A method and device for identifying the sliding of a downhole bridge plug

Abstract

The application discloses a kind of identification method and device of downhole bridge plug sliding, the method comprises: obtaining wellhead high-frequency pressure fluctuation data from fracturing wellhead;Extract a plurality of abnormal features of wellhead high-frequency pressure fluctuation data, and according to a plurality of abnormal features, whether the downhole bridge plug is judged to slide;When downhole bridge plug slides, the target wellhead high-frequency pressure fluctuation data generated when downhole bridge plug slides is obtained;According to target wellhead high-frequency pressure fluctuation data, the type of downhole bridge plug sliding is identified.The application can accurately identify whether downhole bridge plug slides and the type of downhole bridge plug sliding at lower cost.

Description

Technical Field

[0001] This invention relates to the field of horizontal well fracturing technology, and in particular to a method and apparatus for identifying downhole bridge plug slippage. Background Technology

[0002] Horizontal well staged fracturing is a crucial technique for unconventional oil and gas resource development. Bridge plugs, as an indispensable downhole tool in staged fracturing operations, function to isolate each fracturing segment and prevent fracturing fluid cross-contamination between segments. During fracturing, bridge plugs may slip under the significant pressure differential of the fracturing fluid, thus losing their sealing ability. If timely measures such as replacing bridge plugs are not taken, the fracturing fluid will preferentially enter the dominant channels formed in the already fractured formation, ultimately leading to a failure to achieve the desired fracturing effect. Therefore, accurately identifying whether downhole bridge plug slippage has occurred is critical for adjusting subsequent fracturing operation plans.

[0003] Existing methods for identifying bridge plug slippage include downhole microseismic monitoring, distributed fiber optic monitoring, and pressure curve inference. Downhole microseismic monitoring requires monitoring the fracturing process of the drilled well within adjacent wells. When adjacent wells are far apart, the microseismic signal is interfered with and severely attenuated, making it impossible to accurately identify bridge plug slippage in the drilled well. Distributed fiber optic monitoring requires distributing optical fibers outside the casing during the cementing stage, increasing cementing costs. Furthermore, the number of wells with external fiber optic cables is limited, making this method unsuitable for all fracturing wells. As for pressure curve inference, during fracturing operations, when fractures open or the main fracture connects to natural fractures, a large amount of drilling fluid rushes into the formation, causing a sharp drop in the pressure curve. This method cannot accurately identify bridge plug slippage. Existing bridge plug slip identification methods have the following problems: downhole microseismic monitoring and distributed fiber optic monitoring are costly, difficult to perforate afterward, and cannot monitor all fracturing wells. Pressure curve inference methods are prone to sharp drops in pressure curves due to multiple fracturing events, which can lead to confusion in the analysis and thus make it impossible to accurately identify bridge plug slip.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This specification provides a method and apparatus for identifying downhole bridge plug slippage, which can solve the problem that existing technologies cannot accurately identify whether downhole bridge plugs have slipped and the type of slippage at a lower cost.

[0006] Firstly, embodiments of this specification provide a method for identifying downhole bridge plug slippage, the method comprising:

[0007] High-frequency pressure fluctuation data at the wellhead was obtained from the fractured wellhead.

[0008] Extract multiple abnormal features from the high-frequency pressure fluctuation data at the wellhead, and determine whether the downhole bridge plug has slipped based on these features.

[0009] When the downhole bridge plug slips, acquire the high-frequency pressure fluctuation data of the target wellhead generated during the downhole bridge plug slippage;

[0010] Identify the type of downhole bridge plug slippage based on high-frequency pressure fluctuation data at the target wellhead.

[0011] In one embodiment, the method further includes:

[0012] The high-frequency pressure fluctuation data at the wellhead is filtered to obtain filtered high-frequency pressure fluctuation data at the wellhead. The filtered high-frequency pressure fluctuation data at the wellhead is the high-frequency pressure fluctuation data at the wellhead after removing the operating noise of the surface fracturing pump.

[0013] Accordingly, the extracted high-frequency pressure fluctuation data at the wellhead exhibits several anomalous features, including:

[0014] Extract multiple abnormal features from the filtered high-frequency pressure fluctuation data at the wellhead.

[0015] In one embodiment, the method further includes:

[0016] Monitor whether there are any anomalies in the filtered wellhead high-frequency pressure fluctuation data;

[0017] Accordingly, the extracted high-frequency pressure fluctuation data at the wellhead exhibits several anomalous features, including:

[0018] When anomalies are found in the filtered high-frequency pressure fluctuation data at the wellhead, multiple abnormal features of the filtered high-frequency pressure fluctuation data at the wellhead are extracted.

[0019] In one embodiment, the plurality of abnormal features include abnormal water hammer pressure fluctuation features and abnormal pressure drop features, wherein the abnormal water hammer pressure fluctuation features include at least one of the following: abnormal water hammer pressure amplitude, abnormal water hammer pressure frequency, and abnormal water hammer pressure attenuation pattern.

[0020] In one embodiment, determining whether the downhole bridge plug has slipped based on multiple abnormal characteristics includes:

[0021] Correlation analysis was performed on multiple abnormal features and preset standard features of downhole events to obtain multiple correlation analysis results;

[0022] Multiple correlation analysis results are compared with corresponding preset correlation thresholds to obtain multiple comparison results;

[0023] Based on multiple comparison results, determine whether the downhole bridge plug has slipped.

[0024] In one embodiment, the downhole event includes a downhole bridge plug slippage event and a downhole bridge plug non-slippage event; correspondingly, the preset standard characteristics of the downhole event include standard characteristics of the downhole bridge plug slippage event and standard characteristics of the downhole bridge plug non-slippage event. The standard characteristics of the downhole bridge plug non-slippage event include at least one of the following: the initial amplitude of the water hammer pressure is a small negative value; the water hammer pressure frequency is a fixed frequency; the water hammer pressure decay law includes the initial amplitude of the water hammer pressure gradually decreasing from a small negative value; and the difference between the target pressure drop and the initial pressure drop is less than a preset pressure drop threshold, wherein the target pressure drop represents the difference between the initial pressure and the pressure when the pressure recovers to a stable state.

[0025] In one embodiment, identifying the type of downhole bridge plug slippage based on high-frequency pressure fluctuation data at the target wellhead includes:

[0026] Extract the target water hammer pressure fluctuation characteristics and target pressure drop characteristics from the high-frequency pressure fluctuation data at the target wellhead;

[0027] Correlation analysis was performed between the target water hammer pressure fluctuation characteristics and the target pressure drop characteristics and the standard characteristics of downhole bridge plug slippage events;

[0028] Based on the correlation analysis results, the type of downhole bridge plug slippage was identified.

[0029] In one embodiment, the downhole bridge plug slippage event includes a bridge plug re-anchoring event after slippage and a bridge plug complete slippage event; correspondingly, the standard features corresponding to the downhole bridge plug slippage event include: a first standard feature of the bridge plug re-anchoring event after slippage and a second standard feature of the bridge plug complete slippage event; correspondingly, identifying the type of downhole bridge plug slippage based on the correlation analysis results includes:

[0030] The correlation analysis results of the target water hammer pressure fluctuation characteristics and target pressure drop characteristics with the first standard characteristics are compared with the first preset correlation threshold. When the comparison result is greater than the first preset correlation threshold, the type of downhole bridge plug slippage is identified as bridge plug slippage followed by re-anchoring.

[0031] The correlation analysis results of the target water hammer pressure fluctuation characteristics and target pressure drop characteristics with the second standard characteristics are compared with the second preset correlation threshold. When the comparison result is greater than the second preset correlation threshold, the type of downhole bridge plug slippage is identified as complete bridge plug slippage. The first preset correlation threshold and the second preset correlation threshold are different.

[0032] Secondly, embodiments of this specification also provide a downhole bridge plug slippage identification device, the device comprising:

[0033] The first acquisition module is used to acquire high-frequency pressure fluctuation data from the fractured wellhead.

[0034] The judgment module is used to extract multiple abnormal features from the high-frequency pressure fluctuation data at the wellhead, and to determine whether the downhole bridge plug has slipped based on these multiple abnormal features.

[0035] The second acquisition module is used to acquire the high-frequency pressure fluctuation data of the target wellhead generated when the downhole bridge plug slips.

[0036] The identification module is used to identify the type of downhole bridge plug slippage based on the high-frequency pressure fluctuation data at the target wellhead.

[0037] Thirdly, embodiments of this specification also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for identifying downhole bridge plug slippage.

[0038] This specification provides a method and apparatus for identifying downhole bridge plug slippage. First, high-frequency pressure fluctuation data is acquired from the fractured wellhead. Second, multiple abnormal features are extracted from the high-frequency pressure fluctuation data, and based on these features, it is determined whether downhole bridge plug slippage has occurred. Then, when downhole bridge plug slippage occurs, the target wellhead high-frequency pressure fluctuation data generated during the slippage is acquired. Finally, the type of downhole bridge plug slippage is identified based on the target wellhead high-frequency pressure fluctuation data. This specification provides an embodiment that can be directly applied to fractured wells without the need for adjacent wells, accurately identifying downhole bridge plug slippage. Furthermore, no special treatment is required during the initial drilling and cementing stages, reducing cementing costs. In the embodiments described in this specification, high-frequency pressure fluctuation data at the wellhead can be directly obtained from the fracturing wellhead. Based on this data, the identification of downhole bridge plug slippage can be achieved without lowering identification tools downhole, thus avoiding the accident risks associated with such tools and reducing their cost. Furthermore, it eliminates the need for surface-excited water hammer pressure waves; high-frequency pressure fluctuation data can be directly generated from downhole events, ensuring the normal operation of the surface fracturing truck during monitoring or identification. This avoids damage to the fracturing pump truck caused by sudden pump stoppage during surface water hammer pressure activation. By integrating multiple abnormal features to determine whether downhole bridge plug slippage has occurred, the accuracy of downhole bridge plug slippage identification can be improved. When downhole bridge plug slippage occurs, by continuing to acquire the target wellhead high-frequency pressure fluctuation data generated during the slippage, the type of downhole bridge plug slippage can be further identified, providing basic information for subsequent fracturing operations to determine whether additional bridge plugs need to be installed, thereby improving fracturing effectiveness. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0040] Figure 1 This is a flowchart illustrating a method for identifying downhole bridge plug slippage provided in the embodiments of this specification;

[0041] Figure 2 This is a flowchart of the monitoring process for downhole bridge plug slippage provided in the embodiments of this specification;

[0042] Figure 3 This is a schematic diagram illustrating the monitoring principle of downhole bridge plug slippage provided in the embodiments of this specification;

[0043] Figure 4 This is a schematic diagram of water hammer pressure fluctuation during fracture initiation provided in the embodiments of this specification;

[0044] Figure 5 This is a schematic diagram of the water hammer pressure fluctuation during the re-anchoring of the bridge plug after slippage in the fracturing process, provided in the embodiments of this specification.

[0045] Figure 6 This is a schematic diagram of water hammer pressure fluctuations during fracturing when the bridge plug completely slips, as provided in the embodiments of this specification.

[0046] Figure 7 This is a schematic diagram of the structural composition of a downhole bridge plug slippage identification device provided in the embodiments of this specification;

[0047] Figure 8 This is a schematic diagram of the structural composition of the electronic device provided in the embodiments of this specification. Detailed Implementation

[0048] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0049] Low-grade resources, represented by unconventional oil and gas resources, are gradually becoming the main focus of oil exploration and development, and the production share of unconventional oil and gas resources will further increase. Horizontal well staged fracturing technology is an important means of developing unconventional oil and gas resources. Bridge plugs, as an indispensable downhole tool in staged fracturing operations, function to isolate each fracturing segment and prevent fracturing fluid cross-contamination between segments. During operation, bridge plugs may slip under the huge pressure differential of the fracturing fluid, thus losing their sealing ability. If timely measures such as replacing bridge plugs are not taken, the fracturing fluid will first enter the dominant channels formed by the already fractured formation, ultimately leading to the fracturing effect not achieving the expected results. Therefore, accurately identifying whether downhole bridge plug slippage has occurred is crucial for adjusting subsequent fracturing operation plans.

[0050] Existing methods for identifying bridge plug slippage include downhole microseismic monitoring, distributed fiber optic monitoring, and pressure curve inference. Downhole microseismic monitoring requires the insertion of geophones in monitoring wells adjacent to the fractured well. These geophones detect microseismic events generated by formation rock fracturing. Based on the microseismic events recorded by the geophones, the main spatiotemporal distribution characteristics of fluid activity during hydraulic fracturing can be calculated, thus determining the location and time of fracture opening. During staged fracturing, if the detected fracture opening location is within the previous stage, downhole bridge plug slippage can be identified (downhole bridge plug slippage indicates downhole bridge plug seal failure or ineffective setting). Distributed fiber optic monitoring includes acoustic monitoring (DAS) and temperature monitoring (DTS). DTS monitors fluid movement and distribution during fracturing by monitoring formation temperature changes, while DAS monitors the fracturing process by monitoring downhole vibrations. Combining DTS and DAS to monitor downhole fracturing fluid flow and distribution can identify whether downhole bridge plug slippage has occurred. The pressure curve inference method is to infer whether the downhole bridge plug has slipped based on the pressure curve. When the pressure curve drops sharply, it can be identified that the downhole bridge plug has slipped.

[0051] However, all of the above methods have certain limitations. For downhole microseismic monitoring, it is impossible to install geophones inside the drilling well. Instead, it is necessary to monitor the fracturing process of the drilling well (or fractured well) in an adjacent well to obtain the working condition of the downhole bridge plug. When the adjacent well is far away, the microseismic signal is interfered with and severely attenuated. At the same time, when installing geophones in an adjacent well, the fluid level in the adjacent well needs to be controlled to eliminate the influence of wellbore waves, which also affects the normal production of the adjacent well. For distributed fiber optic fracturing monitoring, it is necessary to distribute the optical fiber outside the casing during the cementing stage and complete the cementing together, which increases the cementing cost. At the same time, it is necessary to avoid fiber optic perforation during subsequent fracturing to prevent the perforation from damaging the external optical fiber, affecting data monitoring and transmission, and also increasing the difficulty of perforation in subsequent fracturing operations. In addition, the number of wells with external optical fiber is limited, so this method cannot be applied to all fracturing drilling wells. For the pressure curve inference method, during fracturing operations, when the fracture opens or the main fracture connects with the natural fracture, a large amount of drilling fluid will flow into the formation, which will also cause the pressure curve to drop sharply. Therefore, the pressure curve inference method cannot accurately identify the situation of downhole bridge plug slippage.

[0052] The existing downhole microseismic monitoring and distributed fiber optic monitoring methods suffer from high costs, difficulties in subsequent perforation, and the inability to monitor all fracturing wells. In addition, the pressure curve inference method is prone to confusion in the analysis of sharp drops in pressure curves caused by multiple fracturing events.

[0053] In view of the above-mentioned problems of existing methods and the specific reasons for these problems, this application proposes to introduce a method and device for identifying downhole bridge plug slippage, which can accurately identify whether downhole bridge plug slippage has occurred and the type of slippage, providing guidance for fracturing operations.

[0054] Based on the above approach, this specification proposes a method for identifying downhole bridge plug slippage. First, high-frequency pressure fluctuation data is obtained from the fractured wellhead. Second, multiple anomalous features are extracted from the high-frequency pressure fluctuation data, and based on these features, it is determined whether downhole bridge plug slippage has occurred. Then, when downhole bridge plug slippage occurs, the target wellhead high-frequency pressure fluctuation data generated during the slippage is acquired. Finally, based on the target wellhead high-frequency pressure fluctuation data, the type of downhole bridge plug slippage is identified.

[0055] Figure 1This is a flowchart illustrating a method for identifying downhole bridge plug slippage provided in an embodiment of this specification. While this specification provides method operation steps or apparatus structures as shown in the following embodiments or figures, based on conventional or non-inventive methods, the method or apparatus may include more or fewer operation steps or module units after partial merging. In steps or structures where there is no logically necessary causal relationship, the execution order of these steps or the module structure of the apparatus is not limited to the execution order or module structure shown in the embodiments or figures of this specification. When the method or module structure is applied in actual devices, servers, or terminal products, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or figures (e.g., in a parallel processor or multi-threaded processing environment, or even in a distributed processing or server cluster implementation environment). For specific implementation, please refer to... Figure 1 As shown, the method may include the following:

[0056] S101: Obtain high-frequency pressure fluctuation data from the fractured wellhead.

[0057] In some embodiments, the aforementioned fracturing well may also be referred to as a construction well or a fracturing construction well. The aforementioned fracturing wellhead can be the wellhead of a fracturing well, and a high-frequency, high-precision pressure sensor can be installed on the fracturing wellhead to obtain high-frequency pressure fluctuation data in real time based on this pressure sensor. The aforementioned high-frequency pressure fluctuation data at the wellhead can be high-frequency pressure fluctuation data generated by downhole events, which may include downhole plug slippage events and downhole plug non-slippage events.

[0058] By directly acquiring high-frequency pressure fluctuation data generated by downhole events from the fracturing wellhead, without relying on adjacent wells or requiring special pre-drilling or cementing treatments, cementing costs can be reduced and the accuracy and efficiency of acquiring high-frequency pressure fluctuation data from the wellhead can be improved.

[0059] S102: Extract multiple abnormal features from the high-frequency pressure fluctuation data at the wellhead, and determine whether the downhole bridge plug has slipped based on these multiple abnormal features.

[0060] In some embodiments, after obtaining high-frequency pressure fluctuation data from the fracturing wellhead as described above, the specific implementation may further include:

[0061] The high-frequency pressure fluctuation data at the wellhead is filtered to obtain filtered high-frequency pressure fluctuation data at the wellhead. The filtered high-frequency pressure fluctuation data at the wellhead is the high-frequency pressure fluctuation data at the wellhead after removing the operating noise of the surface fracturing pump. Accordingly, the above-mentioned extraction of multiple abnormal features of the high-frequency pressure fluctuation data at the wellhead may, in specific implementation, include: extracting multiple abnormal features of the filtered high-frequency pressure fluctuation data at the wellhead.

[0062] In some embodiments, the above filtering process may include at least one of the following: superposition averaging method, adaptive noise cancellation method.

[0063] By filtering the high-frequency pressure fluctuation data at the wellhead, noise from the operation of surface fracturing pumps can be removed, ensuring more accurate high-frequency pressure fluctuation data at the wellhead, thereby achieving precise identification of downhole bridge plug slippage. It should be noted that the above filtering method is not limited to the examples described above. Those skilled in the art may make other modifications based on the technical essence of the embodiments in this specification, but as long as the achieved functions and effects are the same as or similar to those in the embodiments of this specification, they should all be covered within the protection scope of the embodiments of this specification.

[0064] In some embodiments, the filtered wellhead high-frequency pressure fluctuation data can be time-domain data, and the aforementioned multiple anomaly features can include abnormal water hammer pressure fluctuation features and abnormal pressure drop features. The abnormal water hammer pressure fluctuation features can include at least one of the following: abnormal water hammer pressure amplitude, abnormal water hammer pressure frequency, and abnormal water hammer pressure attenuation law. Multiple anomaly features of the filtered wellhead high-frequency pressure fluctuation data can be extracted in the following manner:

[0065] To extract abnormal pressure drop features from filtered high-frequency pressure fluctuation data at the wellhead, the stable pressure data (or standard pressure data) under the same injection conditions can be used as a reference. The presence of abnormal pressure drop data in the filtered high-frequency pressure fluctuation data is monitored (abnormal pressure drop data is considered to exist when the pressure drop corresponding to the monitored high-frequency pressure fluctuation data is greater than or less than the pressure drop corresponding to the standard pressure data). When abnormal pressure drop data is present, abnormal pressure drop features can be extracted, such as features of abnormal instantaneous pressure drop or abnormal target pressure drop (defining the instantaneous decrease in pressure as instantaneous pressure drop). When the instantaneous pressure drop is less than or greater than the instantaneous pressure drop corresponding to the standard pressure data, the monitored instantaneous pressure drop can be regarded as an abnormal instantaneous pressure drop. The occurrence time of the abnormal instantaneous pressure drop can be recorded and its characteristics can be analyzed to obtain the characteristics of the abnormal instantaneous pressure drop. Observe the pressure change, and when the pressure stabilizes, obtain the final pressure drop or target pressure drop (the difference between the pressure drop and the initial pressure when the pressure returns to stability is defined as the final pressure drop or target pressure drop; if the monitored final pressure drop is greater than or less than the final pressure drop corresponding to the stable pressure data, the monitored final pressure drop can be regarded as an abnormal final pressure drop). The occurrence time of the abnormal final pressure drop can be recorded and its characteristics can be analyzed to obtain the characteristics of the abnormal final pressure drop.

[0066] To extract the abnormal water hammer pressure fluctuation characteristics from the filtered high-frequency pressure fluctuation data at the wellhead, a Fourier transform can be performed on the filtered high-frequency pressure fluctuation data at the wellhead to convert it into frequency domain data (the frequency domain data can include amplitude spectrum and phase spectrum). Then, spectral analysis can be performed on the frequency domain data to extract the abnormal water hammer pressure fluctuation characteristics from the high-frequency pressure fluctuation data at the wellhead. For example, spectrum analysis can be performed on the amplitude spectrum to obtain the water hammer pressure signal. Then, the water hammer pressure signal can be statistically analyzed to determine the water hammer pressure amplitude, frequency, and attenuation law. Abnormal water hammer pressure amplitude, frequency, and attenuation law can be extracted from the water hammer pressure amplitude, frequency, and attenuation law. (When the water hammer pressure amplitude, frequency, and attenuation law after spectrum analysis are greater than or less than the water hammer pressure amplitude, frequency, and attenuation law corresponding to normal water hammer pressure fluctuations, it can be considered that there are abnormal water hammer pressure amplitude, frequency, and attenuation laws in the monitored wellhead high-frequency pressure fluctuation data.)

[0067] By extracting multiple abnormal features from the filtered high-frequency pressure fluctuation data at the wellhead, a foundation can be laid for accurately identifying whether the downhole bridge plug has slipped.

[0068] In some embodiments, after filtering the wellhead high-frequency pressure fluctuation data as described above, the specific implementation may further include: monitoring whether there are any abnormalities in the filtered wellhead high-frequency pressure fluctuation data;

[0069] Accordingly, the aforementioned abnormal features of the extracted high-frequency pressure fluctuation data at the wellhead can, in specific implementation, include:

[0070] When anomalies are found in the filtered high-frequency pressure fluctuation data at the wellhead, multiple abnormal features of the filtered high-frequency pressure fluctuation data at the wellhead are extracted.

[0071] In some embodiments, the high-frequency pressure fluctuation data at the wellhead after filtering can be monitored for anomalies (i.e., whether there are abnormal data in the filtered high-frequency pressure fluctuation data at the wellhead). If abnormal data is found in the filtered high-frequency pressure fluctuation data (e.g., both abnormal water hammer pressure fluctuation data and abnormal pressure drop data exist simultaneously), then the abnormal features corresponding to multiple abnormal data in the filtered high-frequency pressure fluctuation data at the wellhead can be extracted. This ensures the effective extraction of abnormal features and improves the efficiency and accuracy of abnormal feature extraction. When monitoring whether there are anomalies in the filtered high-frequency pressure fluctuation data at the wellhead, the monitoring can be based on the high-frequency pressure fluctuations and pressure drop during stable operation.

[0072] In some embodiments, determining whether the downhole bridge plug has slipped based on multiple abnormal characteristics may include, in specific implementations, the following:

[0073] Correlation analysis was performed on multiple abnormal features and preset standard features of downhole events to obtain multiple correlation analysis results;

[0074] Multiple correlation analysis results are compared with corresponding preset correlation thresholds to obtain multiple comparison results;

[0075] Based on multiple comparison results, determine whether the downhole bridge plug has slipped.

[0076] In some embodiments, the downhole events may include downhole bridge plug slippage events and downhole bridge plug non-slippage events; correspondingly, the downhole events may include standard features of downhole bridge plug slippage events and standard features of downhole bridge plug non-slippage events. The standard features of downhole bridge plug non-slippage events may include at least one of the following: the initial amplitude of water hammer pressure is a small negative value; the water hammer pressure frequency is a fixed frequency; the water hammer pressure decay law may include the initial amplitude of water hammer pressure gradually decreasing from a small negative value; and the difference between the target pressure drop and the initial pressure drop is less than a preset pressure drop threshold, wherein the target pressure drop represents the difference between the pressure when the pressure recovers to a stable state and the initial pressure.

[0077] In some embodiments, a correlation analysis can first be performed on multiple anomalous features with standard features corresponding to downhole plug slippage events and downhole plug non-slippage events to obtain the correlation analysis results between the multiple anomalous features and downhole plug slippage events, and the correlation analysis results between the multiple anomalous features and downhole plug non-slippage events. Then, the correlation analysis results between the multiple anomalous features and downhole plug slippage events can be compared with a preset correlation threshold corresponding to the downhole plug slippage events, and the correlation analysis results between the multiple anomalous features and downhole plug non-slippage events can be compared with a preset correlation threshold corresponding to the downhole plug non-slippage events. If the comparison result is greater than the preset correlation threshold corresponding to the downhole bridge plug slippage event, it can be identified that the downhole bridge plug has slipped. If the comparison result is greater than the preset correlation threshold corresponding to the downhole bridge plug non-slippage event, it can be identified that the downhole bridge plug has not slipped. Alternatively, if the comparison result is less than the preset correlation threshold corresponding to the downhole bridge plug slippage event, it can be identified that the downhole bridge plug has not slipped. If the comparison result is less than the preset correlation threshold corresponding to the downhole bridge plug non-slippage event, it can be identified that the downhole bridge plug has slipped.

[0078] It should be noted that the aforementioned preset correlation threshold and preset voltage drop threshold can be set according to actual needs, and this manual does not impose specific limitations on them.

[0079] In some embodiments, the abnormal water hammer pressure amplitude, abnormal water hammer pressure frequency, abnormal water hammer pressure attenuation law, and abnormal pressure drop characteristics can be sequentially compared with the standard characteristics (standard water hammer pressure amplitude, standard water hammer pressure frequency, standard water hammer pressure attenuation law, and standard pressure drop characteristics) corresponding to downhole events (downhole bridge plug slippage events and downhole bridge plug non-slippage events) to obtain a first difference processing result related to the downhole bridge plug slippage event and a second difference processing result related to the downhole bridge plug non-slippage event. The first difference processing result and the second difference processing result are then compared to determine whether the downhole bridge plug has slipped. For example, if the first difference processing result is greater than the second difference processing result, it indicates that the downhole bridge plug has not slipped; if the first difference processing result is less than the second difference processing result, it indicates that the downhole bridge plug has slipped.

[0080] In some embodiments, the aforementioned downhole bridge plug non-slippage event may include a fracture initiation event, which refers to the following: during fracturing, fracturing fluid is pumped into the wellbore by the fracturing truck unit. The fracturing fluid is obstructed by the bridge plug, causing the pressure inside the wellbore to rise. When the pressure exceeds the formation fracturing pressure, the formation fracture opens, and the fracturing fluid enters the formation through the blast hole. The standard characteristics of the aforementioned downhole bridge plug non-slip event may include the standard characteristics of a fracture initiation event. These standard characteristics may include: as fracturing fluid flows into the formation through the borehole, an initial pressure drop (also called instantaneous pressure drop, referring to the instantaneous decrease in pressure) is generated at the borehole; the target pressure drop (or final pressure drop, referring to the difference between the initial pressure and the pressure when it stabilizes) is less than a preset pressure drop threshold, meaning the target pressure drop under a fracture initiation event is not significant; when a fracture initiates, a small negative initial amplitude is generated (the initial amplitude refers to the amplitude of the first fluctuation in water hammer pressure); when a fracture initiates, the water hammer pressure signal frequency is fixed, i.e., a fixed frequency; when a fracture initiates, the water hammer pressure attenuation pattern (the water hammer pressure attenuation pattern refers to the change in the peak value of the monitored water hammer pressure signal over time) may include the initial amplitude of the water hammer pressure gradually decreasing from a small negative value.

[0081] By comprehensively utilizing multiple anomalous features (abnormal water hammer pressure amplitude, abnormal water hammer pressure frequency, abnormal water hammer pressure attenuation pattern, and abnormal pressure drop) to determine whether downhole bridge plug slippage has occurred, the accuracy of downhole bridge plug slippage identification can be improved. Furthermore, by performing correlation analysis between multiple anomalous features and preset standard features of downhole events, the efficiency of downhole bridge plug slippage identification can be improved. This eliminates the need to lower identification tools downhole to determine whether slippage has occurred, avoiding the accident risks associated with tool deployment and reducing the cost of using such tools.

[0082] S103: When the downhole bridge plug slips, acquire the high-frequency pressure fluctuation data of the target wellhead generated during the slippage of the downhole bridge plug.

[0083] In some embodiments, when the downhole bridge plug slips, high-frequency pressure fluctuation data of the target wellhead generated during the downhole bridge plug slippage can be acquired based on a high-frequency, high-precision pressure sensor at the fracturing wellhead. The aforementioned high-frequency pressure fluctuation data of the target wellhead can be high-frequency pressure fluctuation data generated by the downhole bridge plug slippage event, and may include water hammer pressure fluctuation data.

[0084] By acquiring high-frequency pressure fluctuation data from the target wellhead, it is possible to avoid impacting fracturing operations. High-frequency pressure fluctuation data generated through downhole bridge plug slippage events, without requiring surface-excited water hammer pressure waves, allows the surface fracturing unit to operate normally during downhole bridge plug monitoring or identification. Simultaneously, it avoids problems such as damage to the fracturing pump truck caused by sudden pump stoppage during surface water hammer pressure activation.

[0085] S104: Identify the type of downhole bridge plug slippage based on the high-frequency pressure fluctuation data at the target wellhead.

[0086] In some embodiments, identifying the type of downhole bridge plug slippage based on high-frequency pressure fluctuation data at the target wellhead can, in specific implementation, include:

[0087] Extract the target water hammer pressure fluctuation characteristics and target pressure drop characteristics from the high-frequency pressure fluctuation data at the target wellhead;

[0088] Correlation analysis was performed between the target water hammer pressure fluctuation characteristics and the target pressure drop characteristics and the standard characteristics of downhole bridge plug slippage events;

[0089] Based on the correlation analysis results, the type of downhole bridge plug slippage was identified.

[0090] In some embodiments, the target pressure water hammer fluctuation characteristics may include the target water hammer pressure amplitude, the target water hammer pressure frequency, and the target water hammer pressure attenuation pattern. The target water hammer pressure fluctuation characteristics and target pressure drop characteristics are used to distinguish them from the abnormal water hammer pressure fluctuation characteristics and abnormal pressure drop characteristics mentioned above, to avoid conceptual confusion. The target water hammer pressure fluctuation characteristics and target pressure drop characteristics represent feature data extracted from currently monitored downhole plug slippage events.

[0091] In some embodiments, the aforementioned downhole bridge plug slippage event may include a bridge plug re-anchoring event after slippage and a bridge plug complete slippage event; correspondingly, the standard characteristics of the aforementioned downhole bridge plug slippage event may include: a first standard characteristic of a bridge plug re-anchoring event after slippage and a second standard characteristic of a bridge plug complete slippage event; correspondingly, the aforementioned identification of the type of downhole bridge plug slippage based on the correlation analysis results may, in specific implementation, include:

[0092] The correlation analysis results of the target water hammer pressure fluctuation characteristics and target pressure drop characteristics with the first standard characteristics are compared with the first preset correlation threshold. When the comparison result is greater than the first preset correlation threshold, the type of downhole bridge plug slippage is identified as bridge plug slippage followed by re-anchoring.

[0093] The correlation analysis results of the target water hammer pressure fluctuation characteristics and target pressure drop characteristics with the second standard characteristics are compared with the second preset correlation threshold. When the comparison result is greater than the second preset correlation threshold, the type of downhole bridge plug slippage is identified as complete bridge plug slippage. The first preset correlation threshold and the second preset correlation threshold are different.

[0094] In some embodiments, the aforementioned bridge plug re-anchoring event after slippage refers to: when the bridge plug cannot withstand the enormous pressure of the fracturing fluid, the bridge plug slips, and after slipping a certain distance, it re-anchors itself on the casing before passing the previous fracturing position. The aforementioned bridge plug complete slippage event refers to: after slippage, the bridge plug has passed the previous fracturing position, and the bridge plug has completely slipped. The first standard characteristics of the aforementioned bridge plug re-anchoring event after slippage may include: when the bridge plug re-anchors after slippage, the initial pressure drop is high, and the target pressure drop is low after re-anchoring; when the bridge plug re-anchors after slippage, this process generates two water hammers, with a large negative initial amplitude during the bridge plug slippage stage and a large positive initial amplitude during re-anchoring; when the bridge plug re-anchors after slippage, the water hammer pressure frequency first decreases (during the bridge plug slippage process) and then remains unchanged (after the bridge plug re-anchors, a new water hammer pressure is generated, and the frequency is fixed); when the bridge plug re-anchors after slippage, a positive water hammer pressure amplitude is generated after the bridge plug re-anchors in the later stage, and the water hammer pressure amplitude gradually decreases over time. The second standard characteristic of the aforementioned bridge plug complete slip event may include: when the bridge plug completely slips, fracturing fluid flows into the next stage, the bridge plug loses its setting ability, and both the initial pressure drop and the target pressure drop are high; when the bridge plug completely slips, a negative initial amplitude is generated, and the amplitude is large; when the bridge plug completely slips, the water hammer pressure frequency becomes lower; when the bridge plug completely slips, a negative water hammer pressure is formed, accompanied by an upward water hammer pressure peak, and the water hammer pressure amplitude decreases over time.

[0095] It should be noted that the above-mentioned downhole bridge plug slippage events are not limited to the examples mentioned above. Those skilled in the art may make other changes based on the technical essence of the embodiments in this specification. However, as long as the functions and effects achieved are the same as or similar to those in the embodiments of this specification, they should all be covered within the protection scope of the embodiments of this specification.

[0096] In some embodiments, the correlation analysis results between the target water hammer pressure fluctuation characteristics and target pressure drop characteristics and the first standard characteristics can be referred to as the first correlation analysis results, and the correlation analysis results between the target water hammer pressure fluctuation characteristics and target pressure drop characteristics and the second standard characteristics can be referred to as the second correlation analysis results. The first correlation analysis results can be compared with a first preset correlation threshold, and the comparison result is referred to as the first comparison result. The second correlation analysis results can be compared with a second preset correlation threshold, and the comparison result is referred to as the second comparison result. When the first comparison result is greater than the first preset correlation threshold, the type of downhole bridge plug slippage is identified as bridge plug slippage followed by re-anchoring. When the second comparison result is greater than the second preset correlation threshold, the type of downhole bridge plug slippage is identified as complete bridge plug slippage.

[0097] It should be noted that the first preset correlation threshold and the second preset correlation threshold mentioned above can be set according to actual needs, and this manual does not impose specific limitations on them.

[0098] In some embodiments, when it is determined that the downhole bridge plug has slipped, the high-frequency pressure fluctuation data of the target wellhead generated during the downhole bridge plug slippage can be acquired, the high-frequency pressure fluctuation data of the target wellhead can be monitored, the high-frequency pressure fluctuation data of the target wellhead can be feature extracted, and then correlation analysis can be performed with the standard features of the downhole bridge plug slippage event, so as to identify the type of the current downhole bridge plug slippage.

[0099] In some embodiments, after identifying the type of downhole bridge plug slippage, the implementation may further include:

[0100] Based on the identified type of downhole bridge plug slippage, a bridge plug replacement strategy corresponding to the downhole bridge plug slippage type is formulated, and bridge plugs are then replaced in the fractured well based on the bridge plug replacement strategy.

[0101] By developing a strategy for replenishing bridge plugs that corresponds to the type of slippage of the downhole bridge plug, suitable bridge plugs can be replenished into the fractured well in a timely and accurate manner to improve the fracturing effect and thus promote the efficient exploration and development of unconventional oil and gas resources.

[0102] The above method will be described below with reference to a specific embodiment. However, it is worth noting that this specific embodiment is only for better illustration of this application and does not constitute an improper limitation of this application.

[0103] In practice, firstly, real-time high-frequency pressure fluctuation data at the wellhead is obtained from a high-frequency, high-precision pressure sensor at the wellhead of the fracturing well. Then, the high-frequency pressure fluctuation data is filtered using methods such as superposition averaging and adaptive noise cancellation to remove the influence of noise from surface fracturing pump operation. Next, the high-frequency pressure fluctuation data is monitored, with stable operating pressure fluctuations used as a basis for monitoring abnormal pressure fluctuations. When abnormal pressure fluctuations are detected, they are analyzed, extracting abnormal pressure fluctuation characteristics and abnormal pressure drop characteristics from the high-frequency pressure fluctuation data at the wellhead. These are compared with pre-acquired water hammer pressure fluctuation characteristics and pressure drop characteristics of known downhole events to determine whether bridge plug slippage has occurred. Finally, fracturing operations continue, and high-frequency pressure fluctuation data at the wellhead is continuously acquired and compared with pre-acquired water hammer pressure fluctuation characteristics and pressure drop characteristics corresponding to downhole bridge plug slippage events to identify the type of downhole bridge plug slippage.

[0104] The above method can accurately and quickly identify whether bridge plug slippage has occurred and the type of downhole bridge plug slippage at a low cost. It can be applied to all fracturing wells and has high applicability, providing reliable guidance for subsequent fracturing operations.

[0105] In a specific scenario example, the identification method of downhole bridge plug slippage can be illustrated by taking downhole events including fracture initiation events, bridge plug slippage and re-anchoring events, and bridge plug complete slippage events. Among them, high-frequency pressure fluctuation data (which may include water hammer pressure fluctuation data) generated by fracture initiation events, bridge plug slippage and re-anchoring events, and bridge plug complete slippage events can be obtained by pressure sensors on the fracture wellhead.

[0106] See Figure 2 As shown, Figure 2 The process for monitoring downhole bridge plug slippage is illustrated. First, high-frequency pressure fluctuation data from the fractured wellhead is acquired. Then, this high-frequency pressure fluctuation data is filtered to remove the influence of surface fracturing pump operation. Next, abnormal fluctuation monitoring is performed on the filtered data to determine if bridge plug slippage has occurred downhole. If slippage has occurred, high-frequency pressure fluctuation data from the fractured wellhead is acquired again and monitored to determine the type of slippage, such as bridge plug re-anchoring. If no slippage has occurred, it indicates borehole opening or fracture initiation. Ultimately, downhole bridge plug slippage can be monitored or identified.

[0107] See Figure 3 The above, Figure 3The monitoring principle of downhole bridge plug slippage is illustrated. When identifying downhole bridge plug slippage, the pressure fluctuation amplitude, frequency, and attenuation pattern of high-frequency pressure fluctuation data from the fracturing wellhead are extracted and then compared with the water hammer pressure characteristics generated by fracture initiation events (or borehole opening events), bridge plug re-anchoring events after slippage, and complete bridge plug slippage events. If the extracted pressure fluctuation amplitude, frequency, and attenuation pattern are close to the water hammer pressure characteristics generated by fracture initiation events, it indicates that the downhole bridge plug has not slipped, and the current state of the downhole bridge plug is fracture initiation or borehole opening. If the extracted pressure fluctuation amplitude, frequency, and attenuation pattern are close to the water hammer pressure characteristics generated by bridge plug re-anchoring events after slippage, it indicates that the downhole bridge plug has slipped, and the current type of downhole bridge plug slippage is bridge plug re-anchoring after slippage. If the extracted pressure fluctuation amplitude, pressure fluctuation frequency, and pressure fluctuation attenuation law characteristics of the water hammer pressure generated by the complete slippage of the bridge plug are similar, it indicates that the downhole bridge plug has slipped, and the current type of downhole bridge plug slippage is complete slippage.

[0108] When identifying bridge plug slippage, the pressure fluctuation amplitude, pressure fluctuation frequency, and pressure fluctuation attenuation law in the high-frequency pressure fluctuation data of the fracturing wellhead can be extracted and combined with the pressure drop characteristics. Then, the characteristic parameters can be compared or correlation analysis can be performed with the water hammer pressure characteristics and pressure drop characteristics generated by the fracture initiation event (or borehole opening event), bridge plug re-anchoring event after slippage, and bridge plug complete slippage event to improve the accuracy of downhole bridge plug slippage identification.

[0109] Among them, the known water hammer pressure characteristics and pressure drop characteristics corresponding to the crack initiation event, the bridge plug re-anchoring event after slippage, and the bridge plug complete slippage event are as follows:

[0110] For crack initiation events, see [reference]. Figure 4 As shown, Figure 4 This diagram illustrates the water hammer pressure fluctuation during fracture initiation in fracturing. When a fracture initiates (or the borehole opens), fracturing fluid rushes into the formation, causing an instantaneous pressure drop. As the fracturing fluid flows through the borehole, the borehole friction causes the pressure to recover to approximately the original pressure level, resulting in a small or negligible final pressure drop. Simultaneously, the instantaneous change in fluid flow characteristics also causes a water hammer pressure fluctuation. The formula for calculating borehole friction is shown below.

[0111]

[0112] Where, p p,j The frictional resistance of the borehole is given by ρ, where ρ is the liquid density and n is the fluid density. p,j d represents the number of blast holes corresponding to the crack. p,j Let Q be the diameter of the perforation hole, K be the dimensionless flow coefficient, and Q be the diameter of the perforation hole.j For traffic.

[0113] When a crack initiates, it generates an initial amplitude of negative water hammer pressure; the signal frequency of the water hammer pressure is fixed and does not change; its attenuation characteristics are as follows: it generates a negative water hammer pressure amplitude, and as time goes by, the water hammer pressure amplitude gradually decreases, while the pressure eventually recovers to a pressure close to the initial pressure, and the final pressure drop is very small.

[0114] For the re-anchoring event following bridge plug slippage, see [link / reference]. Figure 5 As shown, Figure 5 The diagram illustrates the water hammer pressure fluctuations during the re-anchoring of the bridge plug after slippage in the fracturing process. When the bridge plug re-anchors after slippage, the fluid at the rear end of the bridge plug is instantly stopped, and the kinetic energy of the fluid flow is converted into pressure energy, causing the pressure to rise instantaneously and generating water hammer pressure waves.

[0115] During the re-anchoring process after the bridge plug slips, two water hammer events occur. The bridge plug slipping stage generates a large negative initial amplitude, while the re-anchoring stage generates a large positive initial amplitude. The frequency of the water hammer pressure first decreases (during the bridge plug slipping process) and then remains constant (after the bridge plug re-anchors, a new water hammer pressure is generated with a fixed frequency). Its attenuation characteristics are as follows: the first stage is the bridge plug slipping stage, and the later stage, after the bridge plug re-anchors, generates a positive water hammer pressure amplitude, which gradually decreases over time. The initial pressure drop is relatively high, while the final pressure drop after the bridge plug re-anchors is relatively low.

[0116] For the bridge plug complete slippage event, see [reference]. Figure 6 As shown, Figure 6 The diagram shows the water hammer pressure fluctuation when the bridge plug completely slips during fracturing. When the bridge plug completely slips, it loses its ability to set and bear pressure. The fracturing fluid preferentially enters the fracturing section of the previous stage. The fluid flowing through the borehole decreases, and the borehole friction decreases. Therefore, after the water hammer pressure fluctuation occurs, the pressure cannot rise back to approximately the original pressure level, resulting in a large final pressure drop.

[0117] When the bridge plug is fully slipped, a negative initial amplitude is generated, and the amplitude is relatively large; the frequency of the water hammer pressure signal becomes lower; its attenuation characteristics are: a negative water hammer pressure is formed, accompanied by an upward water hammer pressure peak, and the water hammer pressure amplitude decreases over time.

[0118] By filtering the high-frequency, high-precision pressure fluctuation data acquired in real time at the wellhead during fracturing operations to remove the influence of surface fracturing pump trucks, and then monitoring the real-time pressure data, when an abnormal pressure occurs, the abnormal pressure zone is characterized and the abnormal features are extracted. The extracted abnormal features are then correlated with the water hammer pressure fluctuation characteristics and pressure drop characteristics under conditions such as borehole opening (or fracture initiation), complete bridge plug slippage, and bridge plug re-anchoring after slippage. This allows for the accurate identification of downhole bridge plug slippage.

[0119] Compared to downhole microseismic monitoring and distributed fiber optic monitoring, the aforementioned method for identifying bridge plug slippage only requires high-frequency pressure fluctuation data at the wellhead, eliminating the need to run monitoring tools downhole. This avoids the accident risks associated with tool deployment and reduces associated costs. Furthermore, since this method is based on wellhead data, it does not affect fracturing operations. Moreover, it does not rely on adjacent wells and requires no special treatment during the initial drilling or cementing stages, making it applicable to all fracturing wells.

[0120] Compared to the pressure curve inference method, the above-mentioned downhole bridge plug slippage identification method uses high-frequency, high-precision water hammer pressure fluctuation data as the signal for bridge plug slippage identification. The analyzable features mainly include parameters such as pressure fluctuation frequency and amplitude. With a large number of feature parameters, the downhole bridge plug slippage situation can be comprehensively identified based on multiple feature parameters, thereby improving the accuracy of downhole bridge plug slippage identification.

[0121] Furthermore, the aforementioned method for identifying downhole bridge plug slippage collects water hammer pressure waves generated by downhole events (fracture initiation events, bridge plug re-anchoring events after slippage, etc.), without requiring surface excitation of water hammer pressure waves. Therefore, the surface fracturing truck can operate normally during the monitoring process, avoiding the damage to the fracturing pump truck caused by sudden pump stoppage when surface water hammer pressure is activated.

[0122] Although this specification provides the following examples or appendices Figure 7 The methods, steps, or apparatus structures shown may include more or fewer combined operational steps or module units based on conventional or non-inventive methods. In steps or structures where there is no logically necessary causal relationship, the execution order of these steps or the module structure of the apparatus is not limited to the execution order or module structure shown in the embodiments or drawings of this specification. When the methods or module structures described are applied in actual devices, servers, or terminal products, they can be executed sequentially or in parallel according to the methods or module structures shown in the embodiments or drawings (e.g., in parallel processor or multi-threaded processing environments, or even distributed processing or server cluster implementation environments).

[0123] Based on the above-described method for identifying downhole bridge plug slippage, this specification also provides an embodiment of a downhole bridge plug slippage identification device. For example... Figure 7 As shown, the device may specifically include the following modules:

[0124] The first acquisition module 701 can be used to acquire high-frequency pressure fluctuation data from the fractured wellhead.

[0125] The judgment module 702 can be used to extract multiple abnormal features from the high-frequency pressure fluctuation data at the wellhead, and determine whether the downhole bridge plug has slipped based on the multiple abnormal features.

[0126] The second acquisition module 703 can be used to acquire the high-frequency pressure fluctuation data of the target wellhead generated when the downhole bridge plug slips.

[0127] The identification module 704 can be used to identify the type of downhole bridge plug slippage based on the high-frequency pressure fluctuation data at the target wellhead.

[0128] In some embodiments, the first acquisition module 701 may be further used to filter the wellhead high-frequency pressure fluctuation data to obtain filtered wellhead high-frequency pressure fluctuation data, wherein the filtered wellhead high-frequency pressure fluctuation data is wellhead high-frequency pressure fluctuation data after removing the operating noise of the surface fracturing pump.

[0129] In some embodiments, the judgment module 702 can be specifically used to extract multiple abnormal features from the filtered wellhead high-frequency pressure fluctuation data. The judgment module 702 can also be used to monitor whether there are any abnormalities in the filtered wellhead high-frequency pressure fluctuation data; and when there are abnormalities in the filtered wellhead high-frequency pressure fluctuation data, to extract multiple abnormal features from the filtered wellhead high-frequency pressure fluctuation data.

[0130] In some embodiments, the multiple abnormal features in the judgment module 702 may include abnormal water hammer pressure fluctuation features and abnormal pressure drop features. The abnormal water hammer pressure fluctuation features may include at least one of the following: abnormal water hammer pressure amplitude, abnormal water hammer pressure frequency, and abnormal water hammer pressure attenuation law.

[0131] In some embodiments, the judgment module 702 can be specifically used to perform correlation analysis between multiple abnormal features and preset standard features of downhole events to obtain multiple correlation analysis results; compare the multiple correlation analysis results with the corresponding preset correlation thresholds to obtain multiple comparison results; and determine whether the downhole bridge plug has slipped based on the multiple comparison results.

[0132] In some embodiments, the downhole events in the determination module 702 may include downhole bridge plug slippage events and downhole bridge plug non-slippage events; correspondingly, the preset standard features of the downhole events may include standard features of downhole bridge plug slippage events and standard features of downhole bridge plug non-slippage events. The standard features of the downhole bridge plug non-slippage events may include at least one of the following: the initial amplitude of the water hammer pressure is a small negative value; the water hammer pressure frequency is a fixed frequency; the water hammer pressure decay law includes the initial amplitude of the water hammer pressure gradually decreasing from a small negative value; and the difference between the target pressure drop and the initial pressure drop is less than a preset pressure drop threshold, wherein the target pressure drop represents the difference between the initial pressure and the pressure when the pressure recovers to a stable state.

[0133] In some embodiments, the identification module 704 can be specifically used to extract the target water hammer pressure fluctuation characteristics and target pressure drop characteristics from the target wellhead high-frequency pressure fluctuation data; perform correlation analysis between the target water hammer pressure fluctuation characteristics and target pressure drop characteristics and the standard characteristics of downhole bridge plug slippage events; and identify the type of downhole bridge plug slippage based on the correlation analysis results.

[0134] In some embodiments, the downhole bridge plug slippage event in the identification module 704 may include a bridge plug re-anchoring event after slippage and a bridge plug complete slippage event; the standard features of the downhole bridge plug slippage event may include: a first standard feature of the bridge plug re-anchoring event after slippage and a second standard feature of the bridge plug complete slippage event; the identification module 704 may specifically be used to compare the correlation analysis results of the target water hammer pressure fluctuation features and target pressure drop features with the first standard features with a first preset correlation threshold, and when the comparison result is greater than the first preset correlation threshold, the type of downhole bridge plug slippage is identified as bridge plug re-anchoring after slippage; the correlation analysis results of the target water hammer pressure fluctuation features and target pressure drop features with the second standard features are compared with the second preset correlation threshold, and when the comparison result is greater than the second preset correlation threshold, the type of downhole bridge plug slippage is identified as bridge plug complete slippage, wherein the first preset correlation threshold and the second preset correlation threshold are different.

[0135] It should be noted that the units, devices, or modules described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. For ease of description, the above devices are described by dividing them into various modules according to their functions. Of course, in implementing this specification, the functions of each module can be implemented in one or more software and / or hardware, or the module that implements the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection between the devices or units shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0136] As can be seen from the above, the downhole bridge plug slippage identification device provided in the embodiments of this specification can realize real-time monitoring or identification of downhole bridge plug slippage during bridge plug segmented fracturing, and obtain the downhole bridge plug slippage status (whether slippage has occurred and the type of slippage) in a timely manner, providing basic information for subsequent fracturing operations on whether additional bridge plugs need to be installed.

[0137] This specification also provides an electronic device based on the above-described method for identifying downhole bridge plug slippage, including a processor and a memory for storing processor-executable instructions. Specifically, the processor can execute the following steps according to the instructions: acquiring high-frequency pressure fluctuation data from the fractured wellhead; extracting multiple abnormal features from the high-frequency pressure fluctuation data and determining whether downhole bridge plug slippage has occurred based on these abnormal features; when downhole bridge plug slippage occurs, acquiring target wellhead high-frequency pressure fluctuation data generated during the slippage; and identifying the type of downhole bridge plug slippage based on the target wellhead high-frequency pressure fluctuation data.

[0138] To execute the above instructions more accurately, please refer to... Figure 8 As shown in the embodiments of this specification, another specific electronic device is also provided, wherein the electronic device includes a network communication port 801, a processor 802, and a memory 803. The above structures are connected by internal cables so that the various structures can perform specific data interaction.

[0139] Specifically, the network communication port 801 can be used to obtain high-frequency pressure fluctuation data from the fracturing wellhead.

[0140] The processor 802 can be specifically used to extract multiple abnormal features from the high-frequency pressure fluctuation data at the wellhead, and determine whether the downhole bridge plug has slipped based on the multiple abnormal features; when the downhole bridge plug slips, it acquires the target wellhead high-frequency pressure fluctuation data generated when the downhole bridge plug slips; and it identifies the type of downhole bridge plug slip based on the target wellhead high-frequency pressure fluctuation data.

[0141] The memory 803 can be used to store the corresponding instruction program.

[0142] In this embodiment, the network communication port 801 can be a virtual port bound to different communication protocols, thereby enabling the sending or receiving of different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM or CDMA; it can also be a Wi-Fi chip; or it can be a Bluetooth chip.

[0143] In this embodiment, the processor 802 can be implemented in any suitable manner. For example, the processor can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers, etc. This specification is not limiting.

[0144] In this embodiment, the memory 803 may include multiple layers. In a digital system, anything that can store binary data can be a memory. In an integrated circuit, a circuit with storage function but no physical form is also called a memory, such as RAM, FIFO, etc. In a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0145] This specification also provides a computer storage medium based on the above-described method for identifying downhole bridge plug slippage. The computer storage medium stores computer program instructions that, when executed, perform the following: acquiring high-frequency pressure fluctuation data from the fractured wellhead; extracting multiple abnormal features from the high-frequency pressure fluctuation data and determining whether downhole bridge plug slippage has occurred based on these abnormal features; acquiring target high-frequency pressure fluctuation data of the target wellhead when downhole bridge plug slippage occurs; and identifying the type of downhole bridge plug slippage based on the target high-frequency pressure fluctuation data.

[0146] In this embodiment, the storage medium includes, but is not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), cache, hard disk drive (HDD), or memory card. The memory can be used to store computer program instructions. The network communication unit can be an interface configured according to standards specified in the communication protocol for network connection communication.

[0147] While this specification provides the steps of operation for the methods described in the embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible order of execution among many steps and does not represent the only possible order. In actual device or client product execution, the methods shown in the embodiments or drawings may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, the presence of other identical or equivalent elements in a process, method, product, or apparatus that includes said elements is not excluded. The terms "first," "second," etc., are used to denote names and do not indicate any particular order.

[0148] Those skilled in the art will also know that, besides implementing the controller using purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller function as logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices within it used to implement various functions can also be considered structures within that hardware component. Alternatively, the devices used to implement various functions can be considered as both software modules implementing the method and structures within a hardware component.

[0149] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0150] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this specification can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions of this specification can essentially be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments of this specification.

[0151] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. This specification can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0152] Although this specification has been described by way of examples, those skilled in the art will recognize that many variations of this specification are possible without departing from its spirit, and it is intended that the appended claims cover such variations without departing from the spirit of this specification.

Claims

1. A method for identifying slippage of a downhole bridge plug, characterized in that, include: High-frequency pressure fluctuation data at the wellhead was obtained from the fractured wellhead. Extract multiple abnormal features from the high-frequency pressure fluctuation data at the wellhead, and determine whether the downhole bridge plug has slipped based on these multiple abnormal features; When the downhole bridge plug slips, acquire the high-frequency pressure fluctuation data of the target wellhead generated during the downhole bridge plug slippage; Identify the type of downhole bridge plug slippage based on high-frequency pressure fluctuation data at the target wellhead; The step of identifying the type of downhole bridge plug slippage based on high-frequency pressure fluctuation data at the target wellhead includes: Extract the target water hammer pressure fluctuation characteristics and target pressure drop characteristics from the high-frequency pressure fluctuation data at the target wellhead; Correlation analysis was performed between the target water hammer pressure fluctuation characteristics and the target pressure drop characteristics and the standard characteristics of downhole bridge plug slippage events; Based on the correlation analysis results, identify the type of downhole bridge plug slippage; The downhole bridge plug slippage event includes a bridge plug re-anchoring event after slippage and a bridge plug complete slippage event; correspondingly, the standard characteristics of the downhole bridge plug slippage event include: a first standard characteristic of a bridge plug re-anchoring event after slippage and a second standard characteristic of a bridge plug complete slippage event; correspondingly, identifying the type of downhole bridge plug slippage based on the correlation analysis results includes: The correlation analysis results of the target water hammer pressure fluctuation characteristics and target pressure drop characteristics with the first standard characteristics are compared with the first preset correlation threshold. When the comparison result is greater than the first preset correlation threshold, the type of downhole bridge plug slippage is identified as bridge plug slippage followed by re-anchoring. The correlation analysis results of the target water hammer pressure fluctuation characteristics and target pressure drop characteristics with the second standard characteristics are compared with the second preset correlation threshold. When the comparison result is greater than the second preset correlation threshold, the type of downhole bridge plug slippage is identified as complete bridge plug slippage. The first preset correlation threshold and the second preset correlation threshold are different. The bridge plug re-anchoring event after slippage occurs when the bridge plug slips a certain distance and then re-anchors itself on the casing before crossing the previous fracturing position. The complete bridge plug slippage event occurs when the bridge plug has slipped beyond the previous fracturing position and has completely slipped. The first standard feature includes: when the bridge plug re-anchors after slippage, the initial pressure drop is high, and the target pressure drop is low after re-anchoring; when the bridge plug re-anchors after slippage, two water hammer events occur, with a negative initial amplitude during the bridge plug slippage stage and a positive initial amplitude during re-anchoring; when the bridge plug re-anchors after slippage, the water hammer pressure frequency is initially... The initial pressure drop decreases and then remains unchanged; when the bridge plug re-anchors after slippage, a positive water hammer pressure amplitude is generated after the bridge plug re-anchors, and the water hammer pressure amplitude gradually decreases over time; the second standard characteristics include: when the bridge plug is fully slipped, fracturing fluid flows into the next stage, the bridge plug loses its setting ability, and both the initial pressure drop and the target pressure drop are high; when the bridge plug is fully slipped, a negative initial amplitude is generated, and the amplitude is large; when the bridge plug is fully slipped, the water hammer pressure frequency becomes lower; when the bridge plug is fully slipped, a negative water hammer pressure is formed, accompanied by an upward water hammer pressure peak, and the water hammer pressure amplitude decreases over time.

2. The method according to claim 1, characterized in that, The method further includes: The high-frequency pressure fluctuation data at the wellhead is filtered to obtain filtered high-frequency pressure fluctuation data at the wellhead. The filtered high-frequency pressure fluctuation data at the wellhead is the high-frequency pressure fluctuation data at the wellhead after removing the operating noise of the surface fracturing pump. Accordingly, the extracted high-frequency pressure fluctuation data at the wellhead exhibits several anomalous features, including: Extract multiple anomalous features from the filtered wellhead high-frequency pressure fluctuation data.

3. The method according to claim 1, characterized in that, The method further includes: Monitor whether there are any anomalies in the filtered wellhead high-frequency pressure fluctuation data; Accordingly, the extracted high-frequency pressure fluctuation data at the wellhead exhibits several anomalous features, including: When anomalies are found in the filtered high-frequency pressure fluctuation data at the wellhead, multiple abnormal features of the filtered high-frequency pressure fluctuation data at the wellhead are extracted.

4. The method according to claim 1, characterized in that, The multiple abnormal features include abnormal water hammer pressure fluctuation features and abnormal pressure drop features. The abnormal water hammer pressure fluctuation features include at least one of the following: abnormal water hammer pressure amplitude, abnormal water hammer pressure frequency, and abnormal water hammer pressure attenuation pattern.

5. The method according to claim 1, characterized in that, The method of determining whether the downhole bridge plug has slipped based on multiple abnormal characteristics includes: Correlation analysis was performed on multiple abnormal features and preset standard features of downhole events to obtain multiple correlation analysis results; Multiple correlation analysis results are compared with corresponding preset correlation thresholds to obtain multiple comparison results; Based on multiple comparison results, determine whether the downhole bridge plug has slipped.

6. The method according to claim 5, characterized in that, The downhole events include downhole bridge plug slippage events and downhole bridge plug non-slippage events; correspondingly, the preset standard characteristics of the downhole events include standard characteristics of downhole bridge plug slippage events and standard characteristics of downhole bridge plug non-slippage events. The standard characteristics of downhole bridge plug non-slippage events include at least one of the following: the initial amplitude of water hammer pressure is a small negative value; the water hammer pressure frequency is a fixed frequency; the water hammer pressure decay law includes the initial amplitude of water hammer pressure gradually decreasing from a small negative value; and the difference between the target pressure drop and the initial pressure drop is less than a preset pressure drop threshold, wherein the target pressure drop represents the difference between the pressure when the pressure recovers to a stable state and the initial pressure.

7. A device for identifying slippage of a downhole bridge plug, characterized in that, include: The first acquisition module is used to acquire high-frequency pressure fluctuation data from the fractured wellhead. The judgment module is used to extract multiple abnormal features from the high-frequency pressure fluctuation data at the wellhead, and to determine whether the downhole bridge plug has slipped based on these multiple abnormal features. The second acquisition module is used to acquire the high-frequency pressure fluctuation data of the target wellhead generated when the downhole bridge plug slips. The identification module is used to identify the type of downhole bridge plug slippage based on high-frequency pressure fluctuation data at the target wellhead; The step of identifying the type of downhole bridge plug slippage based on high-frequency pressure fluctuation data at the target wellhead includes: Extract the target water hammer pressure fluctuation characteristics and target pressure drop characteristics from the high-frequency pressure fluctuation data at the target wellhead; Correlation analysis was performed between the target water hammer pressure fluctuation characteristics and the target pressure drop characteristics and the standard characteristics of downhole bridge plug slippage events; Based on the correlation analysis results, identify the type of downhole bridge plug slippage; The downhole bridge plug slippage event includes a bridge plug re-anchoring event after slippage and a bridge plug complete slippage event; correspondingly, the standard characteristics of the downhole bridge plug slippage event include: a first standard characteristic of a bridge plug re-anchoring event after slippage and a second standard characteristic of a bridge plug complete slippage event; correspondingly, identifying the type of downhole bridge plug slippage based on the correlation analysis results includes: The correlation analysis results of the target water hammer pressure fluctuation characteristics and target pressure drop characteristics with the first standard characteristics are compared with the first preset correlation threshold. When the comparison result is greater than the first preset correlation threshold, the type of downhole bridge plug slippage is identified as bridge plug slippage followed by re-anchoring. The correlation analysis results of the target water hammer pressure fluctuation characteristics and target pressure drop characteristics with the second standard characteristics are compared with the second preset correlation threshold. When the comparison result is greater than the second preset correlation threshold, the type of downhole bridge plug slippage is identified as complete bridge plug slippage. The first preset correlation threshold and the second preset correlation threshold are different. The bridge plug re-anchoring event after slippage occurs when the bridge plug slips a certain distance and then re-anchors itself on the casing before crossing the previous fracturing position. The complete bridge plug slippage event occurs when the bridge plug has slipped beyond the previous fracturing position and has completely slipped. The first standard feature includes: when the bridge plug re-anchors after slippage, the initial pressure drop is high, and the target pressure drop is low after re-anchoring; when the bridge plug re-anchors after slippage, two water hammer events occur, with a negative initial amplitude during the bridge plug slippage stage and a positive initial amplitude during re-anchoring; when the bridge plug re-anchors after slippage, the water hammer pressure frequency is initially... The initial pressure drop decreases and then remains unchanged; when the bridge plug re-anchors after slippage, a positive water hammer pressure amplitude is generated after the bridge plug re-anchors, and the water hammer pressure amplitude gradually decreases over time; the second standard characteristics include: when the bridge plug is fully slipped, fracturing fluid flows into the next stage, the bridge plug loses its setting ability, and both the initial pressure drop and the target pressure drop are high; when the bridge plug is fully slipped, a negative initial amplitude is generated, and the amplitude is large; when the bridge plug is fully slipped, the water hammer pressure frequency becomes lower; when the bridge plug is fully slipped, a negative water hammer pressure is formed, accompanied by an upward water hammer pressure peak, and the water hammer pressure amplitude decreases over time.

8. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions, which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 6.

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