Marine wide azimuth data deep dessert prediction method, storage medium and equipment

Abstract

The application provides a marine wide-azimuth data deep dessert prediction method, a storage medium and equipment, including the following steps: carrying out nested identification on well logging, identifying a dessert reservoir, and obtaining a well logging elastic threshold value of the dessert reservoir; using marine wide-frequency wide-azimuth seismic data volume to carry out pre-stack inversion, comparing the inversion result with well logging data, and obtaining an elastic parameter volume of the dessert reservoir; and identifying the dessert reservoir according to the elastic parameter threshold value of the dessert reservoir and outputting a result. The application effectively eliminates the interference of mudstone, siltstone and dry layer and the like through the nested identification mode, and the dessert reservoir is identified on the premise, so that the identification accuracy of the dessert reservoir can be improved. The well logging intersection analysis is taken as a benchmark, and the pre-stack inversion of the fine offshore wide-frequency data is taken as a basis, so that the inversion result and well logging error are effectively reduced, and the accuracy of quantitatively explaining the dessert reservoir according to the elastic parameter volume obtained through well analysis is improved.

Description

Technical Field

[0001] This application relates to the field of oil and gas seismic exploration technology, and more specifically, to a method, storage medium, and device for predicting deep sweet spots in ocean wide-azimuth data. Background Technology

[0002] In oil and gas seismic exploration, artificially generated seismic waves are the main means of understanding the properties of underground rock media and provide a basis for oil and gas exploration.

[0003] Deep, tight sandstone reservoirs differ from conventional sandstone reservoirs in their extremely low porosity and permeability. During diagenesis, they exhibit strong compaction and weak dissolution, and are highly heterogeneous due to the influence of sedimentary microfacies and rock grain size. Therefore, the physical properties of tight sandstone reservoirs show relatively subtle variations. Thus, effective reservoirs in tight sandstone are identified using porosity ≥7%, permeability ≥0.2 mD, and water saturation ≤60% as identification criteria. Sweet spot reservoirs developed in tight sandstone have relatively good physical properties and can be considered effective reservoirs. Therefore, identifying sweet spot reservoirs can be used to determine the effective reservoirs.

[0004] In existing technologies, the distribution of sweet spot reservoirs is random, and there is no effective technical process for identifying them. Therefore, designing a method to predict sweet spot reservoirs and identify effective reservoirs has become a necessary research focus. Summary of the Invention

[0005] The first objective of this application is to provide a method for predicting deep sweet spots in ocean wide-azimuth data, which can predict sweet spot reservoirs to identify effective reservoirs.

[0006] The second objective of this application is to provide a computer storage medium storing a computer program that, when executed by a processor, implements the above-described method for predicting deep sweet spots in ocean wide-azimuth data.

[0007] A third objective of this application is to provide a computer device, including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the above-described method for predicting deep sweet spots in ocean wide-azimuth data.

[0008] The first aspect of this application provides a method for predicting the deep sweet spot in ocean wide-azimuth data, including the following steps:

[0009] S1. Perform nested identification on well logging to identify sweet spot reservoirs and obtain the well logging elasticity data threshold value of sweet spot reservoirs;

[0010] S2. Pre-stack inversion is performed using broadband and wide-azimuth seismic data to obtain the inversion elastic parameter volume of sweet spot reservoirs.

[0011] S3. Compare the inversion results with the actual logging data, and control the inversion process based on the comparison results. Finally, determine the inversion reliability based on the error between the two. When the error is within a predetermined range, determine the inversion elastic parameter body.

[0012] S4. Combining the threshold value of the elastic parameters of the sweet spot reservoir and the elastic parameter volume obtained by inversion, quantitative identification of the sweet spot reservoir is performed, and the interpretation results of the sweet spot reservoir are output.

[0013] In one implementation, step S1, the nested identification of well logging, includes:

[0014] S11. Obtain the logging data of the well and configure the sandstone elastic parameter threshold value according to the information of the well; use the logging elastic data to perform intersection, and determine the elastic parameter threshold value corresponding to the actual drilled sandstone on the well according to the intersection result;

[0015] S12. Configure a sweet spot reservoir elastic parameter threshold value based on the well logging information, and use the sweet spot reservoir elastic parameter threshold value to identify sweet spot reservoirs in the sandstone.

[0016] In one embodiment, in step S11, the logging data is a curve characterizing sandstone and mudstone properties, such as the surface mud content or GR curve; the threshold value of the sandstone elastic parameter includes: vp / vs < 1.68.

[0017] In one embodiment, in step S12, the threshold value of the sweet spot reservoir elastic parameter includes: longitudinal wave impedance > 11200 m / s * g / cm. 3 E / Lambda > 3.5.

[0018] In one embodiment, the elastic parameter inversion volume includes at least: P-wave / S-wave velocity ratio, P-wave / S-wave impedance, vp / vs value, and density.

[0019] In one implementation, it further includes:

[0020] S5. Select multiple azimuths of the well logging, repeat step S2, and obtain the inversion elastic parameter volume for each azimuth; correlate multiple inversion elastic parameter volumes to form fracture development information of sweet spot reservoirs.

[0021] In one embodiment, the crack development information includes at least: development orientation and crack density.

[0022] In one implementation, in step S5, at least 6 orientations are selected, and the inversion elasticity parameter volume for each orientation is obtained.

[0023] In one embodiment, the difference between the azimuth angles of two adjacent directions is greater than or equal to 30°.

[0024] In one implementation, before step S1, the logging data is further corrected: density anomalies are corrected based on theoretical and statistical results, and sonic time difference anomalies in abnormal well sections are corrected using theoretical models and the density-sonic time difference relationship statistically obtained from normal well sections.

[0025] A second aspect of this application also provides a computer storage medium storing a computer program that, when executed by a processor, implements the ocean wide-azimuth data deep sweet spot prediction method as provided in the first aspect.

[0026] A third aspect of this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the ocean wide-azimuth data deep sweet spot prediction method as provided in the first aspect.

[0027] Compared with the prior art, the beneficial effects of this application are as follows:

[0028] In the technical solution of this application, the logging curves are first optimized. Then, through detailed petrophysical cross-analysis, sandstone and mudstone are identified first, followed by sweet spot reservoirs. By using nested identification methods, effective threshold values ​​for lithology and sweet spot reservoir identification are found, reducing prediction risks. Since the lithology of mudstone, siltstone, and dry layers overlaps with sweet spot reservoirs within the elastic parameter range, nested identification can effectively eliminate interference from mudstone, siltstone, and dry layers, improving the accuracy of sweet spot reservoir identification. Based on logging cross-analysis and using detailed pre-stack inversion of marine broadband data, the errors in inversion results and logging are effectively reduced, improving the accuracy of quantitative interpretation of elastic parameter volumes based on wellbore analysis. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart illustrating a method for predicting deep sweet spots in ocean wide-azimuth data according to an embodiment of this application;

[0031] Figure 2 for Figure 1 A flowchart of one method for predicting deep sweet spots using wide-azimuth data of the Central Ocean;

[0032] Figure 3 for Figure 1 A VP / VS-P-wave impedance cross-plot for step S1 of the deep sweet spot prediction method for wide-azimuth data in the central ocean;

[0033] Figure 4 for Figure 1 A step S1 of the deep sweet spot prediction method for wide-azimuth data in the central ocean is an E / Lambda-P-wave impedance cross-plot.

[0034] Figure 5 This is a graph comparing the inversion results of multiple well logs with the well log data shown in this embodiment;

[0035] Figure 6 This is a flowchart illustrating an anisotropic inversion according to this embodiment. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0037] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0038] Taking the description of deep reservoir sweet spots in the Yinyue broadband azimuth data of the Xihu Depression as an example, the specific technical solution of the present invention is explained.

[0039] This structure is located in the hydrocarbon generation center of the Santan Deep Depression, west of the Yuquan Structure in the central anticline zone of the Donghai Xihu Depression. It is an early low-amplitude anticline-fault nose structure formed in the northern part of the Santan Deep Depression. Faults were developed in the early and middle stages of the entire structure, but not in the late stage.

[0040] The study area exhibits multiple well-developed reservoir-seal assemblages vertically. The dominant reservoir, the Huagang Formation, is a fluvial delta plain facies, widely distributed throughout the area. Thick sand bodies of 300-500 meters are stably distributed laterally. Due to the burial depths all below 4200 meters, and influenced by strong compaction and weak dissolution during diagenesis, the deep reservoirs are predominantly ultra-low porosity and ultra-low permeability reservoirs. Among them, the H3 gas reservoir exhibits an overall medium-low-ultra-low permeability reservoir pattern, with relatively well-developed "sweet spots" both vertically and horizontally, and a relatively good correlation between porosity and permeability. A nested identification study of sweet spot reservoirs was conducted for the H3 reservoir.

[0041] There are two different types of seismic waves propagating in the formation, with different propagation velocities: P-waves (compression waves) have particle vibrations in the same direction as the wave's propagation; S-waves (shear waves) have particle vibrations perpendicular to the wave's propagation. Cracks and horizontal stress differences can cause anisotropy in formation velocity, meaning the velocity varies with direction.

[0042] First, according to the first aspect of this application, a method for predicting deep sweet spots using ocean wide-azimuth data is specifically provided, see [link to relevant documentation]. Figure 1 This includes the following steps:

[0043] S1. Perform nested identification on well logging to identify sweet spot reservoirs and obtain the well logging elasticity data threshold value for sweet spot reservoirs.

[0044] In one implementation, such as Figure 2 As shown, in step S1, nested identification of well logging includes the following:

[0045] S11. Obtain well logging data and configure sandstone elastic parameter threshold values ​​based on the well logging information; use well logging elastic data for cross-referencing, and determine the elastic parameter threshold values ​​corresponding to the drilled sandstone on the well logging based on the cross-referencing results.

[0046] Specifically, curves characterizing sandstone and mudstone lithology, such as well mud content or GR curves, are selected as well logging data for sandstone identification. Different rock physical elastic curves are used for coordinate intersection to find rock physical elastic parameters that are sensitive to sandstone samples.

[0047] In this embodiment, the intersection is based on the longitudinal wave impedance and vp / vs of the target layer segment, such as... Figure 3 As shown, vp / vs<1.68 is selected as the threshold value for sandstone elastic parameters for sandstone identification.

[0048] It should be noted that the threshold value of the elastic parameter is selected based on information such as different regions, different logging depths, and different geological structures.

[0049] S12. Configure the sweet spot reservoir elastic parameter threshold value based on the well logging information, and use the sweet spot reservoir elastic parameter threshold value to identify sweet spot reservoirs in sandstone.

[0050] Based on the sandstone identified in step S11, intersection was performed using the P-wave impedance and E / lambda of the target layer, selecting layers with a P-wave impedance >11200 m / s*g / cm². 3 E / Lambda > 3.5 is used as a threshold value for the elasticity parameter of the dessert reservoir, such as... Figure 4 As shown, gas-bearing sandstone, i.e. sweet spot reservoirs, are identified.

[0051] In one implementation, prior to step S1, fine correction of the logging data is performed: density anomalies are corrected based on theoretical and statistical results, and sonic transit time anomalies in abnormal well sections are corrected using theoretical models and the density-sonic transit time relationship statistically derived from normal well sections. Considering that density, sonic transit time, and other parameters in logging data are often affected by logging instruments, wellbore collapse, and mud intrusion, anomalies are frequently observed. Therefore, fine correction of these anomalies improves the accuracy of subsequent calculations.

[0052] S2. Pre-stack inversion is performed using broadband and wide-azimuth seismic data to obtain the inversion elastic parameter volume of sweet spot reservoirs.

[0053] Specifically, fine pre-stack inversion is performed using wide-bandwidth and wide-azimuth marine seismic data volumes. The pre-stack inversion process includes at least the following: well-seismic calibration of the angular stack, wavelet extraction, low-frequency model establishment, inversion parameter quality control, and pre-stack inversion.

[0054] S3. Compare the inversion results with the actual logging data, and control the inversion process based on the comparison results. Finally, determine the inversion reliability based on the error between the two. When the error is within the predetermined range, determine the inversion elastic parameter body.

[0055] Figure 5 This is a graph comparing the inversion results of multiple well logs with the well log data. The light-colored lines represent the well log data, and the dark-colored lines represent the inversion results. For example... Figure 5 As shown, P-wave impedance, S-wave impedance, and vp / vs, which have small errors between the inversion results and well logging data, are obtained as inversion elastic parameters for identifying sweet spot reservoirs.

[0056] It should be noted that in this application, the error range between the inversion results and the well logging data should generally be less than or equal to 10%.

[0057] S4. Combining the threshold value of the elastic parameters of the sweet spot reservoir and the elastic parameter volume obtained by inversion, quantitative identification of the sweet spot reservoir is performed, and the interpretation results of the sweet spot reservoir are output.

[0058] It should be noted that in step S4, before outputting the inverted elastic parameter volume, the following content is also included:

[0059] S41. Using vp / vs<1.68 as the standard, sand bodies are identified and characterized on the elastic parameters VP / VS obtained by inversion. The distribution map of sand bodies can be obtained in the vertical and horizontal directions. At the same time, the time thickness of the sand bodies is calculated. The reliability of sand body identification is verified by comparison with the known target well logging interval.

[0060] S42. Calculate the E / lambda body. Based on the vp / vs value obtained in step S3, calculate the E / lambda body using formula (1).

[0061]

[0062] S43. Based on the sand body identification results in S41, the longitudinal wave impedance volume and E / lambda volume are applied to identify those conforming to a longitudinal wave impedance > 11200m / s*g / cm². 3 Data points with E / Lambda > 3.5 were used to identify sweet spot reservoirs both vertically and horizontally. The temporal thickness of the sweet spot reservoirs was also calculated, and the reliability of the sweet spot reservoir identification was verified by comparing it with the interpretation conclusions of known drilling and logging gas layers.

[0063] It should be noted that the wellbore analysis and inversion results were verified against the measured curves for multiple wells to obtain a more accurate body of sensitive elastic parameters for sweet spot reservoirs.

[0064] This application provides a method for predicting deep sweet spots using wide-azimuth marine data. First, well logging curves are optimized. Then, through refined petrophysical cross-analysis, sandstone and mudstone are identified first, followed by sweet spot reservoirs. By employing nested identification, effective threshold values ​​for lithology and sweet spot reservoir identification are found, reducing prediction risk. Since the lithology of mudstone, siltstone, and dry layers overlaps with sweet spot reservoirs within the elastic parameter range, nested identification effectively eliminates interference from mudstone, siltstone, and dry layers, improving the accuracy of sweet spot reservoir identification. Based on well logging cross-analysis and refined pre-stack inversion of wide-bandwidth marine data, the method effectively reduces inversion results and well logging errors, improving the accuracy of quantitative interpretation of elastic parameter volumes based on wellbore analysis.

[0065] When seismic waves propagate through the subsurface medium, various wave fields, such as transmission, reflection, and diffraction, carry information about the geometric and rock properties of the subsurface medium. Various pre-stack gather data can qualitatively understand some changes in the subsurface medium, but this information is not information about changes in subsurface medium parameters. By using pre-stack time and depth migration imaging and pre-stack seismic data inversion imaging techniques, pre-stack data can be transformed into geometric and elastic parameters of the subsurface medium, and information about changes in the subsurface rock medium can be extracted. At the same time, information such as lithology, physical properties, and hydrocarbon-bearing properties obtained from drilling can be used to constrain the inversion imaging and evaluate the reliability of the inversion imaging results. Subsequently, comprehensive sand body and hydrocarbon prediction studies can be carried out from point to surface on the more reliable inversion imaging data volume and post-stack data, providing a basis for hydrocarbon exploration.

[0066] Information on fracture development in sweet spot reservoirs provides a basis for analyzing the formation mechanism of sweet spot / non-sweet spot reservoirs, and offers an effective research method for finding and predicting sweet spot reservoirs in tight sandstone. The research process mainly includes wellbore cross-analysis of sensitive elastic parameters of sweet spot reservoirs, broadband wavelet extraction, broadband pre-stack inversion, correlation interpretation of logging and parameter volumes, anisotropic inversion, and comparison of fracture prediction and identification accuracy.

[0067] In one implementation, it further includes:

[0068] S5. Obtain fracture development information of sweet spot reservoirs through anisotropic inversion.

[0069] Specifically, multiple logging azimuths are selected, and step S2 is repeated to obtain the inversion elastic parameter volume for each azimuth. These multiple inversion elastic parameter volumes are then correlated to form fracture development information for the sweet spot reservoir. Fracture development information includes development azimuth and fracture density, among other things.

[0070] By inverting the anisotropy of wide-azimuth data and mining anisotropic information, the anisotropy information in the wide-azimuth data is extracted. During acquisition, the azimuth information is preserved, recording a series of shot-receiver pairs with different offsets and azimuths, stored within the same element. This allows the anisotropy in the wide-azimuth seismic data to be observed, thus enabling the prediction of fractures and stress fields. Subsequently, the correlation between the planar plots and well logging analysis is compared to provide a basis for obtaining fracture development information from the planar plots.

[0071] Specifically, the detailed process of anisotropic inversion in step S5 is as follows: Figure 6 As shown, it includes the following:

[0072] Basic data such as wide-azimuth seismic, well logging, and stratigraphic data were acquired. Pre-stack seismic gathers from different azimuths and angles were used for azimuth-based inversion to obtain multiple elastic parameter inversion volumes at different azimuth angles, such as P-wave / S-wave velocity ratios, P-wave / S-wave impedance, and density. The variation of amplitude in reflected seismic data with azimuth and incident angle was then investigated. In this anisotropic inversion, the P-wave / S-wave velocity ratio parameter was selected.

[0073] The elastic parameter volumes (longitudinal and transverse wave impedances, density, VP / VS, etc.) obtained from inversion at different azimuth angles are used as input data for anisotropic inversion. The anisotropic inversion parameters are calculated using the Rüger second-order azimuth AVO equation in formula (2), as follows:

[0074] A'=b0+b1 cos[2(ω-φ)]+b2 cos[4(ω-φ)] (2)

[0075] Where: A' is the elastic parameter obtained by anisotropic inversion (VP / VS is selected in this case); b0, b1, and b2 are the model fitting coefficients, respectively; Ф is the orientation of the isotropic surface, which is generally parallel to the crack direction.

[0076] Planar properties obtained through anisotropic inversion (including model fitting coefficients b0, b1, b2 and fracture density), including Biso (isotropic gradient), Bani (anisotropic gradient), and Φiso (azimuth), are used to perform ellipse fitting on these values ​​to obtain fracture development azimuth and fracture density. Fracture density can visually indicate the degree of fracture development in the area. By comparing the correlation between well logging results and these parameters, a basis is provided for analyzing possible factors influencing sweet spot reservoir development.

[0077] It should be noted that at least six azimuth elastic parameter bodies are required, and the difference between the azimuths of two adjacent azimuths must be greater than or equal to 30°. Furthermore, there must be an angular difference of at least 30 degrees between the azimuths.

[0078] Specifically, inversion elastic parameter volumes were obtained for azimuth angles of 8°, 38°, 68°, 98°, 128°, and 158°, respectively.

[0079] By conducting wide-azimuth seismic exploration based on seismic anisotropy, wide-azimuth acquisition can provide better illumination, which is beneficial for imaging under complex structures (such as salt, gas, or basalt); it can achieve more coverage, improve the signal-to-noise ratio, and suppress multiple waves; it can acquire richer azimuth information, making velocity modeling more reliable.

[0080] Secondly, this application also provides a computer storage medium storing a computer program that, when executed by a processor, implements the ocean wide-azimuth data deep sweet spot prediction method as provided in the first aspect.

[0081] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the ocean wide-azimuth data deep sweet spot prediction method as provided in the first aspect.

[0082] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for predicting deep sweet spots using ocean wide-azimuth data, characterized in that, Includes the following steps: S1. Perform nested identification on well logging to identify sweet spot reservoirs and obtain the well logging elasticity data threshold value of sweet spot reservoirs; S2. Pre-stack inversion is performed using broadband and wide-azimuth seismic data to obtain the inversion elastic parameter volume of sweet spot reservoirs. S3. Compare the inversion results with the actual logging data, and control the inversion process based on the comparison results. Finally, determine the inversion reliability based on the error between the two. When the error is within a predetermined range, determine the inversion elastic parameter body. S4. Combine the threshold value of the elastic parameters of the sweet spot reservoir and the elastic parameter volume obtained by inversion to perform quantitative identification of the sweet spot reservoir and output the interpretation results of the sweet spot reservoir. S5. Select multiple azimuths of the well logging, repeat step S2, and obtain the inversion elastic parameter volume for each azimuth. The multiple inverted elastic parameter volumes are correlated to form fracture development information of sweet spot reservoirs; The crack development information includes at least: development azimuth and crack density; at least 6 azimuths are selected, and the inverted elastic parameter volume of each azimuth is obtained; the difference between the azimuth angles of two adjacent azimuths is greater than or equal to 30°.

2. The method for predicting deep sweet spots in ocean wide-azimuth data according to claim 1, characterized in that, In step S1, the nested identification of well logging includes: S11. Obtain the logging data of the well and configure the sandstone elastic parameter threshold value according to the information of the well; use the logging elastic data to perform intersection, and identify the elastic parameter threshold value corresponding to the actual drilled sandstone on the well based on the intersection result; S12. Configure a sweet spot reservoir elastic parameter threshold value based on the well logging information, and use the sweet spot reservoir elastic parameter threshold value to identify sweet spot reservoirs in the sandstone.

3. The method for predicting deep sweet spots in ocean wide-azimuth data according to claim 2, characterized in that, In step S11, the logging data is the surface mud content or GR curve characterizing the sandstone and mudstone lithology; the threshold value of the sandstone elastic parameter includes: vp / vs < 1.

68.

4. The method for predicting deep sweet spots in ocean wide-azimuth data according to claim 3, characterized in that, In step S12, the threshold value of the elastic parameter of the sweet spot reservoir includes: longitudinal wave impedance > 11200 m / s * g / cm 3 E / Lambda > 3.

5.

5. The method for predicting deep sweet spots in ocean wide-azimuth data according to claim 4, characterized in that, The elastic parameter inversion body includes at least: P-wave velocity ratio, P-wave impedance, vp / vs value, and density.

6. The method for predicting deep sweet spots in ocean wide-azimuth data according to claim 1, characterized in that, Before step S1, the well logging data is also corrected: density anomalies are corrected based on theoretical and statistical results, and sonic time difference anomalies in abnormal well sections are corrected by using theoretical models and the density-sonic time difference relationship statistically obtained from normal well sections.

7. A computer storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the ocean wide-azimuth data deep sweet spot prediction method as described in any one of claims 1 to 6.

8. A computer device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the ocean wide-azimuth data deep sweet spot prediction method as described in any one of claims 1 to 6.

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