CN114384587A - Oil gas detection method for biological reef flat reservoir based on prestack wide-angle inversion - Google Patents
Oil gas detection method for biological reef flat reservoir based on prestack wide-angle inversion Download PDFInfo
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Abstract
The invention belongs to the technical field of petroleum exploration reservoir prediction, and particularly relates to a method for detecting oil gas in a reservoir of a biological reef based on prestack wide-angle inversion, which has the advantages of reasonable algorithm, high operation speed, result convergence after finite iterations, good result acquisition and capability of effectively detecting the oil gas content of a complex reef body; the actual work area seismic data test shows that the inversion result is faithful to the seismic data, an oil-gas accumulation area can be identified on the section, and the oil-gas content can be effectively detected through the fluid parameter section after rock physical analysis and multi-attribute calculation; the technical means is utilized to predict the reservoir of an actual test block of an oil field in the Brazilian Mortosus basin, the favorable target area is predicted, and an effective drilling target is provided for the subsequent well position deployment of the oil field.
Description
Technical Field
The invention belongs to the technical field of petroleum exploration reservoir prediction, and particularly relates to a method for detecting oil and gas of a reservoir of a biological reef based on prestack wide-angle inversion.
Background
With the continuous deepening of exploration degree, the continuous increasing of difficulty and the increasing scarcity of structural trapping, the purpose of finding hidden oil gas is more and more urgent, and the target of oil gas exploration and development is not an oil gas reservoir mainly based on the structure, but a more hidden lithologic trapping type oil gas reservoir and other types of oil gas reservoirs. At present, the research on carbonate reservoirs is hot day by day, the oil gas reserves of global carbonate rock account for more than 50% of the total oil gas reserves, and the bio-reef oil gas reserves occupy an important position in the carbonate rock oil gas field.
At present, the method for predicting carbonate reservoirs by using a seismic inversion technology mainly comprises two main categories, namely a post-stack seismic inversion technology and a pre-stack inversion technology, wherein the post-stack seismic inversion technology is early to start and is relatively mature, but seismic data after full-angle repeated stacking is adopted during processing, abundant amplitude and travel time information contained in a pre-stack data body are lacked, and the sensitivity to reservoir characteristics is weakened to a certain extent, so that the oil-gas property of a complex biological reef reservoir cannot be predicted. The pre-stack seismic inversion method is developed, and particularly the pre-stack wide-angle inversion technology developed in recent years reserves the characteristic that the seismic reflection amplitude changes along with the offset or incidence angle, and can provide more, more sensitive and effective data volume results. The method has higher sensitivity to complex biological reef reservoirs and fluid identification, and can provide a basis for oil and gas detection of reef banks.
Although the seismic inversion resolution is improved and the operation speed is high due to the seismic nonlinear inversion method in the prior art, due to the limitation of the method, the inversion algorithm is unstable, a locally optimal trap is easily trapped in the solution, the globally optimal solution cannot be reached, the inversion result is insufficient in feasibility, and the exploration and development requirements cannot be met; therefore, the invention provides an oil-gas detection method of a biological reef reservoir based on prestack wide-angle inversion.
Disclosure of Invention
The invention provides a method for detecting oil and gas in a biological reef storage layer based on prestack wide-angle inversion, which aims to make up the defects of the prior art and solve the problems that although the seismic inversion resolution is improved and the operation speed is high in the inversion method of the seismic nonlinearity in the prior art, due to the limitation of the method, the inversion algorithm is unstable, a locally optimal trap is easy to fall into in the solution, the global optimal solution cannot be achieved, the feasibility of the inversion result is insufficient, and the exploration and development requirements cannot be met.
The technical scheme adopted by the invention for solving the technical problems is as follows: a method for detecting oil and gas of a biological reef reservoir based on prestack wide-angle inversion comprises the following steps:
s1: deducing a prestack wide angle inversion formula, obtaining an inversion result by iteration, namely, establishing a target function based on a convolution model and a Zoeppritz equation, carrying out Taylor expansion and simplification on the target function, forming a joint equation set by the equation sets with large, medium and small angles because large, medium and small angles all meet the equation, establishing an iteration formula of the joint equation set by applying Bayesian theory, and obtaining an inversion result by iteration, namely, obtaining parameters in the equation set;
s2: introducing Bayes theory into a prestack wide angle inversion formula, solving inversion parameters through an iterative formula, namely firstly assuming that the variation of three parameters such as longitudinal wave velocity, transverse wave velocity and density is in accordance with Gaussian distribution with zero mean value, and establishing a prior probability distribution function; secondly, assuming that the omitted items conform to zero mean Gaussian distribution when Taylor expansion is carried out on the target function in the generalized linear inversion, thereby establishing a conditional probability distribution function; finally, establishing a posterior probability distribution function, deducing expressions of the disturbance quantity and the damping factor, and solving inversion parameters through an iterative formula;
s3: the inversion result is utilized to detect oil and gas, the oil and gas distribution rule is predicted, a basis is provided for subsequent reef reservoir prediction and exploration and development, namely, the prestack wide-angle inversion is utilized to obtain speed volume and density data, and then rock physical analysis is carried out by combining logging information to determine reservoir parameters; and through analysis, screening out reservoir parameters with high sensitivity to the reef, and guiding the detection of oil and gas in the reef reservoir.
Preferably, in S1, the reflection angle from the single-layer interface satisfies snell' S law, and the relationship can be expressed as:
according to the theory of elastic mechanics, obtaining a Zoeppritz equation capable of reflecting the relation between reflection and transmission coefficients and an incident angle:
in the formula, Vp1、Vs1、ρ1The longitudinal and transverse wave speeds and the density of the interface I are respectively; vp2、Vs2、ρ2Respectively are parameters of the interface II; rp、Rs、Tp、TsRespectively longitudinal and transverse wave reflection coefficients and longitudinal and transverse wave transmission coefficients; alpha is alpha1、α2、β1、β2Respectively, a longitudinal and transverse wave reflection angle and a longitudinal and transverse wave transmission angle.
Preferably, in S1, since the Zoeppritz equation is too large and too complicated to calculate, the pre-stack inversion formula derivation is performed by introducing Aki & Richards approximation formula, and the reflection coefficient formula is:
wherein the content of the first and second substances,
in the formula, Vp, Vs and rho are mean values of medium speeds and densities at two sides of an interface, and Δ Vp, Δ Vs and Δ rho are difference values of the three, and in a simplified approximate formula, reflection coefficients are only related to three parameters of Vp, Vs and rho, so that conditions are created for subsequent prestack inversion.
Preferably, in S1, an inversion objective function based on a convolution model is established by using a Zoeppritz equation and a Aki & Richards approximation formula:
in the above formula, Vp, Vs, ρ are the longitudinal wave velocity, the transverse wave velocity and the density, respectively, and D is the actual seismic data;
S(Vp,Vs,ρ)＝W*R(Vp,Vs,ρ)；
the above equation is the expected seismic model response S (Vp, Vs, ρ) under the convolution model, where R (Vp, Vs, ρ) is the reflection coefficient and W is the seismic wavelet.
Preferably, in S2, the introduction of bayesian theory includes the following three steps:
a1: establishing a prior probability distribution function, and representing the uncertainty of parameter estimation through the distribution width of PPDF;
a2: selecting a proper likelihood function and a Gaussian distribution function, and obtaining a maximum likelihood inversion method under the condition of assuming that prior random distribution is uniform;
a3: and (4) carrying out prior random constraint, deducing a damping factor expression, and subsequently solving inversion parameters through an iterative formula.
Preferably, in S3, through analysis, a reservoir parameter with high sensitivity to the reef is selected, and the reef reservoir oil-gas detection is guided, and the specific implementation process includes:
b1: obtaining a high-precision three-parameter data body through pre-stack wide-angle inversion, and analyzing the rock physical characteristics of the reef reservoir on the basis of an inversion result;
b2: on the basis of rock physical analysis, 35 fluid sensitive parameters are obtained through multi-attribute interactive calculation, and reservoir parameters with high oil-gas sensitivity are finally determined through theoretical model trial calculation and reservoir AVO analysis;
b2: after the reservoir parameters are obtained, the parameters are utilized to realize the oil-gas containing property prediction and predict the oil-gas distribution rule, thereby providing a basis for the subsequent reef reservoir prediction and exploration and development.
The invention has the technical effects and advantages that:
the invention provides a method for detecting oil and gas of a reservoir of a biological reef flat based on prestack wide-angle inversion, which is characterized in that a prestack wide-angle seismic inversion method aiming at a reef flat is explored by searching a foreperson research method and utilizing high-resolution prestack seismic data, and a Bayesian theory is introduced to re-derive an iterative matrix aiming at inversion instability factors by improving an algorithm, so that an improved prestack seismic inversion method is obtained; through theoretical model simulation, a theoretical initialization model and parameters are given, and an inversion result obtained through calculation is very close to the theoretical model; the method has reasonable algorithm and high operation speed, and after finite iterations, the result is converged, and a good result is obtained, so that the oil-gas content of the complex reef body can be effectively detected; the actual work area seismic data test shows that the inversion result is faithful to the seismic data, an oil-gas accumulation area can be identified on the section, and the oil-gas content can be effectively detected through the fluid parameter section after rock physical analysis and multi-attribute calculation; the technical means is utilized to predict the reservoir of an actual test block of an oil field in the Brazilian Mortosus basin, the favorable target area is predicted, and an effective drilling target is provided for the subsequent well position deployment of the oil field.
Drawings
The invention will be further explained with reference to the drawings.
FIG. 1 is a technical flow chart of the detection method of the present invention;
FIG. 2 is a schematic diagram of a one-dimensional inversion theoretical model of the present invention;
FIG. 3 is a schematic diagram of inversion results of a theoretical model according to the present invention
FIG. 4 is a comparative schematic of the synthetic record of the present invention
FIG. 5 is a schematic diagram of the seismic section of the biological reef of the present invention
FIG. 6 is a schematic view of an inversion profile of a biological reef reservoir according to the present invention;
FIG. 7 is a plot of actual seismic data for a study area in an embodiment of the present invention;
FIG. 8 is a histogram of a multi-well velocity distribution for an area of interest in an embodiment of the present invention;
FIG. 9 is a graphical illustration of inversion predictions for different combinations of lithologies in an embodiment of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
The invention relates to a method for detecting oil and gas of a biological reef reservoir based on prestack wide-angle inversion, which comprises the following steps of:
s1: deducing a prestack wide angle inversion formula, obtaining an inversion result by iteration, namely, establishing a target function based on a convolution model and a Zoeppritz equation, carrying out Taylor expansion and simplification on the target function, forming a joint equation set by the equation sets with large, medium and small angles because large, medium and small angles all meet the equation, establishing an iteration formula of the joint equation set by applying Bayesian theory, and obtaining an inversion result by iteration, namely, obtaining parameters in the equation set;
s2: introducing Bayes theory into a prestack wide angle inversion formula, solving inversion parameters through an iterative formula, namely firstly assuming that the variation of three parameters such as longitudinal wave velocity, transverse wave velocity and density is in accordance with Gaussian distribution with zero mean value, and establishing a prior probability distribution function; secondly, assuming that the omitted items conform to zero mean Gaussian distribution when Taylor expansion is carried out on the target function in the generalized linear inversion, thereby establishing a conditional probability distribution function; finally, establishing a posterior probability distribution function, deducing expressions of the disturbance quantity and the damping factor, and solving inversion parameters through an iterative formula;
s3: the inversion result is utilized to detect oil and gas, the oil and gas distribution rule is predicted, a basis is provided for subsequent reef reservoir prediction and exploration and development, namely, the prestack wide-angle inversion is utilized to obtain speed volume and density data, and then rock physical analysis is carried out by combining logging information to determine reservoir parameters; and through analysis, screening out reservoir parameters with high sensitivity to the reef, and guiding the detection of oil and gas in the reef reservoir.
In S1, the reflection angle from the single-layer interface satisfies snell' S law, and the relationship can be expressed as:
according to the theory of elastic mechanics, obtaining a Zoeppritz equation capable of reflecting the relation between reflection and transmission coefficients and an incident angle:
in the formula, Vp1、Vs1、ρ1The longitudinal and transverse wave speeds and the density of the interface I are respectively; vp2、Vs2、ρ2Respectively are parameters of the interface II; rp、Rs、Tp、TsRespectively longitudinal and transverse wave reflection coefficients and longitudinal and transverse wave transmission coefficients; alpha is alpha1、α2、β1、β2Respectively, a longitudinal and transverse wave reflection angle and a longitudinal and transverse wave transmission angle.
In S1, since the Zoeppritz equation is too large and too complicated to calculate, the pre-stack inversion formula derivation is performed by introducing Aki & Richards approximation formula, and the reflection coefficient formula is as follows:
wherein the content of the first and second substances,
in the formula, Vp, Vs and rho are mean values of medium speeds and densities at two sides of an interface, and Δ Vp, Δ Vs and Δ rho are difference values of the three, and in a simplified approximate formula, reflection coefficients are only related to three parameters of Vp, Vs and rho, so that conditions are created for subsequent prestack inversion.
As an embodiment of the present invention, in S1, an inversion objective function based on a convolution model is established by using a Zoeppritz equation and a Aki & Richards approximation formula:
in the above formula, Vp, Vs, ρ are the longitudinal wave velocity, the transverse wave velocity and the density, respectively, and D is the actual seismic data;
S(Vp,Vs,ρ)＝W*R(Vp,Vs,ρ)；
the above equation is the expected seismic model response S (Vp, Vs, ρ) under the convolution model, where R (Vp, Vs, ρ) is the reflection coefficient and W is the seismic wavelet.
Specifically, the left side of the expected seismic model response S (Vp, Vs, rho) formula under the convolution model is expanded by using the Taylor formula for three parameters, and only a first term is reserved, including:
respectively obtaining first-order partial derivatives of delta Vp, delta Vs and delta rho at two ends of S (Vp, Vs, rho) ═ W × R (Vp, Vs, rho); let Δ di ═ D (Vp, Vs ρ)i-S(Vp,Vs,ρ)iThe method comprises the following steps:
due to the fact that
Cannot be zero, so there are:
order to
Then
Can be converted into:
-d1+G1(vp)ΔVpk+G1(vs)ΔVsk+G1(ρ)Δρ＝0；
-d2+G2(vp)ΔVpk+G2(vs)ΔVsk+G2(ρ)Δρ＝0；
-d3+G3(vp)ΔVpk+G3(vs)ΔVsk+G3(ρ)Δρ＝0；
the above formula is expressed in matrix form as follows:
the best solution estimation is obtained by adopting a least square method to the formula as follows: Δ m ═ GTG+λI]-1GTΔd；
Wherein, the matrix G is a jacobi matrix, which can be expressed as:
by solving for
Finally, a jacobi matrix G is obtained, and Δ m ═ G is substitutedTG+λI]-1GTIn delta d, iteration is carried out to obtain output values of Vp, Vs and rho, so that synchronous deterministic inversion of three parameters before stacking is realized;
it is worth noting that the pre-stack angle set seismic data of small, medium and large angles are respectively substituted into the inversion iteration expression, so that high-precision longitudinal and transverse wave velocity and density parameters can be obtained.
As an embodiment of the present invention, the introduction of bayesian theory in S2 includes the following three steps:
a1: establishing a prior probability distribution function, and representing the uncertainty of parameter estimation through the distribution width of PPDF; specifically, according to the definition of bayesian conditional probability, the posterior probability density formula of the event is as follows:
the Posterior Probability Distribution Function (PPDF) is expressed as p (x | d, I), and the random distribution of the parameter vector x can be obtained given the data vector d (the offset-dependent data vector) and the geological information I. The denominator p (d | I) is a constant that can be ignored if only the shape of the PPDF is of interest. Then there is p (x | d, I) p (x | d), where p (x | d, I) is the likelihood function and p (d | I) is the a priori random distribution function, so the most likely value of PPDF is the maximum value of the multiplication of the two random functions, and the distribution width of PPDF characterizes the magnitude of uncertainty in the parameter estimation.
A2: selecting a proper likelihood function and a Gaussian distribution function, and obtaining a maximum likelihood inversion method under the condition of assuming that prior random distribution is uniform; specifically, the Bayesian theory method mainly utilizes the prior information of solution estimation to overcome the problem of unsuitability during the solution of the inversion problem. If it is assumed that the noise associated with the m-th experimental measurement is the band variable σmGiven a parameter vector x, a reflection coefficient d which varies with the mth offsetmThe random distribution of (a) satisfies the condition:
the gaussian distribution of the measurements obtained for different measurement conditions is independent, the variance of the gaussian distribution is assumed to be uniformly distributed and σmσ, the solution to the likelihood function using the product criterion is:
this method is the maximum likelihood inversion method, assuming that the a priori random distributions are uniform.
A3: carrying out prior random constraint, deducing a damping factor expression, and subsequently solving an inversion parameter through an iterative formula; specifically, if it is assumed that each variable in the prior information is also gaussian distributed, it can be obtained:
the data band characteristics are ignored in the inversion algorithm, so the mean < x > is considered to be 0.
CxIs a parametric covariance matrix, with diagonal elements representing the variance of the parametric variables and off-diagonal elements representing the cross-correlation of the parametric variables.
The Bayesian formula is used to obtain:
the extreme value of the function can be obtained by finding its derivative and equaling zero, and finally by simplifying to find the perturbation of the parameter variation:
in the formula: sigma2Is a weighting factor, σ2The larger the signal to noise ratio of the data is, the lower the signal to noise ratio of the data is, and the solution result is determined by constraint; if it is not
This means a priori variation of the three parameter vectorsThe chemistry is the same; so far, through the probability distribution function, the disturbance quantity and the damping factor expression deduced are completed, and through the subsequent iteration formula, the inversion parameters can be solved. Therefore, compared with the conventional method, the damping factor calculated based on the Bayesian theory changes along with the iteration times, is not influenced by human factors, has sufficient theoretical basis and has clear geophysical significance.
Specifically, through the derivation of the prestack inversion principle, when taylor expansion partial derivation is performed on a target function of the prestack inversion principle, the introduced Jacobi matrix G is usually pathological, so how to solve the Jacobi matrix G is an important step for obtaining an accurate inversion result, and through research on inversion stability, it is found that setting a proper damping factor is a key for solving the Jacobi matrix pathological problem and improving the solution stability; secondly, assuming that the omitted items conform to zero mean Gaussian distribution when Taylor expansion is carried out on the target function in the generalized linear inversion, thereby establishing a conditional probability distribution function; and finally, establishing a posterior probability distribution function, deducing expressions of the disturbance quantity and the damping factor, and solving inversion parameters through an iterative formula.
In an embodiment of the present invention, in S3, through analysis, a reservoir parameter with high sensitivity to a reef is selected, and the reef reservoir oil-gas detection is guided, where the specific implementation process includes:
b1: obtaining a high-precision three-parameter data body through pre-stack wide-angle inversion, and analyzing the rock physical characteristics of the reef reservoir on the basis of an inversion result;
b2: on the basis of rock physical analysis, 35 fluid sensitive parameters are obtained through multi-attribute interactive calculation, and reservoir parameters with high oil-gas sensitivity are finally determined through theoretical model trial calculation and reservoir AVO analysis;
b2: after the reservoir parameters are obtained, the parameters are utilized to realize the oil-gas containing property prediction and predict the oil-gas distribution rule, thereby providing a basis for the subsequent reef reservoir prediction and exploration and development.
FIG. 2 illustrates: designing a nine-layer theoretical model, wherein the sampling point interval is 2ms, and designing a corresponding initial model in the same way, wherein the wavelet is a Rake wavelet with the dominant frequency of 30 HZ.
FIG. 3 illustrates: the inversion adopts five angle gather inversion of 6 degrees, 12 degrees, 18 degrees, 24 degrees and 30 degrees, wherein the theoretical longitudinal and transverse wave velocity density, the initial longitudinal and transverse wave velocity density and the inversion longitudinal and transverse wave velocity density are shown in the figure, and the difference between the theoretical value and the inversion result is small. Under the condition of a nine-layer theoretical model, the longitudinal and transverse wave speed and the density inversion accuracy are high.
FIG. 4 illustrates: the figure is a comparison between 12 and 24 degree inversion synthetic records and theoretical synthetic records. Therefore, in the inversion process, the amplitude of the seismic records has good consistency, and the inversion process is very stable.
FIG. 5 illustrates: the figure is a two-dimensional seismic section of an investigation area, and under the BVE100 top interface, the top hill reflection characteristic, the internal weak reflection characteristic and the clutter reflection characteristic can be seen. The earthquake response characteristic of the biological reef body is considered primarily, only the existence of the biological reef body can be determined qualitatively from an earthquake section, but the specific morphological characteristics of the biological reef body cannot be seen, and the distribution range of the biological reef body cannot be determined.
FIG. 6 illustrates: the figure is a two-dimensional seismic velocity inversion profile of the study area, which is consistent with the survey line in figure 2. In the seismic section of fig. 2, the presence of the biological reef was confirmed, and the present figure more clearly demonstrates the biological reef growth characteristics. As seen in the figure, under the BVE100 top interface, the "shingled" biological reef features are clearly visible, with a distinct difference in velocity from the surrounding rock mass. The velocity inversion section under the same measuring line and the biological reef described by the seismic section are in good balance, but the comparison and observation show that the inversion section can clearly show the morphological characteristics of the biological reef, and the seismic section cannot be identified. This also verifies the feasibility and applicability of the invention from a practical case perspective.
Example (b):
on the basis of the inversion test, a certain Brazilian oil field is selected as an example, the actual three-dimensional seismic data test is carried out on the carbonate rock reservoir under the salt of the Brazilian Santos basin, and the prediction research of the biological reef reservoir is carried out. The area of the three-dimensional seismic data after the study area is overlapped is 240km2, and due to the absorption effect of salt rocks, the seismic data under the salt in the area has poor quality and low dominant frequency of about 17 Hz. As shown in fig. 7. The water depth of the oil field is 2000-2400 m, the target layer is 5000m more than the sea level below the huge thick salt rock, and the BVE group is a main oil reservoir under salt. Drilling reveals that the carbonate reservoir in the area develops, and the oil testing result of part of wells is good. Previous research shows that the main oil reservoir in the area is a set of microbial reef flat layers. The growth characteristics of the biological reef are difficult to identify through a single seismic section, even if the seismic characteristics of the biological reef respond, only a general outline can be identified, and the growth characteristics of the biological reef cannot be accurately predicted. Therefore, how to accurately identify and predict the biological reef body is the key of research.
And selecting logging information in the work area to perform lithology-elasticity parameter analysis, wherein the laminated limestone speed is high and the marlite or argillaceous limestone is low in the target layer section. From a single well phase, the carbonate reservoir has the characteristic rule of 'two low one high' of low GR, low AC and high RT, and the 'box-shaped' curve is obvious in characteristic. Performing lithology classification and speed analysis on 15 wells in the work area to obtain gypsum and argillaceous limestone with high speed in the range of 5500-6200 m/s; the biological limestone and the granular limestone are medium-high speed, and are about 5000-5800 m/s; the salt rock and the marl rock are low-speed, about 4000-.
Through the above petrophysical analysis, we determined the lithology velocity range of the study area. And then the nonlinear chaotic inversion technology of the invention is used for inversion test of the actual seismic data in the research area. The specific implementation steps are as follows: (1) sorting seismic data, analyzing seismic data dominant frequency, and extracting seismic wavelets of a well side channel; (2) arranging the research area to participate in inversion well, and performing standardized correction and normalized processing; (3) and establishing a low-frequency velocity model of seismic inversion, performing inversion trial calculation, continuously adjusting inversion parameters, and finally obtaining an inversion velocity body after multiple times of inversion calculation. And establishing a lithologic combination inversion prediction mode by identifying the specific seismic inversion response of the biological reef, and further guiding the reservoir prediction and favorable area distribution of the biological reef.
As shown in fig. 9, inversion characteristics of strata with different lithologic combinations are different from seismic reflection characteristics, and a biological reef reservoir with high underground concealment is clearly imaged by using a nonlinear chaotic inversion technology, so that prediction accuracy is greatly improved. The growth morphology of the reef can be clearly seen in fig. 3, and the "hill" or "shingle" features of the reef are quite apparent. By utilizing an interpretation mode of inversion prediction, the distribution range of the biological reef can be further tracked and delineated, the reservoir range of the biological reef is finally determined, and a foundation is laid for the subsequent favorable area prediction.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. A method for detecting oil and gas in a reservoir of a biological reef flat based on prestack wide-angle inversion is characterized by comprising the following steps: the method comprises the following steps:
s1: deducing a prestack wide angle inversion formula, obtaining an inversion result by iteration, namely, establishing a target function based on a convolution model and a Zoeppritz equation, carrying out Taylor expansion and simplification on the target function, forming a joint equation set by the equation sets with large, medium and small angles because large, medium and small angles all meet the equation, establishing an iteration formula of the joint equation set by applying Bayesian theory, and obtaining an inversion result by iteration, namely, obtaining parameters in the equation set;
s2: introducing Bayes theory into a prestack wide angle inversion formula, solving inversion parameters through an iterative formula, namely firstly assuming that the variation of three parameters such as longitudinal wave velocity, transverse wave velocity and density is in accordance with Gaussian distribution with zero mean value, and establishing a prior probability distribution function; secondly, assuming that the omitted items conform to zero mean Gaussian distribution when Taylor expansion is carried out on the target function in the generalized linear inversion, thereby establishing a conditional probability distribution function; finally, establishing a posterior probability distribution function, deducing expressions of the disturbance quantity and the damping factor, and solving inversion parameters through an iterative formula;
s3: the inversion result is utilized to detect oil and gas, the oil and gas distribution rule is predicted, a basis is provided for subsequent reef reservoir prediction and exploration and development, namely, the prestack wide-angle inversion is utilized to obtain speed volume and density data, and then rock physical analysis is carried out by combining logging information to determine reservoir parameters; and through analysis, screening out reservoir parameters with high sensitivity to the reef, and guiding the detection of oil and gas in the reef reservoir.
2. The method for detecting the oil and gas in the reservoir of the biological reef based on the prestack wide-angle inversion, according to claim 1, is characterized in that: in S1, the reflection angle from the single-layer interface satisfies snell' S law, and the relationship can be expressed as:
according to the theory of elastic mechanics, obtaining a Zoeppritz equation capable of reflecting the relation between reflection and transmission coefficients and an incident angle:
in the formula, Vp1、Vs1、ρ1The longitudinal and transverse wave speeds and the density of the interface I are respectively; vp2、Vs2、ρ2Respectively are parameters of the interface II; rp、Rs、Tp、TsRespectively longitudinal and transverse wave reflection coefficients and longitudinal and transverse wave transmission coefficients; alpha is alpha1、α2、β1、β2Respectively, a longitudinal and transverse wave reflection angle and a longitudinal and transverse wave transmission angle.
3. The method for detecting the oil and gas in the reservoir of the biological reef based on the prestack wide-angle inversion, according to claim 2, is characterized in that: in the S1, due to the fact that the Zoeppritz equation is too large and too complex to calculate, the pre-stack inversion formula derivation is carried out by introducing Aki & Richards approximation formula, and the reflection coefficient formula is as follows:
Vp＝(Vp1+Vp2)/2,Vs＝(Vs1+Vs2)/2,ρ＝(ρ1+ρ2)/2,
ΔVp＝Vp2-Vp1,ΔVs＝Vs2-Vs1,Δρ＝ρ2-ρ1,
wherein θ ═ α1+β1)/2
In the formula, Vp, Vs and rho are mean values of medium speeds and densities at two sides of an interface, and Δ Vp, Δ Vs and Δ rho are difference values of the three, and in a simplified approximate formula, reflection coefficients are only related to three parameters of Vp, Vs and rho, so that conditions are created for subsequent prestack inversion.
4. The method for detecting oil and gas in a reservoir of a biological reef based on prestack wide-angle inversion, according to claim 3, wherein the method comprises the following steps: in the step S1, an inversion objective function based on a convolution model is established by using a Zoeppritz equation and a Aki & Richards approximation formula:
in the above formula, Vp, Vs, ρ are the longitudinal wave velocity, the transverse wave velocity and the density, respectively, and D is the actual seismic data;
S(Vp,Vs,ρ)＝W*R(Vp,Vs,ρ)；
the above equation is the expected seismic model response S (Vp, Vs, ρ) under the convolution model, where R (Vp, Vs, ρ) is the reflection coefficient and W is the seismic wavelet.
5. The method for detecting oil and gas in a reservoir of a biological reef based on prestack wide-angle inversion, according to claim 4, wherein the method comprises the following steps: in S2, the introduction of bayesian theory includes the following three steps:
a1: establishing a prior probability distribution function, and representing the uncertainty of parameter estimation through the distribution width of PPDF;
a2: selecting a proper likelihood function and a Gaussian distribution function, and obtaining a maximum likelihood inversion method under the condition of assuming that prior random distribution is uniform;
a3: and (4) carrying out prior random constraint, deducing a damping factor expression, and subsequently solving inversion parameters through an iterative formula.
6. The method for detecting oil and gas in a reservoir of a biological reef based on prestack wide-angle inversion, according to claim 5, wherein the method comprises the following steps: in the S3, reservoir parameters with high sensitivity to the reef are screened out through analysis, and the detection of oil and gas in the reef reservoir is guided, and the specific implementation process comprises the following steps:
b1: obtaining a high-precision three-parameter data body through pre-stack wide-angle inversion, and analyzing the rock physical characteristics of the reef reservoir on the basis of an inversion result;
b2: on the basis of rock physical analysis, 35 fluid sensitive parameters are obtained through multi-attribute interactive calculation, and reservoir parameters with high oil-gas sensitivity are finally determined through theoretical model trial calculation and reservoir AVO analysis;
b2: after the reservoir parameters are obtained, the parameters are utilized to realize the oil-gas containing property prediction and predict the oil-gas distribution rule, thereby providing a basis for the subsequent reef reservoir prediction and exploration and development.
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