CN116541627A - Reservoir parameter determining method, device, computer equipment and readable medium - Google Patents
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Abstract
The present disclosure relates to the field of reservoir parameter estimation technologies, and in particular, to a method and apparatus for determining a reservoir parameter, a computer device, and a readable medium. The reservoir parameter determining method comprises the steps of determining an orthogonal medium anisotropy parameter, a low-frequency initial model and a mixed phase wavelet based on logging data and seismic data; combining and updating a preset orthogonal medium longitudinal wave reflection formula to obtain a target longitudinal wave reflection formula; constructing a minimum objective function based on the orthogonal medium anisotropic parameter, the low-frequency initial model and the mixed phase wavelet; and solving a target longitudinal wave reflection formula based on the minimum objective function and the seismic data to obtain target reservoir parameters. By using the embodiment of the specification, based on the updated target longitudinal wave reflection formula and the minimum target function, the solution stability and accuracy of reservoir parameters are improved in an orthotropic medium.
Description
Technical Field
The present disclosure relates to the field of reservoir parameter estimation technologies, and in particular, to a method and apparatus for determining a reservoir parameter, a computer device, and a readable medium.
Background
Currently, parameters used to describe reservoirs include elastic and anisotropic parameters to characterize the reservoir and to predict the important pathways of subsurface fractures. When determining reservoir parameters, a preset orthogonal medium longitudinal wave reflection formula is adopted to solve the reservoir parameters, and the method is a method for improving stability at the cost of losing accuracy. In order to improve both stability and accuracy, the assumption is mostly solved in isotropic media, whereby although stability and accuracy are improved simultaneously, seismic waves are currently propagated in orthotropic media, resulting in lower accuracy relative to the real situation.
How to simultaneously improve the stability and accuracy of solving reservoir parameters in orthotropic media is a problem in the prior art.
Disclosure of Invention
In order to solve the problems in the prior art, the embodiment of the specification provides a method, a device, computer equipment and a readable medium for determining reservoir parameters, which are realized in an orthotropic medium and improve the stability and accuracy of solving the reservoir parameters.
In order to solve the technical problems, the specific technical scheme in the specification is as follows:
In one aspect, embodiments of the present disclosure provide a method of reservoir parameter determination, comprising,
determining an orthogonal medium anisotropic parameter, a low-frequency initial model and a mixed phase wavelet based on the logging data and the seismic data;
combining and updating a preset orthogonal medium longitudinal wave reflection formula to obtain a target longitudinal wave reflection formula;
constructing a minimum objective function based on the orthogonal medium anisotropic parameter, the low-frequency initial model and the mixed phase wavelet; and
and solving the target longitudinal wave reflection formula based on the minimum objective function and the seismic data to obtain target reservoir parameters.
Further, the determining a mixed phase wavelet based on the well log data and the seismic data further comprises,
determining a fracture strike based on the logging data;
determining target seismic data corresponding to the fracture strike from the seismic data; and
the mixed phase wavelet is determined based on the target seismic data.
Further, the combination updating is carried out for the preset orthogonal medium longitudinal wave reflection formula, the obtaining of the target longitudinal wave reflection formula further comprises,
simplifying a preset orthogonal medium crack disturbance reflection coefficient formula in the preset orthogonal medium longitudinal wave reflection formula to obtain a target orthogonal medium crack disturbance reflection coefficient formula; and
And determining the target longitudinal wave reflection formula based on the target orthogonal medium crack disturbance reflection coefficient formula and a preset transverse isotropy longitudinal wave reflection coefficient formula in the preset orthogonal medium longitudinal wave reflection formula.
Further, the target orthogonal dielectric fracture disturbance reflection coefficient formula further comprises,
wherein θ represents the angle of incidence, φ represents the azimuth relative to the fracture normal plane, D=Γ 2 -Γ 1 The e=ε (2) -ε (1 ) The Γ is 2 ＝(δ (2) -8g 2 γ (2) ),Γ 1 ＝(δ (1) -8g 2 γ (1) ) The epsilon characterizes a longitudinal wave anisotropy parameter, the delta characterizes a second derivative of a longitudinal wave phase velocity function at normal incidence, the g characterizes a square term of a transverse-to-longitudinal wave velocity ratio, and the gamma characterizes a transverse wave anisotropy parameter.
Further, the target longitudinal wave reflection formula further includes,
wherein the saidCharacterizing the preset transverse isotropic longitudinal wave reflection coefficient formula, theCharacterizing the target orthogonal medium fracture disturbance reflection coefficient formula, the θ characterizing an angle of incidence, the φ characterizing an azimuth angle relative to a fracture normal plane, the A=ρα, the +.>Said->The ρ characterizes the density, the ε characterizes the longitudinal wave anisotropy parameter, the δ characterizes the second derivative of the longitudinal wave phase velocity function at normal incidence, the g characterizes the square term of the transverse to longitudinal wave velocity ratio, the γ characterizes the transverse wave anisotropy parameter, the α characterizes the longitudinal wave velocity, and the K characterizes the square term of the four times transverse to longitudinal wave velocity ratio.
Further, the minimum objective function further includes,
wherein the d characterizes the seismic data, the G characterizes a linear forward modeling operator determined based on the mixed phase wavelet, the m prior -characterizing prior information of a model parameter vector determined based on the low frequency initial model, and-said m characterizing the target reservoir parameter.
Further, the target storage parameters include acoustic impedance, anisotropic shear modulus, longitudinal wave phase velocity along the fracture strike direction, azimuthal anisotropy gradient, and relative fracture density, the solving the target longitudinal wave reflection formula based on the minimum objective function and the seismic data to obtain target reservoir parameters further includes,
solving the target longitudinal wave reflection formula based on the minimum objective function to obtain the acoustic impedance, the anisotropic shear modulus and the longitudinal wave phase velocity along the crack trend direction;
determining the seismic amplitude difference between the first residual sub-seismic data except for the crack trend corresponding to the observation azimuth in the seismic data and the second residual sub-seismic data parallel to the crack trend;
the azimuthal anisotropy gradient and the relative fracture density are determined based on the minimum objective function and the seismic amplitude difference.
In another aspect, embodiments of the present disclosure also provide a method of determining a reservoir parameter, comprising,
a first determining unit for determining an orthogonal medium anisotropy parameter, a low frequency initial model and a mixed phase wavelet based on the logging data and the seismic data;
the updating unit is used for carrying out combination updating on a preset orthogonal medium longitudinal wave reflection formula to obtain a target longitudinal wave reflection formula;
a constructing unit, configured to construct a minimum objective function based on the orthogonal medium anisotropy parameter, the low-frequency initial model, and the mixed phase wavelet; and
and the solving unit is used for solving the target longitudinal wave reflection formula based on the minimum objective function and the seismic data to obtain target reservoir parameters.
In another aspect, embodiments of the present disclosure further provide a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the method described above when executing the computer program.
In another aspect, embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer instructions that, when executed by a processor, perform the above-described method.
Using embodiments of the present description, determining orthogonal medium anisotropy parameters, a low frequency initial model, and a mixed phase wavelet based on received log data and seismic data; acquiring a preset orthogonal medium longitudinal wave reflection formula, and carrying out combination updating on the preset orthogonal medium longitudinal wave reflection formula to obtain a target longitudinal wave reflection formula; and constructing a minimum objective function based on the determined anisotropic parameters of the orthogonal medium, the low-frequency initial model and the mixed phase wavelet, and further solving a target longitudinal wave reflection formula based on the minimum objective function and the seismic data to obtain the target reservoir parameters. Therefore, the combination updating of a preset orthogonal medium longitudinal wave reflection formula (which can be solved in a weak anisotropic medium) is realized, and a target longitudinal wave reflection formula which can be applied to the orthogonal anisotropic medium is obtained. And then based on the anisotropic parameters of the orthogonal medium, the low-frequency initial model and the mixed phase wavelet, constructing a minimum objective function to solve the objective longitudinal wave reflection formula, thereby determining the objective reservoir parameters, realizing the implementation of the method in the orthogonal anisotropic medium, and improving the solving stability and accuracy of the reservoir parameters.
Drawings
In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present description, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an implementation system of a reservoir parameter determination method according to an embodiment of the present disclosure;
FIG. 2 is a flow chart illustrating a method of reservoir parameter determination in accordance with an embodiment of the present disclosure;
FIG. 3 is a flow chart of a method for determining mixed phase wavelets according to an embodiment of the present disclosure;
FIG. 4 is a flowchart showing a method for determining a reflection formula of a target longitudinal wave according to an embodiment of the present disclosure;
FIG. 5A is a schematic view of seismic wavelets corresponding to seismic data of different azimuth angles according to an embodiment of the present disclosure;
FIG. 5B is a schematic diagram of log data according to an embodiment of the present disclosure;
FIG. 5C is a schematic diagram of a synthetic seismic azimuth gather without noise according to an embodiment of the disclosure;
FIG. 5D is a schematic diagram of a first set of target reservoir parameters according to an embodiment of the present disclosure;
FIG. 5E is a schematic diagram of a second set of target reservoir parameters according to an embodiment of the present disclosure;
FIG. 5F is a schematic diagram of a target reservoir parameter set according to an embodiment of the present disclosure;
FIG. 6 is a schematic diagram of a reservoir parameter determination apparatus according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present disclosure.
[ reference numerals description ]
101. A user terminal;
102. a server;
501. a-C parameter map;
502. a-B parameter map;
503. d, parameter diagram;
504. e, parameter diagram;
610. a first determination unit;
620. an updating unit;
630. a construction unit;
640. a solving unit;
702. a computer device;
704. a processing device;
706. storing the resource;
708. a driving mechanism;
710. an input/output module;
712. an input device;
714. an output device;
716. a presentation device;
718. a graphical user interface;
720. a network interface;
722. a communication link;
724. a communication bus.
Detailed Description
The technical solutions of the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is apparent that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.
It should be noted that the terms "first," "second," and the like in the description and the claims of the specification and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the present description described herein may be capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, apparatus, article, or device that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or device.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
Fig. 1 is a schematic diagram of an implementation system of a reservoir parameter determining method according to an embodiment of the present disclosure, which may include: the user terminal 101 and the server 102 communicate with each other through a network, which may include a local area network (Local Area Network, abbreviated as LAN), a wide area network (Wide Area Network, abbreviated as WAN), the internet, or a combination thereof, and are connected to a website, user equipment (e.g., a computing device), and a backend system. After receiving the logging data and the seismic data, the server 102 determines an orthotropic parameter, a low-frequency initial model and a mixed phase wavelet according to the logging data and the seismic data, and performs combined updating on a preset orthogonal medium longitudinal wave reflection formula to obtain a target longitudinal wave reflection formula; constructing a minimum objective function based on the orthogonal medium anisotropic parameter, the low-frequency initial model and the mixed phase wavelet; and solving a target longitudinal wave reflection formula based on the minimum objective function and the seismic data to obtain a target reservoir parameter, and transmitting the target reservoir parameter to the user terminal 101. In addition, for example, an acquisition terminal may be included, which may be, for example, a sensor or the like, that may acquire log data and send the log data to a server to enable acquisition of the log data and transmission of the log data. The acquisition terminal and the server may communicate over a network, for example.
Alternatively, the servers 102 may be nodes of a cloud computing system (not shown), or each server 102 may be a separate cloud computing system, including multiple computers interconnected by a network and operating as a distributed processing system.
In an alternative embodiment, the user terminal 101 may include electronic devices not limited to smart phones, acquisition devices, desktop computers, tablet computers, notebook computers, smart speakers, digital assistants, augmented Reality (AR, augmented Reality)/Virtual Reality (VR) devices, smart wearable devices, and the like. Alternatively, the operating system running on the electronic device may include, but is not limited to, an android system, an IOS system, linux, windows, and the like.
In addition, it should be noted that, fig. 1 is only one application environment provided in the present specification, and in practical application, a plurality of user terminals 101 may also be included, which is not limited in the present specification.
Fig. 2 is a flow chart illustrating a method of determining reservoir parameters according to an embodiment of the present disclosure. The determination of reservoir parameters is described in this figure, but may include more or fewer operational steps based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one way of performing the order of steps and does not represent a unique order of execution. When a system or apparatus product in practice is executed, it may be executed sequentially or in parallel according to the method shown in the embodiments or the drawings. As shown in fig. 2, the method may include:
S210, determining an orthogonal medium anisotropic parameter, a low-frequency initial model and a mixed phase wavelet based on logging data and seismic data;
s220, carrying out combination updating on a preset orthogonal medium longitudinal wave reflection formula to obtain a target longitudinal wave reflection formula;
s230, constructing a minimum objective function based on the orthogonal medium anisotropic parameter, the low-frequency initial model and the mixed phase wavelet;
s240, solving a target longitudinal wave reflection formula based on the minimum objective function and the seismic data to obtain target reservoir parameters.
Using embodiments of the present description, determining orthogonal medium anisotropy parameters, a low frequency initial model, and a mixed phase wavelet based on received log data and seismic data; acquiring a preset orthogonal medium longitudinal wave reflection formula, and carrying out combination updating on the preset orthogonal medium longitudinal wave reflection formula to obtain a target longitudinal wave reflection formula; and constructing a minimum objective function based on the determined anisotropic parameters of the orthogonal medium, the low-frequency initial model and the mixed phase wavelet, and further solving a target longitudinal wave reflection formula based on the minimum objective function and the seismic data to obtain the target reservoir parameters. Therefore, the combination updating of a preset orthogonal medium longitudinal wave reflection formula (which can be solved in a weak anisotropic medium) is realized, and a target longitudinal wave reflection formula which can be applied to the orthogonal anisotropic medium is obtained. And then based on the anisotropic parameters of the orthogonal medium, the low-frequency initial model and the mixed phase wavelet, constructing a minimum objective function to solve the objective longitudinal wave reflection formula, thereby determining the objective reservoir parameters, realizing the implementation of the method in the orthogonal anisotropic medium, and improving the solving stability and accuracy of the reservoir parameters.
According to one embodiment of the present disclosure, the well logging data is downhole data acquired based on a logging instrument such as a sensor. The seismic data are data acquired based on a seismic signal receiving system and a seismic information recording system.
Determining the orthogonal medium anisotropy parameters based on the logging data in the orthogonal medium anisotropy parameters, the low-frequency initial model and the mixed phase wavelet based on the logging data is specifically to perform deep time conversion processing on the logging data to obtain first processed logging data, and performing orthogonal anisotropy medium petrophysical modeling processing on the first processed logging data to obtain the orthogonal medium anisotropy parameters.
Based on the logging data and the seismic data, determining the orthogonal medium anisotropic parameter, the low-frequency initial model and the logging data and the seismic data based on the mixed phase wavelet, wherein determining the low-frequency initial model is specifically to determine the low-frequency initial model by performing extrapolation calculation on the logging data under the constraint of the seismic horizon data determined by the seismic data. When determining the low-frequency initial model, extrapolation calculation may be performed for the well logging data, or extrapolation calculation may be performed for the roughened well logging data.
Based on the well logging data and the seismic data, determining the well logging data and the seismic data in the orthogonal medium anisotropic parameter, the low-frequency initial model and the mixed phase wavelet, and determining the mixed phase wavelet specifically includes determining target seismic data corresponding to the well logging data from the seismic data based on the well logging data, and determining the mixed phase wavelet based on the target seismic data. The mixed phase wavelet may be, for example, a corresponding mixed wavelet of different azimuth angles extracted from partial angle of incidence seismic data at different azimuth angles of the logging side channel.
The preset orthogonal medium longitudinal wave reflection formula may be, for example, as shown in the following formula (1).
Wherein ρ represents density, α represents longitudinal wave velocity, β represents a seventh parameter, Δ represents the difference between the two adjacent layers above and below the interface, —represents the average of the two adjacent layers of the seismic wave passing through the interface, θ represents the incident angle, δ represents the second derivative of the longitudinal wave phase velocity function at normal incidence, ε represents the longitudinal wave anisotropy parameter, φ represents the azimuth angle relative to the fracture normal plane, γ represents the transverse wave anisotropy parameter, g represents the square term of the transverse-to-longitudinal wave velocity ratio, and
A ij ＝C ij and/ρ is the density normalized orthotropic medium elastic stiffness coefficient.
The longitudinal wave velocity and the seventh parameter are the vertical longitudinal wave velocity and the vertical transverse wave velocity of the isotropic medium, respectively. The square term of the transverse-longitudinal wave speed ratio is specifically
The method comprises the steps of carrying out combined updating on a preset orthogonal medium longitudinal wave reflection formula to obtain a target longitudinal wave reflection formula, namely, separating and updating the preset orthogonal medium longitudinal wave reflection formula into a preset transverse isotropy longitudinal wave reflection coefficient formula and a preset orthogonal medium crack disturbance reflection coefficient formula, determining an optimization term from the preset transverse isotropy longitudinal wave reflection coefficient formula and the preset orthogonal medium crack disturbance reflection coefficient formula, and carrying out optimization processing on the optimization term to obtain the target longitudinal wave reflection formula.
The minimum objective function is a function constructed by taking a preset objective function in the Bayesian framework as a template function. Based on the anisotropic parameters of the orthogonal medium, the low-frequency initial model and the mixed phase wavelet, constructing a minimum objective function, namely, acquiring a preset objective function in a Bayesian framework, and filling the preset objective function by using the anisotropic parameters of the orthogonal medium, the low-frequency initial model and the mixed phase wavelet to obtain the minimum objective function. More specifically, the prior model distribution and likelihood function included in the minimized objective function are both gaussian-shaped.
After a target longitudinal wave reflection formula and a minimum target function are determined, solving the target longitudinal wave reflection formula based on the minimum target function and the seismic data to obtain target reservoir parameters, specifically solving the target longitudinal wave reflection formula based on the minimum target function to obtain first target reservoir parameters; and determining updated seismic data based on the seismic data, and solving a target longitudinal wave reflection formula based on the minimum objective function and the updated seismic data to obtain a second target reservoir parameter. The target reservoir parameter is determined from the first target reservoir parameter and the second target reservoir parameter.
According to another embodiment of the present specification, the minimum objective function may be as shown in the following equation (2), for example.
Wherein d represents seismic data, G represents a linear forward modeling operator determined based on mixed phase wavelets, m prior A priori information characterizing a model parameter vector determined based on the low frequency initial model, and m characterizing a target reservoir parameter.
Target reservoir parameters follow a gaussian distributionWherein mu m Represents a priori average (mean of log data), Σ m The correlation between inversion parameters is estimated by a multivariate gaussian distribution for a deterministic a priori covariance matrix based on orthogonal medium anisotropy parameters. Sigma (sigma) d Is a diagonal matrix of noise covariance, in case of uncorrelated noise of the observed data +.>Wherein->Is the noise variance, follow zero mean gaussian distribution +.>
And (3) deriving the formula (2) to obtain an inversion result, as shown in the following formula (3).
Wherein m characterizes the target reservoir parameters, G characterizes the linear forward modeling operator determined based on the mixed phase wavelet, d characterizes the seismic data, and m prior The prior information characterizing the model parameter vector determined based on the low frequency initial model.
And then solving the target longitudinal wave reflection formula based on the minimum objective function and the seismic data to obtain the target reservoir parameters, specifically solving the target longitudinal wave reflection formula based on the inversion result formula (3) and the seismic data to obtain the target reservoir parameters.
Fig. 3 is a flowchart of a method for determining a mixed phase wavelet according to an embodiment of the present disclosure. A hybrid phase wavelet determination process is described in this figure, but may include more or fewer operational steps based on conventional or non-inventive labor. As shown in fig. 3, the method may include:
s311, determining the trend of the crack based on the logging data;
s312, determining target seismic data corresponding to the crack trend from the seismic data;
S313, determining mixed phase wavelets based on the target seismic data.
According to another embodiment of the present description, electrical imaging logging data is utilized to determine a strike of a well fracture characterized by the logging data and take the strike as a fracture strike.
Sub-target seismic data corresponding to the trend of the crack are respectively determined from the data corresponding to each plane in the three planes in the seismic data, and the three sub-target seismic data are summarized to obtain target seismic data. The three planes are three mutually perpendicular planes in the three-dimensional plane.
After the target seismic data is determined, extracting and mixing wavelets of different azimuth angles from the target seismic data to obtain mixed phase wavelets.
Fig. 4 is a flowchart illustrating a method for determining a target longitudinal wave reflection formula according to an embodiment of the present disclosure. A target longitudinal wave reflection formula determination process is described in this figure, but may include more or fewer operational steps based on conventional or non-inventive labor. As shown in fig. 4, the method may include:
s421, simplifying a preset orthogonal medium crack disturbance reflection coefficient formula in a preset orthogonal medium longitudinal wave reflection formula to obtain a target orthogonal medium crack disturbance reflection coefficient formula;
S422, determining a target longitudinal wave reflection formula based on a target orthogonal medium crack disturbance reflection coefficient formula and a preset transverse isotropy longitudinal wave reflection coefficient formula in a preset orthogonal medium longitudinal wave reflection formula.
According to another embodiment of the present disclosure, the preset orthogonal medium longitudinal wave reflection equation is separated and recombined to obtain a preset transverse isotropic longitudinal wave reflection coefficient equation and a preset orthogonal medium crack disturbance reflection coefficient equation. Specifically, the above formula (1) is split and recombined to obtain a preset transverse isotropy longitudinal wave reflection coefficient formula such as the following formula (4) and a preset orthogonal medium crack disturbance reflection coefficient formula such as the following formula (5). It should be noted that it is based on combining the anisotropic parameters characterizing the same information together and then splitting the two formulas, thereby enhancing the weak anisotropy of the original formula (1).
Wherein ρ represents density, α represents longitudinal wave velocity, β represents seventh parameter, Δ represents difference between two adjacent layers above and below the interface, θ represents incident angle, ε represents longitudinal wave anisotropy parameter, γ represents transverse wave anisotropy parameter, and g represents square term of transverse-longitudinal wave velocity ratio.
Wherein delta refers to the difference between the upper and lower adjacent layers of the interface, theta represents the incident angle, delta represents the second derivative of the longitudinal wave phase velocity function at normal incidence, epsilon represents the longitudinal wave anisotropy parameter, phi represents the azimuth angle relative to the fracture normal plane, gamma represents the transverse wave anisotropy parameter, and g represents the square term of the transverse-to-longitudinal wave velocity ratio.
Further updating is performed with respect to the formula (4), resulting in the following formula (6).
Wherein, A=ρα,and +.>
Simplifying a preset orthogonal medium crack disturbance reflection coefficient formula in a preset orthogonal medium longitudinal wave reflection formula to obtain a target orthogonal medium crack disturbance reflection coefficient formula, specifically deleting sin in the above formula (3) 2 φcos 2 And phi, obtaining a target orthogonal medium crack disturbance reflection coefficient formula. It should be noted that since sin 2 φcos 2 Phi < 0.25, whereby the effect of the term on any azimuth is negligible, the term can be deleted.
After the target orthogonal medium crack disturbance reflection coefficient formula is obtained, the target orthogonal medium crack disturbance reflection coefficient formula and a preset transverse isotropy longitudinal wave reflection coefficient formula form a target longitudinal wave reflection formula.
According to another embodiment of the present disclosure, the deleted target orthogonal dielectric fracture disturbance reflection coefficient formula is shown in the following formula (7).
Where θ represents the angle of incidence, Φ represents the azimuth relative to the fracture normal plane, d=Γ 2 -Γ 1 ，E＝ε (2) -ε (1) ，Γ 2 ＝(δ (2) -8g 2 γ (2) ),Γ 1 ＝(δ (1) -8g 2 γ (1) ) Epsilon characterizes the longitudinal wave anisotropy parameter, delta characterizes the second derivative of the longitudinal wave phase velocity function at normal incidence, g characterizes the square term of the transverse-to-longitudinal wave velocity ratio, and gamma characterizes the transverse wave anisotropy parameter.
According to another embodiment of the present specification, the deleted and combined target longitudinal wave reflection formula may be, for example, as shown in the following formula (8).
Wherein,,representing a preset transverse isotropy longitudinal wave reflection coefficient formula, < + >>Representing a target orthogonal medium crack disturbance reflection coefficient formula, wherein θ represents an incident angle, phi represents an azimuth angle relative to a crack normal plane, and a=ρα, ++>ρ characterizationDensity, epsilon, represents a longitudinal wave anisotropy parameter, delta represents a second derivative of a longitudinal wave phase velocity function at normal incidence, g represents a square term of a transverse-to-longitudinal wave velocity ratio, gamma represents a transverse wave anisotropy parameter, alpha represents a longitudinal wave velocity, and K represents a square term of a transverse-to-longitudinal wave velocity ratio of four times.
As can be seen from equation (8), the target longitudinal wave reflection equation after the combination update has five further reservoir parameters, a (acoustic impedance), B (anisotropic shear modulus), C (longitudinal wave phase velocity along the fracture strike direction), D (azimuthal anisotropy gradient), and E (relative fracture density), respectively, and A, B and C are related only to the incident angle, and D and E are related to both the incident angle and the azimuth angle.
FIG. 5A is a schematic view of seismic wavelets corresponding to seismic data of different azimuth angles according to an embodiment of the present disclosure; FIG. 5B is a schematic diagram of log data according to an embodiment of the present disclosure; FIG. 5C is a schematic diagram of a synthetic seismic azimuth gather without noise according to an embodiment of the disclosure; FIG. 5D is a schematic diagram of a first set of target reservoir parameters according to an embodiment of the present disclosure; FIG. 5E is a schematic diagram of a second set of target reservoir parameters according to an embodiment of the present disclosure; FIG. 5F is a schematic diagram of a target reservoir parameter set according to an embodiment of the present disclosure.
According to another embodiment of the present disclosure, the target storage parameters include acoustic impedance, anisotropic shear modulus, longitudinal wave phase velocity along the fracture strike direction, azimuthal anisotropy gradient, and relative fracture density, solving a target longitudinal wave reflection formula based on a minimum objective function and seismic data, the obtaining the target reservoir parameters includes: solving a target longitudinal wave reflection formula based on a minimum objective function to obtain acoustic impedance, anisotropic shear modulus and longitudinal wave phase velocity along the direction of the crack trend; determining the seismic amplitude difference between the first residual sub-seismic data except for the crack trend corresponding to the observation azimuth in the seismic data and the second residual sub-seismic data parallel to the crack trend; based on the minimum objective function and the seismic amplitude difference, an azimuthal anisotropy gradient and a relative fracture density are determined.
Based on the minimum objective function, solving the objective longitudinal wave reflection formula to obtain acoustic impedance, anisotropic shear modulus and longitudinal wave phase velocity along the direction of fracture strike can be, for example, deriving the above formula (2) to obtain inversion result, as shown in the above formula (3).
Based on the formula (3), inversion is carried out on the target longitudinal wave reflection formula, and acoustic impedance, anisotropic shear modulus and longitudinal wave phase velocity along the direction of the fracture trend are obtained. By way of illustration, d in equation (3) is characterized by the tri-plane in-plane delay [ x ] in the seismic data when inversion is performed to solve for acoustic impedance, anisotropic shear modulus, and longitudinal wave phase velocity along the fracture strike direction 2 ，x 3 ]Amplitude data for the plane. Delay [ x ] 2 ，x 3 ]The determination process of the amplitude data of the plane comprises the steps of determining the trend of the crack corresponding to the logging data, and further extending [ x ] from the seismic data and the three plane data 2 ，x 3 ]Amplitude data corresponding to the fracture trend is determined from the data corresponding to the plane. Further, m= [ a, B, C] T Thereby determining acoustic impedance, anisotropic shear modulus, and longitudinal wave phase velocity along the fracture strike direction.
Because the large offset incidence angle gathers parallel to the fracture trend are selected for carrying out the three-term synchronous inversion which is irrelevant to the azimuth, the three-term approximation which is irrelevant to the azimuth is realized, the approximation degree which is similar to that of the Ruger formula and the accurate formula at each offset is kept, and AVO intercept items, gradient items and anisotropic velocity items can be synchronously inverted instead of the change gradient of the attribute between strata, so that the shale gas reservoir characterization aspect shows good interpretation capability.
Based on the seismic data, updating the formula (3) to obtain an updated formula (3). Specifically, the above formula (3) is updated by using the difference between the amplitude data of the observed azimuth in the seismic data (i.e., data other than the fracture trend) and the amplitude data parallel to the fracture trend to update d in the formula (3), and the updated formula (3) is obtained. And (3) based on the updated formula (3), inverting the target longitudinal wave reflection formula to obtain the azimuth anisotropy gradient and the relative fracture density.
After five target reservoir parameters (acoustic impedance, anisotropic shear modulus, longitudinal wave phase velocity along the fracture strike direction, azimuthal anisotropy gradient, and relative fracture density) are determined, reservoir characterization information may be determined based on the five target reservoir information. For example, the use of the parameter combination a-C (acoustic impedance-longitudinal wave phase velocity in the fracture strike direction) may be used to distinguish brittle related shales from surrounding formations, and the use of the parameter combination a-B (acoustic impedance-anisotropic shear modulus) may be used to successfully identify TOC-rich shales. High TOC shale gas reservoirs exhibit low A and B values, and brittle shale exhibit low A and C values. The parameter D quantifies the azimuthal anisotropy of small deviations and is often used to represent relative fracture density or horizontal stress anisotropy as it is positively correlated with fracture density. The parameter E (relative fracture density) characterizes the difference in longitudinal wave velocity in the x1-x2 direction, which is not only dependent on the fracture density, but is also affected by the packing fluid in the fracture.
For example, seismic wavelets corresponding to the seismic data of different azimuth angles as shown in FIG. 5A and logging data as shown in FIG. 5B are acquired. Denoising the seismic data of different orientations to obtain a noise-free synthetic seismic azimuth gather as shown in fig. 5C. And further determines a minimum objective function based on the data determined by fig. 5B. And carrying out inversion solving on the target longitudinal wave reflection formula by utilizing the minimum objective function and the seismic data to obtain a first target reservoir parameter (acoustic impedance, anisotropic shear modulus and longitudinal wave phase velocity along the direction of the fracture strike) and a second target reservoir parameter (azimuth anisotropy gradient and relative fracture density). The first target reservoir parameter is specifically shown in fig. 5D, where the solid line is the real log data of the noiseless synthetic data, the dotted line is the initial model, and the stippled line is the first target reservoir parameter. The second target reservoir parameter is specifically shown in fig. 5E, where the solid line is the real log data of the noiseless synthetic data, the dotted line is the initial model, and the stippled line is the second target reservoir parameter.
Analysis is performed on the determined first and second target reservoir parameters to construct, for example, an a-C parameter map 501, an a-B parameter map 502, a D parameter map 503, and an E parameter map 504 for identifying brittle-related shales, identifying TOC-rich shales, and quantifying the azimuthal anisotropy of small offsets and quantifying the differences in longitudinal wave velocities in the x1-x2 directions. Note that the coordinates of the lithofacies portions in the a-C parameter map 501 and the a-B parameter map 502 are respectively flexible Shale (durtile scale), brittle Shale (Brittle scale), and Limestone (consumer).
Fig. 6 is a schematic structural diagram of a reservoir parameter determining apparatus according to an embodiment of the present disclosure. As shown in fig. 6, including,
a first determining unit 610 for determining an orthogonal medium anisotropy parameter, a low frequency initial model, and a mixed phase wavelet based on the log data and the seismic data;
an updating unit 620, configured to perform a combination update on a preset orthogonal medium longitudinal wave reflection formula to obtain a target longitudinal wave reflection formula;
a construction unit 630, configured to construct a minimum objective function based on the orthogonal medium anisotropy parameter, the low frequency initial model, and the mixed phase wavelet; and
the solving unit 640 is configured to solve for the target longitudinal wave reflection formula based on the minimum objective function and the seismic data, so as to obtain the target reservoir parameter.
Since the principle of the device for solving the problem is similar to that of the method, the implementation of the device can be referred to the implementation of the method, and the repetition is omitted.
Fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present disclosure, where an apparatus in the present disclosure may be the computer device in the present embodiment, and perform the method of the present disclosure. The computer device 702 may include one or more processing devices 704, such as one or more Central Processing Units (CPUs), each of which may implement one or more hardware threads. The computer device 702 may also include any storage resources 706 for storing any kind of information, such as code, settings, data, etc. For example, and without limitation, storage resources 706 may include any one or more of the following combinations: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any storage resource may store information using any technology. Further, any storage resource may provide volatile or non-volatile retention of information. Further, any storage resources may represent fixed or removable components of computer device 702. In one case, the computer device 702 can perform any of the operations of the associated instructions when the processing device 704 executes the associated instructions stored in any storage resource or combination of storage resources. The computer device 702 also includes one or more drive mechanisms 708, such as a hard disk drive mechanism, an optical disk drive mechanism, and the like, for interacting with any storage resources.
The computer device 702 may also include an input/output module 710 (I/O) for receiving various inputs (via an input device 712) and for providing various outputs (via an output device 714). One particular output mechanism may include a presentation device 716 and an associated Graphical User Interface (GUI) 718. In other embodiments, input/output module 710 (I/O), input device 712, and output device 714 may not be included as just one computer device in a network. The computer device 702 can also include one or more network interfaces 720 for exchanging data with other devices via one or more communication links 722. One or more communication buses 724 couple the above-described components together.
Communication link 722 may be implemented in any manner, for example, through a local area network, a wide area network (e.g., the internet), a point-to-point connection, etc., or any combination thereof. Communication link 722 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.
The embodiments of the present specification also provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the above method.
The present description also provides a computer program product comprising a computer program which, when executed by a processor, implements the above method.
It will be appreciated by those skilled in the art that embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the present specification may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing detailed description of the embodiments has been presented for purposes of illustration and description, and it should be understood that the foregoing is by way of example only, and is not intended to limit the scope of the invention.
Claims (10)
1. A method of reservoir parameter determination, comprising:
determining an orthogonal medium anisotropic parameter, a low-frequency initial model and a mixed phase wavelet based on the logging data and the seismic data;
combining and updating a preset orthogonal medium longitudinal wave reflection formula to obtain a target longitudinal wave reflection formula;
constructing a minimum objective function based on the orthogonal medium anisotropic parameter, the low-frequency initial model and the mixed phase wavelet; and
and solving the target longitudinal wave reflection formula based on the minimum objective function and the seismic data to obtain target reservoir parameters.
2. The method of claim 1, wherein determining a hybrid phase wavelet based on well log data and seismic data comprises:
determining a fracture strike based on the logging data;
determining target seismic data corresponding to the fracture strike from the seismic data; and
the mixed phase wavelet is determined based on the target seismic data.
3. The method of claim 1, wherein the performing a combined update for a predetermined orthogonal medium longitudinal wave reflection formula to obtain a target longitudinal wave reflection formula comprises:
Simplifying a preset orthogonal medium crack disturbance reflection coefficient formula in the preset orthogonal medium longitudinal wave reflection formula to obtain a target orthogonal medium crack disturbance reflection coefficient formula; and
and determining the target longitudinal wave reflection formula based on the target orthogonal medium crack disturbance reflection coefficient formula and a preset transverse isotropy longitudinal wave reflection coefficient formula in the preset orthogonal medium longitudinal wave reflection formula.
4. The method of claim 3, wherein the target orthogonal media fracture perturbed reflection coefficient formula comprises:
wherein θ represents the angle of incidence, φ represents the azimuth relative to the fracture normal plane, D=Γ 2 -Γ 1 The e=ε (2) -ε (1) The Γ is 2 ＝(δ (2) -8g 2 γ (2) ),Γ 1 ＝(δ (1) -8g 2 γ (1) ) The epsilon characterizes a longitudinal wave anisotropy parameter, the delta characterizes a second derivative of a longitudinal wave phase velocity function at normal incidence, the g characterizes a square term of a transverse-to-longitudinal wave velocity ratio, and the gamma characterizes a transverse wave anisotropy parameter.
5. The method of claim 3, wherein the target longitudinal wave reflection formula comprises:
wherein the saidCharacterizing the preset transverse isotropic longitudinal wave reflection coefficient formula, said +. >Characterizing the target orthogonal medium fracture disturbance reflection coefficient formula, the θ characterizing an angle of incidence, the φ characterizing an azimuth angle relative to a fracture normal plane, the A=ρα, the +.>Said->The ρ characterizes the density, the ε characterizes the longitudinal wave anisotropy parameter, the δ characterizes the second derivative of the longitudinal wave phase velocity function at normal incidence, the g characterizes the square term of the transverse to longitudinal wave velocity ratio, the γ characterizes the transverse wave anisotropy parameter, the α characterizes the longitudinal wave velocity, and the K characterizes the square term of the four times transverse to longitudinal wave velocity ratio.
6. The method of claim 1, wherein the minimum objective function comprises:
wherein the d characterizes the seismic data, the G characterizes a linear forward modeling operator determined based on the mixed phase wavelet, the m prior -characterizing prior information of a model parameter vector determined based on the low frequency initial model, and-said m characterizing the target reservoir parameter.
7. The method of claim 1, wherein the target storage parameters include acoustic impedance, anisotropic shear modulus, longitudinal wave phase velocity along a fracture strike direction, azimuthal anisotropy gradient, and relative fracture density, and wherein solving the target longitudinal wave reflection formula based on the minimum objective function and the seismic data to obtain target reservoir parameters comprises:
Solving the target longitudinal wave reflection formula based on the minimum objective function to obtain the acoustic impedance, the anisotropic shear modulus and the longitudinal wave phase velocity along the crack trend direction;
determining the seismic amplitude difference between the first residual sub-seismic data except for the crack trend corresponding to the observation azimuth in the seismic data and the second residual sub-seismic data parallel to the crack trend;
the azimuthal anisotropy gradient and the relative fracture density are determined based on the minimum objective function and the seismic amplitude difference.
8. A reservoir parameter determination apparatus, comprising:
a first determining unit for determining an orthogonal medium anisotropy parameter, a low frequency initial model and a mixed phase wavelet based on the logging data and the seismic data;
the updating unit is used for carrying out combination updating on a preset orthogonal medium longitudinal wave reflection formula to obtain a target longitudinal wave reflection formula;
a constructing unit, configured to construct a minimum objective function based on the orthogonal medium anisotropy parameter, the low-frequency initial model, and the mixed phase wavelet; and
and the solving unit is used for solving the target longitudinal wave reflection formula based on the minimum objective function and the seismic data to obtain target reservoir parameters.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of the preceding claims 1-7 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when executed by a processor, performs the method of any of the preceding claims 1-7.
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