CN113960659A - Seismic rock physical driving coalbed methane reservoir gas content prediction method - Google Patents
Seismic rock physical driving coalbed methane reservoir gas content prediction method Download PDFInfo
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- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 title claims abstract description 94
- 239000011435 rock Substances 0.000 title claims abstract description 79
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Abstract
The invention provides a method for predicting gas content of a coal bed gas reservoir driven by seismic petrophysical, which comprises the following steps of: according to the component attributes of the coal bed gas reservoir, setting organic components including pure coal and adsorption state coal bed gas; inorganic mineral components include clay, quartz, calcite and pyrite; the coal bed gas reservoir rock physical model is equivalent to a coal rock matrix, a coal rock framework, a pore filler and saturated coal rock; constructing a prediction learning sample of the gas content of the coalbed methane reservoir: intercept, gradient, instantaneous amplitude and instantaneous frequency attribute form the input value of the coal bed gas reservoir gas content prediction learning sample, and the coal bed gas reservoir gas content corresponding to the input value is the output value of the learning sample; constructing a prediction model of the gas content of the coal bed gas reservoir; and (3) practice of predicting the gas content of the coal bed gas reservoir: and extracting intercept, gradient, amplitude and frequency attributes of the actual three-dimensional seismic data to serve as input data, and performing practical prediction on the gas content of the coal bed methane reservoir in the target area by adopting a gas content prediction model.
Description
Technical Field
The invention belongs to the technical field of exploration geophysical reservoir prediction, relates to prediction of coal bed gas content, and particularly relates to a seismic rock physical driving coal bed gas reservoir gas content prediction method.
Background
The coal bed gas is a self-generated self-storage unconventional natural gas which is stored in a coal bed and mainly takes an adsorption state (a little of free gas and dissolved gas), the methane content of the unconventional natural gas is more than 90 percent, and coal mines are commonly called as 'gas' and are very important clean energy sources in unconventional oil gas. China is the third global coalbed methane resource next to Canada and Russia, the amount of the coalbed methane resource buried deep by 2000 m and shallow is 36.8 billions of cubic meters, and the development and utilization prospect is wide. The gas content of the coal bed gas reservoir is an essential important parameter for coal bed gas exploration and development, area selection evaluation, safe production and reservoir research, the coal bed gas reserves can be accurately calculated according to the gas content of the coal bed gas reservoir, favorable development areas are screened out, and huge economic loss caused by blind exploitation is avoided. Therefore, the research on the gas content of the coal bed gas reservoir has important theoretical significance and practical application value.
The Chinese patent discloses an intelligent gas-bearing property prediction method of a compact reservoir based on rock physical modeling, and the application number is as follows: 201810657579.7. the prior patents include: step 1, establishing a petrophysical model according to a relation between reservoir physical properties and elastic parameters by a plaque saturation theory; step 2, establishing a rock physical template through forward modeling; step 3, carrying out inversion by utilizing a particle swarm algorithm to obtain velocity volumes under different frequencies; and 4, aiming at different reservoir interval extraction speed dispersion curves, comparing the extracted speed dispersion curves with a rock physical template, and analyzing the gas-bearing characteristics of the reservoir interval. The intelligent gas-bearing property prediction method for the compact reservoir based on the rock physics modeling in the prior art can make full use of the memory and information sharing characteristics of particle groups, quickly search an optimal solution in the whole search space, accurately describe the longitudinal wave velocity frequency dispersion characteristics of the underground reservoir, and overcome the defects that the traditional reservoir prediction technology depends on manual operation more and wastes time and labor.
The conventional coal bed gas content prediction method can be divided into a direct method and an indirect method. The direct method is to take a coal sample by drilling and measure the content of the coal bed gas of the coal sample by a desorption method. The indirect method comprises the following steps: isothermal-adsorption curve method, gas content gradient method, logging curve method, coal quality-ash content-gas content ratio method, modern mathematical method and geological condition synthesis method. However, each method has the characteristics and applicable conditions. During the coring and canning process by the direct method, part of the water-soluble gas and the free gas in the coal sample, even the minimum part of the adsorbed gas which is desorbed is dissipated. Meanwhile, in the desorption process of directly reducing the reservoir pressure to the atmospheric pressure, part of gas is locked by gas and can not be completely desorbed. The reasons often cause the measured value of the gas content of the coal bed by the direct method to be low; the gas content calculation formula of the logging method in the indirect method has regionality and no universality and generality; the isothermal-adsorption curve method has a large error in the prediction of the gas content of high-order coal.
Disclosure of Invention
The invention aims to solve the problems in the prior art, and provides a method for predicting gas content of a coal bed gas reservoir, which is implemented by taking a seismic rock physics theory as a bridge, constructing a rock physics model of the coal bed gas reservoir, and deeply excavating a relation between seismic attributes and gas content of the coal bed gas reservoir by using a support vector machine algorithm in an artificial intelligence method.
The purpose of the invention can be realized by the following technical scheme: a method for predicting gas content of a coal bed gas reservoir driven by seismic rock physics comprises the following steps:
1) physical modeling of coal bed gas reservoir rock
Setting 85% of organic components including pure coal and coal bed gas in an adsorption state according to the component properties of the coal bed gas reservoir; 15% of inorganic mineral components, including 40% of quartz, 40% of clay, 15% of calcite and 5% of pyrite; the physical rock model of the coal bed gas reservoir is equivalent to four parts of a coal rock matrix, a coal rock framework, a pore filler, saturated coal rock and the like;
(1) mixing pure coal with quartz, clay, calcite and pyrite according to a set proportion by using a Voigt-Ruess-Hill boundary theory to obtain a coal-rock matrix elastic modulus parameter without adsorption state coal bed gas;
(2) taking the adsorption-state coal bed gas as one of important components of the coal-rock matrix, and mixing the adsorption-state coal bed gas with the coal-rock matrix by using a Hashin-Shtrikman boundary model to obtain an elastic modulus parameter of the coal-rock matrix with the adsorption-state coal bed gas;
(3) adding dry pores into the coal rock matrix by using a differential equivalent medium model, and calculating the elastic modulus of a dry rock skeleton;
(4) mixing free coal bed gas and water in pores according to a Wood formula to obtain fluid with mixture properties, and solving parameters of equivalent elastic modulus and density of the fluid;
(5) according to a Gassmann rock fluid substitution equation, obtaining the elastic modulus of a saturated coal bed gas reservoir rock physical model, and solving longitudinal wave velocity, transverse wave velocity and density parameters of the saturated coal bed gas reservoir rock physical model;
2) construction of prediction learning sample of gas content of coal bed gas reservoir
(1) According to the coal bed gas reservoir rock physical modeling step, the gas content of the coal bed gas reservoir is calculated from 0-30m3Change in t in increments of 1m3/t；
(2) Taking the longitudinal wave velocity, the transverse wave velocity and the density parameter of the coalbed methane reservoir as basic data, and extracting intercept and gradient attributes sensitive to the coalbed methane gas content by using the following Shuey equation:
R(θ)≈P+G sin2θ
wherein P represents the intercept of AVO and G represents the gradient of AVO;
the meaning of each symbol in the formula:
in the formula, Vp1，Vs1，ρ1Respectively representing the longitudinal wave velocity, the transverse wave velocity and the density of the medium coated on the reflecting interface; vp2，Vs2，ρ2Respectively representing the longitudinal wave velocity, the transverse wave velocity and the density of the underlying medium of the reflecting interface; vp，VsRho respectively represents the average values of the longitudinal wave velocity, the transverse wave velocity and the density of the media on two sides of the reflecting interface; theta is an incident angle;
(3) according to convolution theory, the seismic record of a seismic trace can be expressed as:
y＝w*r
in the formula, y represents a seismic record, w represents a wavelet, and r represents a reflection coefficient; combining the actual situation of coal field seismic exploration, selecting a 45Hz Rake wavelet, synthesizing seismic records under the conditions of different gas contents by utilizing a convolution theory, and extracting the instantaneous amplitude and instantaneous frequency attribute which are sensitive to the content of coal bed gas;
the intercept, the gradient, the instantaneous amplitude and the instantaneous frequency attribute form an input value of a coal bed gas reservoir gas content prediction learning sample, and the coal bed gas reservoir gas content corresponding to the input value is an output value of the learning sample;
3) construction of prediction model for gas content of coal bed gas reservoir
Randomly dividing all the constructed learning samples according to a ratio of 7:3 by utilizing a support vector machine algorithm, wherein 70% of the learning samples are used for learning, 30% of the learning samples are used for testing, and when a test result meets a set threshold value, the constructed gas content prediction model is considered to be qualified and is used for predicting the gas content of the coal bed methane reservoir of actual data;
4) and practice of predicting gas content of coal bed gas reservoir
And extracting intercept, gradient, amplitude and frequency attributes of the actual three-dimensional seismic data to serve as input data, and adopting a qualified gas content prediction model to carry out practical prediction on the gas content of the coal bed methane reservoir in the target area.
In the method for predicting the gas content of the coalbed methane reservoir driven by the seismic rock physics, in the step 1) (3), the pores comprise ellipsoidal matrix pores and coin-shaped cracks, and the size of the cracks is larger than that of the matrix pores.
In the method for predicting the gas content of the coalbed methane reservoir physically driven by the earthquake rock, in the step (1) in the step 2), the calculated porosity is changed from 0-10%, and the increment is 1%; and calculating that the ratio of the ellipsoidal matrix pores to the total porosity is changed from 0-100%, the ratio of the coin-shaped fractures to the total porosity is changed from 100-0%, and the variation is 1% of corresponding longitudinal wave velocity, transverse wave velocity and density parameters of the coalbed methane reservoir.
In the method for predicting the gas content of the coalbed methane reservoir driven by seismic rock physics, in the step 2) (2), the absolute values of intercept and gradient attribute show a trend that the absolute values are gradually increased along with the increase of the gas content of the coalbed methane reservoir, and the trend is used as a sensitive attribute for predicting the coalbed methane content.
In the method for predicting the gas content of the coalbed methane reservoir driven by seismic rock physics, in the step (3) in the step 2), the instantaneous amplitude tends to increase along with the increase of the gas content of the coalbed methane reservoir, and the instantaneous frequency tends to decrease, so that the characteristic is taken as the sensitive attribute of the coalbed methane content prediction.
Compared with the prior art, the method for predicting the gas content of the coal bed methane reservoir driven by the local seismic rock physics has the following beneficial effects:
1. constructing a coal bed gas reservoir rock physical model with adsorptivity and a dual-pore structure:
the constructed physical rock model of the coal bed gas reservoir mainly considers the problems that organic matters and inorganic matters in coal rock matrix, coal bed gas mainly takes an adsorption state, coal rock has a fracture and pore dual pore structure and the like, so that the constructed physical rock model has high goodness of fit with actual conditions, and a reliable theoretical model can be provided for the prediction of gas content.
2. The method for predicting the gas content of the coal bed gas reservoir has high intelligent degree:
based on the constructed coal bed gas reservoir rock physical model, a support vector machine algorithm in an artificial intelligence method is utilized, on the basis of sufficient learning and training, aiming at the coal bed gas reservoir gas content prediction of actual seismic data, only four seismic attributes such as intercept, gradient, amplitude and frequency attribute need to be extracted, the gas content prediction can be realized by utilizing the prediction model, the prediction efficiency is greatly improved, and the interference of human factors is reduced. The method has the characteristics of intellectualization, high speed and high efficiency, can obtain high-precision gas content parameters, and has important theoretical significance and practical application price for the development and utilization of the coal bed gas reservoir.
Drawings
FIG. 1 is a technical flow framework diagram of the present invention.
FIG. 2 is a plot of coalbed methane content as a function of intercept and gradient attributes in accordance with the present invention.
FIG. 3 is a plot of coalbed methane content versus instantaneous amplitude for the present invention.
FIG. 4 is a plot of coalbed methane content versus instantaneous frequency for the present invention.
FIG. 5 is a diagram of the prediction result of the gas content of the coalbed methane reservoir according to the invention.
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings:
as shown in fig. 1, the coal bed gas is mainly present in an adsorbed state on the pore space and the fracture surface of the coal rock (more than 90%), and a smaller amount of coal bed gas is present in a free state in the pore space and the fracture space or dissolved in the pore space and the fracture water. The coal rock mainly comprises organic components and inorganic components, and the content of the organic components is usually higher, most of the organic components are more than 50 percent, even more than 80 percent; the inorganic component comprises clay, quartz, calcite, pyrite, etc. On the basis of the above knowledge, when the physical modeling of the coal bed gas reservoir rock is carried out, 85% of organic components including pure coal and coal bed gas in an adsorption state are set; in 15% of the inorganic mineral components, the quartz content is 40%, the clay content is 40%, the calcite content is 15%, and the pyrite content is 5%. The coal bed gas reservoir rock physical model is equivalent to four parts of a coal rock matrix, a coal rock framework, a pore filler, saturated coal rock and the like, and the coal bed gas reservoir rock physical model which is composed of organic components and inorganic components and has adsorbability and a dual pore structure is constructed. The modeling comprises the following specific steps:
1) pure coal and minerals such as quartz, clay, calcite, pyrite and the like are mixed according to a set proportion by applying a Voigt-Ruess-Hill boundary theory to obtain the coal-rock matrix elastic modulus parameter without the adsorption state coal bed gas.
2) The adsorption-state coal bed gas is used as one of important components of the coal-rock matrix, and the adsorption-state coal bed gas and the coal-rock matrix are mixed by using a Hashin-Shtrikman boundary model to obtain the elastic modulus parameter of the coal-rock matrix with the adsorption-state coal bed gas.
3) Adding dry pores into the coal rock matrix by using a differential equivalent medium model, and calculating the elastic modulus of a dry rock skeleton; wherein the porosity is mainly considered: ellipsoidal matrix pores and coin-shaped fissures, and the dimensions of the fissures are larger than the dimensions of the matrix pores.
4) And (3) mixing the free coal bed gas and the water in the pores by using a Wood formula to obtain a fluid with the property of a mixture, and solving parameters such as equivalent elastic modulus, density and the like of the fluid.
5) And obtaining the elastic modulus of the saturated coal bed gas reservoir rock physical model according to a Gassmann rock fluid substitution equation, and solving parameters such as longitudinal wave velocity, transverse wave velocity and density of the saturated coal bed gas reservoir rock physical model.
The physical modeling method for the coal bed gas reservoir rock considers three characteristics of the coal bed gas reservoir, namely: the coal rock consists of organic components and inorganic components; the coal bed gas is mainly in an adsorption state and is assisted in a free state; coal rock has 2 types of pores, namely matrix pores and fissures.
Second, construction of prediction learning sample of gas content of coal bed gas reservoir
1) According to the coal bed gas reservoir rock physical modeling process, the content of the coal bed gas reservoir is calculatedThe gas amount is from 0 to 30m3Change in t in increments of 1m3T; porosity was varied from 0-10% in 1% increments; the ratio of the ellipsoidal pores to the total porosity is changed from 0-100%, the ratio of the coin-shaped cracks to the total porosity is changed from 100-0%, and the variation is 1% of corresponding parameters such as longitudinal wave, transverse wave speed and density of the coalbed methane reservoir.
2) And (3) extracting intercept and gradient attributes sensitive to the gas content of the coal bed gas by using parameters such as longitudinal wave, transverse wave speed and density of the coal bed gas reservoir as basic data and using a Shuey equation as shown in a formula (1).
R(θ)≈P+G sin2θ
Where P represents the intercept of the AVO and G represents the gradient of the AVO.
The meaning of each symbol in the formula:
in the formula (I), the compound is shown in the specification,
Vp1，Vs1，ρ1respectively representing the longitudinal wave velocity, the transverse wave velocity and the density of the medium coated on the reflecting interface;
Vp2，Vs2，ρ2respectively representing the longitudinal wave velocity of the medium under the reflecting interface,Shear wave velocity and density;
Vp，Vsrho respectively represents the average values of the longitudinal wave velocity, the transverse wave velocity and the density of the media on two sides of the reflecting interface;
θ is the angle of incidence.
3) According to convolution theory, as shown in equation (7), the seismic record of a seismic trace can be expressed as:
y＝w*r
y represents the seismic record, w represents the wavelet, and r represents the reflection coefficient. Combining the actual situation of coal field seismic exploration, selecting 45Hz Rake wavelets as wavelets, synthesizing seismic records under the conditions of different gas contents by utilizing the convolution theory, and extracting the instantaneous amplitude and instantaneous frequency attributes which are sensitive to the content of coal bed gas.
The intercept, the gradient, the amplitude and the frequency attribute form an input value of the coal bed gas reservoir gas content prediction learning sample, and the coal bed gas reservoir gas content corresponding to the input value is an output value of the learning sample.
Third, construction of prediction model of gas content of coal bed gas reservoir
And (3) randomly dividing the constructed learning samples according to a ratio of 7:3 by utilizing a support vector machine algorithm, wherein 70% of the learning samples are used for learning, 30% of the learning samples are used for testing, and when a test result meets a set threshold value, the constructed gas content prediction model is considered to have an ideal effect and can be used for predicting the gas content of the coal bed methane reservoir with actual data.
Fourthly, forecasting gas content of coal bed gas reservoir
And extracting intercept, gradient, amplitude and frequency attributes of actual three-dimensional seismic data to serve as input data, and adopting the trained gas content prediction model to realize prediction of the gas content of the coal bed methane reservoir in the target area.
As shown in fig. 1, the content mainly includes three parts, namely: 1) constructing a physical rock model of the coal bed gas reservoir; 2) extracting attributes of intercept, gradient, instantaneous amplitude and instantaneous frequency; 3) and (3) learning, training and predicting the gas content of the coal bed gas reservoir based on the support vector machine.
As shown in fig. 2, as the gas content of the coalbed methane reservoir increases, the absolute values of the intercept and the gradient attribute show a trend that the absolute values gradually increase, and the absolute values can be used as sensitive attributes for coalbed methane content prediction.
As shown in fig. 3 and 4, as the gas content of the coalbed methane reservoir increases, the instantaneous amplitude tends to increase, and the instantaneous frequency tends to decrease, and the instantaneous amplitude and the instantaneous frequency can also be used as sensitive attributes for coalbed methane content prediction.
The result of the coal bed gas content is predicted by using the four seismic attributes of the actual seismic data, such as intercept, gradient, amplitude, frequency and the like, as shown in fig. 5, the coincidence degree of the gas content value and the actually measured gas content value at the drill hole is higher, and the coal bed gas content of the three wells is higher (the actually measured gas content is 18.02 m)3/t、17.58m3T and 16.86m3T) are positioned in two wells with higher gas content and lower gas content (actually measured gas content is 9.79 m)3T and 8.68m3T) is located in the lower region and the gas content is 12.51m3T and 10.12m3The/t well is located in the transition zone. The prediction result shows that the method for predicting the gas content of the coal bed gas reservoir driven by the seismic rock physics can effectively predict the gas content of the coal bed gas, improve the prediction precision and reduce the prediction risk.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.
Claims (5)
1. A method for predicting gas content of a coal bed gas reservoir driven by seismic rock physics is characterized by comprising the following steps:
1) physical modeling of coal bed gas reservoir rock
Setting 85% of organic components including pure coal and coal bed gas in an adsorption state according to the component properties of the coal bed gas reservoir; 15% of inorganic mineral components, including 40% of quartz, 40% of clay, 15% of calcite and 5% of pyrite; the physical rock model of the coal bed gas reservoir is equivalent to four parts of a coal rock matrix, a coal rock framework, a pore filler and a saturated coal rock;
(1) mixing pure coal with quartz, clay, calcite and pyrite according to a set proportion by using a Voigt-Ruess-Hill boundary theory to obtain a coal-rock matrix elastic modulus parameter without adsorption state coal bed gas;
(2) taking the adsorption-state coal bed gas as one of important components of the coal-rock matrix, and mixing the adsorption-state coal bed gas with the coal-rock matrix by using a Hashin-Shtrikman boundary model to obtain an elastic modulus parameter of the coal-rock matrix with the adsorption-state coal bed gas;
(3) adding dry pores into the coal rock matrix by using a differential equivalent medium model, and calculating the elastic modulus of a dry rock skeleton;
(4) mixing free coal bed gas and water in pores according to a Wood formula to obtain fluid with mixture properties, and solving parameters of equivalent elastic modulus and density of the fluid;
(5) according to a Gassmann rock fluid substitution equation, obtaining the elastic modulus of a saturated coal bed gas reservoir rock physical model, and solving longitudinal wave velocity, transverse wave velocity and density parameters of the saturated coal bed gas reservoir rock physical model;
2) construction of prediction learning sample of gas content of coal bed gas reservoir
(1) According to the coal bed gas reservoir rock physical modeling step, the gas content of the coal bed gas reservoir is calculated from 0-30m3Change in t in increments of 1m3/t；
(2) And (3) taking the longitudinal wave velocity, the transverse wave velocity and the density parameter of the coalbed methane reservoir as basic data, and extracting intercept and gradient attributes sensitive to the coalbed methane gas content by using a Shuey equation:
R(θ)≈P+G sin2θ
wherein P represents the intercept of AVO and G represents the gradient of AVO;
the meaning of each symbol in the formula:
in the formula, Vp1，Vs1，ρ1Respectively representing the longitudinal wave velocity, the transverse wave velocity and the density of the medium coated on the reflecting interface; vp2，Vs2，ρ2Respectively representing the longitudinal wave velocity, the transverse wave velocity and the density of the underlying medium of the reflecting interface; vp，VsRho respectively represents the average values of the longitudinal wave velocity, the transverse wave velocity and the density of the media on two sides of the reflecting interface; theta is an incident angle;
(3) according to convolution theory, the seismic record of a seismic trace can be expressed as:
y＝w*r
in the formula, y represents a seismic record, w represents a wavelet, and r represents a reflection coefficient; combining the actual situation of coal field seismic exploration, selecting a 45Hz Rake wavelet, synthesizing seismic records under the conditions of different gas contents by utilizing a convolution theory, and extracting the instantaneous amplitude and instantaneous frequency attribute which are sensitive to the content of coal bed gas;
the intercept, the gradient, the instantaneous amplitude and the instantaneous frequency attribute form an input value of a coal bed gas reservoir gas content prediction learning sample, and the coal bed gas reservoir gas content corresponding to the input value is an output value of the learning sample;
3) construction of prediction model for gas content of coal bed gas reservoir
Randomly dividing all the constructed learning samples according to a ratio of 7:3 by utilizing a support vector machine algorithm, wherein 70% of the learning samples are used for learning, 30% of the learning samples are used for testing, and when a test result meets a set threshold value, the constructed gas content prediction model is considered to be qualified and is used for predicting the gas content of the coal bed methane reservoir of actual data;
4) and practice of predicting gas content of coal bed gas reservoir
And extracting intercept, gradient, amplitude and frequency attributes of the actual three-dimensional seismic data to serve as input data, and adopting a qualified gas content prediction model to carry out practical prediction on the gas content of the coal bed methane reservoir in the target area.
2. The method for predicting the gas content of the seismic petrophysically-driven coalbed methane reservoir of claim 1, wherein in step 1) (3), the pores comprise ellipsoidal matrix pores and coin-shaped cracks, and the size of the cracks is larger than that of the matrix pores.
3. The method for predicting the gas content of the seismic petrophysically-driven coalbed methane reservoir as claimed in claim 2, wherein in the step 2) (1), the calculated porosity is changed from 0-10% and the increment is 1%; and calculating that the ratio of the ellipsoidal matrix pores to the total porosity is changed from 0-100%, the ratio of the coin-shaped fractures to the total porosity is changed from 100-0%, and the variation is 1% of corresponding longitudinal wave velocity, transverse wave velocity and density parameters of the coalbed methane reservoir.
4. The method for predicting the gas content of the seismic petrophysically-driven coalbed methane reservoir in the step 2), wherein in the step 2), the absolute values of the intercept and the gradient attribute show a trend of gradually increasing the absolute values with the increase of the gas content of the coalbed methane reservoir, and the trend is taken as a sensitive attribute for predicting the gas content of the coalbed methane.
5. The method for predicting the gas content of the seismic petrophysically-driven coalbed methane reservoir in the step 2) is characterized in that in the step 3), the instantaneous amplitude tends to increase along with the increase of the gas content of the coalbed methane reservoir, and the instantaneous frequency tends to decrease, so that the sensitivity of the prediction of the coalbed methane content is obtained.
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| Application Number | Priority Date | Filing Date | Title |
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