CN114428286A - Gas saturation prediction method based on pre-stack seismic data - Google Patents
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Abstract
The invention discloses a gas saturation prediction method based on prestack seismic data, and provides an attribute fusion method based on fuzzy logic for quantitatively predicting gas saturation by comprehensively considering the elasticity (pore volume modulus), attenuation (far offset distance high-frequency attenuation gradient attribute) and AVO characteristic (P x G) difference of rock after gas is contained in a reservoir, so that the problem of multi-resolution in the conventional quantitative prediction of gas saturation based on the difference of single elastic parameters is solved, and the gas saturation prediction reliability is effectively improved.
Description
Technical Field
The invention relates to the field of oil and gas geophysical exploration, in particular to a gas saturation prediction method based on pre-stack seismic data.
Background
Seismic exploration of tight sandstone gas reservoirs is still a relatively difficult field at present. Specifically, because the porosity and the permeability are low, effective reservoir prediction and fluid detection are difficult, and the gas-water relationship of the compact clastic rock reservoir is complex.
At present, the use of seismic data for hydrocarbon prediction is basically a qualitative prediction for the fluids contained in a reservoir. Kassouri qualitatively predicts the gas content by utilizing an AVO gradient and intercept intersection method; peters and the like qualitatively predict the gas content of a reservoir by utilizing a wavelet energy analysis method in the east China sea; li and the like qualitatively predict the gas content of the reservoir in the Chauda basin by utilizing a wavelet scale spectrum method; the potential of elastic impedance in lithology and fluid prediction is discussed in detail by the Ganly lamp et al; the Lizongjie and the like predict the oil content of the Ordovician oil-bearing reservoir in the Tahe oil field by utilizing various seismic attributes; comprehensively utilizing an Absorption Velocity Dispersion (AVD) prediction technology, a dynamic energy spectrum (DR) prediction technology and the like to detect the gas content of the non-uniform compact sandstone of the gas reservoir of the beard family river group of the new field gas field by utilizing the plum is obvious and expensive; in Liezshan et al (2007), a gas reservoir of the middle layer gas reservoir during depression of the victory oil field by utilizing the elastic parameters synchronously inverted by AVA is effectively identified; the Gaojian tiger and the like provide a method for carrying out oil-gas detection by comprehensively utilizing post-stack oil-gas detection technologies such as effective absorption coefficients, seismic wave dynamic parameters, amplitude spectrum identification and the like.
In the aspect of quantitative prediction of fluid saturation, many scholars in China also carry out research, but most scholars are in the theoretical research stage and do not carry out application research. According to the method, the sandstone thickness and the target layer of a section of sandstone oil reservoir in northern Tenebrio of the Mongolian oil field are predicted by utilizing a multivariate regression method based on more than 20 post-stack seismic attributes, and a better effect is obtained, but because the method is based on the seismic attributes rather than seismic inversion parameters, the method has the defects of low resolution and severe dependence on seismic data; the Li-Lelin provides a thought for solving the static residual oil saturation by utilizing seismic and logging data based on a time-averaged equation; liuyang proposed a concept for estimating rock porosity and fluid saturation using seismic data; jinlong and the like provide a method for inverting porosity and saturation based on a rock physical model and a hybrid optimization algorithm, the method takes the porosity and the saturation as inversion constraint conditions, and the inversion stability and effectiveness are subjected to model inspection but are not applied to actual seismic data processing.
Most of the methods are based on the change of elastic characteristics of reservoirs after gas containing, related elastic parameters are obtained through a prestack inversion method, and then the gas saturation is obtained through a conversion relation. The influence of reservoir gas on the rock properties is manifold, and the quantitative description of the gas saturation cannot be effectively carried out by simply considering the change of the elastic parameters.
Disclosure of Invention
The invention aims to: aiming at the problem that quantitative description of gas saturation cannot be effectively carried out by simply considering the change of elastic parameters in the prior art, a gas saturation prediction method based on pre-stack seismic data is provided, the changes of rock elasticity, attenuation and AVO characteristics after gas is contained in a reservoir are comprehensively considered, the gas saturation is quantitatively predicted by an attribute fusion method based on fuzzy logic, and the gas saturation prediction reliability is effectively improved.
In order to achieve the purpose, the invention adopts the technical scheme that:
a method for predicting gas saturation based on pre-stack seismic data comprises the following steps:
s100, sensitive elasticity parameter optimization is carried out; carrying out sensitive AVO attribute optimization; performing sensitive attenuation attribute optimization;
preferred sensitive elastic parameter A1Sensitive AVO Attribute A2Sensitive attenuation Property A3Is denoted as attribute set { A }1(x,y,t),A2(x,y,t),A3(x,y,t)}；
S200, respectively counting correlation functions of each attribute and the gas saturation Sw based on the logging data, namely:
Sw＝f1(A1) Sw＝f2(A2) Sw＝f3(A3)；
s300, respectively normalizing according to the related functions to obtain normalized attribute sets { A'1(x,y,z)，,A'2(x,y,z),A'3(x,y,z)}；
S400, any space point of the three-dimensional space is based on the normalized attribute set { A'1(xi,yi,zi)，,A'2(xi,yi,zi),A'3(xi,yi,zi) Constructing a fuzzy closeness matrix;
s500, obtaining a matrix eigenvalue vector w according to the fuzzy proximity matrix1(xi,yi,zi)，w2(xi,yi,zi),w3(xi,yi,zi) I.e. corresponds to attribute A'1(xi,yi,zi)，,A'2(xi,yi,zi),A'3(xi,yi,zi) The weight coefficient of (a);
s600, calculating the gas saturation Sw of the space point through weighting:
Sw(xi,yi,zi)＝w1(xi,yi,zi)*A'1(xi,yi,zi)+w2(xi,yi,zi)*A'2(xi,yi,zi)+w3(xi,yi,zi)*A'3(xi,yi,zi)。
preferably, in step S100, the method for performing sensitive elasticity parameter optimization includes:
Ksat＝Kdry+Kp，
wherein, KsatIs the bulk modulus, K, of saturated rockdryIs the bulk modulus of the dry rock skeleton; kpIs the pore bulk modulus;
wherein, KSIs the bulk modulus of the matrix, KfIs the bulk modulus of the fluid;
is the porosity; α is the Biot coefficient (between 0 and 1) defined as the ratio of the change in pore volume to the change in total volume at the same pore pressure;
will KpAs a preferred sensitive elastic parameter, based on well log data, K is establishedpRelationship to elasticity parameter:
Kp＝f(attri1,attri2,…)
wherein attri1 and attri2 are elastic fluid factors lambda rho and mu rho, KpAnd obtaining based on pre-stack seismic inversion.
A gas saturation prediction method based on pre-stack seismic data comprehensively considers the elasticity (pore volume modulus), attenuation (far offset distance high-frequency attenuation gradient attribute) and AVO characteristic (P G) difference of a rock after gas containing of a reservoir, quantitatively predicts the gas saturation based on an attribute fusion method of fuzzy logic, avoids the problem of multi-resolution in conventional gas saturation quantitative prediction based on the difference of single elastic parameters, and effectively improves the gas saturation prediction reliability.
Preferably, in step S100, the method for performing sensitive AVO attribute optimization includes:
developing three-dimensional AVO forward modeling, extracting P, G attributes, and performing combination operation on P, G attributes to obtain combination attributes, wherein the method comprises the following steps: p + G, P-G, G-P, aP + bG, P G;
analyzing the combined attribute, setting the change characteristic of the AVO attribute when the water saturation changes from 0 to 100 percent under the reservoir condition, and defining the standard deviation std as a sensitivity indicator:
and taking the combined attribute with the maximum standard deviation std as the preferred sensitive AVO attribute.
Preferably, in step S100, the method for performing sensitivity attenuation attribute optimization includes:
pre-stack gather processing and partial angle stacking;
high-precision time-frequency spectrum decomposition based on far offset seismic data;
and on a time-frequency section, the detected maximum energy frequency is used as an initial attenuation frequency, then 65% and 85% of seismic wave energy are calculated, the attenuation of frequency domain energy is fitted, and a long-offset high-frequency attenuation gradient attribute is obtained and is used as an optimal sensitive attenuation attribute.
A computer device, comprising: a processor and a memory storing computer program instructions, the processor when executing the computer program instructions implementing a method of gas saturation prediction based on pre-stack seismic data as described in any one of the preceding.
A computer storage medium having computer program instructions stored thereon which, when executed by a processor, implement a method of gas saturation prediction based on pre-stack seismic data as recited in any one of the preceding claims.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
the invention discloses a gas saturation prediction method based on prestack seismic data, which comprehensively considers the elasticity (pore volume modulus), attenuation (far offset distance high-frequency attenuation gradient attribute) and AVO characteristic (P G) difference of rocks after reservoir gas containing, quantitatively predicts the gas saturation based on an attribute fusion method of fuzzy logic, avoids the problem of multi-resolution in conventional gas saturation quantitative prediction based on the difference of single elastic parameters, and effectively improves the gas saturation prediction reliability.
Drawings
FIG. 1 is a schematic flow diagram of quantitative prediction of gas saturation in tight clastic reservoirs.
FIG. 2 is a schematic cross-sectional view of an exemplary embodiment of pore volume modulus parametric inversion.
FIG. 3 is a schematic diagram of the attribute of a preferred gas-sensitive AVO based on a "three-dimensional" AVO forward modeling in the embodiment.
Fig. 4 is a schematic cross-sectional view of a typical example of the sensitive AVO attribute P × G in the embodiment.
FIG. 5 is a diagram of the high-frequency attenuation gradient property of the far-offset seismic data in the embodiment.
FIG. 6 is a schematic diagram of the gas saturation sensitivity attribute based on fuzzy logic attribute fusion in the embodiment.
Fig. 7 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, a method for predicting gas saturation based on prestack seismic data of this embodiment includes:
(1) preference for sensitive elastic parameters
A forward simulation can be carried out based on the basic theory of rock physics to obtain a recognized conclusion that the influence of the porosity on elastic parameters such as longitudinal and transverse wave velocity, density and the like is far greater than the gas saturation. Therefore, it is critical to this step to prefer an elastic parameter that is more sensitive to gas saturation.
White rewrites a pair according to the classical Gassmann equation:
Ksat＝Kdry+Kpin which K issatIs the bulk modulus, K, of saturated rockdryIs the bulk modulus of the dry rock skeleton; kpThe pore volume modulus directly reflects the change of the volume modulus after the dry rock is saturated with fluid, and is the most direct physical quantity reflecting the change of the volume modulus of the fluid in the pores.
Based on the well log data, establishing KpRelationship to elasticity parameter:
Kp＝f(attri1,attri2,…)
here, attri1 and attri2 are generally defined as elastic fluid factors λ ρ and μ ρ commonly used by us, and other parameters such as poisson ratio, longitudinal and transverse wave impedance and the like can also be concluded according to the rock physics analysis of the actual work area logging. Because of the K itselfpThe step is easy to realize because of good relation with Sg, and the obtained functional relation fitting correlation coefficient is also high. The prestack seismic inversion method of parameters such as lambda rho, mu rho and Poisson ratio is mature, the effect is stable, and the method is not used any moreDetailed description is given. K can be obtained based on prestack seismic inversionpAs shown in fig. 2.
(2) Sensitive AVO attribute preference
The optimization of the sensitive AVO attribute needs to be combined with an actual well of an actual target area, and finally the sensitive AVO attribute is optimized through AVO forward modeling and analysis. AVO is evolving like the elasticity parameter preference, also the influence of porosity on the AVO properties has to be taken into account. Therefore, it is necessary to develop a "three-dimensional" AVO forward simulation, where the three dimensions are respectively a longitudinal direction representing a time change, an inline direction representing a porosity change with a certain step size, and an Xline direction representing a gas saturation change with a certain step size.
On the basis of obtaining the three-dimensional gather, by extracting the AVO attributes (P attribute and G attribute) and performing various combination operations on the P, G attributes: p + G, P-G, G-P, aP + bG, P × G, etc., which were further analyzed for the change in AVO attributes at varying water saturation from 0 to 100% for certain reservoir conditions (porosity 12%), defining standard deviation
For sensitivity indicators, the most variable AVO attribute is the preferred dense clastic reservoir gas-sensitive AVO attribute.
As shown in fig. 3 for standard deviations of different AVO attributes, it can be seen that P G is most sensitive to changes in gas saturation, and therefore a typical cross-section of an example work area attribute P G is shown in fig. 4.
(3) Preference for sensitive attenuation properties
Theoretical research and practical application show that in a geologic body, if pores develop and are filled with oil, gas and water (particularly for the case of gas), seismic reflection absorption is increased, high-frequency absorption attenuation is increased, and the absorption coefficient of a gas-containing stratum can be several times or even one order of magnitude higher than that of a gas-free stratum with the same lithology. Among the frequency attributes, the frequency decay gradient is an attribute that is relatively sensitive to hydrocarbon reflection. The term "frequency attenuation gradient" as used herein refers to the slope of the fit of the high-frequency-end amplitude envelope based on spectral decomposition.
Most frequency attenuation gradient attributes are obtained based on post-stack, actually, on different offset distances, due to the fact that different attenuation characteristics of propagation distances are different, practice shows that the high-frequency attenuation gradient attributes extracted based on large-angle incident seismic data can reflect the difference of oil gas occurrence characteristics better. The specific extraction technical process based on the pre-stack high-frequency attenuation gradient comprises the following steps: firstly, pre-stack gather processing and partial angle stacking are carried out; high-precision time-frequency spectrum decomposition based on far offset seismic data; thirdly, on the time-frequency section, the maximum energy frequency is used as the initial attenuation frequency, then the seismic wave energy of 65% and 85% is calculated, the attenuation of the frequency domain energy is fitted, and the high-frequency attenuation gradient of the far offset seismic data is obtained, as shown in fig. 5.
(4) Quantitative prediction of gas saturation
And (3) comprehensively considering the elasticity, attenuation and AVO characteristic difference of the rock after the gas is contained in the reservoir, and quantitatively predicting the gas saturation based on an attribute fusion method of fuzzy logic.
A multi-attribute fusion method based on fuzzy logic is characterized in that a fuzzy theory is applied to attribute fusion to obtain a comprehensive quantitative attribute, and the comprehensive attribute can describe underground reservoir information more accurately than a single attribute. The method accurately describes the relationship between elements and fuzzy sets by means of the concept of 'membership degree', simulates human brain to implement rule-type reasoning, and solves various uncertain problems caused by logic deficiency of 'rule of arranging'. When the membership degrees obtained by the attributes are fused, a fusion algorithm is completely established on the basis of the existing data, the information implicit in the data is fully mined, the calculation of the weights of the attributes is completely based on data driving, and the subjectivity of manual control is reduced. Fuzzy logic and information fusion are combined and applied, the problem of inaccurate description can be solved, information can be merged in a self-adaptive mode, and therefore the gas saturation prediction accuracy is improved. Specifically, first, the attribute set { A } is expressed as an attribute set according to the preferred three attributes1(x,y,t),A2(x,y,t),A3(x, y, t) }, respectively counting the relationship between each attribute and the gas saturation based on the logging data, and Sw ═ f1(A1)Sw＝f2(A2)Sw＝f3(A3) Function f1,f2,f3It may be a linear function or a non-linear correlation function. The normalization is performed separately according to the correlation functions,
get normalized sensitivity attribute { A'1(x,y,z)，,A'2(x,y,z),A'3(x,y,z)}；
Then, based on the normalized attribute set { A'1(xi,yi,zi)，,A'2(xi,yi,zi),A'3(xi,yi,zi) Constructing a fuzzy proximity matrix for any point in the three-dimensional space,
matrix eigenvalue vector { w is solved1(xi,yi,zi)，w2(xi,yi,zi),w3(xi,yi,zi) Is corresponding attribute { A'1(xi,yi,zi)，,A'2(xi,yi,zi),A'3(xi,yi,zi) The weighting coefficients of { C };
finally, the gas saturation attribute is obtained through weighting
Sw(xi,yi,zi)＝w1(xi,yi,zi)*A'1(xi,yi,zi)+w2(xi,yi,zi)*A'2(xi,yi,zi)+w3(xi,yi,zi)*A'3(xi,yi,zi)
According to the above method, the fusion result of the entire target layer can be obtained. The fusion result represents the common change of multiple attribute parameters and has the advantages of various attributes, so that the multi-solution of single attribute prediction is reduced, and finally the comprehensive prediction of the attributes based on multi-information combination is realized.
A multi-attribute fusion method based on fuzzy logic, namely applying fuzzy theory to attribute fusion to obtain a methodComprehensive quantitative attributes that describe subsurface reservoir information more accurately than a single attribute can. The method accurately describes the relationship between elements and fuzzy sets by means of the concept of 'membership degree', simulates human brain to implement rule-type reasoning, and solves various uncertain problems caused by logic deficiency of 'rule of arranging'. When the membership degrees obtained by the attributes are fused, a fusion algorithm is completely established on the basis of the existing data, the information implicit in the data is fully mined, the calculation of the weights of the attributes is completely based on data driving, and the subjectivity of manual control is reduced. Fuzzy logic and information fusion are combined and applied, the problem of inaccurate description can be solved, information can be merged in a self-adaptive mode, and therefore the gas saturation prediction accuracy is improved. FIG. 6 shows three properties (pore bulk modulus K) preferred abovepP × G, far offset high frequency attenuation gradient attribute) by an attribute fusion method of fuzzy logic.
As shown in fig. 7, an electronic device (e.g., a computer server with program execution functionality) according to an exemplary embodiment of the present invention includes at least one processor, a power supply, and a memory and an input-output interface communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method disclosed in any one of the preceding embodiments; the input and output interface can comprise a display, a keyboard, a mouse and a USB interface and is used for inputting and outputting data; the power supply is used for supplying electric energy to the electronic equipment.
Those skilled in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.
When the integrated unit of the present invention is implemented in the form of a software functional unit and sold or used as a separate product, it may also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (6)
1. A method for predicting gas saturation based on pre-stack seismic data is characterized by comprising the following steps:
s100, sensitive elasticity parameter optimization is carried out; carrying out sensitive AVO attribute optimization; performing sensitive attenuation attribute optimization;
preferred sensitive elastic parameter A1Sensitive AVO Attribute A2Sensitive attenuation Property A3Is denoted as attribute set { A }1(x,y,t),A2(x,y,t),A3(x,y,t)}；
S200, respectively counting correlation functions of each attribute and the gas saturation Sw based on the logging data, namely:
Sw＝f1(A1) Sw＝f2(A2) Sw＝f3(A3)；
s300, respectively normalizing according to the related functions to obtain normalized attribute sets { A'1(x,y,z)，,A′2(x,y,z),A′3(x,y,z)}；
S400, any space point of the three-dimensional space is subjected to attribute set { A) based on normalization′1(xi,yi,zi)，,A′2(xi,yi,zi),A′3(xi,yi,zi) Constructing a fuzzy closeness matrix;
s500, obtaining a matrix eigenvalue vector w according to the fuzzy proximity matrix1(xi,yi,zi)，w2(xi,yi,zi),w3(xi,yi,zi) I.e. corresponds to attribute A'1(xi,yi,zi)，,A′2(xi,yi,zi),A′3(xi,yi,zi) The weight coefficient of (a);
s600, calculating the gas saturation Sw of the space point through weighting:
Sw(xi,yi,zi)＝w1(xi,yi,zi)*A′1(xi,yi,zi)+w2(xi,yi,zi)*A′2(xi,yi,zi)+w3(xi,yi,zi)*A′3(xi,yi,zi)。
2. the method for predicting gas saturation based on prestack seismic data as claimed in claim 1, wherein in the step S100, the method for performing sensitivity elastic parameter optimization comprises:
Ksat＝Kdry+Kp，
wherein, KsatIs the bulk modulus, K, of saturated rockdryIs the bulk modulus of the dry rock skeleton; kpIs the pore bulk modulus;
wherein, KSIs a matrixBulk modulus, KfIs the bulk modulus of the fluid;
is the porosity; α is the Biot coefficient (between 0 and 1) defined as the ratio of the change in pore volume to the change in total volume at the same pore pressure;
will KpAs a preferred sensitive elastic parameter, based on well log data, K is establishedpRelationship to elasticity parameter:
Kp＝f(attri1,attri2,…)
wherein attri1 and attri2 are elastic fluid factors lambda rho and mu rho, KpAnd obtaining based on pre-stack seismic inversion.
3. The method for predicting gas saturation based on prestack seismic data as claimed in claim 1, wherein in the step S100, the method for performing sensitive AVO attribute optimization comprises:
developing three-dimensional AVO forward modeling, extracting P, G attributes, and performing combination operation on P, G attributes to obtain combination attributes, wherein the method comprises the following steps: p + G, P-G, G-P, aP + bG, P G;
analyzing the combined attribute, setting the change characteristic of the AVO attribute when the water saturation changes from 0 to 100 percent under the reservoir condition, and defining the standard deviation std as a sensitivity indicator:
and taking the combined attribute with the maximum standard deviation std as the preferred sensitive AVO attribute.
4. The method for predicting gas saturation based on prestack seismic data as claimed in claim 1, wherein in the step S100, the method for performing sensitivity attenuation attribute optimization comprises:
pre-stack gather processing and partial angle stacking;
high-precision time-frequency spectrum decomposition based on far offset seismic data;
and on a time-frequency section, the detected maximum energy frequency is used as an initial attenuation frequency, then 65% and 85% of seismic wave energy are calculated, the attenuation of frequency domain energy is fitted, and a long-offset high-frequency attenuation gradient attribute is obtained and is used as an optimal sensitive attenuation attribute.
5. A computer device, comprising: a processor and a memory storing computer program instructions, the processor when executing the computer program instructions implementing a method of gas saturation prediction based on pre-stack seismic data as described in any of 1-4 above.
6. A computer storage medium having computer program instructions stored thereon that, when executed by a processor, implement a method for gas saturation prediction based on pre-stack seismic data as recited in any of 1-4 above.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114966851A (en) * | 2022-05-13 | 2022-08-30 | 广州海洋地质调查局 | Reservoir stratum prediction method and device and storage medium |
| WO2024067835A1 (en) * | 2022-09-30 | 2024-04-04 | 中国石油化工股份有限公司 | Gas-potential prediction method and apparatus based on dominant incident angle and frequency double-domain attenuation |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114966851A (en) * | 2022-05-13 | 2022-08-30 | 广州海洋地质调查局 | Reservoir stratum prediction method and device and storage medium |
| CN114966851B (en) * | 2022-05-13 | 2023-05-05 | 广州海洋地质调查局 | Reservoir prediction method, device and storage medium |
| WO2024067835A1 (en) * | 2022-09-30 | 2024-04-04 | 中国石油化工股份有限公司 | Gas-potential prediction method and apparatus based on dominant incident angle and frequency double-domain attenuation |
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