Seismic random inversion method, device, equipment and medium

Abstract

The invention discloses an earthquake random inversion method and device, equipment and a medium. According to the method, a positive operator is established based on a rock physical model and a seismic convolution model, and a likelihood function between seismic data and reservoir physical property parameters in a target reservoir is determined according to the positive operator; determining a posterior probability density function of the reservoir physical property parameters according to the likelihood function and a prior probability density function of the reservoir physical property parameters under lithofacies constraint; and based on a preset sampling algorithm, according to the posterior probability density function, determining the reservoir physical property parameters and the inversion result of the lithofacies. According to the technical scheme, on one hand, reservoir physical property parameters and lithofacies are directly inverted through seismic data, accumulative errors are avoided, and the inversion precision of the reservoir physical property parameters is improved; on the other hand, the constraint effect of lithofacies is introduced in the process of establishing the reservoir physical property parameter prior probability density function, so that the inversion result has good space continuity, and the precision of reservoir oil-gas possibility detection is improved.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas exploration, and in particular to a seismic random inversion method, device, equipment and medium. Background Art

[0002] Seismic stochastic inversion is a seismic inversion method that is based on geological information, applies random function theory and geostatistical methods, and combines traditional seismic inversion technology to produce multiple optional equal-probability inversion results in each seismic trace or multiple seismic traces.

[0003] At present, the conventional inversion of reservoir physical parameters and lithofacies is mostly completed in two steps under the Bayesian framework, which belongs to the indirect inversion method. Specifically, the elastic parameters are first determined based on the seismic data through inversion; then the reservoir physical parameters are determined based on the elastic parameters using the rock physics statistical relationship, or the lithofacies are determined based on the elastic parameters using intersection analysis. The above indirect inversion method has poor computational efficiency and spatial continuity, and cannot guarantee the matching degree between reservoir physical parameters, lithofacies and seismic data, which easily leads to the generation of cumulative errors and cannot meet the requirements of fine exploration in complex areas.

[0004] Therefore, how to provide a technical solution that can effectively improve inversion accuracy and inversion efficiency is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0005] The present application provides a seismic random inversion method, device, equipment and medium. On the one hand, reservoir physical property parameters and lithofacies are directly inverted through seismic data to avoid the generation of cumulative errors and improve the inversion accuracy of reservoir physical property parameters; on the other hand, the constraint of lithofacies is introduced in the process of establishing a priori probability density function of reservoir physical property parameters, so that the inversion result has better spatial continuity and the accuracy of reservoir oil and gas detection is improved.

[0006] According to one aspect of the present application, a seismic stochastic inversion method is provided, the method comprising:

[0007] Establishing a forward operator based on a rock physics model and a seismic convolution model, and determining a likelihood function between seismic data and reservoir physical property parameters in a target reservoir according to the forward operator;

[0008] Determining the posterior probability density function of the reservoir physical property parameter according to the likelihood function and the prior probability density function of the reservoir physical property parameter under lithofacies constraints;

[0009] Based on a preset sampling algorithm and according to the posterior probability density function, the inversion results of the reservoir physical property parameters and lithofacies are determined.

[0010] According to another aspect of the present application, a seismic stochastic inversion device is provided, the device comprising:

[0011] A likelihood function determination module, used to establish a forward operator based on a rock physics model and a seismic convolution model, and determine a likelihood function between seismic data and reservoir physical property parameters in a target reservoir according to the forward operator;

[0012] A posterior probability construction module, used to determine the posterior probability density function of the reservoir physical property parameters according to the likelihood function and the prior probability density function of the reservoir physical property parameters under lithofacies constraints;

[0013] The seismic random inversion module is used to determine the inversion results of the reservoir physical property parameters and lithofacies based on a preset sampling algorithm and according to the posterior probability density function.

[0014] According to another aspect of the present application, a seismic stochastic inversion device is provided, the device comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the seismic random inversion method described in any embodiment of the present application.

[0018] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the seismic random inversion method described in any embodiment of the present application when executed.

[0019] The technical solution provided by the present application establishes a forward operator based on a rock physics model and a seismic convolution model, and determines the likelihood function between seismic data and reservoir physical parameters in the target reservoir according to the forward operator; determines the posterior probability density function of the reservoir physical parameters according to the likelihood function and the prior probability density function of the reservoir physical parameters under the constraints of lithofacies; and determines the inversion results of the reservoir physical parameters and lithofacies according to the posterior probability density function based on a preset sampling algorithm. The present technical solution, on the one hand, directly inverts reservoir physical parameters and lithofacies through seismic data to avoid the generation of cumulative errors and improve the inversion accuracy of reservoir physical parameters; on the other hand, in the process of establishing the prior probability density function of reservoir physical parameters, the constraints of lithofacies are introduced to make the inversion results have better spatial continuity and improve the accuracy of reservoir oil and gas detection.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A flow chart of a seismic random inversion method provided in Example 1 of the present application;

[0023] Figure 2 A flow chart of a seismic random inversion method provided in Example 2 of the present application;

[0024] Figure 3a A schematic diagram of a priori probability function of a lithofacies provided in Example 2 of the present application;

[0025] Figure 3b A schematic diagram of a variogram of a lithofacies provided in Example 2 of the present application;

[0026] Figure 4a A schematic diagram of a priori probability density function of porosity in sandstone provided in Example 2 of the present application;

[0027] Figure 4b A schematic diagram of a porosity variation function in sandstone provided in Example 2 of the present application;

[0028] Figure 5 A schematic diagram of the structure of a seismic stochastic inversion device provided in Example 3 of the present application;

[0029] Figure 6 It is a structural schematic diagram of a device for implementing a seismic random inversion method according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0031] It should be noted that the terms "first", "second", "target", "candidate", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or precedence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Embodiment 1

[0033] Figure 1 This is a flow chart of a seismic random inversion method provided in Example 1 of the present application. This embodiment is applicable to the case of inverting reservoir physical parameters and lithofacies. The method can be executed by a seismic random inversion device. The seismic random inversion device can be implemented in the form of hardware and / or software. The seismic random inversion device can be configured in a device with data processing capabilities. Figure 1 As shown, the method includes:

[0034] S110, establishing a forward operator based on the rock physics model and the seismic convolution model, and determining a likelihood function between seismic data and reservoir physical property parameters in the target reservoir according to the forward operator.

[0035] The reservoir physical property parameters may be the porosity, clay content, water saturation, etc. of the target reservoir, and the seismic data may be seismic data at different angles.

[0036] Among them, the rock physics model is used to represent the relationship between the reservoir physical parameters and elastic parameters of the target reservoir. In view of different actual rock idealization processes, the rock physics model may include layered model, spherical pore model, inclusion model and contact model. The seismic convolution model is used to represent the relationship between elastic parameters and seismic data.

[0037] Among them, the likelihood function is used to express the matching degree between reservoir physical parameters and seismic data.

[0038] In an embodiment of the present invention, the relationship between reservoir physical property parameters and elastic parameters can be established based on a rock physics model, and the relationship between elastic parameters and seismic data can be established based on a seismic convolution model; then, based on the relationship between physical property parameters and elastic parameters and the relationship between elastic parameters and seismic data, the relationship between reservoir physical property parameters and seismic data can be determined; based on the relationship between reservoir physical property parameters and seismic data, the likelihood function between seismic data and reservoir physical property parameters can be determined.

[0039] Optionally, a forward operator is established based on the rock physics model and the seismic convolution model, and a likelihood function between the seismic data and the reservoir physical property parameters in the target reservoir is determined according to the forward operator, including: determining the likelihood function between the seismic data and the reservoir physical property parameters of the target reservoir using the following formula:

[0040]

[0041] Among them, m is the reservoir physical property parameter, d is the seismic data, n θ is the number of different incident angles of the seismic data, S d is the length of the seismic data, p(d|m) is the likelihood function, g(·) is the forward operator, Σ d is the covariance of the model errors.

[0042] Specifically, the forward modeling problem between seismic data and reservoir physical parameters can be expressed as:

[0043] d=g(m)+n d ;

[0044] Among them, n d is the model error; g(m) is the forward operator that maps the reservoir physical property parameter m to the seismic data d, and is used to represent the mathematical relationship between the seismic data and the reservoir physical property parameter.

[0045] Correspondingly, the inversion problem can be expressed as: to infer the reservoir physical property parameter m under the given seismic data d, the forward operator g(·) and the prior information of the reservoir physical property parameter m.

[0046] Specifically, we can assume that the number of different incident angles in the seismic data is n θ , the length of the seismic data is S d , model error n d The mean is 0 and the covariance is Σ d If the Gaussian distribution is , then the likelihood function can be expressed as the above formula.

[0047] Optionally, a forward operator is established based on a rock physics model and a seismic convolution model, including: determining a first correlation function between reservoir physical parameters and elastic parameters of a target reservoir based on the rock physics model; determining a second correlation function between seismic data of the target reservoir and the elastic parameters based on the seismic convolution model; and determining a forward operator based on the first correlation function and the second correlation function.

[0048] Among them, the elastic parameters can be the speed and density of longitudinal and transverse waves.

[0049] Among them, the first correlation function can be expressed as: E = f RPM (m); where E is the elastic parameter, f RPM (·) is the rock physics model, and m is the reservoir physical property parameter.

[0050] In order to more accurately invert reservoir physical property parameters, the embodiment of the present invention uses pre-stack seismic data d(t,θ) to establish a second correlation function, wherein θ is the incident angle of the seismic data.

[0051] Among them, the pre-stack seismic data d(t,θ) can be expressed as the reflection longitudinal wave coefficient R at different incident angles: PP The convolution of (t,θ) and the sub-wave W(t,θ) with different incident angles is as follows:

[0052] d(t,θ)=R PP (t,θ)*W(t,θ)+n d (t,θ);

[0053] Among them, n d (t,θ) is the model error, which includes all errors of seismic noise, rock physics model and seismic convolution model.

[0054] Among them, the reflected longitudinal wave coefficient R at different incident angles PP (t,θ1) can be expressed using the Zoeppritz equation as follows:

[0055]

[0056]

[0057] Among them, θ1 is the incident angle of the longitudinal wave, θ2 is the transmission angle of the longitudinal wave, is the incident angle of the longitudinal wave, is the longitudinal wave transmission angle, α1 is the longitudinal wave velocity of the rock on the medium interface, α2 is the longitudinal wave velocity of the rock below the medium interface, β1 is the shear wave velocity of the rock on the medium interface, β2 is the shear wave velocity of the rock below the medium interface, ρ1 is the density of the rock on the medium interface, ρ2 is the density of the rock below the medium interface, is the SV wave reflection angle, is the SV wave transmission angle, R PP (t,θ1) is the reflection coefficient of the reflected longitudinal wave, R PS (t,θ1) is the reflection coefficient of the reflected SV wave, T PP (t,θ1) is the transmission coefficient of the transmitted longitudinal wave, T PS (t,θ1) is the reflection coefficient of the transmitted SV wave.

[0058] Therefore, the incident angle coefficient R can be established by the above formula PP The relationship between (t,θ1) and elastic parameters E such as α1, α2, β1, β2, ρ1 and ρ2; and then according to the reflected longitudinal wave coefficient R PP The relationship between (t, θ1) and the seismic data d(t, θ) is derived to obtain the second correlation function between the seismic data d(t, θ) and the elastic parameter E.

[0059] Furthermore, by deriving the first correlation function and the second correlation function jointly, the relationship between the seismic data and the reservoir physical property parameters can be obtained, and then the forward operator can be obtained.

[0060] S120. Determine the posterior probability density function of the reservoir physical property parameters according to the likelihood function and the prior probability density function of the reservoir physical property parameters under lithofacies constraints.

[0061] Since reservoir physical property parameters are affected by lithofacies, the probability density functions of reservoir physical property parameters are different under different lithofacies. Therefore, in the process of establishing the prior probability density function of reservoir physical property parameters, the constraint of lithofacies can be introduced to make the inversion results of reservoir physical property parameters have higher accuracy and spatial continuity.

[0062] Furthermore, based on the Bayesian theory, the posterior probability density function of the reservoir physical property parameters is determined according to the likelihood function and the prior probability density function of the reservoir physical property parameters under different lithofacies. According to the Bayesian theory, all information states are expressed in the form of probability density functions, and the inverted solution is the posterior probability density function. Specifically, the posterior probability density function can be determined using the following formula:

[0063]

[0064] Wherein, m is the reservoir physical property parameter, d is the seismic data, p(m|d) is the posterior probability density function of the reservoir physical property parameter, p(d|m) is the likelihood function, p(m) is the prior probability density function of the reservoir physical property parameter under lithofacies constraints, and p(d) is the prior probability density function of the seismic data.

[0065] Optionally, determining the posterior probability density function of the reservoir physical property parameters according to the likelihood function and the prior probability density function of the reservoir physical property parameters under lithofacies constraints includes: determining the posterior probability density function of the reservoir physical property parameters using the following formula:

[0066]

[0067] Wherein, m is the reservoir physical property parameter, d is the seismic data, p(m|d) is the posterior probability density function of the reservoir physical property parameter, c is a constant, p(d|m) is the likelihood function, and p(m) is the prior probability density function of the reservoir physical property parameter under lithofacies constraints.

[0068] Since the seismic data are known, the prior probability density function of the seismic data is a constant and can be represented by a normalized constant c.

[0069] Optionally, the process of determining the prior probability density function of the reservoir physical property parameters under the lithofacies constraint includes: determining the prior probability density function of the reservoir physical property parameters under the lithofacies constraint using the following formula:

[0070] p(m)=∑ F∈Ω p(m|F)p(F);

[0071] Where m is the reservoir physical property parameter, F is the lithofacies, Ω is the space of all lithofacies, p(m) is the prior probability density function of the reservoir physical property parameter under the lithofacies constraint, p(F) is the prior probability function of the lithofacies, and p(m|F) is the conditional probability density function of the reservoir physical property parameter under the lithofacies constraint.

[0072] In an embodiment of the present invention, the prior probability function p(F) of the lithofacies can be sampled using a sequential indicator simulation algorithm or a first-order Markov model to make the sampled lithofacies have better spatial continuity.

[0073] Among them, the principle of sampling the prior probability function p(F) of the lithofacies by the sequential indicator simulation algorithm is to calculate the conditional cumulative distribution function by solving the indicator kriging equations. Here we use simple indicator kriging, that is:

[0074]

[0075] Where α=1,2,…,n;C1(y β ,y α ; S) is y β and α The covariance function between two points has a cutoff value of S; C1(y,y α ; S) is y and y αCovariance function between two points, with cutoff value S; λ β is the weight.

[0076] It should be noted that in the simulation process of the sequential indicator simulation algorithm, prior information such as seismic data, seismic attributes, and wave impedance can also be considered to more accurately describe the prior probability function of the lithofacies.

[0077] The principle of sampling the prior probability function p(F) of lithofacies using the first-order Markov model is as follows: the prior probability of lithofacies F at sampling time t only depends on the lithofacies at time t-1.

[0078] Therefore, the prior probability function p(F) of lithofacies can be expressed as:

[0079]

[0080] Where T is the time length of lithofacies F; is the probability transfer matrix, which can be obtained through well logging analysis.

[0081] In an embodiment of the present invention, the sampling of the prior probability density function p(m|F) of reservoir physical property parameters under lithofacies constraints can use sequential Gaussian simulation, direct sequential simulation or FFT-MA simulation algorithm to simulate the reservoir physical property parameters in different lithofacies, so that the sampled reservoir physical property parameters have better spatial continuity.

[0082] S130. Based on a preset sampling algorithm and according to the posterior probability density function, determine the inversion results of the reservoir physical property parameters and lithofacies.

[0083] Among them, the preset sampling algorithm can be determined according to actual needs, which can be Gibbs sampling, Metropolis-Hastings sampling, importance sampling, MCMC sampling, acceptance-rejection sampling, etc.

[0084] In the embodiment of the present invention, a fast perturbation sampling is performed on the prior information of reservoir physical property parameters and lithofacies through a preset sampling algorithm, and then the inversion results of reservoir physical property parameters and lithofacies are determined according to the posterior probability density function.

[0085] The embodiment of the present invention provides a seismic random inversion method, which establishes a forward operator based on a rock physics model and a seismic convolution model, and determines the likelihood function between seismic data and reservoir physical parameters in the target reservoir according to the forward operator; determines the posterior probability density function of the reservoir physical parameters according to the likelihood function and the prior probability density function of the reservoir physical parameters under the lithofacies constraint; and determines the inversion results of the reservoir physical parameters and lithofacies according to the posterior probability density function based on a preset sampling algorithm. This technical solution, on the one hand, directly inverts reservoir physical parameters and lithofacies through seismic data to avoid the generation of cumulative errors and improve the inversion accuracy of reservoir physical parameters; on the other hand, in the process of establishing the prior probability density function of the reservoir physical parameters, the constraint of lithofacies is introduced to make the inversion result have better spatial continuity and improve the accuracy of reservoir oil and gas detection.

[0086] Embodiment 2

[0087] Figure 2 This is a flow chart of a seismic random inversion method provided in Example 2 of the present application. This embodiment is optimized based on the above embodiment. Figure 2 As shown, the method of this embodiment specifically includes the following steps:

[0088] S210, establishing a forward operator based on the rock physics model and the seismic convolution model, and determining a likelihood function between seismic data and reservoir physical property parameters in the target reservoir according to the forward operator.

[0089] S220. Determine the posterior probability density function of the reservoir physical property parameters according to the likelihood function and the prior probability density function of the reservoir physical property parameters under lithofacies constraints.

[0090] S230. According to the posterior probability density function, respectively determine the first posterior probability density function value of the target state parameter and the second posterior probability density function value of the candidate state parameter; wherein the state parameter includes at least one of the reservoir physical property parameter and the lithofacies; and the candidate state parameter is a parameter generated by perturbing the target state parameter.

[0091] The target state may be a current state or an initial state, and the candidate state may be a state generated after disturbing the target state.

[0092] In an embodiment of the present invention, the target state parameter may be disturbed within a preset disturbance region to generate candidate state parameters, and the posterior probability density function values ​​of the target state parameter and the candidate state parameter may be calculated based on the posterior probability density function of the reservoir physical property parameters.

[0093] S240. Based on a preset sampling algorithm, determine the inversion results of the reservoir physical property parameters and lithofacies according to the first posterior probability density function value and the second posterior probability density function value.

[0094] Specifically, the first posterior probability density function value and the second posterior probability density function value may be compared to determine the state parameter that is more consistent with the reservoir physical property parameters and the rock phase evolution result among the target state parameters and the candidate state parameters.

[0095] Exemplarily, the ratio of the first posterior probability density function value to the second posterior probability density function value may be compared with a preset threshold value to determine the inversion results of reservoir physical property parameters and lithofacies.

[0096] Optionally, based on a preset sampling algorithm, the inversion results of the reservoir physical properties and lithofacies are determined according to the first posterior probability density function value and the second posterior probability density function value, including: determining the acceptance probability of the candidate state parameters according to the first posterior probability density function value and the second posterior probability density function value; if the acceptance probability is less than or equal to a preset threshold, retaining the target state parameters, and continuing to perturb the target state parameters until a preset number of perturbations is reached; if the acceptance probability is greater than the preset threshold, eliminating the target state parameters and using the candidate state parameters as the target state parameters, and continuing to perturb the target state parameters until a preset number of perturbations is reached; and performing statistics on each of the target state parameters to determine the posterior solutions of the reservoir physical properties and the inversion results of the lithofacies.

[0097] Specifically, the process of determining the inversion results of reservoir physical property parameters and lithofacies according to the first posterior probability density function value and the second posterior probability density function value is as follows:

[0098] The first step is to conduct initial sampling of reservoir physical parameters and lithofacies to obtain the lithofacies initial sampling model F current and reservoir physical property parameter initial sampling model m current ;

[0099] The second step is to randomly select a number γ from a uniform distribution of [0,1];

[0100] The third step is, if γ<σ and σ∈(0,1), then for F current Disturbance is performed within the preset disturbance area to generate a new lithofacies sampling model F propose ;

[0101] Step 4: If γ>σ, then the initial sampling model F of the lithofacies is not used. current To make a disturbance, let F propose =F current ;

[0102] Step 5: F propose Perform sequential Gaussian simulation, direct sequential simulation or spectral simulation to obtain a new reservoir physical property parameter sampling model m propose ; The value of σ can be adjusted according to the actual situation; for the disturbance of lithofacies, a disturbance area of ​​a certain size is randomly selected, and then sampling is performed in the disturbance area to achieve rapid sampling of lithofacies; according to the Metropolis sampling algorithm, the new reservoir physical property parameter sampling model m propose The acceptance probability P can be determined using the following formula:

[0103]

[0104] In each perturbation process, a number u is randomly drawn from a uniform distribution in [0,1]. If P>u, then accept F propose and m propose , otherwise reject F propose and m propose ;

[0105] Step 6: According to the preset disturbance times n iteration ,When the Markov chain converges stably, the sampling of each target state parameter is statistically ,obtained the inversion results of reservoir physical property parameters and lithofacies.

[0106] The embodiment of the present invention provides a seismic random inversion method, which establishes a forward operator based on a rock physics model and a seismic convolution model, and determines the likelihood function between seismic data and reservoir physical parameters in the target reservoir according to the forward operator; determines the posterior probability density function of the reservoir physical parameters according to the likelihood function and the prior probability density function of the reservoir physical parameters under the lithofacies constraint; determines the first posterior probability density function value of the target state parameter and the second posterior probability density function value of the candidate state parameter according to the posterior probability density function; wherein the state parameter includes at least one of the reservoir physical parameter and the lithofacies; the candidate state parameter is a parameter generated by perturbing the target state parameter; based on a preset sampling algorithm, determines the inversion results of the reservoir physical parameter and the lithofacies according to the first posterior probability density function value and the second posterior probability density function value. The technical scheme calculates the posterior probability density function value of the acquired state parameter by perturbation, so as to statistically sample the reservoir physical parameter and the lithofacies and determine the inversion results of the reservoir physical parameter and the lithofacies, thereby improving the inversion accuracy of the reservoir physical parameter.

[0107] The seismic random inversion method provided by the present invention is explained by taking a certain reservoir as an example. It is assumed that the reservoir has two lithofacies: sandstone and mudstone.

[0108] The first step is to obtain pre-stack seismic data of the reservoir at three angles: near, middle and far. The angles are 10°, 20° and 30° respectively. Each pre-stack seismic data has 206 traces in total and 320 time samples.

[0109] In the second step, angular seismic wavelets are extracted from the pre-stack seismic data at three angles: near, medium and far.

[0110] In the third step, four pseudo wells are extracted from the real lithofacies model and the real reservoir physical property parameter model, and the prior statistical information of lithofacies and reservoir physical property parameters is calculated.

[0111] For example, Figure 3a is a schematic diagram of the prior probability function of lithofacies, Figure 3b is the variogram of lithofacies, Figure 4a is a schematic diagram of the prior probability density function of porosity in sandstone. Figure 4b Schematic diagram of the porosity variation function in sandstone.

[0112] The fourth step is to use the soft sand rock-physical model to establish the relationship between elastic parameters and reservoir physical parameters.

[0113] The fifth step is to express the reflection coefficient in the seismic convolution model using the Zoeppritz equation proposed by Knott and Zoeppritz, where the reflection coefficient R PP (t,θ1) can be expressed as:

[0114]

[0115] Among them, θ1 is the incident angle of the longitudinal wave, θ2 is the transmission angle of the longitudinal wave, is the incident angle of the longitudinal wave, is the longitudinal wave transmission angle, α1 is the longitudinal wave velocity of the rock on the medium interface, α2 is the longitudinal wave velocity of the rock below the medium interface, β1 is the shear wave velocity of the rock on the medium interface, β2 is the shear wave velocity of the rock below the medium interface, ρ1 is the density of the rock on the medium interface, ρ2 is the density of the rock below the medium interface, is the SV wave reflection angle, is the SV wave transmission angle, R PP (t,θ1) is the reflection coefficient of the reflected longitudinal wave, R PS (t,θ1) is the reflection coefficient of the reflected SV wave, T PP (t,θ1) is the transmission coefficient of the transmitted longitudinal wave, T PS (t,θ1) is the reflection coefficient of the transmitted SV wave;

[0116] The sixth step is to sample the prior probability function p(F) of lithofacies using the sequential indicator simulation algorithm; the prior information required by the sequential indicator simulation algorithm, such as the prior probability density function and variogram of sandstone and mudstone, can be obtained from the extracted pseudo-wells, and the prior information of sequential Gaussian simulation of different physical property parameters can also be obtained from the extracted pseudo-wells;

[0117] Step 7: sequential Gaussian simulation is used to simulate reservoir physical property parameters in different lithofacies samples, thereby sampling the prior probability density function of the reservoir physical property parameter m;

[0118] The eighth step is to use the sampling single-channel inversion method. In the process of sampling the posterior probability density function of reservoir physical property parameters using the adaptive MCMC sampling algorithm, the value of σ is set to 0.4, the number of iterations per channel is 5000, and the last 1000 samplings are used as the inversion results of reservoir physical property parameters and lithofacies.

[0119] Embodiment 3

[0120] Figure 5 This is a schematic diagram of the structure of a seismic random inversion device provided in Example 3 of the present application. Figure 5 As shown, the device comprises:

[0121] A likelihood function determination module 510 is used to establish a forward operator based on a rock physics model and a seismic convolution model, and determine a likelihood function between seismic data and reservoir physical property parameters in a target reservoir according to the forward operator;

[0122] A posterior probability construction module 520 is used to determine the posterior probability density function of the reservoir physical property parameters according to the likelihood function and the prior probability density function of the reservoir physical property parameters under the lithofacies constraint;

[0123] The seismic random inversion module 530 is used to determine the inversion results of the reservoir physical property parameters and lithofacies based on a preset sampling algorithm and according to the posterior probability density function.

[0124] The embodiment of the present invention provides a seismic random inversion method, which establishes a forward operator based on a rock physics model and a seismic convolution model, and determines the likelihood function between seismic data and reservoir physical parameters in the target reservoir according to the forward operator; determines the posterior probability density function of the reservoir physical parameters according to the likelihood function and the prior probability density function of the reservoir physical parameters under the constraints of lithofacies; and determines the inversion results of the reservoir physical parameters and lithofacies according to the posterior probability density function based on a preset sampling algorithm. This technical solution, on the one hand, directly inverts reservoir physical parameters and lithofacies through seismic data to avoid the generation of cumulative errors and improve the inversion accuracy of reservoir physical parameters; on the other hand, in the process of establishing the prior probability density function of reservoir physical parameters, the constraint of lithofacies is introduced to make the inversion result have better spatial continuity and improve the accuracy of reservoir oil and gas detection.

[0125] Furthermore, the likelihood function determination module 510 includes:

[0126] The likelihood function between seismic data and reservoir physical property parameters in the target reservoir is determined using the following formula:

[0127]

[0128] Among them, m is the reservoir physical property parameter, d is the seismic data, n θ is the number of different incident angles of the seismic data, S d is the length of the seismic data, p(d|m) is the likelihood function, g(·) is the forward operator, Σ d is the covariance of the model errors.

[0129] Furthermore, the likelihood function determination module 510 includes:

[0130] Based on the rock physics model, determining a first correlation function between reservoir physical property parameters and elastic parameters of the target reservoir;

[0131] Determining a second correlation function between the seismic data of the target reservoir and the elastic parameter based on the seismic convolution model;

[0132] A forward operator is determined according to the first correlation function and the second correlation function.

[0133] Furthermore, the posterior probability construction module 520 includes:

[0134] The posterior probability density function of the reservoir physical property parameters is determined using the following formula:

[0135]

[0136] Wherein, m is the reservoir physical property parameter, d is the seismic data, p(m|d) is the posterior probability density function of the reservoir physical property parameter, c is a constant, p(d|m) is the likelihood function, and p(m) is the prior probability density function of the reservoir physical property parameter under lithofacies constraints.

[0137] Furthermore, the process of determining the prior probability density function of the reservoir physical property parameters under the lithofacies constraint includes:

[0138] The following formula is used to determine the prior probability density function of the reservoir physical property parameters under lithofacies constraints:

[0139] p(m)=∑ F∈Ω p(m|F)p(F);

[0140] Where m is the reservoir physical property parameter, F is the lithofacies, Ω is the space of all lithofacies, p(m) is the prior probability density function of the reservoir physical property parameter under the lithofacies constraint, p(F) is the prior probability density function of the lithofacies, and p(m|F) is the conditional probability density function of the reservoir physical property parameter under the lithofacies constraint.

[0141] Furthermore, the seismic stochastic inversion module 530 includes:

[0142] a posterior probability density function value determination unit, used to determine a first posterior probability density function value of a target state parameter and a second posterior probability density function value of a candidate state parameter respectively according to the posterior probability density function; wherein the state parameter includes at least one of the reservoir physical property parameter and the lithofacies; and the candidate state parameter is a parameter generated by disturbing the target state parameter;

[0143] A seismic random inversion unit is used to determine the inversion results of the reservoir physical property parameters and lithofacies based on a preset sampling algorithm and according to the first posterior probability density function value and the second posterior probability density function value.

[0144] Furthermore, the seismic stochastic inversion unit includes:

[0145] an acceptance probability determination subunit, configured to determine the acceptance probability of the candidate state parameter according to the first a posteriori probability density function value and the second a posteriori probability density function value;

[0146] A target state parameter retaining subunit is used to retain the target state parameter if the acceptance probability is less than or equal to a preset threshold, and continue to perturb the target state parameter until a preset number of perturbations is reached;

[0147] A target state parameter replacement subunit is used to, if the acceptance probability is greater than a preset threshold, eliminate the target state parameter and use the candidate state parameter as the target state parameter, and continue to perturb the target state parameter until a preset number of perturbations is reached;

[0148] The seismic random inversion subunit is used to perform statistics on each of the target state parameters to determine the inversion results of the reservoir physical property parameters and lithofacies.

[0149] A seismic random inversion device provided in an embodiment of the present application can execute a seismic random inversion method provided in any embodiment of the present application, and has functional modules and beneficial effects corresponding to the execution method.

[0150] Embodiment 4

[0151] Figure 6 A schematic diagram of a device 10 that can be used to implement an embodiment of the present application is shown. The device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0152] like Figure 6 As shown, the device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the device 10 can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0153] A number of components in the device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0154] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the seismic random inversion method.

[0155] In some embodiments, the seismic stochastic inversion method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the seismic stochastic inversion method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the seismic stochastic inversion method in any other suitable manner (e.g., by means of firmware).

[0156] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0157] The computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer programs are executed by the processor, the functions / operations specified in the flow charts and / or block diagrams are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0158] In the context of the present application, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device or equipment. A computer readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable signal medium. A more specific example of a machine readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0159] To provide interaction with a user, the systems and techniques described herein can be implemented on a device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0160] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0161] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0162] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.

[0163] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A seismic stochastic inversion method, characterized in that: The method comprises: Establishing a forward operator based on a rock physics model and a seismic convolution model, and determining a likelihood function between seismic data and reservoir physical property parameters in a target reservoir according to the forward operator; Determining the posterior probability density function of the reservoir physical property parameter according to the likelihood function and the prior probability density function of the reservoir physical property parameter under lithofacies constraints; Based on a preset sampling algorithm and according to the posterior probability density function, the inversion results of the reservoir physical property parameters and lithofacies are determined.

2. The method according to claim 1, characterized in that Determining the likelihood function between the seismic data and the reservoir physical property parameters in the target reservoir according to the forward operator includes: The likelihood function between seismic data and reservoir physical property parameters in the target reservoir is determined using the following formula: Among them, m is the reservoir physical property parameter, d is the seismic data, n θ is the number of different incident angles of the seismic data, S d is the length of the seismic data, p(d|m) is the likelihood function, g(·) is the forward operator, Σ d is the covariance of the model errors.

3. The method according to claim 1, characterized in that The forward operator is established based on the rock physics model and seismic convolution model, including: Based on the rock physics model, determining a first correlation function between reservoir physical property parameters and elastic parameters of the target reservoir; Determining a second correlation function between the seismic data of the target reservoir and the elastic parameter based on the seismic convolution model; A forward operator is determined according to the first correlation function and the second correlation function.

4. The method according to claim 1, characterized in that: Determining the posterior probability density function of the reservoir physical property parameter according to the likelihood function and the prior probability density function of the reservoir physical property parameter under the lithofacies constraint includes: The posterior probability density function of the reservoir physical property parameters is determined using the following formula: Wherein, m is the reservoir physical property parameter, d is the seismic data, p(m|d) is the posterior probability density function of the reservoir physical property parameter, c is a constant, p(d|m) is the likelihood function, and p(m) is the prior probability density function of the reservoir physical property parameter under lithofacies constraints.

5. The method according to claim 1, characterized in that The process of determining the prior probability density function of the reservoir physical property parameters under the lithofacies constraint includes: The following formula is used to determine the prior probability density function of the reservoir physical property parameters under lithofacies constraints: p(m)=∑ F∈Ω p(m|F)p(F); Where m is the reservoir physical property parameter, F is the lithofacies, Ω is the space of all lithofacies, p(m) is the prior probability density function of the reservoir physical property parameter under the lithofacies constraint, p(F) is the prior probability density function of the lithofacies, and p(m|F) is the conditional probability density function of the reservoir physical property parameter under the lithofacies constraint.

6. The method according to claim 1, characterized in that Based on a preset sampling algorithm and according to the posterior probability density function, the inversion results of the reservoir physical property parameters and lithofacies are determined, including: According to the posterior probability density function, respectively determine a first posterior probability density function value of a target state parameter and a second posterior probability density function value of a candidate state parameter; wherein the state parameter includes at least one of the reservoir physical property parameter and the lithofacies; and the candidate state parameter is a parameter generated by disturbing the target state parameter; Based on a preset sampling algorithm, the inversion results of the reservoir physical property parameters and lithofacies are determined according to the first posterior probability density function value and the second posterior probability density function value.

7. The method according to claim 6, characterized in that Based on a preset sampling algorithm, according to the first posterior probability density function value and the second posterior probability density function value, determining the inversion results of the reservoir physical property parameters and lithofacies, including: Determining the acceptance probability of the candidate state parameter according to the first a posteriori probability density function value and the second a posteriori probability density function value; If the acceptance probability is less than or equal to a preset threshold, the target state parameter is retained, and the target state parameter is continuously disturbed until a preset number of disturbances is reached; If the acceptance probability is greater than a preset threshold, the target state parameter is eliminated and the candidate state parameter is used as the target state parameter, and the target state parameter is continuously disturbed until a preset number of disturbances is reached; Statistics are taken on each of the target state parameters to determine the inversion results of the reservoir physical property parameters and lithofacies.

8. A seismic random inversion device, characterized in that: The device comprises: A likelihood function determination module, used to establish a forward operator based on a rock physics model and a seismic convolution model, and determine a likelihood function between seismic data and reservoir physical property parameters in a target reservoir according to the forward operator; A posterior probability construction module, used to determine the posterior probability density function of the reservoir physical property parameters according to the likelihood function and the prior probability density function of the reservoir physical property parameters under lithofacies constraints; The seismic random inversion module is used to determine the inversion results of the reservoir physical property parameters and lithofacies based on a preset sampling algorithm and according to the posterior probability density function.

9. An electronic device, characterized in that: The device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the seismic stochastic inversion method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the seismic random inversion method according to any one of claims 1 to 7 when executed.

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