A method for enhancing seismic reflection characteristics of a faulted-solution reservoir

Abstract

The application provides a method for enhancing seismic reflection characteristics of fault-dissolved reservoirs, which comprises the following steps: performing smoothing filtering on an initial three-dimensional seismic data volume under the constraint of a stratum three-dimensional model to obtain a smoothed three-dimensional seismic data volume; determining the difference between the initial three-dimensional seismic data volume and the smoothed three-dimensional seismic data volume as an abnormal reflection data volume; performing difference enhancement processing and screening processing on the abnormal reflection data volume to obtain a fault-dissolved reservoir abnormal reflection enhancement data volume; and fusing the fault-dissolved reservoir abnormal reflection enhancement data volume and the smoothed three-dimensional seismic data volume to obtain a final data volume highlighting the abnormal reflection characteristics of the fault-dissolved reservoirs. The application also provides a device for enhancing seismic reflection characteristics of fault-dissolved reservoirs.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of oil and gas exploration and development technology, in particular to the field of carbonate reservoir prediction, and in particular to a method for enhancing post-stack seismic reflection characteristics of fault-dissolution reservoirs. BACKGROUND

[0002] Seismic exploration technology has played a huge role in the fields of infrastructure, mineral resources, energy exploration, and has an indispensable position in the exploration of deep oil and gas resources. Seismic data is the basic result of seismic exploration, and is the basic data for fault interpretation, reservoir prediction, trap description, reserve calculation, and three-dimensional space carving. Therefore, the quality of seismic data is directly related to the accuracy of seismic interpretation, and plays a very important role.

[0003] The reservoir body in the Shunbei area of the Tarim Basin is a fracture-cave system controlled by strike-slip fault zones, i.e., a fault-dissolution reservoir. Carbonate rocks form a fault fracture zone under the action of geological structure, and further dissolution forms a fault-dissolution reservoir. The types of reservoirs are cave type, pore type, pore-fracture type, and fracture type, which are distributed along the fault zone in space. The fault serves as both a channel for oil and gas migration and a space for oil and gas accumulation. The fault-dissolution reservoir has strong vertical and horizontal heterogeneity, with segmentation in the horizontal direction and layering in the vertical direction, and develops in the surface and inner layer of the carbonate rock around the fault zone. Based on the seismic profile of the drilled well and the previous model forward result and understanding, on the seismic profile: the cave type reservoir shows strong "bead" reflection characteristics, the pore type reservoir shows "bead" or chaotic strong reflection, the pore-fracture type and fracture type reservoirs show weak "bead" or chaotic weak reflection, and the fault zone shows faulted phase axis or weak "linear" reflection from bottom to top. Affected by the strong reflection of the carbonate rock top surface or the strong reflection of the inner layer lithology interface, the reflection characteristics of the fault-dissolution reservoir are weakened or even covered. It is difficult to identify the weak reflection and relatively weak reflection characteristics of the reservoir on the seismic profile; therefore, it is an urgent need at the present stage to weaken the reflection characteristics of the surrounding rock and enhance the reflection characteristics of the reservoir in the prediction of carbonate reservoirs; and it plays a very important role in predicting the scale of the reservoir.

[0004] The present inventors know that a lot of research has been done on methods for enhancing seismic abnormal reflection characteristics, which can be roughly divided into two stages: the seismic data processing stage, including filtering, deconvolution, seismic migration imaging, etc.; and the seismic data interpretation stage, including structure-guided filtering, signal separation, etc.

[0005] 1. Seismic data processing stage

[0006] (1) Filtering method

[0007] The filtering method is mainly applied in the seismic data processing stage, and is usually realized by using digital filter. However, the bedrock stratum reflection and the fracture-cave reservoir reflection on the seismic profile are both effective waves, and the difference between them is very small, so it is difficult to separate them by using the ordinary filtering method. Therefore, the filtering method has little effect on highlighting the reflection characteristics of the fracture-cave reservoir.

[0008] (2) Deconvolution method

[0009] Deconvolution, also known as inverse filtering or deconvolution, is a common means in the process of seismic data processing. On the ordinary seismic profile, the transmitted wave of a stratum interface is generally a waveform with a duration of tens of milliseconds. Since the underground reflection interfaces are generally dense layers with a distance of several meters to tens of meters, the arrival time of the seismic wave is only several milliseconds to tens of milliseconds, so they interfere with each other on the seismic profile and are difficult to distinguish. The role of deconvolution is to compress the reflection wave of each interface into a narrow pulse, and the strength of the pulse is proportional to the reflection coefficient of the stratum interface, and the polarity of the pulse is related to the sign of the reflection coefficient of the stratum interface. Therefore, deconvolution can improve the vertical resolution of seismic data by compressing the seismic wavelet, and can also suppress the ringing and multiple waves. The improvement of the vertical resolution of seismic data by deconvolution has a certain effect on the identification of micro-fractures and fractures, but deconvolution will reduce the signal-to-noise ratio of seismic data; therefore, the use of deconvolution often needs to balance the resolution and the signal-to-noise ratio, and can only find a balance point between the resolution and the signal-to-noise ratio.

[0010] (3) Seismic migration imaging method

[0011] Seismic data imaging needs migration velocity, and seismic velocity modeling is an important process of migration imaging. In order to make the reflection characteristics of fractures or fracture-cave bodies more prominent, the conventional method is to encrypt the velocity sampling points, to model the velocity of special geological bodies, to control the velocity by wells, to model the velocity by facies, or to combine several velocity modeling methods; the purpose is to improve the accuracy of the velocity model, to provide more accurate velocity for migration imaging, and to make the reflection characteristics of the geological anomaly more clear. This kind of method has a huge workload, high labor and machine time cost, and high technical threshold requirement.

[0012] 2. Seismic data interpretation stage

[0013] (1) Signal separation class:

[0014] The core of the signal separation class is to separate the reflection of the surrounding rock stratum and the geological anomaly. The predecessors have conducted in-depth research on the blind source separation method, which is mainly used to extract the weak reflection information of the reservoir in the seismic data. The blind source separation method believes that the reflection information received by the geophone is a mixed signal produced by different surrounding rock stratum interfaces and weak signal represented geological bodies. The theoretical basis is that the reflection characteristics of the weak reflection geological body and the surrounding rock are different, and the role of the weak reflection information of the reservoir is to affect the slight change of the reflection wave waveform.

[0015] The specific implementation process of this method is:

[0016] Step 1: Mathematical expression of seismic trace signals

[0017] The physical characteristic parameters of the surrounding rock are very different from the weak reflection information parameters, so the actual seismic record can be described by a model function:

[0018]

[0019] Where x(i,t) represents an actual earthquake data; F k represents the surrounding rock signal source numbered k, t ik represents the time delay; s(i,t) represents the weak information in the channel.

[0020] Step 2: Mathematical expression of weak signal

[0021] Subtracting the surrounding rock record from the seismic record yields the weak reflection information record s(i,t):

[0022]

[0023] The physical meaning of (Equation 2) is clear: it decomposes m seismic records into n surrounding rock signal sources. y(i, t) represents the seismic record generated by each of the n surrounding rock signal sources acting independently. Subtracting this portion from the original seismic record x(i, t) yields the remaining information represented by the weak reflection.

[0024] Step 3: Obtaining the surrounding rock signal source

[0025] ① Obtain the record y(i,t) generated by the surrounding rock signal source, that is, use the similarity between the m-channel seismic record and the target seismic record to achieve it. For the m-channel record, select the middle channel, that is, the first The target trace is the target trace. Compared to the weak-signal geological body, the reflection energy of the surrounding rock or overburden is very strong. Therefore, it can be assumed that the target trace is the reflection record of the surrounding rock signal source:

[0026]

[0027] ② Take the target channel seismic data y * (t) is used as the initial value to calculate the surrounding rock seismic records in the new seismic trace, and an iterative algorithm is introduced to obtain relatively accurate and stable values. The correlation coefficient R between each seismic trace and the target trace is calculated as:

[0028] R xy (i,t)=x(i,t)*y * (i,t)i=1,2,3,…,m (Equation 4)

[0029] ③According to the size of the correlation coefficient R xy (i,t) of each trace, a prediction factor library σ i is established i The average value of the m-1 adjacent seismic traces is obtained by multiplying each corresponding actual seismic trace and then summing and averaging

[0030]

[0031] Although the m-1 adjacent seismic traces still contain weak reflection information, the weak information is weakened after weighted summing and averaging because the weak information reflection characteristics are not similar, and the surrounding rock source information reflection characteristics are retained, and the obtained is closer to the target trace surrounding rock information, which can be used instead of y * (t) to participate in the operation. The termination condition of iteration can be determined by verifying the robustness of Since can be approximately considered to contain only surrounding rock source information, it should satisfy the robustness or certain statistical rules within a certain range, and can be identified to have robustness to a certain extent.

[0032] Step four: weak signal calculation

[0033] The actual seismic signal minus the surrounding rock signal is the weak signal, so the separated weak signal seismic record is:

[0034]

[0035] s(i,t) is the weak signal seismic trace, x(i,t) is the actual seismic trace, and x(i,t) is the surrounding rock source seismic trace.

[0036] However, the current method has imperfections: seismic data interpretation stage mainly uses post-stack seismic data, and if these basic data do not meet the accuracy of fracture interpretation and reservoir prediction, we often perform re-imaging processing or post-stack interpretive processing on the seismic data. The cost of pre-stack re-imaging processing is too high, the cycle is too long, and the difficulty is greater. The post-stack interpretive processing is a more economical and feasible solution for the interpretation personnel. For the reflection characteristic enhancement processing method of fracture-cave reservoir, the signal separation method is a more effective means, but the blind source separation method is not very good and has some defects.

[0037] ①The method only emphasizes the separation of weak signals of reservoirs and does not analyze strong signals of reservoirs.

[0038] ②For formula 3, the middle trace of the m seismic data, i.e., the The target initial trace of the surrounding rock is iteratively calculated. The selection of the target trace is subject to discussion. If the target trace is a strong reflection or a weak reflection trace of the reservoir, a large error will occur in the correlation coefficient of the iterative calculation, which will greatly affect the separation effect. The target trace is a strong reflection or a weak reflection trace of the reservoir, which will cause a large error in the correlation coefficient of the iterative calculation, and greatly affect the separation effect.

[0039] In formula 5, the prediction factor is multiplied by each corresponding trace, and then the sum is averaged, that is, The target trace is a strong reflection or a weak reflection trace of the reservoir, which will cause a large error in the correlation coefficient of the iterative calculation, and greatly affect the separation effect.

[0040] The blind source separation method does not further screen and analyze the separated reservoir information to highlight the reflection characteristics with large differences from the surrounding rock.

[0041] Therefore, it is still desirable to provide a solution that can more effectively enhance the reflection characteristics of fault-dissolution reservoirs

[0042] The above description is only for understanding the background of the related art in the field, and does not admit that it belongs to the prior art. SUMMARY

[0043] The present application aims to establish a method and device that can more effectively enhance the reflection characteristics of fault-dissolution reservoirs. In an embodiment of the present application, a fault-dissolution reservoir seismic reflection characteristic enhancement method is provided, which comprises:

[0044] Conducting smoothing filtering processing on the initial three-dimensional seismic data body under the constraint of the three-dimensional model of the stratum, to obtain a smoothed and filtered three-dimensional seismic data body;

[0045] Determining the difference between the initial three-dimensional seismic data body and the smoothed and filtered three-dimensional seismic data body as an abnormal reflection data body;

[0046] Conducting difference enhancement processing and screening processing on the abnormal reflection data body to obtain a fault-dissolution reservoir abnormal reflection enhancement data body;

[0047] Fusing the fault-dissolution reservoir abnormal reflection enhancement data body with the smoothed and filtered three-dimensional seismic data body to obtain a final data body that highlights the abnormal reflection characteristics of the fault-dissolution reservoir.

[0048] As an explanation but not as a limitation, considering that the seismic reflection characteristics of carbonate reservoirs are often difficult to identify due to being covered by strong reflection of bedrock strata, the method according to the embodiments of the present application can highlight the reflection characteristics of large-scale reservoirs by eliminating or weakening the seismic reflection characteristics of bedrock and enhancing the seismic reflection characteristics of karst reservoirs, especially the abnormal characteristics that are greatly different from surrounding rocks, thereby providing important basic data for seismic attribute analysis, large-scale reservoir prediction and well site optimization deployment.

[0049] In some embodiments, the smoothing filtering processing of the initial three-dimensional seismic data volume under the constraint of the three-dimensional stratum model comprises:

[0050] The spatial smoothing filtering processing of the initial three-dimensional seismic data volume under the control of the stratum dip of the three-dimensional stratum model.

[0051] In some embodiments, the spatial smoothing filtering processing of the initial three-dimensional seismic data volume under the control of the stratum dip of the three-dimensional stratum model comprises:

[0052] The stratum dip of each sampling point of multi-channel seismic data in the three-dimensional stratum model is calculated by scanning the initial three-dimensional seismic data volume.

[0053] The smoothing bin of multi-channel seismic data of the main survey line seismic interpretation profile direction and the connecting line seismic interpretation profile direction is set along the stratum dip direction.

[0054] The three-dimensional seismic data volume is calculated by using spatial smoothing filtering based on the stratum dip and the smoothing bin.

[0055] In some embodiments, the method further comprises:

[0056] Whether there is a horizontally continuous reflection event in the calculated three-dimensional seismic data volume is analyzed.

[0057] When there is a horizontally continuous reflection event, the smoothing bin of multi-channel seismic data of the main survey line seismic interpretation profile direction and the connecting line seismic interpretation profile direction is adjusted.

[0058] The three-dimensional seismic data volume is calculated by using spatial smoothing filtering based on the stratum dip and the adjusted smoothing bin.

[0059] In some embodiments, the smoothing filtering processing of the initial three-dimensional seismic data volume under the constraint of the three-dimensional stratum model comprises:

[0060] Before the smoothing filtering processing is performed, the initial three-dimensional seismic data volume is pre-filtered to improve the signal-to-noise ratio.

[0061] In some embodiments, the difference enhancement processing and the screening processing on the abnormal reflection data volume are performed to obtain an abnormal reflection enhancement data volume of the faulted-solution reservoir, and the difference enhancement processing and the screening processing include:

[0062] The abnormal reflection data volume is processed by an exponential function or a power function to increase the difference between the maximum value and the minimum value of the abnormal reflection data volume;

[0063] The function-processed abnormal reflection data volume is screened based on a predetermined threshold to obtain a screened abnormal reflection enhancement data volume.

[0064] In some embodiments, the method further includes:

[0065] generating the three-dimensional model of the stratum.

[0066] In some embodiments, the generating of the three-dimensional model of the stratum includes:

[0067] tracking seismic reflection events based on the original three-dimensional seismic data volume to obtain horizon data describing the distribution characteristics of the stratum;

[0068] constructing a three-dimensional stratum framework model based on the stratum contact relationship and the horizon data;

[0069] performing spatial interpolation on the interlayer sample points of the three-dimensional stratum framework model under the constraint of the three-dimensional stratum framework model to obtain the three-dimensional model of the stratum.

[0070] In some embodiments, the tracking of the seismic reflection events based on the original three-dimensional seismic data volume to obtain the horizon data describing the distribution characteristics of the stratum includes:

[0071] Before the horizon data is obtained, the original three-dimensional seismic data volume is filtered to eliminate random noise.

[0072] In some embodiments, the constructing of the three-dimensional stratum framework model based on the stratum contact relationship and the horizon data further includes, before the three-dimensional stratum framework model is constructed, performing at least one of the following steps:

[0073] eliminating abnormal data in the horizon data;

[0074] performing planar interpolation processing on a locally missing data region in the horizon data;

[0075] performing smoothing filtering on the horizon data.

[0076] In some embodiments, the initial three-dimensional seismic data volume is a three-dimensional seismic post-stack amplitude-preserved data volume.

[0077] In some embodiments, the method is used to find a scale reservoir or to optimize a drilling trajectory design.

[0078] In embodiments of the present application, a seismic reflection feature enhancement device is provided, comprising:

[0079] A smoothing filtering processing unit configured to perform smoothing filtering processing on the initial 3D seismic data volume under the constraint of the stratum 3D model to obtain a smoothed 3D seismic data volume;

[0080] A determining unit configured to determine the difference between the initial 3D seismic data volume and the smoothed 3D seismic data volume as an abnormal reflection data volume;

[0081] An enhancement and screening unit configured to perform difference enhancement processing and screening processing on the abnormal reflection data volume to obtain a fracture-dissolved reservoir abnormal reflection enhancement data volume;

[0082] A fusion unit configured to fuse the fracture-dissolved reservoir abnormal reflection enhancement data volume and the smoothed 3D seismic data volume to obtain a final data volume highlighting the abnormal reflection features of the fracture-dissolved reservoir.

[0083] Thus, by means of the fracture-dissolved reservoir seismic reflection feature enhancement method of embodiments of the present application, i.e., based on the 3D seismic post-stack data volume, spatial smoothing filtering is performed on the seismic data volume under the control of stratum dip to obtain a smoothed seismic data volume reflecting the background bedrock; the initial seismic data volume and the smoothed seismic data volume are subjected to difference mathematical operation to obtain a seismic abnormal data volume reflecting the fracture-dissolved reservoir; the seismic abnormal data volume is subjected to enhancement mathematical operation and screening to obtain a seismic abnormal enhancement data volume; the abnormal enhancement data volume reflects the reservoir reflection features, the smoothed data volume reflects the background bedrock reflection features, and the two data volumes are fused to obtain a final data volume. Through the method of embodiments of the present application, the reflection features of the fracture-dissolved reservoir can be enhanced, the precision of identifying the fracture zone by seismic amplitude attributes can be improved, and more obvious reflection feature-based data can be provided for fracture-dissolved reservoir prediction, trap, and reserve resource quantity calculation. Meanwhile, drilling trajectory design can be optimized, drilling hit rate can be improved, and drilling risk and cost can be reduced.

[0084] Other features and advantages of embodiments of the present application can be known from the detailed description hereinafter, and can be derived by those skilled in the art from the teachings herein. BRIEF DESCRIPTION OF DRAWINGS

[0085] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings, in which:

[0086] Figure 1 A flowchart of a fracture-dissolved reservoir seismic reflection feature enhancement method according to embodiments of the present application is shown.

[0087] Figure 2 A profile of the direction of vertical fracture zone in a raw seismic data volume according to an embodiment of the present application is shown;

[0088] Figure 3 A profile of the direction of vertical fracture zone in a pre-filtered seismic data volume according to an embodiment of the present application is shown;

[0089] Figure 4 A profile of the direction of vertical fracture zone in a spatially smoothed filtered seismic data volume according to an embodiment of the present application is shown;

[0090] Figure 5 A profile of the direction of vertical fracture zone in a seismic anomaly data volume according to an embodiment of the present application is shown;

[0091] Figure 6 A profile of the direction of vertical fracture zone in a seismic anomaly enhanced data volume according to an embodiment of the present application is shown;

[0092] Figure 7 A profile of the direction of vertical fracture zone in a fused data volume according to an embodiment of the present application is shown;

[0093] Figure 8 A root mean square amplitude attribute plane of raw seismic data of Yijianfang Formation and Yingshan Formation is shown as a comparative example;

[0094] Figure 9 A root mean square amplitude attribute plane of seismic anomaly enhanced data of Yijianfang Formation and Yingshan Formation is shown as an exemplary example, wherein the seismic anomaly enhanced data can be obtained by using a seismic reflection feature enhancement method according to an embodiment of the present application;

[0095] Figure 10 A structural schematic diagram of a seismic reflection feature enhancement device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0096] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application is described in detail below with reference to specific embodiments and drawings. Herein, the exemplary embodiments of the present application and the descriptions thereof are used to explain the present application, but are not intended to limit the present application.

[0097] In one embodiment of the present application, a karst reservoir seismic reflection feature enhancement method is provided, which can highlight the reflection feature of a large-scale reservoir by eliminating or weakening the seismic reflection feature of a bedrock and enhancing the seismic reflection feature of a karst reservoir, especially enhancing the abnormal feature that is greatly different from surrounding rocks, thereby providing important basic data for seismic attribute analysis, large-scale reservoir prediction and well site optimization deployment.

[0098] Specifically, in the embodiment shown in the figure, the method can include steps S101 to S104. Figure 1

[0099] S101: performing smoothing filtering processing on the initial three-dimensional seismic data volume under the constraint of the three-dimensional stratum model to obtain a three-dimensional seismic data volume after smoothing filtering.

[0100] In some embodiments, the obtained three-dimensional seismic data volume after smoothing filtering can reflect the characteristics of the carbonate rock stratum bedrock.

[0101] In some embodiments, the initial three-dimensional seismic data volume is a three-dimensional post-stack preserved-amplitude data volume. In the embodiments of the present application, the terms "post-stack" and "preserved-amplitude" have the conventional meanings in the field.

[0102] In the embodiments of the present application, the "initial three-dimensional seismic data volume" refers to the three-dimensional seismic data volume that is constrained by the three-dimensional stratum model and used for smoothing filtering processing, for distinguishing from the three-dimensional seismic data volume after smoothing filtering; therefore, the initial three-dimensional seismic data volume can not be geological data directly collected, and in some embodiments, one or more preprocessing such as filtering processing can be performed before the smoothing filtering processing.

[0103] For example, in an embodiment, pre-filtering processing can be performed on the initial three-dimensional post-stack seismic data volume V1 before the smoothing filtering.

[0104] Specifically, the step S101 can include:

[0105] A0: performing pre-filtering processing on the initial three-dimensional seismic data volume before the smoothing filtering processing to improve the signal-to-noise ratio.

[0106] In the embodiments of the present application, pre-filtering processing is performed on the three-dimensional post-stack seismic data volume V1 to improve the signal-to-noise ratio of the seismic data, so as to avoid using noise signals as effective signals. The filtering method can be a frequency domain filtering method, a random noise attenuation method, a dip directional filtering method, an FK filtering method, etc. The filtering method is not limited thereto, and filtering method testing can be performed on the original post-stack seismic data volume V1, seismic profile comparison and analysis are performed, and a filtering method with better effect is selected. After filtering processing, a high signal-to-noise ratio seismic data volume V1 is obtained. * .

[0107] In some embodiments, the step S101 can include:

[0108] A1: performing spatial smoothing filtering processing on the initial three-dimensional seismic data volume under the control of the stratum dip of the three-dimensional stratum model.​

[0109] In one specific example, the A1 step can include:

[0110] B0: based on the initial 3D post-stack seismic data volume V1, along the stratigraphic dip direction, setting the trace array of the inline seismic interpretation profile and the xline seismic interpretation profile to form a smoothing panel, performing spatial smoothing filtering processing on the 3D post-stack seismic data volume to obtain a smoothed 3D seismic data volume V2 reflecting the bedrock.

[0111] In some embodiments of the present application, the inline seismic interpretation profile and the xline seismic interpretation profile are determined by the stratigraphic dip.

[0112] More specifically, the A1 step can include:

[0113] B1: scanning the initial 3D seismic data volume to calculate the stratigraphic dip at each sampling point of the multi-channel seismic data in the stratigraphic 3D model.

[0114] In one specific embodiment, the pre-filtered data volume V1 * can be scanned for stratigraphic dip to calculate the stratigraphic dip at each sampling point of each channel of seismic data, which is used for subsequent stratigraphic dip-controlled spatial smoothing filtering processing of the seismic data volume. In embodiments of the present application, various algorithms for calculating the stratigraphic dip of seismic data can be selected, including but not limited to complex trace analysis method, gradient structure tensor algorithm, etc.

[0115] In this example, the complex trace analysis method is used as an example to illustrate the basic principle of dip calculation. For 3D post-stack seismic data, first, the definition of instantaneous frequency is used:

[0116]

[0117] where, is the instantaneous phase, u is the seismic data to be processed (i.e., the seismic data volume V1 * ), u H is the Hilbert transform of u, and u and u H The derivative with respect to time can be realized by finite difference or Fourier transform.

[0118] Similarly, we can get the instantaneous wave number k x of u in the x direction:

[0119]

[0120] Thus, the apparent dip of u in the x direction is:

[0121]

[0122] Similarly, we can get the instantaneous wave number k of u in the y direction y :

[0123]

[0124] Thus, the apparent dip angle of u(y, t) in the y direction is:

[0125]

[0126] On this basis, according to the directional definition relationship, the true dip angle of the corresponding sample point in the stratum can be calculated

[0127]

[0128] Thus, for the post-stack 3D seismic data volume, the stratum dip angle at each sample point of each trace can be calculated, and finally the 3D data θ(x, y, t) of the stratum dip angle is obtained, and the unit of the stratum dip angle is ms / m.

[0129] B2: Set a smoothing bin of multi-channel seismic data in the inline seismic interpretation profile direction and the xline seismic interpretation profile direction along the stratum dip angle direction.

[0130] In some embodiments, m channels of seismic data in the inline direction can be set to participate in the calculation, n channels of seismic data in the xline direction can be set to participate in the calculation, and a data bin of m*n channels is formed, wherein m can be greater than or equal to 3, and n can be greater than or equal to 3.

[0131] B3: Based on the stratum dip angle and the smoothing bin, a 3D seismic data volume is calculated by using spatial smoothing filtering.

[0132] In these embodiments, under the control of the stratum dip angle, the post-stack 3D seismic data volume can be processed by using the aforementioned set data bin for spatial smoothing filtering.

[0133] Specifically, the spatial smoothing processing described by the following formula can be used to process each sampling point of the 3D seismic data space:

[0134]

[0135] In the formula, the spatially smoothed seismic data; m is the number of seismic traces in the inline direction, m is an integer greater than 3; n is the number of seismic traces in the xline direction, n is an integer greater than 3; i is the inline index of the 3D seismic data, j is the xline index of the 3D seismic data, t is the time index of the 3D seismic data, u is the 3D seismic data, and θ is the 3D dip data, the time index of the 3D seismic data under the control of the dip.

[0136] In some embodiments of the present application, the 3D seismic data volume calculated in step B3 can be directly used as the smoothed 3D seismic data volume for subsequent processing.

[0137] In some other embodiments of the present application, the 3D seismic data volume calculated in step B3 can be further used to feedback the setting of the smoothing bin so as to eliminate the horizontally continuous reflection events. In the embodiments of the present application, the terms "event" and "reflection event" have the conventional meanings in the art, for example, the "event" refers to the connecting line of the extreme values (e.g. the peak or the trough) of the same phase of the vibration of each trace in the seismic data.

[0138] Specifically, after the 3D seismic data volume is calculated in step B3, the following steps can be included:

[0139] C1: analyzing whether there is a horizontally continuous reflection event in the calculated 3D seismic data volume;

[0140] C2: adjusting the smoothing bin of the multi-trace seismic data in the main survey line seismic interpretation profile direction and the tie line seismic interpretation profile direction when there is a horizontally continuous reflection event;

[0141] C3: calculating the 3D seismic data volume by using the spatial smoothing filter based on the dip and the adjusted smoothing bin.

[0142] In some embodiments, the steps C1 to C3 can be repeatedly performed until the horizontally continuous reflection event is eliminated.

[0143] In these embodiments, the 3D seismic data volume calculated in step C3 after one or more cycles can be used as the smoothed 3D seismic data volume.

[0144] In some embodiments, the adjustment of the smoothing bin can include adjusting the values of the traces m and n in the inline direction and the xline direction of the smoothing bin, and further adjusting the m*n trace data bin.

[0145] For example, in one instance, it can be assumed that the initial values of the smoothing bin m and n are both 25, and the following steps are completed: the calculation of the three-dimensional seismic data volume whether the horizontal continuous reflection events in the cross-section are eliminated; if the horizontal continuous reflection events still exist, the values of the bins m, n can be appropriately increased; if the horizontal continuous reflection events have been eliminated, the values of the bins m, n can be appropriately decreased. Thus, the spatially smoothed seismic data volume V1 elimination of the horizontal continuous reflection events in the cross-section as a criterion for determining the values of m, n.

[0146] Thus, in the embodiments of the present application, the spatially smoothed seismic data volume V1 can be used as the three-dimensional seismic data volume V2 reflecting the basement.

[0147] S102: determining the difference between the initial three-dimensional seismic data volume and the smoothed three-dimensional seismic data volume as an abnormal reflection data volume.

[0148] In some embodiments, the abnormal reflection data volume can be used to describe the reflection characteristics of the fault-dissolution reservoir.

[0149] In some embodiments, the step S102 can include obtaining the difference V1 * -V2 of the three-dimensional seismic data volumes, to obtain an abnormal reflection data volume V3 reflecting the fault-dissolution reservoir. Here, by means of the subtraction operation of the three-dimensional data volumes, the seismic reflection characteristics of the basement stratum can be eliminated from the original seismic data volume, and the abnormal reflection data volume V3 reflecting the fault-dissolution reservoir can be obtained.

[0150] S103: performing difference enhancement processing and screening processing on the abnormal reflection data volume, to obtain a fault-dissolution reservoir abnormal reflection enhancement data volume.

[0151] In the step S103, the mathematical algorithm is used to make the large values in the data volume V3 more prominent, and the abnormal reflection data volume V3 is screened, to finally obtain an abnormal reflection characteristic enhancement data volume V4.

[0152] In the step S103 of the embodiments of the present application, the process of fault-dissolution reservoir abnormal reflection enhancement is realized, i.e., the process of increasing the difference between the maximum value and the minimum value in the abnormal reflection data volume V3. As an explanation but not limitation, the present inventor proposes that the large values in the abnormal reflection data volume V3 are the reflection characteristics of the fault-dissolution reservoir, which are the most different from the background basement reflection characteristics, and are the most favorable and reliable data for seismic exploration; and the minimum values indicate that the difference from the background basement reflection characteristics is small, and most of the minimum values represent the basement reflection characteristics or random noise; therefore, it is meaningful to enhance the maximum values in the abnormal reflection data volume V3.

[0153] More specifically, the step S103 can include:

[0154] D1: processing the abnormal reflection data volume by means of an exponential function or a power function to increase the difference between the maximum value and the minimum value of the abnormal reflection data volume.

[0155] In the embodiments of the present application, the difference between the maximum value and the minimum value of the abnormal reflection data volume V3 can be increased by means of mathematical tools such as an exponential function or a power function.

[0156] In a specific example, the difference can be increased by means of an exponential function as follows:

[0157]

[0158] wherein V3 is the abnormal reflection data volume; V4* is the abnormal enhancement data volume; a is a real number greater than 1 and b is an odd number, the initial values of a and b can be assumed first, and the values of a and b are finally obtained by repeatedly adjusting a and b through comparative analysis of the profile of the abnormal enhancement data volume V4* and the profile of the abnormal reflection data volume V3; c is a constant greater than 0, and the value range of the abnormal enhancement data volume V4* is obtained by comparing the value range of the original seismic data volume V1 * .

[0159] Thus, in the examples of the present application, the amplitude value of the abnormal weak reflection can be reduced, and the amplitude value of the abnormal strong reflection can be enhanced.

[0160] D2: screening the abnormal reflection data volume processed by the function based on a predetermined threshold to obtain a screened abnormal reflection enhancement data volume.

[0161] In a specific example, the minimum value in the data volume V4* can be screened, and generally, the data greater than or equal to a preset threshold, for example, a given percentage of the absolute value of the maximum value of the data volume, is retained. For example, the data above the absolute value of the maximum value Q% of V4* is retained; the data smaller than the absolute value of the maximum value Q% of V4* is removed as noise, and the specific method is to assign the data to 0. The value of Q is generally a constant Q ∈ (0, 10), and the specific value of Q is determined by the signal-to-noise ratio of the data volume V4*.

[0162] In these examples, the data volume V4 after abnormal enhancement and screening is as follows:

[0163]

[0164] wherein λ is the absolute value of the maximum value of V4*.

[0165] ​In the above preferred embodiments of the application, the differential amplitude enhancement is performed after the screening, but it is contemplated that in further embodiments, the differential amplitude enhancement can be performed after the screening, for example using the aforementioned enhancement methods.

[0166] S104: merging the fault-dissolved reservoir abnormal reflection enhanced data volume with the smoothed filtered three-dimensional seismic data volume to obtain a final data volume highlighting the abnormal reflection characteristics of the fault-dissolved reservoir.

[0167] In one specific example, in the step S104, the abnormal reflection characteristics enhanced volume V4 can be merged with the smoothed filtered three-dimensional seismic data volume V2, V5 = A*V4 + B*V2, to finally obtain a fault-dissolved reservoir reflection characteristics enhanced seismic data volume.

[0168] In this embodiment, the step can be an addition operation of three-dimensional data volumes, A and B being real numbers greater than 0, in preferred embodiments A = 1 and B = 1, and the values of A and B can be finally determined by comparing the profiles of the data volume V5 with the profiles of the data volume V1*,. Here, the three-dimensional data volume V5 can be the final fault-dissolved reservoir seismic reflection characteristics enhanced data volume.

[0169] In some embodiments, the final data volume can be used to highlight the abnormal reflection characteristics of the fault-dissolved reservoir, which is advantageous for the description of the fault-dissolved reservoir.

[0170] Thus, in the fault-dissolved reservoir seismic reflection characteristics enhancement method according to some embodiments of the application, more specifically, the final data volume can be used to search for a scale reservoir or to optimize the drilling trajectory design.

[0171] In further embodiments of the application, the method can further comprise one or more pre-processing steps.

[0172] For example, in embodiments of the application, the method can comprise:

[0173] E0: generating the three-dimensional model of the formation.

[0174] In particular, the step E0 can comprise steps E1 to E3:

[0175] E1 : based on the original three-dimensional seismic data volume, tracking the seismic reflection events to obtain horizon data describing the formation distribution characteristics.

[0176] In the embodiments of the present application, the "original 3D seismic data volume" is for the "initial 3D seismic data volume", which can be data used for constructing a 3D model of a formation, for example, those directly collected original geological data, and has not been processed. However, in the embodiments of the present application, the "original 3D seismic data volume" can also be subjected to preliminary data processing, but is yet to be used for constructing a 3D model of a formation. In the embodiments of the present application, the 3D model of a formation constructed from the "original 3D seismic data volume" by the processing described in the embodiments of the present application can be used to obtain and constrain the "initial 3D seismic data volume".

[0177] In some embodiments, the step E1 can comprise:

[0178] E11: filtering the original 3D seismic data volume to eliminate random noise before obtaining the horizon data.

[0179] Optionally, the 3D seismic data volume is filtered to eliminate random noise, improve the lateral continuity of 3D seismic data events, improve the accuracy of horizon tracking, and obtain high-quality horizon data.

[0180] E2: constructing a 3D framework model of a formation based on the horizon data and the contact relationship of the formation.

[0181] In some embodiments, the step E2 can comprise performing at least one of the following steps before constructing the 3D framework model of a formation:

[0182] E21: removing abnormal data in the horizon data;

[0183] E22: performing planar interpolation processing on a local missing data area in the horizon data;

[0184] E23: smoothing filtering the horizon data.

[0185] In these embodiments, abnormal data can be removed by quality control analysis of the horizon data; a more complete horizon data can be formed by planar interpolation of a local missing data area; and the accuracy of the horizon data in describing the structural features of the formation can be improved by smoothing filtering the horizon data

[0186] E3: performing spatial interpolation on the interlayer sample points of the 3D framework model of a formation under the constraint of the 3D framework model of a formation to obtain the 3D model of a formation.

[0187] In some embodiments, the longitudinal sampling rate and the lateral bin size of the 3D model of a formation can be consistent with the original seismic data.

[0188] To further illustrate the technical process of this method and understand its technical principles, a detailed description of the technical process of this method according to an embodiment of the present invention is now provided in conjunction with 3D seismic data from an actual seismic work area in a specific example of the present invention. Those skilled in the art will appreciate that any features described in this example can be combined with different embodiments of the present invention to create new embodiments, as long as no conflicts arise.

[0189] In this case, consider the carbonate reservoirs in the Shunbei area of ​​the Tarim Basin. Most carbonate reservoirs in this area are distributed along fault zones, forming fracture-cavity systems controlled by strike-slip faults. These systems include those formed by tectonic stresses and those formed by dissolution. Carbonate reservoirs are also called fault-karst reservoirs. Fault zones serve as both pathways for oil and gas migration and as reservoir spaces. Different reservoirs appear differently on seismic profiles: caves appear as "beaded" reflections, pores and fractures as "random" reflections, and faults as blank or weak reflections. Carbonate salts are highly heterogeneous, and strong reflections from internal lithologic interfaces often significantly impact the reflections of active reservoirs. Sometimes, the reflected energy from the background bedrock suppresses the reflection energy of the active reservoir, making it difficult to identify the reservoir's reflection characteristics. Furthermore, energy attributes cannot be used to simultaneously describe reservoirs with both strong and weak reflections on a seismic profile. Based on these existing problems, a method for enhancing the seismic reflection characteristics of fault-karst reservoirs was developed; the strong reflection characteristics of the fault-karst reservoirs are made stronger and the weak reflection characteristics of the reservoirs are enhanced. In this way, the reservoirs can be uniformly described by reflection energy attributes, and the interference of strong horizontal layered reflections is reduced.

[0190] The Shunbei 3D seismic work area in the Shunbei region features the northeast-trending Shunbei No. 1 Fault Zone. By applying actual seismic data volumes and a technical process, we generated the corresponding seismic data volumes. The following describes the characteristics of the seismic data at each stage of the process.

[0191] exist Figures 2 to 9 In the examples shown, the Shunbei No. 1 fault zone is used as an example for description. However, those skilled in the art will appreciate that the description of the examples can be applied to multiple embodiments of the present invention, and in particular, can be applied to other fault zones or other fault-karst reservoirs, thereby obtaining new embodiments, which fall within the scope of the invention.

[0192] Figure 2 The cross section of the original seismic data volume perpendicular to the fault zone is shown. The fault zone is, for example, the aforementioned Shunbei No. 1 fault zone. Figure 2 From the cross-section, it can be seen that there is still some noise in the original seismic data volume. For example, in order to avoid treating the noise in the original seismic data volume as a valid signal, the original seismic data volume can be filtered to improve the signal-to-noise ratio of the seismic section.Figure 2 The original seismic data volume in the illustrated example can involve those described in step E1 above, for example.

[0193] Figure 3 A profile in the direction of the vertical fault zone is shown for the filtered seismic data volume. The noise in the original seismic data is analyzed, and the noise in the work area is mainly distributed in the low and high frequency domain segments. Therefore, in this example, the means and tools for removing noise and improving the signal-to-noise ratio in the work area can be achieved by frequency domain filtering, and the signal-to-noise ratio can be improved by appropriately filtering out low and high frequency data. Figure 3 With Figure 2 , Figure 3 The noise is reduced and the signal-to-noise ratio is improved, providing reliable data for subsequent method and technology applications. Figure 3 In the example shown, the filtering process involves the filtering process described in the foregoing step E11 and the like.

[0194] Figure 4 A profile in the direction of the vertical fault zone is shown for the spatially filtered seismic data volume. In this example, the process is to apply spatial filtering technology under the control of the stratigraphic dip to obtain a seismic data volume reflecting the characteristics of the bedrock. Figure 4 As can be seen, the seismic profile has strong continuity of the event, high signal-to-noise ratio, and few abnormal reflection characteristics. The reflection characteristics of the profile are basically the response of the background bedrock. Figure 4 In the example shown, the filtering process involves the spatial smoothing filtering process described in the foregoing step S101 and the like. Here, the description features of the foregoing step S101 can be combined with this, and vice versa.

[0195] Figure 5 A profile in the direction of the vertical fault zone is shown for the seismic anomaly data volume. The process is to eliminate data reflecting the background bedrock from the original seismic data, thereby obtaining a data volume reflecting the seismic anomaly. Figure 5 The seismic anomaly reflection profile can be obtained; the horizontal events in the profile are basically eliminated, and the reflection information of the background bedrock is basically eliminated; the profile features are bright spots and chaotic reflections, which can roughly reflect the characteristics of the carbonate reservoir, but there is still noise in the profile that needs to be further screened and identified. Figure 5 The processing in the example shown involves the determination of differences described in the foregoing step S102 and the like. Here, the description features of the foregoing step S102 can be combined with this, and vice versa.

[0196] Figure 6 A profile in the direction of the vertical fault zone No. 1 is shown for the seismic anomaly enhancement data volume. The process is to enhance the large values in the seismic anomaly data volume, suppress the small values, and eliminate the values close to the background bedrock as noise, and finally obtain the seismic anomaly enhancement data volume. Figure 6As shown, the bright spot reflection in the seismic anomaly enhancement profile is more prominent, the noise information is suppressed, the reflection characteristics of the longitudinal fault zone are more continuous and obvious, and the reflection characteristics of the hole and fracture reservoir near the fault are enhanced. From Figure 6 As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa. Figure 5 As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa. Figure 6 As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa.

[0197] Figure 7 As shown, the profile in the vertical direction of the No. 1 fault zone in the fusion data volume is shown. The process is to fuse the seismic anomaly enhancement data volume and the background bedrock reflection data volume, which can highlight the dissolution reservoir reflection characteristics and also show the stratum deposition and structure characteristics. From Figure 7 As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa. Figure 3 As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa. Figure 7 As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa. Figure 7 As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa.

[0198] Herein, the present inventors show the root mean square amplitude attribute plane of the Yijianfang Formation and the Yingshan Formation in a contrast example and an example in a manner of example, in order to present the effect of the seismic reflection characteristic enhancement achieved according to the method of the embodiment of the present application. Figure 8 Figure 9 As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa. As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa.

[0199] As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa. Figure 8 As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa. Figure 9 As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa. Figure 9 As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa. Figure 9 As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa. Figure 8 As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa. Figure 8 As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa. Figure 9 As shown in the example, the processing such as the difference enhancement and screening processing described in the foregoing step S103 is involved. Herein, the description features of the foregoing step S103 can be combined with this, and vice versa. Figure 9The displayed root mean square amplitude attributes suppress the reflection energy of the background bedrock of the formation, highlight the reflection characteristics of the reservoir, are more conducive to the identification of fault zones, and provide a better data basis for the prediction of fault-karst reservoirs.

[0200] Here, as mentioned above, the method according to the embodiment of the present invention can be used to find large-scale reservoirs or to optimize drilling trajectory design.

[0201] In some embodiments of the present invention, a device for enhancing seismic reflection characteristics is also provided. Figure 10 In the illustrated embodiment, the seismic reflection feature enhancement device 1000 may include: a smoothing filter processing unit 1001, configured to perform smoothing filter processing on the initial three-dimensional seismic data volume under the constraints of the three-dimensional model of the formation to obtain a three-dimensional seismic data volume after smoothing filter; a determination unit 1002, configured to determine the difference between the initial three-dimensional seismic data volume and the three-dimensional seismic data volume after smoothing filter, as an abnormal reflection data volume; an enhancement and screening unit 1003, configured to perform difference enhancement processing and screening processing on the abnormal reflection data volume to obtain a fault-karst reservoir abnormal reflection enhancement data volume; a fusion unit 1004, configured to fuse the fault-karst reservoir abnormal reflection enhancement data volume with the three-dimensional seismic data volume after smoothing filter to obtain a final data volume that highlights the abnormal reflection characteristics of the fault-karst reservoir.

[0202] In some embodiments, the device may be combined with the method features of any embodiment, and vice versa, which will not be repeated here.

[0203] The methods, programs, systems, and apparatuses of the embodiments of the present invention may be executed or implemented in a single or multiple networked computers, or may be practiced in a distributed computing environment. In the embodiments of this specification, in these distributed computing environments, tasks may be performed by remote processing devices connected via a communication network.

[0204] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, those skilled in the art will appreciate that the functional modules / units or controllers and related method steps described in the above embodiments may be implemented using software, hardware, or a combination of software / hardware.

[0205] In the description, reference has been made to various embodiments of the application. For the sake of brevity, the methodologies, procedures, and / or elements of the various embodiments will not be described in detail with reference to the drawings. Further, the same or similar elements can be numbered the same or similarly throughout the several drawings. Embodiments of the application can be implemented in hardware, software, firmware, or a combination thereof. Embodiments of the application can be implemented in one or more computer programs or code that can be executable on a processor-based system affixed to a device. As used herein, a processor-based system can include any processor-based or similar system including systems based on well-known processing platforms such as IBM® POWER7® or IBM® POWER8®. Similarly, the computer programs can be stored in any

[0206] As used herein, the terms "comprise", "comprising", or "comprises" or any variation thereof are intended to cover a process, method, article, or apparatus whether a complete recitation of elements of which occurs either directly or indirectly in the description. In this context, the terms "include", "including" or "includes" are not intended to be exclusive or exhaustive.

[0207] The exemplary systems and methods of this application have been described with reference to the specific embodiments and applications thereof. It should be understood, however, that many variations, modifications, and / or alterations can be made to the exemplary embodiments and applications of this application without departing from the spirit or scope of the application. In particular, it will be clear to those of ordinary skill in the art that the systems and methods described herein can be embodied in many different forms and that the systems and methods described herein are to be considered in a descriptive sense only and not for purposes of limitation. The scope of the application should be considered in conjunction with the appended claims and their equivalents. The foregoing description of the exemplary systems and methods of this application has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the application to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. It is intended that the scope of the application be limited not with this detailed description, but rather by the claims appended hereto.

Claims

1. A method of enhancing seismic reflection characteristics of a solution-collapse reservoir, the method comprising: The method comprises the following steps: performing smoothing filtering on the initial three-dimensional seismic data volume under the constraint of the three-dimensional stratum model to obtain a three-dimensional seismic data volume after smoothing filtering, wherein spatial smoothing filtering is performed on the initial three-dimensional seismic data volume under the control of the stratum dip angle of the three-dimensional stratum model, the initial three-dimensional seismic data volume is scanned first to calculate the stratum dip angle at each sampling point of multi-channel seismic data in the three-dimensional stratum model, then a smoothing bin of multi-channel seismic data in the direction of the stratum dip angle is set along the direction of the main survey line seismic interpretation profile and the direction of the connecting line seismic interpretation profile, and finally the three-dimensional seismic data volume is calculated by using spatial smoothing filtering based on the stratum dip angle and the smoothing bin; determining the difference between the initial three-dimensional seismic data volume and the three-dimensional seismic data volume after smoothing filtering as an abnormal reflection data volume; performing difference enhancement processing and screening processing on the abnormal reflection data volume to obtain a fracture-cave reservoir abnormal reflection enhancement data volume; fusing the fracture-cave reservoir abnormal reflection enhancement data volume and the three-dimensional seismic data volume after smoothing filtering to obtain a final data volume highlighting the abnormal reflection characteristics of the fracture-cave reservoir.

2. The method of claim 1, wherein, Further comprising: analyzing whether there is a horizontally continuous reflection event in the calculated three-dimensional seismic data volume; when there is a horizontally continuous reflection event, adjusting the smoothing bin of multi-channel seismic data in the direction of the main survey line seismic interpretation profile and the direction of the connecting line seismic interpretation profile; calculating the three-dimensional seismic data volume by using spatial smoothing filtering based on the stratum dip angle and the adjusted smoothing bin.

3. The method of claim 1, wherein, The method of performing smoothing filtering on the initial three-dimensional seismic data volume under the constraint of the three-dimensional stratum model to obtain a three-dimensional seismic data volume after smoothing filtering comprises: performing pre-filtering processing on the initial three-dimensional seismic data volume before performing the smoothing filtering processing to improve the signal-to-noise ratio.

4. The method of claim 1, wherein, The method of performing difference enhancement processing and screening processing on the abnormal reflection data volume to obtain a fracture-cave reservoir abnormal reflection enhancement data volume comprises: processing the abnormal reflection data volume by using an exponential function or a power function to increase the difference between the maximum value and the minimum value of the abnormal reflection data volume; screening the function-processed abnormal reflection data volume based on a predetermined threshold to obtain a screened abnormal reflection enhancement data volume.

5. The method according to any one of claims 1 to 4, characterized in that, Further comprising: generating the three-dimensional stratum model.

6. The method of claim 5, wherein, The method of generating the three-dimensional stratum model comprises: tracking a seismic reflection event based on an original three-dimensional seismic data volume to obtain horizon data describing stratum distribution characteristics; constructing a three-dimensional stratum framework model from the horizon data based on stratum contact relationships; performing spatial interpolation on interlayer sampling points of the three-dimensional stratum framework model under the constraint of the three-dimensional stratum framework model to obtain the three-dimensional stratum model.

7. The method of claim 6, wherein, The method of tracking a seismic reflection event based on an original three-dimensional seismic data volume to obtain horizon data describing stratum distribution characteristics comprises: performing filtering processing on the original three-dimensional seismic data volume before obtaining the horizon data to eliminate random noise.

8. The method according to claim 6 or 7, characterized in that, The three-dimensional stratigraphic framework model is constructed based on stratigraphic contact relations, and before the three-dimensional stratigraphic framework model is constructed, at least one of the following steps is performed: abnormal data in the stratigraphic data is removed; a local missing data area in the stratigraphic data is subjected to planar interpolation processing; the stratigraphic data is subjected to smoothing filtering.

9. The method according to any one of claims 1 to 4, characterized in that, The initial three-dimensional seismic data volume is a three-dimensional seismic post-stack amplitude-preserved data volume.

10. The method according to any one of claims 1 to 4, characterized in that, The method is used for finding a scale reservoir or for optimizing a drilling trajectory design.

11. A seismic reflection feature enhancement apparatus, characterized by, The method comprises: a smoothing filtering processing unit configured to perform smoothing filtering processing on the initial three-dimensional seismic data volume under the constraint of a stratigraphic three-dimensional model, to obtain a smoothed three-dimensional seismic data volume, wherein spatial smoothing filtering processing is performed on the initial three-dimensional seismic data volume under the control of stratigraphic dip angles of the stratigraphic three-dimensional model, the initial three-dimensional seismic data volume is first scanned to calculate the stratigraphic dip angles at each sampling point of multi-channel seismic data in the stratigraphic three-dimensional model, then a smoothing surface element of multi-channel seismic data of a main survey line seismic interpretation profile direction and a liaison line seismic interpretation profile direction is set along the stratigraphic dip angle direction, and finally, based on the stratigraphic dip angles and the smoothing surface element, a three-dimensional seismic data volume is calculated by using spatial smoothing filtering; a determination unit configured to determine a difference between the initial three-dimensional seismic data volume and the smoothed three-dimensional seismic data volume as an abnormal reflection data volume; an enhancement and screening unit configured to perform difference enhancement processing and screening processing on the abnormal reflection data volume, to obtain a fracture-cave reservoir abnormal reflection enhancement data volume; a fusion unit configured to fuse the fracture-cave reservoir abnormal reflection enhancement data volume and the smoothed three-dimensional seismic data volume, to obtain a final data volume highlighting abnormal reflection characteristics of a fracture-cave reservoir.

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