CN110967746B - Fluid saturation seismic inversion method and system - Google Patents
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Abstract
The invention discloses a fluid saturation seismic inversion method and a fluid saturation seismic inversion system. The method can comprise the following steps: constructing a seismic wave attenuation rock physical model, and calculating the compression modulus of the low-frequency fluid-containing medium and the equivalent compression modulus of the high-frequency rock of the seismic wave attenuation rock physical model; calculating a maximum attenuation factor according to the compression modulus of the low-frequency fluid-containing medium and the equivalent compression modulus of the high-frequency rock; establishing a target function according to the inversion likelihood function; calculating an inversion likelihood function, and calculating posterior probability according to the target function and the inversion likelihood function; and taking the reservoir physical property parameter corresponding to the maximum posterior probability as a final solution. According to the invention, the inversion of the water saturation is carried out through the seismic wave attenuation parameters, the logging information is fully utilized to construct a prior model, the multi-parameter joint inversion is realized by combining the rock physical model constraint, and the accuracy of the inversion result of the water saturation is obviously improved.
Description
Technical Field
The invention relates to the field of oil and gas geophysical exploration, in particular to a fluid saturation seismic inversion method and a fluid saturation seismic inversion system.
Background
Porosity and water saturation in rock are important parameters describing reservoir properties. The method for quantitatively inverting the oil reservoir parameters by using the pre-stack seismic data has the advantages of good transverse continuity and high precision, and has important significance for fine oil reservoir description. There have been many studies on the individual estimation of porosity and water saturation using seismic data, and most of them are based on data-driven methods. In contrast, the reservoir fluid identification by adopting the model-driven seismic inversion method has a firmer geophysical theory basis. In recent years, reservoir parameter joint inversion methods based on rock physical model driving under a Bayesian inversion framework are well developed. And the Avseth and the Eidsvik realize reservoir parameter joint inversion by adopting a statistical rock physical model based on the inversion result of the prestack elastic parameters. Bachrach adopts a statistical rock physical model to realize the joint inversion of porosity and saturation. Based on the previous research, Grana and Rossa establish a relatively complete reservoir physical property parameter joint inversion technical process based on prestack earthquake and petrophysical modeling. Hu applies the physical property parameter inversion method based on the Bayesian classification algorithm to the clastic rock reservoir and obtains good effect.
The rock physical model establishes a quantitative relation between rock physical properties and elastic properties, and is an effective tool for evaluating the influence of lithology and pore fluid on elastic properties. Statistical petrophysics can also be used to introduce model uncertainty in order to describe model errors due to media heterogeneity, environmental differences, etc. Generally speaking, the influence of porosity on longitudinal wave velocity and density is large, so that the porosity prediction accuracy based on a conventional rock physical model is higher than the water saturation. In the aspect of seismic fluid identification related to frequency characteristics, stratum absorption parameters and frequency-dependent characteristic parameters have gradually found potential in fluid identification. Carcione et al discovered that reservoir gas and gas saturation have a greater effect on dispersion and attenuation by comparatively analyzing the effects of the White model and the plaque saturation model on attenuation and dispersion. More and more research has shown that dispersion and attenuation have important correlations with fluid properties (permeability, gas saturation, etc.) in the reservoir. Therefore, it is necessary to develop a fluid saturation seismic inversion method and system.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides a fluid saturation seismic inversion method and a fluid saturation seismic inversion system, which can carry out water saturation inversion through seismic wave attenuation parameters, fully utilize logging information to construct a prior model, realize multi-parameter joint inversion by combining rock physical model constraints, and obviously improve the accuracy of a water saturation inversion result.
According to one aspect of the invention, a fluid saturation seismic inversion method is provided. The method may include: constructing a seismic wave attenuation rock physical model, and calculating the compression modulus of a low-frequency fluid-containing medium and the equivalent compression modulus of a high-frequency rock of the seismic wave attenuation rock physical model; calculating a maximum attenuation factor according to the compression modulus of the low-frequency fluid-containing medium and the equivalent compression modulus of the high-frequency rock; establishing a target function according to the inversion likelihood function; calculating the inversion likelihood function, and calculating the posterior probability according to the target function and the inversion likelihood function; and taking the reservoir physical property parameter corresponding to the maximum posterior probability as a final solution.
Preferably, the low frequency fluid-containing medium compressive modulus is calculated by equation (1):
calculating the equivalent compressive modulus of the high-frequency rock by formula (2):
wherein M issat0Is a low frequency fluid medium compression modulus, Ksat0Is a low frequency bulk modulus, G, of a fluid mediumdryIs the shear modulus of the dry rock skeleton,. phi.sat∞Is a high frequency rock equivalent compressive modulus, MwetTo saturate the rock compressive modulus, Msw＝0Is a compressive modulus of hydrocarbon fluid rock, fpIs the volume fraction occupied by the fully saturated fraction.
Preferably, the maximum attenuation factor is calculated by equation (3):
wherein the content of the first and second substances,
as maximum attenuation factor, Msat0For low-frequency fluid-containing medium compression mouldAmount, Msat∞Is the equivalent compression modulus of the high-frequency rock.
Preferably, the objective function is:
wherein J is an objective function, P (R) is a prior distribution of the reservoir property parameter R, and P (m | R) is a likelihood function.
Preferably, the inversion likelihood function is:
wherein P (R) is the prior distribution of the reservoir property parameter R, P (m | R) is the likelihood function, and count (m | R)j) Representing not only m as an elastic parameter but also R as a petrophysical property in a joint sampling datasetjThe number of sampling points of (c), count (R)j) Representing a petrophysical property as R in the joint sample data setjThe number of sampling points of (a).
According to another aspect of the present invention, there is provided a fluid saturation seismic inversion system, comprising: a memory storing computer-executable instructions; a processor executing computer executable instructions in the memory to perform the steps of: constructing a seismic wave attenuation rock physical model, and calculating the compression modulus of a low-frequency fluid-containing medium and the equivalent compression modulus of a high-frequency rock of the seismic wave attenuation rock physical model; calculating a maximum attenuation factor according to the compression modulus of the low-frequency fluid-containing medium and the equivalent compression modulus of the high-frequency rock; establishing a target function according to the inversion likelihood function; calculating the inversion likelihood function, and calculating the posterior probability according to the target function and the inversion likelihood function; and taking the reservoir physical property parameter corresponding to the maximum posterior probability as a final solution.
Preferably, the low frequency fluid-containing medium compressive modulus is calculated by equation (1):
calculating the equivalent compressive modulus of the high-frequency rock by formula (2):
wherein M issat0Is a low frequency fluid medium compression modulus, Ksat0Is a low frequency bulk modulus, G, of a fluid mediumdryIs the shear modulus of the dry rock skeleton,. phi.sat∞Is a high frequency rock equivalent compressive modulus, MwetTo saturate the rock compressive modulus, Msw＝0Is a compressive modulus of hydrocarbon fluid rock, fpIs the volume fraction occupied by the fully saturated fraction.
Preferably, the maximum attenuation factor is calculated by equation (3):
wherein the content of the first and second substances,
as maximum attenuation factor, Msat0For low frequency fluid-containing medium compression modulus, Msat∞Is the equivalent compression modulus of the high-frequency rock.
Preferably, the objective function is:
wherein J is an objective function, P (R) is a prior distribution of the reservoir property parameter R, and P (m | R) is a likelihood function.
Preferably, the inversion likelihood function is:
wherein P (R) is the prior distribution of the reservoir property parameter R, P (m | R) is the likelihood function, and count (m | R)j) Representing not only m as an elastic parameter but also R as a petrophysical property in a joint sampling datasetjThe number of sampling points of (c), count (R)j) Representing a petrophysical property as R in the joint sample data setjThe number of sampling points of (a).
The method and apparatus of the present invention have other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.
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The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts.
FIG. 1 shows a flow chart of the steps of a fluid saturation seismic inversion method according to the invention.
FIGS. 2a, 2b, and 2c are schematic diagrams illustrating compressional velocity, shear velocity, and density, respectively, of an improved Xu & White model for petrophysical model calibration in S-wells, according to an embodiment of the present invention.
Fig. 3a, 3b show schematic diagrams of inversion results of porosity and saturation, respectively, according to an embodiment of the invention.
Fig. 4 is a schematic diagram of compressional and shear wave attenuation estimation according to an embodiment of the invention, wherein the left diagram is a schematic diagram of compressional wave attenuation estimation and the right diagram is a schematic diagram of shear wave attenuation estimation.
FIG. 5 is a diagram illustrating the inversion results of the porosity and saturation of a seismic wave attenuation petrophysical model according to an embodiment of the present invention, wherein the left graph is the porosity inversion result and the right graph is the saturation inversion result.
FIG. 6 shows a schematic diagram of destination layer saturation time slicing along layers according to one embodiment of the invention.
Detailed Description
The invention will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
FIG. 1 shows a flow chart of the steps of a fluid saturation seismic inversion method according to the invention.
In this embodiment, a fluid saturation seismic inversion method according to the present invention may include: 101, constructing a seismic wave attenuation rock physical model, and calculating the compression modulus of a low-frequency fluid-containing medium and the equivalent compression modulus of a high-frequency rock of the seismic wave attenuation rock physical model; 102, calculating a maximum attenuation factor according to the compression modulus of the low-frequency fluid-containing medium and the equivalent compression modulus of the high-frequency rock; 103, establishing a target function according to the inversion likelihood function; step 104, calculating an inversion likelihood function, and calculating posterior probability according to the target function and the inversion likelihood function; and 105, taking the reservoir physical property parameter corresponding to the maximum posterior probability as a final solution.
In one example, the low frequency fluid-containing medium compressive modulus is calculated by equation (1):
calculating the equivalent compressive modulus of the high-frequency rock by the formula (2):
wherein M issat0Is a low frequency fluid medium compression modulus, Ksat0Is a low frequency bulk modulus, G, of a fluid mediumdryIs the shear modulus of the dry rock skeleton,. phi.sat∞Is a high frequency rock equivalent compressive modulus, MwetTo saturate the rock compressive modulus, Msw＝0Is a compressive modulus of hydrocarbon fluid rock, fpIs the volume fraction occupied by the fully saturated fraction.
In one example, the maximum attenuation factor is calculated by equation (3):
wherein the content of the first and second substances,
as maximum attenuation factor, Msat0For low frequency fluid-containing medium compression modulus, Msat∞Is the equivalent compression modulus of the high-frequency rock.
In one example, the objective function is:
wherein J is an objective function, P (R) is a prior distribution of the reservoir property parameter R, and P (m | R) is a likelihood function.
In one example, the inverse likelihood function is:
wherein P (R) is the prior distribution of the reservoir property parameter R, P (m | R) is the likelihood function, and count (m | R)j) Representing not only m as an elastic parameter but also R as a petrophysical property in a joint sampling datasetjThe number of sampling points of (c), count (R)j) Is represented by combined miningThe sample data is concentrated and the rock physical property is characterized by RjThe number of sampling points of (a).
Specifically, the fluid saturation seismic inversion method according to the invention may comprise:
constructing a seismic wave attenuation rock physical model, wherein in a partially saturated medium, under a low-frequency condition, fluid components in pores can be equivalent to uniform mixing of hydrocarbon substances and formation water, and then the equivalent bulk modulus of pore fluid can be obtained by Reuss averaging:
wherein, KhydIs the bulk modulus, K, of the mixed hydrocarbon materialwIs the bulk modulus, S, of the formation waterwThe water saturation. Determining the volume modulus K of the low-frequency fluid-containing medium of the seismic wave attenuation rock physical model by a Gassmann equationsat0Is formula (7):
the compression modulus of the low-frequency fluid-containing medium of the seismic wave attenuation rock physical model is shown as formula (1).
In a partially saturated medium, attenuating high-frequency rock equivalent compression modulus M of rock physical model by seismic wavessat∞Is the saturated rock compressive modulus MwetCompressive modulus M of hydrocarbon-containing fluid rocksw＝0The average of (2) is given as equation (2).
The elastic modulus or propagation velocity of a medium is different under different frequency conditions, which is characterized in that the equivalent modulus and propagation velocity of a medium under a low frequency condition are generally smaller than those under a high frequency condition of the same medium, namely, the modulus dispersion or velocity dispersion. In viscoelastic media, the relationship between quality factor and modulus dispersion is generally described by the Kramers-Kronig equation:
q-1Is the inverse of the longitudinal wave quality factor (attenuation factor), M∞The compression modulus of the medium under the high-frequency condition; m0Is the compression modulus of the medium under low frequency conditions; f. ofcTo distinguish the critical frequencies of high and low frequencies, when f ═ fcAnd (3) calculating the maximum attenuation factor according to the compression modulus of the low-frequency fluid-containing medium and the equivalent compression modulus of the high-frequency rock by using a formula (3).
The model-driven rock physical parameter joint inversion method firstly needs to construct a seismic wave attenuation rock physical model, and the rock physical model can be linked with statistics to describe information which may exist actually and is incomplete in logging data. The petrophysical model considering the statistical concepts can be expressed as:
m＝fRPM(R)+ε (9)
where the variables are randomly distributed vectors, m represents rock mechanical properties (including compressional velocity vp, shear velocity vs, density ρ, compressional quality factor Qp, and shear quality factor Qs), i.e., m ═ vp, vs, ρ, Qp, Qs, … }, R represents reservoir parameters, and lithology Litho, porosity Φ, and pore volume saturation Sw, i.e., R ═ Litho, Φ, Sw, … } are typically of interest. f. ofRPMRepresenting a petrophysical model, which may be an empirical relationship for a region, or a series of theoretical petrophysical models derived from theoretical derivation for a lithology, such as Xu for clastic reservoirs&White model. Of course, the model can also be the seismic wave attenuation rock physics model proposed by the invention. ε is the random error used to describe the accuracy of the model, usually defined as a truncated Gaussian distribution. It is noted that when the petrophysical model is a viscoelastic medium-based seismic wave attenuation petrophysical model, fRPMIt is actually made up of two parts: firstly, selecting a proper elastic model according to the actual geological condition to calculate the longitudinal wave velocity, the transverse wave velocity and the density; then, substituting the elastic parameter and the reservoir parameter obtained by calculation into the groundThe seismic wave attenuation petrophysical model determines attenuation parameters of viscoelastic properties of the medium.
The reservoir physical property parameter joint inversion method based on Bayesian theory can be expressed as follows by using a Bayesian posterior probability formula:
P(R|m)∝P(m|R)·P(R) (10)
R＝MaxP(R|m)|R (11)
wherein P (R) is a prior distribution of a reservoir property parameter R; p (m | R) is a likelihood function, P (R | m) is posterior probability distribution, the solution of the problem is the probability of the reservoir property parameter under the premise that the elasticity parameter or the quality factor is known, and the corresponding reservoir property parameter R is taken as the final solution when the posterior probability P (R | m) is the maximum value.
Assuming that the reservoir parameter R satisfies the Gaussian distribution, the prior distribution can be written as:
P(R)＝N(R；μR,∑R) (12)
wherein, muRFor the expectation of Gaussian distribution, ΣRThe mean value mu of the rock physical property of the target interval is obtained based on logging data and core data combined with geological knowledge statistical analysisRSum variance ΣR。
Once the prior distribution P (R) of the reservoir parameters is obtained, a stochastic simulation dataset { R } of the reservoir physical parameters can be obtained based on MCMC (Monte Carlo Markov chain simulation method) Metropolis-Hastings algorithmi}i＝1…n. The method is substituted into a seismic wave attenuation rock physical model to obtain a combined sampling data set { R } of reservoir physical property parameters, rock elasticity parameters and attenuation parametersi,mi}i＝1…n. Using this jointly sampled data set, the likelihood function P (m | R) can be estimated based on a bayesian classification algorithm.
The bayesian classification algorithm is a statistical classification algorithm, and in the inverse problem described herein, the likelihood function can be expressed as:
in the formula, RjOf reservoir physical parameters, e.g. high porosity reservoir, crFor the number of classes, a prediction accuracy is defined, crAnd needs to be reasonably selected according to the research target. m iskRepresenting any elastic parameter, such as longitudinal wave velocity, Ne Is the kind of elastic parameter involved in inversion, and generally defining Ne as 3, and developing the elastic parameter vector m as m { vp, vs, ρ } or m { Ip, Is, ρ }. In this study, the effect of the presence of pore fluid on seismic wave attenuation was considered, and an elastic parameter vector m was defined as the combination of elastic and attenuation parameters, i.e., Ne 5, m { vp, vs, ρ, Qp,Qs}. Establishing a target function as a formula (4) according to the inversion likelihood function;
P(m|Rj)P(Rj)>P(m|Rt)P(Rt),1≤j,t≤cr,j≠t (14)
joint sampling data set { R) based on obtained reservoir physical property parameters, rock elasticity parameters and attenuation parametersi,mi}i＝1…nAnd (3) calculating an inversion likelihood function through a formula (5), calculating posterior probability according to the target function and the inversion likelihood function, and taking the reservoir physical property parameter corresponding to the maximum posterior probability as a final solution.
According to the method, the inversion of the water saturation is carried out through the seismic wave attenuation parameters, a prior model is built by fully utilizing logging information, the multi-parameter joint inversion is realized by combining the rock physical model constraint, and the accuracy of the inversion result of the water saturation is obviously improved.
Application example
To facilitate understanding of the solution of the embodiments of the present invention and the effects thereof, a specific application example is given below. It will be understood by those skilled in the art that this example is merely for the purpose of facilitating an understanding of the present invention and that any specific details thereof are not intended to limit the invention in any way.
The fluid saturation seismic inversion method according to the invention may comprise: constructing a seismic wave attenuation rock physical model, and calculating the low-frequency fluid medium-containing compression modulus and the high-frequency rock equivalent compression modulus of the seismic wave attenuation rock physical model through a formula (1) and a formula (2) respectively; calculating a maximum attenuation factor through a formula (3) according to the compression modulus of the low-frequency fluid-containing medium and the equivalent compression modulus of the high-frequency rock; establishing a target function as a formula (4) according to the inversion likelihood function; calculating an inversion likelihood function through a formula (5), and calculating posterior probability according to the target function and the inversion likelihood function; and taking the reservoir physical property parameter corresponding to the maximum posterior probability as a final solution.
Take a certain marine carbonate reservoir in China as an example. The oil-water filling in the cracks and pores not only influences the elastic parameters, but also causes the attenuation of seismic waves. In the embodiment, forward modeling calibration of a rock physical model and joint inversion of porosity and saturation are carried out on an S well in a research area, a common carbonate Xu & White model is selected when the forward modeling and the joint inversion of the porosity and the saturation are firstly applied, and inversion is carried out based on longitudinal wave velocity and density, and the result shows that the porosity inversion precision is high, the stability is good, but the saturation inversion is unstable. In subsequent researches, the conventional elastic parameter rock physical model is replaced by the seismic wave attenuation rock physical model established by the invention, and the result shows that the stability of the saturation inversion result is obviously improved after the seismic wave attenuation characteristic is introduced.
(1) Joint inversion of porosity and saturation based on improved Xu & White model
FIGS. 2a, 2b, and 2c are schematic diagrams illustrating compressional velocity, shear velocity, and density, respectively, of an improved Xu & White model for petrophysical model calibration in S-wells, according to an embodiment of the present invention.
Firstly, a carbonate rock petrophysical model proposed by Xu is adopted to obtain a rock physical model calibration result of the improved Xu & White model in an S well, as shown in fig. 2a, 2b and 2c, wherein a solid line in the figure is an original well logging explanation result, and dotted lines are a compressional wave velocity, a shear wave velocity and a density obtained based on forward modeling. The results show that the model can well simulate the influence of lithology, pores and fluid on the elastic properties of the rock under the condition of a carbonate reservoir.
Fig. 3a, 3b show schematic diagrams of inversion results of porosity and saturation, respectively, according to an embodiment of the invention.
And (3) carrying out porosity and saturation joint inversion based on the improved Xu & White model, wherein the inversion result is shown in fig. 3a and 3b, the solid line in the diagram is the logging interpretation result of the original porosity and saturation, and the dotted line is the inversion result. The result shows that the porosity inversion result has high precision and good stability, but the saturation inversion is unstable.
The reason for this phenomenon is mainly because the porosity reflects the information of the rock skeleton, and the change of the porosity has more obvious influence on the mechanical properties of the reservoir rock; the saturation reflects the existence state of the fluid in the pores, and the influence of the existence or change of the fluid on the mechanical property of the rock is much smaller than that of the rock skeleton, especially when the fluid is in an oil-water state, and the influence can be even ignored.
(2) Porosity and saturation joint inversion based on seismic wave attenuation rock physical model
Fig. 4 is a schematic diagram of compressional and shear wave attenuation estimation according to an embodiment of the invention, wherein the left diagram is a schematic diagram of compressional wave attenuation estimation and the right diagram is a schematic diagram of shear wave attenuation estimation.
In order to improve the inversion stability of the saturation result, the rock property sensitive to the saturation needs to be started. When seismic waves propagate in elastic non-uniform rocks or partially saturated rocks, fluid flow which can cause the rocks to vibrate is led into and out of the pores of the rocks, and the seismic wave energy is continuously converted into heat energy, so that the attenuation of the seismic waves is caused. Therefore, it is possible to improve the fluid saturation inversion result based on the seismic wave attenuation characteristics. And replacing the conventional elastic parameter rock physical model with a seismic wave attenuation rock physical model. FIG. 4 shows a schematic diagram of compressional and shear wave attenuation estimation based on raw well log interpretation results, according to an embodiment of the invention. The parameters of the raw well log interpretation include: longitudinal and transverse wave velocities, density, argillaceous content, porosity, water saturation and longitudinal and transverse wave velocity ratio, and the estimation result is a longitudinal and transverse wave attenuation factor. It can be seen that there is a tendency for higher attenuation to occur within the reservoir section.
FIG. 5 is a diagram illustrating the inversion results of the porosity and saturation of a seismic wave attenuation petrophysical model according to an embodiment of the present invention, wherein the left graph is the porosity inversion result and the right graph is the saturation inversion result.
And replacing the improved Xu & White model with a seismic wave attenuation rock physical model, and carrying out porosity and saturation joint inversion, wherein the inversion result is shown in figure 5, the solid line in the figure is the logging interpretation result of the original porosity and saturation, and the dotted line is the inversion result. Compared with the inversion results shown in fig. 3a and 3b, after the seismic wave attenuation characteristic is introduced, the stability of the saturation inversion result is obviously improved.
FIG. 6 shows a schematic diagram of destination layer saturation time slicing along layers according to one embodiment of the invention.
In the application of actual data, in order to realize more accurate water saturation prediction, seismic wave attenuation information needs to be acquired from seismic data, and the seismic wave attenuation information can be estimated by a spectral ratio method or a pre-stack Q value. After the seismic wave attenuation data volume is obtained, according to the method, a saturation seismic inversion result is further obtained, the saturation of a target layer in the embodiment is sliced along the layer time, the brightness in the figure is a region with higher oil saturation, the oil saturation of the underground river region serving as a high-quality reservoir section is higher, and the actual logging interpretation result shows that the oil saturation of the well A is higher than that of the well B, and the inversion result is well matched with production data.
In conclusion, the method carries out water saturation inversion through the seismic wave attenuation parameters, makes full use of logging information to construct a prior model, realizes multi-parameter joint inversion by combining rock physical model constraints, and obviously improves the accuracy of the inversion result of the water saturation.
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention is intended only to illustrate the benefits of embodiments of the invention and is not intended to limit embodiments of the invention to any examples given.
According to an embodiment of the present invention, there is provided a fluid saturation seismic inversion system, including: a memory storing computer-executable instructions; a processor executing computer executable instructions in the memory to perform the steps of: constructing a seismic wave attenuation rock physical model, and calculating the compression modulus of the low-frequency fluid-containing medium and the equivalent compression modulus of the high-frequency rock of the seismic wave attenuation rock physical model; calculating a maximum attenuation factor according to the compression modulus of the low-frequency fluid-containing medium and the equivalent compression modulus of the high-frequency rock; establishing a target function according to the inversion likelihood function; calculating an inversion likelihood function, and calculating posterior probability according to the target function and the inversion likelihood function; and taking the reservoir physical property parameter corresponding to the maximum posterior probability as a final solution.
In one example, the low frequency fluid-containing medium compressive modulus is calculated by equation (1):
calculating the equivalent compressive modulus of the high-frequency rock by the formula (2):
wherein, Msat0Is a low frequency fluid medium compression modulus, Ksat0Is a low frequency bulk modulus, G, of a fluid mediumdryIs the shear modulus of the dry rock skeleton,. phi.sat∞Is a high frequency rock equivalent compression modulus, MwetTo saturate the rock compressive modulus, Msw＝0Is a compressive modulus of hydrocarbon fluid rock, fpIs the volume fraction occupied by the fully saturated fraction.
In one example, the maximum attenuation factor is calculated by equation (3):
wherein the content of the first and second substances,
as maximum attenuation factor, Msat0For low frequency fluid-containing medium compression modulus, Msat∞Is the equivalent compression modulus of the high-frequency rock.
In one example, the objective function is:
wherein J is an objective function, P (R) is a prior distribution of the reservoir property parameter R, and P (m | R) is a likelihood function.
In one example, the inverse likelihood function is:
wherein P (R) is the prior distribution of the reservoir property parameter R, P (m | R) is the likelihood function, and count (m | R)j) Representing not only m as an elastic parameter but also R as a petrophysical property in a joint sampling datasetjThe number of sampling points of (c), count (R)j) Representing a petrophysical property as R in the joint sample data setjThe number of sampling points of (c).
The system carries out water saturation inversion through seismic wave attenuation parameters, makes full use of logging information to construct a prior model, realizes multi-parameter joint inversion by combining rock physical model constraints, and obviously improves the accuracy of water saturation inversion results.
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention is intended only to illustrate the benefits of embodiments of the invention and is not intended to limit embodiments of the invention to any examples given.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.
Claims (8)
1. A method of fluid saturation seismic inversion, comprising:
constructing a seismic wave attenuation rock physical model, and calculating the compression modulus of a low-frequency fluid-containing medium and the equivalent compression modulus of a high-frequency rock of the seismic wave attenuation rock physical model;
calculating a maximum attenuation factor according to the compression modulus of the low-frequency fluid-containing medium and the equivalent compression modulus of the high-frequency rock;
establishing a target function according to the inversion likelihood function;
calculating the inversion likelihood function, and calculating the posterior probability according to the target function and the inversion likelihood function;
taking the physical property parameter of the reservoir corresponding to the maximum posterior probability as a final solution;
wherein the objective function is:
wherein J is an objective function, P (R) is prior distribution of a reservoir physical property parameter R, and P (m | R) is a likelihood function;
considering the effect of the presence of pore fluid on seismic wave attenuation, an elastic parameter vector m is defined as the combination of elastic and attenuation parameters, i.e., Ne 5, m { vp, vs, ρ, Qp,Qs}。
2. The fluid saturation seismic inversion method of claim 1, wherein the low frequency fluid-containing medium compressive modulus is calculated by equation (1):
calculating the high-frequency rock equivalent compressive modulus through formula (2):
wherein M issat0Is a low frequency fluid medium compression modulus, Ksat0Is a low frequency bulk modulus, G, of a fluid mediumdryIs the shear modulus of the dry rock skeleton, phi is the total porosity of the rock, Msat∞Is a high frequency rock equivalent compressive modulus, MwetTo saturate the rock compressive modulus, Msw＝0Compressive modulus of rock for hydrocarbon-containing fluids, fpIs the volume fraction occupied by the fully saturated fraction.
3. The fluid saturation seismic inversion method of claim 1, wherein the maximum attenuation factor is calculated by equation (3):
wherein the content of the first and second substances,
as maximum attenuation factor, Msat0For low frequency fluid-containing medium compression modulus, Msat∞Is the equivalent compression modulus of the high-frequency rock.
4. The fluid saturation seismic inversion method of claim 1, wherein the inversion likelihood function is:
wherein P (R) is the prior distribution of the reservoir property parameter R, P (m | R) is the likelihood function, and count (m | R)j) Represented in a jointly sampled datasetNot only the elastic parameter is m, but also the rock physical property characteristic is RjThe number of sampling points of (c), count (R)j) Representing a petrophysical property as R in the joint sample data setjThe number of sampling points of (a).
5. A fluid saturation seismic inversion system, comprising:
a memory storing computer executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
constructing a seismic wave attenuation rock physical model, and calculating the compression modulus of a low-frequency fluid-containing medium and the equivalent compression modulus of a high-frequency rock of the seismic wave attenuation rock physical model;
calculating a maximum attenuation factor according to the compression modulus of the low-frequency fluid-containing medium and the equivalent compression modulus of the high-frequency rock;
establishing a target function according to the inversion likelihood function;
calculating the inversion likelihood function, and calculating the posterior probability according to the target function and the inversion likelihood function;
taking the physical property parameter of the reservoir corresponding to the maximum posterior probability as a final solution;
wherein the objective function is:
wherein J is an objective function, P (R) is prior distribution of a reservoir physical property parameter R, and P (m | R) is a likelihood function;
considering the effect of the presence of pore fluid on seismic wave attenuation, an elastic parameter vector m is defined as the combination of elastic and attenuation parameters, i.e., Ne 5, m { vp, vs, ρ, Qp,Qs}。
6. The fluid saturation seismic inversion system of claim 5, wherein the low frequency fluid-containing medium compressive modulus is calculated by equation (1):
calculating the equivalent compressive modulus of the high-frequency rock by formula (2):
wherein M issat0Is a low frequency fluid medium compression modulus, Ksat0Is a low frequency bulk modulus, G, of a fluid mediumdryIs the shear modulus of the dry rock skeleton,. phi.sat∞Is a high frequency rock equivalent compressive modulus, MwetTo saturate the rock compressive modulus, Msw＝0Is a compressive modulus of hydrocarbon fluid rock, fpIs the volume fraction occupied by the fully saturated fraction.
7. The fluid saturation seismic inversion system of claim 5, wherein the maximum attenuation factor is calculated by equation (3):
wherein the content of the first and second substances,
as maximum attenuation factor, Msat0For low frequency fluid-containing medium compression modulus, Msat∞Is the equivalent compression modulus of the high-frequency rock.
8. The fluid saturation seismic inversion system of claim 5, wherein the inversion likelihood function is:
wherein P (R) is the prior distribution of the reservoir property parameter R, P (m | R) is the likelihood function, and count (m | R)j) Representing not only m as an elastic parameter but also R as a petrophysical property in a joint sampling datasetjThe number of sampling points of (c), count (R)j) Representing a petrophysical property as R in the joint sample data setjThe number of sampling points of (c).
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102353989A (en) * | 2011-08-24 | 2012-02-15 | 成都理工大学 | Method for estimating velocity of transverse waves based on inversion of equivalent elastic modulus for self-adapting matrix minerals |
| CN103675907A (en) * | 2012-09-20 | 2014-03-26 | 中国石油化工股份有限公司 | AVO inversion hydrocarbon detection method based on petrographic constraints |
| CN106168676A (en) * | 2015-05-22 | 2016-11-30 | 中国石油化工股份有限公司 | Formation lithology based on seismic data and Fluid Identification Method and device |
| CN107765303A (en) * | 2016-08-17 | 2018-03-06 | 中国石油化工股份有限公司 | Estimate the method and system of the attenuation of seismic wave |
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| US9952341B2 (en) * | 2014-12-09 | 2018-04-24 | Chevron U.S.A. Inc. | Systems and methods for aligning a monitor seismic survey with a baseline seismic survey |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102353989A (en) * | 2011-08-24 | 2012-02-15 | 成都理工大学 | Method for estimating velocity of transverse waves based on inversion of equivalent elastic modulus for self-adapting matrix minerals |
| CN103675907A (en) * | 2012-09-20 | 2014-03-26 | 中国石油化工股份有限公司 | AVO inversion hydrocarbon detection method based on petrographic constraints |
| CN106168676A (en) * | 2015-05-22 | 2016-11-30 | 中国石油化工股份有限公司 | Formation lithology based on seismic data and Fluid Identification Method and device |
| CN107765303A (en) * | 2016-08-17 | 2018-03-06 | 中国石油化工股份有限公司 | Estimate the method and system of the attenuation of seismic wave |
Non-Patent Citations (1)
| Title |
|---|
| 基于MCMC算法的地质统计学相控反演技术;张恒山，等;《科技创新与应用》;20131231(第9期);13-15 * |
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