Fine description method for spatial form of geologic body of salt cavern type gas storage

Abstract

The invention provides a salt cavern type gas storage geologic body spatial form fine description method. The method comprises the steps that an observation system is defined, field static correction is carried out, and series fidelity denoising is carried out; performing well control spherical diffusion compensation on the seismic data through a Tar factor and a VSP speed obtained by the VSP data, and performing Q compensation on the seismic data in the aspects of phase and amplitude by using a VSP-Q factor; earth surface consistency amplitude compensation is carried out, and well control verification is carried out on deconvolution of the three-dimensional seismic data by using a VSP-Q factor and corridor superposition data; velocity analysis, earth surface consistency residual static correction, pre-stack migration velocity field establishment and anisotropy analysis are carried out in sequence; target line migration, anisotropic pre-stack time migration, pre-stack time migration and well constraint post-stack frequency expansion processing are carried out in sequence; sequentially performing low-frequency compensation and result output; and carrying out complex structure geologic body identification. According to the method, the salt cavity with poor continuity, strong anisotropy and disordered characteristic reflection can be accurately depicted.

Description

Technical Field

[0001] This application relates to the field of geology, and particularly to a method for finely depicting the spatial morphology of a geological body in a salt cavern type gas storage reservoir. Background Art

[0002] For the selection of the construction area and the construction of a salt cavern type gas storage reservoir, it is necessary to accurately implement the longitudinal and transverse changes of salt rock layers and interbeds in the salt group within the gypsum salt formation, especially to implement the thickness of the top and bottom plates of the gypsum salt layer in the reservoir area, the surrounding fault system, the distribution characteristics of interlayer micro - faults and fractures, and the spatial distribution pattern of the salt cavern cavity.

[0003] However, due to low precision, conventional three - dimensional seismic exploration cannot meet the fine depiction of thin interbeds, interlayer small faults and fissures in the salt cavern geological body. At the same time, due to the extremely irregular characteristics of the salt cavity and the occurrence of residues in the cavity, it is very difficult for traditional sonar cavity measurement technology to accurately measure the overall shape of the salt cavity, which brings difficulties to the secondary development and utilization of the salt cavity. In addition, seismic imaging processing technology is a technology that uses seismic wave data to image the underground structure. With the improvement of seismic data acquisition technology, data transmission technology and computer processing power, high - precision three - dimensional seismic imaging processing technology has developed rapidly. This technology can reconstruct a three - dimensional image of the underground structure by analyzing and processing data such as seismic wave velocity, waveform and propagation path, so as to achieve accurate detection and depiction of underground resources and geological structures.

[0004] Moreover, the technologies for finely depicting the salt cavity geological body mainly include three - dimensional modeling of the salt body, establishment of the salt body velocity model, accurate mastery of the salt bed thickness and structure, etc. The development and application of these technologies require the support of advanced seismic data acquisition, processing and analysis technologies, as well as the application of technical means such as high - performance computers, data mining and artificial intelligence. At the same time, it also requires in - depth research on aspects such as geological structure and the formation mechanism of the salt cavity.

[0005] The formation and evolution process of the salt body geological body is very complex and needs to be studied through various means. The application of high - precision three - dimensional seismic imaging processing technology and salt cavity geological body fine - depiction technology can improve the success rate of exploration and exploitation, reduce exploration costs, and also contribute to the research and utilization of geological structures and oil and gas resources.

[0006] In practice, high - precision three - dimensional seismic imaging processing technology and salt cavity geological body fine - depiction technology usually need to be combined with other technical means, such as geophysical exploration, geochemical analysis and core analysis, etc. Through mutual verification of various means, the underground structure and resource distribution can be depicted more accurately, providing reliable technical support for fields such as oil and gas exploration and exploitation.

[0007] By referring to the current domestic and international characterizations of salt caverns, a comprehensive analysis and integrity evaluation of the geological body of a salt cavern gas storage are carried out by various means: First, VSP data is used to calibrate 3D seismic data, and research such as structural interpretation and lithologic inversion is carried out based on the 3D seismic data to analyze the distribution characteristics of salt layers and interbeds and the fault development characteristics; Second, making full use of geological, seismic and drilling data, a 3D geological model of the salt cavern is constructed by various methods such as stochastic modeling to evaluate the geological characteristics of the salt cavern gas storage; Third, aiming at the geological characteristics of salt layers in different regions, a series of influencing factors for the stability and sealing evaluation of salt caverns are proposed, and a numerical simulation model is established, and the finite element numerical analysis method is used to evaluate the stability and sealing of salt caverns under different working conditions; Fourth, based on the creep characteristics of salt rock, combined with a variety of core analysis and testing experiments, a mechanical model of the salt cavern is established based on the theory of elastic mechanics to carry out a stability evaluation of the salt cavern gas storage. The above research objects of the comprehensive analysis and integrity evaluation of the geological body of the salt cavern gas storage cover various types of salt layers at home and abroad, and the methods used basically cover mature oil and gas geological evaluation means.

[0008] Although there are many research results on the geological evaluation methods of salt cavern gas storage, there is a lack of research on the high-precision 3D seismic processing method of "energy / spectrum" well drive in space that provides seismic wave absorption attenuation and spherical diffusion compensation by VSP, the iterative analysis and improvement model using artificial intelligence I-AI expert database, and the geological body evaluation technology of salt cavern type gas storage based on model-data driven expert modeling and full 3D simulation multi-attribute fusion. Summary of the Invention

[0009] The present application provides a method for finely depicting the spatial morphology of the geological body of a salt cavern type gas storage, aiming to solve the problem that the existing 3D seismic exploration cannot finely depict the spatial morphology of the salt cavern geological body.

[0010] The technical solution of the present application is as follows:

[0011] A method for finely depicting the spatial morphology of the geological body of a salt cavern type gas storage includes the following steps:

[0012] S1, preprocessing stage: Define the observation system, perform field static correction and a series of fidelity denoising, and process the VSP data;

[0013] S2, VSP data assisted processing stage: Perform well-controlled spherical diffusion compensation on the seismic data through the Tar factor obtained from the VSP data and the VSP velocity, and perform Q compensation on the seismic data in terms of phase and amplitude using the VSP-Q factor to make the amplitude and phase of the seismic data match the well data; Perform surface-consistent amplitude compensation, and use the VSP-Q factor and corridor stack data to verify the well control of the deconvolution of the 3D seismic data to increase the matching degree between the section after the deconvolution of the 3D seismic data and the well data;

[0014] S3, Velocity analysis stage: Conduct velocity analysis, surface-consistent residual static correction, pre-stack migration velocity field establishment, and anisotropy analysis in sequence;

[0015] S4, Migration processing stage: Conduct target line migration, anisotropic pre-stack time migration, pre-stack time migration, and post-stack frequency extension processing with well constraint in sequence;

[0016] S5, Result output stage: Conduct low-frequency compensation and result output in sequence;

[0017] S6, Identify complex structural geological bodies.

[0018] As a technical solution in this application, in step S1, first decode the 3D seismic data, define the acquisition system according to the actual field situation to obtain accurate data relationships; then conduct tomographic static correction and pre-stack fidelity denoising to remove the influence of interference waves on the target horizons.

[0019] As a technical solution in this application, in step S2, during the deconvolution process, conduct iteration of Q factor and deconvolution, and obtain data that accurately matches the well-controlled deconvolution and the corridor section through repeated iteration.

[0020] As a technical solution in this application, in step S3, during the velocity analysis stage, conduct velocity analysis and residual static correction processing repeatedly for multiple times. At the same time, use the accurate velocity data at different depths obtained from VSP data during the processing to establish the pre-stack migration velocity field, and conduct anisotropy analysis in advance using VSP data.

[0021] As a technical solution in this application, in step S4, during the migration processing stage, optimize the selection of migration parameters, pick up the fine velocity of the target body to obtain a reasonable and accurate pre-stack time migration profile, and then conduct post-stack frequency extension processing under the constraint of VSP logging data.

[0022] As a technical solution in this application, in step S5, during the result output stage, conduct low-frequency compensation on the post-stack frequency extension data, and then output the results.

[0023] As a technical solution in this application, in step S6, identify complex structural geological bodies through the I-AI expert think tank interpretation system for oil fields:

[0024] Complex faults: Automatically identify and analyze complex faults through the iterative analysis and improvement model and model-data driven in the I-AI expert think tank interpretation system;

[0025] Horizon interpretation: Conduct geological model analysis through small-layer equal-time surface contrast between wells in the I-AI expert think tank interpretation system, so as to conduct horizon interpretation;

[0026] Salt cavern identification: Identify and distinguish salt cavities through multi-attribute fusion of formation bodies, construct a salt cavity model through the I-AI expert think tank interpretation system, and identify salt caverns by suppressing tectonic fracture characteristics;

[0027] Salt cavity connectivity analysis: Construct a three-dimensional solid model of the salt cavity channel through data filtering enhancement, continuous description of salt cavity channel characteristics, and an attribute fusion expert in the I-AI expert think tank interpretation system;

[0028] Salt cavity boundary description: Determine the geometric boundary of the salt cavern body through attribute feature enhancement and fusion, and construct a salt cavern body model in the I-AI expert think tank interpretation system to iteratively learn and predict the three-dimensional salt cavern body;

[0029] Salt dome gypsum rock: Conduct full three-dimensional simulation and prediction of salt dome gypsum rock through data filtering to enhance boundary features and expert modeling in the I-AI expert think tank interpretation system.

[0030] Advantages of this application:

[0031] This application provides a method for finely depicting the spatial morphology of the geological body of a salt cavern type gas storage reservoir. This application extracts various geophysical parameters (velocity, Tar, Q, deconvolution operator, anisotropy, formation dip angle, etc.) through VSP, drives the processing of surface seismic data, improves the ability of surface seismic data to depict fine structures and describe reservoirs, and also conducts a comprehensive evaluation of the salt cavern geological body through automatic identification and analysis of fractures in the I-AI expert think tank; moreover, based on graphic analysis, the I-AI expert think tank effectively synthesizes the geometric characteristics of the spatial structure and the changes in attribute data, enabling the accurate depiction of salt cavity bodies with poor continuity, strong anisotropy, and chaotic reflections. In addition, it provides spatial energy / spectrum parameters for seismic wave absorption attenuation and spherical diffusion compensation from VSP logging data, forming a well-driven high-precision three-dimensional seismic data imaging processing technology based on VSP logging data to improve the resolution of thin interbeds (thickness greater than 10 meters) of gypsum salt layers and the imaging accuracy of salt cavern geological bodies; at the same time, this method uses multi-attribute fusion such as iterative analysis and improvement of models, model-data driven, expert modeling, and full three-dimensional simulation in the I-AI expert think tank to carry out a comprehensive evaluation technology for the geological body of a salt cavern type gas storage reservoir, predict information such as the spatial occurrence form of the salt cavern gas storage reservoir cavity, the thickness of the top and bottom plates of the solution cavity, the integrity of the overlying formation, and the sealing performance of the cavity, delineate the salt cavity boundary, estimate the volume, scale, fault information within the area of the formed old salt cavity, and the possible gas storage risks of the salt cavern cavity. Description of the Drawings

[0032] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for the embodiments. It should be understood that the following drawings only show certain embodiments of the application and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other relevant drawings can be obtained based on these drawings.

[0033] Figure 1 Schematic diagram of the high-precision three-dimensional salt cavity geological body imaging processing flow based on VSP logging provided by the embodiments of the present application. Specific embodiments

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Usually, the components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0035] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0036] Embodiment:

[0037] Please refer to Figure 1 , in the embodiments of the present application, a method for finely depicting the spatial morphology of a geological body of a salt cavern type gas storage is provided. It mainly adopts two technologies: high-precision three-dimensional seismic imaging processing technology based on VSP logging and complex structure geological body recognition technology of an AI expert database. Specifically, the high-precision three-dimensional salt cavity geological body imaging processing flow based on VSP logging and the recognition of complex structure geological bodies mainly include the following steps:

[0038] S1. Pretreatment stage: Define the observation system, perform field static correction and a series of fidelity denoising, and process the VSP data. Specifically, first, decode the three-dimensional seismic data, and obtain accurate data relationships according to the actual field situation for observation system definition. Then, perform tomographic static correction and pre-stack fidelity denoising to remove the influence of interfering waves on the target horizons.

[0039] S2, VSP data assisted processing stage: Using the Tar factor and VSP velocity obtained from VSP data to perform well-controlled spherical divergence compensation on seismic data, and using the VSP-Q factor to perform Q compensation on the phase and amplitude of seismic data, so that the amplitude and phase of seismic data match well data; performing surface-consistent amplitude compensation, and using the VSP-Q factor and corridor stack data to verify the deconvolution of 3D seismic data for well control, so as to increase the matching degree between the section after deconvolution of 3D seismic data and well data; during the deconvolution process, perform iteration of Q factor and deconvolution, and obtain data that accurately matches the well-controlled deconvolution and corridor section through repeated iteration;

[0040] S3, velocity analysis stage: Perform velocity analysis, surface-consistent residual static correction, pre-stack migration velocity field establishment, and anisotropy analysis in sequence; during the velocity analysis stage, perform velocity analysis and residual static correction processing repeatedly for multiple times, and at the same time use the accurate velocity data at different depths obtained from VSP data during the processing to establish the pre-stack migration velocity field, and perform anisotropy analysis in advance using VSP data;

[0041] S4, migration processing stage: Perform target line migration, anisotropic pre-stack time migration, pre-stack time migration, and well-constrained post-stack frequency extension processing in sequence; during the migration processing stage, optimize the selection of migration parameters, pick up the fine velocity of the target body to obtain a reasonable and accurate pre-stack time migration profile, and then perform post-stack frequency enhancement processing under the constraint of VSP logging data to improve the resolution of the target formation data;

[0042] S5, result output stage: Perform low-frequency compensation and result output in sequence, that is, perform low-frequency compensation on the post-stack frequency extension data, and then output the results;

[0043] S6, Identify complex tectonic geological bodies through the I-AI expert think tank interpretation system for oilfields:

[0044] Complex faults: For complex geological and tectonic conditions, it is very difficult to accurately and efficiently identify faults. The strike-slip faults and hidden fault zones are poorly identified. Therefore, use the iterative analysis and improvement model in the I-AI expert think tank interpretation system to perform automatic identification and analysis of complex faults;

[0045] Horizon interpretation: For gypsum-salt layers, it is difficult to compare the isochronous interfaces between wells for the interpretation of thin interbedded and intercalated layers, and the geological model is unreasonable. Therefore, perform geological model analysis through the small-layer well-to-well isochronous surface comparison in the I-AI expert think tank interpretation system, and then perform horizon interpretation;

[0046] Salt cavern identification: When it is difficult to identify non-bright salt caverns and salt caverns in complex structures / fault zones, the salt cavity is identified by multi-attribute fusion of the formation body. A salt cavity model is constructed through the I-AI expert think tank interpretation system, and the salt cavern is identified by suppressing the tectonic fracture characteristics.

[0047] Salt cavity connectivity analysis: For connected salt caverns, when the characteristics of the connected channels are discontinuous and it is difficult to represent them three-dimensionally, a three-dimensional solid model of the salt cavity channel is constructed by data filtering enhancement, continuous description of the salt cavity channel characteristics, and the attribute fusion expert in the I-AI expert think tank interpretation system.

[0048] Salt cavity boundary description: When the amplitude characteristics and geometric shape can be locally described, but the interior is chaotic and difficult to track three-dimensionally, the geometric boundary of the salt cavern body is determined by attribute feature enhancement and fusion. Through the construction of the salt cavern body model in the I-AI expert think tank interpretation system, iterative learning is used to predict the three-dimensional salt cavern body.

[0049] Salt domes and gypsum rocks: When there is strong signal shielding, the boundary response is somewhat distinguishable, but the interior is chaotic and has a certain scale of development, it is difficult to represent them three-dimensionally by conventional means. Therefore, full three-dimensional simulation and prediction of salt domes and gypsum rocks are carried out by enhancing the boundary features through data filtering and expert modeling in the I-AI expert think tank interpretation system.

[0050] It should be noted that the VSP-Q factor is an existing technology. It extracts the formation quality factor using the VSP direct arrival downgoing wave, which is the VSP-Q factor. Usually, the center frequency shift method is adopted. This method uses the downgoing wave field of the zero-offset VSP seismic record. According to the characteristic that the high-frequency components are absorbed faster than the low-frequency parts during the absorption of seismic waves, the absorption coefficient of the medium is estimated by obtaining the shift of the main frequency, thereby obtaining Q. The Q compensation for the seismic data in terms of phase and amplitude also adopts existing technology. It compensates the seismic wave according to the variation law of energy attenuation with the obtained Q value. This compensation restores the vibration amplitude and consistency of the seismic data based on the Q value, which is the phase-amplitude compensation. Moreover, the use of VSP data to perform anisotropy analysis on anisotropic data also adopts existing technology, which analyzes the velocity according to the differences in various azimuth angles of the three-dimensional data.

[0051] It should be noted that in this embodiment, the I-AI Expert Think Tank Interpretation System for Oilfields - InterpretationAI (I-AI) is a prior art. It is a newly developed commercial professional software based on the GPU artificial intelligence deep learning algorithm. Its algorithm principle is as follows: The CNN deep learning neural network based on the U-NET architecture conducts deep learning iterative training on image feature data to obtain a stable and reliable deep learning training model, and outputs a result data volume that can be directly geologically interpreted. In recent years, from AlphaGo to ChatGPT, artificial intelligence has developed rapidly. From the inspiration of bionics to propose computational simulation of human brain thinking to the breakthrough of deep learning driving the development of artificial intelligence, this research uses the new technology of artificial intelligence deep learning to solve problems that were difficult to solve with previous old methods. The technical methods adopted to solve the problems proposed below are all combinations of functional modules in the software. The identification and analysis process is roughly manual sparse interpretation - AI learning training modeling - output of a result data volume that can be directly geologically interpreted. The InterpretationAI (I-AI) software has been commercially released and belongs to the prior art.

[0052] In summary, this application provides a method for finely depicting the spatial morphology of the geological body of a salt cavern type gas storage reservoir. This application extracts various geophysical parameters (velocity, Tar, Q, deconvolution operator, anisotropy, formation dip angle, etc.) through VSP to drive the processing of surface seismic data, improving the ability to finely depict the structure and describe the reservoir of surface seismic data. It also conducts a comprehensive evaluation of the salt cavern geological body through the automatic identification and analysis of fractures by the I-AI expert think tank. Moreover, based on graphical analysis, the I-AI expert think tank effectively synthesizes the geometric characteristics of the spatial structure and the changes in attribute data, enabling the accurate depiction of salt cavity bodies with poor continuity, strong anisotropy, and chaotic feature reflections. In addition, it provides spatial energy / spectrum parameters for seismic wave absorption attenuation and spherical diffusion compensation from VSP logging data, forming a well-driven high-precision 3D seismic data imaging processing technology based on VSP logging data to improve the resolution of thin interbedded gypsum salt layers (with a thickness greater than 10 meters) and the imaging accuracy of the salt cavern geological body. At the same time, this method uses multi-attribute fusion such as iterative analysis and improvement of the model, model-data driven, expert modeling, and full 3D simulation by the I-AI expert think tank to carry out a comprehensive evaluation technology for the geological body of the salt cavern type gas storage reservoir, predicting information such as the spatial occurrence form of the salt cavern gas storage reservoir cavity, the thickness of the top and bottom plates of the dissolution cavity, the integrity of the overlying strata, the sealing performance of the cavity, etc., delineating the boundary of the salt cavity, and estimating the volume, scale, fault information within the area of the formed old salt cavity, as well as the possible gas storage risks of the salt cavern cavity.

[0053] The above are only the preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for finely depicting the spatial form of a geological body of a salt cavern type gas storage reservoir, characterized in that, Including the following steps: S1, preprocessing stage: Define the observation system, perform field static correction and a series of fidelity denoising, and process the VSP data; S2, VSP data assisted processing stage: Perform well-controlled spherical divergence compensation on the seismic data using the Tar factor and VSP velocity obtained from the VSP data, and perform Q compensation on the seismic data in terms of phase and amplitude using the VSP-Q factor to make the amplitude phase of the seismic data match the well data; Perform surface-consistent amplitude compensation, and use the VSP-Q factor and corridor stack data to verify the well control of the deconvolution of the 3D seismic data to increase the matching degree between the section after deconvolution of the 3D seismic data and the well data; S3, velocity analysis stage: Perform velocity analysis, surface-consistent residual static correction, establishment of the prestack migration velocity field, and anisotropy analysis in sequence; S4, migration processing stage: Perform target line migration, anisotropic prestack time migration, prestack time migration, and well-constrained post-stack frequency extension processing in sequence; S5, result output stage: Perform low-frequency compensation and result output in sequence; S6, identify complex tectonic geological bodies.

2. The fine characterization method of the spatial morphology of the geological body of the salt cavern type gas storage reservoir according to claim 1, wherein In step S1, first decode the 3D seismic data, and define the observation system according to the actual field situation to obtain accurate data relationships; Then perform tomographic static correction and prestack fidelity denoising to remove the influence of interfering waves on the target horizon.

3. The method for finely depicting the spatial morphology of the geological body of the salt cavern type gas storage reservoir according to claim 1, wherein In step S2, during the deconvolution process, perform iteration of the Q factor and deconvolution, and obtain data that accurately matches the well-controlled deconvolution and the corridor profile through repeated iteration.

4. The method for finely depicting the spatial morphology of the geological body of the salt cavern type gas storage reservoir according to claim 1, wherein In step S3, during the velocity analysis stage, perform velocity analysis and residual static correction processing repeatedly for multiple times, and at the same time use the accurate velocity data at different depths obtained from the VSP data to establish the prestack migration velocity field during the processing, and perform anisotropy analysis in advance using the VSP data.

5. The method for finely depicting the spatial morphology of the geological body of the salt cavern type gas storage reservoir according to claim 1, characterized in that In step S4, during the migration processing stage, optimize the selection of migration parameters, perform fine velocity picking on the target body to obtain a reasonable and accurate prestack time migration profile, and then perform post-stack frequency lifting processing under the constraint of VSP logging data.

6. The method for finely depicting the spatial morphology of the geological body of the salt cavern type gas storage reservoir according to claim 1, wherein, In step S5, during the result output stage, perform low-frequency compensation on the post-stack frequency extension data, and then output the results.

7. The method for fine characterization of the spatial morphology of the geological body of a salt cavern type gas storage reservoir according to claim 1, wherein, In step S6, identify complex tectonic geological bodies through the I-AI expert think tank interpretation system for oil fields: Complex faults: Automatically identify and analyze complex faults through the iterative analysis and improvement model and model-data driven in the I-AI expert think tank interpretation system; Horizon interpretation: Perform geological model analysis through the isochronal surface comparison between small layers of wells in the I-AI expert think tank interpretation system, so as to perform horizon interpretation; Salt cave identification: Resolve salt cavities through the multi-attribute fusion identification of the formation body, construct a salt cavity model through the I-AI expert think tank interpretation system, and identify salt caves by suppressing tectonic fracture characteristics; Salt cavity connectivity analysis: Construct a three-dimensional solid model of the salt cavity channel through data filtering enhancement, continuous description of the salt cavity channel characteristics, and the attribute fusion expert in the I-AI expert think tank interpretation system; Salt cavity boundary description: Determine the geometric boundary of the salt cavern body through the enhancement and fusion of attribute features. Through the construction of the salt cavern body model in the I-AI expert think tank interpretation system, iteratively learn the three-dimensional salt cavern body prediction; Salt dome gypsum rock: Conduct full three-dimensional simulation and prediction of the salt dome gypsum rock through data filtering to enhance boundary features and expert modeling in the I-AI expert think tank interpretation system.

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