diagram of offset grouping for near-surface velocity inversion based on stepwise constraints of micro-logging, applicable to Embodiment 1 of the present invention.

[0053] Figure 3 This is a schematic diagram of near-surface velocity inversion and geological outcrop superposition based on micro-logging stepwise constraints, applicable to Embodiment 1 of the present invention.

[0054] Figure 4 This is a schematic diagram comparing velocity slices and planar lithological distribution based on near-surface velocity inversion using micro-logging stepwise constraints, applicable to Embodiment 1 of the present invention.

[0055] Figure 5 This is a schematic diagram comparing the imaging speed and VSP speed after inversion with different offset distances and after stepwise constraint inversion, applicable to Embodiment 1 of the present invention.

[0056] Figure 6 This is a schematic diagram illustrating a geological structural model combined with geological outcrops, applicable to Embodiment 1 of the present invention.

[0057] Figure 7 This is a schematic diagram of an initial velocity model with geological significance obtained by combining a structural constraint model with VSP logging velocity, applicable to Embodiment 1 of the present invention.

[0058] Figure 8 This is a schematic diagram of a splicing method based on VSP speed applicable to Embodiment 1 of the present invention;

[0059] Figure 9 This is a comparison diagram of the superposition and splicing of a deep velocity model and VSP velocity applicable to Embodiment 1 of the present invention, showing the superposition of velocity and VSP velocity.

[0060] Figure 10 This is a joint inversion process of refracted and reflected waves applicable to Embodiment 1 of the present invention;

[0061] Figure 11 This is a schematic diagram illustrating the establishment of a near-surface dip model applicable to Embodiment 1 of the present invention;

[0062] Figure 12 This is a schematic diagram of near-surface anisotropic parameter optimization applicable to Embodiment 1 of the present invention;

[0063] Figure 13 This is a schematic diagram comparing gathers before and after full-depth TTI anisotropic velocity iteration, applicable to Embodiment 1 of the present invention.

[0064] Figure 14 This is a schematic diagram comparing the imaging effect of a velocity modeling method applicable to Embodiment 1 of the present invention with that of a conventional velocity modeling method;

[0065] Figure 15 This is a schematic diagram of a geological constraint velocity-depth modeling device based on VSP velocity provided in Embodiment 2 of the present invention;

[0066] Figure 16 This is a schematic diagram of the structure of an electronic device that implements the geological constraint velocity-depth modeling method based on VSP velocity according to an embodiment of the present invention. Detailed Implementation

[0067] 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.

[0068] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0069] Example 1

[0070] Figure 1 This is a flowchart of a geologically constrained velocity depth modeling method based on VSP velocity according to Embodiment 1 of the present invention. This embodiment is applicable to situations requiring accurate structural imaging against a complex geological background in a piedmont zone. The method can be executed by a VSP-based geologically constrained velocity depth modeling device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0071] S110, collect the initial arrival pickup time, micro-logging data and well logging data of the target work area.

[0072] The main requirements are full-offset seismic shot gather data, first arrival travel time and pickup time for full-offset seismic shots, geophone elevation data and shot elevation data for the entire work area, microlog data, surface outcrop profiles, logging velocity and stratigraphic data, VSP logging velocity and corridor overlay, anisotropy parameter survey data, and structural interpretation stratigraphic layers.

[0073] In this embodiment of the invention, after collecting the initial arrival time, micrologging data, and well logging data of the target work area, one or more of the following steps may be included:

[0074] Check the pickup accuracy at the initial pickup time;

[0075] The aforementioned well logging data is interpolated to form a velocity profile to obtain micro-logging velocity data. This data is then combined with surface outcrop data to determine whether it conforms to geological and geophysical laws. Micro-logging points that do not conform to geological laws are corrected or removed.

[0076] The well logging data is subjected to a well-to-well stratification survey and / or an overall curve check.

[0077] The collected initial arrival pickup time, micrologging data, well logging data, and anisotropy parameter data should be checked and evaluated, and problematic data should be corrected or optimized. First, a data quality evaluation module needs to be established, which mainly includes:

[0078] First arrival wave pickup check: Check whether the pickup of the initial wave is appropriate and whether the pickup rate meets the standard; ensure that the accurate pickup rate of the first arrival wave reaches more than 90%;

[0079] Micrologging basic data inspection: interpolation to form a velocity profile, combined with surface outcrop data, to determine whether it conforms to geological and geophysical laws. Micrologging points that do not conform to geological laws are corrected or removed.

[0080] Well logging data inspection: First, conduct a stratified survey of consecutive wells to ensure the accuracy of well logging stratification; second, inspect the overall curves for parameters such as well logging rate, and correct any problematic data before use.

[0081] Based on the results of different data checks, reliable basic data are selected for preliminary optimization processing to better meet the requirements of seismic data processing for pre-stack depth migration.

[0082] S120. Based on the initial arrival pickup time and the micrologging data, perform micrologging-constrained stepwise near-surface velocity inversion to obtain a near-surface velocity model.

[0083] More advanced pre-stack depth migration algorithms demand higher model accuracy. When the velocity model accuracy is low, advanced algorithms may not produce as good imaging results as traditional methods. Therefore, improving the accuracy of the velocity model is crucial in pre-stack depth migration. Due to the low signal-to-noise ratio in shallow layers and the scarcity of usable reflected wave information, traditional methods such as tomographic inversion based on reflected waves struggle to obtain accurate shallow velocities. As is well known, the accuracy of shallow velocities significantly impacts the accuracy of mid-to-deep velocity models in depth migration, thus hindering accurate structural imaging. Therefore, obtaining a surface velocity model is essential for establishing an accurate velocity-depth model. Based on this, the industry commonly uses a statically corrected velocity model as the shallow velocity model for the velocity-depth model, which is a suitable approach but lacks sufficient accuracy.

[0084] Therefore, for obtaining the near-surface velocity model, this embodiment of the invention proposes a step-by-step constraint near-surface velocity modeling method. By iterating step by step through micro-logging, near offset, and medium-to-far offset, the velocity model obtained in this way matches the VSP velocity well in both very shallow and relatively deep formations.

[0085] In this embodiment of the invention, a near-surface velocity model is obtained by performing micro-logging-constrained stepwise near-surface velocity inversion based on the first arrival pickup time and the micro-logging data, including:

[0086] An initial near-surface velocity model is obtained by interpolating the micro-logging velocity data.

[0087] Using the initial arrival pickup data, the initial arrival pickup time within the first distance range with an offset is selected, and the initial near-surface velocity model is used as a constraint to obtain the first near-surface velocity model to be used by tomographic inversion.

[0088] The initial arrival time of the mid-offset distance within the second distance range is selected as the data input, and the first near-surface velocity model to be used is used as the constraint velocity model input to perform near-surface velocity inversion, thereby obtaining the second near-surface velocity model to be used; wherein, the second distance range is greater than the first distance range;

[0089] The initial arrival time within the third distance range of the mid-offset is selected as the data input. The second near-surface velocity model to be used is used as the constraint velocity model input for near-surface velocity inversion to obtain the near-surface velocity model; wherein, the third distance range is greater than the second distance range.

[0090] For example, the specific implementation method for establishing a near-surface velocity model with stepwise constraints is as follows:

[0091] An initial near-surface velocity model was obtained by interpolation using optimized microlog velocity data.

[0092] Using the qualified first-arrival pickup data, the first-arrival pickup data is selected using the offset. First, the first-arrival pickup time within the offset range of 0-600 meters is selected. The near-surface velocity model is used as a constraint, and the near-surface velocity model V1 is obtained by tomographic inversion.

[0093] Using qualified first-arrival data, the first-arrival data is selected using offset distance. Then, the first-arrival time within the offset distance of 0-3000 meters (this range varies depending on the different collected data) is selected as the data input. Then, V1 is used as the input of the constrained velocity model to perform further near-surface velocity inversion, resulting in the near-surface velocity model V2.

[0094] Using qualified first-arrival data, the first-arrival data is selected using the offset distance. Then, the first-arrival time within the full offset distance of 0-7000 meters (this range varies depending on the different collected data) is selected as the data input. Then, V2 is used as the input of the constrained velocity model to perform further near-surface velocity inversion, resulting in near-surface velocity model V3 (i.e., near-surface velocity model).

[0095] The near-surface velocity model obtained in this step is compared and analyzed with the VSP logging velocity. It can be found that the velocity model with stepwise constraints matches the VSP well, which lays a good foundation for subsequent anisotropic velocity modeling and velocity stitching.

[0096] S130. Based on the well logging data, establish an initial velocity model with geological constraints based on VSP velocity.

[0097] In this embodiment of the invention, establishing an initial velocity model based on geological constraints and VSP velocity according to the well logging data includes:

[0098] An equivalent structural model containing near-surface structural information is constructed using time-off domain seismic imaging data and geological outcrop information from the well logging data.

[0099] Using VSP logging data from multiple wells in the logging data, and employing simplified equivalent structural layers as constraints, an initial velocity model based on structural morphology is established.

[0100] The core of pre-stack depth migration imaging (PSMI) is the velocity model, but an accurate and reasonable initial velocity model essentially determines the success or failure of PSMI. An accurate initial velocity model not only reduces the time required to approximate the true model but also reduces the ambiguity of velocity inversion, thus obtaining correct seismic images. For complex regions like the piedmont zone, an accurate initial velocity model is particularly important. Therefore, to address the problem of imaging complex structures in the piedmont zone, firstly, based on time-migrated domain seismic imaging data and referencing geological outcrop information, an equivalent structural model containing near-surface structural information is extracted and interpreted. Secondly, based on the obtained VSP logging data from multiple wells, and using simplified equivalent structural horizons as constraints, an initial velocity model based on structural morphology is established. This results in a velocity model that not only largely conforms to the geological structural morphology but also whose imaging velocity closely matches the VSP velocity.

[0101] S140. Establish a full-depth TTI anisotropic parameter model.

[0102] In seismic exploration of petroleum, due to design limitations of the acquisition and observation system, the signal-to-noise ratio in shallow layers is low, resulting in very limited usable reflected wave information. Traditional methods, such as tomographic inversion based on reflected waves, struggle to accurately determine shallow layer velocities. Therefore, this study utilizes a near-surface velocity model and an initial mid-deep velocity model based on VSP velocity geological constraints. By combining the high degree of agreement between the inverted velocities and VSP velocities, suitable splicing points are selected to establish a full-depth initial velocity model. Based on this full-depth velocity model, an anisotropic parameter model of the full-depth TTI (Transient Intensity Tolerance) is derived by combining it with an isotropic velocity model obtained through data-driven methods.

[0103] In this embodiment of the invention, a full-depth TTI anisotropic parameter model is established, including:

[0104] A preliminary equivalent regional geological structure model is constructed using geological outcrop information and pre-stack time migration data from the well logging data.

[0105] The geological structure model map is offset to the depth domain using the time domain velocity to obtain the initial depth domain geological structure model;

[0106] The initial depth domain geological structure model is spliced ​​with the near-surface velocity model to obtain the full-depth TTI anisotropic velocity model.

[0107] In this embodiment of the invention, the initial depth-domain geological structure model and the near-surface velocity model are spliced ​​together to obtain the full-depth TTI anisotropic velocity model, including:

[0108] Based on the near-surface velocity model, the VSP velocity is compared and calibrated to determine the position where the near-surface velocity model and VSP velocity have a higher degree of agreement than a set value, which is then used as the splicing point. A splicing surface is generated based on the splicing point.

[0109] By stitching the initial depth domain geological structure model with the near-surface velocity model through the stitching surface, the full-depth TTI anisotropic velocity model is obtained.

[0110] Specifically, the implementation method for establishing the full-depth TTI anisotropic parameter model is as follows:

[0111] Based on data-driven principles, an initial isotropic velocity model V(ISO) is obtained using conventional mesh tomography.

[0112] Using the time-off data volume and combining it with digital outcrop information, an initial equivalent construction model is established. Using the initial isotropic velocity model V(ISO) obtained by a, the equivalent construction model in the depth domain is obtained.

[0113] Using the VSP velocity function and the smoothed logging sonic velocity function, an initial velocity model V(TTI) containing geological structural change information is established under the constraints of the equivalent structural model obtained in a.

[0114] The VSP velocity at the survey well points was compared with the near-surface velocity model obtained based on stepwise constraints from micrologging. Depth points H(P) where the near-surface velocity and VSP velocity matched were selected. All spatially distributed H(P) points were identified, and combined with the planar geological structure variation characteristics, a velocity splicing surface was finally obtained. The initial depth-domain geological structure model and the near-surface velocity model were then spliced ​​together using this splicing surface to obtain a full-depth TTI anisotropic velocity model.

[0115] S150. Based on the initial velocity model and the full-depth TTI anisotropic parameter model, an optimization is performed to obtain a geologically constrained velocity-depth model based on VSP velocity.

[0116] Common velocity modeling methods involve using the final velocity from the time migration to obtain an initial velocity-depth model via the DIX formula, followed by equivalent tomographic inversion using layered structural horizons or direct data-driven velocity updates. However, due to biases in the understanding of structural interpreters and the extremely low signal-to-noise ratio of piedmont data, imaging results are often inaccurate. Therefore, this invention, based on the geologically significant initial velocity model obtained through the aforementioned steps, further employs joint tomography of refracted and reflected waves, full-depth TTI tomographic velocity optimization, and multi-azimuth grid tomographic velocity optimization.

[0117] In this embodiment of the invention, an optimization is performed based on the initial velocity model and the full-depth TTI anisotropic parameter model to obtain a geologically constrained velocity-depth model based on VSP velocity, including:

[0118] An initial isotropic velocity model is obtained by using conventional mesh tomography based on data-driven methods.

[0119] Using the gun's first arrival information in the pre-stack time migration data volume, the full-depth TTI anisotropic velocity model is optimized by using a joint inversion method of refracted and reflected waves.

[0120] Based on the difference between the initial isotropic velocity model and the optimized full-depth TTI anisotropic velocity model, the initial anisotropic parameter Delta0 is calculated using the formula.

[0121] The initial anisotropy parameter Epsilon0 is obtained by assigning values ​​to the initial anisotropy parameter Delta0.

[0122] The initial tilt and azimuth models are established using the equivalent construction model.

[0123] Based on the initial velocity model, the initial dip and azimuth model, Delta0 and Epsilon0, the geologically constrained velocity-depth model based on VSP velocity is obtained through optimization.

[0124] Specifically, based on the initial V(TTI) and the optimized V(ISO), the initial anisotropy parameter Delta(0) is calculated using the formula.

[0125] Using the initial anisotropy parameter Delta(0), the anisotropy parameter Epsilon(0) is obtained by assignment.

[0126] Using an equivalent construction model and the plane wave decomposition method, an initial dip and azimuth model is established.

[0127] Using the first arrival information of the full offset obtained from the aforementioned data, a combined tomography method of refracted and reflected waves is adopted to optimize the velocity error in the shallow layer and at the velocity stitching point, and the accuracy of the velocity is judged by imaging criteria.

[0128] The input velocity, along with the tilt volume, azimuth volume, Delta0, and Epsilon0 generated from the aforementioned equivalent structural model, are subjected to full-depth TTI mesh tomography velocity optimization. The full-depth velocity, especially the anisotropic parameters of the shallow layer, is further optimized to obtain a 5-parameter anisotropic parameter volume with a basically accurate global trend.

[0129] Using a 5-parameter data volume, pre-stack Gaussian beam depth migration is performed to output an azimuth gather containing azimuth information.

[0130] Based on the obtained azimuth gathers, the remaining delays in different directions are picked out, and then multi-azimuth mesh tomography is carried out to optimize and modify the speed and anisotropic parameters.

[0131] Using gather flattening and accurate profile imaging as criteria, the process was iterated repeatedly until the velocity model and imaging results were acceptable to the interpreters.

[0132] To make the technical solution of this invention clearer, the technical solution of this invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this invention. The seismic data in the embodiments comes from Block K of an oilfield, which is adjacent to the Southern Tianshan Orogenic Belt. It experiences intense compression, resulting in multiple rows of high and steep thrust faults, exhibiting typical foreland thrust deformation characteristics, with high and steep faults developed in structurally high areas. Furthermore, the surface lithology in area F varies drastically laterally, with strata dip angles all greater than 45 degrees, and locally reaching 90 degrees, almost vertical. Simultaneously, the surface lithology is complex and variable, with exposed strata generally distributed in east-west trending stripes. The work area mainly exposes gravel, conglomerate, and older strata such as the Jurassic. This results in a chaotic seismic wavefield, an extremely low signal-to-noise ratio, and very rapid lateral velocity variations, making it very difficult to accurately establish the seismic velocity field. The imaging results obtained by conventional velocity modeling and optimization methods have a low signal-to-noise ratio, and the shallow main structures are almost impossible to image accurately, while the deep structures are not clear and complete enough. Through a new round of technical breakthroughs, the method of this invention is adopted to enable accurate imaging of shallow structures, clearer and more complete imaging of deep structures, and the occurrence of the structures basically matches the well, while the location of faults in the middle and deep layers is relatively clear.

[0133] This embodiment is implemented according to the following steps:

[0134] 1. We collected pre-stack blast data, over 800 micrologging data, stratification and velocity data from 7 wells, geological interpretation stratigraphic data, time offset data, etc., from the K work area in the piedmont zone.

[0135] 2. Optimize the collection of micro-logging and well logging data to obtain reliable basic data as input for the next step; Figure 2 This is a schematic diagram of offset grouping for near-surface velocity inversion based on stepwise constraints of micro-logging, applicable to Embodiment 1 of the present invention. Figure 3 This is a schematic diagram of the superposition of near-surface velocity inversion velocity and geological outcrops based on stepwise constraints of micrologging, applicable to Embodiment 1 of the present invention. Figure 4 This is a schematic diagram comparing velocity slices and planar lithology distribution based on near-surface velocity inversion using micrologging stepwise constraints, applicable to Embodiment 1 of the present invention (planar lithology on the left, velocity slices on the right).

[0136] 3. Extract the true elevation information of receiver points and shot points from the pre-stack shot gather data, and perform small smoothing processing on the true ground surface to obtain pre-stack data based on the small smoothing surface.

[0137] 4. Based on the optimized micrologging data, the initial arrival pickup time of the offset distance from 0 to 600 meters, the offset distance from 0 to 3000 meters, and the initial arrival pickup time of the offset distance from 0 to 7000 meters, the stepwise constraint near-surface model establishment technology of this invention is used to stepwise invert and obtain a high-precision near-surface velocity model. Figure 5 This is a schematic diagram comparing the imaging speed and VSP speed after inversion with different offset distances and after stepwise constraint inversion, applicable to Embodiment 1 of the present invention.

[0138] 5. Based on the high-precision near-surface velocity model obtained in step 4, compare and calibrate it with the collected VSP velocity, and determine the position where the near-surface model velocity and VSP velocity have a high degree of consistency as the splicing point of the full-depth model.

[0139] 6. Using the collected geological outcrop information and prestack time migration data, the interpreters perform structural interpretation to obtain a preliminary equivalent regional geological structural model. Then, using time domain velocity, the geological structural model map is migrated to the depth domain to obtain an initial depth domain geological structural model. Figure 6 This is a schematic diagram illustrating a geological structural model combined with geological outcrops, applicable to Embodiment 1 of the present invention.

[0140] 7. Based on the depth domain geological structure model obtained in step 6, geostatistical methods are used to constrain the structure model and fill it with the VSP velocity obtained above to obtain an initial TTI anisotropic velocity model that conforms to geological significance. Figure 7 This is a schematic diagram of an initial velocity model with geological significance obtained by combining a structural constraint model with VSP logging velocity, applicable to Embodiment 1 of the present invention.

[0141] 8. Based on the suitable splicing points obtained in step 5, a splicing surface is formed. Using this splicing surface, the near-surface velocity model obtained in step 4 and the intermediate-deep velocity model obtained in step 7 are spliced ​​together to obtain a full-depth TTI anisotropic velocity model. Figure 8 This is a schematic diagram of a splicing method based on VSP speed applicable to Embodiment 1 of the present invention. Figure 9 This is a comparison diagram of the superposition and stitching of a deep velocity model and VSP velocity applicable to Embodiment 1 of the present invention, where the left side is the superposition of the mid-deep velocity model and VSP velocity, and the right side is the superposition of the stitched true surface velocity model and VSP velocity.

[0142] 9. Using the aforementioned gun arrival information, the TTI anisotropic velocity model at full depth is obtained by optimizing step 8 using the method of joint inversion of refracted and reflected waves. Figure 10 This is a flowchart of a joint inversion process of refracted and reflected waves applicable to Embodiment 1 of the present invention.

[0143] 10. Based on data-driven principles, an initial isotropic velocity model V(ISO) is obtained using conventional mesh tomography.

[0144] 11. Based on the V(TTI) obtained in step 9 and the V(ISO) obtained in step 10, the initial anisotropy parameter Delta(0) is calculated using the formula according to the difference between the two velocity models.

[0145] 12. Using the initial anisotropy parameter Delta(0) obtained in step 11, the initial anisotropy parameter Epsilon(0) is obtained by assignment.

[0146] 13. Using the equivalent structural model obtained in step 6, the initial dip and azimuth models are established by employing the plane wave decomposition method. Figure 11 This is a schematic diagram illustrating the establishment of a near-surface dip model applicable to Embodiment 1 of the present invention. Figure 11 This reflects the superposition of dip angle properties with seismic profiles and geological outcrops.

[0147] 14. Input the aforementioned velocity, as well as the tilt volume, azimuth volume, Delta volume, and Epsilon volume generated by the equivalent structural model, and use full-depth TTI mesh tomography to optimize the velocity. Further optimize the full-depth velocity, especially the anisotropic parameters of the shallow layer, to obtain a 5-parameter anisotropic parameter volume with a basically accurate global trend. Figure 12 This is a schematic diagram illustrating the optimization of near-surface anisotropy parameters applicable to Embodiment 1 of the present invention. Figure 12 This reflects the superposition of anisotropic parameter updates with seismic profiles and geological outcrops. Figure 13 This is a schematic diagram comparing gathers before and after full-depth TTI anisotropic velocity iteration, applicable to Embodiment 1 of the present invention.

[0148] 15. Based on multi-angle gather data, utilize ray information from different observation azimuths to further optimize velocity for areas where conventional methods are insufficient for illumination, such as special geological bodies and special structures, thereby improving velocity spatial accuracy.

[0149] 16. Using the flattening of gathers and accurate imaging of profiles as criteria, iterate repeatedly until the velocity model and imaging results approved by the interpreters are obtained, so that the interpreters can identify the structure and traps. Figure 14 This is a schematic diagram comparing the imaging effect of a velocity modeling method applicable to Embodiment 1 of the present invention with that of a conventional velocity modeling method (the imaging of the velocity modeling method is on the right, and the conventional velocity modeling method is on the left).

[0150] In summary, the embodiments of the present invention provide a geologically constrained velocity-depth modeling method based on VSP velocity. This method improves the velocity-depth model of the piedmont zone and makes it more consistent with the geological structure, resulting in a good match with the VSP velocity. This allows for better convergence to the correct velocity, thereby improving the accuracy of seismic imaging, obtaining reasonable geological understanding, and reducing exploration risks.

[0151] Example 2

[0152] Figure 15 This is a schematic diagram of a geologically constrained velocity-depth modeling device based on VSP velocity, provided in Embodiment 2 of the present invention. Figure 15 As shown, the device includes:

[0153] Data collection unit 1510 is used to collect the initial arrival time, micro-logging data and well logging data of the target work area;

[0154] The first model construction unit 1520 is used to perform micro-logging constrained stepwise near-surface velocity inversion based on the first arrival pickup time and the micro-logging data to obtain a near-surface velocity model.

[0155] The second model construction unit 1530 establishes an initial velocity model based on the geological constraints of VSP velocity based on the well logging data.

[0156] The third model building unit 1540 establishes a full-depth TTI anisotropic parameter model;

[0157] The model optimization unit 1550 is used to optimize the initial velocity model and the full-depth TTI anisotropic parameter model to obtain a geologically constrained velocity-depth model based on VSP velocity.

[0158] The geological constraint velocity depth modeling device based on VSP velocity provided in this embodiment of the invention can execute the geological constraint velocity depth modeling method based on VSP velocity provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0159] Example 3

[0160] Figure 16 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0161] like Figure 16 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0162] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0163] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the geologically constrained velocity depth modeling method based on VSP velocity.

[0164] In some embodiments, the VSP-based geological constraint velocity depth modeling method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the VSP-based geological constraint velocity depth modeling method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the VSP-based geological constraint velocity depth modeling method by any other suitable means (e.g., by means of firmware).

[0165] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0166] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0167] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0168] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0169] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0170] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0171] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0172] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method of geologically constrained velocity depth modeling based on VSP velocities, characterized in that, The method comprises the following steps: collecting first arrival picking time, micro-logging data and logging data of a target work area; performing micro-logging constrained step-by-step near-surface velocity inversion based on the first arrival picking time and the micro-logging data to obtain a near-surface velocity model; establishing an initial velocity model based on VSP velocity and geological constraints based on the logging data; establishing a full-depth TTI anisotropy parameter model; performing optimization based on the initial velocity model and the full-depth TTI anisotropy parameter model to obtain a velocity depth model based on VSP velocity and geological constraints.

2. The method of claim 1, wherein, After the step of collecting first arrival picking time, micro-logging data and logging data of a target work area, one or more of the following steps are further included: checking the picking accuracy of the first arrival picking time; interpolating the micro-logging data to form a near-surface velocity profile, combining with surface outcrop data to judge whether it conforms to geological and geophysical rules, and correcting or rejecting micro-logging points that do not conform to geological rules; performing well-to-well layering investigation and / or checking overall curves for the logging data.

3. The method of claim 2, wherein, The step of performing micro-logging constrained step-by-step near-surface velocity inversion based on the first arrival picking time and the micro-logging data to obtain a near-surface velocity model comprises the following steps: obtaining an initial near-surface velocity model by interpolating the micro-logging velocity data; selecting first arrival picking time with offset distance within a first distance range, taking the initial near-surface velocity model as a constraint, and using tomographic inversion to obtain a first near-surface velocity model to be used; selecting first arrival picking time with medium offset distance within a second distance range as data input, taking the first near-surface velocity model to be used as a constraint velocity model input to perform near-surface velocity inversion, and obtaining a second near-surface velocity model to be used; wherein the second distance range is greater than the first distance range; selecting first arrival picking time with medium offset distance within a third distance range as data input, taking the second near-surface velocity model to be used as a constraint velocity model input to perform near-surface velocity inversion, and obtaining the near-surface velocity model; wherein the third distance range is greater than the second distance range.

4. The method of claim 1, wherein, The step of establishing an initial velocity model based on VSP velocity and geological constraints based on the logging data comprises the following steps: constructing an equivalent structure model containing near-surface structure information by time migration domain seismic imaging data and geological outcrop information in the logging data; establishing the initial velocity model based on structure morphology by using a simplified equivalent structure horizon as a constraint based on the multi-well VSP logging data.

5. The method of claim 4, wherein, The step of establishing a full-depth TTI anisotropy parameter model comprises the following steps: constructing a preliminary equivalent regional geological structure model by geological outcrop information and pre-stack time migration data volume in the logging data; migrating the geological structure model graph to the depth domain by using time domain velocity to obtain an initial depth domain geological structure model; splicing the initial depth domain geological structure model with the near-surface velocity model to obtain the full-depth TTI anisotropy velocity model.

6. The method of claim 5, wherein, The splicing the initial depth domain geological structure model with the near-surface velocity model to obtain the full-depth TTI anisotropic velocity model comprises: By the near-surface velocity model basis, comparing and calibrating with VSP velocity, determining the position where the near-surface model velocity and the VSP velocity are consistent and higher than a set value as a splicing point, and generating a splicing surface according to the splicing point; Splicing the initial depth domain geological structure model with the near-surface velocity model through the splicing surface to obtain the full-depth TTI anisotropic velocity model.

7. The method of claim 5, wherein, The optimization based on the initial velocity model and the full-depth TTI anisotropic parameter model to obtain the VSP velocity-based geologically-constrained velocity-depth model comprises: On the basis of data driving, an initial isotropic velocity model is obtained by using a conventional grid tomography method; The full-depth TTI anisotropic velocity model is optimized by using a refraction wave and reflection wave joint inversion method based on the first arrival information in the pre-stack time migration data volume; According to the difference between the initial isotropic velocity model and the optimized full-depth TTI anisotropic velocity model, an initial anisotropic parameter Delta0 is calculated by using a formula; An initial anisotropic parameter Epsilon0 is obtained by using an assignment method based on the initial anisotropic parameter Delta0; An initial dip angle and azimuth angle model is established based on the equivalent structure model; The VSP velocity-based geologically-constrained velocity-depth model is obtained by optimizing the initial velocity model, the initial dip angle and azimuth angle model, the Delta0 and the Epsilon0.

8. Apparatus for geologically constrained velocity depth modeling based on VSP velocities, characterized in that, Comprise: A data collection unit is configured to collect first arrival picking time, microlog data and logging data of a target work area; A first model construction unit is configured to perform microlog-constrained step-by-step near-surface velocity inversion based on the first arrival picking time and the microlog data to obtain a near-surface velocity model; A second model construction unit is configured to establish an initial velocity model based on VSP velocity and geology constraint based on the logging data; A third model construction unit is configured to establish a full-depth TTI anisotropic parameter model; A model optimization unit is configured to perform optimization based on the initial velocity model and the full-depth TTI anisotropic parameter model to obtain the VSP velocity-based geologically-constrained velocity-depth model.

9. An electronic device, characterized by The electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the VSP velocity-based geologically-constrained velocity-depth modeling method in any one of claims 1-7.

10. A computer readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the VSP velocity-based geologically-constrained velocity-depth modeling method in any one of claims 1-7 when executed.

11. Computer program product, characterized by The computer program product comprises a computer program which, when executed by a processor, implements the VSP velocity-based geologically constrained velocity-depth modeling method according to any one of claims 1-7.

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