VSP data acquisition system and processing method based on AI artificial intelligence big data model

Abstract

According to the VSP data acquisition system and processing method based on the AI artificial intelligence big data model, an underground three-dimensional speed model is adopted, according to the arrangement mode of ground or sea surface seismic sources, a full-wave-field artificially synthesized VSP seismic data record recorded by underground three detectors or optical cables and VSP data are subjected to forward modeling, and the VSP seismic data are acquired. A VSP data processing artificial intelligence big data model is trained through AI, and then a VSP data AI processing model is evaporated from the trained VSP data processing artificial big data model. The collected VSP data is processed through VSP data preprocessing software based on AI and input into a VSP data AI processing model to be processed, fine description of geological structures around a well or oil and gas reservoir space distribution is obtained, the distribution range of oil and gas resources in a reservoir is tracked, oil / water or gas / water or oil / gas boundaries are determined, and the saturation degree of oil and gas contained in the reservoir is calculated; and accurate evaluation of the oil and gas resources around the underground well based on the AI artificial intelligence big data model and the VSP data is realized.

Description

Technical Field

[0001] The present invention belongs to the application of the AI large model in the field of geophysical exploration technology, and specifically relates to a microseismic monitoring system and processing method based on the AI big data model. Background Art

[0002] Artificial Intelligence (AI) is an important driving force for the new round of scientific and technological revolution and industrial transformation. It is a new technical science that studies, develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. Artificial Intelligence (AI) is an interdisciplinary and emerging discipline that is based on computer science and is cross-integrated by multiple disciplines such as computer science, psychology, and philosophy. It is a new technical science that studies, develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence, attempting to understand the essence of intelligence and produce a new intelligent machine that can respond in a way similar to human intelligence. The research in this field includes robots, speech recognition, image recognition, natural language processing, and expert systems, etc. Artificial Intelligence is an important part of the intelligent discipline, which attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a way similar to human intelligence. Artificial Intelligence is a very broad science, including robots, speech recognition, image recognition, natural language processing, expert systems, machine learning, computer vision, etc.

[0003] A large model refers to a machine learning model with a large number of parameters and a complex computing structure. These models are usually constructed by deep neural networks and have billions or even hundreds of billions of parameters. The design purpose of the large model is to improve the model's expressive ability and prediction performance, and it can handle more complex tasks and data. Large models have a wide range of applications in various fields, including natural language processing, computer vision, speech recognition, and recommendation systems, etc. The large model learns complex patterns and features by training massive amounts of data and has a stronger generalization ability, and can make accurate predictions for unseen data.

[0004] Big data analysis refers to the process of processing, analyzing, and mining large, high-speed, multi-source, and multi-type data to discover valuable information and knowledge. The core technologies of big data analysis include data storage, data processing, data mining, data analysis, and data visualization, etc. With the development of technologies such as the Internet, artificial intelligence, and the Internet of Things, the scale and complexity of data have been increasing continuously, and big data analysis technology has become an indispensable part of enterprises and organizations.

[0005] In the field of deep learning, model compression and deployment is an important research topic, and model distillation is one of the effective methods. Model Distillation was initially proposed by Hinton et al. in 2015. Its core idea is to transfer the knowledge of a complex large model (teacher model) to a relatively simple small model (student model) through knowledge transfer. Briefly, it uses the predicted probability distribution of the teacher model as soft labels to train the student model, so as to greatly reduce the complexity and computational resource requirements of the model while maintaining high prediction performance, achieving the lightweight and high efficiency of the model.

[0006] Vertical Seismic Profile, i.e., VSP, is a seismic observation method. The vertical profile is in contrast to the surface seismic profile. This method observes the seismic wave field in a well, placing seismic geophones at different depths in the well to record the seismic signals generated by surface seismic sources. The Vertical Seismic Profile (VSP) corresponds to the seismic profile usually observed on the ground. For the seismic profile observed on the ground, seismic waves are excited at some points near the surface, and at the same time, observations are made at some geophone points arranged along the ground survey line; for the vertical seismic profile, seismic waves are also excited at some points near the surface, but observations are made at some geophone points arranged at different depths along the wellbore. In the former, the geophones are placed on the surface and the survey line is arranged along the ground, so it is also called the horizontal (or surface) seismic profile; in the latter, the geophones are placed in the well and the survey line is arranged vertically along the wellbore, so it is called the vertical seismic profile. In the horizontal seismic profile, since the geophones are placed on the ground, in addition to the direct wave and surface wave propagating along the surface, only the up-going wave from underground can be received; in the vertical seismic profile, since the geophones are placed inside the formation through the well, both the up-going wave propagating from bottom to top and the down-going wave propagating from top to bottom can be received. This is perhaps the most important feature of the vertical seismic profile compared with the horizontal seismic profile. The vertical seismic profile is actually also a well observation method, which is a transformation and development of the seismic logging (also known as velocity check shot) method that has long been widely used. The difference between seismic logging and vertical seismic profile lies in: the former only uses the first arrival wave recorded, while the latter not only uses the first arrival wave on the record but also uses the subsequent arrival wave on the record; the observation point spacing of the former is usually larger, while that of the latter is very small; the former only uses the zero-offset observation system with the seismic source near the wellhead, while the latter also uses the offset observation system with the seismic source deviated from the wellhead and the multi-offset observation system; the main purpose of the former is to measure the wave velocity, while the latter is mainly to study the formation profile beside the well and the laws of wave formation and propagation in the actual geological medium. In addition, during its development process, special instrument systems have been developed for the vertical seismic profile, complete sets of field work methods have been tested, and the theoretical basis for interpretation has been developed. So it has far exceeded the original scope of seismic logging and developed into a complete, independent, new observation method. Summary of the Invention

[0007] The present invention proposes a vertical seismic profile (VSP) data acquisition system and processing method based on an AI artificial intelligence big data model, including a seismic source deployed on the ground or sea surface, a VSP data acquisition sensor array deployed downhole, a VSP data acquisition and processing computer workstation system placed on the ground wellhead or offshore platform, a distributed fiber optic acoustic sensing DAS modulation and demodulation instrument placed on the ground wellhead or offshore platform, an AI-trained VSP data processing artificial big data model, and a VSP data AI processing model distilled from the AI-trained VSP data processing artificial big data model;

[0008] The seismic source can be one of an explosive seismic source, a vibroseis source, a drop hammer seismic source, a gas explosion seismic source, an electric energy seismic source, an electric spark seismic source, an air gun seismic source, and a plasma seismic source;

[0009] The VSP data acquisition sensor array can be one of downhole three-component velocity geophones, three-component piezoelectric geophones, three-component accelerometers, and three-component fiber optic geophones, or can also be one of armored straight or helical optical cables fixed or wound inside and outside the casing or inside and outside the pipe string;

[0010] The seismic source is arranged according to the position required by the construction design. It can be excited near the wellhead (zero offset), away from the wellhead (non-zero offset), gradually moved from the wellhead to both ends (variable offset - Walkaway) for sequential excitation, moved around the wellhead in a circular motion at different radial distances (Walkaround) for sequential excitation, uniformly arranged and sequentially excited in a three-dimensional manner (3D VSP) around the wellhead in a grid pattern, or can also be excited by an air gun seismic source or a plasma seismic source on the sea surface in a circular motion around the drilling platform for sequential excitation or uniformly arranged and sequentially excited in a three-dimensional manner (3D VSP) around the drilling platform in a grid pattern;

[0011] The three-component velocity geophones, three-component piezoelectric geophones, three-component accelerometers, and three-component fiber optic geophones of the VSP data acquisition sensor array are connected to the VSP data acquisition and processing computer workstation system through cables or electro-optical composite cables;

[0012] The optical cable is connected to the distributed fiber optic acoustic sensing DAS modulation and demodulation instrument through an armored optical cable near the ground wellhead;

[0013] Using the AI-based underground three-dimensional velocity modeling software, input the three-dimensional geological model, three-dimensional seismic P-wave velocity model, three-dimensional seismic S-wave velocity model, all acoustic logging data, VSP data of other wells, rock physics measurement data, anisotropy coefficient, and attenuation coefficient Q value in the VSP data acquisition work area to establish the three-dimensional viscoelastic medium anisotropic P-wave velocity model and S-wave velocity model underground in the VSP data acquisition work area;

[0014] The artificial big data model for VSP data processing uses various underground three-dimensional seismic P-wave and S-wave velocity models, including homogeneous and inhomogeneous isotropic seismic P-wave and S-wave velocity models, homogeneous and inhomogeneous anisotropic seismic P-wave and S-wave velocity models, acoustic wave seismic velocity models, elastic wave seismic P-wave and S-wave velocity models, viscoelastic medium seismic P-wave and S-wave velocity models, and complex geological structure seismic P-wave and S-wave velocity models. According to the layout of the VSP data acquisition sensor array and the seismic source, it forward simulates the full-wavefield synthetic VSP seismic records recorded in the well for any underground geological structure, as well as the actual VSP data recorded by various existing geophones over the years, and then conducts AI training on the artificial big data model for VSP data processing;

[0015] The AI processing model for VSP data is an AI processing model for VSP data obtained by distillation on the basis of the artificial big data model for VSP data processing, and is used to process the acquired VSP data with the VSP data acquisition and processing computer workstation system on the ground or offshore platform at the site;

[0016] The acquisition and processing method of the VSP data acquisition system based on the AI artificial big data model includes the following steps:

[0017] (1) Layout the optimal VSP data acquisition sensor array according to the construction design and downhole conditions in the VSP data acquisition work area;

[0018] (2) Collect the three-dimensional geological model, three-dimensional velocity model, acoustic logging data of all wells, VSP data of other wells, rock physics measurement data, anisotropy coefficient and attenuation coefficient Q value in the VSP data acquisition work area, and use the AI-based underground three-dimensional velocity modeling software to establish the three-dimensional viscoelastic medium anisotropic P-wave velocity model and three-dimensional viscoelastic medium anisotropic S-wave velocity model under the VSP data acquisition work area;

[0019] (3) Start the VSP data acquisition and processing computer workstation system or the distributed fiber optic acoustic sensing DAS modulation and demodulation instrument;

[0020] (4) In the VSP data acquisition work area, start a single (individual) or multiple (multiple) seismic sources at each pre-designed seismic source point according to the construction design, and simultaneously collect various VSP data through the VSP data acquisition and processing computer workstation system or the distributed fiber optic acoustic sensing DAS modulation and demodulation instrument and the downhole VSP data acquisition sensor array;

[0021] (5) Synchronously start the VSP data acquisition and processing computer workstation system installed with the AI real-time VSP data processing model evaporated from the AI-trained artificial big data model for VSP data processing;

[0022] (6) Processing the VSP data collected in the VSP data collection area on-site in the VSP data collection and processing computer workstation system;

[0023] (7) using the AI-based VSP data preprocessing software installed in the VSP data acquisition and processing computer workstation system to perform amplitude-preserving denoising, surface static correction, surface or downhole consistency processing, deconvolution processing, and wave field separation processing of up and down waves on the VSP data of step (6);

[0024] (8) inputting the VSP data processed in step (7) and the three-dimensional viscoelastic medium anisotropic longitudinal wave velocity model and the three-dimensional viscoelastic medium anisotropic shear wave velocity model into the distilled VSP data AI processing model;

[0025] (9) The VSP data AI processing model in the VSP data acquisition and processing computer workstation system obtains the amplitude-preserving high-resolution VSP longitudinal wave imaging data volume and the amplitude-preserving high-resolution VSP shear wave imaging data volume of the VSP data based on the VSP data processed in step (7) and the three-dimensional viscoelastic medium anisotropic longitudinal wave velocity model and the three-dimensional viscoelastic medium anisotropic shear wave velocity model established in step (2) at the VSP data acquisition site;

[0026] (10) extracting a plurality of VSP longitudinal wave data attributes and a plurality of VSP shear wave data attributes from the amplitude-preserving high-resolution VSP longitudinal wave imaging data volume and the VSP shear wave imaging data volume obtained in step (9);

[0027] (11) Using the multiple VSP longitudinal wave data attributes and the multiple VSP shear wave data attributes extracted in step (10), based on the single attribute data or several combined attribute data that are most sensitive to the comprehensive interpretation target or reservoir physical property parameters, a three-dimensional characterization and detailed description of the geological structure or the spatial distribution of the oil and gas reservoir within a certain range around the VSP data acquisition well is performed, the distribution range of the oil and gas resources in the oil and gas reservoir around the well is tracked, the oil / water boundary or the gas / water boundary or the oil / gas boundary is determined, and the oil and gas saturation in the oil and gas reservoir is calculated, so as to realize the accurate evaluation of the oil and gas resources around the underground well based on the AI ​​artificial intelligence big data model and VSP data, and provide reliable technical support for realizing the efficient development and production of oil and gas resources and improving the recovery rate of oil and gas resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of the VSP data acquisition system and processing method based on the AI ​​artificial intelligence big data model of the present invention.

[0029] Figure 2 It is a schematic diagram of the downhole three-component geophone VSP data acquisition system of the present invention with ring-shaped seismic sources arranged on the ground or sea surface.

[0030] Figure 3 It is a schematic diagram of the downhole three-component geophone VSP data acquisition system for the surface or seabed grid layout of the seismic source of the present invention.

[0031] Figure 4 It is a schematic diagram of the VSP data acquisition system of the armored straight optical cable outside or inside the downhole casing for the surface or seabed grid layout of the seismic source of the present invention.

[0032] Figure 5 It is a schematic diagram of the VSP data acquisition system of the spiral armored optical cable outside the downhole casing for the surface or seabed grid layout of the seismic source of the present invention.

[0033] Marks and corresponding component names in the attached drawings: 1 - seismic source, 2 - VSP data acquisition sensor array, 3 - VSP data acquisition and processing computer workstation system, 4 - distributed fiber optic acoustic sensing DAS modulation and demodulation instrument, 5 - VSP data processing artificial big data model, 6 - VSP data AI processing model, 7 - optical cable, 8 - three-dimensional viscoelastic anisotropic longitudinal wave velocity model and shear wave velocity model. Detailed implementation manners

[0034] For the convenience of understanding the present invention, the present invention will be described in more detail below in conjunction with the attached drawings and specific embodiments. Preferred embodiments of the present invention are shown in the attached drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described in this specification. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present invention more thorough and comprehensive. They do not constitute a limitation to the present invention, but are only examples, and at the same time, the advantages of the present invention will become clearer and easier to understand.

[0035] As Figure 1 shown is a schematic diagram of the VSP data acquisition system and processing method flow based on the AI artificial intelligence big data model of the present invention. A vertical seismic profile (VSP) data acquisition system based on the AI artificial intelligence big data model of the present invention includes a seismic source 1 arranged on the ground or sea surface, a VSP data acquisition sensor array 2 arranged downhole, a VSP data acquisition and processing computer workstation system 3 placed on the ground wellhead or offshore platform, a distributed fiber optic acoustic sensing DAS modulation and demodulation instrument 4 placed on the ground wellhead or offshore platform, a VSP data processing artificial big data model 5 trained by AI, and a VSP data AI processing model 6 evaporated from the VSP data processing artificial big data model 5.

[0036] The seismic source 1 can be one of an explosive seismic source, a vibrator seismic source, a drop hammer seismic source, a gas explosion seismic source, an electric energy seismic source, an electric spark seismic source, an air gun seismic source, and a plasma seismic source; the VSP data acquisition sensor array 2 can be one of a downhole three-component velocity geophone, a three-component piezoelectric geophone, a three-component acceleration geophone, and a three-component fiber optic geophone, or can also be one of the armored straight or helical optical cables 7 fixed or wound inside and outside the casing or inside and outside the pipe string.

[0037] Figure 2 It is a schematic diagram of the downhole three-component geophone VSP data acquisition system with a surface or sea surface annularly arranged seismic source of the present invention. Figure 3 It is a schematic diagram of the downhole three-component geophone VSP data acquisition system with a surface or sea surface grid-like arranged seismic source of the present invention. The seismic source 1 is arranged according to the position requirements of the construction design, and can be excited near the wellhead (zero offset), can be excited away from the wellhead (non-zero offset), can be gradually moved from the wellhead to both ends (variable offset - Walkaway) and excited in sequence, can be moved around the wellhead in a circular motion at different radial distances (Walkaround) and excited in sequence ( Figure 2 ), can be evenly arranged and excited in a three-dimensional manner (three-dimensional VSP) around the wellhead in a grid pattern ( Figure 3 ), can also be excited in sequence by moving in a circular motion with a drilling platform as the center using an air gun seismic source or a plasma seismic source on the sea surface ( Figure 2 ) or can be evenly arranged and excited in a three-dimensional manner (three-dimensional VSP) around the drilling platform in a grid pattern ( Figure 3 ).

[0038] Figure 4 It is a schematic diagram of the downhole armored straight or helical optical cable VSP data acquisition system with a surface or sea surface grid-like arranged seismic source of the present invention. Figure 5 It is a schematic diagram of the downhole outer casing helical armored optical cable VSP data acquisition system with a surface or sea surface grid-like arranged seismic source of the present invention.

[0039] The three-component velocity geophone, three-component piezoelectric geophone, three-component acceleration geophone, and three-component fiber optic geophone of the VSP data acquisition sensor array 2 are connected to the VSP data acquisition and processing computer workstation system 3 through a cable or an optical and electrical composite cable; the optical cable 7 is a downhole fiber optic geophone array or an armored straight ( Figure 4 ) or armored helical optical cable ( Figure 5 ), and the optical cable 7 is connected to the distributed fiber optic acoustic sensing DAS modulation and demodulation instrument 4 through an armored optical cable near the surface wellhead.

[0040] Using AI-based underground three-dimensional velocity modeling software, input the three-dimensional geological model, three-dimensional seismic P-wave velocity model, three-dimensional seismic S-wave velocity model, all acoustic logging data, VSP data of other wells, rock physics measurement data, anisotropy coefficient and attenuation coefficient Q value in the VSP data acquisition work area, and establish an underground three-dimensional viscoelastic medium anisotropic P-wave velocity model and a three-dimensional viscoelastic medium anisotropic S-wave velocity model 8 in the VSP data acquisition work area;

[0041] The VSP data processing artificial big data model 5 uses various underground three-dimensional seismic velocity models, including homogeneous and inhomogeneous isotropic seismic velocity models, homogeneous and inhomogeneous anisotropic seismic velocity models, acoustic wave seismic velocity models, elastic wave seismic velocity models, viscoelastic medium seismic velocity models, and complex geological structure seismic velocity models. According to the layout method of the VSP data acquisition sensor array 2 and the seismic source 1, forward simulate the full-wavefield artificial synthetic VSP seismic records recorded in the well for any underground geological structure, as well as the actual VSP data recorded by various geophones over the years, and then perform AI training on the VSP data processing artificial big data model 5;

[0042] The VSP data AI processing model 6 is obtained by distillation based on the VSP data processing artificial big data model 5, and is used to process the collected VSP data with the VSP data acquisition and processing computer workstation system 3 on the ground or offshore platform at the construction site;

[0043] The acquisition and processing method of the VSP data acquisition system based on the AI artificial intelligence big data model includes the following steps:

[0044] (a) Layout the optimal VSP data acquisition sensor array 2 in the VSP data acquisition work area according to the construction design and downhole conditions;

[0045] (b) Collect the three-dimensional geological model, three-dimensional velocity model, acoustic logging data of all wells, VSP data of other wells, rock physics measurement data, anisotropy coefficient and attenuation coefficient Q value in the VSP data acquisition work area, and use AI-based underground three-dimensional velocity modeling software to establish an underground three-dimensional viscoelastic medium anisotropic P-wave velocity model and a three-dimensional viscoelastic medium anisotropic S-wave velocity model 8 in the VSP data acquisition work area;

[0046] (c) Start the VSP data acquisition and processing computer workstation system 3 or the distributed fiber optic acoustic sensing DAS modulation and demodulation instrument 4;

[0047] (d) In the VSP data acquisition work area, start one or more seismic sources 1 according to the construction design, and conduct excitation at each pre-designed seismic source point. At the same time, collect various VSP data together with the VSP data acquisition sensor array 2 in the well through the VSP data acquisition and processing computer workstation system 3 or the distributed fiber optic acoustic sensing DAS modulation and demodulation instrument 4;

[0048] (e) Synchronously start the VSP data acquisition and processing computer workstation system 3 installed with the AI real-time VSP data processing model 6 evaporated from the VSP data processing artificial big data model 5 trained by AI;

[0049] (f) Conduct on-site processing of the VSP data collected in the VSP data acquisition work area in the VSP data acquisition and processing computer workstation system 3;

[0050] (g) Use the AI-based VSP data preprocessing software installed in the VSP data acquisition and processing computer workstation system 3 to perform amplitude-preserving denoising processing, surface static correction processing, surface or downhole consistency processing, deconvolution processing, and AI artificial intelligence wavefield separation processing of up and down waves on the VSP data in step (f);

[0051] (h) Input the VSP data processed in step (g) and the three-dimensional viscoelastic medium anisotropic P-wave velocity model and three-dimensional viscoelastic medium anisotropic S-wave velocity model (8) into the distilled VSP data AI processing model 6;

[0052] (i) The VSP data AI processing model 6 in the VSP data acquisition and processing computer workstation system 3 obtains the amplitude-preserving high-resolution VSP P-wave imaging data volume and amplitude-preserving high-resolution VSP S-wave imaging data volume of the VSP data according to the VSP data processed in step (g), combined with the three-dimensional viscoelastic medium anisotropic P-wave velocity model and three-dimensional viscoelastic medium anisotropic S-wave velocity model 8 under the VSP data acquisition work area established in step (b);

[0053] (j) Extract various VSP P-wave data attributes and various VSP S-wave data attributes from the amplitude-preserving high-resolution VSP P-wave imaging data volume and amplitude-preserving high-resolution VSP S-wave imaging data volume obtained in step (i);

[0054] (k) Using various VSP P-wave data attributes and various VSP S-wave data attributes extracted in step (j), based on the single attribute data or several combined attribute data that are most sensitive to the comprehensive interpretation target or reservoir physical property parameters, perform three-dimensional characterization and fine delineation of the geological structure or the spatial distribution of the oil and gas reservoir within a certain range around the VSP data acquisition well, trace the distribution range of the oil and gas resources within the oil and gas reservoir around the well, determine the oil / water boundary or gas / water boundary or oil / gas boundary, calculate the oil and gas saturation within the oil and gas reservoir, and achieve an accurate evaluation of the oil and gas resources around the underground well based on the AI artificial intelligence big data model and VSP data, providing reliable technical support for the efficient development and production of oil and gas resources and improving the recovery rate of oil and gas resources.

Claims

1. The VSP data acquisition system based on AI big data model is characterized by: It includes a seismic source (1) arranged on the ground or sea surface, and a VSP data acquisition sensor array (2) arranged downhole; The VSP data acquisition sensor array (2) is one of a downhole three-component velocity detector, a three-component piezoelectric detector, a three-component acceleration detector or a three-component optical fiber detector. The VSP data acquisition sensor array (2) is connected to a VSP data acquisition processing computer workstation system (3) via a cable or an optoelectronic composite cable. The VSP data acquisition processing computer workstation system (3) is installed with an AI-trained VSP data processing artificial big data model (5), and a VSP data AI processing model (6) evaporated from the VSP data processing artificial big data model (5). Using AI-based underground three-dimensional velocity modeling software, a three-dimensional geological model, a three-dimensional seismic longitudinal wave velocity model, a three-dimensional seismic shear wave velocity model, all acoustic logging data, VSP data of other wells, rock physics measurement data, anisotropy coefficient and attenuation coefficient Q value in the VSP data acquisition area are input to establish a three-dimensional viscoelastic medium anisotropic longitudinal wave velocity model and a shear wave velocity model (8) in the VSP data acquisition area. Alternatively, the VSP data acquisition sensor array (2) is one of the optical cables (7); the optical cable (7) is fixed or wound around an armored straight or spiral optical cable inside or outside the casing or inside or outside the pipe column, and the optical cable (7) is connected to a distributed optical fiber acoustic sensor DAS modem (4) near the wellhead on the ground through the armored optical cable; The VSP data acquisition and processing computer workstation system (3) or the distributed optical fiber acoustic wave sensor DAS modulation and demodulation instrument (4) is arranged near the wellhead on the ground or on the offshore platform.

2. The VSP data acquisition system based on the AI ​​artificial intelligence big data model according to claim 1 is characterized in that: The seismic source (1) is one of an explosive seismic source, a controllable seismic source, a heavy hammer seismic source, a gas explosion seismic source, an electric energy seismic source, an electric spark seismic source, an air gun seismic source or a plasma seismic source.

3. The VSP data acquisition system based on the AI ​​artificial intelligence big data model according to claim 1 is characterized in that: The seismic source (1) is arranged according to the position required by the construction design, and is excited near the wellhead, i.e., zero offset VSP, or away from the wellhead, i.e., non-zero offset VSP, or is moved step by step from the wellhead to both ends to sequentially excite, i.e., Walkaway VSP or variable offset VSP, or is moved around the wellhead at different radii to sequentially excite, i.e., Walkaround VSP, or is evenly arranged in a three-dimensional manner around the wellhead in a grid manner to sequentially excite, i.e., onshore three-dimensional VSP, or is moved in a circle on the sea surface with an airgun seismic source or a plasma seismic source around the drilling platform as the center to sequentially excite, or is evenly arranged in a three-dimensional manner around the drilling platform in a grid manner to sequentially excite, i.e., offshore three-dimensional VSP.

4. The VSP data acquisition system based on the AI ​​artificial intelligence big data model according to claim 1 is characterized in that: The VSP data processing artificial big data model (5) adopts various underground three-dimensional seismic P-wave and S-wave velocity models, including uniform and non-uniform isotropic seismic P-wave and S-wave velocity models, uniform and non-uniform anisotropic seismic P-wave and S-wave velocity models, acoustic wave seismic velocity models, elastic wave seismic P-wave and S-wave velocity models, viscoelastic medium seismic P-wave and S-wave velocity models or complex geological structure seismic P-wave and S-wave velocity models, and forward simulates the full wave field artificial synthetic VSP seismic records recorded in the well of any underground geological structure according to the layout of the VSP data acquisition sensor array (2) and the source (1), and then performs AI training of the VSP data processing artificial big data model (5).

5. The VSP data acquisition system based on the AI ​​artificial intelligence big data model according to claim 1 is characterized in that: The VSP data AI processing model (6) is obtained by distillation based on the VSP data processing artificial big data model (5), and is used to process the collected VSP data in the VSP data collection and processing computer workstation system (3).

6. The data collection and data processing method for the VSP data collection system based on the AI ​​artificial intelligence big data model according to any one of claims 1 to 5, characterized in that: The following steps are involved: (a) deploying the optimal VSP data acquisition sensor array (2) in the VSP data acquisition area according to the construction design and downhole conditions; (b) Collect the three-dimensional geological model, three-dimensional P-wave and S-wave velocity models, acoustic logging data of all wells, VSP data of other wells, rock physics measurement data, anisotropy coefficient and attenuation coefficient Q value in the VSP data acquisition area, and use AI-based underground three-dimensional velocity modeling software to establish a three-dimensional viscoelastic medium anisotropic P-wave velocity model and a three-dimensional viscoelastic medium anisotropic S-wave velocity model in the VSP data acquisition area (8); (c) starting the VSP data acquisition and processing computer workstation system (3) or the distributed optical fiber acoustic sensor DAS modulation and demodulation instrument (4); (d) starting a single or multiple seismic sources (1) in the VSP data acquisition area according to the construction design to excite at each pre-designed seismic source point, and simultaneously collecting various VSP data through a VSP data acquisition and processing computer workstation system (3) or a distributed optical fiber acoustic sensor DAS modem instrument (4) and a downhole VSP data acquisition sensor array (2); (e) synchronously starting a VSP data acquisition and processing computer workstation system (3) installed with an AI real-time VSP data processing model (6) evaporated from an AI-trained VSP data processing artificial big data model (5); (f) processing the VSP data collected in the VSP data collection area on-site in the VSP data collection and processing computer workstation system (3); (g) using the AI-based VSP data preprocessing software installed in the VSP data acquisition and processing computer workstation system (3) to perform amplitude-preserving denoising, surface static correction, surface or downhole consistency processing, deconvolution, and AI artificial intelligence wave field separation of up and down waves on the VSP data of step (f); (h) inputting the VSP data processed in step (g) and the three-dimensional viscoelastic medium anisotropic longitudinal wave velocity model and the three-dimensional viscoelastic medium anisotropic shear wave velocity model (8) established in step (b) into the distilled VSP data AI processing model (6); (i) The VSP data AI processing model (6) in the VSP data acquisition and processing computer workstation system (3) obtains an amplitude-preserved high-resolution VSP longitudinal wave imaging data volume and an amplitude-preserved high-resolution VSP shear wave imaging data volume of the VSP data based on the VSP data processed in step (g) and in combination with the three-dimensional viscoelastic medium anisotropic longitudinal wave velocity model and the three-dimensional viscoelastic medium anisotropic shear wave velocity model (8) at the VSP data acquisition site established in step (b); (j) extracting a plurality of VSP longitudinal wave data attributes and a plurality of VSP shear wave data attributes from the amplitude-preserving high-resolution VSP longitudinal wave imaging data volume and the amplitude-preserving high-resolution VSP shear wave imaging data volume obtained in step (i); (k) using the multiple VSP longitudinal wave data attributes and the multiple VSP shear wave data attributes extracted in step (j), based on the single attribute data or several combined attribute data that are most sensitive to the comprehensive interpretation target or reservoir physical property parameters, a three-dimensional characterization and detailed description of the geological structure or the spatial distribution of the oil and gas reservoir within a certain range around the VSP data acquisition well is performed, the distribution range of the oil and gas resources in the oil and gas reservoir around the well is tracked, the oil / water boundary or the gas / water boundary or the oil / gas boundary is determined, and the oil and gas saturation in the oil and gas reservoir is calculated, thereby achieving a comprehensive evaluation of the oil and gas resource potential around the well and the distribution range of the oil and gas reservoir.

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