Reservoir prediction method based on variable angle extension AVO method
By using the variable-angle extended AVO method and leveraging pre-stack CRP gather conversion and wellhead parameter convergence, accurate prediction of mid-deep Paleogene reservoirs was achieved, solving the problem of multiple solutions in reservoir identification in existing technologies and improving the accuracy and efficiency of reservoir prediction.
Patent Information
- Application Number
- CN202511474994.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-15
- Publication Date
- 2025-12-05
AI Technical Summary
Existing technologies struggle to effectively distinguish the weak reflection interface between low-velocity mudstone and low-velocity sandstone in mid-to-deep Paleogene reservoir prediction, leading to multiple interpretations in reservoir identification and an inability to accurately characterize reservoir distribution patterns.
The variable-angle extended AVO method is adopted. The pre-stack CRP gather is converted into a pre-stack angle gather and subjected to frequency division flattening and denoising processing. The pre-stack AVO intercept and gradient are extracted, and the pre-stack AVO forward model is performed in combination with well parameters. The coordinate rotation is performed by the intersection and rotation angle of the well PG curves to achieve accurate reservoir prediction.
It improves the accuracy of reservoir prediction, reduces multiple solutions, enhances the stability of gradient properties, and enables direct analysis of the planar distribution pattern of reservoirs based on data volumes, thus reducing the workload of sedimentary body interpretation.
Smart Images
Figure CN121069488A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of oil and gas field exploration, and particularly relates to a reservoir prediction method based on a variable-angle extended AVO method. BACKGROUND
[0002] With the deepening of oil and gas field exploration, the middle-deep layer Paleogene lithologic exploration has become the focus of Bohai oilfield. For the Sha 1 and Sha 2 formations, far-source deep lacustrine facies low-velocity mudstone is developed, and the reservoirs are mainly of the I and II type "dark spot" type. The sandstone and normal-velocity mudstone impedance is close, thin interbeds are developed, and the reservoirs have the characteristics of low density. The III type "bright spot" type target is rare, the top interface is not the sandstone but the low-velocity mudstone for the low-frequency strong-amplitude wave trough reflection, and the top interface is the low-velocity mudstone and normal-velocity mudstone formation interface for the low-frequency strong-amplitude wave peak reflection, which increases the difficulty of Paleogene exploration. It is crucial to clearly understand the seismic reflection characteristics of the reservoir, make good prediction of sand richness, and find the development area of high-quality reservoirs.
[0003] At present, the technologies applied in the prediction of middle-deep layer Paleogene reservoirs mainly include pre-stack attributes and pre-stack inversion. The pre-stack attributes mainly use the near-far path energy difference (FN attribute) to realize "bright spot" imaging of "dark spot" reservoirs, but cannot effectively distinguish the weak reflection interface formed by low-velocity mudstone and low-velocity mudstone, which brings multiple solutions to reservoir identification. The pre-stack inversion mainly includes pre-stack simultaneous inversion, JI-FI inversion (impedance and phase joint inversion technology), EEI inversion (extended elastic impedance inversion technology), and the above technologies have their own advantages, can overcome the problems of poor petrophysical differentiation of Paleogene reservoirs and thin interbeds, and to some extent, represent the reservoir development, but still have multiple solutions, cannot analyze the AVO characteristics of seismic data in detail, and cannot effectively distinguish the strong-amplitude wave peak reflection formation interface and the weak-amplitude wave peak reflection sand-mud interface, which leads to the difficulty in depicting lithologic traps based on the existing inversion results.
[0004] As a direct detection tool for hydrocarbons, the product of intercept and gradient is widely used in bright spot AVO analysis and interpretation, and the sum of intercept and gradient is widely used in II type AVO anomaly, but when the actual geological conditions of the work area change, the AVO type is more complex, and the simple combination of intercept and gradient cannot directly represent the reservoir. The conventional extended AVO technology can obtain the attribute with the best fluid differentiation capability, but this method can be better applied in the binary lithofacies formation of Neogene, and when Paleogene develops low-velocity mudstone, the data body cannot be used for representation, and only the planar attributes can be extracted from the wave peak and wave trough reflection interfaces to perform coordinate rotation to obtain the planar distribution rule of sandstone and mudstone.
[0005] Therefore, the application provides a reservoir prediction method based on a variable-angle extended AVO method. SUMMARY
[0006] The present application aims to solve the problem of providing a reservoir prediction method based on the variable-angle extended AVO method, which describes the reservoir through a single data body, avoids the problem that the extended AVO method can only be used for reservoir prediction on a plane attribute, and reduces the workload of sedimentary body interpretation when the early understanding is unclear.
[0007] To solve the above technical problems, the technical scheme adopted by the present application is as follows: A reservoir prediction method based on a variable-angle extended AVO method, comprising the following steps, S1: converting pre-stack CRP gathers into pre-stack angle gathers using a velocity field, and performing frequency division flattening and denoising processing; S2: extracting pre-stack AVO intercept P and gradient G using the pre-stack angle gathers; S3: reading the P-and S-wave velocities and density values of sandstone, mudstone and low-velocity mudstone from well logging curves of drilled wells, performing pre-stack AVO forward modeling, determining the AVO types of reservoirs and mudstones in the study area, and determining the intercept value P1 of the difference between sandstone and low-velocity mudstone; S4: extracting the intercept and gradient curves at the well points, strictly selecting the intercept threshold value P2 according to the difference in the intercept values of sandstone and mudstone of the drilled wells, and calculating the maximum intercept value of the mudstone; S5: intersecting the seismic PG attribute of the target layer and the PG curve on the well, respectively performing linear fitting within the intercept threshold values less than P1 and greater than P2, and calculating the optimal rotation angle for sandstone and mudstone differentiation; S6: using the variable-angle extended AVO method, performing coordinate rotation according to the corresponding rotation angle for different intercept values, obtaining the AVO attribute data body of the work area, and comprehensively interpreting the reservoir prediction results.
[0008] Further, in the S1, the frequency division flattening and denoising processing on the pre-stack angle gathers comprises the following steps, S11: extracting statistical wavelets of different incidence angles for the pre-stack gathers of the target layer; S12: analyzing the spectrum differences of near, middle and far traces, and extracting the low, middle and high frequency bands in the near, middle and far traces by linear filtering, so that the spectrum frequency bands and the main frequencies of the wavelets in the near, middle and far traces are consistent in different frequency bands; S13: flattening the seismic traces; S14: suppressing noise by Radon denoising to ensure the signal-to-noise ratio of seismic data and enhance the stability of gradient attributes.
[0009] Further, in the S2, the pre-stack AVO intercept and gradient body are obtained by Shuey approximation of the Zoeppritz equation, and when the incidence angle is between 0° and 30°, the Shuey two-term approximation equation is as follows, Wherein, R0 is the reflection coefficient at normal incidence, the amplitude picked up along the time difference correction in-phase axis on the CMP gather is relative to the amplitude of the reference trace, and the amplitude of the reference trace is the amplitude of the trace at the zero offset. The drawing of the formula (1) conforms to a straight line graph, and the slope of the straight line gives the AVO gradient attribute G, and the ordinate at the zero angle gives the AVO intercept attribute P.
[0010] Further, in the S4, the threshold P2 is selected as the minimum value of the maximum intercept of the mudstone.
[0011] Further, in the S5, in order to reduce the singular value, the rotation angle of the middle section between P1 and P2 is gradually changed along the slope.
[0012] Further, in the S6, the variable-angle extended AVO method expression is as follows, Wherein, Wherein, P is the intercept, G is the gradient, alpha is the coordinate rotation angle, and E(alpha) is the seismic data body representing the formation interface information; when the intercept is less than the intercept value P1, E(alpha1) effectively distinguishes the interface between the conventional mudstone and the low-velocity mudstone and the interface between the conventional mudstone and the reservoir; when the intercept is greater than the intercept value P2, E(alpha2) effectively distinguishes the formation interface between the low-velocity mudstone and the high-velocity mudstone and the interface between the low-velocity mudstone and the reservoir; and when the intercept is between P1 and P2, in order to reduce the singular value, the rotation angle is gradually changed along the slope with the increase of the intercept sampling point.
[0013] Further, the application provides a device for running the data processing method.
[0014] Further, the application provides an equipment, which comprises a memory, a processor, and an algorithm stored in the memory and executable on the processor, and the processor realizes the data processing method when the computer program is executed.
[0015] Further, the application provides a computer readable storage medium, which stores a computer algorithm, and the computer algorithm realizes the data processing when the processor is executed.
[0016] The application has the advantages and positive effects that: 1. The application clearly shows the AVO characteristics of reservoir and mudstone by well up parameter pre-stack AVO forward, strictly selects the threshold of intercept according to the difference of sand and mudstone intercept value of drilled well, and realizes "weak mud shows sand" by selecting different angles for coordinate rotation to enlarge the difference of low velocity mudstone, conventional mudstone and sandstone according to different intercept values through the intersection of intercept (P) and gradient (G) attribute and well PG curve, so that the reservoir can be well described by single data body, the problem that the extended AVO method can only be used for reservoir prediction on plane attribute is avoided, the workload of sedimentary body interpretation when the early understanding is unclear is reduced, and the reservoir plane distribution law can be directly analyzed based on the data body.
[0017] 2. The pre-stack angle gather is subjected to frequency division flattening and denoising processing, the optimization effect of the frequency divided gather is obviously better than that of the non-frequency divided gather, and the gradient attribute stability is greatly enhanced.
[0018] 3. The variable angle extended AVO method is used to accurately characterize the reservoir top interface distribution law, the reservoir prediction accuracy is improved, and the multi-solution is reduced. DETAILED DESCRIPTION
[0019] Figure 1 is the overall flowchart of the embodiment of the application.
[0020] Figure 2 is the approximate simple mathematical model diagram of the Paleogene actual stratum of the embodiment of the application.
[0021] Figure 3 is the model forward result diagram of the embodiment of the application.
[0022] Figure 4 is the diagram of the embodiment of the application using intercept threshold and coordinate rotation angle.
[0023] Figure 5 is the reservoir data diagram characterized by the conventional extended AVO method based on model data of the embodiment of the application.
[0024] Figure 6 is the reservoir data diagram characterized by the variable angle extended AVO method based on model data of the embodiment of the application.
[0025] Figure 7 is the original pure wave seismic data diagram of the embodiment of the application.
[0026] Figure 8 is the reservoir prediction result diagram of the embodiment of the application. DETAILED DESCRIPTION
[0027] The technical solutions of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present application.
[0028] The embodiments of the present application will be further described below in conjunction with the drawings: As shown in the drawings, a reservoir prediction method based on a variable-angle extension AVO method comprises the following steps. Figure 1 S1: converting pre-stack CRP gathers into pre-stack angle gathers by using a velocity field, performing frequency division flattening and denoising processing, and ensuring that the pre-stack gathers have a high signal-to-noise ratio. Specifically, the frequency division flattening and denoising processing of the pre-stack angle gathers comprises the following steps,
[0029] S11: extracting statistical wavelets of different incident angles for a target layer section of the pre-stack gathers. S12: analyzing the spectrum differences of near, middle and far traces. Due to the influence of absorption and attenuation on seismic data, the far trace has a low main frequency and a narrow frequency band, and the near trace has a high main frequency and a wide frequency band. The low, middle and high frequency bands in the near, middle and far traces are extracted by linear filtering, so that the spectrum frequency bands and main frequencies of the wavelets in the near, middle and far traces are consistent in different frequency bands.
[0030] S13: flattening the seismic traces.
[0031] S14: suppressing noise by Radon denoising, ensuring that the seismic data have a high signal-to-noise ratio, and enhancing the stability of gradient attributes.
[0032] S2: extracting pre-stack AVO intercept P and gradient G bodies by using the pre-stack angle gathers. Specifically, the pre-stack AVO intercept and gradient bodies are obtained by performing Shuey approximation on the Zoeppritz equation. When the incident angle is between 0° and 30°, the Shuey two-term approximation equation is as follows,
[0033] In the formula, R0 is the reflection coefficient at the vertical incidence, and the amplitude picked up along the moveout corrected phase axis on the CMP gather is plotted against to obtain a straight line graph. The slope of the straight line gives the AVO gradient attribute G, and the ordinate at the zero angle gives the AVO intercept attribute P. S3: reading the P-wave and S-wave velocities and density values of sandstone, mudstone and low-velocity mudstone from the well logging curves of drilled wells, performing pre-stack AVO forward modeling, determining the AVO types of the reservoir and mudstone in the study area, and determining the intercept value P1 of the difference between the sandstone and low-velocity mudstone.
[0034]
[0035] S4: Extract the intercept and gradient curve at the well point, strictly select the intercept threshold P2 according to the difference of sand shale intercept value of the drilled well, calculate the maximum intercept value of the mudstone, and the threshold P2 is selected as the minimum value of the maximum intercept of the mudstone as far as possible.
[0036] S5: Through the intersection of the target layer seismic PG attribute and the PG curve on the well, linear fitting is carried out in the intercept threshold less than P1 and greater than P2 respectively, and the rotation angle with the best sand shale discrimination is calculated. In order to reduce singular values, the rotation angle in the middle section of P1 and P2 gradually changes along the slope.
[0037] S6: Using the variable angle extension AVO method, for different intercept values, the corresponding rotation angle is used for coordinate rotation to obtain the AVO attribute data volume with the best lithology discrimination ability in the work area, and the reservoir prediction results are comprehensively interpreted. Specifically, the expression of the variable angle extension AVO method is as follows, In the formula, P is the intercept, G is the gradient, a is the coordinate rotation angle, and E(a) is the seismic data volume representing the formation interface information; when the intercept is less than the intercept value P1, E(a1) effectively distinguishes the interface between conventional mudstone and low-velocity mudstone and the interface between conventional mudstone and reservoir; when the intercept is greater than the intercept value P2, E(a2) effectively distinguishes the formation interface between low-velocity mudstone and high-velocity mudstone and the interface between low-velocity mudstone and reservoir; when the intercept is between P1 and P2, in order to reduce singular values, the rotation angle gradually changes along the slope as the intercept sampling point increases.
[0038] In order to better verify the feasibility of the above steps, a specific example is described below.
[0039] According to the actual formation and logging analysis, a simple approximate model is designed as shown in Figure 2 The low-velocity mudstone has a thickness of 150m, a longitudinal wave velocity of 3040m / s, a transverse wave velocity of 1680m / s, and a density of 2350kg / m 3 ; the normal velocity mudstone has a thickness of 150m, a longitudinal wave velocity of 3800m / s, a transverse wave velocity of 1900m / s, and a density of 2400kg / m 3 ; the sandstone has a thickness of 150m, a longitudinal wave velocity of 3600m / s, a transverse wave velocity of 2100m / s, and a density of 2270kg / m 3 The three-dimensional wave equation forward is used, the main frequency of 20 Hz is used, the number of shots is 200, the initial shot point is located at (0, 0), the shot interval is 50 m, the trace interval is 25 m, the offset is 1000 m, and the sampling interval is 2 ms. The common offset gathers are extracted from the model forward data, converted into angle gathers, stacked to obtain the post-stack pure wave seismic data as shown in Figure 3 It can be seen that the sandstone top interface is a strong amplitude peak reflection feature, and the constant velocity mudstone top interface is also a strong amplitude peak reflection feature, which is difficult to distinguish.
[0040] The threshold selected by the variable angle extension AVO method and the rotation angle selected by the intersection analysis are shown in Figure 4 Different work areas and seismic data are different, and the parameters are selected according to the technical steps.
[0041] Further use of the theoretical model tests the effectiveness of the reservoir prediction technology based on the variable angle extension AVO method, and the conventional extension AVO reservoir prediction method is shown in Figure 5 The variable angle extension AVO reservoir prediction method is shown in Figure 6 Comparative analysis can see that the strong trough is considered as the top interface of the reservoir in the result, the conventional extension AVO method can highlight the reservoir, but fails to effectively suppress the trough interface of the low-velocity mudstone, resulting in multiple solutions. The variable angle extension AVO method effectively distinguishes mudstone and sandstone by variable angle coordinate rotation, and the top and bottom interfaces of the sandstone are very clear, which well displays the top interface of the reservoir while suppressing the low-velocity mudstone interface, and the effect is obviously better than that of the conventional method.
[0042] The reservoir prediction technology based on the variable angle extension AVO method is applied to the actual seismic data, the original seismic pure wave data is shown in Figure 7 The reservoir prediction result of the variable angle extension AVO method is shown in Figure 8 Through comparison Figure 7 , Figure 8 It can be seen that in the seismic pure wave profile, the oil-bearing reservoir of the Sha 1 member of Well A is a medium-weak amplitude reflection feature, and Well B does not drill the reservoir, which is a strong reflection feature. The reservoir prediction result of the variable angle extension AVO method highlights the reservoir and suppresses the mudstone, amplifies the difference between the pre-stack AVO of the mudstone and the sandstone, suppresses the low-velocity mudstone and the constant velocity mudstone, and the result is consistent with the drilled well, which improves the identification accuracy of the sand body.
[0043] In summary, the present application is based on the optimization of pre-stack seismic data, and intercept gradient data volume with pre-stack AVO characteristic information is used to innovate a reservoir prediction technology based on variable angle extended AVO method. Compared with the prior art, the reservoir prediction accuracy is greatly improved, the workload of the interpreter is reduced, and the efficiency is improved. The present application mainly uses pre-stack AVO forward on well parameters to determine the AVO characteristics of the reservoir and the mudstone, then strictly selects the threshold value of the intercept according to the difference of the intercept value of the drilled sand and mudstone, and the intersection of the intercept (P) and gradient (G) attributes and the well PG curve, different angles are selected for coordinate rotation according to different intercept values to amplify the difference of low velocity mudstone, conventional mudstone and sandstone, and the "weak mud shows sand" is realized. The reservoir prediction result is a powerful evidence for the sand body description of the Paleogene rich sand analysis, and has high application and promotion value for the offshore Paleogene exploration and evaluation.
[0044] The above has carried out the detailed description to one embodiment of the present application, but the content described is only the preferred embodiment of the present application, and cannot be considered to limit the implementation range of the present application. Any equivalent change and improvement made according to the scope of the present application should still belong to the patent coverage range of the present application.
Claims
1. A reservoir prediction method based on variable angle spreading (AVO) method, characterized in that: The method comprises the following steps, S1: converting pre-stack CRP gathers into pre-stack angle gathers by using a velocity field, performing frequency division flattening and de-noising; S2: extracting pre-stack AVO intercept P and gradient G by using the pre-stack angle gathers; S3: reading the P-and S-wave velocities and density values of sand, mud and low-velocity mud from well logging curves of drilled wells, performing pre-stack AVO forward modeling, determining the AVO types of reservoirs and mud in the study area, and determining the intercept value P1 of the difference between sand and low-velocity mud; S4: extracting the intercept and gradient curves at well points, strictly selecting an intercept threshold P2 according to the intercept value difference between sand and mud of drilled wells, and calculating the maximum intercept value of mud; S5: performing linear fitting within the intercept threshold less than P1 and greater than P2, respectively, by intersecting the seismic PG attribute of the target layer and the PG curve on the well, and calculating the optimal rotation angle for sand and mud differentiation; S6: using the variable-angle extended AVO method, performing coordinate rotation according to the corresponding rotation angle for different intercept values, obtaining the AVO attribute data volume of the work area, and comprehensively interpreting the reservoir prediction results.
2. The reservoir prediction method based on variable angle spreading AVO method according to claim 1, characterized in that: In the S1, the frequency division flattening and de-noising of the pre-stack angle gathers comprises the following steps, S11: extracting statistical wavelets of different incidence angles for the target layer of the pre-stack gathers; S12: analyzing the spectrum difference between near, middle and far traces, and extracting the low, medium and high frequency bands in the near, middle and far traces by linear filtering, so that the spectrum frequency bands and dominant frequencies of the near, middle and far trace wavelets are consistent in different frequency bands; S13: flattening the seismic traces; S14: suppressing noise by Radon de-noising, ensuring the signal-to-noise ratio of seismic data, and enhancing the stability of gradient attributes.
3. The reservoir prediction method based on variable angle spreading AVO method according to claim 1 or 2, characterized in that: In the S2, the pre-stack AVO intercept and gradient volume are obtained by Shuey approximation of the Zoeppritz equation, and when the incidence angle is between 0° and 30°, the Shuey two-term approximation equation is as follows, where R0 is the reflection coefficient at normal incidence, and A is the amplitude picked up on the CMP gather along the time-corrected event The plot of P versus G is a straight line, the slope of which gives the AVO gradient attribute G, and the ordinate at zero angle gives the AVO intercept attribute P.
4. The reservoir prediction method based on variable angle spreading AVO method according to claim 1 or 2, characterized in that: In the S4, the threshold P2 selects the minimum value of the maximum intercept of mud.
5. The reservoir prediction method based on variable angle spreading AVO method according to claim 1 or 2, characterized in that: In the S5, in order to reduce singular values, the rotation angle in the middle section of P1 and P2 gradually changes along the slope.
6. The reservoir prediction method based on variable angle spreading AVO method according to claim 1 or 2, characterized in that: In the S6, the expression of the variable-angle extended AVO method is as follows, wherein, In the formula, P is the intercept, G is the gradient, a is the coordinate rotation angle, and E(a) is the seismic data volume representing the information of the formation interface; when the intercept is less than the intercept value P1, E(a1) effectively distinguishes the interfaces between conventional mud and low-velocity mud and between conventional mud and reservoirs; when the intercept is greater than the intercept value P2, E(a2) effectively distinguishes the formation interfaces between low-velocity mud and high-velocity mud and between low-velocity mud and reservoirs; when the intercept is between P1 and P2, in order to reduce singular values, the rotation angle gradually changes along the slope as the intercept sampling point increases.
7. An apparatus, characterized by: The data processing method as claimed in any one of claims 1 to 6 is run.
8. An apparatus comprising a memory, a processor, and an algorithm stored in the memory and executable on the processor, wherein: The processor implements the data processing method as claimed in any one of claims 1 to 6 when executing the computer program.
9. A computer readable storage medium having stored thereon a computer algorithm, the computer algorithm comprising: The computer algorithm implements the data processing as claimed in any one of claims 1 to 6 when executed by the processor.