CN107797139A - Shale reservoir free gas air content earthquake prediction method and system - Google Patents
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Abstract
Disclose a kind of shale reservoir free gas air content earthquake prediction method and system.This method can include：Based on work area earthquake data before superposition body and Depth Domain log data, work area elastic parameter and time-domain log data are obtained respectively；Based on work area elastic parameter and time-domain log data, petrophysical model is established, with reference to Bayes's inversion algorithm, obtains porosity and gas saturation；Based on seismic data and geothermal gradient, strata pressure and formation temperature are obtained respectively, and then obtain volume factor；And based on porosity, gas saturation, volume factor and strata pressure, the computation model of free gas air content is established, free gas air content is obtained, wherein, work area elastic parameter includes p-wave impedance, S-wave impedance and density.The embodiment realizes the high-precision forecast of shale reservoir free gas air content by factor constraints such as formation temperature, strata pressure, porosity and gas saturation.
Description
Technical Field
The invention relates to the field of oil and gas geophysical exploration, in particular to a shale reservoir free gas content earthquake prediction method and system.
Background
Shale gas is natural gas energy which is stored in shale layers and can be exploited, has the advantages of long exploitation life, long production period and the like, and is a clean and efficient energy resource. The shale gas in a free state exists in pores or fissures of the shale, the quantity of the shale gas is determined by the space of the pore fissures in the shale, and the shale gas content has important significance for shale reservoir evaluation and favorable area optimization. The existing methods for predicting gas content include an on-site desorption method, a well logging desorption method, a TOC fitting method and the like.
The inventor finds that the existing method has the problems of small estimated quantity, large error and the like because other influence factors are ignored, so that the obtained gas content value has large difference, and difficulty is brought to the prediction of resource reserves and the optimization of a favorable area by using the gas content. Therefore, a method and a system capable of predicting the free gas content of the shale reservoir with high accuracy are needed to be developed.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art that is already known to a person skilled in the art.
Disclosure of Invention
The invention provides a shale reservoir free gas content seismic prediction method and a shale reservoir free gas content seismic prediction system, which can realize high-precision prediction of shale reservoir free gas content through constraint of factors such as stratum temperature, stratum pressure, porosity, gas saturation and the like.
According to an aspect of the invention, a shale reservoir free gas content seismic prediction method is provided, which may include: respectively acquiring elastic parameters of a work area and logging data of a time domain based on the pre-stack seismic data volume and the logging data of the depth domain of the work area; establishing a rock physical model based on the elastic parameters of the work area and the logging data of the time domain, and obtaining porosity and gas saturation by combining a Bayesian inversion algorithm; respectively obtaining the formation pressure and the formation temperature based on the seismic data and the geothermal gradient, and further obtaining a volume coefficient; and establishing a calculation model of the free gas content based on the porosity, the gas saturation, the volume coefficient and the formation pressure to obtain the free gas content, wherein the work area elastic parameters comprise longitudinal wave impedance, transverse wave impedance and density.
According to another aspect of the invention, a shale reservoir free gas content seismic prediction system is provided, which may comprise: a unit for respectively acquiring the elastic parameters of the work area and the logging data of the time domain based on the logging data of the pre-stack seismic data volume and the depth domain of the work area; a unit for establishing a rock physical model based on the work area elastic parameters and the logging data of the time domain, and obtaining porosity and gas saturation by combining a Bayesian inversion algorithm; a unit for obtaining the formation pressure and the formation temperature respectively based on the seismic data and the geothermal gradient, and further obtaining the volume coefficient; and a unit for establishing a calculation model of the free gas content based on the porosity, the gas saturation, the volume coefficient and the formation pressure to obtain the free gas content, wherein the work area elastic parameters comprise longitudinal wave impedance, transverse wave impedance and density.
The method and apparatus of the present invention have other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts throughout.
Fig. 1 shows a flow chart of the steps of the shale reservoir free gas content seismic prediction method according to the invention.
Fig. 2 shows a schematic representation of the free gas content according to an embodiment of the invention.
Detailed Description
The invention will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Embodiment mode 1
Fig. 1 shows a flow chart of the steps of the shale reservoir free gas content seismic prediction method according to the invention.
In this embodiment, the shale reservoir free gas content seismic prediction method according to the invention may include: step 101, respectively acquiring elastic parameters of a work area and logging data of a time domain based on a pre-stack seismic data volume and the logging data of a depth domain of the work area; 102, establishing a rock physical model based on the elastic parameters of the work area and the logging data of the time domain, and obtaining the porosity and the gas saturation by combining a Bayesian inversion algorithm; 103, respectively obtaining formation pressure and formation temperature based on the seismic data and the earth temperature gradient, and further obtaining a volume coefficient; and step 104, establishing a calculation model of the free gas content based on the porosity, the gas saturation, the volume coefficient and the formation pressure to obtain the free gas content, wherein the elastic parameters of the work area comprise longitudinal wave impedance, transverse wave impedance and density.
According to the implementation mode, the high-precision prediction of the free gas content of the shale reservoir is realized through the constraints of factors such as the formation temperature, the formation pressure, the porosity and the gas saturation.
The concrete steps of the shale reservoir free gas content earthquake prediction method according to the invention are explained in detail below.
In one example, a work area elastic parameter and time domain well logging data are respectively obtained based on a work area pre-stack seismic data volume and depth domain well logging data, wherein the work area elastic parameter comprises longitudinal wave impedance, transverse wave impedance and density.
Specifically, based on the work area prestack seismic data volume, by a seismic inversion method, commercial software such as Jason, strata and the like can be utilized to select appropriate wavelets, low-frequency models and appropriate inversion parameters, so that longitudinal wave impedance Ip (x, y, t), transverse wave impedance Is (x, y, t) and density ρ (x, y, t) with high longitudinal resolution can be obtained.
Specifically, the Well log data Well (z) of the depth domain may include: longitudinal wave impedance Ip (z), transverse wave impedance Is (z), density rho (z), porosity phi (z), gas saturation S g (z) longitudinal wave velocity vp (z).
Specifically, time-depth conversion is performed on the logging data of the depth domain to obtain the logging data of the time domain, and a relational expression of the time-depth conversion may be:
where t is the time domain, z is the depth domain, and vp (z) is the longitudinal wave velocity.
Specifically, the time domain well log data may include: longitudinal wave impedance curve Ip (t) of time domain, transverse wave impedance Is (t) of time domain, density curve rho (t) of time domain, porosity phi (t) of time domain, and gas saturation S of time domain g (t)。
In one example, a petrophysical model is established based on the work area elastic parameters and the logging data of a time domain, and the porosity and the gas saturation are obtained by combining a Bayesian inversion algorithm.
Specifically, based on the work area elastic parameters and the logging data of the time domain, an anisotropic rock physical model of the shale reservoir is established, and further, a functional relation among the porosity, the gas saturation and the work area elastic parameters is established, wherein the functional relation can be as follows:
{Ip(t),Is(t),ρ(t)}＝f(φ(t),S g (t)…) (2)
in one example, an objective function is constructed based on a Bayesian inversion algorithm and an a priori distribution of porosity and gas saturation is determined.
Specifically, the invention can adopt a Bayesian classification algorithm, and the objective function is expressed as the porosity phi and the gas saturation S under the condition of the known elastic parameters of the work area g Maximum a posteriori probability distribution of:
{φ,S g }＝argMaxp(φ j ,S gj …|Ip,Is,ρ) j＝1,2…,Nc (3)
wherein, { phi, S g Denotes the solution for porosity and gas saturation, p (phi) j ,S gj … | Ip, is, ρ) represents the posterior probability of porosity and gas saturation, nc represents the number of classified classes, j represents the class number, and Nc can be set by those skilled in the art to control the prediction accuracy.
That is, if:
p(φ j ,S gj …|Ip,Is,ρ)>p(φ m ,S gm …|Ip,Is,ρ) 1≤j,m≤Nc,j≠m (4)
then:
φ＝φ j ，S g ＝S gj (5)
wherein, the significance of (4) and (5) Is that under the condition of known longitudinal wave impedance Ip, transverse wave impedance Is and density rho, the porosity phi and the gas saturation S are obtained g A posterior probability of different values, wherein the value of the maximum posterior probability is phi = phi j ，S g ＝S gj Is a solution of porosity and gas saturation.
In one example, the objective function may be:
{φ,S g }＝argMax{p(Ip,Is,ρ|φ,S g …)·p(φ,S g …)} (6)
wherein, { phi, S g Denotes the solution of porosity and gas saturation, ip denotes the longitudinal wave impedance, is denotes the transverse wave impedance, p denotes the density, p (phi, S) g …) represents an a priori distribution of porosity and gas saturation, p (Ip, is, ρ | φ, S) g …) represents a likelihood function.
Specifically, based on the bayesian formula, formula (3) can be rewritten as formula (6). Assuming that porosity and gas saturation obey a multidimensional gaussian distribution, i.e. the prior distribution of porosity and gas saturation may be:
wherein F represents a multi-dimensional Gaussian distribution,is the mean of a multi-dimensional gaussian distribution,is the variance of multidimensional Gaussian distribution, nr is the number of the variables of porosity and gas saturation in the rock physical model, and the weight coefficient alpha k Satisfy the requirements of
In one example, joint random simulation results of porosity, gas saturation and work area elasticity parameters are obtained based on prior distribution of porosity and gas saturation in combination with a sampling algorithm.
Specifically, based on the prior distribution of porosity and gas saturation, the porosity and gas saturation are randomly simulated by using a sampling algorithm, and a random simulation result { phi } of the porosity and the gas saturation can be obtained i ,S gi …} i＝1…N . The random simulation can be performed by using the MCMC sampling Metropolis Hastings sampling algorithm and other existing sampling algorithms.
Can be converted into [ phi ] i ,S gi …} i＝1…N And (2) obtaining a joint random simulation result of the work area elasticity parameter, the porosity and the gas saturation as follows:
{Ip i ,Is i ,ρ i ,φ i ,S gi …} i＝1…N (8)
where N is the number of random samples, i represents the ith random sample, i =1 … N, ip i Is the longitudinal wave impedance of the ith random sample i Transverse wave impedance, p, for the ith random sample i Is the density of the ith random sample, phi i Porosity, S, for the ith random sample gi The gas saturation for the ith random sample.
In one example, the objective function is solved based on a bayesian classification algorithm and joint stochastic simulation results of porosity, gas saturation, and work area elasticity parameters.
In one example, solving the objective function may be:
where N represents the number of random samples, N represents the count statistics,represents a statistic Ip i ,Is i ,ρ i ,φ i ,S gi ,…} i＝1…N The value of middle phi is equal to phi i ，S g Has a value of S gi And the number of randomly sampled sampling points n (Is # phi) with longitudinal wave impedance Ip i ,S gi …) represents the statistics Ip i ,Is i ,ρ i ,φ i ,S gi ,…} i＝1…N The value of middle phi is equal to phi i ，S g Has a value of S gi And the number of randomly sampled sampling points whose transverse wave impedance Is, n: (ρ∩φ i ,S gi …) represents the statistics Ip i ,Is i ,ρ i ,φ i ,S gi ,…} i＝1…N The value of middle phi is equal to phi i ，S g Has a value of S gi And the number of randomly sampled sampling points with the density of rho.
Specifically, based on the conditional independence assumption of the bayesian classification algorithm, the calculation form of the objective function can be rewritten as:
p(φ j ,S gj …|Ip,Is,ρ)＝p(Ip|φ j ,S gj ,…)·p(Is|φ j ,S gj ,…)·p(ρ|φ j ,S gj ,…)·p(φ j ,S gj ,…) (10)
bringing (8) into (10) can result in:
in one example, the porosity and gas saturation are obtained by fitting the work zone elasticity parameters into an objective function.
Specifically, the longitudinal wave impedance Ip (x, y, t), the transverse wave impedance Is (x, y, t), and the density ρ (x, y, t) may be substituted into (11) to obtain the porosity Φ (x, y, t) and the gas saturation S g (x,y,t)。
In one example, the formation pressure and the formation temperature, and thus the volume factor, are obtained based on the seismic data and the geothermal gradient, respectively.
In one example, the volume factor may be:
wherein, B g Representing the volume coefficient, Z representing the natural gas compressibility factor, and T representing the formation temperature; p represents the formation pressure.
In particular, the formation temperature may be
T＝T 0 +d*ΔT (13)
Wherein T represents the formation temperature, T 0 The temperature of the constant temperature zone is expressed in units of DEG C, d represents the buried depth of the stratum, delta T represents the ground temperature gradient which means the change condition of the ground temperature under the constant temperature zone every 100 meters, and the actual ground temperature test result is used as the standard in the actual application process.
In particular, the porosity φ (x, y, t) and the gas saturation S are determined g (x, y, t) are all time domain data, so the conversion of (12) into a time domain may be:
wherein, B g (x, y, T) represents a volume coefficient of the time domain, Z represents a natural gas compressibility factor, T (x, y, T) represents a formation temperature of the time domain, and P (x, y, T) represents a formation pressure of the time domain.
In one example, a calculation model of the free gas content is established based on porosity, gas saturation, volume coefficient and formation pressure to obtain the free gas content.
In one example, the free gas content may be:
wherein G is g Denotes the gas content of free gas, phi denotes the porosity, S g Indicating gas saturation and p density.
In particular, the porosity phi (x, y, t) and the gas saturation S are determined g (x, y, t) and volume factor B g (x, y, t) are all time domain data, so the conversion of (13) into a time domain may be:
wherein G is g (x, y, t) represents free gas content in time domainAmount of the compound (A).
Application example
To facilitate understanding of the aspects of the embodiments of the present invention and their effects, a specific application example is given below. It will be understood by those skilled in the art that this example is merely for the purpose of facilitating an understanding of the present invention and that any specific details thereof are not intended to limit the invention in any way.
101 Based on a work area prestack seismic data volume, a RockTrace module of Jason inversion software Is used for replacing the RockTrace module with other prestack inversion methods or software capable of obtaining longitudinal wave impedance, transverse wave impedance and density, and proper wavelets, low-frequency models and proper inversion parameters are selected to obtain longitudinal wave impedance Ip (x, y, t), transverse wave impedance Is (x, y, t) and density rho (x, y, t) with high longitudinal resolution.
Carrying out time-depth conversion on the logging data of the depth domain to obtain the logging data of the time domain, wherein the relation of the time-depth conversion is as follows:
where t is the time domain, z is the depth domain, and vp (z) is the longitudinal wave velocity. The obtained time domain well log data may include: longitudinal wave impedance curve Ip (t) of time domain, transverse wave impedance Is (t) of time domain, density curve rho (t) of time domain, porosity phi (t) of time domain, and gas saturation S of time domain g (t)。
102 Based on the work area elastic parameters and the time domain well logging data, establishing an anisotropic rock physical model of the shale reservoir, and further establishing a functional relation among the porosity, the gas saturation and the work area elastic parameters as follows:
{Ip(t),Is(t),ρ(t)}＝f(φ(t),S g (t)…) (2)
the Bayesian classification algorithm is adopted, and the objective function is expressed as the porosity phi and the gas saturation S under the condition of the known elastic parameters of the work area g Maximum a posteriori probability distribution of:
{φ,S g }＝argMaxp(φ j ,S gj …|Ip,Is,ρ) j＝1,2…,Nc (3)
wherein, { phi, S g Denotes the solution of porosity and gas saturation, p (phi) j ,S gj … | Ip, is, ρ) represents the posterior probability of porosity and gas saturation, nc represents the number of classified classes, j represents the class number, and Nc can be set by those skilled in the art to control the prediction accuracy.
That is, if:
p(φ j ,S gj …|Ip,Is,ρ)>p(φ m ,S gm …|Ip,Is,ρ) 1≤j,m≤Nc,j≠m (4)
then:
φ＝φ j ，S g ＝S gj (5)
(4) The significance of (5) and (5) Is that under the condition that the longitudinal wave impedance Ip, the transverse wave impedance Is and the density rho are known, the porosity phi and the gas saturation S are obtained g A posterior probability of different values, wherein the value of the maximum posterior probability is phi = phi j ，S g ＝S gj Is a solution of porosity and gas saturation.
Based on the Bayesian formula, (3) can be rewritten as:
{φ,S g }＝argMax{p(Ip,Is,ρ|φ,S g …)·p(φ,S g …)} (6)
wherein Ip represents the longitudinal wave impedance, is represents the transverse wave impedance, ρ represents the density, and p (φ, S) g …) represents an a priori distribution of porosity and gas saturation, p (Ip, is, ρ | φ, S) g …) represents a likelihood function.
Assuming that porosity and gas saturation obey a multidimensional gaussian distribution, i.e. the prior distribution of porosity and gas saturation may be:
wherein F represents a multi-dimensional Gaussian distribution,is the mean of a multi-dimensional gaussian distribution,is the variance of multidimensional Gaussian distribution, nr is the number of the variables of porosity and gas saturation in the rock physical model, and the weight coefficient alpha k Satisfy the requirements of
Based on the prior distribution of porosity and gas saturation, the porosity and the gas saturation are randomly simulated by using MCMC sampling Metropolis Hastings sampling algorithm, and the random simulation result (phi) of the porosity and the gas saturation can be obtained i ,S gi …} i＝1…N 。
Can be converted into [ phi ] i ,S gi …} i＝1…N And (2) obtaining a joint random simulation result of the elasticity parameter, the porosity and the gas saturation of the work area as follows:
{Ip i ,Is i ,ρ i ,φ i ,S gi …} i＝1…N (8)
where N is the number of random samples, i represents the ith random sample, i =1 … N, ip i Is the longitudinal wave impedance of the ith random sample i Transverse wave impedance, p, for the ith random sample i Is the density of the ith random sample, phi i Porosity, S, for the ith random sample gi The gas saturation for the ith random sample.
Based on the conditional independence assumption of the Bayesian classification algorithm, the calculation form of the objective function is rewritten as follows: p (phi) j ,S gj …|Ip,Is,ρ)＝p(Ip|φ j ,S gj ,…)·p(Is|φ j ,S gj ,…)·p(ρ|φ j ,S gj ,…)·p(φ j ,S gj ,…) (10)
Bringing (8) into (10) can result in:
wherein N represents the number of random sampling times, N represents the count statistics, N (Ip #. Phi.) i ,S gi …) represents the statistics Ip i ,Is i ,ρ i ,φ i ,S gi ,…} i＝1…N The value of middle phi is equal to phi i ，S g Has a value of S gi And the number of randomly sampled sampling points n (Is # phi) with longitudinal wave impedance Ip i ,S gi …) represents the statistics Ip i ,Is i ,ρ i ,φ i ,S gi ,…} i＝1…N The value of middle phi is equal to phi i ，S g Has a value of S gi And the number of randomly sampled sampling points with transverse wave impedance Is, n (rho # phi #) i ,S gi …) represents the statistics Ip i ,Is i ,ρ i ,φ i ,S gi ,…} i＝1…N The value of middle phi is equal to phi i ，S g Has a value of S gi And the number of randomly sampled sampling points with the density of rho.
Introducing (11) the longitudinal wave impedance Ip (x, y, t), the transverse wave impedance Is (x, y, t) and the density rho (x, y, t) to obtain the porosity phi (x, y, t) and the gas saturation S g (x,y,t)。
103 Based on the seismic data and the geothermal gradient, obtaining a formation temperature of
T＝T 0 +d*ΔT (13)
Wherein T represents the formation temperature, T 0 Denotes the temperature of the constant temperature zone, d denotes the buried depth of the formation, and Δ T denotes the earth temperature gradient.
Based on the formation pressure and the formation temperature, the volume coefficient is obtained as follows:
wherein, B g (x, y, T) represents a volume coefficient of the time domain, Z represents a natural gas compressibility factor, T (x, y, T) represents a formation temperature of the time domain, and P (x, y, T) represents a formation pressure of the time domain.
104 Fig. 2) shows a schematic diagram of free gas content according to an embodiment of the invention. Establishing a calculation model of the free gas content based on the porosity, the gas saturation, the volume coefficient and the formation pressure, and obtaining the free gas content as follows:
wherein, G g (x, y, t) represents the free gas content of the time domain.
According to the method, under a Bayesian framework, a porosity and gas saturation parameter seismic prediction method based on multi-attribute constraints of longitudinal wave impedance, transverse wave impedance and density parameters is combined with a stratum pressure and stratum temperature prediction result, high-precision prediction of the free gas content of a shale reservoir is achieved, and a more stable and reliable free gas content prediction result can be obtained.
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention is intended only to illustrate the beneficial effects of embodiments of the invention and is not intended to limit embodiments of the invention to any of the examples given.
Embodiment mode 2
According to an embodiment of the invention, a shale reservoir free gas content seismic prediction system is provided, which may include: a unit for respectively acquiring the elastic parameters of the work area and the logging data of the time domain based on the logging data of the pre-stack seismic data volume and the depth domain of the work area; the unit is used for establishing a rock physical model based on the elastic parameters of the work area and the logging data of a time domain, and obtaining the porosity and the gas saturation by combining a Bayesian inversion algorithm; a unit for obtaining formation pressure and formation temperature respectively based on the seismic data and the geothermal gradient, and further obtaining a volume coefficient; and a unit for establishing a calculation model of the free gas content based on the porosity, the gas saturation, the volume coefficient and the formation pressure to obtain the free gas content, wherein the elastic parameters of the work area comprise longitudinal wave impedance, transverse wave impedance and density.
According to the implementation mode, the high-precision prediction of the free gas content of the shale reservoir is realized through the constraints of factors such as the formation temperature, the formation pressure, the porosity and the gas saturation.
In one example, the volume factor may be:
wherein, B g Representing the volume factor, Z the natural gas compressibility factor, T the formation temperature, P the formation pressure,
in one example, the free gas content may be:
wherein, G g Denotes the gas content of the free gas, phi denotes the porosity, S g Indicating gas saturation and p density.
In one example, the porosity and gas saturation may include: constructing an objective function based on a Bayesian inversion algorithm, and determining prior distribution of porosity and gas saturation; obtaining a joint random simulation result of the porosity, the gas saturation and the work area elastic parameter based on the prior distribution of the porosity and the gas saturation and by combining a sampling algorithm; solving an objective function based on a Bayesian classification algorithm and a joint random simulation result of porosity, gas saturation and work area elastic parameters; and bringing the elastic parameters of the work area into an objective function to obtain the porosity and the gas saturation.
In one example, the objective function may be:
{φ,S g }＝argMax{p(Ip,Is,ρ|φ,S g …)·p(φ,S g …)} (6)
wherein, { phi, S g } tableSolutions for porosity and gas saturation, ip for longitudinal wave impedance, is for transverse wave impedance, p for density, p (phi, S) g …) represents the prior distribution of the inversion target parameters, p (Ip, is, ρ | φ, S g …) represents a likelihood function.
In one example, solving the objective function may be:
wherein N represents the number of random sampling times, N represents count statistics, N (Ip #φ) i ,S gi …) represents the statistics Ip i ,Is i ,ρ i ,φ i ,S gi ,…} i＝1…N The value of middle phi is equal to phi i ，S g Has a value of S gi And the number of randomly sampled sampling points n (Is # phi) with the longitudinal wave impedance Ip i ,S gi …) represents the statistics Ip i ,Is i ,ρ i ,φ i ,S gi ,…} i＝1…N The value of middle phi is equal to phi i ，S g Has a value of S gi And the number of randomly sampled sampling points with transverse wave impedance Is, n (rho # phi #) i ,S gi …) represents the statistics Ip i ,Is i ,ρ i ,φ i ,S gi ,…} i＝1…N The value of middle phi is equal to phi i ，S g Has a value of S gi And the number of randomly sampled sampling points with the density of rho.
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention is intended only to illustrate the beneficial effects of embodiments of the invention and is not intended to limit embodiments of the invention to any of the examples given.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A shale reservoir free gas content seismic prediction method, the method comprising:
respectively acquiring elastic parameters of a work area and logging data of a time domain based on the pre-stack seismic data volume and the logging data of the depth domain of the work area;
establishing a rock physical model based on the elastic parameters of the work area and the logging data of the time domain, and obtaining porosity and gas saturation by combining a Bayesian inversion algorithm;
respectively obtaining the formation pressure and the formation temperature based on the seismic data and the geothermal gradient, and further obtaining a volume coefficient; and
establishing a calculation model of the free gas content based on the porosity, the gas saturation, the volume coefficient and the formation pressure to obtain the free gas content,
wherein, the work area elastic parameters comprise longitudinal wave impedance, transverse wave impedance and density.
2. The shale reservoir free gas content seismic prediction method of claim 1, wherein the volume factor is:
wherein, B g Representing the volume factor, Z the natural gas compressibility factor, T the formation temperature, P the formation pressure,
wherein, the free gas content is:
wherein, G g Denotes the gas content of free gas, phi denotes the porosity, S g Indicating gas saturation and p density.
3. The shale reservoir free gas content seismic method of claim 2, wherein obtaining the porosity and the gas saturation comprises:
constructing an objective function based on the Bayesian inversion algorithm, and determining prior distribution of the porosity and the gas saturation;
based on the prior distribution of the porosity and the gas saturation, combining a sampling algorithm to obtain a joint random simulation result of the porosity, the gas saturation and the work area elastic parameter;
solving the objective function based on a Bayesian classification algorithm and a joint random simulation result of the porosity, the gas saturation and the work area elastic parameter; and
and substituting the work area elastic parameters into the objective function to obtain the porosity and the gas saturation.
4. The shale reservoir free gas content seismic prediction method of claim 3, wherein the objective function is:
{φ,S g }＝argMax{p(Ip,Is,ρ|φ,S g …)·p(φ,S g …)} (6)
wherein, { phi, S g Denotes the solution of porosity and gas saturation, ip denotes the longitudinal wave impedance, is denotes the transverse wave impedance, p denotes the density, p (φ, S) g …) represents an a priori distribution of the porosity and the gas saturation, p (Ip, is, ρ | φ, S) g …) represents a likelihood function.
5. The shale reservoir free gas content seismic prediction method of claim 4, wherein solving the objective function is:
wherein N represents the number of random sampling times, N represents the count statistics, N (Ip #. Phi.) i ,S gi …) represents the statistics Ip i ,Is i ,ρ i ,φ i ,S gi ,…} i＝1…N The value of middle phi is equal to phi i ，S g Has a value of S gi And the number of randomly sampled sampling points n (Is # phi) with longitudinal wave impedance Ip i ,S gi …) represents the statistics Ip i ,Is i ,ρ i ,φ i ,S gi ,…} i＝1…N The value of middle phi is equal to phi i ，S g Has a value of S gi And the number of randomly sampled sampling points with transverse wave impedance Is, n (rho # phi #) i ,S gi …) represents the statistics Ip i ,Is i ,ρ i ,φ i ,S gi ,…} i＝1…N The value of middle phi is equal to phi i ，S g Has a value of S gi And the number of randomly sampled sampling points with the density of rho.
6. A shale reservoir free gas content seismic prediction system, the system comprising:
a unit for respectively acquiring the elastic parameters of the work area and the logging data of the time domain based on the logging data of the pre-stack seismic data volume and the depth domain of the work area;
a unit for establishing a rock physical model based on the work area elastic parameters and the logging data of the time domain, and obtaining porosity and gas saturation by combining a Bayesian inversion algorithm;
a unit for obtaining formation pressure and formation temperature respectively based on the seismic data and the geothermal gradient, and further obtaining a volume coefficient; and
means for modeling a calculation of free gas content based on the porosity, the gas saturation, the volume factor, and the formation pressure to obtain the free gas content,
wherein, the work area elastic parameters comprise longitudinal wave impedance, transverse wave impedance and density.
7. The shale reservoir free gas content seismic forecasting system of claim 6, wherein the volume factor is:
wherein, B g Representing the volume factor, Z the natural gas compressibility factor, T the formation temperature, P the formation pressure,
wherein, the free gas content is:
wherein G is g Denotes the gas content of free gas, phi denotes the porosity, S g Indicating gas saturation and p density.
8. The shale reservoir free gas content seismic prediction system of claim 7, wherein the obtaining porosity and gas saturation comprises:
constructing an objective function based on the Bayesian inversion algorithm, and determining prior distribution of the porosity and the gas saturation;
based on the prior distribution of the porosity and the gas saturation, combining a sampling algorithm to obtain a joint random simulation result of the porosity, the gas saturation and the work area elastic parameter;
solving the objective function based on a Bayesian classification algorithm and a joint random simulation result of the porosity, the gas saturation and the work area elastic parameter; and
and substituting the work area elastic parameters into the objective function to obtain the porosity and the gas saturation.
9. The shale reservoir free gas content seismic forecasting system of claim 8, wherein the objective function is:
{φ,S g }＝argMax{p(Ip,Is,ρ|φ,S g …)·p(φ,S g …)} (6)
wherein, { phi, S g Denotes the solution of porosity and gas saturation, ip denotes the longitudinal wave impedance, is denotes the transverse wave impedance, p denotes the density, p (φ, S) g …) represents an a priori distribution of the porosity and the gas saturation, p (Ip, is, ρ | φ, S) g …) represents a likelihood function.
10. The shale reservoir free gas content seismic forecasting system of claim 9, wherein solving the objective function is:
wherein N represents the number of random sampling times, N represents the count statistics, N (Ip #. Phi.) i ,S gi …) represents statistics Ip i ,Is i ,ρ i ,φ i ,S gi ,…} i＝1…N The value of middle phi is equal to phi i ，S g Has a value of S gi And the number of randomly sampled sampling points n (Is # phi) with longitudinal wave impedance Ip i ,S gi …) represents the statistics Ip i ,Is i ,ρ i ,φ i ,S gi ,…} i＝1…N The value of middle phi is equal to phi i ，S g Has a value of S gi And the number of randomly sampled sampling points with transverse wave impedance Is, n (rho # phi #) i ,S gi …) represents the statistics Ip i ,Is i ,ρ i ,φ i ,S gi ,…} i＝1…N The value of middle phi is equal to phi i ，S g Has a value of S gi And the number of randomly sampled sampling points with the density of rho.
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