A method and system for multi-parameter seismic imaging

Abstract

The application discloses a kind of multi-parameter seismic imaging method and system, it is related to oil and gas geophysical engineering technical field, including the following steps: offset velocity, offset porosity, the velocity disturbance and porosity disturbance imaging result after initialization is input to anti-offset operator, obtain predicted seismic data;Residual of predicted seismic data and observed seismic data is input to accompanying operator, obtain gradient direction for updating velocity disturbance and porosity disturbance;Velocity disturbance and porosity disturbance imaging result are updated based on gradient direction;The updating process of velocity disturbance and porosity disturbance imaging result is iterated, when reaching iteration number, output final velocity disturbance and porosity disturbance imaging result.The method of the application has higher resolution and amplitude fidelity compared with acoustic least square reverse time migration method, while effectively solving the problem that elastic parameters and physical parameters disturbance cannot be simultaneously imaged with high precision in existing seismic imaging technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas geophysical engineering, in particular to a multi-parameter seismic imaging method and system. BACKGROUND

[0002] Among the remaining oil and gas resources in China, tight sandstone oil and gas reservoirs occupy an important position. However, due to the small difference in wave impedance between the tight reservoir and the surrounding rock, it is difficult to accurately identify the reservoir only by using elastic parameters such as velocity and wave impedance. When seismic waves propagate in the tight reservoir, their response is not only affected by elastic parameters, but also by physical parameters sensitive to the reservoir (such as porosity and absorption attenuation). These reservoir parameters are closely related to the physical properties of the reservoir. Therefore, for tight reservoirs with small differences in wave impedance from surrounding rocks, fully utilizing elastic parameters and physical parameters of the reservoir for identification is the key to accurate evaluation of high-quality reservoirs.

[0003] Seismic imaging is a key technology for imaging the perturbation of underground medium parameters based on pre-stack seismic data, and its imaging results can be used for oil and gas reservoir identification and evaluation. However, the conventional migration imaging methods such as Kirchhoff migration and reverse-time migration are easily affected by incomplete observation systems and limited seismic frequency bands, and their imaging results are often disturbed by migration noise, and have problems such as uneven imaging amplitude and low resolution. Least squares reverse-time migration is a seismic imaging method based on an inversion framework, which uses a linearized wave equation as a forward operator to build an inversion model to iteratively solve the imaging problem, which can effectively suppress migration noise, compensate for imaging amplitude, and improve imaging resolution. In theory, this method can achieve high-resolution imaging of any medium parameter sensitive to seismic waves. Therefore, studying the least squares reverse-time migration method that can simultaneously image the elastic parameters and physical parameters of tight oil and gas reservoirs is of great significance for the exploration of tight oil and gas reservoirs.

[0004] However, there are two challenges when using conventional least squares reverse-time migration methods for elastic parameter and physical parameter imaging. First, traditional reverse migration operators are mostly based on elastic parameters such as acoustic waves, elastic waves, and viscoelastic waves, and cannot image physical parameter perturbations. Second, these equations do not fully consider the seismic wave attenuation caused by the dispersion effect of the fluid in the reservoir, resulting in a decrease in imaging amplitude fidelity and resolution. SUMMARY

[0005] Based on the defects of the prior art, the present application provides a multi-parameter seismic imaging method and system, which solves the problem that the existing seismic imaging technology cannot simultaneously image the elastic and physical parameter perturbations with high resolution, and cannot compensate for the decrease in imaging amplitude fidelity and resolution caused by the dispersion effect of the fluid in the reservoir.

[0006] The present application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a multi-parameter seismic imaging method, comprising the following steps:

[0008] obtaining observed seismic data, a migration velocity field and a migration porosity field of a target tight oil and gas reservoir working area, and initializing velocity perturbation and porosity perturbation imaging results; wherein the migration velocity field is used to describe the distribution of the seismic wave velocity of the underground background medium, and the migration porosity field is used to describe the distribution of the porosity of the underground background medium;

[0009] inputting the migration velocity field, the migration porosity field, the initialized velocity perturbation and porosity perturbation imaging results into a reverse migration operator to obtain predicted seismic data; inputting the residual of the predicted seismic data and the observed seismic data into an adjoint operator to obtain a gradient direction for updating the velocity perturbation and the porosity perturbation; updating the velocity perturbation and the porosity perturbation imaging results based on the gradient direction; wherein the reverse migration operator is obtained by linear approximation of the seismic wave equation of the tight oil and gas medium, and the adjoint operator is obtained by the adjoint state method for deriving the reverse migration operator;

[0010] iterating the updating process of the velocity perturbation and the porosity perturbation imaging results, and outputting the final velocity perturbation and porosity perturbation imaging results when the number of iterations is reached.

[0011] Preferably, the reverse migration operator is specifically as follows:

[0012] ;

[0013] wherein, f is a seismic wavelet, p s is a scattered wave field generated by parameter perturbation, v 0 and m v is a migration velocity and a velocity perturbation, and is a porosity and a porosity perturbation, b f is a viscous attenuation parameter, is a physical parameter related to permeability, t is time, is a Laplace operator;

[0014] wherein, p 0 is a background wave field, which is calculated by solving the following tight oil and gas medium seismic wave equation:

[0015] .

[0016] Preferably, the adjoint operator is specifically as follows:

[0017] ;

[0018] ;

[0019] wherein, g v and g are the gradients of the update velocity perturbation and the porosity perturbation respectively, r is the adjoint wavefield of the residual, which is obtained by reverse time propagating the residual based on the seismic wave equation of the dense oil and gas medium.

[0020] Preferably, the gradient direction based update is performed on the velocity perturbation and the porosity perturbation imaging results, and the specific implementation is as follows:

[0021] ;

[0022] wherein,

[0023] ;

[0024] wherein, k is the iteration number, is the update step length, is the weighting coefficient, x is the spatial sampling interval, t is the time sampling interval.

[0025] Preferably, the migration velocity is obtained by a conventional travel time tomography inversion method, and the migration porosity is obtained by a conventional porosity inversion method.

[0026] In a second aspect, the present application further provides a multi-parameter seismic imaging system, comprising:

[0027] an acquisition module, configured to acquire observed seismic data, a migration velocity field and a migration porosity field of a target dense oil and gas reservoir working area, and initialize velocity perturbation and porosity perturbation imaging results; wherein the migration velocity field is used to describe the distribution of the seismic wave velocity of the underground background medium, and the migration porosity field is used to describe the distribution of the porosity of the underground background medium;

[0028] a calculation module, configured to input the migration velocity field, the migration porosity field, the initialized velocity perturbation and porosity perturbation imaging results into a reverse migration operator to obtain predicted seismic data; input the residual of the predicted seismic data and the observed seismic data into an adjoint operator to obtain a gradient direction for updating the velocity perturbation and the porosity perturbation; and update the velocity perturbation and the porosity perturbation imaging results based on the gradient direction; wherein the reverse migration operator is obtained by linear approximation on the seismic wave equation of the dense oil and gas medium, and the adjoint operator is obtained by deducing the reverse migration operator by the adjoint state method;

[0029] The iteration module iterates the updating process of the velocity disturbance and the porosity disturbance imaging result, and outputs the final velocity disturbance and porosity disturbance imaging result when the number of iterations is reached.

[0030] Compared with the prior art, the at least one technical scheme of the present application can achieve the following beneficial effects:

[0031] Firstly, the target dense oil and gas reservoir work area observation seismic data, migration velocity and migration porosity are obtained, and the velocity disturbance and porosity disturbance imaging result are initialized. Then, by linearizing approximation to the dense oil and gas medium wave equation, the reverse migration operator containing the physical property parameter disturbance is constructed, and on this basis, the adjoint operator which can be used to update the velocity disturbance and porosity disturbance imaging result is derived, and then the high-resolution joint imaging of the velocity disturbance and porosity disturbance is realized by using the iterative optimization algorithm. Specifically, the migration velocity and migration porosity are input into the reverse migration operator to obtain the predicted seismic data; the residual of the predicted seismic data and the observed seismic data is input into the adjoint operator to obtain the gradient direction for updating the velocity disturbance and porosity disturbance; and the velocity disturbance and porosity disturbance imaging result are updated based on the gradient direction. The present application can simultaneously calculate the high-resolution imaging result of the velocity disturbance and porosity disturbance, and effectively solve the problem that the elastic parameter and physical property parameter disturbance cannot be simultaneously imaged with high precision in the existing seismic imaging technology.

[0032] In addition, the dense oil and gas medium seismic wave equation can describe the dispersion effect of the fluid in the reservoir, so the method of the present application can compensate the seismic wave attenuation caused by the dispersion effect of the fluid in the reservoir, has higher resolution and amplitude fidelity than the acoustic least square reverse time migration method, and solves the problem that the imaging amplitude fidelity and resolution are reduced due to the dispersion effect of the reservoir fluid which cannot be compensated in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0034] Figure 1 The flow chart of a multi-parameter seismic imaging method of the present application;

[0035] Figure 2 The migration velocity field map of the embodiment of the present application;

[0036] Figure 3 The migration porosity field map of the embodiment of the present application;

[0037] Figure 4 a velocity perturbation imaging result map of an embodiment of the present application;

[0038] Figure 5 a porosity perturbation imaging result map of an embodiment of the present application;

[0039] Figure 6 a velocity perturbation imaging result map of a conventional acoustic wave least square reverse time migration of the present application. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the present application.

[0041] The tight oil and gas medium seismic wave equation considers the dispersion of fluid in the reservoir, and explicitly links the seismic data and the physical parameters such as porosity, thereby providing a model basis with less parameters and feasible calculation for reservoir parameter inversion and imaging. Therefore, the purpose of the present application is to provide a multi-parameter least square reverse time migration method based on the tight oil and gas medium seismic wave equation, aiming to solve the technical problem that the existing seismic imaging technology cannot simultaneously perform high-resolution imaging on elastic and physical parameter perturbations. Figure 1 The present application comprises the following steps:

[0042] S1: setting an observation system and migration parameters, and initializing the velocity perturbation and porosity perturbation imaging results to 0.

[0043] The migration parameters include the spatial sampling point number and sampling interval of the migration parameter field (migration velocity field and migration porosity field), the observation time and time sampling interval of the seismic data, the seismic wavelet, and the maximum iteration number.

[0044] Obtaining observed seismic data (seismic shot data), obtaining a migration velocity field (input parameter v 0) by a conventional travel time tomography inversion method, and obtaining a migration porosity field (input parameter ) by a conventional porosity inversion method.

[0045] The migration velocity field is used to describe the distribution of the seismic wave velocity of the background medium underground. Further, the migration velocity field is the background component of the seismic wave velocity of the underground medium, which is the low wave number information and is used to calculate the background wave field in the reverse migration operator.

[0046] The shifted porosity field is used to describe the distribution of the porosity of the background medium in the subsurface. Further, the shifted porosity field is a background component of the porosity of the subsurface medium, which is the low-to-mid wave number information, and is used to calculate the background wave field in the reverse migration operator.

[0047] S2: input the shifted velocity field, the shifted porosity field, the initialized velocity perturbation and the porosity perturbation imaging result into the reverse migration operator to obtain predicted seismic data.

[0048] The predicted seismic data is predicted by the reverse migration operator based on the seismic wave equation of the tight oil and gas medium, and the calculation formula is:

[0049] (1);

[0050] (2);

[0051] wherein, p 0 is the background wave field, f is the seismic wavelet, p s is the scattering wave field generated by the parameter perturbation (i.e. the predicted seismic data), v 0 and m v is the migration velocity and the velocity perturbation, and is the porosity and the porosity perturbation, and the viscous decay parameter b f is set to be a constant of the order of 10 -4 , and the physical parameter related to the permeability is set to be a constant of the order of 10 2 . The reverse migration operator is a forward equation for calculating the seismic data from the velocity perturbation imaging result and the porosity perturbation imaging result.

[0052] S3: input the residual of the predicted seismic data and the observed seismic data into the adjoint operator to obtain the gradient direction for updating the velocity perturbation and the porosity perturbation.

[0053] The data residual is calculated from the predicted seismic data and the observed seismic data predicted by step S2:

[0054] (3);

[0055] wherein, r is the data residual, p obs is the observed seismic data.

[0056] The gradient is calculated by the adjoint operator of the reverse migration operator:

[0057] (4);

[0058] (5);

[0059] wherein, g v and g are the gradients of the update velocity perturbation and the porosity perturbation respectively, r *is the adjoint wavefield of the data residual, which is obtained by reverse-time propagating the data residual based on the seismic wave equation of the dense oil and gas medium.

[0060] Further, in order to ensure that the update amount of the porosity perturbation and the porosity perturbation are of the same dimension, a weighting coefficient of the porosity perturbation imaging result is calculated:

[0061] (6);

[0062] wherein, x is the spatial sampling interval, t is the time sampling interval.

[0063] The velocity perturbation and the porosity perturbation imaging result are updated by the steepest descent method:

[0064] (7);

[0065] wherein, the upper side represents the iteration number.

[0066] S4: repeatedly performing S2 and S3 until the iteration number is greater than the maximum iteration number set in S1, and outputting the velocity perturbation imaging result and the porosity perturbation imaging result.

[0067] Based on the same concept, the application further provides a multi-parameter seismic imaging system, comprising an acquisition module, a calculation module and an iteration module.

[0068] The acquisition module is used to acquire observed seismic data, a migration velocity field and a migration porosity field of a target dense oil and gas reservoir working area, and initialize velocity perturbation and porosity perturbation imaging results; wherein the migration velocity field is used to describe the distribution of the seismic wave velocity of the underground background medium, and the migration porosity field is used to describe the distribution of the porosity of the underground background medium.

[0069] The calculation module is used to input the migration velocity field, the migration porosity field, the initialized velocity perturbation and the porosity perturbation imaging result into a reverse migration operator to obtain predicted seismic data; input the residual of the predicted seismic data and the observed seismic data into an adjoint operator to obtain a gradient direction for updating the velocity perturbation and the porosity perturbation; update the velocity perturbation and the porosity perturbation imaging result based on the gradient direction; wherein the reverse migration operator is obtained by linear approximation on the seismic wave equation of the dense oil and gas medium, and the adjoint operator is obtained by deducing the reverse migration operator by the adjoint state method.

[0070] The iteration module iterates the updating process of the velocity perturbation and porosity perturbation imaging results, and outputs the final velocity perturbation and porosity perturbation imaging results when the number of iterations is reached.

[0071] Embodiment

[0072] In order to make the technical solutions of the present application more simple and clear, a specific application example is given below for illustration:

[0073] When the imaging method of the present application is applied to two-dimensional seismic data, the input observation data, the migration velocity field (as shown in Figure 2 ) and the porosity field (as shown in Figure 3 ) are inputted;

[0074] The observation system and migration parameters are set, the observation system distribution is that 90 shot points are uniformly arranged at an interval of 60 meters on the ground surface, and 552 geophones on the ground surface are received at an interval of 10 meters, the migration parameters are as follows: the migration velocity field has 219 lateral sampling points and 101 vertical sampling points, the spatial sampling interval is 10 meters, the time sampling interval is 0.5 milliseconds, the sampling time is 1 second, the seismic wavelet is a Ricker wavelet with a main frequency of 40 Hz, the viscous attenuation parameter is 0.0001, and the physical parameter related to permeability is 100.

[0075] The initial velocity perturbation and porosity perturbation imaging results are set to 0, and the maximum number of iterations is set to 10.

[0076] According to the migration velocity field, the migration porosity field, the velocity perturbation imaging result and the porosity perturbation imaging result, the seismic data is predicted by the inverse migration operator based on the seismic wave equation of the dense oil and gas medium, the data residual is calculated, the gradient is calculated by the adjoint operator of the inverse migration operator, and the velocity perturbation imaging result and the porosity perturbation imaging result are updated.

[0077] When the number of iterations is 10, the velocity perturbation imaging result of the 10th iteration (as shown in Figure 4 ) and the porosity perturbation imaging result (as shown in Figure 5 ) are outputted.

[0078] In order to compare the imaging method of the present application with the data-driven seismic imaging method, the same migration velocity model and migration parameters are used, the observed seismic data is imaged by the acoustic least squares reverse time migration, and the velocity perturbation imaging result (as shown in Figure 6 ) is obtained.

[0079] The imaging method of the present application can compensate for the seismic wave attenuation caused by the dispersion effect of the fluid in the reservoir,

[0080] The imaging result is relative to the velocity disturbance imaging result of the conventional acoustic wave least square reverse time migration (as shown in Figure 6 The imaging resolution of the underground structure is improved, especially the deep structure is clearer. In addition, the imaging method of the application can not only calculate the velocity disturbance imaging result (as shown in Figure 4 The imaging result is relative to the velocity disturbance imaging result of the conventional acoustic wave least square reverse time migration (as shown in Figure 5 The imaging result is relative to the velocity disturbance imaging result of the conventional acoustic wave least square reverse time migration (as shown in Figure 6 The imaging result is relative to the velocity disturbance imaging result of the conventional acoustic wave least square reverse time migration (as shown in

[0081] The method of the application constructs an explicit migration operator containing the property parameter disturbance and capable of describing the reservoir fluid diffusion effect by linearizing and approximating the tight oil and gas medium wave equation. On this basis, the gradient operator for jointly updating the velocity disturbance and porosity disturbance imaging result is derived, and then the high-resolution joint imaging of the velocity disturbance and porosity disturbance is realized by using the iterative optimization algorithm. The method effectively solves the problem that the existing seismic imaging technology cannot simultaneously perform high-precision imaging of elastic parameters and property parameter disturbance, and provides technical support for fine description of the tight oil and gas reservoir.

[0082] Therefore, compared with the prior art method, the method of the application can improve the imaging resolution of the velocity disturbance,

[0083] The imaging result of the porosity disturbance can also be calculated, which is of great significance for the exploration of the tight oil and gas reservoir.

[0084] Although the preferred embodiments of the application have been described, those skilled in the art can make additional changes and modifications to the embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be interpreted as including all the changes and modifications falling within the scope of the application.

[0085] Obviously, those skilled in the art can make various modifications and changes to the application without departing from the spirit and scope of the application. Thus, if these modifications and changes of the application fall within the scope of the claims of the application and their equivalent technologies, the application also intends to include these modifications and changes.

Claims

1. A multi-parameter seismic imaging method, characterized in that, The method comprises the following steps: Obtaining observed seismic data, a migration velocity field and a migration porosity field of a target tight oil and gas reservoir working area, and initializing velocity perturbation and porosity perturbation imaging results; the migration velocity field is used to describe the distribution of the seismic wave velocity of the underground background medium, and the migration porosity field is used to describe the distribution of the porosity of the underground background medium; Inputting the migration velocity field, the migration porosity field, the initialized velocity perturbation and porosity perturbation imaging results into a reverse migration operator to obtain predicted seismic data; inputting the residual of the predicted seismic data and the observed seismic data into an adjoint operator to obtain a gradient direction for updating the velocity perturbation and the porosity perturbation; updating the velocity perturbation and the porosity perturbation imaging results based on the gradient direction; the reverse migration operator is obtained by linearizing and approximating a seismic wave equation of the tight oil and gas medium, and the adjoint operator is derived from the reverse migration operator by using an adjoint state method. The updating process of the velocity perturbation and the porosity perturbation imaging results is iterated, and the final velocity perturbation and porosity perturbation imaging results are output when the iteration number is reached.

2. A multi-parameter seismic imaging method as claimed in claim 1, characterized in that, The reverse migration operator is specifically as follows: ; where f is the seismic wavelet, p s is the scattered wavefield due to parameter perturbation, v 0 and m v is the migration velocity and velocity perturbation, and is the porosity and porosity perturbation, b f is the viscous attenuation parameter, is the petrophysical parameter related to permeability, t is time, is the Laplacian operator; where, p 0 is the background wavefield, computed by solving the seismic wave equation for a dense oil and gas medium: 。 3. A multi-parameter seismic imaging method as claimed in claim 2, characterized in that, The adjoint operator is specifically as follows: ; ; wherein g v and are the gradients of the update velocity perturbation and porosity perturbation, respectively, r is the residual's adjoint wavefield, which is obtained by reverse-time propagating the residual based on the seismic wave equation for a dense hydrocarbon medium.

4. A multi-parameter seismic imaging method as claimed in claim 3, characterized in that, The updating of the velocity perturbation and the porosity perturbation imaging results based on the gradient direction is specifically as follows: ; Wherein, ; wherein k is the iteration number, is the update step size, is the weighting coefficient, x is the spatial sampling interval, t is the time sampling interval.

5. A multi-parameter seismic imaging method as claimed in claim 1, characterized in that, The migration velocity is obtained by a conventional travel time tomography inversion method, and the migration porosity is obtained by a conventional porosity inversion method.

6. A multi-parameter seismic imaging system characterized by, The method comprises the following steps: The obtaining module is configured to obtain observed seismic data, a migration velocity field and a migration porosity field of a target tight oil and gas reservoir working area, and initialize velocity perturbation and porosity perturbation imaging results; the migration velocity field is used to describe the distribution of the seismic wave velocity of the underground background medium, and the migration porosity field is used to describe the distribution of the porosity of the underground background medium; The calculation module is configured to input the migration velocity field, the migration porosity field, the initialized velocity perturbation and porosity perturbation imaging results into a reverse migration operator to obtain predicted seismic data; input the residual of the predicted seismic data and the observed seismic data into an adjoint operator to obtain a gradient direction for updating the velocity perturbation and the porosity perturbation; update the velocity perturbation and the porosity perturbation imaging results based on the gradient direction; the reverse migration operator is obtained by linearizing and approximating a seismic wave equation of the tight oil and gas medium, and the adjoint operator is derived from the reverse migration operator by using an adjoint state method. The iteration module is configured to iterate the updating process of the velocity perturbation and the porosity perturbation imaging results, and output the final velocity perturbation and porosity perturbation imaging results when the iteration number is reached.

Images