CN112305602B - Carbonate reservoir prediction method based on prestack multi-attribute and ancient landform fusion technology - Google Patents
Carbonate reservoir prediction method based on prestack multi-attribute and ancient landform fusion technology Download PDFInfo
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Abstract
The invention provides a carbonate reservoir prediction method based on a prestack multi-attribute and ancient landform fusion technology. The method comprises the following steps: 1) Performing petrophysical analysis on the region where the interval to be predicted is located based on the drilled well data, and determining a prestack elastic parameter sensitive to the carbonate reservoir; 2) Determining a relational expression between longitudinal wave velocity and transverse wave velocity and a relational expression between density and longitudinal wave velocity respectively based on the drilled well data for the interval to be predicted, and then performing prestack AVO attribute inversion calculation to obtain specific values of each prestack elastic parameter which is sensitive to the carbonate reservoir; 3) Respectively determining the current structural form and the ancient landform aiming at the intervals to be predicted; 4) And respectively carrying out data normalization and fusion processing on the current structural form, the ancient landform and each prestack elastic parameter obtained by inverting the determined AVO attribute before the prestack aiming at the interval to be predicted, and predicting the carbonate reservoir by using the fused result.
Description
Technical Field
The invention belongs to the field of oil exploration, relates to a carbonate reservoir prediction technology in the field of seismic data interpretation in oil exploration, and particularly relates to a carbonate reservoir prediction method based on a prestack multi-attribute and ancient landform fusion technology, in particular to a technology for predicting a carbonate reservoir by using the seismic data interpretation multi-attribute fusion technology.
Background
Various geophysical techniques have emerged in carbonate reservoir prediction, including post-stack amplitude attribute analysis, pre-stack AVO analysis, geophysical inversion, waveform classification, spectral analysis, three-dimensional coherence analysis, and the like. In the 70's of the 20 th century, ostrander proposed predicting reservoirs by applying AVO analysis technology of ' bright spots ' and reflection coefficients varying with incident angles, and it was based on the Zoeppritz equation of reflection and projection of seismic waves on a horizontal interface established in 1919, but the equation was complex in analysis and difficult to solve practical problems, and later Aki Richards, shuy, fatti et al used simplified analysis of the equation for practical seismic data interpretation (left-hand Ping, lu-Fu, van-national Chapter, etc.. Application of AVO technology in Gagla Bay air-bearing detection. Oil geophysical exploration, 2011,46 (1): 60-66. Reynaud, wang, liu-Fang, etc. application of prestack AVO inversion technology in carbonate reservoir oil-gas-bearing prediction in the southwestern region. Engineering geophysical report, 2018,15 (3): 292. Over decades of development, this technology has become an important reservoir and fluid prediction method in seismic exploration. In addition, the prestack elastic parameter research and prestack elastic inversion provide rich information and provide more reliable basis for lithology and fluid identification. The AVO technology is applied to describe the carbonate reservoir with certain success, the porosity and the gas content are important parameters of the carbonate reservoir, and the AVO is the comprehensive response of the speed, the porosity and the gas content (Lijianhua, liubaihong, zhang Yangqing, and the like. The application of prestack AVO inversion in the prediction of the oil and gas content of the reservoir. The petroleum geophysical exploration 2016,51 (6): 1180-1187). Geophysical inversion techniques, such as wave impedance inversion, prestack elastic inversion and the like, can effectively determine the spatial distribution rule of the carbonate reservoir by means of high resolution and high precision of logging data, and improve the reliability of carbonate reservoir prediction.
However, with the continuous development of exploration, the carbonate reservoir is predicted by purely depending on the amplitude and the reservoir elastic parameters, and the limitation is gradually exposed in the practical application. Carbonate reservoirs are various in types, strong in heterogeneity, rapid in reservoir transverse change, unobvious in seismic amplitude response characteristics and elastic parameter abnormity, the conventional method has multiple resolvability, particularly, the inversion reliability is greatly reduced in areas with few drilled wells, and the reservoir prediction accuracy is influenced. In practical application, the carbonate reservoir amplitude is large in transverse change, the change rule of the amplitude along with the offset is influenced, and in addition, due to the limitation of the buried depth and application conditions, the application effect in the middle-deep stratum and the area with large structural fluctuation is not good. The main information of seismic inversion comes from earthquake, and due to the influence of high density and high speed of a carbonate reservoir, reflection is weak in places mixed with other lithologies, the reflection exists in the situation of large difference of actual geological features, and in addition, the number of general well drilling in an exploration block is small, so that the effect of the inversion technology in carbonate reservoir prediction is not ideal frequently.
However, the requirement on the carbonate reservoir prediction precision is higher and higher at present, only the high-precision carbonate reservoir prediction can effectively realize the carbonate exploration target evaluation and well position demonstration, and particularly in the ocean deep water carbonate reservoir oil and gas exploration, the high-precision carbonate reservoir prediction has great significance.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a carbonate reservoir prediction method with higher precision and higher efficiency. The prediction method is based on the fusion technology of the prestack multi-attribute and the ancient landform, improves the accuracy and efficiency of carbonate reservoir prediction, and reduces the multi-solution property and uncertainty.
In order to achieve the purpose, the invention provides a carbonate reservoir prediction method based on a prestack multi-attribute and ancient landform fusion technology, wherein the method comprises the following steps:
1) Performing petrophysical analysis on the region where the interval to be predicted is located based on drilled data, and determining prestack elastic parameters sensitive to the carbonate reservoir, namely judging which types of prestack elastic parameters have good differentiation on the carbonate reservoir and a non-reservoir by means of intersection analysis of different elastic parameters and porosity or oil-gas content and the like;
2) Determining a relational expression between longitudinal wave velocity and transverse wave velocity and a relational expression between carbonate rock density and longitudinal wave velocity respectively based on drilled well data for an interval to be predicted, and then performing prestack AVO attribute inversion calculation to obtain a specific numerical value of the prestack elastic parameter sensitive to the carbonate rock reservoir determined in the step 1);
3) Respectively performing construction interpretation on the intervals to be predicted to determine the current construction form and performing ancient landform restoration;
4) And (3) aiming at the interval to be predicted, respectively carrying out data normalization and fusion processing on the current structural form and the restored ancient landform determined in the step 3) and each prestack elastic parameter obtained by inverting the prestack AVO attribute determined in the step 2), and predicting the carbonate reservoir by using the fused result.
In the method for predicting the carbonate reservoir, the interval to be predicted can be one interval or a plurality of intervals; when the interval to be predicted is multiple intervals, carbonate reservoir prediction is carried out on each interval to be predicted respectively, namely after the step 1) is finished, the operations related to the steps 2), 3) and 4) are carried out on each interval to be predicted respectively.
In the carbonate reservoir prediction method, the current structural form and the restored ancient landform determined in the step 3) and the prestack elastic parameters obtained by inverting the prestack AVO attributes determined in the step 2) are subjected to data normalization and fusion processing in the step 4), and the carbonate reservoir prediction is performed by using the fused results. For each interval to be predicted, respectively carrying out data normalization processing on the current structural form and the restored paleotopographic determined in the step 3) and each pre-stack elastic parameter (which is determined in the step 1) and is sensitive to the carbonate reservoir) to obtain dimensionless values in the same range, fusing the normalized current structural form data, the restored paleotopographic data and the pre-stack elastic parameter data, and finally predicting the carbonate reservoir by using the fused result.
In the method for predicting a carbonate reservoir, preferably, the prestack elastic parameters in the step 1) include one or a combination of more than two of longitudinal and transverse wave impedance change rates, longitudinal and transverse wave velocity change rates, poisson ratio change rates, fluid factors, lambda · ρ, and mu · ρ; more preferably, the prestack elastic parameters in the step 1) comprise one or more of shear wave impedance, longitudinal wave impedance and μ · ρ; wherein lambda is the first parameter of Lam, mu is the second parameter of Lam, namely the shear modulus, and rho is the density (g/cm) of the carbonate rock 3 )。
In the method for predicting the carbonate reservoir, the relational expression between the longitudinal wave velocity and the transverse wave velocity can be implemented by adopting a conventional method in the field; preferably, the relation between the longitudinal wave velocity and the transverse wave velocity is determined by logging information; specifically, the relationship between the longitudinal wave velocity and the transverse wave velocity is: vs = m · Vp-n, i.e. shear velocity equals m times longitudinal velocity minus n; wherein Vs is transverse wave velocity (m/s), vp is longitudinal wave velocity (m/s), m and n are parameters in the relational expression, and m and n are obtained by fitting according to logging data.
In the method for predicting the carbonate reservoir, the relational expression between the carbonate rock density and the longitudinal wave velocity can be implemented by adopting a conventional method in the field; preferably, the relation between the carbonate rock density and the longitudinal wave velocity is determined by logging information; specifically, the relation between the carbonate rock density and the longitudinal wave velocity is as follows: log (ρ) = j · Log (Vp) -k,wherein rho is the density (g/cm) of carbonate rock 3 ) Vp is the longitudinal wave velocity (m/s), j and k are parameters in the relational expression, and j and k are obtained by fitting according to logging data.
In the method for predicting the carbonate reservoir, the method for performing pre-stack AVO attribute inversion in step 2) may be: and calculating pre-stack elasticity parameters by using a Shuey formula and/or an Aki Richards formula based on the pre-stack CRP gather according to the determined relational expression between the longitudinal wave velocity and the transverse wave velocity and the relational expression between the carbonate rock density and the longitudinal wave velocity, so as to realize pre-stack AVO attribute inversion. The Shuey formula comprises an original Shuey formula and an approximate formula of the original Shuey formula, and the Aki Richards formula comprises an original Aki Richards formula and an approximate formula of the original Aki Richards formula. The Aki Richards formula is usually used to calculate the longitudinal and transverse wave impedance, the longitudinal and transverse wave velocity change rate, the Poisson ratio change rate, the fluid factor and other properties. Properties such as poisson ratio change rate, λ · ρ, μ · ρ, etc. can be calculated generally using Shuey's formula. Calculating the prestack AVO attribute using Shuey's formula and/or Aki Richards' formula based on the prestack CRP gather according to the determined relationship between longitudinal and transverse wave velocities, and the relationship between carbonate rock density and longitudinal wave velocity may be, but is not limited to, using conventional methods in the art. In one embodiment, the Shuey formula is R (θ) = P + G · sin 2 θ, where R (θ) is the longitudinal wave reflection coefficient (dimensionless), θ is the incident angle, P is the intercept (dimensionless), and G is the gradient, i.e., slope (dimensionless). In another embodiment, the Aki Richards formula is
Wherein R (theta) is a longitudinal wave reflection coefficient, theta is an incident angle, vs is a transverse wave velocity, vp is a longitudinal wave velocity, and rho is a carbonate rock density.
In the above carbonate reservoir prediction method, it may be, but is not limited to, a conventional normalization processing method. Preferably, the normalization process in step 4) is performed by a dispersion normalization method, such that the result value is mapped to [0-1 ]]In the meantime. The transfer function is as follows:
wherein max is the maximum value of the sample data, min is the minimum value of the sample data, f is the sample data, and f is the normalized sample data.
In the above method for predicting a carbonate reservoir, preferably, in step 4), the calculation method of the fusion process is:
wherein F (i) represents the fused attribute value; x (i) represents the value of i point on the attribute plane before the stack after the normalization processing, X (Min) represents the minimum value on the attribute plane before the stack after the normalization processing, and X (Max) represents the maximum value on the attribute plane before the stack after the normalization processing; y (i) represents the value of the point i on the ancient geomorphic attribute plane after normalization processing, Y (Min) represents the minimum value on the ancient geomorphic attribute plane after normalization processing, and Y (Max) represents the maximum value on the ancient geomorphic attribute plane after normalization processing; z (i) represents the value of the point i on the current structure attribute plane after normalization processing, Z (Min) represents the minimum value of the current structure attribute after normalization processing, and Z (Max) represents the maximum value of the current structure attribute after normalization processing; in the formula, alpha, beta and delta are weight coefficients of different attributes after normalization processing, and are selected according to the weight occupied by the different attributes in carbonate reservoir prediction, preferably, alpha is more than or equal to 1 and is more than or equal to 0, beta is more than or equal to 1 and is more than or equal to 0, delta is more than or equal to 1 and is more than or equal to 0, and alpha + beta + delta =1; a. b and c are index influence factors with different attributes after being distributed according to the weight, the values of a, b and c are determined according to the influence degrees of the different attributes on the reservoir, preferably, a is more than or equal to 3 and more than or equal to 1, b is more than or equal to 3 and more than or equal to 1, and c is more than or equal to 3 and more than or equal to 1. The values of alpha, beta, delta, a, b and c can be obtained by fitting according to the carbonate reservoir development area determined by the drilled well data; in practical application, the parameters can be debugged for many times by combining the carbonate rock reservoir development area determined by drilling and petroleum geological conditions to achieve the best fusion effect, namely, the parameters are adjusted and determined by experimental study on the known carbonate rock development area to be consistent with the known condition, and then the carbonate rock reservoir prediction is carried out on other non-drilling areas.
In the above method for predicting a carbonate reservoir, it is preferable that the method for predicting a carbonate reservoir using the fused result is such that whether or not the point i is a carbonate reservoir is determined using the value of F (i), and the larger the numerical value of F (i), the higher the possibility that the point i is a carbonate reservoir. More preferably, the threshold value F for judging the carbonate reservoir is determined by utilizing the drilled data, the petroleum and geological comprehensive condition analysis and combining the F (i) value obtained by calculation, when the F (i) is more than or equal to F, the point i is the carbonate reservoir, and when the F (i) is less than F, the point i is not the carbonate reservoir.
The invention provides a carbonate reservoir prediction method based on AVO multi-attribute and ancient landform fusion technology, which can be suitable for carbonate reservoir prediction in areas with complex structures, takes the reservoir development condition and oil and gas migration and accumulation into consideration, and can improve the carbonate reservoir prediction quality in the structural development areas. Compared with the prior art, the technical scheme provided by the invention has the following advantages:
1) The prediction method provided by the invention has higher prediction precision;
2) The prediction method provided by the invention is simple and convenient, is beneficial to realizing high-efficiency prediction, and has a good application prospect.
Drawings
FIG. 1A is a cross-sectional view of longitudinal wave impedance and porosity.
FIG. 1B is a cross-sectional view of transverse wave impedance and porosity.
FIG. 1C is a mu. Rho and porosity intersection.
FIG. 2A is a cross-sectional view of the velocity of a longitudinal wave and a transverse wave.
FIG. 2B is a cross plot of longitudinal wave velocity and density.
FIG. 3 is a flow chart of an AVO multi-attribute and ancient landform fusion technique.
FIG. 4A is a current configuration diagram before normalization processing.
Fig. 4B is a graph of ancient structures before normalization.
Fig. 4C is a graph of longitudinal wave impedance before normalization processing.
Fig. 4D is a graph of the transverse wave impedance before normalization processing.
Fig. 4E is a μ · ρ property map before normalization processing.
FIG. 5A is a current configuration diagram after normalization processing.
Fig. 5B is a ancient structural diagram after the normalization processing.
Fig. 5C is a graph of the longitudinal wave impedance after the normalization process.
Fig. 5D is a graph of the transverse wave impedance after the normalization process.
Fig. 5E is a μ · ρ property map after normalization processing.
Fig. 6A is a plan view of the ancient structure, the current structure, and the normalized fusion of the longitudinal wave impedance.
Fig. 6B is a plan view of the ancient structure, the current structure, and the shear wave impedance normalized and fused.
Fig. 6C is a plan view of the ancient and present structures fused with μ · ρ normalization.
Detailed Description
The technical solutions of the present invention will be described in detail below in order to clearly understand the technical features, objects, and advantages of the present invention, but the present invention is not limited to the practical scope of the present invention.
Example 1
The embodiment provides a carbonate reservoir prediction method based on a prestack multi-attribute and ancient landform fusion technology (refer to a technical process in a figure 3), wherein an area to be predicted is a chalk carbonate rock stratum of a santos basin in a brazil sea area, and the specific process is as follows:
1) Performing petrophysical analysis on the region where the interval to be predicted is located based on the drilled well data, and determining prestack elastic parameters sensitive to the carbonate reservoir;
petrophysical analysis is shown in fig. 1A to fig. 1C (fig. 1A, fig. 1B, and fig. 1C are intersection graphs of compressional wave impedance, shear wave impedance, μ · ρ, and porosity, respectively), and it can be seen from the graphs that carbonate reservoirs generally exhibit high impedance and non-reservoirs exhibit low impedance. Longitudinal wave impedance, transverse wave impedance and mu rho elastic parameters are distinguished from the carbonate reservoir, and the longitudinal wave impedance, the transverse wave impedance and the mu rho are determined as prestack elastic parameters sensitive to the carbonate reservoir; however, it can be seen from fig. 1A to 1C that there are still some reservoirs difficult to identify only by means of shear wave impedance, longitudinal wave impedance and μ · ρ, i.e. there is still some ambiguity in identifying carbonate rock only by means of AVO method. Further analysis shows that the low-impedance carbonate reservoir is mainly located at a position with relatively high structure and ancient landform, namely the distribution of the carbonate reservoir is obviously controlled by the ancient landform, and in addition, the accumulation of hydrocarbons in the high-quality carbonate reservoir is influenced by the current structure.
2) Determining a relational expression (see figure 2A) between longitudinal wave velocity and transverse wave velocity and a relational expression (see figure 2B) between carbonate rock density and longitudinal wave velocity based on drilled well data for an interval to be predicted, and then performing prestack AVO attribute inversion calculation to obtain specific values of each prestack elastic parameter sensitive to the carbonate reservoir, determined in the step 1);
the relation between the longitudinal wave velocity and the transverse wave velocity is as follows: vs = m · Vp-n; wherein Vs is transverse wave velocity (m/s), vp is longitudinal wave velocity (m/s), m and n are parameters in a relational expression, and m and n are obtained by fitting according to logging data; the obtained relational expression is concretely Vs =0.547Vp-23;
the relationship between carbonate rock density and longitudinal wave velocity is: log (ρ) = j.log (Vp) -k, wherein ρ is carbonate rock density (g/cm) 3 ) Vp is the longitudinal wave velocity (m/s), j and k are parameters in the relational expression, and j and k are obtained by fitting according to logging data; the obtained relational expression is concretely Log (rho) =0.259Log (Vp) -0.539;
the method for performing pre-stack AVO attribute inversion comprises the following steps: calculating each pre-stack elastic parameter determined in the step 1) by using a Shuey formula and/or an Aki Richards formula based on a pre-stack CRP gather according to a relation between the determined longitudinal wave velocity and the transverse wave velocity and a relation between the carbonate rock density and the longitudinal wave velocity, and realizing pre-stack AVO attribute inversion; wherein, the longitudinal wave impedance and the transverse wave impedance are calculated by utilizing an Aki Richards formula, and the mu.rho is calculated by utilizing a Shuey formula; wherein the Shuey formula is R (theta) = P + G.sin 2 θ, where R (θ) is the longitudinal reflection coefficient, θ is the angle of incidence, P is the intercept, G is the gradient, and the Aki Richards formula is
Wherein R (theta) is a longitudinal wave reflection coefficient, theta is an incident angle, vs is a transverse wave velocity, vp is a longitudinal wave velocity, and rho is carbonate rock density; the calculated longitudinal wave impedance, transverse wave impedance and μ · ρ are shown in plan view in fig. 4C to 4E. Fig. 4C is a longitudinal wave impedance plane view, fig. 4D is a transverse wave impedance plane view, and fig. 4E is a μ · ρ plane view.
3) For the interval to be predicted, performing structure interpretation to determine the current structure form and performing ancient landform restoration, where fig. 4A and 4B are the current structure form and a plan view of the ancient landform before normalization (fig. 4A is a current structure plan view, and fig. 4B is an ancient landform (i.e., ancient structure) plan view).
4) For the interval to be predicted, respectively carrying out data normalization and fusion treatment on the current structural form and the restored ancient landform determined in the step 3) and longitudinal wave impedance, transverse wave impedance and mu & rho obtained by inverting the pre-stack AVO attribute determined in the step 2), and predicting the carbonate reservoir by using the fused result;
wherein the normalization process is performed by a dispersion normalization method: the current structure, ancient landform, longitudinal wave impedance, transverse wave impedance, and mu.p value are converted by a conversion function
Mapping to [0-1]Wherein max is the maximum value of the sample data, min is the minimum value of the sample data, f is the sample data, and f is the normalized sample data; the plan views of the normalized current structure form, ancient landform, longitudinal wave impedance, transverse wave impedance, and μ · ρ are shown in fig. 5A to 5E, where fig. 5A is the current structure plan view, fig. 5B is the ancient structure plan view, fig. 5C is the longitudinal wave impedance plan view, fig. 5D is the transverse wave impedance plan view, and fig. 5E is the μ · ρ plan view;
the calculation method of the fusion processing comprises the following steps:
wherein F (i) represents the fused attribute value; x (i) represents the value of i point on the attribute plane before the stack after the normalization processing, X (Min) represents the minimum value on the attribute plane before the stack after the normalization processing, and X (Max) represents the maximum value on the attribute plane before the stack after the normalization processing; y (i) represents the value of the point i on the ancient geomorphic attribute plane after normalization processing, Y (Min) represents the minimum value on the ancient geomorphic attribute plane after normalization processing, and Y (Max) represents the maximum value on the ancient geomorphic attribute plane after normalization processing; z (i) represents the value of the i point on the current structure attribute plane after normalization processing, Z (Min) represents the minimum value of the current structure attribute after normalization processing, and Z (Max) represents the maximum value of the current structure attribute after normalization processing; in the formula, alpha, beta and delta are weight coefficients of different attributes after normalization processing, and a number between 0 and 1 is selected according to the weight occupied by the different attributes in carbonate reservoir prediction, so that alpha + beta + delta =1; a. b and c are index influence factors of different attributes after being distributed according to the weight, and any number between 1 and 3 is distributed according to the influence degree of the different attributes on the reservoir; in this embodiment, the values of α, β, and δ are 0.2, and 0.6, respectively, and the values of a, b, and c are 1,2, and 1, respectively. The result of the fusion processing is shown in fig. 6A to 6C, in which fig. 6A is a plan view of the ancient structure, the present structure, and the longitudinal wave impedance fused together, fig. 6B is a plan view of the ancient structure, the present structure, and the transverse wave impedance fused together, and fig. 6C is a plan view of the ancient structure, the present structure, and μ · ρ fused together.
And judging whether the point i is a carbonate reservoir or not by using the value of F (i) obtained by fusion processing, wherein the larger the value of the F (i) is, the higher the possibility that the point i is the carbonate reservoir is, determining a threshold value F for judging the carbonate reservoir by using the drilled well data and the F (i) value obtained by calculation, wherein when the F (i) is not less than F, the point is the carbonate reservoir, and when the F (i) is less than F, the point is not the carbonate reservoir. In this embodiment, the analysis of the drilled well information and the petroleum geological conditions is combined, the selected paleo-structure, the current structure and the longitudinal wave impedance are fused to form an F-threshold of 0.7, the paleo-structure, the current structure and the transverse wave impedance are fused to form an F-threshold of 0.75, and the paleo-structure, the current structure and the μ · ρ are fused to form an F-threshold of 0.65.
As can be seen from comparison of the attribute diagrams of FIGS. 5C, 5D and 5E with the attribute diagrams of FIGS. 6A, 6B and 6C, the attribute diagrams of FIGS. 6A, 6B and 6C are obviously improved compared with the attribute diagrams of FIGS. 5C, 5D and 5E, the reservoir and non-reservoir are more obviously differentiated, and favorable carbonate development areas are indicated, and the method effectively predicts the carbonate reservoir distribution and guides the oil and gas exploration of the research area.
Claims (12)
1. A carbonate reservoir prediction method based on a prestack multi-attribute and ancient landform fusion technology is disclosed, wherein the method comprises the following steps:
1) Performing petrophysical analysis on the region where the interval to be predicted is located based on the drilled well data, and determining prestack elastic parameters sensitive to the carbonate reservoir;
2) Determining a relational expression between longitudinal wave velocity and transverse wave velocity and a relational expression between carbonate rock density and longitudinal wave velocity respectively based on drilled well data for an interval to be predicted, and then performing pre-stack AVO attribute inversion calculation to obtain specific values of the pre-stack elastic parameters sensitive to the carbonate reservoir determined in the step 1);
3) Respectively performing construction interpretation on the intervals to be predicted to determine the current construction form and performing ancient landform restoration;
4) Respectively carrying out data normalization and fusion processing on the current structural form and the restored ancient landform determined in the step 3) and each prestack elastic parameter obtained by inverting the prestack AVO attribute determined in the step 2) aiming at the interval to be predicted, and predicting the carbonate reservoir by using the fused result;
in step 4), the calculation method of the fusion process is as follows:
wherein F (i) represents the fused attribute value; x (i) represents the value of i point on the elastic parameter plane before the stack after the normalization processing, X (Min) represents the minimum value on the elastic parameter plane before the stack after the normalization processing, and X (Max) represents the maximum value on the elastic parameter plane before the stack after the normalization processing; y (i) represents the value of the point i on the ancient geomorphic attribute plane after normalization processing, Y (Min) represents the minimum value on the ancient geomorphic attribute plane after normalization processing, and Y (Max) represents the maximum value on the ancient geomorphic attribute plane after normalization processing; z (i) represents the value of the point i on the current structural form plane after normalization processing, Z (Min) represents the minimum value of the current structural form after normalization processing, and Z (Max) represents the maximum value of the current structural form after normalization processing; alpha, beta and delta are weight coefficients of different attributes after normalization processing, 1 is more than or equal to alpha and more than or equal to 0,1 is more than or equal to beta and more than or equal to 0,1 is more than or equal to delta and more than or equal to 0, and alpha + beta + delta =1; a. b and c are index influence factors with different attributes after being distributed according to the weight, wherein a is more than or equal to 3 and is more than or equal to 1, b is more than or equal to 3 and is more than or equal to 1, and c is more than or equal to 3 and is more than or equal to 1.
2. The prediction method according to claim 1, wherein the prestack elastic parameters in step 1) include one or more of longitudinal and transverse wave impedance change rate, longitudinal and transverse wave velocity change rate, poisson's ratio change rate, fluid factor, λ ρ, μ ρ; wherein lambda is the first parameter of Lam é, mu is the second parameter of Lam, namely the shear modulus, and rho is the carbonate rock density.
3. The prediction method according to claim 1, wherein the prestack elastic parameters of step 1) include one or a combination of two or more of transverse wave impedance, longitudinal wave impedance, μ · ρ; wherein mu is the second parameter of Lame, namely the shear modulus, and rho is the density of the carbonate rock.
4. The prediction method according to claim 1,
determining the relation between the longitudinal wave velocity and the transverse wave velocity through logging information; the relation between the longitudinal wave velocity and the transverse wave velocity is as follows: vs = m · V p -n; wherein Vs is the transverse wave velocity, m/s; vp is the longitudinal wave velocity, m/s; m and n are parameters in the relational expression and are obtained by fitting according to logging information;
the density and longitudinal wave velocity of the carbonate rockThe relation between the two is determined by logging information; the relation between the carbonate rock density and the longitudinal wave velocity is as follows: log (ρ) = j · Log (Vp) -k; wherein rho is the density of carbonate rock in g/cm 3 (ii) a Vp is the longitudinal wave velocity, m/s; j. k is a parameter in the relational expression, and j and k are obtained by fitting according to logging data.
5. The prediction method according to claim 1, wherein the method of performing pre-stack AVO attribute inversion of step 2) is: and calculating pre-stack elasticity parameters by using a Shuey formula and/or an Aki Richards formula based on the pre-stack CRP gather according to the determined relational expression between the longitudinal wave velocity and the transverse wave velocity and the relational expression between the carbonate rock density and the longitudinal wave velocity, so as to realize pre-stack AVO attribute inversion.
6. The prediction method of claim 5, wherein the Shuey formula is R (θ) = P + G sin 2 And theta, wherein R (theta) is the longitudinal wave reflection coefficient, theta is the incident angle, P is the intercept, and G is the gradient.
7. The prediction method of claim 5, wherein the Aki Richards formula is
Wherein R (theta) is a longitudinal wave reflection coefficient, theta is an incident angle, vs is a transverse wave velocity, vp is a longitudinal wave velocity, and rho is a carbonate rock density.
8. The prediction method according to claim 1, wherein the data normalization process in step 4) is performed by a dispersion normalization method, and the result value is mapped to [0-1 ]]In the middle of; the transfer function is as follows:
wherein max is the maximum value of the sample data, min is the minimum value of the sample data, f is the sample data, and f is the sample data after data normalization.
9. The prediction method of claim 1, wherein the values of α, β, δ and a, b, c are fitted according to the carbonate reservoir development zone determined from the well data.
10. The prediction method of claim 1, wherein the values of α, β, δ are 0.2,0.6, respectively; a. the values of b and c are respectively 1,2,1.
11. The prediction method according to claim 1, wherein the carbonate reservoir prediction is performed using the fused result by determining whether or not the point i is a carbonate reservoir using a value of F (i), and the greater the value of F (i), the greater the possibility that the point i is a carbonate reservoir.
12. The prediction method of claim 11, wherein the threshold F for the carbonate reservoir determination is determined using the well data in combination with the calculated F (i) value, wherein when F (i) ≧ F, point i is the carbonate reservoir, and when F (i) < F, point i is not the carbonate reservoir.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103488971A (en) * | 2013-09-06 | 2014-01-01 | 电子科技大学 | Method for identifying geometrical morphology of organic reef storage layer |
| CN103527184A (en) * | 2013-10-28 | 2014-01-22 | 北京大学 | Method and system for predicting dolomite reservoir |
| WO2017024702A1 (en) * | 2015-08-11 | 2017-02-16 | 深圳朝伟达科技有限公司 | Inversion system for ray elastic parameter |
| CN107340542A (en) * | 2017-07-12 | 2017-11-10 | 中国石油化工股份有限公司 | Carbonate rock fractured cave locus Forecasting Methodology |
| WO2017205307A1 (en) * | 2016-05-25 | 2017-11-30 | Schlumberger Technology Corporation | Elastic parameter estimation |
| CN109597126A (en) * | 2018-12-19 | 2019-04-09 | 中国地质大学（北京） | A kind of carbonate platform marginal texture meticulous depiction and prediction technique |
| CN109870719A (en) * | 2019-01-25 | 2019-06-11 | 中国石油天然气集团有限公司 | A kind of carbonate rock thin tight reservoir distribution determination method, apparatus and system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009011737A1 (en) * | 2007-07-16 | 2009-01-22 | Exxonmobil Upstream Research Company | Retrodicting source-rock quality and paleoenvironmental conditions |
| CN104853822A (en) * | 2014-09-19 | 2015-08-19 | 杨顺伟 | Method for evaluating shale gas reservoir and searching sweet spot region |
| CN105089657B (en) * | 2015-06-15 | 2018-05-04 | 中国石油天然气股份有限公司 | The physical simulating method and experimental provision of fracture-cavity type carbonate reservoir hydrocarbons filling |
-
2019
- 2019-08-01 CN CN201910706564.XA patent/CN112305602B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103488971A (en) * | 2013-09-06 | 2014-01-01 | 电子科技大学 | Method for identifying geometrical morphology of organic reef storage layer |
| CN103527184A (en) * | 2013-10-28 | 2014-01-22 | 北京大学 | Method and system for predicting dolomite reservoir |
| WO2017024702A1 (en) * | 2015-08-11 | 2017-02-16 | 深圳朝伟达科技有限公司 | Inversion system for ray elastic parameter |
| WO2017205307A1 (en) * | 2016-05-25 | 2017-11-30 | Schlumberger Technology Corporation | Elastic parameter estimation |
| CN107340542A (en) * | 2017-07-12 | 2017-11-10 | 中国石油化工股份有限公司 | Carbonate rock fractured cave locus Forecasting Methodology |
| CN109597126A (en) * | 2018-12-19 | 2019-04-09 | 中国地质大学（北京） | A kind of carbonate platform marginal texture meticulous depiction and prediction technique |
| CN109870719A (en) * | 2019-01-25 | 2019-06-11 | 中国石油天然气集团有限公司 | A kind of carbonate rock thin tight reservoir distribution determination method, apparatus and system |
Non-Patent Citations (7)
| Title |
|---|
| Modeling elastic properties in carbonate rocks;SHIYU XU et al.;《The Leading Edge》;20090131;第66-74页 * |
| Study of Hydrocarbon Detection Methods in Offshore Deepwater Sediment: An Example in Equatorial Guinea;Zuo Guoping et al.;《SPG/SEG Shenzhen 2011 International Geophysical Conference Technical Program Expanded Abstracts》;20111231;第1947-1954页 * |
| 储层综合预测技术在塔里木盆地碳酸盐岩中的应用;师永民等;《天然气工业》;20041231;第24卷(第12期);第51-53页 * |
| 孟加拉湾浅层气成藏条件及地震识别技术;左国平等;《岩性油气藏》;20180430;第30卷(第2期);第110-119页 * |
| 巴西桑托斯盆地Ｓ油田碳酸盐岩储层预测;王朝锋等;《2017年物探技术研讨会》;20171231;第1-3页 * |
| 现代体属性分析技术在风化壳气藏勘探中的应用—以苏里格气田桃 7 区块马五段为例;韩翀等;《物探与化探》;20160630;第40卷(第3期);第445-451页 * |
| 相控地震多属性预测技术在南堡凹陷碳酸盐岩储层的应用;杨晓利等;《地球物理学进展》;20120831;第27卷(第4期);第1565-1571页 * |
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