CN112630841B - Microseism event detection and analysis method - Google Patents
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Abstract
The invention discloses a microseism event detection and analysis method, which comprises the following steps: acquiring perforation and background monitoring data, establishing an initial velocity model, and performing iterative processing on the initial velocity model to obtain an anisotropic velocity model; denoising the perforation and background monitoring data, filtering strong pulse interference data in the background, and optimizing the signal-to-noise ratio of the data to obtain optimized perforation and background monitoring data; performing energy cluster focusing and in-phase axis leveling on the optimized perforation and background monitoring data through the anisotropic velocity model, and determining the occurrence position of the microseism event; according to the technical scheme, the technical problems that micro-seismic positioning is easy to be unstable and low in precision in the prior art are solved, and the micro-seismic event is accurately positioned.
Description
Technical Field
The invention relates to the technical field of microseism detection, in particular to a method, a device, a medium and a terminal device for detecting and analyzing microseism events.
Background
The underground microseism monitoring is one of microseism observation modes and is characterized in that an underground three-component detector receives microseism full wavefield signals, and compared with ground microseism monitoring, the underground microseism monitoring system has the advantages that the signal-to-noise ratio of data received in a well is high, the number and types of microseism events are rich, but the defect is that the azimuth of the underground detector is unknown, so that the X-component microseism data and the Y-component microseism data of the detector are disordered, and subsequent positioning processing is influenced. Meanwhile, the observation distance of the micro-seismic in the well is short (generally about 200-100 meters) and the number of detectors is limited (generally 12-32-level three-component borehole detectors), that is, the signal receiving range is too narrow, so that the micro-seismic positioning in some wells is easy to have the phenomena of instability, low precision and the like.
At present, the borehole microseism positioning technology mainly has two ideas: firstly, forward modeling is carried out when events of P waves and S waves travel, a representative algorithm comprises a network search method, a simulated annealing method, a geiger method and the like, the method has the advantages of easiness in realization and the defects that the events of the P waves and the S waves are difficult to accurately pick up when traveling due to weak first arrival phase signals, and positioning results are influenced; the second positioning idea is based on wave equation convolution, and the representative algorithm comprises an interference method, a reverse time migration method and a passive source imaging method. In addition, the method is suitable for being based on isotropic uniform media, but in the fracturing microseism development of unconventional tight sandstone gas and shale gas reservoir reservoirs, the stratum has heterogeneity and belongs to anisotropic media, and the travel time and the propagation path of longitudinal and transverse microseism waves are different from isotropy, so that the microseism event is not accurately positioned by the conventional method. Therefore, there is a need to provide a method for borehole microseismic location of anisotropic media.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides a microseism event detection and analysis method, which aims to solve the technical problems that microseism positioning is easy to be unstable and low in precision in the prior art.
In order to solve the above technical problem, an embodiment of the present invention provides a method for detecting and analyzing a microseismic event, including:
acquiring perforation and background monitoring data, establishing an initial velocity model, and performing iterative processing on the initial velocity model to obtain an anisotropic velocity model;
denoising the perforation and background monitoring data, filtering strong pulse interference data in the background, and optimizing the signal-to-noise ratio of the data to obtain optimized perforation and background monitoring data;
and carrying out energy cluster focusing and in-phase axis leveling on the optimized perforation and background monitoring data through the anisotropic velocity model, and determining the occurrence position of the microseism event.
As a preferred scheme, the step of obtaining the perforation and background monitoring data and establishing an initial velocity model, and obtaining the anisotropic velocity model after performing iterative processing on the initial velocity model specifically includes:
performing median filtering processing on the perforation and background monitoring data, and removing abnormal points in the data to obtain initial optimization data;
resampling the perforation and background monitoring data, and establishing an initial speed model;
performing static correction processing on the initial optimization data to obtain secondary optimization data;
inputting the secondary optimization data into the initial velocity model for iterative processing to obtain an anisotropic velocity model.
As a preferred scheme, the step of performing static correction processing on the initial optimization data to obtain secondary optimization data includes:
forming a set patch for every N adjacent detection points on each measurement line in the initial optimization data, wherein N is a positive integer;
calculating static correction values among the detectors in each set patch as first correction values;
calculating a static correction value between every two set patches as a second correction value;
and according to the first correction amount and the second correction amount, performing signal leveling on the initial optimization data to obtain secondary optimization data.
As a preferred scheme, the step of denoising the perforation and background monitoring data, filtering strong pulse interference data in the background, optimizing the signal-to-noise ratio of the data, and obtaining optimized perforation and background monitoring data specifically includes:
carrying out amplitude anomaly detection on the frequency data of the middle frequency band of the perforation and background monitoring data within a preset value range, deleting the frequency data with abnormal amplitude, and filtering strong pulse interference data in the background;
and performing signal-to-noise ratio optimization on the filtered perforation and background monitoring data to obtain optimized perforation and background monitoring data.
Preferably, the step of deleting the frequency data with abnormal amplitude includes: the frequency data that the amplitude energy exceeds the threshold value and the amplitude is irregular are deleted.
As a preferred scheme, in the step of performing signal-to-noise ratio optimization on the filtered perforation and background monitoring data to obtain optimized perforation and background monitoring data, the method specifically comprises the following steps:
classifying and extracting the filtered perforation and background monitoring data, and setting a data abnormal threshold;
respectively cutting different types of extracted data into data blocks with preset data density as a unit, and transmitting the data blocks into preset data rules for secondary filtering, wherein the preset data rules are rules for judging whether the data blocks are abnormal according to the different types of data blocks;
when the data block is determined to be abnormal, deleting the data block, otherwise, keeping the data block; and finally, combining the data blocks of the same type which are reserved to obtain optimized perforation and background monitoring data.
As a preferred scheme, the step of performing energy cluster focusing and in-phase axis flattening on the optimized perforation and background monitoring data through the anisotropic velocity model to determine the occurrence position of the microseism event specifically comprises the following steps:
performing energy cluster focusing and in-phase axis flattening on the optimized perforation and background monitoring data through the anisotropic velocity model, and determining the type of the micro-seismic event, wherein the type of the micro-seismic event comprises a strong signal event and a weak signal event;
when a strong signal event is determined, determining the occurrence position of the micro-seismic event through field or seismic source mechanism inversion;
when the weak signal event is determined, determining the occurrence position of the micro-seismic event when the micro-seismic positioning speed reaches the field fracturing speed in a data interaction pickup mode.
Preferably, when determining the weak signal event, the method further comprises: and determining the occurrence position of the microseism event by adjusting the relative travel time value and combining the energy cluster focusing and the same-phase axis leveling data condition.
Accordingly, another embodiment of the present invention provides a microseismic event detection and analysis apparatus, comprising:
the model establishing module is used for acquiring perforation and background monitoring data, establishing an initial velocity model, and performing iterative processing on the initial velocity model to obtain an anisotropic velocity model;
the data optimization module is used for denoising the perforation and background monitoring data, filtering strong pulse interference data in the background, and optimizing the signal-to-noise ratio of the data to obtain optimized perforation and background monitoring data;
and the event position module is used for carrying out energy cluster focusing and in-phase axis leveling on the optimized perforation and background monitoring data through the anisotropic velocity model, and determining the occurrence position of the microseism event.
Preferably, the model building module includes:
the filtering processing unit is used for carrying out median filtering processing on the perforation and background monitoring data, removing abnormal points in the data and obtaining initial optimization data;
the resampling processing unit is used for resampling the perforation and background monitoring data and establishing an initial speed model;
the static correction processing unit is used for carrying out static correction processing on the initial optimized data to obtain secondary optimized data;
and the iteration processing unit is used for inputting the secondary optimization data into the initial velocity model for iteration processing to obtain an anisotropic velocity model.
Preferably, the static correction processing unit includes:
the set patch subunit is configured to form a set patch for every N adjacent detection points on each measurement line in the initial optimization data, where N is a positive integer;
a first correction subunit, configured to calculate a static correction value between the detectors in each set patch as a first correction value;
a second correction subunit, configured to calculate a static correction amount between every two set patches as a second correction amount;
and the optimized data subunit is used for carrying out signal leveling on the initial optimized data according to the first correction amount and the second correction amount to obtain secondary optimized data.
As a preferred scheme, the data optimization module comprises:
the data filtering unit is used for carrying out amplitude abnormity detection on the frequency data of the perforation and background monitoring data, wherein the frequency range of the perforation and background monitoring data is within a preset value range, deleting the frequency data with abnormal amplitude, and filtering strong pulse interference data in the background;
and the signal-to-noise ratio optimization unit is used for carrying out signal-to-noise ratio optimization on the filtered perforation and background monitoring data to obtain optimized perforation and background monitoring data.
Preferably, the data filtering unit is configured to delete frequency data with abnormal amplitude, and includes: the frequency data that the amplitude energy exceeds the threshold value and the amplitude is irregular are deleted.
As a preferred scheme, the signal-to-noise ratio optimization unit specifically includes:
the classification extraction subunit is used for performing classification extraction on the filtered perforation and background monitoring data and setting a data abnormal threshold;
the secondary filtering subunit is used for respectively cutting the extracted data of different types into data blocks taking preset data density as a unit, and transmitting the data blocks into preset data rules for secondary filtering, wherein the preset data rules are rules for judging whether the data blocks are abnormal or not according to the data blocks of different types;
the data optimizing subunit is used for deleting the data block when the data block is determined to be abnormal, and otherwise, reserving the data block; and finally, combining the data blocks of the same type which are reserved to obtain optimized perforation and background monitoring data.
Preferably, the event location module includes:
the event type unit is used for carrying out energy cluster focusing and in-phase axis flattening on the optimized perforation and background monitoring data through the anisotropic velocity model, and determining the type of the micro-seismic event, wherein the type of the micro-seismic event comprises a strong signal event and a weak signal event;
the first determination unit is used for determining the occurrence position of the micro-seismic event through field or seismic source mechanism inversion when the strong signal event is determined;
and the second determining unit is used for determining the occurrence position of the micro-seismic event when the micro-seismic positioning speed reaches the field fracturing speed in a data interaction pickup mode when the weak signal event is determined.
Preferably, the second determining unit is further configured to: and determining the occurrence position of the microseism event by adjusting the relative travel time value and combining the energy cluster focusing and the same-phase axis leveling data condition.
An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program; wherein the computer program, when executed, controls an apparatus in which the computer readable storage medium is located to perform a method of microseismic event detection analysis as defined in any of the above.
An embodiment of the present invention further provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor, when executing the computer program, implements the micro-seismic event detection and analysis method according to any one of the above items.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
according to the technical scheme, an anisotropic velocity model is obtained by acquiring perforation and background monitoring data, establishing an initial velocity model and performing iterative processing on the initial velocity model; denoising the perforation and background monitoring data, filtering strong pulse interference data in the background, and optimizing the signal-to-noise ratio of the data to obtain optimized perforation and background monitoring data; energy cluster focusing and in-phase axis leveling are carried out on the optimized perforation and background monitoring data through the anisotropic velocity model, and the occurrence position of the micro-seismic event is determined, so that the technical problems that micro-seismic positioning is unstable and low in precision in the prior art are solved, and the micro-seismic event is accurately positioned.
Drawings
FIG. 1: the method comprises the steps of providing a flow chart of the steps of the microseism event detection and analysis method provided by the embodiment of the invention;
FIG. 2: the structure schematic diagram of the microseism event detection and analysis device provided by the embodiment of the invention is shown;
FIG. 3: the structure diagram of an embodiment of the terminal device provided by the embodiment of the invention is shown.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1, a flow chart of steps of a method for detecting and analyzing a microseismic event according to an embodiment of the present invention includes steps 101 to 103, and the steps are as follows:
step 101, acquiring perforation and background monitoring data, establishing an initial velocity model, and performing iterative processing on the initial velocity model to obtain an anisotropic velocity model.
In an implementation manner of this embodiment, the step 101 includes steps 1011 to 1014, and each step specifically includes the following steps: step 1011, performing median filtering processing on the perforation and background monitoring data, and removing abnormal points in the data to obtain initial optimized data; step 1012, resampling the perforation and background monitoring data, and establishing an initial velocity model; step 1013, performing static correction processing on the initial optimization data to obtain secondary optimization data; and 1014, inputting the quadratic optimization data into the initial velocity model for iterative processing to obtain an anisotropic velocity model.
In an implementation manner of this embodiment, the step 1013 includes the step 10131 to the step 10134, which is as follows: step 10131, forming a set patch for every N adjacent detection points on each line in the initial optimization data, where N is a positive integer. Step 10132, calculating a static correction between the detectors within each set patch as a first correction. Step 10133, a static correction amount between each two set patches is calculated as a second correction amount. Step 10134, performing signal leveling on the initial optimized data according to the first correction amount and the second correction amount to obtain secondary optimized data.
Specifically, the information for establishing the initial velocity model is provided for well acoustic logging data, the data is subjected to median filtering to remove abnormal points, then the data is subjected to resampling processing according to the thickness of 10m to obtain the initial velocity model, and the initial velocity model can be optimized by combining a perforation signal and a strong event, so that an accurate velocity model is obtained for event positioning.
Because the surface fluctuation in the research area range is small, the micro-terrain is relatively developed, and the speed change of the surface environment has certain influence on the leveling of the signal, thereby influencing the positioning precision of the event. Without static correction, perforation signal positioning deviation is large. Therefore, the ground environment needs to be statically corrected in a consistent manner, but if the static correction iterative calculation is uniformly performed on all the detection points, the calculation amount is very large. In order to complete the static correction work quickly, every 25 detection points on each line form a 'patch', and 4 patches on each line are 46 patches in total. Firstly, calculating static correction values among all detectors in a patch, namely high-frequency static correction; then, regarding each patch as a point, and calculating the static correction value between patches, namely low-frequency static correction; finally, the two are combined to achieve the purpose of leveling the signal. On the basis of bridge plug effect removal and static correction, anisotropic iteration is carried out by adopting a Monte Carlo algorithm based on an anisotropic model, and the anisotropic model has large change after iteration.
Experiments were performed based on the post-iterative anisotropy model: because the fracturing is carried out in a mode of immediately fracturing within 1-2 hours after perforation, the time for establishing the speed model is short. Initial velocity model and anisotropic model corrections based on the perforation signals are therefore done only before fracturing in a relatively time-slack way through real-time processing. Based on the set of models, calibration and speed correction are carried out on each section of perforation signals until the transverse error and the longitudinal error meet the requirement of real-time processing. And (3) counting the positioning result and the actual position error of 21 sections of perforation signals (table 1), wherein the absolute error in the X direction is less than 24m, the absolute error in the y direction is less than 27m, and the absolute error in the z direction is less than 23 m.
Table 1: perforation positioning error table
Therefore, the velocity model after iteration can improve the positioning accuracy of the micro-seismic event and is also beneficial to improving the detection capability of the weak micro-seismic event.
And 102, denoising the perforation and background monitoring data, filtering strong pulse interference data in the background, and optimizing the signal-to-noise ratio of the data to obtain optimized perforation and background monitoring data.
In an implementation manner of this embodiment, the step 102 includes steps 1021 to 1022, and each step is specifically as follows: and 1021, performing amplitude anomaly detection on the frequency data of the frequency band in the perforation and background monitoring data within a preset value range, deleting the frequency data with abnormal amplitude, and filtering strong pulse interference data in the background. And 1022, performing signal-to-noise ratio optimization on the filtered perforation and background monitoring data to obtain optimized perforation and background monitoring data.
In an implementation manner of this embodiment, the deleting, at the step 1021, the frequency data with abnormal amplitude includes: the frequency data that the amplitude energy exceeds the threshold value and the amplitude is irregular are deleted.
In an implementation manner of this embodiment, the step 1022 includes steps 10221 to 10223, and each step is as follows: step 10221, classifying and extracting the filtered perforation and background monitoring data, and setting a data abnormal threshold; step 10222, respectively cutting different types of extracted data into data blocks with preset data density as a unit, and transmitting the data blocks to a preset data rule for secondary filtering, wherein the preset data rule is a rule for judging whether the data blocks are abnormal according to the different types of data blocks; step 10223, when the data block is determined to be abnormal, deleting the data block, otherwise, reserving the data block; and finally, combining the data blocks of the same type which are reserved to obtain optimized perforation and background monitoring data.
In particular, during the ground micro-seismic monitoring process, the ground micro-seismic monitoring process is influenced by various factors. There can be a large amount of strong energy interference in the surface microseismic data recording. These strong energy disturbances also present different manifestations due to different causes. And the noise is removed in different domains according to different noise types.
Strong pulse interference: this type of noise is generated in field acquisitions by mechanical, traffic, and man-made vibrations. The appearance of these noises in the surface microseismic data is: the energy is strong and the irregularity appears. Strong impulsive interference is generally present in isolation in a single track.
Local amplitude anomaly: the noise is mainly concentrated in local tracks, and the tracks have certain correlation; the frequencies are mainly concentrated in a very narrow frequency band.
Processing the noise, selecting a proper denoising module, combining deconvolution and an inter-patch/intra-patch static correction module, optimizing the signal-to-noise ratio of the original data, and highlighting signal characteristics; and clearly identify events on the secondary overlay data and planar imaging through unique narrow beam filtering.
And 103, performing energy cluster focusing and in-phase axis leveling on the optimized perforation and background monitoring data through the anisotropic velocity model, and determining the occurrence position of the microseism event.
In an implementation manner of this embodiment, the step 103 includes steps 1031 to 1033, and each step is as follows: and step 1031, performing energy cluster focusing and in-phase axis flattening on the optimized perforation and background monitoring data through the anisotropic velocity model, and determining the type of the micro-seismic event, wherein the type of the micro-seismic event comprises a strong signal event and a weak signal event. Step 1032, when a strong signal event is determined, the location of the micro-seismic event is determined by inversion by a field or source mechanism. And 1033, when the weak signal event is determined, determining the occurrence position of the microseism event when the microseism positioning speed reaches the site fracturing speed in a data interaction pickup mode.
In one implementation manner of this embodiment, when determining the weak signal event, the method further includes: and determining the occurrence position of the microseism event by adjusting the relative travel time value and combining the energy cluster focusing and the same-phase axis leveling data condition.
In particular, in the experiment, the surface lithology is loose sandy soil, the coverage is thick, and the condition that the propagation process of the underground micro seismic signal is seriously attenuated generally exists. In order to solve the technical problems, the scheme is improved from the aspects of data processing and actual operation flow, and specifically comprises the following steps:
on the first hand, in the aspect of data processing improvement, a method combining plane domain, depth domain focusing and in-phase axis leveling is adopted for identifying microseism events; the plane area has a more focused energy mass, which obviously suppresses surrounding noise, and meanwhile, the corresponding time window range of the gather overlapping area has a more obvious phenomenon of flattening the same phase axis, and the two mutually prove that the signal is a weak event.
Secondly, in the aspect of improvement of an actual operation process, a signal acquisition range is set in a signal acquisition area, part of loose sandy soil is excavated in the ground surface of the signal acquisition range, water and liquid nitrogen are poured back into the surface layer, so that the stratum of the surface layer within a certain thickness range can be solidified in a short time, and then earthquake construction and signal acquisition are carried out, so that the signal acquisition equipment can be more stable in the acquisition process, the signal attenuation is weakened, and the accuracy of signal acquisition is improved; and because only a few minutes are needed in the signal acquisition process, the consolidation time of the liquid nitrogen is enough for signal acquisition, and the consolidation is removed after the acquisition is finished, the operation is convenient, the liquid nitrogen does not pollute the soil after volatilization, and the method is green and environment-friendly.
In consideration of the cost problem, the second aspect of improvement can be performed in a small-range area of a work area, the seismic signals acquired through the liquid nitrogen freezing area are processed and compared with the actual loose surface seismic signals to establish a correction coefficient, and the surface seismic signals in a large range of the whole work area are corrected based on the acquired correction coefficient to obtain accurate seismic data signals, so that the construction cost is reduced.
The microseism event is generally positioned by adopting a method of on-site real-time positioning (seismic source scanning positioning) and interactive pickup (relative travel time positioning), and can be judged by energy cluster focusing and gather homophase axis leveling, wherein the strong signal event can realize on-site direct positioning and can also be inverted and accurately positioned by a seismic source mechanism; the weak signal event can be positioned by an interactive pickup method, and the positioning speed can meet the field fracturing speed; further identification of suspected events is needed at a later stage. The method for judging the weak signal event generally checks the focusing and the same-phase axis leveling conditions of an energy mass by adjusting a relative travel time value on the basis of automatically identifying the event by microseismic software, and obtains a more credible microseismic event earthquake-initiating position by repeated positioning error check, head wave re-picking and inversion processing.
According to the technical scheme, an anisotropic velocity model is obtained by acquiring perforation and background monitoring data, establishing an initial velocity model and performing iterative processing on the initial velocity model; denoising the perforation and background monitoring data, filtering strong pulse interference data in the background, and optimizing the signal-to-noise ratio of the data to obtain optimized perforation and background monitoring data; energy cluster focusing and in-phase axis leveling are carried out on the optimized perforation and background monitoring data through the anisotropic velocity model, and the occurrence position of the micro-seismic event is determined, so that the technical problems that micro-seismic positioning is unstable and low in precision in the prior art are solved, and the micro-seismic event is accurately positioned.
Example two
Accordingly, referring to fig. 2, a schematic structural diagram of a microseismic event detection and analysis apparatus provided in an embodiment of the present invention includes a model building module, a data optimization module, and an event location module, where each module is as follows:
and the model establishing module is used for acquiring perforation and background monitoring data, establishing an initial velocity model, and performing iterative processing on the initial velocity model to obtain an anisotropic velocity model.
In an implementation manner of this embodiment, the model building module includes: the filtering processing unit is used for carrying out median filtering processing on the perforation and background monitoring data, removing abnormal points in the data and obtaining initial optimization data; the resampling processing unit is used for resampling the perforation and background monitoring data and establishing an initial speed model; the static correction processing unit is used for carrying out static correction processing on the initial optimized data to obtain secondary optimized data; and the iteration processing unit is used for inputting the secondary optimization data into the initial velocity model for iteration processing to obtain an anisotropic velocity model.
In one implementation manner of this embodiment, the static correction processing unit includes: the set patch subunit is configured to form a set patch for every N adjacent detection points on each measurement line in the initial optimization data, where N is a positive integer; a first correction subunit, configured to calculate a static correction value between the detectors in each set patch as a first correction value; a second correction subunit, configured to calculate a static correction amount between every two set patches as a second correction amount; and the optimized data subunit is used for carrying out signal leveling on the initial optimized data according to the first correction amount and the second correction amount to obtain secondary optimized data.
And the data optimization module is used for denoising the perforation and background monitoring data, filtering strong pulse interference data in the background, and optimizing the signal-to-noise ratio of the data to obtain optimized perforation and background monitoring data.
In an implementation manner of this embodiment, the data optimization module includes: the data filtering unit is used for carrying out amplitude abnormity detection on the frequency data of the perforation and background monitoring data, wherein the frequency range of the perforation and background monitoring data is within a preset value range, deleting the frequency data with abnormal amplitude, and filtering strong pulse interference data in the background; and the signal-to-noise ratio optimization unit is used for carrying out signal-to-noise ratio optimization on the filtered perforation and background monitoring data to obtain optimized perforation and background monitoring data.
In an implementation manner of this embodiment, the step of deleting the frequency data with abnormal amplitude by the data filtering unit includes: the frequency data that the amplitude energy exceeds the threshold value and the amplitude is irregular are deleted.
In an implementation manner of this embodiment, the signal-to-noise ratio optimizing unit specifically includes: the classification extraction subunit is used for performing classification extraction on the filtered perforation and background monitoring data and setting a data abnormal threshold; the secondary filtering subunit is used for respectively cutting the extracted data of different types into data blocks taking preset data density as a unit, and transmitting the data blocks into preset data rules for secondary filtering, wherein the preset data rules are rules for judging whether the data blocks are abnormal or not according to the data blocks of different types; the data optimizing subunit is used for deleting the data block when the data block is determined to be abnormal, and otherwise, reserving the data block; and finally, combining the data blocks of the same type which are reserved to obtain optimized perforation and background monitoring data.
And the event position module is used for carrying out energy cluster focusing and in-phase axis leveling on the optimized perforation and background monitoring data through the anisotropic velocity model, and determining the occurrence position of the microseism event.
In an implementation manner of this embodiment, the event location module includes: the event type unit is used for carrying out energy cluster focusing and in-phase axis flattening on the optimized perforation and background monitoring data through the anisotropic velocity model, and determining the type of the micro-seismic event, wherein the type of the micro-seismic event comprises a strong signal event and a weak signal event; the first determination unit is used for determining the occurrence position of the micro-seismic event through field or seismic source mechanism inversion when the strong signal event is determined; and the second determining unit is used for determining the occurrence position of the micro-seismic event when the micro-seismic positioning speed reaches the field fracturing speed in a data interaction pickup mode when the weak signal event is determined.
In an implementation manner of this embodiment, the second determining unit is further configured to: and determining the occurrence position of the microseism event by adjusting the relative travel time value and combining the energy cluster focusing and the same-phase axis leveling data condition.
EXAMPLE III
An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program; wherein the computer program, when running, controls the device on which the computer readable storage medium is located to execute the method for microseismic event detection and analysis according to any of the above embodiments.
Example four
Referring to fig. 3, a schematic structural diagram of a terminal device according to an embodiment of the present invention is shown, where the terminal device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the microseismic event detection and analysis method according to any one of the embodiments when executing the computer program.
Preferably, the computer program may be divided into one or more modules/units (e.g., computer program) that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the terminal device.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, etc., the general purpose Processor may be a microprocessor, or the Processor may be any conventional Processor, the Processor is a control center of the terminal device, and various interfaces and lines are used to connect various parts of the terminal device.
The memory mainly includes a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like, and the data storage area may store related data and the like. In addition, the memory may be a high speed random access memory, may also be a non-volatile memory, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), and the like, or may also be other volatile solid state memory devices.
It should be noted that the terminal device may include, but is not limited to, a processor and a memory, and those skilled in the art will understand that the terminal device is only an example and does not constitute a limitation of the terminal device, and may include more or less components, or combine some components, or different components.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, and it should be understood that the above-mentioned embodiments are only examples of the present invention and are not intended to limit the scope of the present invention. It should be understood that any modifications, equivalents, improvements and the like, which come within the spirit and principle of the invention, may occur to those skilled in the art and are intended to be included within the scope of the invention.
Claims (9)
1. A method for microseismic event detection analysis, comprising:
acquiring perforation and background monitoring data, establishing an initial velocity model, and performing iterative processing on the initial velocity model to obtain an anisotropic velocity model;
denoising the perforation and background monitoring data, filtering strong pulse interference data in the background, and optimizing the signal-to-noise ratio of the data to obtain optimized perforation and background monitoring data;
carrying out energy cluster focusing and in-phase axis leveling on the optimized perforation and background monitoring data through the anisotropic velocity model, and determining the occurrence position of the microseism event, wherein the method specifically comprises the following steps:
performing energy cluster focusing and in-phase axis flattening on the optimized perforation and background monitoring data through the anisotropic velocity model, and determining the type of the micro-seismic event, wherein the type of the micro-seismic event comprises a strong signal event and a weak signal event;
when a strong signal event is determined, determining the occurrence position of the micro-seismic event through field or seismic source mechanism inversion;
when the weak signal event is determined, determining the occurrence position of the micro-seismic event when the micro-seismic positioning speed reaches the field fracturing speed in a data interaction pickup mode.
2. The method for microseismic event detection and analysis of claim 1 wherein the step of obtaining perforation and background monitoring data and establishing an initial velocity model, and obtaining an anisotropic velocity model after iterative processing of the initial velocity model, comprises:
performing median filtering processing on the perforation and background monitoring data, and removing abnormal points in the data to obtain initial optimization data;
resampling the perforation and background monitoring data, and establishing an initial speed model;
performing static correction processing on the initial optimization data to obtain secondary optimization data;
inputting the secondary optimization data into the initial velocity model for iterative processing to obtain an anisotropic velocity model.
3. The method for microseismic event detection and analysis of claim 1 wherein the step of de-noising the perforation and background monitoring data, filtering the strong impulse interference data in the background, optimizing the signal-to-noise ratio of the data, and obtaining the optimized perforation and background monitoring data specifically comprises:
carrying out amplitude anomaly detection on the frequency data of the middle frequency band of the perforation and background monitoring data within a preset value range, deleting the frequency data with abnormal amplitude, and filtering strong pulse interference data in the background;
and performing signal-to-noise ratio optimization on the filtered perforation and background monitoring data to obtain optimized perforation and background monitoring data.
4. A method for microseismic event detection and analysis as defined in claim 3 wherein the step of removing frequency data for amplitude anomalies comprises: the frequency data that the amplitude energy exceeds the threshold value and the amplitude is irregular are deleted.
5. The method for microseismic event detection analysis of claim 1 wherein when a weak signal event is determined, further comprising: and determining the occurrence position of the microseism event by adjusting the relative travel time value and combining the energy cluster focusing and the same-phase axis leveling data condition.
6. A microseismic event detection analysis device, comprising:
the model establishing module is used for acquiring perforation and background monitoring data, establishing an initial velocity model, and performing iterative processing on the initial velocity model to obtain an anisotropic velocity model;
the data optimization module is used for denoising the perforation and background monitoring data, filtering strong pulse interference data in the background, and optimizing the signal-to-noise ratio of the data to obtain optimized perforation and background monitoring data;
the event position module is used for carrying out energy cluster focusing and homophase axis leveling on the optimized perforation and background monitoring data through the anisotropic velocity model to determine the occurrence position of the microseism event; the event location module includes:
the event type unit is used for carrying out energy cluster focusing and in-phase axis flattening on the optimized perforation and background monitoring data through the anisotropic velocity model, and determining the type of the micro-seismic event, wherein the type of the micro-seismic event comprises a strong signal event and a weak signal event;
the first determination unit is used for determining the occurrence position of the micro-seismic event through field or seismic source mechanism inversion when the strong signal event is determined;
and the second determining unit is used for determining the occurrence position of the micro-seismic event when the micro-seismic positioning speed reaches the field fracturing speed in a data interaction pickup mode when the weak signal event is determined.
7. The microseismic event detection and analysis device of claim 6 wherein the model building module comprises:
the filtering processing unit is used for carrying out median filtering processing on the perforation and background monitoring data, removing abnormal points in the data and obtaining initial optimization data;
the resampling processing unit is used for resampling the perforation and background monitoring data and establishing an initial speed model;
the static correction processing unit is used for carrying out static correction processing on the initial optimized data to obtain secondary optimized data;
and the iteration processing unit is used for inputting the secondary optimization data into the initial velocity model for iteration processing to obtain an anisotropic velocity model.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored computer program; wherein the computer program, when executed, controls an apparatus on which the computer readable storage medium is located to perform the method of microseismic event detection analysis of any of claims 1-5.
9. A terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor when executing the computer program implementing the microseismic event detection analysis method of any of claims 1-5.
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