阅读说明：本技术 基于叠前地震属性的碳酸盐岩储集相预测方法及装置 (Carbonate reservoir facies prediction method and device based on pre-stack seismic attributes ) 是由 苑书金 高君 陈志海 于 2018-06-14 设计创作，主要内容包括：本发明提供一种基于叠前地震属性的碳酸盐岩储集相预测方法,包括：碳酸盐岩叠前储层弹性参数反演；建立基于测井储层参数信息的碳酸盐岩储集相先验概率模型；基于第一步和第二步的阶段成果,应用贝叶斯分类识别方法实现碳酸盐岩油藏的储层三维储集相预测。本发明的基于叠前地震属性的碳酸盐岩储集相预测方法,基于测井储层信息的碳酸盐岩油藏的储集相划分及碳酸盐岩储集相的先验概率模型为依据,应用叠前地震储层弹性参数反演成果及储层多参数贝叶斯识别分类方法,实现了碳酸盐岩油藏的优质储集相预测,能够提高碳酸盐岩优质储层描述的精度,降低碳酸盐岩油藏勘探开发风险,同时为该类碳酸盐岩油藏的井位部署提供决策依据。(The invention provides a carbonate reservoir facies prediction method based on pre-stack seismic attributes, which comprises the following steps: inversion of elastic parameters of a carbonate rock prestack reservoir; establishing a carbonate reservoir phase prior probability model based on logging reservoir parameter information; and based on the stage results of the first step and the second step, the reservoir three-dimensional reservoir facies prediction of the carbonate reservoir is realized by applying a Bayesian classification identification method. According to the carbonate reservoir phase prediction method based on the prestack seismic attributes, the carbonate reservoir phase division of the carbonate reservoir based on the well logging reservoir information and the prior probability model of the carbonate reservoir phase are used as the basis, the prestack seismic reservoir elastic parameter inversion result and the reservoir multi-parameter Bayes identification classification method are applied, the high-quality reservoir phase prediction of the carbonate reservoir is achieved, the accuracy of carbonate reservoir high-quality description can be improved, the carbonate reservoir exploration and development risks are reduced, and meanwhile, decision basis is provided for the well position deployment of the carbonate reservoir.)

1. The carbonate reservoir facies prediction method based on the pre-stack seismic attribute is characterized by comprising the following steps of:

step 1, inverting elastic parameters of a carbonate rock prestack reservoir;

step 2, establishing a carbonate reservoir phase prior probability model based on logging reservoir parameter information;

and 3, based on the stage results of the step 1 and the step 2, realizing the reservoir three-dimensional reservoir facies prediction of the carbonate reservoir by applying a Bayesian classification recognition method.

2. The method for predicting carbonate reservoir facies based on prestack seismic attributes as claimed in claim 1, wherein the step 1 specifically comprises:

step 1.1, carrying out environmental correction and pretreatment on logging information, and carrying out lithology interpretation and physical property interpretation on reservoir parameters;

step 1.2, carrying out reservoir facies division on a reservoir according to lithology explanation and physical explanation of logging reservoir parameters;

step 1.3, carrying out well constraint-based pre-stack seismic reservoir elastic parameter inversion to obtain P wave impedance and V wave impedance of a target layer_p/V_sThe inverted data volume of (1).

3. The method for carbonate reservoir facies prediction based on pre-stack seismic attributes as claimed in claim 2, wherein the step 3 specifically comprises:

step 3.1, obtaining P-wave impedance and longitudinal-transverse wave velocity ratio V according to inversion_p/V_SCalculating the posterior probability of the carbonate reservoir parameters by using a prior probability model of the parameters and the reservoir parameters;

and 3.2, completing reservoir facies prediction of the carbonate reservoir by applying a reservoir multi-parameter Bayes classification identification method.

4. The carbonate reservoir facies prediction method based on the pre-stack seismic attribute of claim 3, wherein in the step 3.2, the application of the Bayesian classification recognition method to realize reservoir three-dimensional reservoir facies prediction of the carbonate reservoir specifically comprises the following steps:

giving an unclassified data sample X, applying a Bayesian classification algorithm, predicting that the sample data X belongs to the class with the highest posterior probability and the unknown sample X belongs to the class c_iThe essential condition for this is that,

P(c_i|X)＞P(c_j|X),1≤j≤k,j≠i

thus, to classify the unknown sample X, each class c is classified_iCalculation if P (X | c)_i).P(c_i)>P(X|c_j).P(c_j) J is not less than 1 and not more than k, j is not equal to i, then the sample X belongs to the class c_i，

Wherein, the sample data X ═ is<X₁，X₂，…，X_n>X has n attributes, X_iRepresents the ith attribute A_iValue of (d), any class y e { c₁,...,c_kY has k classes, c_jRepresenting the probability of the jth class.

5. The method for carbonate reservoir facies prediction based on prestack seismic attributes as claimed in claim 2 wherein in step 1.3, elastic parameter inversion is computed based on well constraints and prestack gathers according to the Zoeppritz equation approximation of the Fatti method, said Zoeppritz equation approximation being:

wherein, I_p＝V_pρ is the acoustic wave impedance, I_s＝V_sρ is the shear wave impedance and θ is the value of the angle of incidence.

6. The carbonate rock reservoir facies prediction method based on the pre-stack seismic attributes as claimed in any one of claims 1 to 5, wherein in the Bayesian classification and identification method, a Bayesian probability formula is as follows:

wherein, P (y)_i| X) is X conditional probability distribution function, y_iRepresents the i-th reservoir phase, P (y)_i) Is y_iPrior probability distribution, P (X) carbonate reservoir parameter vector X prior probability distribution, P (X | y)_i) Representing a reservoir facies y_iThe reservoir parameter prior conditional probability distribution model of (1).

7. A carbonate reservoir facies prediction device based on pre-stack seismic attributes is characterized by comprising,

the elastic parameter inversion module is used for inverting the elastic parameters of the pre-stack reservoir of the carbonate rock;

the prior probability model module is used for establishing a carbonate reservoir phase prior probability model based on the logging reservoir parameter information;

and the posterior probability module is used for calculating the posterior probability of the carbonate reservoir parameters according to the prior probability model and realizing the reservoir three-dimensional reservoir facies prediction of the carbonate reservoir by applying a Bayesian classification recognition method.

8. The pre-stack seismic attribute-based carbonate reservoir facies prediction apparatus of claim 7 wherein the elastic parameter inversion module is specifically configured to,

carrying out environmental correction and pretreatment on logging data, carrying out lithology interpretation and physical interpretation of reservoir parameters, carrying out pre-stack seismic reservoir elastic parameter inversion based on well constraint to obtain P-wave impedance and V-wave impedance of a target layer_P/V_SThe inverted data volume of (1).

9. The pre-stack seismic attribute-based carbonate reservoir facies prediction apparatus of claim 7 wherein the a priori probability model module is specifically configured to,

according to lithology explanation and physical explanation of logging reservoir parameters, carrying out reservoir facies division on a reservoir;

and establishing a prior probability model of the reservoir facies and the reservoir parameters of the logging reservoir according to the reservoir facies classification of the logging reservoir and the well reservoir parameter information.

10. The pre-stack seismic attribute-based carbonate reservoir facies prediction apparatus of claim 7 wherein the a posteriori probability module is specifically configured to,

calculating the posterior probability of the carbonate reservoir parameters according to the P wave impedance, the Vp/Vs parameters and the prior probability model of the reservoir parameters obtained by inversion;

and (3) completing reservoir facies prediction of the carbonate reservoir by applying a reservoir multi-parameter Bayes classification identification method.

Technical Field

The invention relates to the field of seismic oil and gas exploration and development, in particular to a carbonate reservoir facies prediction method based on pre-stack seismic attributes.

Background

The reserves of carbonate rock type oil and gas fields in the world account for about 50 percent of the total reserves, and the yield of the carbonate rock type oil and gas fields also reaches more than 60 percent of the total yield. Carbonate reservoirs are a very complex reservoir type and a detailed description of carbonate reservoirs remains a long-standing worldwide problem.

Carbonate rocks are of various types and complex in mineral structure and formation evolution. Carbonate reservoirs are a large number of reservoir space types, have large secondary variations, and are of greater complexity and diversity than sandstone reservoirs. The carbonate reservoir pores are generally characterized by 'superficial pore-forming, medium-depth storage and adjustment'. Generally, carbonate reservoirs can be classified into pore type carbonate reservoirs, cavern type carbonate reservoirs, karst type carbonate reservoirs, fracture type carbonate reservoirs, and the like according to the carbonate reservoir space characteristics. The different types of carbonate reservoir parameter characteristics have great difference, and the seismic response characteristics also have great difference, so the carbonate reservoir description method based on the seismic attribute parameters is also different. At present, no universal and effective carbonate reservoir seismic attribute parameter fine description method is available, and the method is suitable for describing all types of carbonate reservoirs, and various challenges still face to the fine description of different types of carbonate reservoir seismic attribute parameters.

At present, the conventional carbonate reservoir parameter seismic description technologies include a seismic amplitude analysis technology, a seismic reflection structure analysis technology, a seismic waveform clustering analysis technology, a three-dimensional seismic coherent body processing technology, an ancient geomorphologic visual analysis technology, a post-stack reservoir wave impedance inversion, a pre-stack reservoir elastic parameter inversion, a pre-stack seismic anisotropy analysis technology, a hydrocarbon direct detection technology (HDI, AVO) and the like, and the seismic attribute parameter reservoir description technologies play an important role in the carbonate reservoir exploration and development, can reduce exploration and development risks, but the drilling risk of the carbonate reservoir is still high.

Disclosure of Invention

Aiming at the problems of high description difficulty, low precision and high drilling risk of a carbonate reservoir in the existing method, the invention provides the carbonate reservoir phase prediction method based on the pre-stack seismic attribute, which can improve the description precision of the carbonate reservoir seismic attribute parameters and reduce the exploration and development risks of the carbonate reservoir.

The invention discloses a carbonate reservoir facies prediction method based on pre-stack seismic attributes, which comprises the following steps of:

step 1, inverting elastic parameters of a carbonate rock prestack reservoir;

step 2, establishing a carbonate reservoir phase prior probability model based on logging reservoir parameter information;

and 3, based on the stage results of the step 1 and the step 2, realizing the reservoir three-dimensional reservoir facies prediction of the carbonate reservoir by applying a Bayesian classification recognition method.

Further, the step 1 specifically includes:

step 1.1, carrying out environmental correction and pretreatment on logging information, and carrying out lithology interpretation and physical property interpretation on reservoir parameters;

step 1.2, carrying out reservoir facies division on a reservoir according to lithology explanation and physical explanation of logging reservoir parameters;

step 1.3, carrying out well constraint-based pre-stack seismic reservoir elastic parameter inversion to obtain P wave impedance and V wave impedance of a target layer_p/V_sThe inverted data volume of (1).

Further, the step 3 specifically includes:

step 3.1, obtaining P-wave impedance and longitudinal-transverse wave velocity ratio V according to inversion_p/V_SCalculating the posterior probability of the carbonate reservoir parameters by using a prior probability model of the parameters and the reservoir parameters;

and 3.2, completing reservoir facies prediction of the carbonate reservoir by applying a reservoir multi-parameter Bayes classification identification method.

Further, in the step 3.2, the method for realizing the reservoir three-dimensional reservoir facies prediction of the carbonate reservoir by applying the Bayesian classification and identification method specifically comprises the following steps:

known sample data X ═<X₁，X₂，…，X_n>X has n attributes, X_iRepresents the ith attribute A_iValue of (d), any class y e { c₁,...,c_kY has k classes, c_jRepresenting the probability of the jth class.

Giving an unclassified data sample X, applying a Bayesian classification algorithm, and predicting that the sample data X belongs to

Class with the highest posterior probability, the condition for the unknown sample X to belong to the class ci is if and only if

P(c_i|X)＞P(c_j|X),1≤j≤k,j≠i，

Thus, to classify the unknown sample X, each class c is classified_iCalculation if and only if P (X | c)_i).P(c_i)>P(X|c_j).P(c_j) J is not less than 1 and not more than k, j is not equal to i, then the sample X belongs to the class c_i。

Further, in step 4, elastic parameter inversion is calculated and inverted according to a Zoeppritz equation approximation expression of the Fatti method on the basis of well constraints and prestack gathers, where the Zoeppritz equation approximation expression is:

wherein, I_p＝V_pρ is the acoustic wave impedance, I_s＝V_sρ is the shear wave impedance and θ is the value of the angle of incidence.

Furthermore, in the step 3.2, the reservoir facies prediction of the carbonate reservoir is based on the bayesian theory, and the probability that the sample belongs to a certain reservoir facies category is predicted by solving posterior probability distribution and applying a statistical bayesian classification and identification method on the basis of the carbonate reservoir elastic parameter inversion data result before the carbonate reservoir is stacked and the carbonate reservoir facies prior probability of drilling test.

Further, in the bayesian classification and identification method, a bayesian probability formula is as follows:

wherein, P (y)_i| X) is X conditional probability distribution function, y_iRepresents the i-th reservoir phase, P (y)_i) Is y_iP (X) is a carbonate reservoir parameter vector X prior probability distribution, X represents a reservoir parameter vector, e.g., X ═ Z (Z)_P，V_P/V_S)，P(X|y_i) Representing a reservoir facies y_iThe reservoir parameter prior conditional probability distribution model of (1).

Furthermore, the Bayesian classification and identification method is that the prior probability of an object is used, the Bayesian formula is used for calculating the posterior probability, namely the probability that the object belongs to a certain class, and the class with the maximum posterior probability is selected as the class to which the object belongs.

The invention also provides a carbonate reservoir facies prediction device based on the pre-stack seismic attribute, which comprises,

the elastic parameter inversion module is used for inverting the elastic parameters of the pre-stack reservoir of the carbonate rock;

the prior probability model module is used for establishing a carbonate reservoir phase prior probability model based on the logging reservoir parameter information;

and the posterior probability module is used for calculating the posterior probability of the carbonate reservoir parameters according to the prior probability model and realizing the reservoir three-dimensional reservoir facies prediction of the carbonate reservoir by applying a Bayesian classification recognition method.

Further, the elastic parameter inversion module is specifically used for carrying out environment correction and preprocessing on logging data, carrying out lithology interpretation and physical property interpretation on reservoir parameters, and carrying out pre-stack seismic reservoir elastic parameter inversion based on well constraint to obtain an inversion data volume of P-wave impedance and Vp/Vs of a target layer.

Further, the prior probability model module is specifically used for carrying out reservoir facies division on the reservoir according to lithology interpretation and physical property interpretation of the logging reservoir parameters;

and establishing a prior probability model of the reservoir facies and the reservoir parameters of the logging reservoir according to the reservoir facies classification of the logging reservoir and the well reservoir parameter information.

Further, the posterior probability module is specifically configured to obtain the P-wave impedance and V according to inversion_P/V_SCalculating the posterior probability of the carbonate reservoir parameters by using a prior probability model of the parameters and the reservoir parameters; and (3) completing reservoir facies prediction of the carbonate reservoir by applying a reservoir multi-parameter Bayes classification identification method.

Compared with the prior art, the carbonate reservoir facies prediction method based on the pre-stack seismic attributes is used for comprehensively constructing sediment, drilling reservoir parameter information, carbonate rock physical reservoir parameter characteristics, pre-stack seismic reservoir parameter-containing information and reservoir prediction technologies aiming at the problems that the carbonate reservoir is low in description precision and a high-quality reservoir is difficult to predict, so that a well-seismic and reservoir-sediment integrated multi-parameter reservoir facies prediction technology is formed.

According to the method, the reservoir phase and reservoir phase prediction of the carbonate reservoir is realized by applying an advanced Bayesian classification method and a carbonate reservoir phase prior probability model of well reservoir parameters according to the parameter information of the drilled carbonate reservoir and the inversion result of the elastic parameters of the prestack seismic reservoir, and the optimization of the carbonate reservoir development scheme is facilitated. The method has good applicability to the prediction of the high-quality reservoir of the pore type carbonate reservoir, can effectively predict the high-quality reservoir phase of the carbonate reservoir, further improves the description precision of the reservoir, and provides a more reliable basis for exploration and development well position deployment.

The above-described method features can be combined in various suitable ways or replaced by equivalent method features as long as the object of the invention is achieved.

Drawings

The invention will be described in more detail hereinafter on the basis of non-limiting examples only and with reference to the accompanying drawings. Wherein:

FIG. 1 is a flow chart of a carbonate reservoir facies prediction method based on pre-stack seismic attributes of the present invention.

FIG. 2 is a representation of a carbonate rock oil according to an embodiment of the present inventionGR curve, reservoir phase, P-wave impedance curve, porosity curve and longitudinal-transverse wave velocity ratio V of field L well_P/V_SCurve line.

FIG. 3 shows P-wave impedance and V of reservoir elastic parameters established based on carbonate reservoir information of an oil field L well in an embodiment of the invention_P/V_SThe type 3 reservoir facies prior probability density function.

FIG. 4 is a cross-sectional view and a cross-sectional view of P-wave impedance of pre-stack inversion elastic parameters of a well passing through an L-well according to an embodiment of the present invention_P/V_SA cross-sectional view.

FIG. 5 is a high-quality reservoir facies prediction profile of an L-well-crossing based on the inversion result of prestack reservoir elastic parameters in an embodiment of the invention.

Fig. 6 is a plan view of a high quality reservoir facies for a target interval of a target interval in an embodiment of the present invention.

Detailed Description

The invention will be described in further detail below with reference to the drawings and specific examples. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the formed method scheme is within the protection scope of the present invention.

As shown in FIG. 1, the carbonate reservoir facies prediction method based on the pre-stack seismic attributes comprises the following steps:

step 1, inverting elastic parameters of a carbonate rock prestack reservoir;

the method specifically comprises the following steps:

step 1.1, carrying out environmental correction and pretreatment on logging information, and carrying out lithology interpretation and physical property interpretation of reservoir parameters, including effective porosity analysis;

step 1.2, according to lithology explanation and physical explanation of logging reservoir parameters, reservoir facies of the reservoir are divided, and the general division does not exceed 5 types;

step 1.3, carrying out well constraint-based pre-stack seismic reservoir elastic parameter inversion to obtain P wave impedance and V wave impedance of a target layer_p/V_sThe inverted data volume of (1).

Step 2, establishing a carbonate reservoir facies prior probability model based on the logging reservoir parameter information according to the reservoir facies classification of the logging reservoir and the well reservoir parameter information;

and 3, based on the stage results of the step 1 and the step 2, realizing the reservoir three-dimensional reservoir facies prediction of the carbonate reservoir by applying a Bayesian classification recognition method.

The method specifically comprises the following steps:

step 3.1, obtaining P-wave impedance and longitudinal-transverse wave velocity ratio V according to inversion_p/V_SCalculating the posterior probability of the carbonate reservoir parameters by using a prior probability model of the parameters and the reservoir parameters;

step 3.2, completing reservoir facies prediction of the carbonate reservoir by applying a reservoir multi-parameter Bayes classification identification method; and are

According to the reservoir facies prediction result based on seismic prestack inversion, the comprehensive evaluation and analysis of the carbonate rock high-quality reservoir are completed by combining real drilling information and reservoir deposition characteristics, and the carbonate rock reservoir exploration and development scheme is optimized.

Further, in step 1.3, elastic parameter inversion is computed according to a Zoeppritz equation approximation expression of the Fatti method based on well constraints and prestack gathers, where the Zoeppritz equation approximation expression is:

wherein, I_p＝V_pρ is the acoustic wave impedance, I_s＝V_sρ is the shear wave impedance and θ is the value of the angle of incidence.

Further, in the step 3.2, the reservoir facies prediction of the carbonate reservoir is based on the Bayes theory, and the probability that the sample belongs to a certain reservoir facies category is predicted by solving posterior probability distribution and applying a statistical Bayes classification identification method on the basis of the carbonate prestack reservoir elastic parameter inversion data result and the carbonate reservoir facies prior probability of drilling test.

Further, in the bayesian classification and identification method, a bayesian probability formula is as follows:

wherein, P (y)_i| X) is X conditional probability distribution function, y_iRepresents the i-th reservoir phase, P (y)_i) Is y_iPrior probability distribution, p (X) is a carbonate reservoir parameter vector X prior probability distribution, X represents a reservoir parameter vector, e.g. X ═ (Z)_P，V_P/V_S)，P(X|y_i) Representing a reservoir facies y_iThe reservoir parameter prior conditional probability distribution model of (1).

The Bayes classification identification method is that the prior probability of a certain object is used, the Bayes formula is used for calculating the posterior probability, namely the probability that the object belongs to a certain class, and the class with the maximum posterior probability is selected as the class to which the object belongs.

The principle is that known sample data X ═ is<X₁，X₂，…，X_n>X has n attributes, X_iRepresents the ith attribute A_iValue of (d), any class y e { c₁,...,c_kY has k classes, c_jRepresenting the probability of the jth class.

Giving an unclassified data sample X, applying a Bayesian classification algorithm, predicting that the sample data X belongs to the class with the highest posterior probability and the unknown sample X belongs to the class c_iProvided that if and only if

P(c_i|X)＞P(c_j|X),1j≤k,j≠i

Thus, to classify the unknown sample X, each class c is classified_iCalculation if and only if P (X | c)_i).P(c_i)>P(X|c_j).P(c_j) J is not less than 1 and not more than k, j is not equal to i, then the sample X belongs to the class c_i。

The invention also provides a carbonate reservoir facies prediction device based on the pre-stack seismic attribute, which comprises,

the elastic parameter inversion module is used for inverting the elastic parameters of the pre-stack reservoir of the carbonate rock;

the prior probability model module is used for establishing a carbonate reservoir phase prior probability model based on the logging reservoir parameter information;

and the posterior probability module is used for calculating the posterior probability of the carbonate reservoir parameters according to the prior probability model and realizing the reservoir three-dimensional reservoir facies prediction of the carbonate reservoir by applying a Bayesian classification recognition method.

Further, the elastic parameter inversion module is specifically used for carrying out environment correction and pretreatment on logging data, carrying out lithology interpretation and physical property interpretation of reservoir parameters, and carrying out pre-stack seismic reservoir elastic parameter inversion based on well constraint to obtain P wave impedance and V wave impedance of a target layer_P/V_SThe inverted data volume of (1).

Further, the prior probability model module is specifically used for carrying out reservoir facies division on the reservoir according to lithology explanation and physical property explanation of the logging reservoir parameters; and establishing a prior probability model of the reservoir facies and the reservoir parameters of the logging reservoir according to the reservoir facies classification of the logging reservoir and the well reservoir parameter information.

Further, the posterior probability module is specifically used for calculating the posterior probability of the carbonate reservoir parameters according to the P-wave impedance, the Vp/Vs parameters and the prior probability model of the reservoir parameters obtained by inversion; and (3) completing reservoir facies prediction of the carbonate reservoir by applying a reservoir multi-parameter Bayes classification identification method.

According to the carbonate reservoir facies prediction method based on the prestack seismic attribute, which is provided by the invention, a certain pore type carbonate reservoir under basci salt is taken as an example, and the method is implemented on site.

A certain porous carbonate reservoir under Brazilian salt is chalk series reef flat phase carbonate, the average oil field water depth is 2400 m, the development cost is high, and the prediction difficulty of a high-quality reservoir is high. The development block carries out pre-stack seismic reservoir parameter inversion work constrained by well information on the basis of implementing three-dimensional earthquake and on the basis of rock physical parameter characteristics of a carbonate reservoir, tests by applying a carbonate reservoir phase prediction method of pre-stack seismic attributes, obtains a good application effect, realizes prediction of a high-quality reservoir phase, improves reservoir description precision and optimizes an oil field development scheme.

The method comprises the following specific steps:

s1, performing lithology and physical interpretation on the reservoir of the well test data of the target interval, and further dividing the reservoir phase, as shown in fig. 2, into a high pore reservoir phase, a medium pore reservoir phase, and a low pore reservoir phase according to the effective porosity of the reservoir.

S2, establishing reservoir phase and reservoir parameter P wave impedance and V according to the reservoir phase classification and the reservoir parameter of the well point test reservoir_P/V_SA prior probability function model of (2). FIG. 3 is a graph of the carbonate reservoir information for L-wells established with respect to reservoir elastic parameters P-wave impedance and longitudinal-to-transverse-wave velocity ratio V_p/V_sThe type 3 reservoir facies prior probability density function.

S3, inverting the elastic parameters of the prestack seismic reservoir constrained by well information to obtain an inverted data volume of P-wave impedance and Vp/Vs, as shown in figure 2, showing GR curve, reservoir phase, P-wave impedance curve, porosity curve and V of L well of a carbonate rock oil field in a work area_P/V_SThe curve can explain the lithology, physical property and oil-gas containing property of the reservoir according to the logging response characteristics of the reservoir, wherein the high-quality carbonate reservoir has low V_P/V_SAnd medium and low wave impedance characteristics.

FIG. 4 shows the cross-sectional view of the pre-stack inversion elastic parameters P-wave impedance (top view in FIG. 4) and the velocity ratio V of the longitudinal wave and the transverse wave of the L-well_P/V_SProfile (lower panel of fig. 4) of a premium reservoir of carbonate reservoir with low V_P/V_SAlthough the pre-stack reservoir parameter inversion can improve the reservoir prediction accuracy compared with the reservoir single-parameter longitudinal wave impedance, the carbonate reservoir description accuracy is still insufficient at the moment, and the drilling risk is still high.

S4, obtaining reservoir P-wave impedance and V of carbonate reservoir according to pre-stack seismic inversion_P/V_SCalculating the posterior probability of the reservoir facies of the carbonate reservoir according to the prior probability model of the reservoir facies of the parameters and the carbonate, and completing the reservoir facies prediction of the carbonate reservoir according to the Bayes recognition classification technology of multi-reservoir elastic parameters, wherein FIG. 5 shows the inversion result of the L-well based on the elastic parameters of the prestack reservoir and the inversion result of the storage faciesThe deep black area of the section of the reservoir facies section predicted by the layer prior probability model represents a high-hole reservoir facies position, and the deep black high-quality reservoir facies can be easily identified from the predicted reservoir facies section, so that a basis is provided for well position deployment.

S5, extracting a plane distribution diagram of a reservoir facies of the carbonate reservoir in the target interval, and according to a reservoir facies data body predicted by pre-stack seismic data, the extracted plane distribution diagram of the reservoir facies of the target interval in figure 6, the distribution characteristics of a high-quality reservoir are easy to identify (deep black represents a high-hole reservoir facies and is a favorable area of the carbonate reservoir), the capacity of identifying the high-quality reservoir facies by earthquake is improved, and a reliable basis is provided for well position deployment of the carbonate reservoir.

S6, the method determines the research of the spatial distribution characteristics of the high-quality reservoir of the carbonate reservoir by applying the geological structure deposition characteristics, the drilling information, the target interval amplitude root-mean-square plane distribution diagram and the target reservoir phase plane distribution characteristic diagram of the work area, optimizes the development scheme of the oil field according to the research results and obtains good economic benefits.

Thus, it will be appreciated by those skilled in the art that while a number of illustrative embodiments of the invention have been shown and described in detail herein, many other variations or modifications can be made, directly or by derivation of teachings consistent with the principles of the invention without departing from the spirit or scope thereof, and thus, the scope of the invention should be understood and considered to cover all such other variations or modifications.

Moreover, while the operations of the invention are depicted in the drawings in a particular order, this does not necessarily imply that the operations must be performed in that particular order, or that all of the operations shown must be performed, to achieve desirable results. Certain steps may be omitted, multiple steps combined into one step or a step divided into multiple steps performed.

While the invention has been described with reference to a preferred embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the method features mentioned in the individual embodiments can be combined in any desired manner, as long as no conflict exists. The present invention is not limited to the particular embodiments disclosed herein, but encompasses all method aspects falling within the scope of the claims.