A method and apparatus for progressive microseismic model correction with multi-source constraints

Abstract

This invention discloses a multi-source constrained progressive microseismic model correction method and equipment, belonging to the field of geophysical exploration technology. First, high signal-to-noise ratio perforation effective channels and strong fracturing signals distributed on both sides of the wellbore are screened to construct an initial velocity model. Only the effective channels of the perforations are used for initial inversion to obtain a preliminary velocity model and static correction values. This model is then used to locate strong fracturing signals, and the model's rationality is verified using waveform similarity coefficients. The accurately located strong fracturing events are used as virtual sources, and a small-step strategy is employed to re-invert and correct the model. This process is repeated iteratively until the multi-source strong signal gathers are consistently leveled. This invention significantly improves the location accuracy of microseismic events under deep, weak signal conditions by eliminating low-quality data contamination through effective channel screening, compensating for insufficient spatial sampling through multi-source joint constraints, and ensuring stable convergence through progressive iteration.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, specifically to a method and equipment for constructing velocity and static correction models in ground microseismic monitoring, which is particularly suitable for high-precision positioning of microseismic events in complex monitoring scenarios where the target layer is deeply buried, the perforation signal energy is weak, and only some detector channels can effectively receive signals. Background Technology

[0002] Surface microseismic monitoring technology is currently an important means of evaluating the effectiveness of hydraulic fracturing in unconventional oil and gas resources. It involves deploying a large array of geophones on the surface to receive microseismic event signals induced by fracturing operations. After data processing, the spatial distribution of fractures is inverted, providing a basis for optimizing reservoir stimulation schemes. As my country's oil and gas exploration and development moves towards deeper formations, the monitoring targets have gradually shifted from the relatively shallow and homogeneous marine shale of the early stages to complex reservoirs such as continental shale and tight sandstone, which are generally buried at depths of 3000-5000 meters. This change has brought severe challenges to surface microseismic monitoring: the increased depth of the target layer leads to severe energy attenuation of perforation signals during propagation to the surface, significantly reducing the signal-to-noise ratio; the strong heterogeneity and relatively weak fracture energy of continental reservoirs further exacerbate the difficulty of signal acquisition. In actual operations, it is common to find that only a small number of geophone channels near the source can effectively record usable perforation signals, while signals from distant gathers are completely submerged in noise, forming a difficult data pattern of "locally effective, globally sparse".

[0003] To construct subsurface velocity models and near-surface static correction models for microseismic event location, the common practice in the industry is as follows: calculate travel time parameters based on the actual perforation locations and the initial model, and calculate the superposition energy of the first arrival times of each trace; then, disturb the velocity model and the static correction model, and update the travel time and superposition energy; the model that yields the strongest superposition energy is the accurate model. The logical premise of this technique is to utilize all available trace data as much as possible to pursue the stability of the inversion system and the spatial integrity of the model.

[0004] Existing technologies are relatively mature for monitoring shallow, high signal-to-noise ratio signals, providing velocity models and positioning results that meet basic engineering requirements. However, their limitations are clearly exposed when facing current monitoring targets that are deep, low-energy, and highly heterogeneous. The main problems are concentrated in the following aspects: ① Low-quality data contaminates the inversion process, leading to overall distortion of the velocity model. ② The calculation error of static correction quantities is amplified, resulting in a systematic decrease in positioning accuracy.

[0005] Therefore, there is an urgent need to develop a new method that can break through the traditional framework and specifically target "local high signal-to-noise ratio effective signals" for high-precision modeling and correction, in order to solve the technical problem of insufficient positioning accuracy of ground microseismic monitoring under deep and complex reservoir conditions. Summary of the Invention

[0006] This invention provides a progressive microseismic model correction method and device with multi-source constraints. It addresses the problems of weak perforation signal energy, only some detector channels being effective, and the decline in positioning accuracy caused by low-quality data contamination in traditional global inversion in deep reservoir surface microseismic monitoring. The invention proposes an iterative correction scheme that combines perforation signals with high-energy fracturing signals for joint constraints.

[0007] In a first aspect, the present invention provides a method for correcting a multi-source constrained progressive microseismic model, comprising: S1. Obtain basic data for ground microseismic monitoring. The basic data includes: spatial coordinates of geophone points, true coordinates of perforation points, seismic waveform data recorded by each geophone during perforation operation, and microseismic waveform data continuously recorded by each geophone during hydraulic fracturing operation. S2. Quality assessment and screening of the acquired perforation records and fracturing records: pick out the detector channels with a signal-to-noise ratio higher than the preset threshold from the perforation records as effective perforation channels; select microseismic event signals with energy intensity that meet the preset conditions from the fracturing records as supplementary constraint sources; S3. Establish an initial velocity model based on prior geological information of the work area; S4. Using only the effective perforation channel data, combined with the known perforation point location, detector location, and initial velocity model, perform the first inversion calculation to obtain the preliminary corrected velocity model and static correction amount. S5. Using the obtained velocity model and static correction, locate the selected high-energy fracturing signal, and judge the rationality of the current model based on the imaging energy focusing degree of the location result. S6. The location of the high-pressure fracturing event that has been accurately located is regarded as the known location of the virtual source. Its waveform data is used as input. Combined with the current velocity model and the detector position, the joint inversion of velocity and static correction is performed again, and the model parameters are corrected using a small step size strategy. S7. Repeat steps S5 and S6 until all strong signal events involved in the constraints are uniformly flattened on the gather, and obtain the final velocity model and static correction model.

[0008] In some instances, the preset threshold for signal-to-noise ratio (SNR) in step S2 is greater than 3, meaning that detector channels with an SNR greater than 3 are determined to be effective perforation channels.

[0009] In some instances, the selection of high-energy fracturing events in step S2 requires that the selected events be evenly distributed on both sides of the wellbore, with at least 2-3 typical events selected on each side.

[0010] In some instances, the initial velocity model in step S3 is a layered model, and the prior geological information includes at least one of well logging velocity data, geological stratification data, regional velocity statistics, or VSP data.

[0011] In some instances, the initial inversion calculation in step S4 starts with this initial velocity model, employs the Monte Carlo inversion method, and takes the maximum superposition energy of the direct waves as the objective function.

[0012] In some instances, the criteria for judging the rationality of the current model in step S5 include: after locating multiple strong fracturing signals selected in S2 using the velocity model and static correction obtained in S4, if multiple strong fracturing signals can be focused on spatial locations that conform to geological laws and the gathers have good phase inconsistency, then the current model is judged to be reasonable and S6 is continued; if the location results are divergent or significantly inconsistent with geological understanding, then the iteration is terminated and the reason is output or the parameter settings are checked or the data is re-filtered.

[0013] In some instances, the small step strategy in step S6 is specifically as follows: the model parameters are slightly adjusted only based on the residuals corresponding to the strong fracturing event in S5, which can be achieved by setting a damping factor, limiting the maximum single modification range, or using the trust region method, wherein the modification range of the model parameters in a single iteration does not exceed 2% of the current value.

[0014] In some instances, the convergence criterion described in step S7 is: all strong signal events involved in the constraints are uniformly flattened on the gather, and the model change over three consecutive iterations is less than 2%.

[0015] In some instances, the source scanning localization method is used to locate the high-energy fracturing signal in step S5.

[0016] In a second aspect, the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0017] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: ① Avoid low-quality data pollution: By using only the selected effective perforation channels in the core modeling stage, the interference of low signal-to-noise ratio far-end channels on the inversion is completely eliminated, ensuring the accuracy and stability of the initial model.

[0018] ② Achieve multi-source joint constraint: Introduce a high-energy fracturing signal as a supplementary seismic source, which effectively makes up for the defects of weak energy and insufficient spatial sampling of deep perforation signals, and is especially suitable for complex scenarios without strong perforation signals.

[0019] ③ Gradual Iterative Approximation of Reality: Adopting a robust optimization strategy of "one fine-tuning and multiple iterations", the model avoids drastic fluctuations in model parameters in traditional methods by alternating verification and correction of positioning results and model updates, which significantly improves the convergence accuracy and reliability of inversion.

[0020] ④ Improve deep positioning accuracy: Through the above technical approach, the velocity model and static correction value obtained can accurately reflect the characteristics of the underground medium and the near-surface delay effect, thereby significantly improving the positioning accuracy of deep microseismic events and providing more reliable data support for the evaluation of fracturing effect. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0022] Figure 1 This is a schematic diagram of the method provided in an embodiment of the present invention; Figure 2 These are the perforation signals and fracturing signals monitored in the embodiments of this invention; Figure 3 This is a schematic diagram of the distribution locations of the six fracturing signals selected for model calculation in this embodiment of the invention; Figure 4 This is a velocity model obtained by correcting a portion of the perforation signal gather in an embodiment of the present invention; Figure 5 This is an image of a strong fracturing signal obtained by applying the velocity model after the first correction in this embodiment of the invention. Figure 6 This is an image of a strong fracturing signal obtained by applying the final velocity model and the static correction model in an embodiment of the present invention; Figure 7 This is a perforation signal imaging diagram using conventional methods in an embodiment of the present invention; Figure 8 This is an image of a perforation signal using the method of the present invention in an embodiment of the present invention; Figure 9 This is a schematic diagram of an embodiment of the computer device provided in this invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] In the following description, specific embodiments of the invention will be illustrated with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be referred to several times as being performed by a computer, and computer execution as referred to herein includes operations by a computer processing unit representing electronic signals of data in a structured format. This operation transforms the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise alter the operation of the computer in a manner well known to those skilled in the art. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of the invention described above are not intended to be limiting, and those skilled in the art will understand that many of the following steps and operations can also be implemented in hardware.

[0025] The terms "module" or "unit" as used herein can be considered as software objects executing on the computing system. Different components, modules, engines, and services described herein can be considered as implementations on the computing system. The apparatus and methods described herein are preferably implemented in software, but can also be implemented in hardware, both of which are within the scope of this invention.

[0026] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0027] This invention discloses a progressive microseismic model correction method with multi-source constraints. This method addresses the problems of weak perforation signal energy, only some detector channels being effective, and the susceptibility of traditional global inversion to low-quality data contamination leading to decreased positioning accuracy in deep reservoir surface microseismic monitoring. It proposes an iterative correction scheme that jointly constrains perforation signals and high-energy fracturing signals. First, the detector positions, actual perforation locations, and perforation and fracturing signal records are acquired. High signal-to-noise ratio effective perforation channels and strong fracturing signals distributed on both sides of the wellbore are selected to construct an initial velocity model. Only the effective perforation channels are used for the initial inversion to obtain a preliminary velocity model and static correction values. This model is then used to locate strong fracturing signals, and the model's rationality is verified using waveform similarity coefficients. The accurately located strong fracturing events are used as virtual sources, and a small-step strategy is employed to re-invert and correct the model. This iteration is repeated until the multi-source strong signal gathers are consistently aligned. This invention significantly improves the positioning accuracy of microseismic events under deep weak signal conditions by effectively identifying and eliminating low-quality data pollution, compensating for insufficient spatial sampling through multi-source joint constraints, and employing a progressive iterative strategy to ensure stable model convergence, thus providing a reliable basis for evaluating fracturing effects.

[0028] In an embodiment of the present invention, a progressive microseismic model correction method with multi-source constraints is provided, such as... Figure 1 As shown, it includes the following steps: S1. Obtain basic data for ground microseismic monitoring. The basic data includes: spatial coordinates of geophone points, true coordinates of perforation points, seismic waveform data recorded by each geophone during perforation operation, and microseismic waveform data continuously recorded by each geophone during hydraulic fracturing operation. S2. Quality assessment and screening of the acquired perforation records and fracturing records: pick out the detector channels with a signal-to-noise ratio higher than the preset threshold from the perforation records as effective perforation channels; select microseismic event signals with energy intensity that meet the preset conditions from the fracturing records as supplementary constraint sources; S3. Establish an initial velocity model based on prior geological information of the work area; S4. Using only the effective perforation channel data, combined with the known perforation point location, detector location, and initial velocity model, perform the first inversion calculation to obtain the preliminary corrected velocity model and static correction amount. S5. Using the obtained velocity model and static correction, locate the selected high-energy fracturing signal, and judge the rationality of the current model based on the imaging energy focusing degree of the location result. S6. The location of the high-pressure fracturing event that has been accurately located is regarded as the known location of the virtual source. Its waveform data is used as input. Combined with the current velocity model and the detector position, the joint inversion of velocity and static correction is performed again, and the model parameters are corrected using a small step size strategy. S7. Repeat steps S5 and S6 until all strong signal events involved in the constraints are uniformly flattened on the gather, and obtain the final velocity model and static correction model.

[0029] In some instances, step S1 involves acquiring basic ground microseismic monitoring data. This includes: the spatial coordinates (X, Y, Z coordinates) of each geophone; the known true coordinates of the perforation points; seismic waveform data recorded by each geophone during perforation operations; and microseismic waveform data continuously recorded by each geophone during hydraulic fracturing operations. All data must be synchronized in coordinate system and time recording to ensure the accuracy of subsequent processing.

[0030] like Figure 2 The diagram shown illustrates the perforation and fracturing signals monitored in an embodiment of the present invention. The horizontal axis represents the detector channel number, and the vertical axis represents the recording time. From... Figure 1 It can be clearly seen that in the perforation signal (upper waveform), only some detector channels can record clear and effective signals, while most of the in-phase axes of the gather are completely submerged in noise; while in the strong fracturing signal (lower waveform), the distal channel also records identifiable and effective waveforms, providing a data basis for subsequent multi-source joint constraints.

[0031] In some instances, in step S2, the acquired perforation records and fracturing records are subjected to quality assessment and screening. For perforation data, detector channels with high signal-to-noise ratio (SNR) and clear first arrival are selected as "effective perforation channels," and their effective channel information is recorded. The SNR threshold can be set according to the actual data. Far-end channels with low SNR or no effective signal are excluded from subsequent inversion. Specifically, the SNR of each detector channel record is calculated, and detector channels with an SNR greater than a preset threshold (set to SNR>3 in this embodiment) and clear first arrival are determined as "effective perforation channels," and their effective channel information is recorded. Figure 2 Analysis of the perforation signals revealed 722 valid channel sets, with channels having a signal-to-noise ratio below the threshold excluded from subsequent inversion.

[0032] For fracturing data, microseismic events are detected and extracted from continuous records. High-energy microseismic events with significant waveform characteristics are selected as supplementary constraint sources, and their waveform data are recorded. To ensure the lateral constraint capability of the velocity model, the azimuth distribution of the selected strong fracturing events on both sides of the wellbore should be as balanced as possible, such as selecting at least 2-3 typical events on each side. In this embodiment, 6 high-energy events were selected, 3 on the left and 3 on the right side of the wellbore (…). Figure 3 This ensures that the velocity model has a balanced planar constraint capability.

[0033] In some instances, in step S3, an initial velocity model is established based on prior geological information of the work area. Prior geological information includes sonic logging data, geological stratification data (such as well logging velocity data, geological stratification data, regional velocity statistics, or VSP data). The initial velocity model can be a layered model, serving as the starting point for subsequent iterative corrections.

[0034] In some instances, in step S4, only the effective perforation channel data selected in step S2 is used, combined with the known perforation point locations, detector locations, and the initial velocity model, to perform the initial inversion calculation. The inversion can employ travel-time inversion or tomographic imaging methods. This step uses only the effective perforation channel data to preliminarily correct the velocity model and calculate the static corrections corresponding to these effective channels. By limiting the data range for calculation to high signal-to-noise ratio (SNR) effective channels, the contamination of the inversion process by low SNR far-end channels is fundamentally avoided. The inversion uses the maximum direct wave superposition energy as the objective function to preliminarily correct the velocity model parameters and calculate the static corrections corresponding to these effective channels. The key to this step is using only high SNR effective channels for inversion, fundamentally avoiding the contamination of the inversion process by low-quality far-end channel data and ensuring the reliability of the initial model.

[0035] like Figure 4 The diagram shown is a schematic of the velocity model after the first correction obtained using the effective channel of the perforation in this embodiment. The green line represents the corrected velocity model, and the red line represents the model before correction. The velocity model has undergone local adjustments, but the overall structure of the model remains stable, and no abnormal distortions caused by low-quality data contamination have occurred.

[0036] In some instances, in step S5, the velocity model and static correction values ​​updated in step S4 are used to relocate multiple high-energy fracturing signals selected in step S2. The relocation method employs source scanning. The rationality of the current model parameters is judged based on the imaging energy focusing degree of the relocation results (e.g., maximum superimposed energy, best gather phase integrator, etc.). Specifically, if multiple high-energy fracturing signals can be focused at spatial locations consistent with geological patterns, and the gather phase integrator is enhanced and the superimposed energy is increased, then the current model is deemed reasonable, and the process proceeds to step S6; if the energy focusing degree of the relocation results is poor or significantly inconsistent with geological understanding, the iteration is terminated, and the reason is output or the process returns to check parameter settings or re-filter data.

[0037] like Figure 5The figure shows the localization effect of the velocity model after the first correction on the strong fracturing signal in this embodiment. The figure displays the waveform (right) and spatial localization result (left, planar projection) of one of the strong fracturing signals after gather correction. As can be seen from the right figure, after the first correction, the gather of the fracturing signal has initially shown some in-phase characteristics, but some gathers still have residual time differences (marked by ellipses in the figure), and are not completely flattened. As can be seen from the right figure, although the localization result roughly falls near the fracturing section, the energy focusing still needs further improvement. This indicates that the model after the first correction has a certain degree of rationality, but still needs further optimization. Therefore, the model is deemed reasonable, and the process proceeds to step 6 for further iteration.

[0038] In some instances, in step S6, the location of the strong fracturing event from step S5 is treated as a known location of a "virtual source," and its waveform data is used as new input. Combined with the current velocity model and detector location, a joint inversion of velocity and static corrections is performed again. In this step, the model correction employs a small-step strategy, meaning that only small adjustments are made to the model parameters based on the current residuals. Small steps can be achieved by setting a damping factor, limiting the maximum modification range, or using a trust region method to prevent model oscillations or divergence due to excessively large single corrections. Specifically, the number of iterations is set to 100, limiting the modification range of each iteration to no more than 2% of the model parameters. This strategy prevents model oscillations or divergence due to excessively large single corrections, ensuring the stability of the iteration process.

[0039] In some instances, steps S5 and S6 are repeated in step S7, forming a closed-loop iterative process of "location verification → model fine-tuning → relocation verification". Each iteration uses the latest model to relocate the strong fracturing signal and corrects the model parameters based on the location results. As the iteration progresses, the velocity model and static correction gradually approach the true values. The final convergence criterion is that all constrained strong fracturing signal events show a consistent flattening on the trace set, i.e., the remaining time difference of each trace approaches zero. At this point, the final velocity model and static correction model that meet the accuracy requirements are obtained. The convergence criterion for the iteration is: all constrained strong fracturing signal events show a consistent flattening on the trace set, and the model change over three consecutive iterations is less than 2%.

[0040] In this embodiment, after six iterations of correction of the fracturing signal, the velocity model and static correction model reach convergence, and the output is the final model. The final model is used to reposition the fracturing signal, resulting in a concentrated positioning result with high energy focus. The dynamically corrected gather flattening effect is good, and the superimposed energy is strong. The waveform similarity coefficient is reduced from 270 (…). Figure 5 ) increased to 748 ( Figure 6 This verified the accuracy of the final model.

[0041] To further confirm the effectiveness of this method, conventional methods were first used to... Figure 2 The perforation signal in the image was used for positioning, with a waveform similarity coefficient of 22.5 and a positioning error of 12.8m. Figure 7 The method mentioned in this patent has a positioning error of 5.4m and the waveform similarity coefficient increases to 55, further verifying the effectiveness of the method in this patent. Figure 8 ).

[0042] The method in this embodiment is particularly suitable for complex monitoring scenarios where the target layer is buried at a depth of 3000-5000 meters, the perforation signal energy is weak, and only some detector channels can record effective signals. It can achieve high-precision iterative solutions for velocity models and static correction models by introducing fracturing signals as supplementary constraints when strong perforation signals are missing or of poor quality.

[0043] Through the above steps, this embodiment achieves the following technical effects: First, an effective trace screening mechanism is used, utilizing only high signal-to-noise ratio perforation traces in the initial modeling, fundamentally eliminating the contamination of inversion by low-quality data. Second, strong-energy fracturing signals are introduced as a supplementary constraint source, forming a joint inversion framework with the perforation data, effectively compensating for insufficient spatial sampling under conditions of weak signals in deep reservoirs. Third, a gradual optimization strategy of "small step size, multiple iterations" is adopted, allowing the velocity model and static correction values ​​to steadily approach the true values ​​during iterations. Finally, consistent flattening of multi-source strong signal gathers is used as a convergence criterion, ensuring the accuracy and reliability of the model. The resulting high-precision model significantly improves the location accuracy of deep microseismic events, providing solid data support for fracturing effect evaluation and reservoir stimulation scheme optimization.

[0044] In another embodiment of the present invention, a computer device is also provided, such as... Figure 9 As shown, it illustrates a structural schematic diagram of a computer device involved in an embodiment of the present invention, specifically: The computer device may include components such as a processor 901 with one or more processing cores, a memory 902 with one or more computer-readable storage media, a power supply 903, and an input unit 904. Those skilled in the art will understand that... Figure 9 The computer device structure shown does not constitute a limitation on the computer device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 901 is the control center of the computer device. It connects various parts of the computer device via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the memory 902, and by calling data stored in the memory 902, thereby providing overall monitoring of the computer device. Optionally, the processor 901 may include one or more processing cores; preferably, the processor 901 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operation of the storage medium, user interface, and application programs, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor 901.

[0045] The memory 902 can be used to store software programs and modules. The processor 901 executes various functional applications and data processing by running the software programs and modules stored in the memory 902. The memory 902 may mainly include a program storage area and a data storage area. The program storage area may store application programs required for operating the storage medium and at least one function (such as sound playback function, image playback function, etc.); the data storage area may store data created according to the use of the computer device. In addition, the memory 902 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 902 may also include a controller to provide the processor 901 with access to the memory 902.

[0046] The computer device also includes a power supply 903 that supplies power to various components. Preferably, the power supply 903 can be logically connected to the processor 901 via a power management storage medium, thereby enabling functions such as charging, discharging, and power consumption management through the power management storage medium. The power supply 903 may also include one or more DC or AC power supplies, recharge storage media, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0047] The computer device may also include an input unit 904, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0048] Although not shown, the computer device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 901 in the computer device loads the executable files corresponding to the processes of one or more applications into the memory 902 according to the following instructions, and the processor 901 runs the applications stored in the memory 902, thereby implementing the steps in the above method embodiment.

[0049] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0050] Therefore, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to execute the steps of any method provided in the embodiments of the present invention.

[0051] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0052] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0053] Since the computer program stored in the computer-readable storage medium can execute the steps of any of the methods provided in the embodiments of the present invention, the beneficial effects that any of the methods provided in the embodiments of the present invention can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0054] The foregoing has provided a detailed description of a multi-source constrained progressive microseismic model correction method and device provided by the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A progressive microseismic model correction method with multi-source constraints, characterized in that, include: Acquire basic data for ground microseismic monitoring, including: spatial coordinates of geophone points, true coordinates of perforation points, seismic waveform data recorded by each geophone during perforation operation, and microseismic waveform data continuously recorded by each geophone during hydraulic fracturing operation. The acquired perforation records and fracturing records were subjected to quality assessment and screening: the detector channels with a signal-to-noise ratio higher than the preset threshold were picked from the perforation records as effective perforation channels; and the microseismic event signals with energy intensity meeting the preset conditions were selected from the fracturing records as supplementary constraint sources. An initial velocity model was established based on prior geological information of the work area; Using only the effective perforation channel data, combined with the known perforation point location, detector location, and initial velocity model, the first inversion calculation is performed to obtain the initially corrected velocity model and static correction amount; Using the obtained velocity model and static correction, the selected high-energy fracturing signal is located, and the rationality of the current model is judged based on the imaging energy focusing degree of the location results. The location of the high-pressure fracturing event that has been accurately located is regarded as the known location of the virtual source. Its waveform data is used as input. Combined with the current velocity model and the location of the geophone, the joint inversion of velocity and static correction is performed again, and the model parameters are corrected using a small step size strategy. Return to the execution step of using the modified velocity model and static correction to locate the selected high-energy fracturing signal, and judge the rationality of the current model based on the imaging energy focusing degree of the location result, until all high-energy signal events involved in the constraint show a consistent flattening on the gather, and obtain the final velocity model and static correction model.

2. The method according to claim 1, characterized in that, The preset threshold for signal-to-noise ratio is greater than 3, meaning that detector channels with a signal-to-noise ratio greater than 3 are considered valid perforation channels.

3. The method according to claim 2, characterized in that, Microseismic events with energy intensity meeting preset conditions are selected from the fracturing record and are evenly distributed on both sides of the wellbore, with at least 2-3 typical events selected on each side.

4. The method according to claim 3, characterized in that, The initial velocity model is a layered model, and the prior geological information includes at least one of the following: well logging velocity data, geological stratification data, regional velocity statistics, or VSP data.

5. The method according to claim 4, characterized in that, The initial inversion calculation started with the initial velocity model and used the Monte Carlo inversion method, with the objective function being to maximize the superposition energy of the direct waves.

6. The method according to claim 5, characterized in that, The criteria for judging the rationality of the current model include: after locating multiple selected strong fracturing signals using the obtained initial velocity model and static correction, if multiple strong fracturing signals can be focused on spatial locations that conform to geological laws and the gathers have good phase inconsistency, then the current model is judged to be reasonable; if the location results are divergent or significantly inconsistent with geological understanding, then the iteration is terminated and the reason is output or the parameter settings are checked or the data is re-filtered.

7. The method according to claim 6, characterized in that, The small step size strategy is as follows: the model parameters are adjusted slightly only based on the residuals corresponding to the strong fracturing event. This can be achieved by setting a damping factor, limiting the maximum modification range in a single iteration, or using the trust region method. The modification range of the model parameters in a single iteration does not exceed 2% of the current value.

8. The method according to claim 7, characterized in that, All strong signal events involved in the constraints show a consistent flattening on the gather, and the model change is less than 2% over three consecutive iterations.

9. The method according to claim 8, characterized in that, The source scanning method is used to locate high-energy fracturing signals.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method according to any one of claims 1 to 9.

Images