An intelligent microseismic monitoring system for horizontal wells with small wellbores and a data processing method

Abstract

The present invention relates to the technical field of data processing, and is an intelligent horizontal well slimhole microseismic monitoring system and data processing method. The specific method includes: collecting and transmitting microseismic data to a data processing center in real time; processing and integrating the microseismic data through a data correction algorithm; extracting the corrected microseismic data, identifying microseismic events using an automatic detection algorithm, and evaluating the magnitude of each microseismic event; identifying the fracture regions of microseisms with corresponding magnitudes, and simultaneously simulating and evaluating the connectivity of the fractures, and displaying the microseismic and fracture evaluation data through a data visualization tool. The present invention solves the problem in the prior art that, for the monitoring of gas leakage in a gas storage reservoir, there is a lack of dynamic evaluation of the connectivity between fractures in the microseismic region.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and is an intelligent horizontal well small borehole microseismic monitoring system and a data processing method. Background Art

[0002] During the gas injection and storage process in a gas storage reservoir, due to the flow of gas in the underground reservoir, a series of microseismic events may be triggered. These microseismic events usually have a relatively low energy level, but to a certain extent, they reflect the dynamic changes in the fracture system in the underground reservoir. Microseismic monitoring technology is of great significance in the operation and management of gas storage reservoirs, and can help identify possible fracture propagation in the reservoir and its impact on gas storage safety.

[0003] As an important facility for underground gas storage, the stability and safety of a gas storage reservoir are directly related to the stability of energy supply. The gas injection and storage operations in a gas storage reservoir will cause changes in the underground gas pressure, and this pressure change may lead to a change in the rock stress state in the reservoir, thereby triggering microseismic events. By analyzing the spatial distribution and temporal characteristics of microseismic events, the evolution trend of the fracture system in the reservoir and its interaction with natural fractures can be identified. When fractures in the reservoir expand or connect with the natural fracture system, it may lead to unplanned gas leakage, which poses a threat to the safety of the gas storage reservoir and environmental protection. Therefore, through the analysis of microseismic events, it is possible to identify whether fractures are likely to connect with natural fractures, and corresponding measures can be taken for prevention and intervention before the gas leakage risk becomes an actual problem, thereby ensuring the safe operation of the gas storage reservoir and the reliability of gas storage.

[0004] In the existing publicly disclosed invention technologies, for example, the patent with the publication number CN117150591A discloses a method for constructing a multi-well enhanced geothermal system, which includes: (1) drilling a vertical well into a heat reservoir as an injection well for the multi-well enhanced geothermal system, and simultaneously arranging a microseismic monitoring shallow well station centered on the wellhead and around it; (2) carrying out large-scale hydraulic fracturing on the vertical well, and carrying out moment tensor inversion on the stress waves received by the microseismic monitoring shallow well station to obtain source mechanism information and determine important parameters of the main hydraulic fractures; (3) establishing a continuous fracture network model, and displaying the fracture permeability distribution in the heat reservoir calculated according to the fracture network model with a geological honeycomb volume image; (4) drilling multi-target directional wells as production wells for the multi-well enhanced geothermal system, then carrying out secondary large-scale hydraulic fracturing on the directional wells to form a connection with the vertical well hydraulic fractures, and finally circulating for heat extraction.

[0005] The above patent does not take into account that during the hydraulic fracturing process, the evolution of fractures is dynamic, and microseismic monitoring can only provide the fracture state at a certain moment, lacking the magnitude classification processing of microseisms and lacking the real-time tracking of the expansion and connectivity changes of fractures. Summary of the Invention

[0006] The technical problem to be solved by the present invention is that in the prior art, for the monitoring of gas leakage in gas storage caverns, there is a lack of dynamic evaluation of the connectivity between fractures in the microseismic area. Therefore, an intelligent horizontal well small-borehole microseismic monitoring system and a data processing method are proposed.

[0007] In order to achieve the above object, the technical solution of an intelligent horizontal well small-borehole microseismic data processing method of the present invention includes the following steps:

[0008] S1: Arrange and install highly sensitive seismic sensors inside and outside the gas storage cavern, and collect and transmit microseismic data to the data processing center in real time;

[0009] S2: Process and integrate the microseismic data through a data correction algorithm;

[0010] S3: Extract the corrected microseismic data, identify microseismic events using an automatic detection algorithm, and evaluate the magnitude of each microseismic event;

[0011] S4: Identify the fracture area of the microseismic events with corresponding magnitudes, and simultaneously simulate and evaluate the connectivity of the fractures;

[0012] S5: Establish a real-time monitoring platform, generate a microseismic fracture evaluation report, and display the microseismic and fracture evaluation data through a data visualization tool.

[0013] Specifically, the microseismic data in S1 includes: amplitude data of seismic waves, frequency data of seismic waves, timestamp data of seismic waves reaching the detection sensor, and waveform types of seismic waves. Among them, the waveform types of seismic waves include: longitudinal waves, transverse waves, reflected waves, and refracted waves.

[0014] Specifically, S2 includes the following specific steps:

[0015] S21: Collect the basic information of the soil layer corresponding to the microseismic data. The basic information of the soil layer includes: soil layer thickness, soil layer density;

[0016] S22: According to the basic information of the soil layer, construct a layered medium model, including the acoustic impedance Z of each soil layer i , where Z i =ρ i ×V p,i , ρ i is the soil layer density, and V p,i is the longitudinal wave velocity of the soil layer;

[0017] S23: Import the acoustic impedance values of each soil layer in the layered medium model into the soil layer reflection coefficient calculation strategy to calculate the acoustic wave reflection coefficients of each soil layer interface. The specific soil layer reflection coefficient calculation strategy is: Among them, R i represents the soil layer reflection coefficient of the i-th soil layer;

[0018] S24: Extract the acoustic wave reflection coefficients of each soil layer interface to form a soil layer reflection coefficient sequence, and perform correction processing on the amplitude data of the seismic wave according to the soil layer reflection coefficient sequence. The specific correction strategy is: A r,i (t) = R i ×A i where A i represents the amplitude of the incident signal of the i-th soil layer; A r,i (t) represents the amplitude of the corrected reflected signal of the i-th soil layer.

[0019] Specifically, S2 further includes the following specific steps:

[0020] S25: Set the cut-off frequency and the order of the denoising filter according to the acoustic wave reflection coefficient and the layered medium model, design the filter transfer function, and perform digital filtering. Among them, the cut-off frequency is 0.1 Hz - 10 Hz, and the order of the filter is 5;

[0021] S26: Combine the calculated amplitude of the reflected signal with the collected microseismic signal to perform signal correction, where S corrected (t) is the corrected microseismic signal, and S observed (t) is the actually collected microseismic signal; is the correction scale factor; I is the total number of soil layers.

[0022] Specifically, in S3, the identification of microseismic events using the automated detection algorithm specifically includes:

[0023] S31: Extract the corrected microseismic data, and calculate the STA and LTA values through the STA / LTA trigger, where Both N and M are window length parameters, and STA(t) and LTA(t) are functions of the STA value and the LTA value with respect to the data acquisition time point t respectively;

[0024] S32: Perform exponential smoothing processing on the STA and LTA values. The exponential smoothing processing of the STA and LTA values includes:

[0025] STA sm (t) = p × STA(t) + (1 - p) × STA(t - 1);

[0026] LTA sm (t) = p × LTA(t) + (1 - p) × LTA(t - 1);

[0027] Among them, STA sm(t) is the STA value after smoothing;

[0028] LTA sm (t) is the LTA value after smoothing; p is the smoothing factor;

[0029] S33: Extract the actual microseismic signal data collected by the sensor, calculate the change rate of the signal, ΔS corrected (v) = S corrected (v) - S corrected (v - 1); Preset the weighting function J(v), and perform weighted supplementary processing on the STA and LTA values through the weighting function. The specific weighted supplementary processing is as follows:

[0030]

[0031] Among them, STA ra (t) is the STA value after weighted supplementary processing;

[0032] LTA ra (t) is the LTA value after weighted supplementary processing;

[0033] J(v) is the weight function at time v;

[0034] S34: Calculate the stability wd of the filtered microseismic signal according to S31 - S33. Among them, the calculation strategy of the stability wd of the microseismic signal is:

[0035]

[0036] Among them, α1 and α2 are respectively data processing proportionality coefficients, and α1 + α2 = 1;

[0037] S35: Extract the stability wd data of the microseismic signal, and preset the multi-level trigger thresholds TH1, TH2, and TH3 of the microseismic data. Specifically: when wd < TH1, it is judged that no microseismic event has occurred; when TH1 ≤ wd < TH2, it is judged that a slight microseismic event has occurred in the reservoir to be detected; when TH2 ≤ wd < TH3, it is judged that a general microseismic event has occurred in the reservoir to be detected; when TH3 ≤ wd, it is judged that a major microseismic event has occurred in the reservoir to be detected.

[0038] Specifically, in S3, the evaluation of the magnitude of each microseismic event specifically includes the following steps:

[0039] S36: Screen the signal data of the reservoir where a major microseismic event has occurred collected by the sensor, and perform magnitude evaluation. The evaluation strategy of the magnitude evaluation is specifically:

[0040]

[0041] Wherein, L is the microseismic magnitude of the reservoir where major microseismic events occur;

[0042] S corrected max (t), S mb are respectively the maximum amplitude data and the target amplitude data in the microseismic signal data; D1 and D2 are respectively the epicentral distance and the focal depth; k1 and k2 are respectively the correction coefficients of the epicentral distance and the focal depth.

[0043] Specifically, S4 includes the following specific steps:

[0044] S41: Preset the magnitude monitoring threshold level, screen the microseismic areas to be monitored in the reservoir that exceed the magnitude monitoring threshold level, establish a three-dimensional geological model of the microseismic areas to be monitored, construct a three-dimensional coordinate system with the epicenter as the vertex in the three-dimensional geological model, and simulate the distribution of the fracture areas in the three-dimensional geological model;

[0045] S42: Calculate the straight-line distance between each fracture and the epicenter, and sort the straight-line distances in ascending order to obtain a fracture distance sequence. Calculate the three-dimensional fracture volume of each fracture in the three-dimensional geological model in turn through three-dimensional modeling software. The three-dimensional fracture volume of the u-th fracture is TJ u ;

[0046] S43: Simulate and calculate the overlapping volume V between two adjacent fractures in the fracture distance sequence through three-dimensional modeling software overlap 。

[0047] Specifically, S4 also includes the following specific steps:

[0048] S44: Introduce the time factor to simulate the expansion or contraction of the fractures, establish a dynamic overlapping model, calculate the dynamic overlapping volume between two adjacent fractures in the fracture distance sequence at each moment, and update the fracture volume. Among them, the calculation strategy of the dynamic overlapping volume is:

[0049]

[0050] Among them, V′ overlap (t) is the dynamic overlapping volume at time t;

[0051] is the overlapping volume change rate; τ is the integration variable.

[0052] Specifically, S4 also includes the following specific steps:

[0053] S45: Extract the dynamic overlapping volume between two adjacent fractures in the fracture distance sequence, preset the fracture connectivity ratio coefficient as B overlap , and conduct fracture connectivity judgment, specifically:

[0054] When V′ overlap (t) < B overlap × min[TJ u , TJ u+1 , it is determined that the u-th crack and the (u + 1)-th crack in the crack distance sequence are not connected;

[0055] When V′ overlap (t) ≥ B overlap × min[TJ u , TJ u+1 , it is determined that there is a connectivity trend between the u-th crack and the (u + 1)-th crack in the crack distance sequence;

[0056] Among them, TJ u+1 represents the three-dimensional crack volume of the (u + 1)-th crack in the crack distance sequence.

[0057] In addition, an intelligent horizontal well slimhole microseismic monitoring system of the present invention includes the following modules:

[0058] Data acquisition module, data correction module, microseismic positioning module, crack state evaluation module, monitoring data display module;

[0059] The data acquisition module is used to arrange and install highly sensitive seismic sensors inside and outside the gas storage reservoir, and collect and transmit microseismic data to the data processing center in real time;

[0060] The data correction module processes and integrates microseismic data through a data correction algorithm;

[0061] The microseismic positioning module is used to extract the corrected microseismic data, identify microseismic events using an automated detection algorithm, and evaluate the magnitude of each microseismic event;

[0062] The crack state evaluation module is used to identify the crack area of microseismic with corresponding magnitude, and simultaneously simulate and evaluate the connectivity of the cracks;

[0063] The monitoring data display module is used to establish a real-time monitoring platform, generate a microseismic crack evaluation report, and display microseismic and crack evaluation data through a data visualization tool.

[0064] A storage medium stores instructions, and when a computer reads the instructions, the computer executes the above-mentioned intelligent horizontal well slimhole microseismic data processing method.

[0065] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned intelligent horizontal well slimhole microseismic data processing method is implemented.

[0066] Compared with the prior art, the technical effects of the present invention are as follows:

[0067] 1. The present invention adopts an automated detection algorithm that can adaptively process and analyze microseismic data, automatically identify microseismic events and their magnitudes. This reduces the dependence on manual data analysis, reduces the risk of errors caused by human operation, and improves the speed and consistency of detection and evaluation.

[0068] 2. Considering the problem of heterogeneity of soil layers in gas storage reservoirs in different regions and areas, the present invention reduces noise interference and improves the quality of microseismic signals through a data correction algorithm. This method not only considers the basic information of the soil layer, but also designs a calculation strategy for the reflection coefficient of the layered medium model and corrects the microseismic signals, which helps to more accurately evaluate the magnitude and fracture connectivity.

[0069] 3. Through the preset magnitude monitoring threshold levels, the system of the present invention can screen out microseismic events exceeding the threshold and establish a three-dimensional geological model to simulate the distribution of fracture areas. The introduction of the dynamic overlap model considers the simulation of fracture propagation with time factors, further improving the accuracy of fracture connectivity evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0071] Among them:

[0072] Figure 1 is a schematic flow chart of an intelligent horizontal well small hole microseismic data processing method of the present invention;

[0073] Figure 2 is a schematic structural diagram of an intelligent horizontal well small hole microseismic monitoring system of the present invention;

[0074] Figure 3 is a structural diagram of a magnet device in a seismic logging instrument for obtaining microseismic data of the present invention;

[0075] Figure 4 is a structural diagram of an electromagnetic probe in a seismic logging instrument for obtaining microseismic data of the present invention.

[0076] Reference numerals: 1, instrument housing; 2, magnet device; 3, setscrew for fixing magnet device; 4, circuit part of instrument; 5, sensor; 21, magnet seat; 22, magnet sleeve; 23, magnet; 24, screw. Detailed implementation manners

[0077] To make the above objects, features and advantages of the present invention more obvious and understandable, the following detailed description of the specific implementation manners of the present invention will be given with reference to the accompanying drawings of the specification.

[0078] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0079] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure or characteristic that may be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0080] Embodiment 1:

[0081] As Figure 1 shown, a method for processing microseismic data of small boreholes in intelligent horizontal wells according to an embodiment of the present invention includes the following specific steps:

[0082] S1: Arrange and install highly sensitive seismic sensors inside and outside the gas storage reservoir, and collect and transmit microseismic data to the data processing center in real time;

[0083] The microseismic data in S1 includes: amplitude data of seismic waves, frequency data of seismic waves, timestamp data of seismic waves reaching the detection sensor, and waveform types of seismic waves. Among them, the waveform types of seismic waves include: longitudinal waves, transverse waves, reflected waves, and refracted waves.

[0084] S2: Process and integrate the microseismic data through a data correction algorithm;

[0085] S2 includes the following specific steps:

[0086] S21: Collect the basic information of the soil layer corresponding to the microseismic data. The basic information of the soil layer includes: soil layer thickness and soil layer density;

[0087] S22: Construct a layered medium model according to the basic information of the soil layer, including the acoustic impedance Z i of each soil layer, where Z i = ρ i × Vp,i , Z i is the acoustic impedance of the i-th soil layer, ρ i is the soil density of the i-th soil layer, V p,i is the P-wave velocity of the i-th soil layer;

[0088] S23: Import the acoustic impedance values of each soil layer in the layered medium model into the soil layer reflection coefficient calculation strategy to calculate the acoustic wave reflection coefficients of each soil layer interface. The specific soil layer reflection coefficient calculation strategy is as follows: where, R i represents the soil layer reflection coefficient of the i-th soil layer;

[0089] S24: Extract the acoustic wave reflection coefficients of each soil layer interface to form a soil layer reflection coefficient sequence, and perform correction processing on the amplitude data of the seismic wave according to the soil layer reflection coefficient sequence. The specific correction strategy is: A r,i (t) = R i × A i , A i represents the incident signal amplitude of the i-th soil layer; A r,i (t) represents the corrected reflection signal amplitude of the i-th soil layer.

[0090] S2 also includes the following specific steps:

[0091] S25: Set the cut-off frequency and the order of the denoising filter according to the acoustic wave reflection coefficient and the layered medium model, design the filter transfer function, and perform digital filtering. Among them, the cut-off frequency is 0.1 Hz - 10 Hz, and the filter order is 5;

[0092] S26: Combine the calculated reflection signal amplitude with the collected microseismic signal for signal correction, where S corrected (t) is the corrected microseismic signal, S observed (t) is the actually collected microseismic signal; is the correction scale factor; I is the total number of soil layers.

[0093] S3: Extract the corrected microseismic data, use an automatic detection algorithm to identify microseismic events, and evaluate the magnitude of each microseismic event;

[0094] In S3, the specific process of using the automatic detection algorithm to identify microseismic events includes:

[0095] S31: Extract the corrected microseismic data, and calculate the STA and LTA values through the STA / LTA trigger. Among them, Both N and M are window length parameters, and STA(t) and LTA(t) are functions of the STA value and the LTA value with respect to the data acquisition time point t;

[0096] Specifically, the STA / LTA trigger is a triggering mechanism used in seismic monitoring and seismic data analysis. Here, STA / LTA is an abbreviation for "Short-Term Average and Long-Term Average", and its basic principle includes: detecting sudden increases in seismic signals by comparing short-term and long-term variations, thereby achieving the monitoring and response to seismic activities;

[0097] S32: Perform exponential smoothing on the STA and LTA values. The exponential smoothing of the STA and LTA values includes:

[0098] STA sm (t) = p × STA(t) + (1 - p) × STA(t - 1);

[0099] LTA sm (t) = p × LTA(t) + (1 - p) × LTA(t - 1);

[0100] Wherein, STA sm (t) is the STA value after smoothing;

[0101] LTA sm (t) is the LTA value after smoothing; p is the smoothing factor;

[0102] S33: Extract the actual microseismic signal data collected by the sensor, calculate the change rate of the signal, ΔS corrected (v) = S corrected (v) - S corrected (v - 1); Preset a weighting function J(v), and perform weighted supplementary processing on the STA and LTA values through the weighting function. The specific weighted supplementary processing is:

[0103]

[0104] Wherein, STA ra (t) is the STA value after weighted supplementary processing;

[0105] LTA ra (t) is the LTA value after weighted supplementary processing;

[0106] J(v) is the weight function at time v;

[0107] Exemplarily, in this embodiment, J(v) = e -f×(t-v) ; f is the weight decay control coefficient;

[0108] S34: Calculate the stability wd of the filtered microseismic signal according to S31 - S33. The calculation strategy for the stability wd of the microseismic signal is as follows:

[0109]

[0110] where α1 and α2 are respectively data processing proportionality coefficients, and α1 + α2 = 1;

[0111] S35: Extract the data of the stability wd of the microseismic signal, and preset the multi - level triggering thresholds TH1, TH2, and TH3 of the microseismic data. Specifically: when wd < TH1, it is judged that no microseismic event has occurred; when TH1 ≤ wd < TH2, it is judged that a slight microseismic event has occurred in the reservoir to be detected; when TH2 ≤ wd < TH3, it is judged that a general microseismic event has occurred in the reservoir to be detected; when TH3 ≤ wd, it is judged that a major microseismic event has occurred in the reservoir to be detected.

[0112] In S3, the specific steps for evaluating the magnitude of each microseismic event are as follows:

[0113] S36: Screen the signal data of the reservoir where a major microseismic event occurs collected by the sensor, and conduct magnitude evaluation. The evaluation strategy for the magnitude evaluation is specifically:

[0114]

[0115] where L is the microseismic magnitude of the reservoir where a major microseismic event occurs;

[0116] S corrected max (t), S mb are respectively the maximum amplitude data and the target amplitude data in the microseismic signal data; D1 and D2 are respectively the epicentral distance and the focal depth; k1 and k2 are respectively the correction coefficients of the epicentral distance and the focal depth.

[0117] Here, it should be noted that the epicentral distance is the horizontal distance between the epicenter of the earthquake source (the location where the earthquake occurs on the ground) and the seismic station (observation point); the focal depth is the vertical distance between the earthquake source (the underground point where the earthquake occurs) and the ground.

[0118] S4: Identify the fracture area of the microseismic event with the corresponding magnitude, and simultaneously conduct a simulation evaluation on the connectivity of the fractures;

[0119] S4 includes the following specific steps:

[0120] S41: Preset the magnitude monitoring threshold level, screen the micro-seismic areas to be monitored in the reservoir that exceed the magnitude monitoring threshold level, establish a three-dimensional geological model of the micro-seismic areas to be monitored, construct a three-dimensional coordinate system with the earthquake source center as the vertex in the three-dimensional geological model, and simulate the distribution of the fracture areas in the three-dimensional geological model;

[0121] S42: Calculate the straight-line distance between each fracture and the earthquake source center, arrange the straight-line distances in ascending order to obtain a fracture distance sequence, and sequentially calculate the three-dimensional fracture volume of each fracture in the three-dimensional geological model through three-dimensional modeling software. The three-dimensional fracture volume of the u-th fracture is TJ u ;

[0122] S43: Simulate and calculate the overlapping volume V between two adjacent fractures in the fracture distance sequence through three-dimensional modeling software overlap .

[0123] S4 also includes the following specific steps:

[0124] S44: Introduce the time factor to simulate the expansion or contraction of fractures, establish a dynamic overlapping model, calculate the dynamic overlapping volume between two adjacent fractures in the fracture distance sequence at each moment, and update the fracture volume. Among them, the calculation strategy of the dynamic overlapping volume is:

[0125]

[0126] Among them, V′ overlap (t) is the dynamic overlapping volume at time t;

[0127] is the overlapping volume change rate; τ is the integration variable.

[0128] S4 also includes the following specific steps:

[0129] S45: Extract the dynamic overlapping volume between two adjacent fractures in the fracture distance sequence, preset the fracture connectivity ratio coefficient as B overlap , and perform fracture connectivity judgment, specifically:

[0130] When V′ overlap (t) < B overlap × min[TJ u , TJ u+1 , it is judged that the u-th fracture and the (u + 1)-th fracture in the fracture distance sequence are not connected;

[0131] When V′ overlap (t) ≥ B overlap × min[TJ u , TJ u+1 , it is judged that there is a connectivity trend between the u-th fracture and the (u + 1)-th fracture in the fracture distance sequence;

[0132] Among them, TJ u+1 represents the three-dimensional fracture volume of the (u + 1)-th fracture in the fracture distance sequence.

[0133] S5: Establish a real-time monitoring platform, generate a microseismic fracture evaluation report, and display the microseismic and fracture evaluation data through a data visualization tool.

[0134] Embodiment 2:

[0135] As Figure 2 shown, an intelligent horizontal well slimhole microseismic monitoring system according to an embodiment of the present invention includes the following modules:

[0136] A data acquisition module, a data correction module, a microseismic positioning module, a fracture state evaluation module, and a monitoring data display module;

[0137] The data acquisition module is used to arrange and install highly sensitive seismic sensors inside and outside the gas storage reservoir, and collect and transmit microseismic data to the data processing center in real time;

[0138] The data correction module processes and integrates the microseismic data through a data correction algorithm;

[0139] The microseismic positioning module is used to extract the corrected microseismic data, identify microseismic events using an automated detection algorithm, and evaluate the magnitude of each microseismic event;

[0140] The fracture state evaluation module is used to identify the fracture area of the microseismic with the corresponding magnitude, and simultaneously simulate and evaluate the connectivity of the fractures;

[0141] The monitoring data display module is used to establish a real-time monitoring platform, generate a microseismic fracture evaluation report, and display the microseismic and fracture evaluation data through a data visualization tool.

[0142] Embodiment 3:

[0143] Exemplarily, in this embodiment, a structural diagram of a seismic logging device for obtaining microseismic data is given. As Figure 3 and Figure 4 shown, a magnet device is installed at each end of the instrument. In a cased well, the instrument can be attached to the inner wall of the casing by the magnetic force of the magnet to achieve seismic logging.

[0144] Among them, 1 is the instrument housing, 2 is the magnet device, 3 is the set screw for fixing the magnet device, 4 is the circuit part of the instrument, and 5 is the sensor. There are three sensors in total. The magnet device is fixed to the instrument housing with set screws.

[0145] As shown in the magnet device diagram: 21 is the magnet base, 22 is the magnet sleeve, 23 is the magnet, and 24 is the screw. There are four magnet bases and 4 magnets, with one magnet distributed at each corner of the magnet base. There are 8 magnet sleeves, and one magnet is installed in every two magnet sleeves. The magnet sleeves are used to protect the magnets from being crushed due to friction and collision during the process of installing the instrument. The screw fixes the four magnet bases together to form a magnet device. The magnets are distributed at the four corners of the magnet base, and two magnets will be attracted to the inner wall of the casing, making the instrument adsorbed firmly without rotation, improving the quality of receiving seismic waves.

[0146] Embodiment 4:

[0147] This embodiment provides an electronic device, including: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory;

[0148] The processor executes the above-mentioned intelligent horizontal well small hole microseismic data processing method by calling the computer program stored in the memory.

[0149] This electronic device may have relatively large differences due to configuration or performance, and can include one or more processors (Central Processing Units, CPU) and one or more memories. Among them, at least one computer program is stored in the memory, and this computer program is loaded and executed by the processor to implement the intelligent horizontal well small hole microseismic data processing method provided by the above method embodiment. This electronic device can also include other components for realizing the functions of the device. For example, this electronic device can also have components such as wired or wireless network interfaces and input / output interfaces for data input and output. This embodiment will not be elaborated here.

[0150] Embodiment 5:

[0151] This embodiment proposes a computer-readable storage medium, on which a rewritable computer program is stored;

[0152] When the computer program runs on a computer device, it enables the computer device to execute the above-mentioned intelligent horizontal well small hole microseismic data processing method.

[0153] For example, the computer-readable storage medium can be a read-only memory (Read-Only Memory, abbreviated as: ROM), a random access memory (RandomAccess Memory, abbreviated as: RAM), a compact disc read-only memory (Compact Disc Read-OnlyMemory, abbreviated as: CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0154] It should be understood that in various embodiments of the present application, the sequence numbers of the above processes do not indicate the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0155] It should be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0156] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0157] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0158] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0159] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only one way, and in actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0160] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0161] In addition, the functional units in various embodiments of the present invention can be integrated in one processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0162] In the description of this specification, the descriptions with reference to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0163] In summary of the above embodiments, compared with the prior art, the technical effects of the present invention are as follows:

[0164] 1. The present invention adopts an automated detection algorithm that can adaptively process and analyze microseismic data, automatically identify microseismic events and their magnitudes. This reduces the dependence on manual data analysis, reduces the risk of errors caused by human operations, and improves the speed and consistency of detection and evaluation.

[0165] 2. The present invention takes into account the problem of the inhomogeneity of soil layers in gas storage layers in different regions and areas. Through a data correction algorithm, noise interference is reduced and the quality of microseismic signals is improved. This method not only considers the basic information of the soil layer, but also designs a calculation strategy for the reflection coefficient of the layered medium model and corrects the microseismic signals, which helps to more accurately evaluate the magnitude and fracture connectivity.

[0166] 3. Through the preset magnitude monitoring threshold levels, the system of the present invention can screen out microseismic events exceeding the thresholds and establish a three-dimensional geological model to simulate the distribution of fracture zones. The introduction of the dynamic overlapping model takes into account the influence of time factors on the simulation of fracture propagation, further improving the accuracy of fracture connectivity assessment.

[0167] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only to illustrate the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will also have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. An intelligent horizontal well small hole microseismic data processing method, characterized in that: The method comprises the following specific steps: S1: Arrange and install seismic sensors inside and outside the gas storage to collect and transmit microseismic data to the data processing center in real time; S2: Process and integrate microseismic data through data correction algorithms; S3: Extract the corrected microseismic data, identify microseismic events using automated detection algorithms, and evaluate the magnitude of each microseismic event; S4: Identify the fracture areas of microearthquakes of corresponding magnitudes and simulate and evaluate the connectivity of the fractures; S41: Preset a magnitude monitoring threshold level, screen the microseismic areas to be monitored in the reservoir that exceed the magnitude monitoring threshold level, establish a three-dimensional geological model of the microseismic areas to be monitored, construct a three-dimensional coordinate system with the earthquake source center as the vertex in the three-dimensional geological model, and simulate the distribution of fracture areas in the three-dimensional geological model; S42: Calculate the straight-line distance between each fracture and the epicenter, and arrange the straight-line distances in ascending order to obtain a fracture distance sequence. Calculate the three-dimensional fracture volume of each fracture in the three-dimensional geological model in turn using three-dimensional modeling software. The three-dimensional fracture volume of the u-th fracture is TJ u ; S43: Calculate the overlap volume V between two adjacent fractures in the fracture distance sequence by 3D modeling software overlap ; S44: Introducing a time factor to simulate the expansion or contraction of the crack, establishing a dynamic overlap model, calculating the dynamic overlap volume between two adjacent cracks in the crack distance sequence at each moment, and updating the crack volume, wherein the calculation strategy of the dynamic overlap volume is: Among them, V o ' verlap (t) is the dynamic overlapping volume at time t; is the overlapping volume change rate; τ is the integral variable; S45: Extract the dynamic overlapping volume between two adjacent fractures in the fracture distance sequence, and preset the fracture connectivity ratio coefficient as B overlap , and make a judgment on the connectivity of the cracks, specifically: When V o ' verlap (t)<B overlap ×min[TJ u ,TJ u+1 ], it is judged that the u-th crack and the u+1-th crack in the crack distance sequence are not connected; When V o ' verlap (t)≥B overlap ×min[TJ u ,TJ u+1 ], it is judged that there is a connection trend between the u-th crack and the u+1-th crack in the crack distance sequence; Among them, TJ u+1 represents the three-dimensional fracture volume of the u+1th fracture in the fracture distance sequence; S5: Establish a real-time monitoring platform to generate microseismic fracture assessment reports and display microseismic and fracture assessment data through data visualization tools.

2. The intelligent horizontal well small hole microseismic data processing method according to claim 1 is characterized in that: The microseismic data in S1 include: amplitude data of seismic waves, frequency data of seismic waves, timestamp data of seismic waves arriving at detection sensors, and waveform types of seismic waves, wherein the waveform types of seismic waves include: longitudinal waves, transverse waves, reflected waves, and refracted waves.

3. The intelligent horizontal well small hole microseismic data processing method according to claim 2 is characterized in that: S2 includes the following specific steps: S21: Collecting basic information of soil layers corresponding to microseismic data, wherein the basic information of soil layers includes: soil layer thickness and soil layer density; S22: Based on the basic information of the soil layer, a layered medium model is constructed, including the acoustic impedance Z of each soil layer. i , where Z i =ρ i ×V p,i , ρ i is the soil density, V p,i is the longitudinal wave velocity of the soil layer; S23: Importing the acoustic impedance value of each soil layer in the layered medium model into the soil layer reflection coefficient calculation strategy to calculate the acoustic wave reflection coefficient of each soil layer interface. The soil layer reflection coefficient calculation strategy is specifically as follows: Among them, R i represents the soil reflection coefficient of the i-th soil layer; S24: Extract the acoustic reflection coefficients of each soil layer interface to form a soil layer reflection coefficient sequence, and perform correction processing on the amplitude data of the seismic wave according to the soil layer reflection coefficient sequence. The specific correction strategy is: A r,i (t) = R i ×A i , A i represents the incident signal amplitude of the i-th soil layer; A r,i (t) represents the reflected signal amplitude after correction of the i-th soil layer.

4. The intelligent horizontal well slim hole microseismic data processing method according to claim 3 is characterized in that: S2 also includes the following specific steps: S25: setting a cutoff frequency and a denoising filter order according to the acoustic wave reflection coefficient and the layered medium model, designing a filter transfer function, and performing digital filtering, wherein the cutoff frequency is 0.1 Hz-10 Hz, and the filter order is 5; S26: Combining the calculated reflection signal amplitude with the collected microseismic signal to perform signal correction, wherein: S corrected (t) is the corrected microseismic signal, S observed (t) is the microseismic signal actually acquired; is the correction scale factor; I is the total number of soil layers.

5. The intelligent horizontal well slim hole microseismic data processing method according to claim 4 is characterized in that: In S3, the use of an automated detection algorithm to identify microseismic events specifically includes: S31: Extract the corrected microseismic data and calculate the STA and LTA values ​​through the STA / LTA trigger, where: N and M are both window length parameters, STA(t) and LTA(t) are respectively the functions of STA value and LTA value with respect to the data acquisition time point t; S32: performing exponential smoothing processing on the STA and LTA values, wherein the exponential smoothing processing on the STA and LTA values ​​includes: STA sm (t)=p×STA(t)+(1-p)×STA(t-1); LTA sm (t)=p×LTA(t)+(1-p)×LTA(t-1); Among them, STA sm (t) is the STA value after smoothing; LTA sm (t) is the LTA value after smoothing; p is the smoothing factor; S33: Extract the actual microseismic signal data collected by the sensor and calculate the rate of change of the signal, ΔS corrected (v) = S corrected (v)-S corrected (v-1); a preset weighting function J(v) is used to perform weighted supplementation processing on the STA and LTA values, wherein the weighted supplementation processing is specifically as follows: Among them, STA ra (t) is the STA value after weighted supplementation processing; LTA ra (t) is the LTA value after weighted supplementation processing; J(v) is the weight function at time v; S34: Calculate the stability wd of the filtered microseismic signal according to S31-S33, wherein the calculation strategy of the microseismic signal stability wd is: Among them, α1 and α2 are data processing proportional coefficients, α1+α2=1; S35: Extract the microseismic signal stability wd data, and preset the microseismic data multi-level trigger thresholds TH1, TH2, and TH3, specifically: when wd<TH1, it is judged that no microseismic event has occurred; when TH1≤wd<TH2, it is judged that a slight microseismic event has occurred in the reservoir to be detected; when TH2≤wd<TH3, it is judged that a general microseismic event has occurred in the reservoir to be detected; when TH3≤wd, it is judged that a major microseismic event has occurred in the reservoir to be detected.

6. The intelligent horizontal well slim hole microseismic data processing method according to claim 5 is characterized in that: In S3, the evaluation of the magnitude of each micro-earthquake specifically includes the following steps: S36: Screening the signal data of the reservoir where the major microseismic event occurred collected by the sensor, and performing magnitude assessment, wherein the magnitude assessment strategy is specifically as follows: Where L is the microseismic magnitude of the reservoir where the major microseismic event occurred; S corrected max (t),S mb are the maximum amplitude data and target amplitude data in the microseismic signal data respectively; D1 and D2 are the epicenter distance and focal depth respectively; k1 and k2 are the correction coefficients of the epicenter distance and focal depth respectively.

7. An intelligent horizontal well small hole microseismic monitoring system, which is used to implement an intelligent horizontal well small hole microseismic data processing method as claimed in any one of claims 1 to 6, characterized in that: The system includes the following modules: Data acquisition module, data correction module, microseismic positioning module, crack state assessment module, monitoring data display module; The data acquisition module is used to arrange and install seismic sensors inside and outside the gas storage reservoir to collect and transmit microseismic data to the data processing center in real time; The data correction module processes and integrates microseismic data through a data correction algorithm; The microseismic location module is used to extract the corrected microseismic data, identify microseismic events using an automated detection algorithm, and evaluate the magnitude of each microseismic event; The fracture state assessment module is used to identify fracture areas of micro-earthquakes of corresponding magnitudes and to simulate and assess the connectivity of fractures; The monitoring data display module is used to establish a real-time monitoring platform, generate a microseismic crack assessment report, and display microseismic and crack assessment data through a data visualization tool.

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