Well leakage risk early warning method based on multi-scale fracture-cavity body

Abstract

The invention discloses a well leakage risk early warning method based on a multi-scale fracture-cavity body, and belongs to the technical field of oil exploitation, and the method comprises the following steps: dividing the well leakage risk level of a Maokou group into medium and large leakage and small and micro leakage; obtaining pre-stack gather data and post-stack seismic data of the OVT domain; according to the pre-stack gather data of the OVT domain, carrying out azimuth superposition offset processing to obtain a pre-stack crack attribute body; preliminarily selecting to obtain an attribute body corresponding to the post-stack seismic data; optimal characterization attribute bodies corresponding to different seismic response features are selected and obtained; performing threshold value range test and well matching rate verification to obtain an optimal threshold value range of the chaos attribute body, the dessert attribute body and the pre-stack crack attribute body; and well leakage risk early warning of medium and large leakage and small and micro leakage is carried out on the new drilling well. The problem that well leakage early warning is insufficient in comprehensiveness and accuracy is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of oil extraction, and in particular relates to a well leakage risk early warning method based on multi-scale fracture-cavity bodies. Background Art

[0002] During oil extraction, although it is possible to monitor well leakage in real time based on logging and mud recording data, through the relative changes in the total mud pool volume, well temperature gradient, drilling fluid outlet flow rate and field experience, it is impossible to provide pre-drilling warning. The prediction results are lagging and it is difficult to support subsequent development of well locations and optimization of well trajectories. Seismic data is currently still the only objective data that can be used for pre-drilling warning.

[0003] At present, there are many methods for pre-drilling well leakage warning using seismic data. The existing scheme 1 reflects formation fractures and cracks by extracting coherence, curvature, ants and structural tensor attributes, and further indicates the occurrence of well leakage; the existing scheme 2 obtains parameters such as Young's modulus based on pre-stack data inversion to obtain the formation fracture index, and uses the fracture index to characterize the risk of well leakage; the existing scheme 3 obtains the comprehensive index of well leakage by weighted average coherence, ants, and formation fracture parameters, and then evaluates the risk of well leakage; the existing scheme 4 predicts well leakage by predicting the formation pressure coefficient. The low-value area of ​​the pressure coefficient represents weak pressure bearing capacity and a high probability of well leakage; the existing scheme 5 uses machine learning methods to perform pre-drilling intelligent identification to predict the risk of well leakage before drilling. However, most of the above existing methods do not consider the scale of the fractures and holes that cause well leakage, and most of them are pre-drilling predictions based on medium and large leakage. There is a lack of pre-drilling prediction and systematic research on the Maokou Formation and small and micro leakage, and it is impossible to accurately warn of well leakage risk. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for early warning of well leakage risk based on multi-scale fracture-cavity bodies, which solves the problem of insufficient comprehensiveness and accuracy of well leakage early warning.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] The present invention provides a method for early warning of well leakage risk based on multi-scale fracture-cavity bodies, comprising the following steps:

[0007] S1. Based on the leakage data of Maokou Formation shale gas wells actually drilled, combined with leakage mechanism and leakage rate, the leakage risk level of Maokou Formation wells is divided into medium-large leakage and small-micro leakage;

[0008] S2, obtaining pre-stack gather data and post-stack seismic data in the OVT domain;

[0009] S3, according to the OVT domain pre-stack gather data, perform azimuth stack migration processing to obtain the pre-stack fracture attribute volume;

[0010] S4, preliminarily selecting an attribute body corresponding to the post-stack seismic data according to the post-stack seismic data;

[0011] S5. Compare and analyze the fracture-cavity bodies predicted based on the pre-stack fracture attribute body and the attribute body corresponding to the post-stack seismic data under different leakage mechanisms, and select the optimal characterization attribute body corresponding to different seismic response characteristics;

[0012] S6. Perform threshold value range tests and well matching rate verification on the optimal characterization attribute bodies corresponding to different seismic response characteristics, and obtain the optimal threshold value ranges of the chaos attribute body, the sweet spot attribute body and the prestack fracture attribute body;

[0013] S7. According to the optimal threshold value range of the chaotic attribute body, the sweet spot attribute body and the prestack fracture attribute body, the hollowed-out chaotic attribute body, the sweet spot attribute body and the prestack fracture attribute body are used to carry out the risk warning of medium-large leakage and small leakage for the newly drilled wells.

[0014] The beneficial effects of the present invention are as follows: a well leakage risk warning method based on a multi-scale fracture-cavity body provided by the present invention determines two well leakage risk levels of medium-large leakage and small and micro leakage according to leakage mechanism and leakage rate, providing a basis for well leakage risk warning; the present invention selects the optimal characterization attribute body corresponding to different seismic response characteristics by comparing and analyzing the fracture-cavity body predicted by different attribute bodies under different leakage mechanisms according to the pre-stack gather data and post-stack seismic data in the OVT domain, determines that it is most effective to characterize the well leakage risk area of ​​medium-large leakage by using the chaotic attribute body, determines that for the small and micro leakage wells caused by small and micro-scale fractures and caves, the pre-stack fracture and sweet spot attribute prediction method can further improve the well matching rate; the present invention determines the optimal threshold value range of the chaotic attribute body, the sweet spot attribute body and the pre-stack fracture attribute body through the threshold value range test and the well matching rate verification, effectively improving the accuracy of the well leakage risk warning; the present invention can implement the well leakage risk area of ​​the Maokou Formation based on the multi-scale fracture-cavity prediction results, and through the well location tracking work, the well leakage prediction matching rate reaches more than 80%, which has a strong reference value.

[0015] Other advantages of the present invention will be analyzed in more detail in subsequent embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0017] Figure 1The present invention is a flowchart of a method for early warning of well leakage risk based on multi-scale fracture-cavity bodies in an embodiment of the present invention.

[0018] FIG. 2( a ) is a superimposed seismic profile of amplitude difference attributes through Well A in an embodiment of the present invention.

[0019] FIG. 2( b ) is a maximum likelihood attribute superimposed seismic profile through Well A in an embodiment of the present invention.

[0020] FIG. 2( c ) is a superimposed seismic profile of coherent attributes through Well A in an embodiment of the present invention.

[0021] FIG. 2( d ) is a superimposed seismic profile of a chaotic attribute body passing through Well A in an embodiment of the present invention.

[0022] FIG. 3( a ) is a superimposed seismic profile of a variance texture attribute volume through Well B in an embodiment of the present invention.

[0023] FIG3( b ) is a superimposed seismic profile through the sweet spot attribute volume of Well B in an embodiment of the present invention.

[0024] FIG. 4( a ) is a superimposed seismic profile of the ant attribute body through Well C in an embodiment of the present invention.

[0025] FIG4( b ) is a superimposed seismic profile of the pre-stack fracture attribute body through Well C in an embodiment of the present invention.

[0026] Figure 5 This is a superimposed seismic profile of the chaotic attribute body and the sweet spot attribute body through the D well in an embodiment of the present invention.

[0027] FIG. 6( a ) is a plan view of a fusion sculpture of a chaotic attribute body and a dessert attribute body in an embodiment of the present invention.

[0028] FIG6( b ) is a plane showing the effect of the pre-stack fracture attribute volume prediction in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0030] OVT (Offset Vector Tile), offset vector tile.

[0031] like Figure 1 As shown, in one embodiment of the present invention, the present invention provides a method for early warning of well leakage risk based on multi-scale fracture-cavity bodies, comprising the following steps:

[0032] S1. Based on the leakage data of Maokou Formation shale gas wells actually drilled, combined with leakage mechanism and leakage rate, the leakage risk level of Maokou Formation wells is divided into medium-large leakage and small-micro leakage;

[0033] In this embodiment, the leakage rate of the medium and large leakage is greater than 10m 3 / h; the leakage rate of the small leakage is less than 10m 3 / h. The leakage mechanism of medium-large leakage is: medium-large scale fractures and caves caused by drilling into fault fracture zones; small and micro leakage is divided into two leakage mechanisms: one is small-scale fractures caused by drilling into tectonic stress, and the other is small-scale caves caused by drilling into the exposure and denudation of the Maokou Formation. Fractures and caves of different scales are closely related to the risk level of well leakage.

[0034] The seismic response characteristics of medium- and large-scale fractures and caves caused by drilling fault fracture zones are mainly manifested as dislocation and disorder of the phase axis, and the lateral change of amplitude energy near the fault is obvious; the seismic response characteristics of small-scale caves caused by the exposure and denudation of the Maokou Formation are mainly manifested as intermittent medium-strong wave peak reflections at the top of the Maokou Formation; the seismic response characteristics caused by small-scale cracks caused by drilling tectonic stress are mainly manifested as discontinuous phase axis and waveform changes. The seismic waveform characteristics corresponding to different leakage types and leakage mechanisms are shown in Table 1:

[0035] Table 1

[0036]

[0037] S2, obtaining pre-stack gather data and post-stack seismic data in the OVT domain;

[0038] S3, according to the OVT domain pre-stack gather data, perform azimuth stack migration processing to obtain the pre-stack fracture attribute volume;

[0039] The S3 comprises the following steps:

[0040] S31, dividing the OVT domain pre-stack gathers evenly into several sector-shaped areas according to the azimuth angle; in this embodiment, the OVT domain pre-stack gathers are preferably divided into 6 sector-shaped areas, each sector-shaped area covers a range of 30°, to ensure that the data is basically evenly distributed in azimuth angle.

[0041] S32, performing stacking processing based on the medium-long offset distance in each sector area to obtain the stacked seismic data in each sector area; the stacking processing based on the medium-long offset distance can effectively improve the signal-to-noise ratio, thereby highlighting the effective signal. In this embodiment, six sector areas are divided, so six stacking processes are performed;

[0042] S33, performing offset processing on the superimposed seismic data in each fan-shaped area to obtain offset seismic data; by offsetting the seismic data, the seismic wave can be "offset" from the receiving point back to the real position of the low reflection point, thereby improving the imaging quality of the seismic data;

[0043] S34, obtaining a pre-stack fracture attribute body according to the migrated seismic data. In this embodiment, the migrated seismic data is input into EPoffice software for attribute calculation to obtain a pre-stack fracture attribute body.

[0044] S4, preliminarily selecting an attribute body corresponding to the post-stack seismic data according to the post-stack seismic data;

[0045] The attribute bodies corresponding to the post-stack seismic data include a chaos attribute body, a maximum likelihood attribute body, an amplitude difference attribute body, a coherence attribute body, a sweet spot attribute body, a variance texture attribute body and an ant attribute body.

[0046] In this embodiment, based on the post-stack seismic data, the chaotic attribute body, the sweet spot attribute body and the ant attribute body are calculated using the Petrel software, the coherent attribute body, the amplitude difference attribute body and the variance texture attribute body are calculated using the Geoeast software, and the maximum likelihood attribute body is calculated using the SMT software. The principles and application characteristics of each seismic attribute body are shown in Table 2:

[0047] Table 2

[0048]

[0049]

[0050] S5. Compare and analyze the fracture-cavity bodies predicted based on the pre-stack fracture attribute body and the attribute body corresponding to the post-stack seismic data under different leakage mechanisms, and select the optimal characterization attribute body corresponding to different seismic response characteristics;

[0051] The S5 comprises the following steps:

[0052] S51. For the case where the leakage mechanism is medium-to-large-scale fractures and holes caused by drilling faults, the fracture and hole bodies predicted based on the amplitude difference attribute body, the maximum likelihood attribute body, the coherence attribute body and the chaos attribute body are compared and analyzed, and the chaos attribute body is selected as the optimal characterization attribute body when the seismic response characteristics are the seismic event axis misalignment and disorder;

[0053] In this embodiment, after comparing the chaotic attribute body, the amplitude difference attribute body, the coherent attribute body and the maximum likelihood attribute body, the chaotic attribute body is selected to characterize the seismic attribute of the fault characteristics. The reason is that: taking Well A as an example, Well A had a medium-large leakage at 2943.1m to 2947.9m in the second section of the Maokou Formation, that is, the rectangular box at the center position of Figure 2 (a), Figure 2 (b), Figure 2 (c) and Figure 2 (d), and the leakage reached 938.6m 3 , the logging data show medium-to-large scale cracks caused by drilling faults. As shown in Figure 2(a), the fracture-cavity body described by the amplitude difference attribute not only does not develop along the fault, but also is small and discontinuous, which is inconsistent with the medium-to-large leakage in drilling; as shown in Figure 2(b), the maximum likelihood attribute has a certain deviation from the real fault plane and cannot effectively and accurately describe the leakage interval; as shown in 2(c), the coherence attribute is also unable to effectively describe the fracture-cavity body related to the fault; as shown in Figure 2(d), the fracture-cavity body described by the chaotic attribute develops continuously on a large scale along both sides of the fault, which can better characterize the seismic reflection characteristics related to the fault, such as the phase axis misalignment and disorder, and thus provide a well leakage warning.

[0054] S52. When the leakage mechanism is a small-scale cave, the fracture-cave body predicted based on the variance texture attribute body and the sweet spot attribute body are compared and analyzed, and the sweet spot attribute body is selected as the optimal characterization attribute body when the seismic response characteristics are intermittent medium-strong amplitude at the top of the Maokou Formation;

[0055] In this embodiment, after comparing the sweet spot attribute body and the variance texture attribute body, the seismic attribute of the sweet spot attribute body is selected to represent the cave characteristics. The reason is that: taking Well B as an example, when Well B was drilled to the 2664m to 2677.9m of the Maokou Formation, that is, the rectangular frame in the center of Figure 3 (a) and Figure 3 (b), a small leak occurred, and 22.5m was lost. 3 , logging data show that small-scale caves were encountered. The through-well profile shows that the leakage interval has obvious medium-strong peak reflection characteristics. As shown in Figure 3(a), the prediction result of the variance texture attribute does not match the leakage point. The small-scale caves described are developed in the middle and lower parts of the Maokou Formation, which is obviously inconsistent with the previous geological understanding; as shown in Figure 3(b), the small-scale caves described by the sweet spot attribute are mainly developed in the upper part of the Maokou Formation, which is consistent with the geological understanding that the top of the Maokou Formation was exposed and eroded after the Dongwu Movement. Therefore, the prediction result matches the seismic reflection well.

[0056] S53. When the leakage mechanism is small-scale fractures, the fracture-cavity bodies predicted based on the ant attribute body and the prestack fracture attribute body are compared and analyzed, and the prestack fracture attribute body is selected as the optimal characterization attribute body when the seismic response characteristics are phase axis discontinuity and waveform change.

[0057] In this embodiment, after comparing the prestack fracture attribute body and the ant attribute body, the prestack fracture attribute body is selected to represent the seismic attribute body of fracture characteristics. The reason is that: taking Well C as an example, when Well C was drilled to the 2257.9m to 2262.3m of the Maokou Formation, that is, the rectangular frame in the center of Figures 4(a) and 4(b), a small leak occurred, and 18.6m was lost. 3 , the logging data showed that small micro-scale fractures were encountered. As shown in Figure 4(a), the post-stack seismic profile of Well C shows that the lost layer has no obvious seismic reflection characteristics, and the ant attribute cannot effectively characterize the small-scale fractures, and the prediction result does not match the lost layer; as shown in Figure 4(b), the small-scale fractures characterized by the pre-stack fracture attribute are small in scale and intermittently developed, which matches the lost layer better.

[0058] S6. Perform threshold value range tests and well matching rate verification on the optimal characterization attribute bodies corresponding to different seismic response characteristics, and obtain the optimal threshold value ranges of the chaos attribute body, the sweet spot attribute body and the prestack fracture attribute body;

[0059] The optimal threshold value range of the chaotic attribute body is above 0.3; the optimal threshold value range of the sweet spot attribute body is below 300; the optimal threshold value range of the prestack fracture attribute body is above 5.

[0060] In this embodiment, when testing the threshold value range of the chaotic attribute body, the threshold value range is taken as above 0.2, above 0.25, above 0.3, above 0.35 and above 0.4, and the well matching rate corresponding to each threshold value range is calculated by petrel software, and the corresponding well matching rates are 58.6%, 72.5%, 82.8%, 71.5% and 68.7% respectively. Therefore, the optimal threshold value range of the chaotic attribute body is selected as above 0.3, and the chaotic attribute body is hollowed out to predict medium and large scale fractures and caves; when testing the threshold value range of the sweet spot attribute body, the threshold value range is taken as below 200, below 250, below 300, below 350 and below 400, and the threshold values ​​are calculated by petrel software respectively. The well matching rates corresponding to the range are 64.6%, 71.8%, 81.6%, 70.2% and 62.9% respectively. Therefore, the optimal threshold value range of the sweet spot attribute body is below 300, and hollowing out the sweet spot attribute body can be used to predict small-scale caves. When testing the threshold value of the prestack fracture attribute body, the threshold value ranges are above 3, above 4, above 5, above 6 and above 7 respectively. The well matching rates corresponding to each threshold value range are calculated by petrel software respectively, and the well matching rates are 61.6%, 73.8%, 83.4%, 73.5% and 61.1% respectively. Therefore, the optimal threshold value range of the prestack fracture attribute body is above 5, and hollowing out the prestack fracture attribute body can be used to predict small-scale fractures.

[0061] S7. According to the optimal threshold value range of the chaotic attribute body, the sweet spot attribute body and the prestack fracture attribute body, the hollowed-out chaotic attribute body, the sweet spot attribute body and the prestack fracture attribute body are used to carry out the risk warning of medium-large leakage and small leakage for the newly drilled wells.

[0062] Based on the pre-stack gather data of the newly drilled well in the OVT domain, the small-scale fractures of the newly drilled well are predicted by using the pre-stack fracture attribute body after setting the optimal threshold value range and hollowing out. If small-scale fractures exist, a small and micro leakage alarm is issued; based on the post-stack seismic data set of the newly drilled well, the medium- and large-scale fractures and small-scale caves caused by the drilling faults of the newly drilled well are fused and predicted by using the chaotic attribute body and sweet spot attribute body after setting the optimal threshold value range and hollowing out. If there are medium- and large-scale fractures and caves caused by drilling faults, a medium- and large leakage alarm is issued, and if there are small-scale caves, a small and micro leakage alarm is issued.

[0063] like Figure 5 As shown, in this embodiment, taking the newly drilled well D as an example, when the well D is drilled to the second section of the Maokou Formation from 2796.2m to 2803.2m, that is, Figure 5 A medium-large leak occurred in the rectangular frame in the center, with a loss of 387.5m 3 The logging results show that there are medium-to-large-scale fractures and holes caused by drilling faults. Figure 5 The superimposed seismic profile of the D well is obtained based on the prediction of the chaotic attribute body and the sweet spot attribute body after the optimal threshold value range is set and hollowed out. The fracture-cave body described by the fused attribute body is mainly attached to the fault and the top of the Maokou Formation, which effectively predicts the fracture-cave body developed in the Maokou Formation. Figure 5 It can be found that the leakage layer is located in the composite fracture-cavity body of medium-to-large-scale fractures and caves caused by faults and small-scale caves caused by erosion. It is more accurate to describe the fracture-cavity body by using the attribute body fused with the chaotic attribute body and the sweet spot attribute body, and it can realize the well leakage warning more accurately.

[0064] As shown in Figure 6(a), in the plane view of the attribute body after fusion, the predicted composite fracture-cave body is developed along the fault and has a northeast distribution feature. Due to the influence of the cave at the top of the Maokou Formation, the fracture-cave bodies caused by different faults are connected, and the area and volume of different fracture-cave development can be calculated. Among them, the development area of ​​the fracture-cave body through Well D (dark green range in Figure 6) is 2509626m 2 , with a volume of 97047000m 3It should be noted that the fracture-cavity volume and area calculated here are obtained from the disorder and waveform changes of the seismic phase axis, and there is a difference in the order of magnitude of the fracture-cavity body actually connected underground. The reason is that most of the fracture-cavity space has been filled. It is necessary to further study the filling type and mechanism of the drilling to determine the unfilled fracture-cavity volume consistent with the leakage volume, and then fit the fracture-cavity volume fitting coefficient predicted by seismic to achieve the purpose of quantitative characterization of the fracture-cavity body. In addition, the small-scale fractures that cannot be predicted by the fusion attribute body developed in the local area of ​​the syncline can be used as an auxiliary means to further improve the consistency rate of well leakage prediction. As shown in Figure 6(b), the prestack fracture attribute body can effectively predict the small-scale fractures developed in the C well area, including fracture density and direction. Finally, the leakage encountered by 32 wells in the study area in the Maokou Formation was counted, including 24 wells with leakage and 8 wells without leakage. The consistency rate of fracture-cavity prediction with well leakage warning reached 82.8%.

[0065] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for early warning of well leakage risk based on multi-scale fracture-cavity bodies, characterized in that: The steps include: S1. Based on the leakage data of Maokou Formation shale gas wells actually drilled, combined with leakage mechanism and leakage rate, the leakage risk level of Maokou Formation wells is divided into medium-large leakage and small-micro leakage; S2, obtaining pre-stack gather data and post-stack seismic data in the OVT domain; S3, according to the OVT domain pre-stack gather data, perform azimuth stack migration processing to obtain the pre-stack fracture attribute volume; S4, preliminarily selecting an attribute body corresponding to the post-stack seismic data according to the post-stack seismic data; S5. Compare and analyze the fracture-cavity bodies predicted based on the pre-stack fracture attribute body and the attribute body corresponding to the post-stack seismic data under different leakage mechanisms, and select the optimal characterization attribute body corresponding to different seismic response characteristics; S6. Perform threshold value range tests and well matching rate verification on the optimal characterization attribute bodies corresponding to different seismic response characteristics, and obtain the optimal threshold value ranges of the chaos attribute body, the sweet spot attribute body and the prestack fracture attribute body; S7. According to the optimal threshold value range of the chaotic attribute body, the sweet spot attribute body and the prestack fracture attribute body, the hollowed-out chaotic attribute body, the sweet spot attribute body and the prestack fracture attribute body are used to carry out the risk warning of medium-large leakage and small leakage for the newly drilled wells.

2. The method for early warning of well leakage risk based on multi-scale fracture-cavity bodies according to claim 1 is characterized in that: The S3 comprises the following steps: S31, dividing the OVT domain pre-stack gathers evenly into a number of fan-shaped areas according to the azimuth angle; S32, performing stacking processing in each fan-shaped area based on the medium-long offset distance to obtain stacked seismic data in each fan-shaped area; S33, performing migration processing on the superimposed seismic data in each fan-shaped area to obtain migrated seismic data; S34. Obtain a pre-stack fracture attribute volume based on the migrated seismic data.

3. The method for early warning of well leakage risk based on multi-scale fracture-cavity bodies according to claim 1 is characterized in that: The attribute bodies corresponding to the post-stack seismic data include a chaos attribute body, a maximum likelihood attribute body, an amplitude difference attribute body, a coherence attribute body, a sweet spot attribute body, a variance texture attribute body and an ant attribute body.

4. The method for early warning of well leakage risk based on multi-scale fracture-cavity bodies according to claim 3 is characterized in that: The S5 comprises the following steps: S51. For the case where the leakage mechanism is medium-to-large-scale fractures and holes caused by drilling faults, the fracture and hole bodies predicted based on the amplitude difference attribute body, the maximum likelihood attribute body, the coherence attribute body and the chaos attribute body are compared and analyzed, and the chaos attribute body is selected as the optimal characterization attribute body when the seismic response characteristics are the seismic event axis misalignment and disorder; S52. When the leakage mechanism is a small-scale cave, the fracture-cave body predicted based on the variance texture attribute body and the sweet spot attribute body are compared and analyzed, and the sweet spot attribute body is selected as the optimal characterization attribute body when the seismic response characteristics are intermittent medium-strong amplitude at the top of the Maokou Formation; S53. When the leakage mechanism is small-scale fractures, the fracture-cavity bodies predicted based on the ant attribute body and the prestack fracture attribute body are compared and analyzed, and the prestack fracture attribute body is selected as the optimal characterization attribute body when the seismic response characteristics are phase axis discontinuity and waveform change.

5. The method for early warning of well leakage risk based on multi-scale fracture-cavity bodies according to claim 4 is characterized in that: The optimal threshold value range of the chaotic attribute body is above 0.3; the optimal threshold value range of the sweet spot attribute body is below 300; the optimal threshold value range of the prestack fracture attribute body is above 5.

6. The method for early warning of well leakage risk based on multi-scale fracture-cavity bodies according to claim 5, characterized in that: Based on the OVT domain pre-stack gather data of the newly drilled well, the newly drilled well is predicted by using the pre-stack fracture attribute body after setting the optimal threshold value range and hollowing out. If there are small-scale fractures, a small leakage alarm is issued; Based on the post-stack seismic data set of the new well, the chaotic attribute body and the sweet spot attribute body with the optimal threshold value range set and hollowed out are used to make a fusion prediction of the new well. If there are medium-to-large-scale fractures and caves caused by drilling faults, a medium-to-large leakage alarm is issued. If there are small-scale caves, a small-to-micro leakage alarm is issued.

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