A lost circulation risk warning method based on multi-scale fracture-cavity bodies

Abstract

The present invention discloses a method for early warning of well leakage risk based on multi-scale fracture-cavity bodies, belonging to the field of oil production technology. The method comprises the following steps: classifying the well leakage risk level of the Maokou Formation into medium-large leakage and small-micro leakage; obtaining pre-stack gather data and post-stack seismic data in the OVT domain; performing azimuth stacking and migration processing on the pre-stack gather data in the OVT domain to obtain a pre-stack fracture attribute body; preliminarily selecting an attribute body corresponding to the post-stack seismic data; selecting the optimal characterization attribute body corresponding to different seismic response characteristics; performing threshold value range testing and well matching rate verification to obtain the optimal threshold value range of the chaotic attribute body, the sweet spot attribute body, and the pre-stack fracture attribute body; and performing well leakage risk early warning for medium-large leakage and small-micro leakage in newly drilled wells. The present invention solves the problem of insufficient comprehensiveness and accuracy in well leakage early warning.

Description

Technical Field

[0001] The present 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 production, although it is possible to monitor well leakage in real time based on logging and well recording data, through 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, making it difficult to support subsequent development well locations and optimize well trajectories. Seismic data is currently the only objective data that can be used for pre-drilling warning.

[0003] Currently, there are numerous pre-drilling methods for lost circulation warning using seismic data. The first method extracts coherence, curvature, ANT, and structural tensor attributes to reflect formation fractures and cracks, further indicating the occurrence of lost circulation. The second method uses pre-stack data inversion to obtain parameters such as Young's modulus to derive a formation fracture index, which is then used to characterize lost circulation risk. The third method uses a weighted average of coherence, ANT, and formation fracture parameters to obtain a comprehensive lost circulation index, which is then used to assess lost circulation risk. The fourth method predicts lost circulation by predicting formation pressure coefficients; low pressure coefficient values ​​indicate weak pressure bearing capacity and a high probability of lost circulation. The fifth method uses machine learning to perform pre-drilling intelligent identification to predict lost circulation risk. However, most of these existing methods fail to consider the scale of the fractures and vugs that cause lost circulation, and most focus on medium- to large-scale lost circulation for pre-drilling prediction. Pre-drilling prediction and systematic research on the Maokou Formation and small and micro-leaks are lacking, making them incapable of providing accurate lost circulation risk warnings. Summary of the Invention

[0004] In response to the above-mentioned deficiencies in the prior art, the present invention provides a method for early warning of lost circulation risk based on multi-scale fractures and caverns, which solves the problem of insufficient comprehensiveness and accuracy of lost circulation 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 lost circulation 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 drilled, combined with the 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. Perform azimuth stack migration processing based on the OVT domain pre-stack gather data to obtain the pre-stack fracture attribute volume;

[0010] S4. Preliminarily selecting an attribute body corresponding to the post-stack seismic data based on 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 loss mechanisms, and select the optimal characterization attribute body corresponding to different seismic response characteristics;

[0012] S6. Conduct 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. Based on the optimal threshold value range of the chaotic attribute body, sweet spot attribute body, and prestack fracture attribute body, the hollowed chaotic attribute body, sweet spot attribute body, and prestack fracture attribute body are used to provide early warning of medium-large and small leakage risks for newly drilled wells.

[0014] The beneficial effects of the present invention are as follows: a well leakage risk warning method based on multi-scale fracture-cavity bodies provided by the present invention determines two well leakage risk levels, medium-large leakage and small and micro leakage, according to the leakage mechanism and leakage rate, providing a basis for well leakage risk warning; based on OVT domain pre-stack gather data and post-stack seismic data, the present invention compares and analyzes fracture-cavity bodies predicted by different attribute bodies under different leakage mechanisms, selects the optimal characterization attribute body corresponding to different seismic response characteristics, determines that the chaotic attribute body is most effective in characterizing the medium-large leakage well leakage risk area, and determines that for small and micro leakage wells caused by small and micro fractures and caves, the use of pre-stack fracture and sweet spot attribute prediction methods 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 threshold value range testing and well matching rate verification, effectively improving the accuracy of well leakage risk warning; based on the multi-scale fracture-cavity prediction results, the present invention can identify the well leakage risk area of ​​the Maokou Formation, and through well location tracking, the well leakage prediction matching rate reaches more than 80%, which has 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 is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

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

[0018] FIG2( a ) is a superimposed seismic profile of amplitude difference attributes through Well A in an embodiment of the present invention.

[0019] FIG2( b ) is a maximum likelihood attribute superimposed seismic profile through Well A in an embodiment of the present invention.

[0020] FIG2( c ) is a superimposed seismic profile of coherent attributes through Well A in an embodiment of the present invention.

[0021] FIG2( d ) is a superimposed seismic cross-section of the chaotic attribute body through Well A in an embodiment of the present invention.

[0022] FIG3( a ) is a superimposed seismic cross-section of the variance texture attribute volume through Well B according to an embodiment of the present invention.

[0023] FIG3( b ) is a superimposed seismic cross-section through the sweet spot attribute volume of Well B according to an embodiment of the present invention.

[0024] FIG4( a ) is a superimposed seismic cross-section of the ant attribute body through well C in an embodiment of the present invention.

[0025] FIG4( b ) is a superimposed seismic cross-section 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 well D in an embodiment of the present invention.

[0027] FIG6( a ) is a plan view of a fusion sculpture of a chaotic attribute body and a dessert attribute body according to 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, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein 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 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 are within 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 lost circulation 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 drilled, combined with the 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 mechanism of medium-to-large leakage is caused by encountering medium-to-large-scale fractures and vugs in fault fracture zones. Micro-leakage can be divided into two mechanisms: encountering small-scale fractures caused by tectonic stress, and encountering small-scale karst caves caused by exposure and erosion of the Maokou Formation. The different sizes of fractures and vugs are closely related to the level of lost circulation risk.

[0034] The seismic response characteristics of medium- and large-scale fractures and cavities caused by drilling into fault fracture zones are primarily characterized by dislocated and chaotic events, with significant lateral variations in amplitude energy near the fault. The seismic response characteristics of small-scale karst caves caused by exposure and denudation of the Maokou Formation are primarily characterized by intermittent medium- to strong-peak reflections at the top of the Maokou Formation. The seismic response characteristics of small-scale fractures caused by drilling into tectonic stress are primarily characterized by discontinuous events and waveform variations. Table 1 shows the seismic waveform characteristics corresponding to different leakage types and mechanisms.

[0035] Table 1

[0036]

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

[0038] S3. Perform azimuth stack migration processing based on the OVT domain pre-stack gather data to obtain the pre-stack fracture attribute volume;

[0039] The S3 comprises the following steps:

[0040] S31. Divide 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 covering a range of 30° to ensure that the data are basically evenly distributed in the azimuth angle.

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

[0042] S33, performing migration processing on the superimposed seismic data in each sector to obtain migrated seismic data; by migrating the seismic data, the seismic wave can be "migrated" from the receiving point back to the true position of the low reflection point, thereby improving the imaging quality of the seismic data;

[0043] S34. Obtaining a pre-stack fracture attribute volume based on 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 volume.

[0044] S4. Preliminarily selecting an attribute body corresponding to the post-stack seismic data based on 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 example, based on post-stack seismic data, Petrel software was used to calculate the chaotic attribute volume, sweet spot attribute volume, and ant attribute volume. Geoeast software was used to calculate the coherence attribute volume, amplitude difference attribute volume, and variance texture attribute volume. SMT software was used to calculate the maximum likelihood attribute volume. The principles and application characteristics of each seismic attribute volume 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 loss 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 seismic phase axis misalignment and disorder;

[0053] In this embodiment, after comparing the chaotic attribute body, the amplitude difference attribute body, the coherence attribute body, and the maximum likelihood attribute body, the chaotic attribute body is selected to represent the seismic attribute of the fault characteristics. The reason is that: taking Well A as an example, Well A experienced 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 Figures 2(a), 2(b), 2(c), and 2(d), with a leakage of 938.6m. 3 The logging data shows medium-to-large-scale fractures 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 caused by drilling. As shown in Figure 2(b), the maximum likelihood attribute has a certain deviation from the actual fault plane and cannot effectively and accurately describe the leakage interval. As shown in Figure 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 phase axis offset and disorder, and thus provide a lost circulation warning.

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

[0055] In this example, after comparing the sweet spot attribute volume and the variance texture attribute volume, the sweet spot attribute volume is selected to represent the seismic attribute of the cave characteristics. The reason is that: taking Well B as an example, when Well B was drilled to the 2664m to 2677.9m position of the Maokou Formation, that is, the rectangular box in the center of Figure 3(a) and Figure 3(b), a small leak occurred, with a loss of 22.5m. 3 Mud logging data indicates that small-scale karst caves were encountered. A through-hole profile reveals that this leak-off interval has distinct medium-to-strong peak reflection characteristics. As shown in Figure 3(a), the prediction using variance texture attributes does not match the leak-off point. The small-scale karst caves depicted are located in the middle and lower parts of the Maokou Formation, which clearly contradicts previous geological understanding. As shown in Figure 3(b), the small-scale karst caves depicted using sweet spot attributes are primarily located in the upper part of the Maokou Formation, consistent with the geological understanding that the top of the Maokou Formation was exposed and eroded after the Dongwu Movement. Therefore, the prediction results match well with seismic reflections.

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

[0057] In this embodiment, after comparing the pre-stack fracture attribute volume and the ant attribute volume, the pre-stack fracture attribute volume is selected as the seismic attribute volume representing the fracture characteristics. The reason is that: taking Well C as an example, when Well C was drilled to the 2257.9m to 2262.3m position of the Maokou Formation, that is, the rectangular frame in the center of Figure 4(a) and Figure 4(b), a small leak occurred, with a loss of 18.6m. 3 Mud logging data indicates that small micro-scale fractures were encountered during drilling. As shown in Figure 4(a), the post-stack seismic profile through Well C shows that the leaking interval has no obvious seismic reflection characteristics. Ant attributes cannot effectively characterize small-scale fractures, and the prediction results do not match the leaking interval. However, as shown in Figure 4(b), the small-scale fractures characterized by pre-stack fracture attributes are small and discontinuous, which better match the leaking interval.

[0058] S6. Conduct 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 greater than 0.3; the optimal threshold value range of the sweet spot attribute body is less than 300; and the optimal threshold value range of the prestack fracture attribute body is greater than 5.

[0060] In this embodiment, when testing the threshold value range of the chaotic attribute body, the threshold value range is respectively 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 the 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 hollowing out the chaotic attribute body can be used 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 respectively taken as below 200, below 250, below 300, below 350 and below 400, and the threshold values ​​are respectively calculated by the petrel software. The well matching rates corresponding to the ranges 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 selected to be below 300. 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 3 or above, 4 or above, 5 or above, 6 or above and 7 or above. The well matching rates corresponding to each threshold value range are calculated by Petrel software. 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 selected to be above 5. Hollowing out the prestack fracture attribute body can be used to predict small-scale fractures.

[0061] S7. Based on the optimal threshold value range of the chaotic attribute body, sweet spot attribute body, and prestack fracture attribute body, the hollowed chaotic attribute body, sweet spot attribute body, and prestack fracture attribute body are used to provide early warning of medium-large and small leakage risks for newly drilled wells.

[0062] Based on the OVT domain pre-stack gather data of the newly drilled well, 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 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 drilled 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 medium- and large-scale fractures and caves caused by the drilled faults exist, a medium- and large leakage alarm is issued, and if small-scale caves exist, a small 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 leakage occurred in the rectangular frame in the center, with a loss of 387.5m 3 The well logging shows 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 by predicting the chaotic attribute body and the sweet spot attribute body after the optimal threshold value range is set and hollowed out. The fracture-cavity 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-cavity body developed in the Maokou Formation. Figure 5 It can be found that the lost layer is located in the composite fracture-cavity body composed of medium- and large-scale fractures and caves caused by faults and small-scale caves caused by erosion. The attribute body obtained by fusion of chaotic attribute body and sweet spot attribute body is more accurate in characterizing the fracture-cavity body, which can realize more accurate well leakage warning.

[0064] As shown in Figure 6(a), in the plane view of the attribute body after fusion, the predicted composite fracture-cavity body develops along the fault and has a northeastern distribution feature. Due to the influence of the cave at the top of the Maokou Formation, the fracture-cavity bodies caused by different faults are connected, and the area and volume of different fracture-cavity development can be calculated. Among them, the development area of ​​the fracture-cavity body through Well D (the dark green range in Figure 6) is 2509626m 2 , with a volume of 97,047,000 m 3It should be noted that the fracture-vuggy volume and area calculated here are derived from the chaotic and waveform variations of seismic events, and differ in magnitude from the actual connected fracture-vuggy volume in the subsurface. This is because most of the fracture-vuggy space has already been filled. Further research is needed on the filling type and mechanism of the drilling process to determine the unfilled fracture-vuggy volume consistent with the lost circulation volume. This can then be used to fit the seismically predicted fracture-vuggy volume coefficient and achieve quantitative characterization of the fracture-vuggy volume. Furthermore, small-scale fractures that cannot be predicted by the fusion attribute model in localized areas of the syncline can be supplemented by the prestack fracture attribute model to further improve the accuracy of lost circulation prediction. As shown in Figure 6(b), the prestack fracture attribute model can effectively predict the small-scale fractures developed in the C well area, including fracture density and direction. Finally, the lost circulation encountered by 32 wells drilled in the Maokou Formation in the study area was analyzed, including 24 wells that experienced lost circulation and 8 wells that did not. The accuracy of the fracture-vuggy prediction and lost circulation warning reached 82.8%.

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

Claims

1. A method for early warning of lost circulation 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 drilled, combined with the 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. Perform azimuth stack migration processing based on the OVT domain pre-stack gather data to obtain the pre-stack fracture attribute volume; S4. Preliminarily selecting an attribute body corresponding to the post-stack seismic data based on 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 loss mechanisms, and select the optimal characterization attribute body corresponding to different seismic response characteristics; S6. Conduct 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. Based on the optimal threshold value range of the chaotic attribute body, sweet spot attribute body, and prestack fracture attribute body, the hollowed chaotic attribute body, sweet spot attribute body, and prestack fracture attribute body are used to provide early warning of medium-large and small leakage risks for newly drilled wells.

2. The method for early warning of lost circulation risk based on multi-scale fracture-cavity bodies according to claim 1 is characterized in that: The S3 comprises the following steps: S31, divide the OVT domain pre-stack gathers evenly into several fan-shaped areas according to the azimuth angle; S32, performing stacking processing in each sector area based on the medium-long offset distance to obtain stacked seismic data in each sector area; S33, performing migration processing on the superimposed seismic data in each sector 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 lost circulation 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 lost circulation 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 seismic phase axis misalignment and disorder; S52. When the leakage mechanism is a small-scale cave, the fracture-cavity body predicted based on the variance texture attribute and the sweet spot attribute are compared and analyzed, and the sweet spot attribute is selected as the optimal characterization attribute when the seismic response characteristic is discontinuous medium-strong amplitude at the top of the Maokou Formation; S53. When the leakage mechanism is small-scale fractures, a comparative analysis is conducted on the fracture-cavity bodies predicted based on the ant attribute body and the prestack fracture attribute body, and the prestack fracture attribute body is selected as the optimal characterization attribute body when the seismic response characteristics are event axis discontinuity and waveform change.

5. The method for early warning of lost circulation 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 greater than 0.3; the optimal threshold value range of the sweet spot attribute body is less than 300; and the optimal threshold value range of the prestack fracture attribute body is greater than 5.

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