Integrated device and method for improving fracturing efficiency of unconventional reservoir

Abstract

The invention relates to the technical field of oil well monitoring, in particular to an integrated device and method for improving the fracturing efficiency of an unconventional reservoir. The method comprises the following steps: carrying out anomaly detection on a plurality of original pressure data of a construction well to determine a plurality of anomaly moments; by analyzing the formation change and the pressure change corresponding to each abnormal moment and combining the flow velocity change of the energizing liquid, the target abnormal moment is identified from the multiple abnormal moments, and the original pressure data corresponding to the target abnormal moment is removed from the multiple pieces of original pressure data, so that multiple pieces of target pressure data are obtained; finally, energy storage state monitoring information is obtained through analysis according to the multiple pieces of target pressure data, and then whether the actual reservoir pressure of the construction well is matched with a preset energy storage pressure target or not is determined. According to the method, interference noise can be fully inhibited, the accuracy of energy storage state monitoring information obtained subsequently is improved, extra time expenditure caused by supplementary measurement and even repeated construction operation is avoided, and the fracturing construction efficiency of an oil well is improved.

Description

Technical Field

[0001] This invention relates to the technical field of oil well monitoring, and specifically to an integrated device and method for improving the fracturing efficiency of unconventional reservoirs. Background Technology

[0002] In unconventional reservoir fracturing, the common practice is to first inject carbon dioxide or energy-enhancing fluid for energy storage, and then perform staged fracturing to produce oil.

[0003] During the energy storage stage, when monitoring reservoir pressure, the complex environment of the oil well leads to strong interference in the collected pressure data. The reservoir pressure obtained from this analysis is prone to deviate significantly from the actual reservoir pressure. To compensate for this deviation, related technologies use supplementary measurements or even repeated operations to manage errors. However, these methods incur significant time costs, resulting in low efficiency in oil well fracturing operations. Summary of the Invention

[0004] The purpose of this invention is to provide an integrated device and method for improving the fracturing efficiency of unconventional reservoirs, thereby solving the technical problem of low fracturing efficiency in oil wells.

[0005] In a first aspect, one embodiment of the present invention provides an integrated method for improving the fracturing efficiency of unconventional reservoirs, the method comprising:

[0006] Anomaly detection is performed on multiple raw pressure data of the construction well to identify multiple abnormal moments, wherein the abnormal moment is the moment indicated by the raw pressure data whose pressure anomaly probability value is greater than a first threshold.

[0007] Correlation analysis is performed on the formation changes and pressure changes corresponding to each anomalous moment to determine the pressure confidence value for each anomalous moment. Based on the pressure confidence value for each anomalous moment, formation anomalous moments and anomalous moments to be investigated are distinguished among the multiple anomalous moments. The pressure confidence value for formation anomalous moments is greater than the pressure confidence value for anomalous moments to be investigated.

[0008] The flow rate change of the booster fluid at each time of an anomaly to be investigated is analyzed to determine the flow rate interference value at each time of anomaly. Based on the flow rate interference value at each time of anomaly to be investigated, the flow rate anomaly time and the target anomaly time are distinguished among the multiple times of anomaly to be investigated. The flow rate interference value at the time of flow rate anomaly is greater than the flow rate interference value at the target anomaly time.

[0009] The original pressure data corresponding to the target abnormal moment is removed from the multiple original pressure data to obtain multiple target pressure data;

[0010] Data analysis is performed based on the multiple target pressure data to obtain energy storage status monitoring information, which is used to indicate whether the actual reservoir pressure of the well matches the preset energy storage pressure target.

[0011] In some embodiments, the step of performing correlation analysis between formation changes and pressure changes corresponding to each anomalous time to determine the pressure confidence value for each anomalous time includes:

[0012] In the multiple anomalous moments, the seismic wave data corresponding to each anomalous moment and the seismic wave data of its adjacent moments are analyzed to obtain the seismic wave influence factor for each anomalous moment. The seismic wave influence factor is used to indicate the severity of the seismic wave changes at the corresponding anomalous moment.

[0013] In the multiple abnormal moments, the original pressure data corresponding to each abnormal moment and the original pressure data of the adjacent moments are analyzed to obtain the pressure influence factor for each abnormal moment. The pressure influence factor is used to indicate the degree of matching between the measured pressure change at the corresponding abnormal moment and the ideal pressure change caused by the formation change.

[0014] In the aforementioned multiple anomalous moments, the pressure confidence value for each anomalous moment is determined based on the seismic wave influence factor and pressure influence factor for each anomalous moment.

[0015] In some embodiments, the step of analyzing the seismic wave data corresponding to each anomalous time and the seismic wave data of its adjacent times to obtain the seismic wave influence factor for each anomalous time includes:

[0016] In the multiple abnormal moments, the forward wave amplitude and backward wave amplitude corresponding to each abnormal moment are obtained. The forward wave amplitude is the absolute difference between the seismic wave data at the corresponding abnormal moment and the seismic wave data at the moment before the corresponding abnormal moment, and the backward wave amplitude is the absolute difference between the seismic wave data at the corresponding abnormal moment and the seismic wave data at the moment after the corresponding abnormal moment.

[0017] In the multiple abnormal moments, the average value of the forward vibration amplitude and the backward vibration amplitude corresponding to each abnormal moment is calculated to obtain the vibration transient amplitude corresponding to each abnormal moment.

[0018] In the multiple anomalous moments, the ratio of the vibration transient amplitude corresponding to each anomalous moment to its corresponding seismic wave data is calculated to obtain the seismic wave influence factor for each anomalous moment.

[0019] In some embodiments, the step of analyzing the original pressure data corresponding to each abnormal time and the original pressure data of its adjacent times to obtain the pressure influence factor for each abnormal time includes:

[0020] In the multiple abnormal moments, the forward pressure average and the backward pressure average corresponding to each abnormal moment are obtained. The forward pressure average is the average of multiple forward raw pressure data at the corresponding abnormal moment. The continuous time period indicated by the multiple forward raw pressure data at the abnormal moment is a time period starting from the corresponding abnormal moment and going backward for a set time. The backward pressure average is the average of multiple backward raw pressure data at the corresponding abnormal moment. The continuous time period indicated by the multiple backward raw pressure data at the abnormal moment is a time period starting from the corresponding abnormal moment and going backward for a set time.

[0021] In the multiple abnormal moments, the ratio of the average forward pressure to the average backward pressure corresponding to each abnormal moment is calculated to obtain the pressure influence factor for each abnormal moment.

[0022] In some embodiments, the pressure confidence value is used to indicate the confidence level of the original pressure data at the corresponding anomalous moment, and the seismic wave influence factor is positively correlated with the pressure confidence value.

[0023] In some embodiments, the step of analyzing the change in the flow rate of the booster fluid corresponding to each time of an anomaly to determine the flow rate interference value at each time of an anomaly includes:

[0024] Anomaly detection is performed on multiple flow rate data of the energy-enhancing fluid to determine multiple reference times, wherein the reference times are the times indicated by flow rate data with an anomaly probability value greater than a second threshold.

[0025] Among the multiple anomaly times to be investigated, a neighboring reference time is determined for each anomaly time to be investigated. The neighboring reference time is the reference time that is located before the corresponding anomaly time to be investigated and has the smallest time difference with the corresponding anomaly time to be investigated among the multiple reference times.

[0026] In the multiple anomaly times to be investigated, the time difference between each anomaly time and its nearest reference time is analyzed to obtain the flow velocity disturbance time domain factor for each anomaly time.

[0027] In the multiple anomaly times to be investigated, the dispersion of at least two flow velocity data associated with each anomaly time to be investigated is analyzed to obtain the flow velocity disturbance intensity factor for each anomaly time to be investigated.

[0028] In the multiple anomaly moments to be investigated, the flow velocity disturbance value for each anomaly moment is determined based on the flow velocity disturbance time-domain factor and flow velocity disturbance intensity factor for each anomaly moment to be investigated.

[0029] In some embodiments, the flow velocity disturbance value is used to characterize the probability that the corresponding abnormal time indicates an abnormal flow velocity condition. The flow velocity disturbance time domain factor is negatively correlated with the flow velocity disturbance value, and the flow velocity disturbance intensity factor is positively correlated with the flow velocity disturbance value.

[0030] In some embodiments, the step of performing data analysis based on the plurality of target pressure data to obtain energy storage status monitoring information includes:

[0031] The data interference corresponding to the multiple target pressure data is analyzed to obtain the monitoring interference value, wherein the monitoring interference value is used to characterize the intensity of the data interference received by the multiple target pressure data;

[0032] A wavelet denoising threshold is determined based on the monitored interference value, wherein the wavelet denoising threshold and the monitored interference value are positively correlated.

[0033] The multiple target pressure data are subjected to wavelet denoising processing according to the wavelet denoising threshold to obtain denoised pressure information.

[0034] Data analysis is performed on the denoised pressure information to obtain energy storage status monitoring information.

[0035] In some embodiments, the step of analyzing the data interference corresponding to the plurality of target pressure data to obtain the monitoring interference value includes:

[0036] Curve fitting is performed on multiple target pressure data to obtain a pressure fitting curve;

[0037] Obtain the curvature of the curve point corresponding to each target pressure data point in the pressure fitting curve to obtain multiple curvature data.

[0038] A straight line is fitted to the multiple curvature data to obtain a curvature fitting straight line;

[0039] A first disturbance factor is determined based on the slope of the curvature fitting line, and a second disturbance factor is determined based on the number of multiple target abnormal moments, wherein the slope and the first disturbance factor are positively correlated, and the number of multiple target abnormal moments is positively correlated with the second disturbance factor.

[0040] The monitoring interference value is determined based on the first disturbance factor and the second disturbance factor.

[0041] Secondly, another embodiment of the present invention provides an integrated device for improving the fracturing efficiency of unconventional reservoirs, the device comprising:

[0042] The initial screening module is used to detect anomalies in multiple raw pressure data of the construction well to identify multiple abnormal moments, wherein the abnormal moment is the moment indicated by the raw pressure data whose pressure anomaly probability value is greater than a first threshold.

[0043] The rescreening module is used to perform correlation analysis on the formation changes and pressure changes corresponding to each anomalous moment to determine the pressure confidence value of each anomalous moment. Based on the pressure confidence value of each anomalous moment, it distinguishes the formation anomalous moment from the anomaly moment to be investigated among the multiple anomalous moments, wherein the pressure confidence value of the formation anomalous moment is greater than the pressure confidence value of the anomaly moment to be investigated.

[0044] The final screening module is used to analyze the flow rate change of the energy-enhancing liquid corresponding to each abnormal time to determine the flow rate interference value at each abnormal time. Based on the flow rate interference value at each abnormal time to be investigated, the module distinguishes the abnormal flow rate time from the target abnormal time among the multiple abnormal times to be investigated. The flow rate interference value at the abnormal flow rate time is greater than the flow rate interference value at the target abnormal time.

[0045] The anomaly cleanup module is used to remove the original pressure data corresponding to the target anomaly time from the multiple original pressure data to obtain multiple target pressure data.

[0046] The energy storage status monitoring module is used to perform data analysis based on the multiple target pressure data to obtain energy storage status monitoring information. The energy storage status monitoring information is used to indicate whether the actual reservoir pressure of the construction well matches the preset energy storage pressure target.

[0047] Thirdly, in another embodiment of the present invention, an electronic device is provided, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method described in the first aspect.

[0048] Fourthly, in another embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0049] The present invention has the following beneficial effects:

[0050] This invention performs anomaly detection on multiple raw pressure data from the well to initially locate abnormal moments that may interfere with reservoir pressure monitoring. Then, by analyzing the formation and pressure changes corresponding to each abnormal moment, it distinguishes between formation anomalies indicating formation changes and those indicating no formation changes. Based on this, it further analyzes the energy-enhancing fluid velocity changes corresponding to each abnormal moment to distinguish between velocity anomalies indicating velocity disturbances and target anomalies indicating actual environmental disturbances. Finally, by removing the raw pressure data indicated by the target anomaly, it suppresses interference noise while retaining as much pressure data as possible indicating formation changes and energy-enhancing fluid injection fluctuations. Ultimately, the energy storage status monitoring information obtained from this analysis is more accurate and reliable, avoiding the additional time cost of supplementary measurements or even repeated operations, and improving the efficiency of oil well fracturing operations. Attached Figure Description

[0051] To more clearly illustrate the technical solutions and advantages 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.

[0052] Figure 1 This is a structural diagram corresponding to an integrated process method for two-stage fluid injection and fracturing, fishing, and testing provided in an embodiment of the present invention;

[0053] Figure 2 This is a schematic flowchart of an integrated method for improving the fracturing efficiency of unconventional reservoirs provided in an embodiment of the present invention;

[0054] Figure 3 This is a schematic diagram of the structure of an integrated device for improving the fracturing efficiency of unconventional reservoirs provided in an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0056] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an integrated device and method for improving fracturing efficiency in unconventional reservoirs according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0058] The following description, in conjunction with the accompanying drawings, details the specific scheme of an integrated device and method for improving the fracturing efficiency of unconventional reservoirs provided by the present invention.

[0059] In one embodiment, the present invention provides an integrated process method for two-stage fluid injection and fracturing and retrieval testing, the corresponding structural diagram of which is shown below. Figure 1 As shown, Figure 1 In the diagram, number 1 is the casing, number 2 is the tubing, number 3 is the safety joint, number 4 is the hydraulic anchor, number 5 is the high-pressure balanced packer, number 6 is the sliding sleeve blasting device, number 7 is the sliding sleeve eccentric injector, number 8 is the differential pressure sliding sleeve blasting device, number 9 is the open eccentric injector, and number 10 is the guide plug.

[0060] The specific implementation process of the above technology is as follows:

[0061] Connect the above tools through tubing and move them to the predetermined layer by inserting the casing. Pressurize the tubing by pumping, and generate a throttling pressure differential with an open-type eccentric injector to set the high-pressure balanced packers at each stage. At the same time, the hydraulic anchor claws extend to support the entire tubing string and prevent it from creeping.

[0062] The first layer of energy-enhancing fluid (such as liquid carbon dioxide) is injected through an open-type eccentric injector. After the formation pressure of the first layer reaches the energy storage requirement, a sealing ball of the appropriate size is inserted to open the sliding-sleeve eccentric injector. At the same time, the sliding sleeve moves down to close the open-type eccentric injector, and the second layer of energy-enhancing fluid is injected.

[0063] After monitoring that the formation pressure in the second stage has reached the energy storage requirement, the non-perforated water nozzle is put into operation to close the sliding sleeve eccentric injector, and the fracturing operation procedure is initiated. The differential pressure sliding sleeve sandblaster is activated by pressurizing the tubing to carry out the fracturing operation in the first stage.

[0064] After the first stage of fracturing is completed, a sealing ball of the appropriate size is inserted to activate the sliding sleeve sandblasting device to carry out the second stage of fracturing. Once the entire well is completed, the well can be shut in and left to simmer or the packers at each level can be lifted to release the tubing string for this trip and proceed with subsequent operations.

[0065] The first segment indicates the reservoir portion located below the open-type eccentric dispenser, and the second segment indicates the reservoir portion located between the open-type eccentric dispenser and the sliding-type eccentric dispenser.

[0066] It should be noted that the high-pressure balanced packer needs to meet the different requirements of energy storage and fracturing operations. It must not only be able to maintain its position during long-term fluid injection, but also meet the high temperature and high pressure requirements of fracturing operations. To this end, balancing mechanisms are added to both sides of the packer to prevent the packer from being subjected to additional stress during operation, ensuring that the packer can be successfully released after long-term operation.

[0067] The sliding sleeve type eccentric dispenser and the open type eccentric dispenser meet the requirements of high temperature resistance, high pressure resistance and erosion wear resistance.

[0068] The above process achieves different procedures such as layered injection, pressure testing, and fracturing through a single tubing string, which can reduce the number of tubing string replacements, reduce construction time, improve construction efficiency, shorten construction cycle and cost, and significantly improve economic benefits.

[0069] It should be noted that during the injection of boosting fluid into the first and second fracturing layers, the monitored layer pressure is subject to significant interference, leading to a large deviation between the reservoir pressure indicated by the monitoring data and the actual reservoir pressure. To suppress this deviation, this invention proposes an integrated method to improve the fracturing efficiency of unconventional reservoirs, such as... Figure 2 As shown, the method includes:

[0070] Step S1: Perform anomaly detection on multiple raw pressure data of the construction well to determine multiple abnormal moments.

[0071] The abnormal moment is the moment indicated by the original pressure data when the pressure anomaly probability value is greater than the first threshold.

[0072] In this invention, the construction well should be understood as an oil well in the stage of energy-enhancing fluid injection.

[0073] It should be noted that for oil wells in the energy-enhancing fluid injection stage, formation pressure data (obtained based on the quartz pressure gauge deployed to the current injection section, or the second injection section), energy-enhancing fluid injection velocity data (obtained based on the flow meter deployed to the current injection section), and seismic wave data (obtained based on a three-component geophone deployed in the other oil well closest to the drilling well at the same depth as the quartz pressure gauge, with a sampling frequency of 300Hz, and the average absolute value of the three seismic wave vibration components collected each time is a seismic wave data point) will be collected periodically.

[0074] For example, the duration of a single data acquisition cycle can be set to 5 minutes.

[0075] The aforementioned multiple raw pressure data should be understood as multiple formation pressure data collected within a certain data acquisition cycle.

[0076] The above anomaly detection process can be completed based on any anomaly detection algorithm, such as the random forest algorithm.

[0077] The process of anomaly detection for multiple raw pressure data of the construction well to determine multiple abnormal moments can be as follows: Anomaly detection is performed on multiple raw pressure data of the construction well to determine the pressure anomaly probability value of each raw pressure data (used to indicate the probability that the raw pressure data has a numerical anomaly, with a value between 0 and 1). Among the multiple raw pressure data, the raw pressure data with a pressure anomaly probability value greater than a first threshold (which can be set to 0.7 based on experience) is identified as suspected abnormal pressure data, and the data acquisition time corresponding to the suspected abnormal pressure data (i.e., the time it indicates) is identified as the abnormal moment.

[0078] Step S2: Perform correlation analysis on the formation changes and pressure changes corresponding to each anomalous moment to determine the pressure confidence value for each anomalous moment, and distinguish the formation anomalous moment from the anomalous moment to be investigated among the multiple anomalous moments based on the pressure confidence value for each anomalous moment.

[0079] Among them, the pressure confidence value at the time of formation anomaly is greater than the pressure confidence value at the time of the anomaly to be investigated.

[0080] In this embodiment, the step of performing correlation analysis on formation changes and pressure changes corresponding to each anomalous moment to determine the pressure confidence value for each anomalous moment includes:

[0081] In the multiple anomalous moments, the seismic wave data corresponding to each anomalous moment and the seismic wave data of its adjacent moments are analyzed to obtain the seismic wave influence factor for each anomalous moment. The seismic wave influence factor is used to indicate the severity of the seismic wave changes at the corresponding anomalous moment.

[0082] In the multiple abnormal moments, the original pressure data corresponding to each abnormal moment and the original pressure data of the adjacent moments are analyzed to obtain the pressure influence factor for each abnormal moment. The pressure influence factor is used to indicate the degree of matching between the measured pressure change at the corresponding abnormal moment and the ideal pressure change caused by the formation change.

[0083] In the aforementioned multiple anomalous moments, the pressure confidence value for each anomalous moment is determined based on the seismic wave influence factor and pressure influence factor for each anomalous moment.

[0084] Ideally, the current injection zone is a completely closed structure. As the energy-enhancing fluid is continuously injected, the reservoir pressure detected in the current injection zone will continuously increase (that is, the original pressure data will continuously increase over time). However, in actual working conditions, during the injection of energy-enhancing fluid, a series of micro-fractures will expand in the current injection zone. These expanding micro-fractures will increase the original volume of the current injection zone and absorb the injected energy-enhancing fluid, thus causing the reservoir pressure to decrease. This pressure anomaly caused by the expansion of micro-fractures can truly reflect the geological structure changes of the current injection zone during injection. If it is indiscriminately judged as abnormal data and removed, it will lead to the loss of high-value reservoir pressure data (reflecting changes in geological structure). This will not only affect the accuracy of subsequent reservoir pressure monitoring, but also reduce the effectiveness of collaborative analysis of reservoir pressure data with other geological data (such as core data, well logging data, geological modeling data, etc.).

[0085] Analysis revealed that the propagation of micro-fractures is specifically the fracturing of rock mass. The instantaneous release of elastic waves during fracturing causes an abnormal increase in seismic wave energy (increased seismic wave amplitude). Simultaneously, the newly propagated micro-fractures require time to fill, meaning that after a decrease in reservoir pressure, it takes time for the pressure to return to pre-depression levels. Furthermore, sharp drops in reservoir pressure caused by various environmental disturbances typically manifest as isolated spikes. In other words, for the identified anomalous moments, the more drastic (increased) the seismic wave amplitude change is detected at the corresponding anomalous moment, the more likely the original pressure data for the detected numerical anomaly at that moment is due to actual fracture propagation, and the more likely the original pressure data for the detected numerical anomaly at that moment should be retained. Similarly, the closer the pressure change detected at the corresponding anomalous moment matches the pressure drop caused by fracture propagation (i.e., the ideal pressure change caused by formation changes), the more likely the original pressure data for the detected numerical anomaly at that moment is due to actual fracture propagation, and the more likely the original pressure data for the detected numerical anomaly at that moment should be retained.

[0086] Specifically, the step of analyzing the seismic wave data corresponding to each anomalous moment and the seismic wave data of its adjacent moments to obtain the seismic wave influence factor for each anomalous moment includes:

[0087] In the multiple abnormal moments, the forward wave amplitude and backward wave amplitude corresponding to each abnormal moment are obtained. The forward wave amplitude is the absolute difference between the seismic wave data at the corresponding abnormal moment and the seismic wave data at the moment before the corresponding abnormal moment, and the backward wave amplitude is the absolute difference between the seismic wave data at the corresponding abnormal moment and the seismic wave data at the moment after the corresponding abnormal moment.

[0088] In the multiple abnormal moments, the average value of the forward vibration amplitude and the backward vibration amplitude corresponding to each abnormal moment is calculated to obtain the vibration transient amplitude corresponding to each abnormal moment.

[0089] In the multiple anomalous moments, the ratio of the vibration transient amplitude corresponding to each anomalous moment to its corresponding seismic wave data is calculated to obtain the seismic wave influence factor for each anomalous moment.

[0090] For example, in multiple abnormal moments, the first Seismic wave influence factors at anomalous moments It can be represented as:

[0091]

[0092] in, Indicates the first The forward wave amplitude corresponding to each abnormal moment Indicates the first The amplitude of the backward vibration at each abnormal moment Indicates the first Seismic wave data corresponding to each anomalous moment.

[0093] Furthermore, the step of analyzing the original pressure data corresponding to each abnormal time and the original pressure data of its adjacent times within the multiple abnormal times to obtain the pressure influence factor for each abnormal time includes:

[0094] In the multiple abnormal moments, the forward pressure average and the backward pressure average corresponding to each abnormal moment are obtained. The forward pressure average is the average of multiple forward raw pressure data at the corresponding abnormal moment. The continuous time period indicated by the multiple forward raw pressure data at the abnormal moment is a time period starting from the corresponding abnormal moment and going backward for a set time. The backward pressure average is the average of multiple backward raw pressure data at the corresponding abnormal moment. The continuous time period indicated by the multiple backward raw pressure data at the abnormal moment is a time period starting from the corresponding abnormal moment and going backward for a set time.

[0095] In the multiple abnormal moments, the ratio of the average forward pressure to the average backward pressure corresponding to each abnormal moment is calculated to obtain the pressure influence factor for each abnormal moment.

[0096] The above-mentioned setting time can be adaptively set based on actual needs, such as by statistically analyzing the minimum pressure drop time (the time difference between the time when the micro-fracture expansion was detected and the time when the reservoir pressure recovered) of other oil wells in the history of micro-fracture expansion. In this invention, the setting time is defined as 3 seconds.

[0097] For example, in multiple abnormal moments, the first Pressure influencing factors at abnormal moments It can be represented as:

[0098]

[0099] in, Indicates the first The average forward pressure corresponding to each abnormal moment. Indicates the first The average backward pressure corresponding to each abnormal moment.

[0100] In this embodiment, the pressure confidence value is used to indicate the confidence level of the original pressure data at the corresponding anomalous time (that is, to indicate the probability that the original pressure data at the corresponding anomalous time represents crack propagation). The seismic wave influence factor is positively correlated with the pressure confidence value.

[0101] In applications, to eliminate differences in numerical dimensions and ensure the accuracy of the pressure confidence value determined based on the seismic wave influence factor and the pressure influence factor, the seismic wave influence factor and the pressure influence factor can be normalized separately (such as maximum-minimum normalization, which aims to transform the values ​​to the 0-1 range) and then the corresponding pressure confidence value can be obtained by weighted calculation.

[0102] For example, in multiple abnormal moments, the first Pressure confidence value at an abnormal moment It can be represented as:

[0103]

[0104] in, Indicates the first Normalized seismic wave influence factors at each anomalous moment. Indicates the first Normalized stress influence factors at each anomalous moment The weights representing the calculated seismic wave influence factors are... Greater than 0 and less than 1.

[0105] Step S3: Analyze the change in the flow rate of the booster fluid corresponding to each abnormal time to determine the flow rate interference value at each abnormal time, and distinguish the abnormal flow rate time from the target abnormal time among the multiple abnormal times to be investigated based on the flow rate interference value at each abnormal time to be investigated.

[0106] Among them, the flow velocity disturbance value at the time of flow velocity anomaly is greater than the flow velocity disturbance value at the time of target anomaly.

[0107] Specifically, the steps for analyzing the flow rate change of the booster fluid at each time of an anomaly to determine the flow rate interference value at each anomaly time include:

[0108] Anomaly detection is performed on multiple flow rate data of the energy-enhancing fluid to determine multiple reference times, wherein the reference times are the times indicated by flow rate data with an anomaly probability value greater than a second threshold.

[0109] Among the multiple anomaly times to be investigated, a neighboring reference time is determined for each anomaly time to be investigated. The neighboring reference time is the reference time that is located before the corresponding anomaly time to be investigated and has the smallest time difference with the corresponding anomaly time to be investigated among the multiple reference times.

[0110] In the multiple anomaly times to be investigated, the time difference between each anomaly time and its nearest reference time is analyzed to obtain the flow velocity disturbance time domain factor for each anomaly time.

[0111] In the multiple anomaly times to be investigated, the dispersion of at least two flow velocity data associated with each anomaly time to be investigated is analyzed to obtain the flow velocity disturbance intensity factor for each anomaly time to be investigated.

[0112] In the multiple anomaly moments to be investigated, the flow velocity disturbance value for each anomaly moment is determined based on the flow velocity disturbance time-domain factor and flow velocity disturbance intensity factor for each anomaly moment to be investigated.

[0113] Analysis revealed that, in addition to the expansion of micro-fractures, abnormal fluctuations in the injected booster fluid (due to drastic changes in the flow resistance of the booster fluid within the rock formation) can also cause reservoir pressure anomalies. Similar to reservoir pressure anomalies caused by fracture expansion, reservoir pressure anomalies caused by abnormal fluctuations in booster fluid are also of high value. If they are identified as anomalous data and removed, they will also affect the accuracy of subsequent reservoir pressure monitoring.

[0114] Specifically, abnormal fluctuations in the reservoir fluid usually lead to corresponding changes in reservoir pressure, and the timing of these changes is usually quite close. Therefore, the closer the time of the abnormal fluctuations in the reservoir fluid is to the time of the anomaly to be investigated, the higher the probability that the data anomaly in the original pressure data at the time of the anomaly to be investigated originates from the abnormal fluctuations in the reservoir fluid. Consequently, the original pressure data at the time of the anomaly to be investigated should be preserved.

[0115] In addition, the stronger the abnormal fluctuation of the boosting fluid, the longer it will affect the reservoir pressure. Even if there is a certain time difference between the abnormal fluctuation of the boosting fluid and the change in reservoir pressure, the probability that the data change of the original pressure data at the time of the anomaly to be investigated is caused by the abnormal fluctuation of the boosting fluid may be higher. Therefore, the original pressure data at the time of the anomaly to be investigated should be preserved.

[0116] The aforementioned velocity disturbance time-domain factor is used to characterize the time interval between the reservoir pressure anomaly indicated by the corresponding anomaly time and the abnormal fluctuation of the booster fluid indicated by its nearest reference time. The larger the velocity disturbance time-domain factor, the longer the indicated time interval.

[0117] The aforementioned velocity disturbance intensity factor is used to characterize the intensity of abnormal fluctuations in the booster fluid indicated by a reference time adjacent to the time of the anomaly to be investigated. The larger the velocity disturbance intensity factor, the higher the intensity of the indicated fluctuation.

[0118] The flow velocity disturbance value is used to characterize the probability that the corresponding abnormal time indicates an abnormal flow velocity situation. The flow velocity disturbance time domain factor is negatively correlated with the flow velocity disturbance value, and the flow velocity disturbance intensity factor is positively correlated with the flow velocity disturbance value.

[0119] The method for obtaining the flow velocity anomaly probability value is similar to that for obtaining the pressure anomaly probability value, and will not be repeated here to avoid repetition. The second threshold mentioned above can be set to 0.75 based on experience.

[0120] In one example, the time difference between each time point of an anomaly to be investigated and its nearest reference time can be normalized (the value of the time difference can be transformed to the range of 0-1) to obtain the flow velocity disturbance time domain factor for each time point of an anomaly to be investigated.

[0121] In this invention, numerical discrete indicators such as variance, standard deviation, and coefficient of variation can be used to quantify the degree of dispersion of at least two flow velocity data associated with each time an anomaly is to be investigated. The at least two flow velocity data associated with the time an anomaly is to be investigated can be understood as a number of flow velocity data (such as 10, 100, etc.) traced back from the time an anomaly is to be investigated.

[0122] For example, the first of multiple anomaly times to be investigated Pressure confidence value at the moment of an anomaly to be investigated It can be represented as:

[0123]

[0124] in, Indicates the first The normalized (transforming the numerical range to the 0-1 range) flow velocity disturbance intensity factor at each anomaly to be investigated. Indicates the first The time-domain factor of the velocity disturbance at the moment of the anomaly to be investigated. Represents extremely small positive numbers (e.g., 0.0001), used to avoid... A value of zero will cause the calculation to fail.

[0125] It should be noted that, under actual operating conditions, the propagation of micro-fractures has a more significant impact on reservoir pressure (compared to abnormal fluctuations in the energy-enhancing fluid). Therefore, in this invention, we first analyze the relevant features associated with the propagation of micro-fractures to complete the initial screening of abnormal moments. Then, we use the relevant features associated with abnormal fluctuations in the energy-enhancing fluid to further screen the initially screened abnormal moments to ensure that the abnormal reservoir pressure data corresponding to the propagation of micro-fractures is fully preserved, thereby ensuring the accuracy and reliability of subsequent energy storage status monitoring information.

[0126] Step S4: Remove the original pressure data corresponding to the target abnormal moment from the multiple original pressure data to obtain multiple target pressure data.

[0127] In practical applications, the steps for obtaining the above-mentioned multiple target pressure data are as follows:

[0128] The original pressure data corresponding to the target abnormal moment is removed from the multiple original pressure data to obtain multiple remaining pressure data.

[0129] Interpolation processing (including but not limited to spline interpolation, linear interpolation, piecewise interpolation, etc.) is performed on multiple residual pressure data to obtain the multiple target pressure data.

[0130] Based on the above settings, the integrity of the obtained multiple target pressure data in the time domain is ensured.

[0131] Step S5: Perform data analysis based on the multiple target pressure data to obtain energy storage status monitoring information.

[0132] The energy storage status monitoring information is used to indicate whether the actual reservoir pressure of the drilling well matches the preset energy storage pressure target. Here, "actual reservoir pressure matching the preset energy storage pressure target" should be understood as: the actual reservoir pressure value of the drilling well reaches the set energy storage pressure target value (e.g., 37 MPa or 32 MPa). Conversely, if the actual reservoir pressure value of the drilling well fails to reach the set energy storage pressure target value, it is considered that the actual reservoir pressure of the drilling well does not match the preset energy storage pressure target.

[0133] The steps of performing data analysis based on the multiple target pressure data to obtain energy storage status monitoring information include:

[0134] The data interference corresponding to the multiple target pressure data is analyzed to obtain the monitoring interference value, wherein the monitoring interference value is used to characterize the intensity of the data interference received by the multiple target pressure data;

[0135] A wavelet denoising threshold is determined based on the monitored interference value, wherein the wavelet denoising threshold and the monitored interference value are positively correlated.

[0136] The multiple target pressure data are subjected to wavelet denoising processing according to the wavelet denoising threshold to obtain denoised pressure information.

[0137] Data analysis is performed on the denoised pressure information to obtain energy storage status monitoring information.

[0138] The above settings dynamically determine the wavelet denoising threshold based on the intensity of data interference to multiple target pressure data, so as to adapt to the actual interference situation in the corresponding data acquisition cycle and enable wavelet denoising processing to achieve better denoising effect.

[0139] For example, if the current data acquisition period is set to the th data acquisition period among multiple data acquisition periods... For each data acquisition cycle, the wavelet denoising threshold for the current data acquisition cycle is... It can be represented as:

[0140]

[0141] in, This indicates the monitoring interference value for the current data collection period. This indicates the total number of target pressure data points in the current data acquisition period.

[0142] Furthermore, the step of analyzing the data interference corresponding to the multiple target pressure data to obtain the monitoring interference value includes:

[0143] Curve fitting is performed on multiple target pressure data to obtain a pressure fitting curve;

[0144] Obtain the curvature of the curve point corresponding to each target pressure data point in the pressure fitting curve to obtain multiple curvature data.

[0145] A straight line is fitted to the multiple curvature data to obtain a curvature fitting straight line;

[0146] A first disturbance factor is determined based on the slope of the curvature fitting line, and a second disturbance factor is determined based on the number of multiple target abnormal moments, wherein the slope and the first disturbance factor are positively correlated, and the number of multiple target abnormal moments is positively correlated with the second disturbance factor.

[0147] The monitoring interference value is determined based on the first disturbance factor and the second disturbance factor.

[0148] Although a certain number of micro-cracks propagate during the injection process, overall, the reservoir pressure in the current injection zone shows a gradual upward trend with the continuous injection of the boosting fluid, and this upward trend gradually slows down. It is particularly noteworthy that the smoother the upward trend, the closer the boosting fluid injection is to completion, indicating that the boosting fluid has essentially filled all types of cavities, making it less susceptible to interference from external factors. In this invention, a first perturbation factor is used to characterize the progress of the boosting fluid injection process. The smaller the first perturbation factor, the closer the boosting fluid injection process is to completion, and the stronger the anti-interference capability of the reservoir pressure collected during the injection process in the current injection zone.

[0149] In addition, in the process of obtaining multiple target pressure data by removing some original pressure data at the target abnormal moment, the more target abnormal moments there are, the stronger the disturbance to the reservoir pressure collected during the injection process of the current injection segment.

[0150] For example, if the current data acquisition period is set to the th data acquisition period among multiple data acquisition periods... For each data acquisition cycle, the monitoring interference value for the current data acquisition cycle is... It can be represented as:

[0151]

[0152] in, This represents the number of target anomalies identified within the current data acquisition cycle (i.e., the second disturbance factor). This represents the first disturbance factor corresponding to the current data acquisition period. (representing the natural constant). This represents the slope of the curvature fitting line corresponding to the current data acquisition period. and All of these indicate the normalization function (such as the tanh function, sigmoid function, etc.).

[0153] In one example, the process of performing data analysis on the denoised pressure information to obtain energy storage status monitoring information can be as follows:

[0154] Data prediction is performed based on the denoised pressure information (such as using an LSTM model) to predict the reservoir pressure prediction value (the average of multiple predicted pressure data) for the next data acquisition cycle corresponding to the denoised pressure information.

[0155] If the predicted reservoir pressure is greater than or equal to the target value of the set energy storage pressure target indication, then target energy storage status monitoring information is generated to indicate that the actual reservoir pressure of the well being constructed matches the preset energy storage pressure.

[0156] If the predicted reservoir pressure is less than the target value indicated by the set energy storage pressure target, then target energy storage status monitoring information is generated indicating that the actual reservoir pressure of the well being constructed does not match the preset energy storage pressure.

[0157] In one embodiment, the present invention also proposes an integrated device for improving the fracturing efficiency of unconventional reservoirs, such as... Figure 3 As shown, the device 200 includes:

[0158] The initial screening module 201 is used to perform anomaly detection on multiple raw pressure data of the construction well to determine multiple abnormal moments, wherein the abnormal moment is the moment indicated by the raw pressure data whose pressure anomaly probability value is greater than a first threshold.

[0159] The rescreening module 202 is used to perform correlation analysis on the formation changes and pressure changes corresponding to each abnormal time to determine the pressure confidence value of each abnormal time, and to distinguish the formation abnormal time and the abnormal time to be investigated among the multiple abnormal times based on the pressure confidence value of each abnormal time, wherein the pressure confidence value of the formation abnormal time is greater than the pressure confidence value of the abnormal time to be investigated.

[0160] The final screening module 203 is used to analyze the change in the flow rate of the energy-enhancing liquid corresponding to each abnormal time to determine the flow rate interference value at each abnormal time, and to distinguish the abnormal flow rate time from the target abnormal time among the multiple abnormal times to be investigated based on the flow rate interference value at each abnormal time to be investigated, wherein the flow rate interference value at the abnormal flow rate time is greater than the flow rate interference value at the target abnormal time.

[0161] The anomaly cleanup module 204 is used to remove the original pressure data corresponding to the target anomaly time from the multiple original pressure data to obtain multiple target pressure data.

[0162] The energy storage status monitoring module 205 is used to perform data analysis based on the multiple target pressure data to obtain energy storage status monitoring information. The energy storage status monitoring information is used to indicate whether the actual reservoir pressure of the construction well matches the preset energy storage pressure target.

[0163] It should be noted that the apparatus provided in the above embodiments is only illustrative of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the integrated apparatus for improving the fracturing efficiency of unconventional reservoirs and the integrated method for improving the fracturing efficiency of unconventional reservoirs provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0164] This invention also provides an electronic device. Please refer to [link to relevant documentation]. Figure 4 The electronic device may include a processor 301, a memory 302, and a program 3021 stored in the memory 302 and capable of running on the processor 301.

[0165] When program 3021 is executed by processor 301, it can achieve the following: Figure 2 Any steps in the corresponding method embodiments and the achievement of the same beneficial effects will not be repeated here.

[0166] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by hardware related to program instructions, and the program can be stored in a readable medium.

[0167] This invention also provides a readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described functions. Figure 2 Any step in the corresponding method embodiment can achieve the same technical effect, and will not be repeated here to avoid repetition.

[0168] The computer-readable storage medium of this invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0169] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0170] The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0171] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or terminal. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0172] This invention also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to achieve an integrated method for improving the fracturing efficiency of unconventional reservoirs provided in the above embodiments.

[0173] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0174] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. An integrated method for improving the fracturing efficiency of unconventional reservoirs, characterized in that, The method includes: Anomaly detection is performed on multiple raw pressure data of the construction well to identify multiple abnormal moments, wherein the abnormal moment is the moment indicated by the raw pressure data whose pressure anomaly probability value is greater than a first threshold. Correlation analysis is performed on the formation changes and pressure changes corresponding to each anomalous moment to determine the pressure confidence value for each anomalous moment. Based on the pressure confidence value for each anomalous moment, formation anomalous moments and anomalous moments to be investigated are distinguished among the multiple anomalous moments. The pressure confidence value for formation anomalous moments is greater than the pressure confidence value for anomalous moments to be investigated. The flow rate change of the booster fluid at each time of an anomaly to be investigated is analyzed to determine the flow rate interference value at each time of anomaly. Based on the flow rate interference value at each time of anomaly to be investigated, the flow rate anomaly time and the target anomaly time are distinguished among the multiple times of anomaly to be investigated. The flow rate interference value at the time of flow rate anomaly is greater than the flow rate interference value at the target anomaly time. The original pressure data corresponding to the target abnormal moment is removed from the multiple original pressure data to obtain multiple target pressure data; Data analysis is performed based on the multiple target pressure data to obtain energy storage status monitoring information, which is used to indicate whether the actual reservoir pressure of the well matches the preset energy storage pressure target.

2. The integrated method for improving fracturing efficiency of unconventional reservoirs according to claim 1, characterized in that, The steps for performing correlation analysis between formation changes and pressure changes at each anomalous time point to determine the pressure confidence value at each anomalous time point include: In the multiple anomalous moments, the seismic wave data corresponding to each anomalous moment and the seismic wave data of its adjacent moments are analyzed to obtain the seismic wave influence factor for each anomalous moment. The seismic wave influence factor is used to indicate the severity of the seismic wave changes at the corresponding anomalous moment. In the multiple abnormal moments, the original pressure data corresponding to each abnormal moment and the original pressure data of the adjacent moments are analyzed to obtain the pressure influence factor for each abnormal moment. The pressure influence factor is used to indicate the degree of matching between the measured pressure change at the corresponding abnormal moment and the ideal pressure change caused by the formation change. In the aforementioned multiple anomalous moments, the pressure confidence value for each anomalous moment is determined based on the seismic wave influence factor and pressure influence factor for each anomalous moment.

3. The integrated method for improving fracturing efficiency of unconventional reservoirs according to claim 2, characterized in that, The steps for analyzing the seismic wave data corresponding to each anomalous time and the seismic wave data of its adjacent times to obtain the seismic wave influence factor for each anomalous time include: In the multiple abnormal moments, the forward wave amplitude and backward wave amplitude corresponding to each abnormal moment are obtained. The forward wave amplitude is the absolute difference between the seismic wave data at the corresponding abnormal moment and the seismic wave data at the moment before the corresponding abnormal moment, and the backward wave amplitude is the absolute difference between the seismic wave data at the corresponding abnormal moment and the seismic wave data at the moment after the corresponding abnormal moment. In the multiple abnormal moments, the average value of the forward vibration amplitude and the backward vibration amplitude corresponding to each abnormal moment is calculated to obtain the vibration transient amplitude corresponding to each abnormal moment. In the multiple anomalous moments, the ratio of the vibration transient amplitude corresponding to each anomalous moment to its corresponding seismic wave data is calculated to obtain the seismic wave influence factor for each anomalous moment.

4. The integrated method for improving fracturing efficiency of unconventional reservoirs according to claim 2, characterized in that, The steps for analyzing the original pressure data corresponding to each abnormal time and the original pressure data of its adjacent times to obtain the pressure influence factor for each abnormal time include: In the multiple abnormal moments, the forward pressure average and the backward pressure average corresponding to each abnormal moment are obtained. The forward pressure average is the average of multiple forward raw pressure data at the corresponding abnormal moment. The continuous time period indicated by the multiple forward raw pressure data at the abnormal moment is a time period starting from the corresponding abnormal moment and going backward for a set time. The backward pressure average is the average of multiple backward raw pressure data at the corresponding abnormal moment. The continuous time period indicated by the multiple backward raw pressure data at the abnormal moment is a time period starting from the corresponding abnormal moment and going backward for a set time. In the multiple abnormal moments, the ratio of the average forward pressure to the average backward pressure corresponding to each abnormal moment is calculated to obtain the pressure influence factor for each abnormal moment.

5. The integrated method for improving fracturing efficiency of unconventional reservoirs according to claim 2, characterized in that, The pressure confidence value is used to indicate the confidence level of the original pressure data at the corresponding anomalous moment. The seismic wave influence factor is positively correlated with the pressure confidence value.

6. The integrated method for improving fracturing efficiency of unconventional reservoirs according to claim 1, characterized in that, The steps for analyzing the flow rate change of the booster fluid at each time of an anomaly to determine the flow rate interference value at each time of an anomaly include: Anomaly detection is performed on multiple flow rate data of the energy-enhancing fluid to determine multiple reference times, wherein the reference times are the times indicated by flow rate data with an anomaly probability value greater than a second threshold. Among the multiple anomaly times to be investigated, a neighboring reference time is determined for each anomaly time to be investigated. The neighboring reference time is the reference time that is located before the corresponding anomaly time to be investigated and has the smallest time difference with the corresponding anomaly time to be investigated among the multiple reference times. In the multiple anomaly times to be investigated, the time difference between each anomaly time and its nearest reference time is analyzed to obtain the flow velocity disturbance time domain factor for each anomaly time. In the multiple anomaly times to be investigated, the dispersion of at least two flow velocity data associated with each anomaly time to be investigated is analyzed to obtain the flow velocity disturbance intensity factor for each anomaly time to be investigated. In the multiple anomaly moments to be investigated, the flow velocity disturbance value for each anomaly moment is determined based on the flow velocity disturbance time-domain factor and flow velocity disturbance intensity factor for each anomaly moment to be investigated.

7. The integrated method for improving fracturing efficiency of unconventional reservoirs according to claim 6, characterized in that, The flow velocity disturbance value is used to characterize the probability that the corresponding abnormal time indicates an abnormal flow velocity situation. The flow velocity disturbance time domain factor is negatively correlated with the flow velocity disturbance value, and the flow velocity disturbance intensity factor is positively correlated with the flow velocity disturbance value.

8. The integrated method for improving fracturing efficiency of unconventional reservoirs according to claim 1, characterized in that, The steps of performing data analysis based on the multiple target pressure data to obtain energy storage status monitoring information include: The data interference corresponding to the multiple target pressure data is analyzed to obtain the monitoring interference value, wherein the monitoring interference value is used to characterize the intensity of the data interference received by the multiple target pressure data; A wavelet denoising threshold is determined based on the monitored interference value, wherein the wavelet denoising threshold and the monitored interference value are positively correlated. The multiple target pressure data are subjected to wavelet denoising processing according to the wavelet denoising threshold to obtain denoised pressure information. Data analysis is performed on the denoised pressure information to obtain energy storage status monitoring information.

9. The integrated method for improving fracturing efficiency of unconventional reservoirs according to claim 8, characterized in that, The step of analyzing the data interference corresponding to the multiple target pressure data to obtain the monitoring interference value includes: Curve fitting is performed on multiple target pressure data to obtain a pressure fitting curve; Obtain the curvature of the curve point corresponding to each target pressure data point in the pressure fitting curve to obtain multiple curvature data. A straight line is fitted to the multiple curvature data to obtain a curvature fitting straight line; A first disturbance factor is determined based on the slope of the curvature fitting line, and a second disturbance factor is determined based on the number of multiple target abnormal moments, wherein the slope and the first disturbance factor are positively correlated, and the number of multiple target abnormal moments is positively correlated with the second disturbance factor. The monitoring interference value is determined based on the first disturbance factor and the second disturbance factor.

10. An integrated device for improving the fracturing efficiency of unconventional reservoirs, characterized in that, The device includes: The initial screening module is used to detect anomalies in multiple raw pressure data of the construction well to identify multiple abnormal moments, wherein the abnormal moment is the moment indicated by the raw pressure data whose pressure anomaly probability value is greater than a first threshold. The rescreening module is used to perform correlation analysis on the formation changes and pressure changes corresponding to each anomalous moment to determine the pressure confidence value of each anomalous moment. Based on the pressure confidence value of each anomalous moment, it distinguishes the formation anomalous moment from the anomaly moment to be investigated among the multiple anomalous moments, wherein the pressure confidence value of the formation anomalous moment is greater than the pressure confidence value of the anomaly moment to be investigated. The final screening module is used to analyze the flow rate change of the energy-enhancing liquid corresponding to each abnormal time to determine the flow rate interference value at each abnormal time. Based on the flow rate interference value at each abnormal time to be investigated, the module distinguishes the abnormal flow rate time from the target abnormal time among the multiple abnormal times to be investigated. The flow rate interference value at the abnormal flow rate time is greater than the flow rate interference value at the target abnormal time. The anomaly cleanup module is used to remove the original pressure data corresponding to the target anomaly time from the multiple original pressure data to obtain multiple target pressure data. The energy storage status monitoring module is used to perform data analysis based on the multiple target pressure data to obtain energy storage status monitoring information. The energy storage status monitoring information is used to indicate whether the actual reservoir pressure of the construction well matches the preset energy storage pressure target.

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