Method for identifying channel sand bodies based on AVO response characteristics of sand bodies
Through well earthquake calibration, AVO forwarding and global automatic body tracking technology, the problem of difficult to identify river sand bodies after stacking attribute analysis is solved, and high-precision river sand bodies are recognized and reservoir prediction is achieved.
Patent Information
- Application Number
- CN202310381553.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-04-11
- Publication Date
- 2025-07-04
- Estimated Expiration
- 2043-04-11
AI Technical Summary
The existing post-stack attribute analysis methods are difficult to accurately identify low-speed bright spot type, low, medium and high-speed river sand bodies. Especially when the physical properties of the sand bodies are large, the seismic response characteristics of the sand bodies are unclear, and the morphological characteristics on the conventional amplitude plane are hidden, making it difficult to portray river sand bodies with high precision.
The pre-stack response characteristics of river sand bodies are determined through well earthquake calibration, and the AVO response characteristics and types of river sand bodies are determined by AVO forward performance, and the pre-stack CRP channel set processing and advantageous angle superposition are carried out. Combined with the global automatic body tracking to extract the amplitude value of high-density isometric slices, and the plane layout of the sand body is portrayed.
It realizes high-precision portrayal of different types of river sand bodies, can simultaneously evaluate the physical properties and gas-containing properties of the reservoir, improves the accuracy of river channel description, and enhances the ability to analyze river channel differences and identify the distribution of gas-containing river channels.
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Figure CN116609830B_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of oil exploration, and relates to the technology of channel sand body characterization in the field of seismic data interpretation in oil exploration, and particularly relates to a method for identifying channel sand bodies based on the AVO response characteristics of sand bodies. Background Art
[0002] In recent years, a series of major breakthroughs have been made in the exploration and development of global unconventional oil and gas. Tight gas, coalbed methane, heavy oil, oil sands, etc. have become key areas for the exploration and development of global unconventional oil and gas, and tight oil and gas have become a highlight area for the exploration and development of global unconventional oil. Tight reservoirs have characteristics different from conventional reservoirs in terms of genetic type, pore space, pore-permeability characteristics, etc. Traditional geophysical theories and conventional technical methods are difficult to gain a deeper understanding of the reservoir characteristics and seepage mechanisms of rocks. In the research of high-precision reservoir prediction, data such as drilling, logging, seismic, and oil and gas should be fully utilized, emphasizing the combination of seismic and geology, theoretical models and practice, studying the spatial distribution characteristics of reservoirs, establishing corresponding models between geophysical exploration and geology, and on the basis of high-resolution seismic data processing, studying the reservoir prediction methods and technologies for thin interbeds of sandstone and mudstone.
[0003] Reservoir prediction based on seismic data is the data basis for oil and gas exploration and development, and has important guiding significance for improving the success rate of drilling. Channel characterization is the key to subsequent reservoir prediction and target optimization. Previously, channel carving mainly relied on post-stack attributes, and the amplitude and structure information were used to characterize the channel distribution. However, with the in-depth research, it is found that the existing technology for channel sand body characterization using full-stack seismic for attribute analysis has the following problems and disadvantages:
[0004] Post-stack attribute analysis for channel characterization is mainly applicable to low-velocity bright spot type channels. For the situation where low-, medium-, and high-velocity channel sand bodies are developed simultaneously and the physical properties of channel sand bodies in the same period vary greatly, the seismic response characteristics of sand bodies are not clear, the morphological characteristics of sand bodies on the conventional amplitude plane are not prominent, and overall it shows hidden channels, making it difficult to characterize sand bodies mainly based on post-stack amplitude attributes. Summary of the Invention
[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method for identifying channel sand bodies based on the AVO response characteristics of sand bodies.
[0006] The purpose of the present invention is achieved through the following technical solutions: A method for identifying channel sand bodies based on the AVO response characteristics of sand bodies, including:
[0007] Step S100. Perform well-seismic calibration to determine the pre-stack response characteristics of channel sand bodies, and then determine the AVO response characteristics and AVO types of channel sand bodies through AVO forward modeling;
[0008] Step S200. Perform pre-stack CRP gather processing under the AVO response characteristics and AVO type quality control of the channel sand body, so that the actual pre-stack gather AVO characteristics of the channel sand body match the AVO response characteristics of the channel sand body;
[0009] Step S300. Under the guidance of the AVO response characteristics of the channel sand body, perform dominant angle stacking on the CRP gather obtained in Step S200 to obtain a seismic data volume;
[0010] Step S400. Based on the seismic data volume obtained in Step S300, use the amplitude value of the fused attribute volume extracted by high-density isochronous slice extraction based on global automatic body tracking, and characterize the planar distribution of the sand body according to the amplitude value of the fused attribute volume.
[0011] Furthermore, the method of AVO forward modeling is as follows:
[0012] By inputting the P-wave travel time curve, S-wave travel time curve, density curve and the extracted wavelet, applying the Aki-Richards equation, the forward modeling result of the angle gather is obtained.
[0013] Furthermore, the AVO type is the result obtained by classifying AVO anomalies according to the wave impedance relationship between sandstone and overlying shale.
[0014] Furthermore, the method of pre-stack CRP gather processing is as follows: perform Radon transform denoising and Trim static correction processing on the CRP gather to obtain high signal-to-noise ratio and flattened gather data, and then perform subsequent AVO type analysis and in-phase stacking of dominant offset distances.
[0015] Furthermore, the method of performing dominant angle stacking on the CRP gather is as follows:
[0016] Perform equally spaced partial stacking on the CRP angle gather at intervals of 5°. The stacking is to synthesize multiple seismic traces within the 5° interval of the common reflection point angle gather into one trace and place it at the position of the reflection point;
[0017] Based on the AVO type of sand body reflection, select the data volume with the most prominent response characteristics of the channel sand body on the cross-section of the equally spaced scanned partial stacking data volume.
[0018] Furthermore, the method of extracting the high-density isochronous slice is as follows:
[0019] Establish a relative isochronous framework model based on global automatic body tracking;
[0020] Based on the relative isochronous framework model, extract high-density isochronous interpretation horizon slices according to the top and bottom windows of the target layer required to obtain high-density isochronous slices.
[0021] The beneficial effects of the present invention are:
[0022] (1) Due to the differences in physical properties and gas-bearing properties of reservoirs in different river channels, it is difficult to accurately identify these differences from post-stack data. The information on the variation of amplitude with offset in pre-stack data is correlated with the lithology, physical properties, and gas-bearing properties of river channels. Therefore, based on the amplitude differences of different types of river channel sands in different offset ranges, the method of the present invention can effectively depict different types of river channels, and at the same time evaluate the physical properties and gas-bearing properties of different types of river channels, and can take into account river channel sands with different velocities, with higher accuracy in river channel delineation;
[0023] (2) The method of the present invention is helpful for the fine dissection of river channels, and uses different parts of the stacked data volume to depict the river channel differences and analyze and evaluate the reservoir conditions of different river channels;
[0024] (3) The pre-stack data in the method of the present invention is rich in information, which is helpful for analyzing the distribution of gas-bearing river channels. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] Figure 1 is a flowchart of a method for identifying river channel sands in the present invention;
[0026] Figure 2 is a schematic diagram of the pre-stack gather response characteristics of river channel sands;
[0027] Figure 3 is the effect diagram of pre-stack CRP gather processing;
[0028] Figure 4 is a schematic diagram after dominant angle stacking of CRP gathers;
[0029] Figure 5 is a schematic diagram of the root mean square amplitude of the full stacked data volume;
[0030] Figure 6 is the root mean square amplitude of the dominant stacked data volume. DETAILED DESCRIPTION OF THE INVENTION
[0031] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.
[0032] Refer to Figures 1 to 6 , the present invention provides a method for identifying river channel sands based on the AVO response characteristics of sand bodies:
[0033] As Figure 1 shown, the method for identifying river channel sands based on the AVO response characteristics of sand bodies includes steps S100 to S400.
[0034] Step S100. Perform well-seismic calibration to determine the pre-stack response characteristics of the channel sand body, and then determine the AVO response characteristics and AVO types of the channel sand body through AVO forward modeling.
[0035] The well-seismic calibration is a process of obtaining simulated seismic traces by convolving the wavelet with the seismic reflection coefficient. The mathematical form of the convolution model is:
[0036] X(t) = W(t) * R(t) + N(t)
[0037] In the formula, X(t) represents the seismic record; W(t) is the seismic wavelet; R(t) is the formation reflection coefficient; N(t) is the random environmental noise.
[0038] Specifically, a time-depth table is obtained through well-seismic calibration to complete the matching of the well in the depth domain with the seismic data in the time domain. Among them, the amplitude, phase, and frequency characteristics of the seismic reflection corresponding to the channel sand body after calibration are the seismic response characteristics of the channel sand body.
[0039] Forward modeling is the synthesis of well curves, that is, the theoretical type. The pre-stack gather can also be used to calibrate the AVO type of the sand body. For actual data, the two must be consistent through comparison to indicate that the data is usable.
[0040] In one embodiment, the method of AVO forward modeling is: applying the Aki-Richards equation to the input P-wave traveltime curve, S-wave traveltime curve, density curve, and the extracted wavelet to obtain the forward modeling result of the angle gather.
[0041] The expression of the Aki-Richards equation is:
[0042]
[0043] R P (θ) ≈ P + Gsin 2 θ + C(tg 2 θ - sin 2 θ)
[0044] In the formula, P represents the intercept, G represents the gradient, C represents the curvature, Vp represents the P-wave velocity, Vs represents the S-wave velocity, ρ represents the density, θ represents the incident angle, and Rp represents the reflection coefficient.
[0045] The specific AVO types refer to: classifying AVO anomalies into 4 categories based on the wave impedance relationship between sandstone and overlying shale. Specifically, the AVO types include the following categories:
[0046] Type Ⅰ: High-impedance gas-bearing sandstone with a wave impedance value higher than that of the surrounding rock, accompanied by AVO attenuation. At normal incidence, there is a relatively high positive reflection coefficient. As the offset increases, the reflection coefficient becomes smaller, and the reflection coefficient transitions from a positive value to a negative value;
[0047] Class II: Gas-bearing sandstones with wave impedance values very close to those of the surrounding rock formations (less than a preset difference), approaching zero impedance. The reflection coefficient changes from positive to negative as the offset increases and is generally submerged in noise.
[0048] Class III: Low-impedance gas-bearing sandstones with wave impedance values lower than those of the surrounding rock. Along with the enhancement of AVO, negative reflection coefficient, no polarity reversal, and the absolute value of the reflection coefficient increases as the offset increases.
[0049] Class IV: Low-impedance sandstones with wave impedance lower than that of the surrounding rock. Along with the attenuation of AVO, negative reflection coefficient, and the absolute value of the reflection coefficient decreases as the offset increases, making it difficult to identify.
[0050] Step S200. Perform pre-stack CRP gather processing under the quality control of the AVO response characteristics and AVO types of the channel sand body, so that the actual pre-stack gather AVO characteristics of the channel sand body match the AVO response characteristics of the channel sand body.
[0051] In this embodiment, by performing pre-stack CRP gather processing under the quality control of the AVO response characteristics and AVO types of the channel sand body, the data quality of the obtained CRP gather is improved.
[0052] The method of the pre-stack CRP (common reflection point) gather processing is as follows: Perform Radon transform denoising (denoising is used to improve the signal-to-noise ratio) and Trim static correction processing (Trim static correction is used to eliminate residual moveout) on the CRP gather to obtain high signal-to-noise ratio and flattened gather data, and then perform subsequent AVO type analysis and in-phase stacking of the dominant offset.
[0053] The Radon transform is also called the τ - p transform. After the normal moveout correction of the CRP gather, the travel-time curve of the primary reflection wave is flattened, and there is residual moveout in the travel-time curve of the multiple waves, and its curve fitting is approximately a parabola or a hyperbola. The Radon transform transforms the seismic data into the τ - p domain, cuts off the multiple waves, and then inverse-transforms to restore the seismic signal to the x - t domain.
[0054] Trim static correction is a method to correct the unflattened CRP gather. Although its basis rule is energy maximization, the principle is to correlate the input seismic trace with the model trace within a given time window, obtain the time difference between the two, and align them by time shift, so that the amplitude energy of the final output converges to the model trace.
[0055] Step S300. Under the guidance of the AVO response characteristics of the channel sand body, perform dominant angle stacking on the CRP gather obtained in step S200 to obtain a seismic data volume and enhance the response characteristics of the channel sand body on the stacked section.
[0056] The AVO response feature refers to the variation law of vibration with the increase of offset. Both the CRP gather and the prestack gather of forward modeling have this feature. Therefore, calibration and comparison are required to ensure that the AVO response feature of the sand body is consistent with that of the CRP gather. Then, based on these two AVO response features, the dominant offset is determined, and stacking is performed on the dominant offset of the CRP gather.
[0057] The method for performing dominant angle stacking on the CRP gather is as follows: perform equally spaced partial stacking on the CRP angle gather at intervals of 5°. Stacking synthesizes multiple seismic traces within a 5° interval of the common reflection point angle gather into one trace and places it at the position of the reflection point to improve the signal-to-noise ratio of seismic reflections. Based on the AVO type of sand body reflection, select the data volume with the most prominent response characteristics of channel sand bodies from the equally spaced scanned partial stacking data volume section.
[0058] In this embodiment, if the AVO feature shows strong far-offset energy, then stack the far-offset part of the CRP gather.
[0059] Step S400. Based on the seismic data volume obtained in step S300, extract the amplitude value of the fusion attribute volume using high-density isochronal slices based on global automatic volume tracking, and depict the planar distribution of the sand body according to the amplitude value of the fusion attribute volume.
[0060] The global optimization automatic tracking technology is as follows: using an algorithm of the optimization cost function, based on considering comprehensive seismic and geological factors such as seismic reflection characteristics, global consistency of formation deposition, deposition thickness, and formation inheritance, adopting a "global thinking" mode, considering the entire 3D seismic data volume as a whole, and establishing an accurate relative isochronal framework model based on the seismic and geological information contained in the entire 3D data volume.
[0061] The expression of the cost function is:
[0062]
[0063] In the formula, P(i) and P(j) represent the coordinates of the seismic "voxel", V i and V j represent the image pixel feature values of the seismic "voxel", N′ represents the total number of "voxels" in the seismic data, represents the size of the seismic "voxel".
[0064] The high-density isochronal formation slice refers to: a series of high-density isochronal interpretation horizon slices extracted according to the top and bottom windows of the target layer on the result obtained by global optimization tracking.
[0065] The high-density interpretation horizon slice means that each wave peak, wave trough, and zero-phase point in the vertical direction is interpreted as a horizon, and the number of horizons is large.
[0066] The isochronous interpreted horizon slice means that each interpreted horizon is closed and traced throughout the region according to seismic reflections, and a single interpreted horizon slice represents a slice of a certain sedimentary period.
[0067] The method of this embodiment will be described below in combination with a case. The area to be predicted is the channel sand body of the first member of the Jurassic Shaximiao Formation in the Sichuan Basin. The specific process is as follows:
[0068] First, determine the reflection characteristics of the "peak-bottom valley" of the target layer channel sand body through well-seismic calibration, and then determine the pre-stack gather response characteristics of the channel sand body through AVO forward modeling, such as Figure 2 As shown, the sand body has the "peak-bottom valley" response characteristics, and its AVO forward modeling result is the characteristic of weak amplitude at near offset and weak amplitude at far offset. Combining with the AVO plate, the target layer channel sand body in the prediction area is a type I AVO response characteristic, a high-impedance gas-bearing sandstone with a wave impedance value higher than that of the surrounding rock, accompanied by AVO attenuation. There is a relatively high normal reflection coefficient at normal incidence, and the reflection coefficient becomes smaller as the offset increases. Specifically, the AVO forward modeling uses the longitudinal wave travel time curve, transverse wave travel time curve, and density curve as inputs and calculates using the Aki-Richards equation.
[0069] Well-seismic joint AVO feature matching, under the quality control of the AVO forward modeling features of the sand body, optimize the original pre-stack CRP gather, so that the AVO features of the actual pre-stack gather of the channel sand body match the AVO features of the sand body forward modeling, such as Figure 3 As shown in the corrected result of the original gather, after Radon transform and Trim static correction, the quality of the gather is optimized, the signal-to-noise ratio quality of the gather is improved, the far-offset gather is flattened, the AVO features of the actual gather of the sand body match the forward modeling result better, and the type I AVO features of the channel sand body on the original gather are enhanced.
[0070] Specifically, the principle of Radon transform is to transform the seismic data into the τ-p domain, cut off the multiple waves, and then inverse transform to restore the seismic signal to the x-t domain, thereby eliminating the noise influence of the far-offset multiple waves. Trim static correction is to correlate the input seismic trace with the model trace within a given time window, obtain the time difference between the two and align them by time shift, so that the amplitude energy of the final output converges to the model trace, thereby achieving the correction of the unflattened CRP gather.
[0071] For the optimization of the seismic stacking data of the channel sand body, under the guidance of the AVO response characteristics of the sand body, perform dominant angle stacking on the CRP gather to enhance the response characteristics of the channel sand body on the stacked section, such as Figure 4 As shown, the bright spot response characteristics of the "peak-bottom valley" of the sand body on some stacked sections are more prominent than those on the full stacked section, and it is easier to identify the channel sand body.
[0072] Specifically, the method for stacking and optimizing CRP gathers from advantageous angles is as follows: multiple seismic traces within the 5° incident angle range are synthesized into one trace and placed at the position of the reflection point to achieve equidistant stacking. The optimization criterion is the prominence degree of the response characteristics of channel sandbodies on some stacked sections.
[0073] For planar identification of channel sandbodies, the global automatic volume tracking technology is first used to track the entire target layer. On this basis, high-density isochronal slices are obtained to extract seismic attributes to depict the distribution of channels. Figure 5 and Figure 6 (This is a comparison of the root-mean-square amplitudes of the advantageous stacked data volume and the fully stacked data volume extracted from an isochronal slice of a certain section of the first member of the Shaximiao Formation. On the advantageous stacked data volume, the clarity of the abnormal reflections of the channels shown by the root-mean-square seismic attributes on the isochronal slice is higher, the lateral continuity of the channels is enhanced, and the channel sandbodies are more easily identified and split.
[0074] The above are only the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications, and environments, and can be changed within the scope of the concept described herein through the above teachings or the techniques or knowledge in related fields. Any changes and modifications made by those skilled in the art without departing from the spirit and scope of the present invention shall fall within the protection scope of the appended claims of the present invention.
Claims
1. A method for identifying channel sand bodies based on the AVO response characteristics of sand bodies, characterized in that, include: Step S100. Perform well-seismic calibration to determine the pre-stack response characteristics of the channel sand body, and then determine the AVO response characteristics and AVO type of the channel sand body through AVO forward modeling; Step S200. Perform pre-stack CRP gather processing under the AVO response characteristics and AVO type quality control of the channel sand body, so that the actual pre-stack gather AVO characteristics of the channel sand body match the AVO response characteristics of the channel sand body; Step S300. Under the guidance of the AVO response characteristics of the channel sand body, the CRP gathers obtained in step S200 are stacked at a dominant angle to obtain a seismic data volume; Step S400. Based on the seismic data volume obtained in step S300, high-density isochronous slices based on global automatic volume tracking are used to extract the amplitude value of the fused attribute volume, and the plane distribution of the sand body is described according to the amplitude value of the fused attribute volume; The AVO forward modeling method is: By inputting the P-wave time difference curve, S-wave time difference curve, density curve and extracted wavelet, the Aki-Richards equation is applied to obtain the forward modeling result of the angle gather. The AVO type is the result of classifying AVO anomalies based on the wave impedance relationship between sandstone and overlying shale; The method for processing the pre-stack CRP gathers is as follows: performing Radon transform denoising and Trim static correction processing on the CRP gathers to obtain gather data with a high noise-to-new ratio and flattened, and then performing subsequent AVO type analysis and in-phase stacking of dominant offsets; The method of superimposing the CRP gathers at the dominant angle is: The CRP angle gathers are partially stacked at 5° intervals. The stacking is to combine multiple seismic traces within the 5° interval of the common reflection point angle gathers into one and place it at the reflection point. According to the AVO type of sand body reflection, the data volume with the most prominent channel sand body response characteristics is selected from the partially stacked data volume sections scanned at equal intervals; The extraction method of the high-density isochronous slices is as follows: Establishing a relative isochronous frame model based on global automatic body tracking; Based on the relative isochronous framework model, high-density isochronous interpretation horizon slices are extracted according to the top and bottom windows of the required target layer to obtain high-density isochronous slices; The well seismic calibration is a process of obtaining a simulated seismic trace by convolution of wavelet and seismic reflection coefficient. The mathematical form of the seismic convolution model is: X(t)=W(t)*R(t)+N(t); where X(t) represents the seismic record; W(t) is the seismic wavelet; R(t) is the formation reflection coefficient; and N(t) is the random environmental noise. The expression of the Aki-Richards equation is: ， Wherein, P represents the intercept, G represents the gradient, and C represents the curvature, V P represents the longitudinal wave velocity, V S represents the shear wave velocity, ρ represents Table density, θ represents the incident angle, R P represents the reflection coefficient.