CN112505764A - High-porosity hydrocarbon-containing sandstone reservoir prediction method and device - Google Patents
High-porosity hydrocarbon-containing sandstone reservoir prediction method and device Download PDFInfo
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Abstract
The invention provides a method and a device for predicting a high-porosity hydrocarbon-containing sandstone reservoir. The reservoir prediction method comprises the following steps: (1) acquiring logging curve data and longitudinal and transverse wave impedance seismic data of a work area; (2) constructing a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor with two-dimensional parameters to be determined based on the logging curve data obtained in the step (1); (3) constructing an objective function based on the hydrocarbon-containing pore attributes and the high-pore hydrocarbon-containing sandstone reservoir discrimination factors obtained in the step (2); (4) solving the objective function to obtain undetermined parameter values in the discrimination factors of the high-porosity hydrocarbon-bearing sandstone reservoir; (5) and calculating a discrimination factor seismic data body of the high-porosity hydrocarbon-bearing sandstone reservoir and predicting the reservoir. The reservoir prediction method effectively ensures the accuracy of reservoir prediction and reduces exploration risks.
Description
Technical Field
The invention relates to the field of geophysical exploration of petroleum, in particular to a method and a device for predicting a high-porosity hydrocarbon-containing sandstone reservoir.
Background
With the progress of seismic exploration technology, reservoir prediction means are continuously updated and iterated, and the petroleum exploration target develops towards the direction of refinement and complication. Conventional reservoir prediction techniques based on elastic, single-phase fluid saturation are increasingly challenged in terms of prediction accuracy and effectiveness, and particularly, expected accuracy requirements are difficult to achieve for lithologic hydrocarbon reservoir exploration and reservoir description of multiphase fluid saturated rocks. In recent years, with the development of petrophysical theory, reservoir prediction technology based on elastic information receives more and more attention, and theoretical research considers that non-zero offset seismic data contain more reservoir fluid information, particularly shear wave velocity and density attributes. Based on rock physical analysis, in the aspect of seismic prediction, the accuracy of lithology prediction and fluid detection is effectively improved by combining prestack inversion with fluid factor construction; in the well logging interpretation method, the well logging interpretation method based on elastic information such as longitudinal and transverse wave speed, density and the like greatly enriches the method theory of stratum lithology and reservoir physical property interpretation, and improves the accuracy of reservoir prediction. Theoretically, compared with a logging interpretation method based on electrical information, the logging interpretation method based on the elastic information has better physical consistency with seismic exploration, can effectively ensure comparability of detection results of different scales, is favorable for combined application of logging and seismic information, and promotes full linkage and fusion application of information of two scales. Based on longitudinal and transverse wave velocity and density information, geophysicists in recent years propose various methods for describing reservoir lithology and physical properties, the aim of fine reservoir prediction is achieved to a certain extent and in certain block ranges, and a theoretical basis and a guidance method are provided for a well logging interpretation method based on elastic information, and the method comprises the following steps: reservoir mudstone baseline methods proposed in Smith and Gidlow (1987), ramet coefficient intersection analysis methods proposed by Goodway (1997), elastic impedance theory proposed by Connolly (1999), poisson impedance properties proposed by quekenbush (2006), and the like. Although the former research method is widely applied in actual oil field development and has better application effect, the former research method has certain limitation, and for various complex geological problems in different work areas, the selection of an applicable exploration method according to local conditions is a necessary means for ensuring the exploration success rate. Therefore, a reservoir prediction method with high accuracy and low exploration risk is needed.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a high-porosity hydrocarbon-containing sandstone reservoir prediction method, which effectively ensures the accuracy of reservoir prediction and reduces exploration risks.
Another object of the present invention is to provide a high porosity hydrocarbon-containing sandstone reservoir prediction device.
To achieve the above objects, in one aspect, the present invention provides a method for predicting a high-porosity hydrocarbon-containing sandstone reservoir, wherein the method comprises: (1) acquiring logging curve data and longitudinal and transverse wave impedance seismic data of a work area; (2) constructing a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor with two-dimensional parameters to be determined based on the logging curve data obtained in the step (1); (3) constructing an objective function based on the hydrocarbon-containing pore attributes and the high-pore hydrocarbon-containing sandstone reservoir discrimination factors obtained in the step (2); (4) solving the objective function to obtain undetermined parameter values in the discrimination factors of the high-porosity hydrocarbon-bearing sandstone reservoir; (5) and (3) calculating a discrimination factor seismic data body of the high-porosity hydrocarbon-bearing sandstone reservoir and predicting the reservoir according to the undetermined parameter values and the longitudinal wave impedance seismic data and the transverse wave impedance seismic data in the step (1).
According to some embodiments of the invention, the well log data includes compressional velocity, shear velocity, density, porosity, and water saturation curves.
According to some embodiments of the invention, the compressional and shear wave impedance seismic data volumes are obtainable based on conventional prestack inversion.
According to some embodiments of the present invention, the formula for calculating the high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor is:
F(x,y)＝(ρ×vp+x)×(vp/vs+y)
wherein F (x, y) is a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor and is to-be-consultedThe number x and y are functions of undetermined parameters, p is density and is given in g/cm3，vpIs the longitudinal wave velocity in m/s, vsThe unit is the transverse wave velocity in m/s.
According to some embodiments of the invention, the hydrocarbon-containing pore property calculation formula is:
HPCV＝φ×(1-Sw)
where HPCV is the hydrocarbon-containing porosity attribute, φ is the porosity, and Sw is the water saturation. The distribution rule of the effective reservoir of the high-porosity hydrocarbon-bearing sandstone is characterized when the value of the hydrocarbon porosity attribute (HPCV) is larger along with the increase of the porosity and the decrease of the water saturation (the increase of the hydrocarbon saturation).
According to some embodiments of the invention, the expression of the objective function is:
J(x,y)＝corr(F(x,y),HPCV)
j (x, y) is an objective function value which is a function of undetermined parameters x, y, F (x, y) is a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor, HPCV is a hydrocarbon-bearing pore attribute, and corr is a correlation operation symbol.
According to some specific embodiments of the present invention, the method for obtaining the value of the undetermined parameter in step (4) includes: and calculating the correlation coefficient of the discrimination factor of the high-porosity hydrocarbon-bearing sandstone reservoir and the hydrocarbon-bearing pore attribute by using a two-dimensional space parameter scanning method in a two-dimensional space of the x and y parameters to obtain specific values of the undetermined parameters x and y corresponding to the maximum objective function value.
According to some embodiments of the invention, the specific method for reservoir prediction in step (5) comprises: step one, substituting the undetermined parameter values obtained by solving the objective function and longitudinal and transverse wave impedance seismic data into a calculation formula of a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor to obtain a discrimination factor seismic data volume; and secondly, obtaining a reservoir prediction threshold value according to the high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor petrophysical analysis, and comparing the seismic data volume obtained by the first step with the threshold value to predict the reservoir of the research work area, so as to obtain the distribution rule of the high-porosity hydrocarbon-bearing sandstone reservoir.
According toIn some embodiments of the invention, Zp is the longitudinal wave impedance in g/cm3M/s, Zs is the transverse wave impedance in g/cm3M/s, p is the density in g/cm3，vpIs the longitudinal wave velocity in m/s, vsThe unit is m/s for transverse wave velocity, and Zp is rho × vp，Zs＝ρ×vs，vp/vs＝Zp/Zs。
According to some embodiments of the invention, the high pore hydrocarbon containing sandstone reservoir discrimination petrophysical analysis is characterized by hydrocarbon containing pore properties, preferably, porosity and water saturation.
The above formula for calculating the hydrocarbon-containing pore properties is: HPCV ═ Φ × (1-Sw), where HPCV is the hydrocarbon-containing porosity, Φ is the porosity, and Sw is the water saturation. From this equation, it follows that: the higher the porosity, the lower the water saturation, the greater the value of this property and vice versa. The high values of the hydrocarbon-containing porosity attribute correspond to high porosity and low water saturation, and conversely, the low values of the hydrocarbon-containing porosity attribute correspond to low porosity and high water saturation. This hydrocarbon-containing pore property can characterize the reservoir porosity as well as the water saturation. In short, it is the case that reservoir porosity and water saturation are characterized by the magnitude of the value of this hydrocarbon-containing pore property.
In another aspect, the present invention further provides a high pore hydrocarbon-containing sandstone reservoir prediction device, where the device is configured to implement the high pore hydrocarbon-containing sandstone reservoir prediction method, and the device includes: the earthquake and logging data input unit is used for inputting logging curve data and longitudinal and transverse wave impedance earthquake data of a work area; the discrimination factor construction unit is used for constructing a discrimination factor of the high-porosity hydrocarbon-bearing sandstone reservoir with two-dimensional parameters to be determined based on the input logging curve data; the target function construction unit is used for constructing a target function based on the high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor and the hydrocarbon-bearing pore attribute; the objective function solving unit is used for obtaining undetermined parameter values in the discrimination factors of the high-porosity hydrocarbon-bearing sandstone reservoir by solving an objective function; and the calculation and prediction unit is used for calculating the high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor seismic data volume according to the undetermined parameter value and performing reservoir prediction.
According to some embodiments of the invention, the well log data includes compressional velocity, shear velocity, density, porosity, and water saturation curves.
According to some embodiments of the invention, the compressional and shear wave impedance seismic data volumes are obtainable based on prestack inversion.
According to some embodiments of the present invention, the formula for calculating the high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor is:
F(x,y)＝(ρ×vp+x)×(vp/vs+y)
wherein F (x, y) is a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor which is a function of undetermined parameters x, y, rho is density and the unit is g/cm3，vpIs the longitudinal wave velocity in m/s, vsThe unit is the transverse wave velocity in m/s.
According to some embodiments of the invention, the hydrocarbon-containing pore property calculation formula is:
HPCV＝φ×(1-Sw)
where HPCV is the hydrocarbon-containing porosity attribute, φ is the porosity, and Sw is the water saturation.
According to some embodiments of the invention, the expression of the objective function is:
J(x,y)＝corr(F(x,y),HPCV)
j (x, y) is an objective function value which is a function of undetermined parameters x, y, F (x, y) is a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor, HPCV is a hydrocarbon-bearing pore attribute, and corr is a correlation operation symbol.
According to some specific embodiments of the present invention, the method for obtaining the value of the pending parameter includes: and calculating the correlation coefficient of the discrimination factor of the high-porosity hydrocarbon-bearing sandstone reservoir and the hydrocarbon-bearing pore attribute by using a two-dimensional space parameter scanning method to obtain the undetermined parameter value corresponding to the maximum objective function value.
According to some embodiments of the invention, the method of making a reservoir prediction comprises: step one, substituting the undetermined parameter values obtained by solving the objective function and longitudinal and transverse wave impedance seismic data into a calculation formula of a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor to obtain a discrimination factor seismic data volume; and secondly, obtaining a reservoir prediction threshold value according to the high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor petrophysical analysis, and comparing the seismic data body obtained by the first step with the threshold value to predict the reservoir of the research work area.
According to some embodiments of the invention, Zp is the longitudinal wave impedance in g/cm3M/s, Zs is the transverse wave impedance in g/cm3M/s, p is the density in g/cm3，vpIs the longitudinal wave velocity in m/s, vsThe unit is m/s for transverse wave velocity, and Zp is rho × vp，Zs＝ρ×vs，vp/vs＝Zp/Zs。
According to some embodiments of the invention, the high pore hydrocarbon containing sandstone reservoir discrimination petrophysical analysis is characterized by hydrocarbon containing pore properties.
The invention provides a high-porosity hydrocarbon-bearing sandstone reservoir prediction method aiming at continental facies oil and gas reservoir exploration in China, and aims to construct a discriminant attribute factor capable of effectively identifying a high-porosity hydrocarbon-bearing sandstone reservoir based on longitudinal and transverse wave velocity and density information, wherein petrophysical constraints are introduced into the attribute factor in the construction process, and an optimal undetermined parameter is obtained by solving a two-dimensional parameter space objective function, so that the optimal fitting with the reservoir hydrocarbon-bearing pore attribute is realized, the reservoir prediction accuracy is effectively ensured, and the exploration risk is reduced.
Drawings
Fig. 1 is a flow chart of a high pore hydrocarbon-containing sandstone reservoir prediction method of the present invention;
fig. 2 is a flow chart of the prediction device for the high-porosity hydrocarbon-containing sandstone reservoir according to the invention;
FIG. 3 is a diagram of a distribution of an objective function in a two-dimensional space of parameters to be determined in embodiment 1 of the present invention;
FIG. 4 is a diagram of the discrimination factor petrophysical analysis of the high-porosity hydrocarbon-bearing sandstone reservoir in the petrophysical prediction template in example 1 of the present invention;
fig. 5 is a hydrocarbon reservoir distribution profile predicted from high pore hydrocarbon sandstone reservoir discrimination according to example 1 of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
The embodiment provides a high-porosity hydrocarbon-containing sandstone reservoir prediction method (detailed flow is shown in fig. 1), which comprises the following steps:
step 101: and inputting logging curve data and longitudinal and transverse wave impedance seismic data of the work area.
In the implementation process, the longitudinal wave impedance seismic data volume and the transverse wave impedance seismic data volume are obtained through conventional prestack seismic inversion, and the logging data comprise longitudinal wave velocity, transverse wave velocity, density, porosity and water saturation curves.
Step 102: constructing a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor with two-dimensional parameters to be determined based on input logging curve data, wherein the method comprises the following steps:
the formula for calculating the discrimination factor of the high-porosity hydrocarbon-bearing sandstone reservoir is as follows:
F(x,y)＝(ρ×vp+x)×(vp/vs+y)
f (x, y) is a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor which is a function of undetermined parameters x and y, x and y are undetermined parameters, rho is density and has the unit of g/cm3，vpIs the longitudinal wave velocity in m/s, vsThe unit is the transverse wave velocity in m/s.
Step 103: constructing an objective function based on the high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor and the hydrocarbon-bearing pore attribute, wherein the method comprises the following steps:
in the implementation process, firstly, a hydrocarbon-containing pore property curve is calculated by using an input porosity and water saturation logging curve to characterize the distribution characteristics of a high-porosity hydrocarbon-containing sandstone reservoir, and the calculation formula is as follows:
HPCV＝φ×(1-Sw)
where HPCV is the hydrocarbon-containing porosity attribute, φ is the porosity, and Sw is the water saturation. It can be seen that the distribution rule of the effective reservoir of the high-porosity hydrocarbon-bearing sandstone is represented as the value of the hydrocarbon porosity attribute (HPCV) is larger along with the increase of the porosity and the decrease of the water saturation (the increase of the hydrocarbon saturation).
Then, constructing an objective function based on a correlation analysis method, wherein the expression is as follows:
J(x,y)＝corr(F(x,y),HPCV)
wherein J (x, y) is the objective function value, corr is the correlation operation sign. The objective function J (x, y) is a function of the pending parameters x and y.
Step 104: solving an objective function by utilizing a two-dimensional space parameter scanning technology to obtain undetermined parameter values in the discrimination factors of the high-porosity hydrocarbon-bearing sandstone reservoir, wherein the undetermined parameter values comprise the following steps:
in the implementation process, the x and y parameters are scanned one by one, and the correlation coefficients of the discrimination factors of the high-porosity hydrocarbon-bearing sandstone reservoir and the hydrocarbon-bearing pore attributes corresponding to different (x, y) parameter combinations are respectively calculated, so that the corresponding parameter values of x and y are obtained when the maximum objective function value is obtained. The specific calculation process comprises the following steps:
firstly, setting the value range of a parameter x to be-50 to 30, the sampling interval to be 1 and the sampling point to be 81; the value range of the parameter y is-5 to 5, the sampling interval is 0.5, and the sampling point is 21; then, sequentially fixing the value of the parameter x (for example, when the value of x is 10), and calculating 21 high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor curves corresponding to all different values of y (namely-5 to 5); then, by repeating the above process while sequentially changing the value of the parameter x, all the discriminant factor curves 1701 (21 × 81) are obtained, and each discriminant factor curve corresponds to a different (x, y) parameter combination. Finally, the calculated discrimination factor curves are respectively subjected to correlation analysis with the hydrocarbon-containing pore property curves to obtain the correlation coefficients (1701) of each discrimination factor curve and the hydrocarbon-containing pore property curve. When the correlation coefficient is maximized, the corresponding discrimination factor attribute can best represent the pore development and the hydrocarbon-containing condition of the reservoir, and can be used for reservoir prediction and fluid detection, and the x and y parameter values corresponding to the maximized correlation coefficient are the parameter values of the undetermined parameters of the high-pore hydrocarbon-containing sandstone reservoir determination factor corresponding to the embodiment. Fig. 3 shows a distribution diagram of an objective function in a two-dimensional space of x and y of undetermined parameters in the embodiment, where the abscissa in the diagram is an x parameter value, the ordinate is a y parameter value, a dark color represents an area with a large correlation coefficient, and a light color represents an area with a small correlation coefficient, it can be seen that when (x, y) takes a value of (-18, -1.5), the correlation coefficient reaches 0.76 at maximum, and it can be determined that the values of the undetermined parameter values in the discrimination factor of the high-porosity hydrocarbon-bearing sandstone reservoir in this embodiment are x-18, and y-1.5, respectively. Fig. 4 shows a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor petrophysical analysis graph obtained by calculation in this example, in which the abscissa represents porosity and the ordinate represents water saturation, and it can be seen from the graph that the low value of the discrimination factor attribute corresponds to a high-quality reservoir region (lower right corner) with high porosity and low water saturation. According to geological knowledge of predecessors, when the stratum porosity is greater than 0.25 and the water saturation is less than 0.3, the stratum is determined to be a high-quality hydrocarbon-containing reservoir area, and according to the knowledge and the distribution rule of the discrimination factor of the high-porosity hydrocarbon-containing sandstone reservoir, the prediction threshold value of the discrimination factor of the high-porosity hydrocarbon-containing sandstone reservoir is obtained to be F < -1.2, namely when F < -1.2, the stratum corresponds to the distribution area of the high-porosity hydrocarbon-containing sandstone reservoir, and when F is greater than or equal to-1.2, the stratum corresponds to a non-reservoir area or a differential reservoir area.
Step 105: calculating a discriminating factor seismic data volume of the high-porosity hydrocarbon-bearing sandstone reservoir and predicting the reservoir, wherein the method comprises the following steps:
in the implementation process, input seismic inversion data and the undetermined parameter value of the discrimination factor obtained by analysis are substituted into a discrimination factor calculation formula of the high-porosity hydrocarbon-bearing sandstone reservoir to obtain a discrimination factor seismic data body, and reservoir prediction and fluid detection are carried out by combining with a reservoir discrimination factor prediction threshold value. The specific calculation process is as follows:
F＝(ρ×vp-18)×(vp/vs-1.5)
wherein,
Zp＝ρ×vp
Zs＝ρ×vs
vp/vs＝Zp/Zs
further, an expression for calculating the discrimination factor of the high-porosity hydrocarbon-bearing sandstone reservoir based on the wave impedance seismic data can be obtained:
F＝(Zp-18)×(Zp/Zs-1.5)
wherein Zp is input longitudinal wave impedance, Zs is input transverse wave impedance, and F is calculated high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor.
In the embodiment, the reservoir distribution range with the attribute value of the discrimination factor lower than the prediction threshold value (-1.2) is defined as a high-porosity and low-water-saturation high-quality reservoir area on the basis of the calculated discrimination factor seismic data of the high-porosity hydrocarbon-bearing sandstone reservoir, and the range higher than the threshold value is determined as a non-reservoir or poor reservoir area.
In order to verify the accuracy of the prediction result, the information of the drilled wells in the work area is utilized to carry out contrastive analysis, as shown in fig. 5, a distribution profile of the high-porosity hydrocarbon-bearing reservoir predicted according to the discrimination factor of the high-porosity hydrocarbon-bearing sandstone reservoir is shown, two wells pass through the profile, wherein the oil testing result of the W-1 well in the target interval (the elliptic dotted line region) is a developed three sets of high-yield oil and gas sandstone reservoirs, the oil testing result of the W-2 well in the target interval (the elliptic solid line region) is a developed set of thin reservoir, and the W-2 well is considered to be close to an oil-water interface through comprehensive construction and reservoir analysis, so that formation water is. In the prediction section shown in fig. 5, W-1 well corresponds to the low value of the discriminant factor attribute (F ═ 3.5 or so) in the target interval, and W-2 well corresponds to the high value of the discriminant factor attribute (F ═ 0.5 or so), which shows that the prediction result based on the discriminant factor attribute at the drilled well position is matched with the drilling conclusion, thus confirming the effectiveness of the method.
Example 2
The embodiment also provides a high-porosity hydrocarbon-bearing sandstone reservoir prediction device for implementing the high-porosity hydrocarbon-bearing sandstone reservoir prediction method, which is detailed in fig. 2 and comprises the following components:
the earthquake and logging data input unit 201 is used for inputting logging curve data and longitudinal and transverse wave impedance earthquake data of a work area; the logging curve data comprise longitudinal wave velocity, transverse wave velocity, density, porosity and water saturation curves, and the longitudinal wave impedance and the transverse wave impedance seismic data bodies can be obtained based on pre-stack inversion.
The discrimination factor construction unit 202 is used for constructing a discrimination factor of the high-porosity hydrocarbon-bearing sandstone reservoir with two-dimensional parameters to be determined based on the input logging curve data; wherein, the calculation formula of the discrimination factor of the high-porosity hydrocarbon-bearing sandstone reservoir is as follows:
F(x,y)＝(ρ×vp+x)×(vp/vs+y)
wherein F (x, y) is a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor which is a function of undetermined parameters x, y, rho is density and the unit is g/cm3，vpIs the longitudinal wave velocity in m/s, vsThe unit is the transverse wave velocity in m/s.
The objective function constructing unit 203 is used for constructing an objective function based on the high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor and the hydrocarbon-bearing pore property; wherein, the hydrocarbon-containing pore property calculation formula is as follows:
HPCV＝φ×(1-Sw)
wherein HPCV is a hydrocarbon-containing pore property,
for porosity, Sw is the water saturation;
the expression of the objective function is:
J(x,y)＝corr(F(x,y),HPCV)
j (x, y) is an objective function value which is a function of undetermined parameters x, y, F (x, y) is a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor, HPCV is a hydrocarbon-bearing pore attribute, and corr is a correlation operation symbol.
The objective function solving unit 204 is used for obtaining the undetermined parameter value in the high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor by solving an objective function; the method for acquiring the undetermined parameter value comprises the following steps: and calculating the correlation coefficient of the discrimination factor of the high-porosity hydrocarbon-bearing sandstone reservoir and the hydrocarbon-bearing pore attribute by using a two-dimensional space parameter scanning method to obtain the undetermined parameter value corresponding to the maximum objective function value.
The calculation and prediction unit 205 is used for calculating a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor seismic data volume according to the undetermined parameter values and the longitudinal and transverse wave impedance seismic data and performing reservoir prediction; the method for reservoir prediction specifically comprises the following steps: step one, substituting the undetermined parameter values obtained by solving the objective function and longitudinal and transverse wave impedance seismic data into a calculation formula of a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor to obtain a discrimination factor seismic data volume; secondly, obtaining a reservoir prediction threshold value according to the high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor petrophysical analysis, and comparing the seismic data volume obtained by the first step with the threshold value to predict the reservoir of the research work area; zp is longitudinal wave impedance, Zs is transverse wave impedance, ρ is density, vpIs the velocity of longitudinal wave, vsFor transverse wave velocity, Zp ═ ρ × vp，Zs＝ρ×vs，vp/vsZp/Zs; the high-porosity hydrocarbon-bearing sandstone reservoir discrimination petrophysical analysis is characterized by hydrocarbon-bearing pore properties.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only examples of the present invention, and are not intended to limit the scope of the present invention, and any modifications or equivalent substitutions made within the spirit and principle of the present invention should be covered by the claims of the present invention.
Claims (11)
1. A high pore hydrocarbon-bearing sandstone reservoir prediction method, wherein the method comprises the following steps:
(1) acquiring logging curve data and longitudinal and transverse wave impedance seismic data of a work area;
(2) constructing a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor with two-dimensional parameters to be determined based on the logging curve data obtained in the step (1);
(3) constructing an objective function based on the hydrocarbon-containing pore attributes and the high-pore hydrocarbon-containing sandstone reservoir discrimination factors obtained in the step (2);
(4) solving the objective function to obtain undetermined parameter values in the discrimination factors of the high-porosity hydrocarbon-bearing sandstone reservoir;
(5) and (3) calculating a discrimination factor seismic data body of the high-porosity hydrocarbon-bearing sandstone reservoir and predicting the reservoir according to the undetermined parameter values and the longitudinal wave impedance seismic data and the transverse wave impedance seismic data in the step (1).
2. The method of claim 1, wherein the well log data comprises compressional velocity, shear velocity, density, porosity, and water saturation curves.
3. The method of claim 1 or 2, wherein the compressional and shear wave impedance seismic data volumes are obtainable based on prestack inversion.
4. The method of any one of claims 1 to 3, wherein the high pore hydrocarbon containing sandstone reservoir discrimination is calculated by the formula:
F(x,y)＝(ρ×vp+x)×(vp/vs+y)
wherein F (x, y) is a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor which is a function of undetermined parameters x, y, rho is density and the unit is g/cm3，vpIs the longitudinal wave velocity in m/s, vsThe unit is the transverse wave velocity in m/s.
5. The method of any of claims 1-4, wherein the hydrocarbon-containing pore property is calculated by the formula:
HPCV＝φ×(1-Sw)
wherein HPCV is a hydrocarbon-containing pore property,
for porosity, Sw is the water saturation.
6. The method of any of claims 1-5, wherein the objective function is expressed as:
J(x,y)＝corr(F(x,y),HPCV)
j (x, y) is an objective function value which is a function of undetermined parameters x, y, F (x, y) is a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor, HPCV is a hydrocarbon-bearing pore attribute, and corr is a correlation operation symbol.
7. The method according to any one of claims 1-6, wherein the method for obtaining the value of the parameter to be determined in step (4) comprises: and calculating the correlation coefficient of the discrimination factor of the high-porosity hydrocarbon-bearing sandstone reservoir and the hydrocarbon-bearing pore attribute by using a two-dimensional space parameter scanning method to obtain the undetermined parameter value corresponding to the maximum objective function value.
8. The method of any one of claims 1-7, wherein the specific method of reservoir prediction in step (5) comprises:
step one, substituting the undetermined parameter values obtained by solving the objective function and longitudinal and transverse wave impedance seismic data into a calculation formula of a high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor to obtain a discrimination factor seismic data volume;
and secondly, obtaining a reservoir prediction threshold value according to the high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor petrophysical analysis, and comparing the seismic data body obtained by the first step with the threshold value to predict the reservoir of the research work area.
9. The method of claim 8, wherein Zp is longitudinal wave impedance in g/cm3M/s, Zs is the transverse wave impedance in g/cm3M/s, p is the density in g/cm3，vpIs the longitudinal wave velocity in m/s, vsThe unit is m/s for transverse wave velocity, and Zp is rho × vp，Zs＝ρ×vs，vp/vs＝Zp/Zs。
10. The method of claim 8, wherein the high pore hydrocarbon containing sandstone reservoir discrimination petrophysical analysis is characterized by hydrocarbon containing pore properties.
11. A high pore hydrocarbon-bearing sandstone reservoir prediction device, wherein the device is used for realizing the high pore hydrocarbon-bearing sandstone reservoir prediction method of any one of claims 1-10, and the device comprises:
the earthquake and logging data input unit is used for inputting logging curve data and longitudinal and transverse wave impedance earthquake data of a work area;
the discrimination factor construction unit is used for constructing a discrimination factor of the high-porosity hydrocarbon-bearing sandstone reservoir with two-dimensional parameters to be determined based on the input logging curve data;
the target function construction unit is used for constructing a target function based on the high-porosity hydrocarbon-bearing sandstone reservoir discrimination factor and the hydrocarbon-bearing pore attribute;
the objective function solving unit is used for obtaining undetermined parameter values in the discrimination factors of the high-porosity hydrocarbon-bearing sandstone reservoir by solving an objective function;
and the calculation and prediction unit is used for calculating the discrimination factor seismic data volume of the high-porosity hydrocarbon-bearing sandstone reservoir and performing reservoir prediction according to the undetermined parameter values and the longitudinal wave impedance seismic data and the transverse wave impedance seismic data.
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