A 3D modeling method for subdividing seismic attributes of deep carbonate rock conduits based on their advantages

Abstract

The present invention discloses a method for fine three-dimensional modeling of the advantages of deep carbonate rock conduits, including: optimizing three-dimensional seismic data through multi-scale and multi-directional decomposition and reconstruction to establish an optimal three-dimensional seismic data volume that can best characterize deep carbonate rock reservoirs. Based on the heterogeneity of deep carbonate rocks, an isochronous stratigraphic division method is adopted to conduct fine subdivision of small layers for the optimized seismic data. Wellbore synthetic records are used for seismic horizon calibration, and the conduit-land ratio of each small layer is calculated. By extracting and analyzing seismic attributes, the best seismic attributes that can effectively predict the conduit-land ratio are screened out. Using these indicators, a mathematical equation is established to convert seismic attributes into a conduit-land ratio data volume. Combining the conduit-land ratio of small layers and paleogeomorphic depth values in each hydrocarbon accumulation period, a three-dimensional conduit-land ratio distribution model is constructed to provide a scientific basis for hydrocarbon conduction and hydrocarbon accumulation analysis. This method significantly improves the recognition accuracy of reservoir characteristics and has important application value.

Description

Technical Field

[0001] The present invention belongs to the technical field of geophysical exploration, and particularly relates to a method for three-dimensional modeling of the dominant seismic genus subdivision of deep carbonate rock conductors. Background Art

[0002] Microbial mound shoals play an important controlling role in the formation of reservoirs and can often form industrial gas reservoirs in the high-energy zones of the platform margin and within the platform. However, due to the large burial depth of the mound shoals, the complexity of reservoir types and combinations, and the rapid lateral variation of the reservoirs, different types of conduction systems will have different effects on the migration and accumulation of oil and gas reservoirs. Therefore, the accurate three-dimensional characterization of the conduction parameters of the mound shoals is of great significance for the exploration and development of mound shoal oil and gas reservoirs.

[0003] By carrying out downhole conductor layer calibration through synthetic seismograms and logging profiles, dividing the mound shoal types based on actual drilled cores, and clarifying the seismic response characteristics of different conductors, and then carrying out three-dimensional prediction of different mound shoal types, is the key step for three-dimensional modeling of conduction parameters. However, due to the thin thickness and strong heterogeneity of the mound shoal reservoirs, there are usually high requirements for the quality and resolution of seismic data. Conventional seismic data is difficult to distinguish the seismic response characteristics of different types of conductor layers, so it often causes the multi-solution and inaccuracy of the interpretation results. At present, there is no effective seismic processing method for accurately characterizing the conduction parameters of different mound shoal types. Therefore, the resolution of seismic data severely restricts the accurate identification of the conduction performance of the mound shoals. Summary of the Invention

[0004] In order to solve the technical problems existing in the background art, the present invention aims to provide a method for three-dimensional modeling of the dominant seismic genus subdivision of deep carbonate rock conductors, so as to improve the recognition and modeling accuracy of the complex reservoirs of the mound shoals.

[0005] In order to solve the technical problems, the technical solution of the present invention is as follows:

[0006] A method for three-dimensional modeling of the dominant seismic genus subdivision of deep carbonate rock conductors, the method comprising:

[0007] S1: Seismic data optimization. Decompose and reconstruct the three-dimensional seismic data in multiple scales and multiple directions, optimize the seismic data that matches well with the downhole synthetic seismogram, and establish a three-dimensional seismic data volume of dominant decomposition and reconstruction that best characterizes the characteristics of deep carbonate rock reservoir conductors;

[0008] S2: Isochronous stratigraphic division of small layers of seismic data. Based on the strong heterogeneity of deep-ultra-deep carbonate rocks, combined with the minimum bin thickness of the mound shoals, use the isochronous stratigraphic division method to carry out fine isochronous stratigraphic subdivision of the target layer of the optimized seismic data, and establish a fine high-resolution isochronous stratigraphic framework;

[0009] S3: Pick up the ratio of reservoir thickness to formation thickness for each sub-layer of the target formation underground. Using the synthetic seismogram underground, conduct fine seismic-geological horizon calibration on the seismic profile beside the well, establish the corresponding relationship between the interfaces of each sub-layer of the target formation in the seismic data and the underground formation interfaces of each well, conduct reservoir interpretation and reservoir thickness statistics for each sub-layer of the target formation underground, and calculate the ratio of reservoir thickness to formation thickness for each sub-layer underground;

[0010] S4: Optimize the dominant seismic attributes. Extract the seismic attributes of each sub-layer, conduct correlation analysis and multi-attribute fusion, screen out the attributes with high correlation, and optimize the best seismic attributes, which can be used to predict the ratio of reservoir thickness to formation thickness for each sub-layer;

[0011] S5: Convert the ratio of reservoir thickness to formation thickness using seismic attributes. Conduct crossplot analysis between the best seismic attributes and the ratio of reservoir thickness to formation thickness underground, establish a mathematical equation for the best characterization of the transport parameter, determine the conversion relationship corresponding to each sub-layer section of the target formation, and convert the dominant seismic attributes of each sub-layer into the data volume of the ratio of reservoir thickness to formation thickness for this sub-layer;

[0012] S6: Construct a 3D distribution model of the ratio of reservoir thickness to formation thickness. Combine the ratio of reservoir thickness to formation thickness of each sub-layer and the paleogeomorphic depth value during each hydrocarbon accumulation period, and establish a fine 3D spatial distribution model of the ratio of reservoir thickness to formation thickness of multiple sub-layers superimposed during each hydrocarbon accumulation period in the target formation of the study area, which is used for hydrocarbon migration and accumulation analysis.

[0013] Among them, the input and output of each step are as follows:

[0014] S1: Optimize seismic data. Input: Original 3D seismic data and synthetic seismogram underground; Output: High-resolution and reconstructed seismic data obtained after navigation pyramid processing and wavelet frequency division processing, and the 3D seismic data volume (dominant decomposition and reconstructed 3D seismic data volume) that is well matched with the synthetic seismogram underground;

[0015] S2: Conduct isochronous stratigraphic division of sub-layers for seismic data. Input: Optimized 3D seismic data; Output: High-resolution isochronous stratigraphic framework (subdivided sub-layer information) of each sub-layer of the target formation;

[0016] S3: Pick up the ratio of reservoir thickness to formation thickness for each sub-layer of the target formation underground. Input: Synthetic seismogram underground, seismic data of each sub-layer of the target formation, and underground formation thickness information of the well; Output: The corresponding relationship between the interfaces of each sub-layer and the underground formation interfaces, the statistical results of the reservoir thickness of each sub-layer, and the ratio of reservoir thickness to formation thickness of each sub-layer (the ratio of the reservoir thickness of the sub-layer to the thickness of the sub-layer);

[0017] S4: Optimize the dominant seismic attributes: Input: Various seismic attributes extracted from each sub-layer, and the data of the ratio of reservoir thickness to formation thickness at the well point location; Output: The dominant seismic attributes with the largest correlation coefficient, and the best seismic data volume of each sub-layer;

[0018] S5: Convert the well - to - formation ratio using seismic attributes: Input: The selected optimal seismic attributes, well - to - formation ratio data downhole; Output: The established mathematical equation (relationship for each sub - layer segment of each sub - layer), well - to - formation ratio data volume for each sub - layer (obtained by converting through the dominant seismic attributes).

[0019] S6: Three - dimensional well - to - formation ratio spatial distribution model for each hydrocarbon accumulation period based on the well - to - formation ratio of sub - layers. Input: Well - to - formation ratio values of each sub - layer for each hydrocarbon accumulation period, paleogeomorphic depth values; Output: Fine three - dimensional spatial distribution model of multi - sub - layer superposition for each hydrocarbon accumulation period of the target layer, providing a basis for hydrocarbon migration and accumulation analysis.

[0020] Through the above input and output steps, a logical chain is formed among the steps, and finally, a fine three - dimensional modeling of deep carbonate rock conduits based on the dominant seismic attributes of subdivided sub - layers is achieved.

[0021] Further, step S1 includes:

[0022] Decompose and reconstruct the three - dimensional seismic data in multiple scales and directions through navigation pyramid processing and wavelet frequency division methods; compare and analyze the processed seismic data with the downhole synthetic seismic record, and select the seismic data with excellent matching with the downhole synthetic seismic record; establish a dominant decomposed and reconstructed three - dimensional seismic data volume that best represents the characteristics of deep carbonate rock reservoir conduits.

[0023] Further, step S4 includes:

[0024] Extract various seismic attributes for each sub - layer using the dominant seismic data, conduct correlation analysis on various seismic attributes, select various attributes with poor correlation, perform multi - attribute fusion, extract various seismic attributes and attribute fusion values selected at each well point position of the same sub - layer, conduct cross - plot analysis of their various attribute values with the well - to - formation ratio data downhole at each well point, select the corresponding seismic attribute with the largest correlation coefficient as the dominant seismic data volume and dominant seismic attribute for predicting the well - to - formation ratio of this sub - layer. This method is used for each sub - layer, and finally, the optimal seismic data volume and optimal seismic attribute data volume for each sub - layer predicting the well - to - formation ratio of the target layer in the study area are selected.

[0025] Compared with the prior art, the advantages of the present invention are:

[0026] (1) Component information in different frequency bands of seismic data has different sensitivities to various geological information. The present invention uses methods such as navigation pyramid hierarchical decomposition, reconstruction, and wavelet frequency division processing to decompose and reconstruct the seismic data in the study area in multiple scales and directions, and compare and analyze it with the synthetic seismic record passing through the well to verify the accuracy of the mined information, improving the recognition and characterization ability of thin conduits, and solving the problem that the resolution of the conventional seismic data volume is low and insufficient to reflect the strong heterogeneity of the mound - beach complex.

[0027] (2) The present invention fully realizes the close combination of seismic and geology. Within a fine high-resolution framework, the measured information from geological drilling is fully utilized for the optimization and verification of seismic attributes, establishing a mathematical conversion equation between seismic attribute parameters and actual geological parameters, and developing a reliable method for quantitatively predicting the ratio of hydrocarbon migration to geological body volume suitable for each sub-layer conductor.

[0028] The main part of this model is data-driven. Based on the advantageous seismic data volume excavated and optimized from seismic data in this study, the target layer is divided into sub-layers according to the minimum modeling grid, and various prediction method analyses and optimizations are carried out in combination with the actual parameters of the underground conductor layer in the study area. The ratio of hydrocarbon migration to geological body volume and its distribution of the conductor layer are quantitatively predicted using the optimized prediction method.

[0029] (3) The present invention fully considers the strong longitudinal and transverse heterogeneity of the mound-beach body, establishes a high-resolution isochronous stratigraphic framework for the target layer by subdividing it into sub-layers (different frequency-divided seismic volumes and seismic attributes can be used for each sub-layer), and constructs a precise three-dimensional parameter model of the conductor layer in the depth domain in combination with complex situations such as tectonic paleogeomorphology and fault distribution, providing important data support for the analysis of multi-stage hydrocarbon accumulation.

[0030] The reservoir types of the mound-beach body are complex, and the oil reservoirs will experience different evolutionary processes in different geological periods. This modeling fully considers the pre-depositional paleogeomorphology, the three-dimensional digital model of the ratio of hydrocarbon migration to geological body volume for each sub-layer, the seismic interpretation horizon data, and the structural surface modeling using fault distribution data, establishing a carbonate reservoir body (algal mound) model for different hydrocarbon accumulation periods, providing a reliable three-dimensional geological model of the conductor layer in the depth domain for the simulation and analysis of hydrocarbon migration and accumulation in different periods. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 - Comparison diagram of seismic profiles of different seismic data volumes (taking well8 as an example);

[0032] Figure 2 - Isochronous stratigraphic division diagram of sub-layers under the high-resolution framework;

[0033] Figure 3 - Crossplot analysis diagram of the ratio of hydrocarbon migration to geological body volume in the wellbore and seismic attributes within the sub-layers of the framework;

[0034] Figure 4 - Prediction diagram of the three-dimensional spatial distribution of the ratio of hydrocarbon migration to geological body volume in the depth domain of the target layer section. DETAILED DESCRIPTION OF THE INVENTION

[0035] The following describes the specific implementation manners of the present invention in combination with embodiments:

[0036] It should be noted that the structures, ratios, sizes, etc. shown in this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the implementation conditions of the present invention. Any modification of the structure, change of the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0037] Meanwhile, the terms such as "upper", "lower", "left", "right", "middle", and "one" cited in this specification are only for the convenience of clear narration and are not used to limit the scope of implementation of the present invention. The change or adjustment of their relative relationships, without substantial change in the technical content, should also be regarded as the scope where the present invention can be implemented.

[0038] Example 1:

[0039] The present invention provides a fine three-dimensional modeling method for deep carbonate rock conduits based on the advantages of subdivided small layers of seismic attributes, mainly solving the following two problems: (1) The origin of mound-beach bodies is complex and the heterogeneity is strong. The resolution of conventional seismic data is relatively low, which will cause the seismic reflection characteristics of different conduit types to cross and confuse each other, resulting in inaccurate interpretation results. The purpose of the present invention is to extract post-stack seismic data with higher resolution, and combined with downhole synthetic seismic records, further optimize the data volume with both high resolution and high credibility to fully carry out the prediction of mound-beach conduit parameters. (2) Conventional three-dimensional modeling of conduit parameters is based on the research of the entire target layer, but this is only a general description of the conduit information of this stratum, lacking a fine description of the internal heterogeneity. The purpose of the present invention is to combine the maximum recognition ability of seismic data, establish a high-resolution stratigraphic framework and subdivide small layers, carry out multi-seismic attribute optimization within the target layer section by small layers, and then carry out the characterization of conduit parameters for each small layer to fully describe the strong vertical and horizontal heterogeneity of the mound-beach complex. Its technical solution includes the following steps:

[0040] S1: Optimization of seismic data: Through processing methods such as navigation pyramid processing and wavelet frequency division, decompose and reconstruct the three-dimensional seismic data in multiple scales and multiple directions, and conduct a comparative analysis with the downhole synthetic seismic record to optimize the seismic data that matches well with the downhole synthetic seismic record, so as to improve the recognition and characterization ability of seismic data for deep carbonate rock reservoir conduits and establish a three-dimensional seismic data volume of dominant decomposition and reconstruction that best represents the characteristics of deep carbonate rock reservoir conduits (such as Figure 1 );

[0041] S2: Isochronous stratigraphic division of seismic data for small layers: Fully considering the strong heterogeneity of deep to ultra-deep carbonate rocks and combining with the minimum bin thickness of mound-beach bodies, the isochronous stratigraphic division method is used to conduct fine isochronous stratigraphic subdivision of small layers in the target layer for the selected seismic data. As much as possible, the small layers are subdivided according to the quality of the seismic data to establish a fine high-resolution isochronous stratigraphic framework for each small layer in the target layer (such as Figure 2 );

[0042] S3: Pickup of the reservoir-to-seal ratio for small layers in the downhole target layer: Using downhole synthetic seismograms to conduct fine seismic-geological horizon calibration on the seismic profile beside the well, establishing the corresponding relationship between the interfaces of each small layer in the target layer of the seismic data and the downhole stratigraphic interfaces of each well, conducting reservoir interpretation and reservoir thickness statistics for each small layer in the downhole target layer, and calculating the reservoir-to-seal ratio for each small layer in the downhole (the ratio of the reservoir thickness of the small layer to the thickness of the small layer), providing a basis for predicting the reservoir-to-seal ratio using the seismic data volume;

[0043] S4: Optimization of dominant seismic attributes: Using dominant seismic data to extract various seismic attributes for each small layer, conducting correlation analysis on various seismic attributes, selecting various attributes with poor correlation, and performing multi-attribute fusion. Extract the various seismic attributes and attribute fusion values selected at each well point position in the same small layer, and conduct crossplot analysis of their various attribute values data with the downhole reservoir-to-seal ratio data at each well point. The dominant seismic data volume and dominant seismic attributes with the largest correlation coefficient are selected as the dominant seismic data for predicting the reservoir-to-seal ratio of this small layer. This method is used for each small layer, and finally the optimal seismic data volume and optimal seismic attribute data volume for each small layer predicting the reservoir-to-seal ratio of the target layer in the study area are optimized;

[0044] S5: Converting the reservoir-to-seal ratio using seismic attributes:

[0045] Through attribute optimization, crossplot analysis is conducted on the best seismic attributes and the downhole reservoir-to-seal ratio, establishing a mathematical equation for the best representation of the transport-conducting parameters, clarifying the corresponding conversion relationships for each small layer segment in the target layer (different seismic volumes can be used for each small layer), and converting the dominant seismic attributes of each small layer into the reservoir-to-seal ratio data volume of this small layer (such as Figure 3 );

[0046] S6: Three-dimensional reservoir-to-seal ratio spatial distribution model for each hydrocarbon accumulation period based on the small-layer reservoir-to-seal ratio: Combining the paleogeomorphic depth values corresponding to the reservoir-to-seal ratios of each small layer in each hydrocarbon accumulation period, establishing a fine three-dimensional spatial distribution model of the superimposed reservoir-to-seal ratios of multiple small layers in the target layer of the study area for each hydrocarbon accumulation period, providing a basis for the hydrocarbon transport-conductivity and hydrocarbon accumulation analysis of the target layer in the study area for each hydrocarbon accumulation period (such as Figure 4 ).

[0047] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0048] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0049] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that realizes the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0050] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, so that the instructions executed on the computer or other programmable devices provide steps for realizing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0051] The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the above embodiments. Within the knowledge scope of those of ordinary skill in the art, various changes can be made without departing from the purpose of the present invention.

[0052] Many other changes and modifications can be made without departing from the concept and scope of the present invention. It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A method for fine three-dimensional modeling of deep carbonate rock conduits based on dominant seismic attributes, characterized in that, The method includes: S1: Perform multi-scale and multi-directional decomposition and reconstruction on 3D seismic data, optimize the seismic data that matches well with the downhole synthetic seismic record, and establish a dominant decomposition and reconstruction 3D seismic data volume that best represents the characteristics of the deep carbonate reservoir conductor; S2: Based on the strong heterogeneity of deep to ultra-deep carbonates, combined with the minimum bin thickness of mound-beach bodies, use the isochronous stratigraphic division method to perform isochronous stratigraphic subdivision of the target layers of the optimized seismic data, and establish a fine high-resolution isochronous stratigraphic framework; S3: Use the downhole synthetic record to perform fine seismic-geological horizon calibration on the seismic profile beside the well, establish the corresponding relationship between the interfaces of each small layer of the target layer of the seismic data and the downhole stratigraphic interfaces of each well, conduct reservoir interpretation and reservoir thickness statistics for each small layer of the downhole target layer, and calculate the land-transport ratio of each small layer downhole; S4: Extract the seismic attributes of each small layer, conduct correlation analysis and multi-attribute fusion, screen out the attributes with high correlation, and optimize the best seismic attributes, which can be used to predict the land-transport ratio of each small layer; S5: Conduct cross-plot analysis of the best seismic attributes and the downhole land-transport ratio, establish a mathematical equation that best represents the transport parameters, determine the conversion relationship corresponding to each small layer segment of the target layer, and convert the dominant seismic attributes of each small layer into the land-transport ratio data volume of this small layer; S6: Combine the land-transport ratio of each small layer and the paleogeomorphic depth value of each hydrocarbon accumulation period to establish a fine three-dimensional spatial distribution model of the land-transport ratio of multiple small layers superimposed in each hydrocarbon accumulation period of the target layer in the study area, which is used for oil and gas transportability and hydrocarbon accumulation analysis; The step S1 includes: Perform multi-scale and multi-directional decomposition and reconstruction on 3D seismic data through navigation pyramid processing and wavelet frequency division methods; compare and analyze the processed seismic data with the downhole synthetic seismic record, and optimize the seismic data that matches well with the downhole synthetic seismic record; establish a dominant decomposition and reconstruction 3D seismic data volume that best represents the characteristics of the deep carbonate reservoir conductor.

2. The method for fine three-dimensional modeling of dominant seismic attributes of deep carbonate rock conduits according to claim 1, characterized in that The step S4 includes: Extract various seismic attributes for each small layer using the dominant seismic data, conduct correlation analysis on each seismic attribute, select various attributes with high correlation, perform multi-attribute fusion, extract the various seismic attributes and attribute fusion values selected at each well point position of the same small layer, conduct cross-plot analysis of their various attribute values data with the downhole land-transport ratio data of each well point, and optimize the corresponding seismic attribute with the largest correlation coefficient as the dominant seismic data volume and dominant seismic attribute for predicting the land-transport ratio of this small layer. This method is used for each small layer, and finally, the optimal seismic data volume and optimal seismic attribute data volume of each small layer for predicting the land-transport ratio of the target layer in the study area are optimized.

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