Reservoir identification and description method based on relative velocity correlation analysis of seismic inversion

Abstract

The present invention provides a reservoir identification and description method based on relative velocity correlation analysis seismic inversion, comprising: step 1, using well logging data to complete the correct calibration of wellside seismic traces; step 2, analyzing the correlation between the velocity of the area and actual geological data such as well logging; step 3, performing relative velocity correlation homing processing at the same depth point, completing relative velocity normalization analysis, and obtaining a relative velocity geological model of the study area; step 4, using the geological model constructed by relative velocity analysis established in step 3 as a constraint, performing relative velocity wave impedance inversion processing on the study area; step 5, using step 3 to construct relative discrimination data for lithology and reservoir to discriminate the inversion data, and completing automatic identification, tracking, and description of lithology and reservoir. The reservoir identification and description method based on relative velocity correlation analysis seismic inversion greatly improves lithology identification, reservoir prediction, and description, and can greatly improve the drilling success rate and exploration efficiency.

Description

Technical Field

[0001] The invention relates to the technical field of geophysical exploration, and in particular to a reservoir identification and description method based on relative velocity correlation analysis and seismic inversion. Background Art

[0002] The conventional seismic velocity processing method, wave impedance inversion, is generally the product of underground lithology velocity and density, which can be considered as absolute wave impedance inversion. Due to the influence of underground compaction, the lithology velocity and density gradually increase with depth, resulting in the superposition of wave impedance values ​​of different lithologies, making it difficult to identify lithology and reservoirs.

[0003] In the Chinese patent application with application number: CN202010799977.X, a reservoir oil and gas identification method and device are involved, and the method includes: constructing a sandstone reservoir oil and gas identification factor calculation formula for the target drilling according to the logging curve data of the target layer segment in the target drilling; based on the logging interpretation results data of the target layer segment in the target drilling and the sandstone reservoir oil and gas identification factor calculation formula, obtaining the sandstone reservoir oil and gas identification factor curve corresponding to the target layer segment in the target drilling; using the sandstone reservoir oil and gas identification factor curve to identify the oil and gas distribution range of the target layer segment in the target drilling.

[0004] In the Chinese patent application with application number: CN201910067743.3, a sandstone reservoir identification method is involved, which includes the following steps: step S1, extracting a sensitive identification factor, wherein the sensitive identification factor is the ratio of the product of the longitudinal wave velocity and the shear wave velocity to the density; step S2, using the sensitive identification factor to identify the sandstone reservoir.

[0005] In the Chinese patent application with application number: CN201310439924.7, a semi-quantitative identification method of reservoir gas content based on frequency-divided AVO inversion is involved, including: (1) using three-dimensional seismic data to calculate the P-wave velocity attenuation gradient and the S-wave velocity attenuation gradient respectively; drawing the relationship diagram between the P-wave velocity attenuation gradient and the production well capacity and the relationship diagram between the S-wave velocity attenuation gradient and the production well capacity respectively, performing linear fitting, and calculating the correlation coefficient R2 between the two attenuation gradients and the capacity; the initial value of the weight value x between the P-wave velocity attenuation gradient and the S-wave velocity attenuation gradient is selected as 1, that is, x0=1; (2) extracting the P-wave velocity attenuation gradient and the S-wave velocity attenuation gradient along each well, and calculating the weighted sum gi,0 of the inversion value of the production layer section of each well, where i represents the well number; (3) performing least squares linear fitting with the well capacity Y as the ordinate and G as the abscissa.

[0006] The above existing technologies are greatly different from the present invention and fail to solve the technical problem we want to solve. For this reason, we have invented a new reservoir identification and description method based on relative velocity correlation analysis and seismic inversion. Summary of the invention

[0007] The purpose of the present invention is to provide a method based on relative velocity correlation analysis, which selects standard values ​​of the same phase type and different lithology velocities of multiple target layers according to the established correlation formula to perform relative velocity correlation homing processing at the same depth point, and completes the normalized correction correlation curves and profiles of lithology, target layer relative velocity, acoustic wave, porosity, density, etc. by logging. The geological model constructed by relative velocity is used as a constraint to perform relative wave impedance inversion on the study area, and the relative discrimination data of lithology and reservoir construction is used to discriminate the inversion data, and the lithology and reservoir are automatically identified, tracked, and described. The establishment of this method greatly improves lithology identification, reservoir prediction, and description, and can significantly improve the success rate of drilling and improve exploration efficiency.

[0008] This technical method breaks through the limitation that conventional inversion can only invert a single target layer segment and time domain wave impedance, eliminates the influence of different velocities in the time domain and depth domain of multiple target layers, and provides a new means of directly using the relative wave impedance inversion data obtained by inversion based on the relative velocity model to predict lithology or reservoirs in complex situations. This research result is unique and innovative, and can fill the research gap at home and abroad. To achieve the above purpose, the present invention adopts the following technical solutions:

[0009] The object of the present invention can be achieved by the following technical measures: a reservoir identification description method based on relative velocity correlation analysis of seismic inversion, the reservoir identification description method based on relative velocity correlation analysis of seismic inversion comprises:

[0010] Step 1: Use logging data to correctly calibrate the seismic traces near the well;

[0011] Step 2, analyzing the correlation between the velocity and well logging data of the study area obtained by the division in step 1;

[0012] Step 3, perform relative velocity correlation homing processing at the same depth point to complete relative velocity normalization analysis;

[0013] Step 4, perform relative velocity wave impedance inversion processing on the study area;

[0014] Step 5, using the relative discrimination data of lithology and reservoir constructed in step 3 to discriminate the inversion data, and complete the automatic identification, tracking and description of lithology and reservoir.

[0015] The purpose of the present invention can also be achieved by the following technical measures:

[0016] In step 1, the extracted seismic wavelet needs to match the characteristics of the seismic data, and the well logging data is used to complete the correct calibration of the wellside seismic traces to clarify the correspondence between the lithology, reservoir, target layer segment and the seismic time domain and depth domain.

[0017] In step 2, the lithology and target layer sections in step 1 are divided in detail to analyze the correlation between the velocity and actual geological data such as logging in this area, clarify the negative correlation between velocity and acoustic wave and porosity, and the positive correlation with density, and establish the velocity, porosity and depth change curves, correlation equations and charts of the main lithologies in this area, including sandstone and mudstone.

[0018] In step 3, standard values ​​of the same phase type and different lithology velocities of multiple target layers are selected to perform relative velocity correlation processing at the same depth point to complete the relative velocity normalization analysis.

[0019] In step 3, the relative velocity normalization analysis of the study area is completed, and the calibration well velocity is converted into relative velocity; standard values ​​of the same phase type and different lithology velocities of multiple target layers are selected to perform relative velocity correlation processing at the same depth, and at the same time, the relevant relative acoustic wave, relative porosity, and relative density normalization processing are completed for the calibration well. Based on this, a geological model is constructed, and synthetic seismic records are remade to match the seismic traces.

[0020] In step 4, relative velocity wave impedance inversion is performed on the study area based on the relative velocity analysis and construction of the geological model established in step 3 as a constraint.

[0021] In step 4, based on the relative velocity analysis and geological model established in step 3, relative velocity wave impedance inversion is performed on the study area, and the relative velocities of the main lithologies in the target layer are normalized and inverted for multiple times to obtain the best inversion effect.

[0022] In step 5, the relative wave impedance inversion is performed based on the relative velocity analysis of the lithology established in step 3, and the relative discriminant data of the lithology and reservoir in the area are finally used to discriminate the inversion data, thus completing the automatic identification, tracking and description of the lithology and reservoir.

[0023] The reservoir identification and description method based on seismic inversion of relative velocity correlation analysis in the present invention constructs a geological model based on relative velocity correlation analysis, and performs relative wave impedance inversion technology on large well sections and multi-target layers. It breaks through the limitation that conventional inversion can only invert wave impedance of a single target layer section and time domain, eliminates the influence of different velocities in the time domain and depth domain of multi-target layers, and provides a new means of directly using relative wave impedance data obtained by inversion based on relative velocity model to predict lithology or reservoirs in complex situations.

[0024] The reservoir identification and description method based on relative velocity correlation analysis seismic inversion in the present invention selects the same-phase type of multiple target layers and standard values ​​of different lithology velocities according to the established correlation formula to perform relative velocity correlation homing processing at the same depth point, and completes the normalized correction correlation curves and profiles of lithology, target layer relative velocity, sound wave, porosity, density, etc. by logging. The geological model constructed using relative velocity is used as a constraint to perform relative wave impedance inversion on the study area, and the relative discrimination data of lithology and reservoir construction is used to discriminate the inversion data, and the lithology and reservoir are automatically identified, tracked, and described. The establishment of this method greatly improves lithology identification, reservoir prediction, and description, and can significantly improve the success rate of drilling and improve exploration efficiency.

[0025] This technical method breaks through the limitation that conventional inversion can only invert the wave impedance of a single target layer and time domain, eliminates the influence of different velocities in the time domain and depth domain of multiple target layers, and provides a new means of directly using the relative wave impedance inversion data obtained by inversion based on the relative velocity model to predict lithology or reservoirs in complex situations. This research result is unique and innovative, and can fill the gap in research at home and abroad. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flowchart of a specific embodiment of the reservoir identification description method based on relative velocity correlation analysis seismic inversion of the present invention;

[0027] Figure 2 It is a schematic diagram of the full-well velocity and sandstone velocity analysis in a specific embodiment of the present invention;

[0028] Figure 3 It is a schematic diagram of critical porosity and depth analysis of sandstone in a target layer in a specific embodiment of the present invention;

[0029] Figure 4 A graph showing a normalized curve of relative velocity of a single well in a specific embodiment of the present invention;

[0030] Figure 5 It is a schematic diagram of synthesizing seismic records after normalization of relative velocity of a single well in a specific embodiment of the present invention;

[0031] Figure 6 It is a time-depth relationship diagram of the study area in a specific embodiment of the present invention;

[0032] Figure 7 A diagram showing the relationship between velocity and depth in a study area in a specific embodiment of the present invention;

[0033] Figure 8 It is a theoretical normalized chart of relative velocity and depth in the study area in a specific embodiment of the present invention;

[0034] Fig. 9It is a relative velocity impedance profile of the study area in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0035] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0036] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations and / or combinations thereof.

[0037] The reservoir identification and description method based on relative velocity correlation analysis seismic inversion of the present invention comprises the following steps:

[0038] In step 1, the well logging data is used to correctly calibrate the seismic traces near the well.

[0039] In step 2, the velocity of the study area obtained in step 1 is subjected to correlation analysis with actual geological data such as well logging.

[0040] In step 3, standard values ​​of the same phase type and different lithology velocities of multiple target layers are selected to perform relative velocity correlation processing at the same depth point to complete the normalization analysis of relative velocity.

[0041] In step 4, the relative velocity analysis established in step 3 is used as a constraint to construct a geological model, and relative wave impedance inversion and other processing are performed on the study area ( Fig. 9 ).

[0042] In step 5, the relative discrimination data of lithology and reservoir constructed in step 3 are used to discriminate the inversion data, and the lithology and reservoir are automatically identified, tracked and described.

[0043] Compared with the prior art, the present invention has the following advantages:

[0044] This technical method breaks through the limitation that conventional inversion can only invert the wave impedance of a single target layer segment and time domain, eliminates the influence of different velocities in the time domain and depth domain of multiple target layers, and provides a new means of directly using the relative wave impedance inversion data obtained by inversion based on the relative velocity model to predict lithology or reservoirs in complex situations. This research result is unique and innovative, and can fill the gap in domestic and foreign research. This method is applicable to the relative wave impedance inversion of seismic data with well-constrained relative velocity analysis, which has far-reaching significance for guiding the exploration and development of the study area.

[0045] In a specific embodiment 1 of the present invention, Figure 1 As shown, Figure 1 A flow chart describing the technique of reservoir identification based on relative velocity correlation analysis seismic inversion of the present invention.

[0046] In step 101, it is required to extract seismic wavelets to match the characteristics of seismic data, use well logging data to correctly calibrate the wellside seismic traces, and clarify the corresponding relationship between lithology, reservoir, target layer segment and seismic time domain and depth domain.

[0047] In step 102, the velocity in the study area is analyzed for correlation with actual geological data such as well logging, and the velocity is negatively correlated with acoustic waves and porosity, and positively correlated with density, and velocity, porosity and depth variation curves and charts of the main lithologies in the area, such as sandstone and mudstone, are established. Figure 2 , Figure 3 ).

[0048] In step 103, the relative velocity normalization analysis of the study area is completed, and the calibration well velocity is converted into a relative velocity. Specifically, the standard values ​​of the same phase type and different lithology velocities in multiple target layers below 1400 meters are selected to perform relative velocity correlation processing at the same depth, and the calibration well is normalized by the relative acoustic wave, relative porosity, and relative density. Figure 4 ), based on which the geological model is constructed, and synthetic seismic records are re-made to match the seismic traces ( Figure 5 ).

[0049] In step 104, using the geological model constructed by relative velocity analysis established in step 103 as a constraint, relative wave impedance inversion is performed on the study area, and the relative velocities of the main lithologies in the target layer below 1400 meters are normalized and inverted multiple times to obtain the best inversion effect.

[0050] In step 105, the relative discrimination data of lithology and reservoir constructed in step 103 is used to discriminate the inversion data, and the lithology and reservoir are automatically identified, tracked and described.

[0051] In the specific embodiment 2 of the present invention, the process requirements are consistent with those of the embodiment 1, but the parameters and methods used in the specific steps are different.

[0052] In step 101, the VSP and well logging data of the study area are used to correctly calibrate the seismic traces near the well, and the corresponding relationship between the time domain and the depth domain of the seismic in the study area is clarified ( Figure 6 ).

[0053] In step 102, the correlation between the velocity in the study area and actual geological data such as VSP and well logging is analyzed to establish the main average velocity, porosity and depth change curves and charts in the study area ( Figure 7 ).

[0054] In step 103, the relative velocity normalization analysis of the study area is completed. Specifically, the standard values ​​of the same phase type and different lithology velocities at depths of multiple target layers of 2000-3000, 3000-3700, and below 3700 meters are selected to perform relative velocity correlation processing at the same depth point, and the relative velocity and depth theoretical plate of the study area is obtained ( Figure 8 ); then use the formula to convert all VSP and calibration well velocities in the study area into relative velocities. At the same time, normalize the relative acoustic waves, relative porosity, and relative density of the calibration wells, and use this as a basis to build a geological model and re-make synthetic seismic records to match the seismic traces.

[0055] In step 104, using the geological model constructed by the relative velocity analysis established in step 103 as a constraint, multi-well constrained relative wave impedance inversion is performed on the study area, and the relative velocities of the main lithologies in the target layer below 2000 meters are normalized and inverted multiple times to obtain the best inversion effect.

[0056] In step 105, the relative discrimination data of lithology and reservoir constructed in step 103 is used to discriminate the inversion data, and the lithology and reservoir are automatically identified, tracked and described.

[0057] The above embodiments are preferred implementations of the present invention, but the implementations of the present invention are not limited by the above embodiments and can be applied to different research areas with well constraints. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principle of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

[0058] In summary, the present invention implements relative wave impedance inversion technology for large well sections and multi-target layers by constructing a geological model based on relative velocity correlation analysis. This method breaks through the limitation that conventional inversion can only perform wave impedance inversion on a single target layer section and time domain, eliminates the influence of different velocities in the time domain and depth domain of multiple target layers, and provides a new means of directly using the relative wave impedance inversion data obtained by inversion based on the relative velocity model to predict lithology or reservoirs in complex situations. This research result is unique and innovative, and can fill the research gap at home and abroad. This method is applicable to relative wave impedance inversion of seismic data with well-constrained relative velocity analysis, and has far-reaching significance for guiding the exploration and development of the study area.

[0059] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0060] Except for the technical features described in the specification, all other technical features are known technologies to those skilled in the art.

Claims

1. A reservoir identification and description method based on relative velocity correlation analysis of seismic inversion, characterized in that: The method includes: Step 1: Use logging data to correctly calibrate the seismic traces near the well; Step 2: Conduct correlation velocity analysis on the lithology of the study area, velocity of the target layer and well logging data; Step 3, perform relative velocity correlation processing of the same-phase lithology at the same depth point to complete relative velocity normalization analysis; Step 4, perform relative velocity wave impedance inversion processing on the study area; Step 5, using the relative discrimination data constructed after normalizing the lithology and reservoir velocity in step 3 to discriminate the inversion data, and complete the automatic identification, tracking and description of the lithology and reservoir; In step 1, the extracted seismic wavelet must be matched with the characteristics of the seismic data, and the well logging data must be used to correctly calibrate the wellside seismic traces to clarify the corresponding relationship between lithology, reservoir, target layer, and seismic time domain and depth domain; In step 2, the lithology and target layer sections in step 1 are divided in detail, and the correlation between the velocity and the actual geological data of well logging in this area is analyzed to clarify the negative correlation between velocity and acoustic wave and porosity and the positive correlation with density. The velocity, porosity and depth variation curves, correlation equations and charts of the main lithology in this area, including sandstone and mudstone, are established. In step 3, the relative velocity normalization analysis of the study area is completed, and the calibration well velocity is converted into relative velocity; the standard values ​​of the same phase type and different lithology velocities of multiple target layers are selected to perform relative velocity correlation processing at the same depth point, and the calibration well is normalized by the relevant relative acoustic wave, relative porosity, and relative density. Based on this, the geological model is constructed and the synthetic seismic record is remade to match the seismic trace; In step 4, the relative velocity analysis and construction of the geological model established in step 3 is used as a constraint to perform relative velocity wave impedance inversion processing on the study area, and the relative velocity of the main lithology of the target layer is normalized and inverted for multiple times to obtain the best inversion effect; In step 5, the relative wave impedance inversion is performed based on the relative velocity analysis of the lithology established in step 3, and the relative discriminant data of the lithology and reservoir in the area are finally used to discriminate the inversion data, thus completing the automatic identification, tracking and description of the lithology and reservoir.

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