CN106772577B - Source inversion method based on microseism data and SPSA optimization algorithm - Google Patents
Source inversion method based on microseism data and SPSA optimization algorithm Download PDFInfo
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Abstract
The source inversion method based on microseism data and SPSA optimization algorithm that the present invention relates to a kind of, method includes the following steps: establishing horizontal layer rate pattern according to well-log information and subsurface rock property analysis；Forward simulation is carried out to micro-seismic event, the preferred ray tracing path of x-ray angle is adjusted with dichotomy, first-arrival traveltime is calculated, then calculates difference when walking of adjacent two wave detector；Random perturbation is added as observation in difference when will walk；Microseism hypocentral location that iteration updates is calculated to the difference of first-arrival traveltime using SPSA algorithm according to preceding method, hypocentral location is continued to optimize by the Fitting Calculation data and observation, check the difference for calculating data and observation, stop updating if meeting precision or reaching maximum number of iterations, export result, positioning is completed, next iteration is otherwise continued.The present invention improves the accuracy of seismic source location by solution micro-seismic event source rapidly and efficiently.
Description
Technical field
The invention belongs to petroleum gas field of seismic exploration, and in particular, to one kind is based on microseism data and SPSA
The source inversion method of optimization algorithm.
Background technique
In low permeability oil and gas field production process, fracturing technology is an important measures of increasing yield and injection, and pressure break is produced
Raw crack (there is close relationship in fracture orientation with crustal stress again) and pressure break scale, are the important references of well net deployment,
Fracture orientation and form are thus in depth studied, in time carries out Well pattern edjustment, this is always oil field project urgently to be resolved.
The artificial microseism real-time monitoring assessment technique of fractured well is built upon an oil on the basis of microseismic
Field production performance observation technology.Micro-seismic monitoring is most accurate, most timely, the most abundant monitoring hand of information in current reservoir fracturing
Section.Microseismic imaging can instruct fracturing engineering in time, adjust fracturing parameter in due course；Range, fracture development to pressure break
Direction, size be tracked, position, objectively evaluate the effect of fracturing engineering, the production development of next step provided effective
Guidance reduces development cost, preferably provides foundation for subsequent field management.
The most crucial problem of the data processing of micro-seismic monitoring is seismic source location, and the most commonly used is based on inverting side when walking
Method, such as longitudinal and shear wave time difference method or homotype wave time difference method, such methods are the conventional methods in microseism source inversion, but the party
Method needs to solve linearly or nonlinearly equation group, in the solution procedure of equation group there may be overdetermination, owe fixed or ill-conditioning problem,
Furthermore event time detected on this method heavy dependence wave detector, positioning accuracy cannot be met the requirements.
Currently used inversion method is also varied, including some gradient class algorithms, such as Newton method, conjugate gradient method
It in advantage is that convergence is very fast, but is easily trapped into locally optimal solution, and big to the selection dependence of initial value Deng, such method；
There are also some random class algorithms, such as particle swarm algorithm, genetic algorithm, simulated annealing, such algorithm to well solve
The problem of locally optimal solution, but the speed that such algorithm solves is slower.
Summary of the invention
To overcome defect of the existing technology, the present invention provides a kind of based on microseism data and SPSA optimization algorithm
Source inversion method can rapidly and efficiently realize seismic source location.
To achieve the above object, the present invention uses following proposal:
Microseism seismic source location method based on SPSA algorithm, comprising the following steps:
Step 1: horizontal layer rate pattern is established according to well-log information and subsurface rock property analysis；
Step 2: forward simulation is carried out to micro-seismic event, first-arrival traveltime is calculated, then calculates adjacent two wave detector
Difference when walking；
Step 3: difference when walking being calculated in step 2 is added to one group of Gaussian distributed in (0,1)
The random perturbation of random number composition, in this, as observation；
Step 4: SPSA algorithm, by the Fitting Calculation data and observation, the optimal hypocentral location of Optimization Solution are applied.
In terms of ray tracing positioning, depending critically upon radiographic density factor for local ray casting leads to accuracy
The disadvantage of difference, the present invention propose the shooting method that the ray tracing based on Snell law is improved using dichotomy, are given first
Then initial ray angle is constantly adjusted according to dichotomy, this method is examined to not only increase convergence rate, and precision
It is significantly promoted, improves the speed and accuracy of positioning.
In the selection of objective function, difference when walking that the present invention uses same focus to monitor in adjacent wave detector as
Fitting parameter has not only been evaded solve the cumbersome of focus generation moment in this way, but also reduced seismic wave in earth-layer propagation process
In the annoyance level that receives, improve anti-noise ability.
In addition, the invention also provides using SPSA algorithm to come inverting hypocentral location, this method gradient class algorithm and with
Machine class algorithm is combined together consideration, gives the direction of search and stochastic gradient using random class algorithm, then utilizes gradient optimizing
It solves, can consider the rapid solving of global optimum and objective function, convergence rate is very fast, can be realized quick standard
True seismic source location has the advantages that calculating speed is fast, high-efficient.
Detailed description of the invention
Fig. 1 is the source inversion method flow schematic diagram based on microseism data and SPSA optimization algorithm；
Fig. 2 is that the ray tracing based on Snell law positions schematic diagram；
Fig. 3 is SPSA algorithm optimization inversion process figure；
Fig. 4 is the ray tracing path profile that forward simulation obtains；
Fig. 5 is the first-arrival traveltime diagram that forward simulation obtains；
Fig. 6 is the difference diagram for the first-arrival traveltime that forward simulation obtains；
Fig. 7 is that observation data diagram is obtained after random perturbation is added；
Fig. 8 is that true hypocentral location and inverting obtain hypocentral location diagram.
Specific embodiment
As shown in Figure 1, the source inversion method based on microseism data and SPSA optimization algorithm, includes the following steps:
Step 1: horizontal layer rate pattern is established according to well-log information and subsurface rock property analysis；
The specific method is as follows:
1. carrying out classifying and dividing layer position to stratum according to well-log information and subsurface rock property analysis, stratum is divided into M
Layer position, number consecutively, dielectric interface are all horizontal planes from deep to shallow, and each layer is Layer_h=apart from ground depth
[Layer_h1Layer_h2……Layer_hM]；
2. assuming oil reservoir and upper overlying strata stone homogeneous, it is full of fluid in pore media, then can establish according to Raymer model
Formation velocity field:
V=(1- φ)2·vsolid+φ·vfluid
Wherein,
Kfliud=Kw×Sw+Ko×So+Kg×Sg；
ρfliud=ρw×Sw+ρo×So+ρg×Sg；
In formula, v is speed (m/s), and φ is porosity, and K is elasticity modulus (Pa), and μ is modulus of shearing (Pa), and ρ is density
(kg/cm3), fiFor the shared volume fraction of i-th kind of composition in solid, NsolidFor number of components included in solid, SmFor phase m
Saturation degree；Subscript solid, fliud respectively indicates solid, fluid, and w, o, g respectively indicate water, oil, gas three-phase.
As can be seen from the above equation, Raymer model has comprehensively considered the essential characteristic of underground solid and fluid, passes through well logging
The above parameter value can be obtained in data and subsurface rock property analysis, and seismic wave can thus be calculated in each Es-region propagations
Speed.Since to have the advantages that spread speed fast, easy to identify for P wave (longitudinal wave), so generally selecting the P wave of bed boundary to carry out
Seismic source location, such velocity field is from shallowly to deep respectively Layer_v=[Layer_v1Layer_v2……Layer_vM]。
Step 2: forward simulation is carried out to micro-seismic event, first-arrival traveltime is calculated, then calculates adjacent two wave detector
Difference when walking；
The specific method is as follows:
1. the ray tracing based on Snell law is defined as follows:
Stratum is divided into M layers in test area, and the spread speed of P wave (longitudinal wave) is Layer_v=in every layer from shallow to deep
[Layer_v1Layer_v2……Layer_vM], wave detector series is N, and seismic wave, which is pointed out to be dealt into up to i-stage wave detector from S, to be somebody's turn to do
Ray propagation angle, propagation path and calculation formula when walking in stratum is as follows:
Wherein, siIt is that seismic wave points out the path being dealt into up to the i-stage wave detector ray in stratum, n from SiIt is wave detector
It numbers (number in corresponding stratum from top to bottom), hijIt is ray in longitudinal projection's height, θijIt is seimic wave propagation to
Pass through the angle (angle with vertical direction) when jth layer stratum, Layer_v during i grades of wave detectorsjIt is seismic wave in jth
The speed propagated in layer stratum, ToibsBe seismic wave pointed out from S be dealt into up to the i-stage wave detector ray in stratum when walking.
2. improve shooting method progress ray tracing, the launch angle θ of ray initial position given first:
A=0, b=90
θ=(a+b)/2 (θ ∈ [a, b])
Wherein, θ is the launch angle of ray initial position, and a, b are the bound of initial position launch angle.
From focal point, the propagation of seismic wave between the layers follows Snell law, judges the final position of ray
Whether be receiving point position, if ray final position is not receiving point position, if be higher than receiving point position, have:
B=(a+b)/2
θ=(a+b)/2 (θ ∈ [a, b])
Conversely, then having:
A=(a+b)/2
θ=(a+b)/2 (θ ∈ [a, b])
X-ray angle is constantly adjusted according to dichotomy, is recalculated until meeting the requirements.
3. since the fracturing process duration is longer, focus time of origin is not easy to determine, therefore by calculating detection
The difference of first-arrival traveltime between device is evaded the origin time of earthquake of focus, and the annoyance level that can also be effectively reduced in stratum, is calculated
It is as follows:
Wherein,Difference when being walked for adjacent two-stage wave detector.
Step 3: the random number that one group of Gaussian distributed in (0,1) is added in difference when walking in step 2 is formed
Random perturbation as observation；
The specific method is as follows:
The random perturbation of the random number composition of one group of Gaussian distributed is generated in (0,1)
Δ t=[Δ t1Δt2……ΔtN-1]
Then observation is
Wherein, Δ t is random perturbation vector, Δ TobsTo observe data vector.
Step 4: SPSA algorithm, by the Fitting Calculation data and observation, the optimal hypocentral location of Optimization Solution are applied；
The specific method is as follows:
1. given micro-seismic event initial position X at random0=(x0,y0,z0), according to first arrival is calculated described in step (2)
When walkingAnd then obtain the difference of first-arrival traveltime
It obtains calculating data and observes the difference of data
Given k=0 (k is current search number), maxIter=100 (maximum search number).
Wherein, Δ Tcal0For the difference vector of the first-arrival traveltime of initial position, | | Δ T0| | for calculating data and observation at this time
The difference of data.
0) and direction step delta 2. searching times k adds 1, a random given iteration direction α is not (between [- 1,1] and for
X then has
K=k+1
X1=X0+αΔX
Also according to according to first-arrival traveltime is calculated described in step 2And then obtain first arrival
Difference when walking
It can equally succeed in one's scheme and count according to and observe the difference of data
If | | Δ T0| | > | | Δ T1| |, then selected directions areOtherwise
Wherein, Δ Tcal1To calculate the difference vector for updating obtained first-arrival traveltime, | | Δ T1| | at this time calculating data with
The difference of data is observed, α is iteration direction, and Δ X is the step-length of iteration direction.
Micro-seismic event position is updated 3. calculating
Xupdate=X0+αgStep
Wherein, XupdateFor updated hypocentral location, Step is iteration step length.
4. according to according to first-arrival traveltime is calculated described in step 2And then it obtains
The difference of first-arrival traveltime
It obtains calculating data and observes the difference of data
Wherein, g (Xupdate) it is the objective function that iteration updates, Δ TupdateFor the difference vector of the first-arrival traveltime of update, Δ
TobsTo observe data vector.
5. judging g (Xupdate) whether meet precision, X is exported if meetingbest=Xupdate；Otherwise X0=Xupdate, return
It returns and continues to execute 3. iteration update micro-seismic event position, until g (Xupdate) no longer decline, it returns execute 2. at this time, judge k
Whether maxIter is greater than, if then exporting Xbest=Xupdate, iteration direction α is otherwise chosen again, and searching times k adds 1.This
Outside, if there is g (X in iteration renewal processupdate) meet required precision and then export Xbest=Xupdate, stop updating.
Embodiment
Based on step 1, horizontal layer rate pattern is established according to well-log information and subsurface rock property analysis: this calculation
400 meters long, 400 meters wide, high 400 meters of the region of example choosing, stratum are divided into 4 layer positions, from deep to shallow number consecutively, medium boundary
Face is all horizontal plane, and each layer is Layer_h=[2,100 2,150 2,300 2400] m, the P wave of bed boundary apart from ground depth
(longitudinal wave) speed is from shallowly to deep respectively Layer_v=[1,600 2,000 2,400 2800] m/s.
Based on step 2, forward simulation is carried out to given micro-seismic event, first-arrival traveltime is calculated, then calculates adjacent
Difference when walking of two wave detectors:
It is as shown in table 1 below that acceleration geophone parameter is set:
1 acceleration geophone parameter of table
| Series | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| X | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Y | 0 | 0 | 0 | 0 | 0 | 0 | 600 | 600 | 600 | 600 | 600 | 600 |
| Z | 2017 | 2032 | 2047 | 2062 | 2077 | 2092 | 2107 | 2122 | 2137 | 2152 | 2167 | 2182 |
| Series | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 |
| X | 600 | 600 | 600 | 600 | 600 | 600 | 600 | 600 | 600 | 600 | 600 | 600 |
| Y | 0 | 0 | 0 | 0 | 0 | 0 | 600 | 600 | 600 | 600 | 600 | 600 |
| Z | 2197 | 2212 | 2227 | 2242 | 2257 | 2272 | 2287 | 2302 | 2317 | 2332 | 2347 | 2362 |
Z coordinate is the depth on relative degree ground, and first order wave detector depth is -2017 meters, the 24th grade of wave detector depth be -
2362 meters, every grade 15 meters of wave detector height difference.
It is as shown in table 2 below that true focus event location is set:
The true focus event argument of table 2
X-ray angle is adjusted according to improved ray tracing shooting method, and based on dichotomy, forward simulation obtains ray and chases after
Fig. 4 is seen in track path.
After obtaining path, divided by the seismic wave propagation speed of place layer, first arrival of the focus at detector position is obtained
Fig. 5 is shown in distribution when walking, and then the difference cloth for obtaining first-arrival traveltime is shown in Fig. 6.
Based on step 3, one group of Gaussian distributed in (0,1) is added in difference when walking that step 2 is calculated
Random number composition random perturbation, in this, as observation；
Random perturbation Δ t=[the Δ t of the random number composition of one group of Gaussian distributed is generated in (0,1)1Δt2……
ΔtN-1], then observation is
Observation data distribution is obtained after addition random perturbation sees Fig. 7.
Based on step 4, using SPSA algorithm, by the Fitting Calculation data and observation, the optimal focus position of Optimization Solution
It sets:
Fig. 3 is seen using SPSA optimization method inverting hypocentral location flow chart, is executed according to step shown in Fig. 3, inverting obtains
Focal shock parameter is as shown in table 3:
The hypocentral location that 3 inverting of table obtains
True hypocentral location and inverting obtain hypocentral location and see Fig. 8.
Can intuitively it be found out by table 3 and Fig. 8, due to it joined certain disturbance on the basis of true value after, really
The hypocentral location error that hypocentral location and inverting obtain is up to 21.9219m, minimum 4.0864m, total through program test inverting
About 140s when shared, the result of inverting is in the reasonable scope.
Claims (1)
1. a kind of source inversion method based on microseism data and SPSA optimization algorithm, comprising the following steps:
Step 1: horizontal layer rate pattern is established according to well-log information and subsurface rock property analysis；
Step 2: forward simulation is carried out to micro-seismic event, first-arrival traveltime is calculated, then calculates walking for adjacent two wave detector
When difference；
Step 3: the random number of one group of Gaussian distributed in (0,1) is added in difference when walking that step 2 is calculated
The random perturbation of composition, in this, as observation；
Step 4: SPSA algorithm, by the Fitting Calculation data and observation, the optimal hypocentral location of Optimization Solution are applied；
It is characterized by:
Step 1 the following steps are included:
1. carrying out classifying and dividing layer position to stratum according to well-log information and subsurface rock property analysis, stratum is divided into M layer position,
Number consecutively from deep to shallow, dielectric interface are all horizontal planes, give each layer apart from ground depth；
2. assuming oil reservoir and upper overlying strata stone homogeneous, and it is full of fluid in pore media, then can establish ground according to Raymer model
Layer longitudinal wave (P wave) velocity field；
Step 2 the following steps are included:
(1), based on the ray tracing localization method of Snell law:
Stratum is divided into M layers in presumptive test region, can calculate seismic wave according to Snell law and points out from S and is dealt into up to i-th
Grade the wave detector ray propagation angle, ray propagation path and first-arrival traveltime in stratum；
(2), when improving shooting method progress ray tracing, the launch angle θ of ray initial position given first:
The propagation of seismic wave between the layers follows Snell law, judge ray final position whether be monitoring point position
It sets, if ray final position is not monitoring location, x-ray angle is constantly adjusted according to dichotomy, recalculate until full
Foot requires；
(3), since the fracturing process duration is longer, focus time of origin is not easy to determine, therefore, by calculating wave detector
Between the difference of first-arrival traveltime evade origin time of earthquake of focus, and the annoyance level in stratum can also be effectively reduced in this method,
Improve the robustness of inverting；
Step 3 the following steps are included:
The random perturbation that the random number composition of one group of Gaussian distributed is generated in (0,1), the first-arrival traveltime being calculated
Difference observation can be obtained plus random perturbation；
Step 4 the following steps are included:
(1), firstly, setting initial parameter, according to first-arrival traveltime is calculated described in step 2, and then obtain first-arrival traveltime it
Then difference is calculated observation data and calculates the difference of data；
(2), iterative parameter is updated, also according to the difference and calculating that first-arrival traveltime, first-arrival traveltime are calculated described in step 2
Data and the difference for observing data select search iteration direction；
(3), it is calculated according to the iteration direction of selection, obtains updated micro-seismic event position；
(4), according to updated first-arrival traveltime is calculated described in step 2, so obtain the difference of updated first-arrival traveltime with
And it calculates data and observes the difference of data；
(5), judge whether calculate data and the difference of observation data meets precision, if it is satisfied, stopping updating；Conversely, selected new
The direction of search continue to iterate to calculate.
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| CN110967756B (en) * | 2018-09-30 | 2021-09-17 | 中国石油化工股份有限公司 | Microseism positioning precision evaluation method and system based on normal distribution |
| CN109188515B (en) * | 2018-10-31 | 2021-02-26 | 中国石油化工股份有限公司 | Method and system for calculating position of seismic source of microseism monitoring crack |
| CN110727028A (en) * | 2019-09-17 | 2020-01-24 | 河南理工大学 | Coal reservoir fracture evaluation method based on ground microseism monitoring |
| CN111324968B (en) * | 2020-03-06 | 2023-03-28 | 西南大学 | Laying method of microseismic monitoring sensors for inclined stratum tunnel engineering |
| CN112114359B (en) * | 2020-08-13 | 2021-07-02 | 中南大学 | Dangerous area detection method, system and terminal based on active and passive seismic source signals and readable storage medium |
| CN112255671A (en) * | 2020-08-28 | 2021-01-22 | 长江大学 | Method and device for forward modeling of seismic waves between two points |
| CN112630833B (en) * | 2020-11-19 | 2022-12-02 | 安徽理工大学 | Fast seismic first-arrival travel time joint inversion method based on logging curve |
| CN114486680B (en) * | 2022-01-24 | 2022-09-13 | 北京市生态环境保护科学研究院 | Method and device for determining permeability coefficient of deep unsaturated zone rock soil and electronic equipment |
| CN114879249B (en) * | 2022-04-13 | 2023-04-28 | 中国海洋大学 | Earthquake wave front travel time calculation method based on tetrahedron unit travel time disturbance interpolation |
| CN115616659B (en) * | 2022-10-10 | 2023-06-30 | 中国矿业大学(北京) | Microseism event type determining method and device and electronic equipment |
| CN117991349A (en) * | 2024-04-07 | 2024-05-07 | 吉林大学 | Microseism positioning method based on improved ant lion optimization algorithm |
| CN117991331B (en) * | 2024-04-07 | 2024-05-31 | 山东省地震局 | Method for tracking multiple rays in two-dimensional complex model based on seismic monitoring |
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| CN102841373B (en) * | 2012-08-23 | 2015-02-04 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Microseism positioning method based on azimuth angle constraint |
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