CN110618450B - Intelligent gas-bearing property prediction method for tight reservoir based on rock physical modeling - Google Patents
Intelligent gas-bearing property prediction method for tight reservoir based on rock physical modeling Download PDFInfo
- Publication number
- CN110618450B CN110618450B CN201810657579.7A CN201810657579A CN110618450B CN 110618450 B CN110618450 B CN 110618450B CN 201810657579 A CN201810657579 A CN 201810657579A CN 110618450 B CN110618450 B CN 110618450B
- Authority
- CN
- China
- Prior art keywords
- reservoir
- particle
- frequency
- particle swarm
- inversion
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/282—Application of seismic models, synthetic seismograms
Abstract
The invention provides an intelligent gas-bearing property prediction method for a tight reservoir based on petrophysical modeling, which comprises the following steps: step 1, establishing a petrophysical model according to a relation between reservoir physical properties and elastic parameters by a plaque saturation theory; step 2, establishing a rock physical template through forward modeling; step 3, carrying out inversion by utilizing a particle swarm algorithm to obtain velocity volumes under different frequencies; and 4, aiming at different reservoir interval extraction speed dispersion curves, comparing the extracted speed dispersion curves with a rock physical template, and analyzing the gas-bearing characteristics of the reservoir interval. The intelligent gas content prediction method for the compact reservoir based on the petrophysical modeling can make full use of the memory and information sharing characteristics of particle groups, quickly search an optimal solution in the whole search space, accurately describe the longitudinal wave velocity frequency dispersion characteristics of the underground reservoir, and overcome the defects that the traditional reservoir prediction technology depends on manual operation more and wastes time and labor.
Description
Technical Field
The invention relates to the field of exploration geophysical processing and explanation, in particular to a compact reservoir intelligent gas-bearing property prediction method based on petrophysical modeling.
Background
With the gradual orientation of oil and gas exploration objects to unconventional oil and gas reservoirs with complex structures, complex media, complex earth surfaces and deep layers, the exploration difficulty is higher and higher, the obtained oil and gas exploration bright spots are fewer and fewer, the search of oil and gas in the deep layers and the unconventional oil and gas reservoirs becomes one of the main research directions for maintaining stable yield or improving yield of the oil industry at the present stage, and the method also has important practical significance for ensuring the sustainable development of the oil industry and energy safety in China. The strong heterogeneity and anisotropy of the unconventional oil and gas reservoir enables reservoir geology, logging response and seismic prediction results to present a more complex nonlinear relation, and compared with conventional oil and gas prediction, the unconventional oil and gas reservoir prediction method is stronger in multi-solution. Although, in recent years, a large number of oil and gas prediction techniques have been proposed by scholars at home and abroad, such as,
zusanxi et al studied the inversion method of longitudinal wave velocity dispersion attributes ("longitudinal wave velocity dispersion attribute inversion method study", Zusanxi et al, oil geophysical prospecting, vol. 50, No. 3, p. 219, 287, 5 months 2011), indicated that hydrocarbon-containing reservoirs can cause seismic wave attenuation and velocity dispersion, deduced a reflection coefficient approximation formula containing attributes representing longitudinal wave velocity dispersion degrees by studying the change rule of reflection amplitude along with frequency at normal incidence of longitudinal waves, constructed an inversion equation, and respectively obtained frequency division seismic data and relative density variation through wavelet domain frequency division and Bayes three-parameter inversion, and finally obtained a dispersion attribute data volume.
However, although the research proposes a method for inverting the dispersion attribute, the method only aims at the normal incidence condition, does not utilize rich information recorded by the seismic before the superposition, especially information of large angle and large offset distance sensitive to the fluid, and uses a linear inversion method for inversion, which has high requirement on initial solution, is very easy to fall into a local extreme value and cannot jump out.
The reservoir permeability seismic response characteristics of the guoqiqi et al based on the patch saturation model are analyzed (reservoir permeability seismic response characteristic analysis based on the patch saturation model, guoqiqi et al, APPLIED geophhysic, vol. 12, No. 2, p. 187-. The reflection coefficient and the synthetic seismic recording algorithm are designed based on the propagation matrix theory, and seamless connection of the frequency domain rock physical model and seismic response calculation is achieved.
Although the analysis reveals the influence of fluid properties and permeability on seismic dispersion characteristics, the idea of analyzing fluid saturation is not given, and only the sensitivity analysis of seismic response with fluid and permeability is carried out.
Therefore, the existing technology can not meet the actual requirements far, because the conventional linear inversion method depends on the initial model, the conventional linear inversion method is very easy to fall into a local extreme value and cannot jump out, and in practical application, the given initial model is difficult to ensure to be good certainly.
Disclosure of Invention
In order to solve the technical problem, the invention provides a compact reservoir intelligent gas content prediction method based on rock physical modeling, which comprises the following steps:
step 1, establishing a petrophysical model according to a relation between reservoir physical properties and elastic parameters by a plaque saturation theory;
step 3, carrying out inversion by utilizing a particle swarm algorithm to obtain velocity volumes under different frequencies;
and 4, comparing the extracted velocity dispersion curves of different reservoir sections with a rock physical template, and analyzing the gas-bearing characteristics of the reservoir sections.
Preferably, in the step 1, the rock physical model corresponding to the plaque saturation model is recorded as F, a function of the longitudinal wave velocity is obtained,
Vp＝F(Km,Ks,Kf,μm,ρs,ρf,p1,p2,f)
wherein, VpFor longitudinal wave velocity, K, calculated by the plaque saturation modelmIs the bulk modulus, K, of the dry rock skeletonsIs the bulk modulus, K, of a solid matrixfBulk modulus of fluid, mumShear modulus, rho, for dry rock skeletonsAnd ρfDensity of solid matrix and fluid, p1And p2Respectively, the gas and water content, which is used for describing the gas content of the reservoir, and f is the frequency.
Under the condition of fixing the physical property of the reservoir, the corresponding longitudinal wave velocity V can be obtained by inputting different frequenciespThereby characterizing the velocity dispersion characteristics of the longitudinal wave.
Further, in step 2, for the P-wave incidence, the reflection and transmission coefficient vector r is solved by the following formula:
r＝-[A1-BA2]-1ip
wherein A is1、A2Respectively propagation matrices related to the elastic modulus of the upper and lower layer media, B propagation matrix of the intermediate layer, ipIs a phasor that is related to the elastic parameters and angular frequency of the medium.
Further, the propagation matrix of the upper medium:
propagation matrix of the underlying medium:
wherein subscript 1 represents an upper layer media parameter, subscript 2 represents an upper layer media parameter, no subscript represents an intermediate layer parameter,
propagation matrix of the interlayer medium: b ═ T (0) T-1(h)，
Wherein the content of the first and second substances,
where h is the intermediate layer thickness, ω is the angular frequency (ω ═ 2 π f, f is the frequency), i denotes the complex number unit (-square root of 1),
WPj＝2μjsPjsVPj,WSj＝μj(sSj 2-s2)VSj,
ZPj＝(λjs2+EjsPj 2)VPj,ZSj＝-2μjsSjsVSj,
s＝sinθ/VP1,sPj＝cosθ/VPj,sSj＝cosθ/VSj,
Ej＝ρjVPj 2,μj＝ρjVSj 2,λj＝Ej-2μj,j＝1,2，
where ρ is the density of the medium and VPIs the medium longitudinal wave velocity, VSThe transverse wave velocity of the medium and theta is an incident angle.
Further, the step 2 is based on the velocity V of the longitudinal wavepObtaining a reflection coefficient vector R at a frequency fpp，
Rpp＝G(Vp,Vs,ρ,θ,f,h)，
Where G is a propagation matrix system, VpVector formed by longitudinal wave velocities of the respective layers of the medium, VsTransverse wave of each layer mediumVector composed of velocity, rho is density of each layer of medium, theta is inverted angle vector, f is frequency, and h is middle reservoir thickness; rppEach component of the vector is a reflection coefficient vector at frequency f, which corresponds one-to-one to angle θ.
Further, the step 2 also comprises the steps of,
and after the reflection coefficients of all the frequencies are calculated, obtaining a wavelet frequency spectrum, multiplying the wavelet frequency spectrum by the reflection coefficient spectrum, and obtaining the seismic record of a time domain through inverse Fourier transform.
Further, in the step 3, the objective function is set as
Wherein N isθAnd NωThe number of angles and the number of frequencies for inversion, and d is expressed as dobs＝[Re(Rpp obs),Im(Rpp obs)]T,dsyn＝[Re(Rpp syn),Im(Rpp syn)]TSuperscript obs denotes the seismic record solution from the actual observation, superscript syn denotes the seismic record solution from the forward evolution of the solution in the particle swarm, wherein both the real and imaginary parts of the reflection coefficient are applied, meaning that both the amplitude and phase of the reflection coefficient spectrum are taken into account in the inversion process, thereby reducing the uncertainty of the inversion result.
And evaluating the quality degree of each particle in the particle swarm by taking the reciprocal of the objective function as a fitness function, wherein the larger the quality degree is, the closer the position of the particle is to the real solution is.
Further, the step 4 specifically includes selecting the velocity dispersion of the longitudinal wave from the inversion results of all the inversion parameters, and determining the gas content of the reservoir by analyzing the velocity dispersion curve of the longitudinal wave.
Further, the step of taking the reciprocal of the objective function as the fitness to evaluate the degree of goodness and badness of each particle in the particle swarm further comprises the step of updating the position and the speed of each individual according to the fitness and experience of each individual in the particle swarm until the maximum iteration number or the global optimal position meets the minimum limit.
Further, the inversion process specifically includes:
preprocessing pre-stack seismic data, and extracting seismic wavelets corresponding to three angle ranges of low, medium and high from seismic records;
calculating the frequency spectrums of the seismic records and the seismic wavelets, and obtaining a reflection coefficient spectrum according to the frequency spectrums;
setting the size of the particle swarm and the maximum iteration number kmaxGenerating an initial particle swarm in a specified search space, wherein the iteration number k is 1;
then evaluating the initial particle swarm by utilizing a fitness function, and searching the individual optimal position and the overall optimal position of the particle;
updating the speed and the position of the particles according to an iterative formula, limiting an out-of-limit variable, evaluating the particle swarm by using a fitness function again, preferentially changing the state of each particle and updating the individual optimal position and the overall optimal position, and if k is k, updating the speed and the position of the particle, and if k is k, updating the individual optimal position and the overall optimal position of the particle swarmmaxOr J<And if the obtained result is not k ═ k, the longitudinal wave velocity dispersion of the intermediate reservoir is taken outmaxOr J<And returning to search the individual optimal position and the overall optimal position of the particle.
Compared with the prior art, the intelligent gas content prediction method for the compact reservoir based on the petrophysical modeling can fully utilize the memory and information sharing characteristics of the particle swarm, quickly search the optimal solution in the whole search space, greatly improve the speed compared with a simulated annealing and genetic algorithm, improve the speed of the particle swarm optimization algorithm by more than 15 times after testing to achieve the same precision, accurately describe the longitudinal wave velocity frequency dispersion characteristics of the underground reservoir, and then utilize the frequency dispersion characteristics of the longitudinal wave velocity to predict the gas content, wherein the prediction result is well consistent with the actual situation, which is very important for implementing oil and gas exploration and well position arrangement. The method overcomes the defects that the traditional reservoir prediction technology depends on manual operation more and wastes time and labor, and provides the gas content prediction result with higher reliability in the shortest time.
The features mentioned above can be combined in various suitable ways or replaced by equivalent features as long as the object of the invention is achieved.
Drawings
The invention will be described in more detail hereinafter on the basis of non-limiting examples only and with reference to the accompanying drawings. Wherein:
FIG. 1 is a flow chart of an intelligent gas content prediction method for a tight reservoir based on petrophysical modeling;
FIG. 2 is a theoretical model of a three-layer media in an embodiment of the invention;
FIG. 3 is a graph of reflection coefficient as a function of angle and frequency for an embodiment of the present invention;
FIG. 4 is a synthetic seismic record for different fluid contents in a reservoir in an embodiment of the invention;
FIG. 5 is a result of an inversion of a synthetic seismic record according to an embodiment of the invention;
FIG. 6 is a residual between a seismic record obtained by forward modeling the inversion result and the original synthetic seismic record in an embodiment of the invention;
FIG. 7 is two CDP gathers of a field prestack data volume according to an embodiment of the present invention;
FIG. 8 is inversion usage data obtained by intercepting an original seismic record using a Hamming window in an embodiment of the present invention;
FIG. 9 is a reservoir longitudinal wave velocity dispersion curve obtained by inversion of a particle swarm optimization algorithm in the embodiment of the invention.
Detailed Description
The invention will be described in further detail below with reference to the drawings and specific examples. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
The intelligent tight reservoir gas-bearing property prediction method based on the petrophysical modeling has a flow chart shown in figure 1 and mainly comprises the following steps:
step 1, establishing a petrophysical model according to the relation between the physical property and the elastic parameter of a reservoir stratum by a plaque saturation theory.
Noting that the mathematical model corresponding to the plaque saturation model is F, then there are
Vp＝F(Km,Ks,Kf,μm,ρs,ρf,p1,p2,f)
Wherein VpFor longitudinal wave velocity, K, calculated by the plaque saturation modelmIs the bulk modulus, K, of the dry rock skeletonsIs the bulk modulus, K, of a solid matrixfBulk modulus of fluid, mumShear modulus, rho, for dry rock skeletonsAnd ρfDensity of the solid matrix and the fluid, respectively; p is a radical of1And p2Respectively, the content of gas and water, and is used for describing the gas content of the reservoir; f is the frequency. Under the condition of fixing the physical property of the reservoir, the corresponding longitudinal wave velocity V can be obtained by inputting different frequenciespThereby characterizing the velocity dispersion characteristics of the longitudinal wave.
And 2, establishing a rock physical template by forward modeling.
Considering the case of three layers of media, the permeable sandstone reservoir is the non-permeable surrounding rock above and below. For P-wave incidence, the reflection and transmission coefficient vector r is solved by the following formula:
r＝-[A1-BA2]-1ip
wherein A is1、A2Respectively propagation matrices related to the elastic modulus of the upper and lower layer media, B propagation matrix of the intermediate layer, ipIs a phasor that is related to the elastic parameters and angular frequency of the medium. If the propagation matrix system is denoted as G, then:
Rpp＝G(Vp,Vs,ρ,θ,f,h)
wherein: vpVector formed by longitudinal wave velocities of the respective layers of the medium, VsIs the vector formed by the transverse wave velocity of each layer of medium, rho is the density of each layer of medium, and theta is the inverted angle directionQuantity, f is frequency, h is intermediate reservoir thickness; rppEach component of the vector is a reflection coefficient vector at frequency f, which corresponds one-to-one to angle θ. After the reflection coefficients (namely reflection coefficient spectrums) of all frequencies are calculated, the wavelet frequency spectrum is obtained, multiplied by the reflection coefficient spectrum, and subjected to inverse Fourier transform to obtain the seismic record of a time domain.
And 3, carrying out inversion by utilizing a particle swarm algorithm to obtain velocity volumes under different frequencies.
The objective function is set as:
wherein: n is a radical ofθAnd NωThe number of angles and the number of frequencies for inversion, and d is expressed as dobs＝[Re(Rpp obs),Im(Rpp obs)]T,dsyn＝[Re(Rpp syn),Im(Rpp syn)]TThe superscript obs represents the calculation from the actually observed seismic record, the superscript syn represents the calculation from the seismic record obtained by forward calculation of the solution in the particle swarm, and the real part and the imaginary part of the reflection coefficient are applied, which means that the amplitude and the phase of the reflection coefficient spectrum are considered in the inversion process, thereby reducing the uncertainty of the inversion result.
And taking the reciprocal of the objective function as a fitness function to evaluate the quality degree of each particle in the particle swarm, wherein the larger the quality degree is, the closer the position of the particle is to the real solution, and according to the property of the particle swarm algorithm, all solutions can converge to the real solution or a very good solution, but only one particle is required to reach the position.
And updating the position and the speed of the individual according to the fitness and experience of each individual in the particle swarm until the maximum iteration times or the global optimal position meets the minimum limit.
As shown in fig. 1, the inversion process specifically includes:
preprocessing pre-stack seismic data, and extracting seismic wavelets corresponding to three angle ranges of low, medium and high from seismic records;
calculating the frequency spectrums of the seismic records and the seismic wavelets, and obtaining a reflection coefficient spectrum according to the frequency spectrums;
setting the size of the particle swarm and the maximum iteration number kmaxGenerating an initial particle swarm in a specified search space, wherein the iteration number k is 1;
then evaluating the initial particle swarm by utilizing a fitness function, and searching the individual optimal position and the overall optimal position of the particle;
updating the speed and the position of the particles according to an iterative formula, limiting an out-of-limit variable, evaluating the particle swarm by using a fitness function again, preferentially changing the state of each particle and updating the individual optimal position and the overall optimal position, and if k is k, updating the speed and the position of the particle, and if k is k, updating the individual optimal position and the overall optimal position of the particle swarmmaxOr J<And if the obtained result is not k ═ k, the longitudinal wave velocity dispersion of the intermediate reservoir is taken outmaxOr J<And returning to search the individual optimal position and the overall optimal position of the particle.
The Particle Swarm Optimization (PSO) has many excellent characteristics, does not have complicated intersection and variation operations like the Genetic Algorithm (GA), only depends on the particle speed to complete the search, and only has the optimal particles to transmit position information to other particles in the iterative evolution process, so that the whole system has the characteristics of memorability and group information sharing, and has extremely high search speed relative to the genetic algorithm and the simulated annealing algorithm (SA); the PSO algorithm has fewer parameters needing to be adjusted, has a simple structure and is easy to realize in engineering; unlike genetic algorithms which sometimes use binary coding, the PSO algorithm uses real number coding, which is directly determined by the solution of the problem, whose variable number is directly used as the dimension of the particle; therefore, the PSO algorithm can efficiently find the optimal solution of the inverted reservoir parameters, so that the analytical prediction basis is provided for the oil and gas prediction and the like of unconventional oil and gas reservoir reservoirs such as compact sandstone gas, shale gas and the like at the fastest speed.
And 4, comparing the extracted velocity dispersion curves of different reservoir sections with a rock physical template, and analyzing the gas-bearing characteristics of the reservoir sections.
And selecting the velocity dispersion of the longitudinal wave from the inversion results of all the inversion parameters, and analyzing the velocity dispersion curve of the longitudinal wave to judge the gas containing condition of the reservoir.
According to the intelligent gas content prediction method of the compact reservoir based on the petrophysical modeling, a field test is carried out by utilizing a theoretical model of three layers of media and a prestack data body of an unconventional compact sandstone reservoir of a certain gas field, and as the seismic data in the frequency band range of 5Hz to 30Hz are more reliable, the longitudinal wave speed under 6 frequencies is inverted only by taking 5Hz as an interval in the frequency band range of 5Hz to 30 Hz.
FIG. 2 is a theoretical layered media model used in the present example; FIG. 3 is a graph of the reflection coefficient with angle and frequency for different fluid-containing cases obtained by petrophysical modeling and propagation matrix forward calculation according to low pore low permeability parameters, wherein (a) in FIG. 3 is a case where the reservoir is a gas layer; fig. 3 (b) shows a case where the reservoir is a water layer. FIG. 4 is a synthetic seismic record corresponding to FIG. 3, where (a) in FIG. 4 is for a reservoir that is a gas layer; fig. 4 (b) shows a case where the reservoir is a water layer. FIG. 5 is the inversion result of the theoretical model, in which the two curves are the real velocity dispersion curves in the case of water and gas respectively, the dispersion point is the longitudinal wave velocity of the inverted 6 frequencies, and (a) in FIG. 5 is the case where the reservoir is a gas layer; fig. 5 (b) shows a case where the reservoir is a water layer. FIG. 6 is a residual error between the seismic record obtained by forward modeling using the inversion results and the original synthetic seismic record, wherein (a) in FIG. 6 is a case where the reservoir is a gas layer; fig. 6 (b) shows a case where the reservoir is a water layer.
By analyzing the inversion result diagram of fig. 5, it can be found that the longitudinal wave velocities of the inverted 6 frequencies are basically on the real velocity dispersion curve, and under the condition that the reservoir contains gas, the longitudinal wave velocities have an obvious dispersion phenomenon, that is, the longitudinal wave velocities have obvious changes along with the changes of the frequencies within the seismic frequency band, especially in the low frequency band; and under the condition that the reservoir contains water, the velocity frequency dispersion of the longitudinal wave is not obvious, namely the inversion result is quite consistent with the reservoir fluid containing condition of the real model, and the fluid properties contained in the reservoir can be identified by utilizing the velocity frequency dispersion of the longitudinal wave.
Fig. 7 shows two CDP gathers of actual data for a field, where (a) in fig. 7 is CDP 1; fig. 7 (b) is CDP2, where the location of the reservoir is known from the data recorded in the field about 4.5s to 4.6s, and in order to approximate the assumption of the three-layer model, the target locations of the two gathers are time-windowed with Hamming time-window function, and it is noted that there is also a distinct homophase axis around 4.9s of time, and this is also truncated for comparison. The data used for inversion is shown in FIG. 8; fig. 9 is the result of inversion on actual data.
As can be seen from the analysis of fig. 8 and 9, the dispersion phenomenon of the longitudinal wave velocity obtained by inverting the gas layer in the seismic record is very obvious, while the dispersion phenomenon of the longitudinal wave velocity obtained by inverting the non-target layer is almost zero, and the longitudinal wave changes slowly with the frequency. Therefore, the inversion result of the particle swarm global optimization algorithm is well consistent with the actual gas containing condition of the reservoir, and the prediction result has high reliability.
The intelligent tight reservoir gas-bearing prediction method based on the petrophysical modeling comprises the construction of a particle swarm system, an algorithm improvement method and an algorithm combined with gas-bearing detection. The method provided by the invention is used for carrying out inversion and gas content identification on the theoretical model and the actual seismic data of a certain gas field, so that a more accurate result can be obtained, and the method has better generalization.
Thus, it will be appreciated by those skilled in the art that while a number of illustrative embodiments of the invention have been shown and described in detail herein, many other variations or modifications can be made, directly or by derivation of teachings consistent with the principles of the invention without departing from the spirit or scope thereof, and thus, the scope of the invention should be understood and considered to cover all such other variations or modifications.
Moreover, while the operations of the invention are depicted in the drawings in a particular order, this does not necessarily imply that the operations must be performed in that particular order, or that all of the operations shown must be performed, to achieve desirable results. Certain steps may be omitted, multiple steps combined into one step or a step divided into multiple steps performed.
While the invention has been described with reference to a preferred embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the technical features mentioned in the embodiments can be combined in any way as long as no conflict exists. It is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (4)
1. An intelligent tight reservoir gas-bearing property prediction method based on rock physics modeling is characterized by comprising the following steps:
step 1, establishing a petrophysical model according to a relation between reservoir physical properties and elastic parameters by a plaque saturation theory; recording the rock physical model corresponding to the plaque saturation model as F to obtain a function of the longitudinal wave velocity,
Vp＝F(Km,Ks,Kf,μm,ρs,ρf,p1,p2,f)
wherein, VpFor longitudinal wave velocity, K, calculated by the plaque saturation modelmIs the bulk modulus, K, of the dry rock skeletonsIs the bulk modulus, K, of a solid matrixfBulk modulus of fluid, mumShear modulus, rho, for dry rock skeletonsAnd ρfDensity of the solid matrix and of the fluid, p, respectively1And p2Respectively the content of gas and water, and f is frequency;
step 2, forward modeling is carried out to establish a rock physical template;
for P-wave incidence, the reflection and transmission coefficient vector r is solved by the following formula:
r＝-[A1-BA2]-1ip
wherein A is1、A2Respectively, propagation matrices related to the elastic modulus of the upper and lower layer media, B is the propagation matrix of the middle layer，ipIs a complex phasor, which is related to the elastic parameters and angular frequency of the medium;
according to the velocity V of longitudinal wavespObtaining a reflection coefficient vector R at a frequency fpp，
Rpp＝G(Vp,Vs,ρ,θ,f,h)
Where G is a propagation matrix system, VpVector formed by longitudinal wave velocities of the respective layers of the medium, VsIs a vector formed by the transverse wave velocity of each layer of medium, rho is the density of each layer of medium, theta is an inverted angle vector, f is frequency, h is the thickness of the middle reservoir, RppEach component of the vector corresponds to an angle theta in a one-to-one mode, namely a reflection coefficient vector under the frequency f;
frequency dependent reflection coefficient vector RppObtaining wavelet frequency spectrum, multiplying the wavelet frequency spectrum by reflection coefficient spectrum, and obtaining time domain seismic record by inverse Fourier transform
Step 3, carrying out inversion by utilizing a particle swarm algorithm to obtain velocity volumes under different frequencies;
the objective function is set to be,
wherein N isθAnd NωThe number of angles and the number of frequencies for inversion, and d is expressed as dobs＝[Re(Rpp obs),Im(Rpp obs)]T,dsyn＝[Re(Rpp syn),Im(Rpp syn)]TSuperscript obs means from the actually observed seismic records, superscript syn means from the seismic records derived from the forward evolution of the solution in the particle swarm,
evaluating the quality degree of each particle in the particle swarm by taking the reciprocal of the target function as a fitness function, wherein the larger the fitness function is, the closer the position of the particle is to the real solution is;
and 4, comparing the extracted velocity dispersion curves of different reservoir sections with the rock physical template in the step 2, and analyzing the gas-bearing characteristics of the reservoir section.
2. The method for predicting the intelligent gas content of the tight reservoir based on the petrophysical modeling according to claim 1, wherein the step 4 specifically comprises selecting the velocity dispersion of the longitudinal wave from the inversion results of all the inversion parameters, and analyzing the velocity dispersion curve of the longitudinal wave to judge the gas content of the reservoir.
3. The method for predicting intelligent gas content of tight reservoir based on petrophysical modeling according to claim 1, wherein in the step 3, the inverse of the objective function is used as the fitness to evaluate the degree of superiority and inferiority of each particle in the particle swarm, and further comprising updating the position and speed of each individual according to the fitness and experience of each individual in the particle swarm until the maximum number of iterations is reached or the global optimal position meets the minimum limit.
4. The tight reservoir intelligent gas fraction prediction method based on petrophysical modeling according to claim 1 or claim 3, wherein in the step 3, the inversion process specifically comprises:
preprocessing pre-stack seismic data, and extracting seismic wavelets corresponding to three angle ranges of low, medium and high from seismic records;
calculating the frequency spectrums of the seismic records and the seismic wavelets, and obtaining a reflection coefficient spectrum according to the frequency spectrums;
setting the size of the particle swarm and the maximum iteration number kmaxGenerating an initial particle swarm in a specified search space, wherein the iteration number k is 1;
evaluating an initial particle swarm by utilizing a fitness function, and searching an individual optimal position and an overall optimal position of the particle;
updating the speed and position of the particles according to an iterative formula, limiting the out-of-limit variable, evaluating the particle swarm by using the fitness function again, preferentially changing the state of each particle and updating the individual optimal position and the overall optimal position at the moment,
if k is equal to kmaxOr J<Outputting an inversion result and taking out longitudinal wave velocity dispersion of the middle reservoir;
if the result is not k ═ kmaxOr J<And returning to search the individual optimal position and the overall optimal position of the particle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810657579.7A CN110618450B (en) | 2018-06-20 | 2018-06-20 | Intelligent gas-bearing property prediction method for tight reservoir based on rock physical modeling |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810657579.7A CN110618450B (en) | 2018-06-20 | 2018-06-20 | Intelligent gas-bearing property prediction method for tight reservoir based on rock physical modeling |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110618450A CN110618450A (en) | 2019-12-27 |
| CN110618450B true CN110618450B (en) | 2021-07-27 |
Family
ID=68920878
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810657579.7A Active CN110618450B (en) | 2018-06-20 | 2018-06-20 | Intelligent gas-bearing property prediction method for tight reservoir based on rock physical modeling |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110618450B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111399044B (en) * | 2020-04-13 | 2021-05-25 | 中国石油大学(北京) | Reservoir permeability prediction method and device and storage medium |
| CN112748464A (en) * | 2020-12-25 | 2021-05-04 | 青岛黄海学院 | Rayleigh surface wave frequency dispersion curve rapid inversion method |
| CN113534262B (en) * | 2021-06-24 | 2023-02-03 | 中国海洋石油集团有限公司 | Big data analysis-based sand-mud interbed reservoir development zone earthquake prediction method |
| CN113960659B (en) * | 2021-10-14 | 2023-02-03 | 中国矿业大学 | Seismic rock physical driving coalbed methane reservoir gas content prediction method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103605151A (en) * | 2013-11-20 | 2014-02-26 | 中北大学 | Distributed group wave shallow-layer slight shock positioning method based on phase measuring |
| CN105467451A (en) * | 2016-01-13 | 2016-04-06 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Seismic reflection coefficient inversion method based on total variation minimized constraint |
| CN107843920A (en) * | 2017-09-29 | 2018-03-27 | 中国石油化工股份有限公司 | A kind of cluster analysis optimizes population reservoir attribute Forecasting Methodology |
| CN108181654A (en) * | 2018-01-18 | 2018-06-19 | 中国石油大学(北京) | AVAF analogy methods and device based on multi-scale rock physical model |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2012376236B2 (en) * | 2012-04-02 | 2014-11-13 | Landmark Graphics Corporation | VSP systems and methods representing survey data as parameterized compression, shear, and dispersive wave fields |
-
2018
- 2018-06-20 CN CN201810657579.7A patent/CN110618450B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103605151A (en) * | 2013-11-20 | 2014-02-26 | 中北大学 | Distributed group wave shallow-layer slight shock positioning method based on phase measuring |
| CN105467451A (en) * | 2016-01-13 | 2016-04-06 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Seismic reflection coefficient inversion method based on total variation minimized constraint |
| CN107843920A (en) * | 2017-09-29 | 2018-03-27 | 中国石油化工股份有限公司 | A kind of cluster analysis optimizes population reservoir attribute Forecasting Methodology |
| CN108181654A (en) * | 2018-01-18 | 2018-06-19 | 中国石油大学(北京) | AVAF analogy methods and device based on multi-scale rock physical model |
Non-Patent Citations (1)
| Title |
|---|
| "利用粒子群优化算法快速、稳定反演瑞雷波频散曲线";蔡伟 等;《石油地球物理勘探》;20180228;第53卷(第1期);第25-34页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110618450A (en) | 2019-12-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110618450B (en) | Intelligent gas-bearing property prediction method for tight reservoir based on rock physical modeling | |
| WO2019062655A1 (en) | Method and device for determining thin interlayer | |
| CN101551466B (en) | Method for improving prediction precision of oil and gas reservoir by using seismic attribute related to offset distance | |
| CN103163553B (en) | Based on earthquake detecting method of hydrocarbon and the device of multiple pore medium model | |
| CN104316966B (en) | A kind of Fluid Identification Method and system | |
| CN104459778A (en) | Prestack seismic inversion method and system based on double-phase medium solid-liquid decoupling | |
| CN103643949A (en) | Quantitatively forecasting method and device for oil-gas possibility of reservoirs | |
| CN111897011A (en) | Reservoir pore characteristic determination method, device and equipment | |
| Li et al. | Estimation of porosity, fluid bulk modulus, and stiff-pore volume fraction using a multitrace Bayesian amplitude-variation-with-offset petrophysics inversion in multiporosity reservoirs | |
| Faraji et al. | Integration of core data, well logs and seismic attributes for identification of the low reservoir quality units with unswept gas in the carbonate rocks of the world’s largest gas field | |
| CN103364834B (en) | A kind of method utilizing pre-stack seismic dispersion analysis predicting reservoir permeability | |
| Zhang et al. | An intelligent approach for gas reservoir identification and structural evaluation by ANN and Viterbi algorithm—A case study from the Xujiahe Formation, Western Sichuan Depression, China | |
| Tamaki et al. | Prediction and validation of gas hydrate saturation distribution in the eastern Nankai Trough, Japan: Geostatistical approach integrating well-log and 3D seismic data | |
| CN110187389A (en) | A kind of AVA inversion method based on thin bed reflection theory | |
| Anyiam et al. | A study of hydrocarbon generation and expulsion of the Nkporo Shales in Anambra Basin, Nigeria | |
| CN113219531A (en) | Method and device for identifying gas-water distribution of tight sandstone | |
| CN106249294A (en) | A kind of reservoir detecting method of hydrocarbon | |
| Wang et al. | Gas prediction using low-frequency components of variable-depth streamer seismic data applied to the deepwater area of the South China Sea | |
| CN105527648A (en) | Calculation method and system of sensitivity matrix for anisotropy parameter inversion | |
| Wu et al. | An Unsupervised Inversion Method for Seismic Brittleness Parameters Driven by the Physical Equation | |
| Sabinin et al. | Machine Learning for Fracture Parameter Estimation in Fractured Reservoirs from Seismic Data | |
| CN109239777B (en) | Method for detecting development of tectonic coal by using joint inversion method | |
| Li et al. | Time-Lapse Seismic Inversion for Predicting Reservoir Parameters Based on a Two-Stage Dual Network | |
| Ma et al. | Direct inversion of Young's modulus and Poisson's ratio using exact Zoeppritz equations based on double constraints | |
| CN104570071A (en) | Bayesian linear AVA and AVF retrieval method of sticky sound medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |