Seismic wave velocity model construction method, device, electronic equipment and storage medium

Abstract

The present invention discloses a method, device, electronic device and storage medium for constructing a seismic wave velocity model, and relates to the technical field of seismic data processing. The method includes collecting seismic monitoring data; identifying microseismic events on the seismic monitoring data; obtaining the initial arrival of each microseismic event after identifying the microseismic event generated by the fracturing operation; obtaining the formation parameters of the fracturing operation area according to the logging curve and three-dimensional seismic profile data of the fracturing operation area, and establishing the initial velocity model of the seismic wave of each formation based on the formation parameters; constructing the phase time difference function of all microseismic events according to the theoretical travel time and the initial arrival of the seismic phase of each microseismic event; optimizing the formation parameters using a nonlinear optimization algorithm to obtain the velocity optimization model of the seismic wave of each formation; and repeating the optimization multiple times to obtain the final velocity model of the seismic wave of each formation. The method, device, electronic device and storage medium disclosed in the present invention can accurately establish the velocity model of the seismic wave of each formation.

Description

Technical Field

[0001] The present invention belongs to the technical field of seismic data processing, and in particular relates to a method, device, electronic equipment and storage medium for constructing a seismic wave velocity model. Background Art

[0002] With the continuous deepening of oil and gas exploration and development, unconventional oil and gas have shown great potential. Horizontal well technology and hydraulic fracturing technology have become important means to improve the efficiency of oil and gas extraction. Hydraulic fracturing microseismic monitoring technology is one of the key technologies in the field of fracturing transformation of low permeability oil and gas reservoirs in recent years. It analyzes and monitors the development of fracturing cracks by observing and locating weak seismic events induced by water injection fracturing process, so as to achieve the purpose of real-time monitoring of fracturing process and evaluation of reservoir transformation effect. The accuracy of the source location results of fracturing-induced microseismic source is directly related to the credibility of the description of fracturing cracks. The velocity model within the range of the microseismic observation system in the fracturing area is a key factor affecting the source location.

[0003] Conventional microseismic monitoring collects and processes data for each fracturing section. The velocity model used for earthquake source location usually uses the first arrival of the perforation event in each fracturing section to correct the initial velocity model obtained from the logging curve. The resulting velocity model results are local and uncertain, and there may be large mutations in the velocity models of adjacent fracturing sections, which is not conducive to the accurate positioning of fracturing microseismicity in horizontal wells with multiple fracturing sections.

[0004] Therefore, how to provide an effective solution to accurately construct the velocity model of seismic waves during fracturing construction is of great significance for the accurate positioning of fracturing microearthquakes. Summary of the invention

[0005] The purpose of the present invention is to provide a method, device, electronic device and storage medium for constructing a seismic wave velocity model to solve the above-mentioned problems existing in the prior art.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for constructing a seismic wave velocity model, comprising:

[0008] Collecting seismic monitoring data through an observation system, wherein the observation system includes a plurality of seismic detectors;

[0009] Identify microseismic events on the collected seismic monitoring data;

[0010] After identifying the microseismic events generated by the fracturing operation, the first arrival of each microseismic event is obtained;

[0011] According to the well logging curve and three-dimensional seismic profile data of the fracturing construction area, the formation parameters of the fracturing construction area are obtained, and the initial velocity model of the seismic wave of each formation is established based on the formation parameters;

[0012] According to the theoretical travel time of each microseismic event and the first arrival of each microseismic event, the phase time difference function of all microseismic events is constructed;

[0013] For all the microseismic events, the nonlinear optimization algorithm is used to optimize the formation parameters with the minimum phase time difference as the goal, and the velocity optimization model of seismic waves in each formation is obtained;

[0014] The optimization is repeated many times to obtain multiple groups of velocity optimization models, and the final velocity model of the seismic waves of each formation is obtained based on the multiple groups of velocity optimization models.

[0015] In one possible design, the method further includes:

[0016] Based on the final velocity model of seismic waves in each formation, the fracturing construction area is inverted and positioned.

[0017] In a possible design, the phase time difference function of all microseismic events is constructed based on the theoretical travel time of each microseismic event and the first arrival of each microseismic event, including:

[0018] According to the theoretical travel time of each microseismic event and the first arrival of each microseismic phase, the inter-trace time difference and phase time difference of all microseismic events are determined;

[0019] Based on the inter-trace time difference and phase time difference of each microseismic event, the phase time difference function of all microseismic events is constructed.

[0020] In a possible design, the trace time difference and phase time difference of all microseismic events are:

[0021]

[0022] Among them, f p It represents the sum of the theoretical travel time of the longitudinal wave of the microseismic event detected by all seismic detectors and the time difference of the first arrival of the seismic phase, f s represents the sum of the theoretical travel time of the shear wave of the microseismic event detected by all seismic detectors and the time difference of the first arrival of the seismic phase, f ps represents the sum of the theoretical travel time of the longitudinal constant wave of the microseismic event detected by all seismic detectors and the time difference of the first arrival of the seismic phase, M represents the total number of seismic detectors, ttp(m) represents the theoretical travel time of the longitudinal wave detected by the mth seismic detector, atp(m) represents the first arrival of the longitudinal wave detected by the mth seismic detector, It represents the average theoretical travel time of the longitudinal waves detected by all seismic detectors. represents the average value of the first arrival of the P-wave phase detected by all seismic detectors, l represents the function norm, which takes a value of 1 or 2, tts(m) represents the theoretical travel time of the S-wave detected by the m-th seismic detector, ats(m) represents the first arrival of the S-wave phase detected by the m-th seismic detector, represents the average theoretical travel time of the shear waves detected by all seismic detectors, It represents the average value of the first arrival of the shear wave phase detected by all seismic geophones;

[0023] The phase time difference function of all microseismic events is:

[0024]

[0025] Where K represents the total number of microseismic events, f p (k) represents the sum of the theoretical travel time of the longitudinal wave of the kth microseismic event detected by all seismic detectors and the time difference of the first arrival of the seismic phase, f s (k) represents the sum of the theoretical travel time of the shear wave of the kth microseismic event detected by all seismic detectors and the time difference between the first arrival of the seismic phase, f ps (k) represents the sum of the theoretical travel time of the longitudinal constant wave of the kth microseismic event detected by all seismic detectors and the time difference of the first arrival of the seismic phase, and ω is the normalization factor of the inter-trace time and the seismic phase time, with a value range of 0-1.

[0026] In a possible design, the microseismic event identification of the collected seismic monitoring data includes:

[0027] Microseismic events are identified from the collected seismic monitoring data based on the before-after energy ratio or waveform similarity.

[0028] In a possible design, the first arrival of the seismic phase of each microseismic event includes the first arrival of the seismic phase of the constant wave of each microseismic event and the first arrival of the seismic phase of the longitudinal wave of each microseismic event.

[0029] In a possible design, the formation parameters of the fracturing operation area include the number of formations, the upper interface depth and the lower interface depth of each formation, the dip angle of each formation, the inclination of each formation, the measured longitudinal wave velocity of each formation, and the measured transverse wave velocity of each formation.

[0030] In a second aspect, the present invention provides a seismic wave velocity model construction device, comprising:

[0031] A collection unit, used for collecting seismic monitoring data through an observation system, wherein the observation system includes a plurality of seismic detectors;

[0032] An identification unit, used for identifying microseismic events from the collected seismic monitoring data;

[0033] An acquisition unit, used for acquiring the first arrival of each microseismic event after identifying the microseismic event generated by the fracturing operation;

[0034] A model building unit, used to obtain the formation parameters of the fracturing construction area according to the well logging curve and three-dimensional seismic profile data of the fracturing construction area, and to establish an initial velocity model of seismic waves of each formation based on the formation parameters;

[0035] A function construction unit is used to construct a phase time difference function of all microseismic events according to the theoretical travel time of each microseismic event and the first arrival of each microseismic event;

[0036] An optimization unit is used to optimize the formation parameters with the minimum phase time difference as the goal for the phase time difference function of all microseismic events by using a nonlinear optimization algorithm to obtain the velocity optimization model of the seismic waves of each formation; and

[0037] Repeat the optimization process multiple times to obtain multiple groups of speed optimization models;

[0038] The determination unit is used to obtain the final velocity model of the seismic waves of each formation based on multiple groups of velocity optimization models.

[0039] In a third aspect, the present invention provides an electronic device comprising a memory, a processor and a transceiver which are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the seismic wave velocity model construction method as described in the first aspect or any possible design of the first aspect.

[0040] In a fourth aspect, the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed on a computer, executes the method for constructing a seismic wave velocity model according to the first aspect or any possible design of the first aspect.

[0041] In a fifth aspect, the present invention provides a computer program product comprising instructions, which, when executed on a computer, causes the computer to execute the method for constructing an X-seismic wave velocity model as described in the first aspect or any possible design of the first aspect.

[0042] Beneficial effects:

[0043] The invention provides a method, device, electronic device and storage medium for constructing a seismic wave velocity model. The method collects seismic monitoring data through an observation system, wherein the observation system includes a plurality of seismic detectors; performs microseismic event identification on the collected seismic monitoring data; obtains the first arrival of the seismic phase of each microseismic event after identifying the microseismic event generated by the fracturing operation; obtains the formation parameters of the fracturing operation area according to the well logging curve and three-dimensional seismic profile data of the fracturing operation area, and establishes the initial velocity model of the seismic wave of each formation based on the formation parameters; constructs the seismic phase time difference function of all microseismic events according to the theoretical travel time of each microseismic event and the first arrival of the seismic phase of each microseismic event; optimizes the formation parameters for the seismic phase time difference function of all microseismic events by using a nonlinear optimization algorithm with the minimum seismic phase time difference as the goal, and obtains the velocity optimization model of the seismic wave of each formation; repeats the optimization multiple times to obtain multiple groups of velocity optimization models, and obtains the final velocity model of the seismic wave of each formation based on the multiple groups of velocity optimization models. In this way, the velocity model of seismic waves in each formation can be established more accurately, which is conducive to the accurate positioning and quality control of fracturing microseismicity in horizontal wells with multiple fracturing sections, and is convenient for practical application and promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A flow chart of a method for constructing a seismic wave velocity model provided in an embodiment of the present application;

[0045] Figure 2 Schematic diagram of the observation system for Case 1;

[0046] Figure 3 This is the first arrival of the microseismic event with noise simulated in Case 1;

[0047] Figure 4 Velocity model inversion results for Case 1;

[0048] Figure 5 It is a schematic diagram of the observation system of Case 2;

[0049] Figure 6 This is the inversion result of the seismic wave velocity model of Case 2;

[0050] Figure 7 is a schematic diagram of the observation system of Case 3;

[0051] Figure 8 It is the record and first arrival information of microseismic events in Case 3;

[0052] Fig. 9 This is the seismic wave velocity model obtained by conventional inversion in Case 3;

[0053] Fig.10 The seismic wave velocity model obtained by inversion using the method of the present invention in Case 3;

[0054] Fig.11 Comparison of the source location effect of the seismic wave velocity model obtained by inversion in Case 3;

[0055] Fig.12 A schematic block diagram of a seismic wave velocity model building device provided in an embodiment of the present application;

[0056] Fig.13 A schematic block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0058] It should be understood that although the terms first, second, etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another unit. For example, a first unit can be referred to as a second unit, and similarly, a second unit can be referred to as a first unit without departing from the scope of the exemplary embodiments of the present invention.

[0059] It should be understood that the term "and / or" that may appear in this article is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can represent three situations: A exists alone, B exists alone, and A and B exist at the same time. The term " / and" that may appear in this article describes another type of association object relationship, indicating that two relationships may exist. For example, A / and B can represent two situations: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this article generally indicates that the previous and next associated objects are in an "or" relationship.

[0060] In order to facilitate the accurate positioning of fracturing microseisms, the embodiments of the present application provide a method, device, electronic device and storage medium for constructing a seismic wave velocity model. The method, device, electronic device and storage medium for constructing a seismic wave velocity model can accurately establish the velocity model of seismic waves in each formation, which is beneficial to the accurate positioning and quality control of fracturing microseisms in horizontal wells with multiple fracturing sections.

[0061] The seismic wave velocity model construction method provided in the embodiment of the present application will be described in detail below.

[0062] like Figure 1 As shown, it is a flowchart of the seismic wave velocity model construction method provided in the first aspect of the embodiment of the present application. The seismic wave velocity model construction method may include but is not limited to the following steps S101-S107.

[0063] Step S101. Collect earthquake monitoring data through an observation system.

[0064] In the embodiment of the present application, before the fracturing construction, a suitable observation system can be selected for the fracturing construction area. The observation system can include multiple seismic detectors. The layout positions of the multiple seismic detectors can be divided into three types: in the well, on the ground, and in the shallow well. No specific limitation is made in the embodiment of the present application.

[0065] Step S102: Identify microseismic events on the collected earthquake monitoring data.

[0066] When identifying microseismic events in the collected seismic monitoring data, microseismic event identification can be achieved by, but is not limited to, analyzing the energy ratio before and after the first arrival of the seismic monitoring data (seismic wave data) collected by the seismic detector or by comparing the waveform of the seismic monitoring data with the seismic waveform of known microseismic events (mainly referring to microseismic events caused by fracturing construction) for similarity.

[0067] Among them, in seismic exploration, the moment when the seismic wave front reaches a certain observation point and the seismic detector at the observation point detects the vibration of the particle is called the first arrival time of the wave, abbreviated as the first arrival.

[0068] Step S103: After identifying the microseismic events generated by the fracturing operation, the first arrival of each microseismic event is obtained.

[0069] The first arrival of the seismic phase of each microseismic event may include the first arrival of the seismic phase of the constant wave (S wave) of each microseismic event and / or the first arrival of the seismic phase of the longitudinal wave (P wave) of each microseismic event.

[0070] In the embodiment of the present application, the first arrival of each microseismic event includes the first arrival of the S wave of each microseismic event and the first arrival of the P wave of each microseismic event. Since the observation system includes multiple seismic detectors, each seismic detector has a first arrival of the S wave and a first arrival of the P wave for each microseismic event.

[0071] Step S104: According to the well logging curve and three-dimensional seismic profile data of the fracturing construction area, the formation parameters of the fracturing construction area are obtained, and the initial velocity model of the seismic wave of each formation is established based on the formation parameters.

[0072] Well logging curves and 3D seismic profile data of the fracturing construction area can be obtained during the exploration phase. The formation parameters of the fracturing construction area may include but are not limited to the number of formations, the depth of the upper interface of each formation, and the depth of the upper interface of each formation. top (i) and the underlying interface depth interface bottom (i), the dip angle θ(i) of each formation, the inclination α(i) of each formation, the measured P-wave velocity vp(i) of each formation and the measured S-wave velocity vs(i) of each formation, etc., where i represents the formation number, the dip is based on the north direction and the positive direction is clockwise, the dip angle is the angle relative to the horizontal plane, the upper interface and the measured P-wave velocity can be obtained through sound waves and logging curves, and then the measured S-wave velocity is given by the proportional relationship between the P-wave and S-wave velocities.

[0073] Step S105. Construct the phase time difference function of all microseismic events according to the theoretical travel time of each microseismic event and the first arrival of each microseismic phase.

[0074] Specifically, the inter-trace time difference and phase time difference of all microseismic events can be determined based on the theoretical travel time of each microseismic event and the first arrival of each microseismic phase. Then, based on the inter-trace time difference and phase time difference of each microseismic event, the phase time difference function of all microseismic events is constructed.

[0075] The inter-trace time difference and phase time difference of all microseismic events can be expressed as:

[0076]

[0077] Among them, f p It represents the sum of the theoretical travel time of the longitudinal wave of the microseismic event detected by all seismic detectors and the time difference of the first arrival of the seismic phase, f s represents the sum of the theoretical travel time of the shear wave of the microseismic event detected by all seismic detectors and the time difference of the first arrival of the seismic phase, f ps represents the sum of the theoretical travel time of the longitudinal constant wave of the microseismic event detected by all seismic detectors and the time difference of the first arrival of the seismic phase, M represents the total number of seismic detectors (i.e., the number of seismic detectors), ttp(m) represents the theoretical travel time of the longitudinal wave detected by the mth seismic detector, atp(m) represents the first arrival of the longitudinal wave detected by the mth seismic detector, It represents the average theoretical travel time of the longitudinal waves detected by all seismic detectors. It represents the average value of the first arrival of the longitudinal wave detected by all seismic detectors, l represents the function norm, which takes the value of 1 or 2. The value of l can be determined according to the data quality. When the data quality is good, the value of l is 1. When the data quality is poor, that is, when there is obvious abnormal interference, the value of l is 2. tts(m) represents the theoretical travel time of the shear wave detected by the mth seismic detector, ats(m) represents the first arrival of the shear wave detected by the mth seismic detector, and tts represents the average value of the theoretical travel time of the shear wave detected by all seismic detectors. It represents the average value of the first arrival of the shear wave phase detected by all seismic geophones;

[0078] The phase time difference function of all microseismic events can be expressed as:

[0079]

[0080] Where K represents the total number of microseismic events, f p (k) represents the sum of the theoretical travel time of the longitudinal wave of the kth microseismic event detected by all seismic detectors and the time difference of the first arrival of the seismic phase, f s (k) represents the sum of the theoretical travel time of the shear wave of the kth microseismic event detected by all seismic detectors and the time difference between the first arrival of the seismic phase, f ps (k) represents the sum of the theoretical travel time of the longitudinal constant wave of the kth microseismic event detected by all seismic detectors and the time difference of the first arrival of the seismic phase. ω is the normalization factor of the inter-trace time length and the seismic phase length, and its value range is 0-1. The value of ω can be the average value of the seismic phase time difference divided by the average value of the inter-trace time difference.

[0081] Step S106. For the phase time difference functions of all microseismic events, a nonlinear optimization algorithm is used to optimize the formation parameters with the goal of minimizing the phase time difference, and a velocity optimization model of seismic waves in each formation is obtained.

[0082] Among them, the formation parameters to be optimized in this step are mainly the measured longitudinal wave velocity vp(i) of each formation and the measured shear wave velocity vs(i) of each formation.

[0083] The nonlinear optimization algorithm may be, but is not limited to, a simulated annealing algorithm, a particle swarm algorithm or a genetic algorithm. In the embodiment of the present application, the nonlinear optimization algorithm adopts a simulated annealing algorithm.

[0084] The basic idea of ​​the simulated annealing algorithm is to generate a series of parameter vectors to simulate the thermal motion of particles, and to slowly reduce a control parameter of the simulated temperature so that the simulated system eventually cools and crystallizes to reach the minimum energy value of the system.

[0085] The simulated annealing algorithm steps are as follows:

[0086] 1) Given the initial model parameters X0 and initial temperature T, set the temperature drop coefficient α, the maximum number of iterations Num and the objective function threshold ε.

[0087] 2) Give a random disturbance to the current value of the model parameter to generate the model parameter X, and calculate the change of the objective function ΔE = E(X0)-E(X). If ΔE>0, accept X, otherwise accept X with a certain probability according to a certain acceptance criterion.

[0088] 3) Check whether equilibrium is achieved at temperature T (i.e., whether the objective function is less than the threshold ε). If not, return to step 2).

[0089] 4) Select an annealing scheme and slowly lower the temperature.

[0090] 5) Check whether the minimum temperature is reached (i.e., whether the maximum number of iterations Num is reached). If so, the annealing process ends, otherwise return to step 2).

[0091] The final model is the speed optimization model obtained by inversion search.

[0092] Step S107. Repeat the optimization process multiple times to obtain multiple groups of velocity optimization models, and obtain the final velocity model of the seismic waves of each formation based on the multiple groups of velocity optimization models.

[0093] In the embodiment of the present application, the optimization process can be repeated multiple times to obtain velocity optimization models of multiple groups of seismic waves in each formation, and then the parameters of the velocity optimization models of multiple groups of seismic waves in each formation are averaged to obtain the final velocity model of the seismic waves in each formation.

[0094] In one or more embodiments, after obtaining the final velocity model of the seismic waves of each formation, the fracturing construction area can be inverted and located based on the final velocity model of the seismic waves of each formation.

[0095] The seismic wave velocity model construction method provided by the present invention collects seismic monitoring data through an observation system, and the observation system includes multiple seismic detectors; the collected seismic monitoring data is subjected to microseismic event identification; after identifying the microseismic events generated by the fracturing construction, the seismic phase first arrival of each microseismic event is obtained; according to the well logging curve and three-dimensional seismic profile data of the fracturing construction area, the formation parameters of the fracturing construction area are obtained, and the initial velocity model of the seismic wave of each formation is established based on the layer parameters; according to the theoretical travel time of each microseismic event and the seismic phase first arrival of each microseismic event, the seismic phase time difference function of all microseismic events is constructed; for the seismic phase time difference function of all microseismic events, the formation parameters are optimized with the minimum seismic phase time difference as the goal by using a nonlinear optimization algorithm, and the velocity optimization model of the seismic wave of each formation is obtained; the optimization is repeated for multiple times to obtain multiple groups of velocity optimization models, and the final velocity model of the seismic wave of each formation is obtained based on the multiple groups of velocity optimization models. In this way, the velocity model of the seismic wave of each formation can be established more accurately, which is conducive to the accurate positioning and quality control of the fracturing microseism in the case of multiple fracturing sections of horizontal wells, and is convenient for practical application and promotion.

[0096] Case 1

[0097] This case is the surface microseismic monitoring of multi-stage fracturing in horizontal wells. The layered velocity model and simulated fracturing stages and observation system are as follows: Figure 2 As shown in a, the triangle in the figure is the location of the seismic detector, and the star is the perforation location of the fracturing stage. Use 10 depths of 1700 meters, with a spacing of 40 meters, and the plane distribution is as follows Figure 2 The star-shaped image shown in b is used as the simulated horizontal multi-stage fracturing perforation position, and the radial arrangement is adopted for reception, such as Figure 2 As shown in Fig. 2b, this arrangement consists of 8 equal-length survey lines, each with 10 seismic detectors, a trace spacing of 50 meters, and an angle of 45 degrees between two adjacent survey lines. Velocity model Horizontal layered velocity model (stratum dip and azimuth are 0), the theoretical velocity model settings of each layer are shown in Table 1.

[0098] Table 1 Case 1 velocity model

[0099] Upper interface depth (m) Depth of lower interface (m) P wave velocity (m / s) S wave speed (m / s) 0 300 1500 1000 300 600 2000 1300 600 1000 2500 1600 1000 1500 3000 1900 1500 1900 3500 2200

[0100] The simulated recording sampling rate is 500Hz. The travel time of each microseismic event is obtained through forward modeling, and the disturbances of 5ms and 10ms are set for the first arrival of the P-wave and S-wave phases of each microseismic event to simulate the actual situation where there is an error in the first arrival picking. Figure 3is the noisy first arrival information of one of the simulated microseismic events. In the process of velocity model inversion, the depth of the stratum interface and the interface dip and inclination are set to fixed values ​​to simulate the horizontal layered geological conditions. In the inversion optimization process, the search ranges of P-wave and S-wave velocities are set to [1000,4000] (unit: m) and [800,2500] (unit: m), respectively. The results of velocity model inversion using a single source and the results of velocity model inversion using all sources are shown in the figure. Figure 4 As shown, Figure 4 (a) and (b) are the P-wave and S-wave velocity results obtained by inversion of the first arrival of a single source event. Figure 4 (c) and (d) are the P-wave and S-wave velocity results obtained by the inversion method of the present invention, respectively. The gray line in the figure is the inversion result repeated 50 times, the black line in the figure is the average value of all results, and the dotted line is the true value of the velocity model of each layer. The comparison of the results shows that the method of the present invention can reduce the inversion error of the seismic wave velocity model in the case of ground microseismic monitoring.

[0101] Case 2

[0102] This case simulates the microseismic monitoring of a horizontal well with multiple stages of fracturing. The layered velocity model and the simulated fracturing stages and observation system are as follows: Figure 5 As shown in the figure, the triangle is the location of the seismic detector, and the star is the perforation location of the fracturing stage. The observation system consists of a group of seismic detector strings in the well, and the distance between the seismic detectors is 10m. The theoretical velocity model is a layered velocity model with an inclination of 4°. The interface depth and velocity settings of each layer are shown in Table 2.

[0103] Table 2 Case 2 velocity model

[0104] Upper interface depth (m) Depth of lower interface (m) P wave velocity (m / s) S wave speed (m / s) 1100 1300 2200 1500 1300 1500 2500 1800 1500 1700 3000 2000 1700 1900 3500 2200

[0105] The simulated record sampling rate is 4000Hz. The phase travel time of each microseismic event is obtained through forward modeling, and the P-wave and S-wave phase first arrivals of each microseismic event are set to 1ms and 2ms perturbations to simulate the actual situation where there are errors in the first arrival picking. In the inversion optimization process, the depth of the layer interface at the fixed lateral distance of 0m remains unchanged, the search range of P-wave and S-wave velocities is set to [1500,4000] and [1000,3000] respectively, and the search range of dip angle is 0~8°. The results of the velocity model inversion of all sources are used as shown in the figure. Figure 6 As shown, Figure 6 (a) and (b) are the P-wave and S-wave velocity results obtained by the inversion method of the present invention, respectively. The gray line in the figure is the inversion result repeated 50 times, the black line in the figure is the average value of all results, and the dotted line is the true value of the velocity model of each layer. Figure 6(c) is the interface dip distribution obtained by repeating the inversion 50 times, with an average value of 3.95°. The results show the feasibility of the inversion method of the seismic wave velocity model in the case of inclined layers.

[0106] Case 3

[0107] This case contains actual data of microseismic monitoring of 10 sections of hydraulic fracturing in the same horizontal well. The observation system used to collect this data is a 15-level downhole seismic detector string, such as Figure 7 The initial velocity model was established using the acoustic logging curve of a well in the work area, and its model parameters are shown in Table 3. Afterwards, the initial velocity model was corrected using the perforation records of each fracturing stage. Figure 8 The active source monitoring record of one of the fracturing sections in the actual data, where the black solid line and dashed line represent the first arrival time of the picked P wave and S wave, respectively. The conventional method takes the arrival time data of the active source events of each fracturing section and the initial horizontal layered velocity model as input, and uses the simulated annealing algorithm to invert the P wave and S wave velocity models as shown in the figure. Fig. 9 As shown in the figure, there is a large mutation in the velocity model results of adjacent fracturing sections, which will have a great impact on the subsequent microseismic source location.

[0108] Table 3 Initial velocity model for Case 3

[0109] Upper interface depth (m) Depth of lower interface (m) P wave velocity (m / s) S wave speed (m / s) 2420 2614 2695 1245 2614 1500 2877 1378 2683 1700 3039 1533 2715 1900 3749 1986 2739 2748 2725 1355 2748 2764 3723 1971 2764 2787 2867 1467 2787 2794 3602 1859 2794 2820 2619 1354

[0110] The method of the present invention takes the arrival time data of microseismic events in all fracturing stages and the initial velocity model as input, and uses the simulated annealing algorithm to invert the P-wave and S-wave velocity models and the dip angle of the layer interface, such as Fig.10 The conventional method and the method of the present invention are used to locate the microseismic event, and the positioning results are shown in Fig.11 As shown in the figure, the stars are the positions of microseismic events, the hollow circles and solid circles are the inversion positioning results of the velocity model of the conventional method and the velocity model of the method of the present invention, respectively. From the comparison between the source positioning results and the actual microseismic event positions, it can be seen that the positioning results of the velocity model of the method of the present invention are more accurate, which also shows that the obtained velocity model is more reliable.

[0111] See also Fig.12 According to a second aspect of an embodiment of the present application, there is provided a seismic wave velocity model construction device, the seismic wave velocity model construction device comprising:

[0112] A collection unit, used for collecting seismic monitoring data through an observation system, wherein the observation system includes a plurality of seismic detectors;

[0113] An identification unit, used for identifying microseismic events from the collected seismic monitoring data;

[0114] An acquisition unit, used for acquiring the first arrival of each microseismic event after identifying the microseismic event generated by the fracturing operation;

[0115] A model building unit, used to obtain the formation parameters of the fracturing construction area according to the well logging curve and three-dimensional seismic profile data of the fracturing construction area, and to establish an initial velocity model of seismic waves of each formation based on the formation parameters;

[0116] A function construction unit is used to construct a phase time difference function of all microseismic events according to the theoretical travel time of each microseismic event and the first arrival of each microseismic event;

[0117] An optimization unit is used to optimize the formation parameters with the minimum phase time difference as the goal for the phase time difference function of all microseismic events by using a nonlinear optimization algorithm to obtain the velocity optimization model of the seismic waves of each formation; and

[0118] Repeat the optimization process multiple times to obtain multiple groups of speed optimization models;

[0119] The determination unit is used to obtain the final velocity model of the seismic waves of each formation based on multiple groups of velocity optimization models.

[0120] The working process, working details and technical effects of the seismic wave velocity model building device provided in the second aspect of this embodiment can be found in the first aspect of the embodiment and will not be described in detail here.

[0121] like Fig.13 As shown, the third aspect of an embodiment of the present application provides an electronic device, comprising a memory, a processor and a transceiver which are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the seismic wave velocity model construction method as described in the first aspect of the embodiment.

[0122] For specific examples, the memory may include but is not limited to random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out memory (FIFO) and / or first-in-last-out memory (FILO), etc.; the processor may be but is not limited to a microprocessor of the STM32F105 series, an ARM (Advanced RISC-Machines), an X86 or other architecture processor, or a processor with an integrated NPU (neural-network processing units); the transceiver may be but is not limited to a WiFi (Wireless Fidelity) wireless transceiver, a Bluetooth wireless transceiver, a General Packet Radio Service (GPRS) wireless transceiver, a ZigBee protocol (a low-power local area network protocol based on the IEEE802.15.4 standard, ZigBee) wireless transceiver, a 3G transceiver, a 4G transceiver and / or a 5G transceiver, etc.

[0123] In a fourth aspect of the present embodiment, there is provided a computer-readable storage medium storing instructions for the method for constructing a seismic wave velocity model as described in the first aspect of the embodiment, that is, the computer-readable storage medium stores instructions, and when the instructions are run on a computer, the method for constructing a seismic wave velocity model as described in the first aspect is executed. The computer-readable storage medium refers to a carrier for storing data, which may include but is not limited to a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive, and / or a memory stick, etc., and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0124] The fifth aspect of this embodiment provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the method for constructing a seismic wave velocity model as described in the first aspect of the embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0125] It should be understood that certain details are provided in the following description to facilitate a complete understanding of the example embodiments. However, it should be understood by those of ordinary skill in the art that the example embodiments can be implemented without these certain details. For example, the system can be shown in a block diagram to avoid obscuring the example with unnecessary details. In other examples, well-known processes, structures, and techniques may not be shown in unnecessary detail to avoid obscuring the example embodiments.

[0126] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for constructing a seismic wave velocity model, characterized in that: include: Collecting seismic monitoring data through an observation system, wherein the observation system includes a plurality of seismic detectors; Identify microseismic events on the collected seismic monitoring data; After identifying the microseismic events generated by the fracturing operation, the first arrival of each microseismic event is obtained; According to the well logging curve and three-dimensional seismic profile data of the fracturing construction area, the formation parameters of the fracturing construction area are obtained, and the initial velocity model of the seismic wave of each formation is established based on the formation parameters; According to the theoretical travel time of each microseismic event and the first arrival of each microseismic event, the phase time difference function of all microseismic events is constructed; For all the microseismic events, the nonlinear optimization algorithm is used to optimize the formation parameters with the minimum phase time difference as the goal, and the velocity optimization model of seismic waves in each formation is obtained; Repeat the optimization process multiple times to obtain multiple groups of velocity optimization models, and obtain the final velocity model of the seismic waves of each formation based on the multiple groups of velocity optimization models; The formation parameters of the fracturing construction area include the number of formations, the upper interface depth and the lower interface depth of each formation, the dip angle of each formation, the inclination of each formation, the measured longitudinal wave velocity of each formation, and the measured transverse wave velocity of each formation.

2. The method for constructing a seismic wave velocity model according to claim 1, characterized in that: The method further comprises: Based on the final velocity model of seismic waves in each formation, the fracturing construction area is inverted and positioned.

3. The method for constructing a seismic wave velocity model according to claim 1, characterized in that: The phase time difference function of all microseismic events is constructed according to the theoretical travel time of each microseismic event and the first arrival of each microseismic event, including: According to the theoretical travel time of each microseismic event and the first arrival of each microseismic phase, the inter-trace time difference and phase time difference of all microseismic events are determined; Based on the inter-trace time difference and phase time difference of each microseismic event, the phase time difference function of all microseismic events is constructed.

4. The method for constructing a seismic wave velocity model according to claim 3, characterized in that: The inter-trace time difference and phase time difference of all microseismic events are: Among them, f p It represents the sum of the theoretical travel time of the longitudinal wave of the microseismic event detected by all seismic detectors and the time difference of the first arrival of the seismic phase, f s represents the sum of the theoretical travel time of the shear wave of the microseismic event detected by all seismic detectors and the time difference of the first arrival of the seismic phase, f ps represents the sum of the theoretical travel time of the longitudinal constant wave of the microseismic event detected by all seismic detectors and the time difference of the first arrival of the seismic phase, M represents the total number of seismic detectors, ttp(m) represents the theoretical travel time of the longitudinal wave detected by the mth seismic detector, atp(m) represents the first arrival of the longitudinal wave detected by the mth seismic detector, It represents the average theoretical travel time of the longitudinal waves detected by all seismic detectors. represents the average value of the first arrival of the P-wave phase detected by all seismic detectors, l represents the function norm, which takes a value of 1 or 2, tts(m) represents the theoretical travel time of the S-wave detected by the m-th seismic detector, ats(m) represents the first arrival of the S-wave phase detected by the m-th seismic detector, represents the average theoretical travel time of the shear waves detected by all seismic detectors, It represents the average value of the first arrival of the shear wave phase detected by all seismic geophones; The phase time difference function of all microseismic events is: Where K represents the total number of microseismic events, f p (k) represents the sum of the theoretical travel time of the longitudinal wave of the kth microseismic event detected by all seismic detectors and the time difference of the first arrival of the seismic phase, f s (k) represents the sum of the theoretical travel time of the shear wave of the kth microseismic event detected by all seismic detectors and the time difference between the first arrival of the seismic phase, f ps (k) represents the sum of the theoretical travel time of the longitudinal constant wave of the kth microseismic event detected by all seismic detectors and the time difference of the first arrival of the seismic phase, and ω is the normalization factor of the inter-trace time and the seismic phase time, with a value range of 0-1.

5. The method for constructing a seismic wave velocity model according to claim 1, characterized in that: The microseismic event identification of the collected seismic monitoring data includes: Microseismic events are identified from the collected seismic monitoring data based on the before-after energy ratio or waveform similarity.

6. The method for constructing a seismic wave velocity model according to claim 1, characterized in that: The first arrival of the seismic phase of each microseismic event includes the first arrival of the seismic phase of the constant wave of each microseismic event and the first arrival of the seismic phase of the longitudinal wave of each microseismic event.

7. A seismic wave velocity model construction device, characterized in that: include: A collection unit, used for collecting seismic monitoring data through an observation system, wherein the observation system includes a plurality of seismic detectors; An identification unit, used for identifying microseismic events from the collected seismic monitoring data; An acquisition unit, used for acquiring the first arrival of each microseismic event after identifying the microseismic event generated by the fracturing operation; A model building unit, used to obtain the formation parameters of the fracturing construction area according to the well logging curve and three-dimensional seismic profile data of the fracturing construction area, and to establish an initial velocity model of seismic waves of each formation based on the formation parameters; A function construction unit is used to construct a phase time difference function of all microseismic events according to the theoretical travel time of each microseismic event and the first arrival of each microseismic event; The optimization unit is used to optimize the formation parameters with the minimum phase time difference as the goal for the phase time difference function of all microseismic events using a nonlinear optimization algorithm to obtain the velocity optimization model of the seismic waves of each formation; as well as Repeat the optimization process multiple times to obtain multiple groups of speed optimization models; A determination unit, used for obtaining a final velocity model of seismic waves of each formation based on multiple groups of velocity optimization models; The formation parameters of the fracturing construction area include the number of formations, the upper interface depth and the lower interface depth of each formation, the dip angle of each formation, the inclination of each formation, the measured longitudinal wave velocity of each formation, and the measured transverse wave velocity of each formation.

8. An electronic device, characterized in that: It comprises a memory, a processor and a transceiver which are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the seismic wave velocity model construction method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the method for constructing a seismic wave velocity model according to any one of claims 1 to 6 is executed.

Images