阅读说明：本技术 储层孔隙特征确定方法、装置及设备 (Reservoir pore characteristic determination method, device and equipment ) 是由 杜炳毅 张广智 高建虎 王洪求 李林 蔺营 周游 于 2020-07-27 设计创作，主要内容包括：本说明书实施例公开了一种储层孔隙特征确定方法、装置及设备,所述方法包括获取目标储层的岩石骨架体积含量、岩石骨架剪切模量、流体体积模量以及等效体积模量、等效剪切模量；其中,等效体积模量、等效剪切模量通过综合考虑纵波及横波数据确定。利用孔隙特征参数反演模型对岩石骨架体积含量、岩石骨架剪切模量、流体体积模量以及等效体积模量、等效剪切模量进行反演处理,获得目标储层的孔隙特征参数数据；其中,孔隙特征参数包括孔隙度以及孔隙形态指数；孔隙形态指数用于表征储层的孔隙结构特征；基于反演获得的所述孔隙特征参数数据对所述目标储层的孔隙特征进行分析。利用本说明书各个实施例,可以更加准确的确定碳酸盐储层的孔隙特征。(The embodiment of the specification discloses a method, a device and equipment for determining reservoir pore characteristics, wherein the method comprises the steps of obtaining rock framework volume content, rock framework shear modulus, fluid volume modulus, equivalent volume modulus and equivalent shear modulus of a target reservoir; wherein the equivalent bulk modulus and the equivalent shear modulus are determined by comprehensively considering longitudinal wave data and transverse wave data. Carrying out inversion processing on the volume content of the rock framework, the shear modulus of the rock framework, the volume modulus of the fluid, the equivalent volume modulus and the equivalent shear modulus by using a pore characteristic parameter inversion model to obtain pore characteristic parameter data of a target reservoir stratum; wherein the pore characteristic parameters comprise porosity and pore form index; the pore morphology index is used for characterizing the pore structure of the reservoir; and analyzing the pore characteristics of the target reservoir stratum based on the pore characteristic parameter data obtained by inversion. With the various embodiments of the present description, the pore characteristics of a carbonate reservoir may be more accurately determined.)

1. A method for reservoir pore characterization, comprising:

obtaining the volume content of a rock framework, the shear modulus of the rock framework, the volume modulus of fluid, the equivalent volume modulus and the equivalent shear modulus of a target reservoir; the equivalent volume modulus and the equivalent shear modulus are obtained by calculation according to elastic parameters determined by utilizing longitudinal and transverse wave joint inversion;

carrying out inversion processing on the volume content of the rock framework, the shear modulus of the rock framework, the volume modulus of the fluid, the equivalent volume modulus and the equivalent shear modulus by using a pore characteristic parameter inversion model to obtain pore characteristic parameter data of the target reservoir; wherein the pore characteristic parameters include porosity and pore morphology index; the pore morphology index is used for characterizing the pore structure of the reservoir; the pore characteristic parameter inversion model comprises a model for representing the functional relationship between pore characteristic parameters and the volume content, the shear modulus, the fluid volume modulus, the equivalent volume modulus and the equivalent shear modulus of the rock framework;

and analyzing the pore characteristics of the target reservoir stratum based on the pore characteristic parameter data obtained by inversion to obtain a reservoir stratum pore characteristic analysis result of the target reservoir stratum.

2. The method of claim 1, wherein the pore feature parametric inversion model comprises:

μ＝μ_S(1-φ)^γ

wherein, K_SRepresents the volume content of the rock skeleton, mu_SExpressing shear modulus of rock skeleton, K_fDenotes the fluid bulk modulus, K denotes the equivalent bulk modulus, μ denotes the equivalent shear modulus, phi denotes the porosity, and gamma denotes the pore morphology index.

3. The method of claim 1, wherein the equivalent bulk modulus and the equivalent shear modulus are calculated by:

performing transverse interpolation on initial longitudinal wave impedance, initial transverse wave impedance and initial density obtained by calculation based on logging data by taking longitudinal wave horizon data of the target reservoir as transverse constraint to obtain an initial elastic parameter model;

taking the longitudinal wave seismic data, the transverse wave seismic data and the longitudinal wave seismic wavelet data under the longitudinal wave domain and the transverse wave seismic wavelet data of the target reservoir stratum as input, and performing inversion processing on the initial elastic parameter model to obtain the longitudinal wave impedance, the transverse wave impedance and the density of the target reservoir stratum;

and calculating to obtain the equivalent bulk modulus and the equivalent shear modulus of the target reservoir according to the longitudinal wave impedance, the transverse wave impedance and the density obtained by inversion.

4. The method of claim 3, wherein said inverse processing of said initial elastic parametric model comprises:

carrying out inversion processing on the initial elastic parameter model according to the following longitudinal and transverse wave joint inversion model:

wherein R is_ppDenotes the longitudinal wave reflection coefficient, R_psRepresents the transverse wave reflection coefficient, theta,

5. The method of claim 1, further comprising:

and analyzing the pore aspect ratio, the relation between longitudinal wave impedance and porosity, the relation between transverse wave impedance and porosity and the relation between permeability and porosity of the target reservoir based on the pore characteristic parameter data obtained by inversion to obtain a reservoir characteristic analysis result of the target reservoir.

6. A reservoir pore characteristics determination apparatus, the apparatus comprising:

the data acquisition module is used for acquiring the rock framework volume content, the rock framework shear modulus, the fluid volume modulus, the equivalent volume modulus and the equivalent shear modulus of the target reservoir; the equivalent volume modulus and the equivalent shear modulus are obtained by calculation according to elastic parameters determined by utilizing longitudinal and transverse wave joint inversion;

the first inversion processing module is used for performing inversion processing on the rock framework volume content, the rock framework shear modulus, the fluid volume modulus, the equivalent volume modulus and the equivalent shear modulus by using a pore characteristic parameter inversion model to obtain pore characteristic parameter data of the target reservoir; wherein the pore characteristic parameters include porosity and pore morphology index; the pore morphology index is used for characterizing the pore structure of the reservoir; the pore characteristic parameter inversion model comprises a model for representing the functional relationship between pore characteristic parameters and the volume content, the shear modulus, the fluid volume modulus, the equivalent volume modulus and the equivalent shear modulus of the rock framework;

and the pore characteristic determining module is used for analyzing the pore characteristics of the target reservoir stratum based on the pore characteristic parameter data obtained by inversion to obtain a reservoir stratum pore characteristic analysis result of the target reservoir stratum.

7. The apparatus of claim 6, wherein the pore characteristic parametric inversion model comprises:

μ＝μ_S(1-φ)^γ

wherein, K_SRepresents the volume content of the rock skeleton, mu_SExpressing shear modulus of rock skeleton, K_fDenotes the fluid bulk modulus, K denotes the equivalent bulk modulus, μ denotes the equivalent shear modulus, phi denotes the porosity, and gamma denotes the pore morphology index.

8. The apparatus of claim 6, further comprising:

the elastic parameter model building module is used for taking longitudinal wave horizon data of the target reservoir as transverse constraint, and carrying out transverse interpolation on initial longitudinal wave impedance, initial transverse wave impedance and initial density which are obtained based on logging data calculation to obtain an initial elastic parameter model;

the second inversion processing module is used for performing inversion processing on the initial elastic parameter model by taking the longitudinal wave seismic data of the target reservoir stratum, the transverse wave seismic data and the longitudinal wave seismic wavelet data under the longitudinal wave domain and the transverse wave seismic wavelet data as input so as to obtain the longitudinal wave impedance, the transverse wave impedance and the density of the target reservoir stratum;

and the calculation module is used for calculating to obtain the equivalent bulk modulus and the equivalent shear modulus of the target reservoir according to the longitudinal wave impedance, the transverse wave impedance and the density obtained by inversion.

9. The apparatus of claim 6, further comprising:

and the reservoir characteristic determining module is used for analyzing the pore aspect ratio, the relation between longitudinal wave impedance and porosity, the relation between transverse wave impedance and porosity and the relation between permeability and porosity of the target reservoir based on the pore characteristic parameter data obtained by inversion to obtain a reservoir characteristic analysis result of the target reservoir.

10. A reservoir pore characterisation apparatus comprising a processor and a memory for storing processor executable instructions which, when executed by the processor, carry out the steps of the method of any of claims 1 to 5.

Technical Field

The specification relates to the technical field of oil exploration, in particular to a method, a device and equipment for determining reservoir pore characteristics.

Background

With the gradual deepening of oil and gas exploration and development at home and abroad, a carbonate reservoir has the characteristics of large reserves, high yield and easy formation of large oil and gas fields, and becomes one of the key points of the current oil exploration and research. However, the deposition environment and diagenesis of the carbonate reservoir are complex, so that the reservoir characteristics have the characteristics of diversification and strong heterogeneity. The reservoir space of the carbonate reservoir comprises various types such as holes, seams and the like, and the formation mechanism of the reservoir space is difficult to accurately determine due to the complexity and the variety of the formation mechanism of the reservoir space. Therefore, reservoir pore characteristic analysis becomes an important link in reservoir space formation mechanism analysis, and the accuracy of reservoir pore characteristic analysis has an important influence on accurate prediction of a reservoir space formation mechanism.

At present, the reservoir pore characteristic analysis mostly uses porosity as a characteristic parameter, analyzes the influence relationship between the porosity and other reservoir parameters, and further predicts the formation mechanism of a reservoir space. However, the pore characteristics of the carbonate reservoir are complex and changeable, and the accuracy of the analysis of the formation mechanism of the carbonate reservoir space is influenced only by using the porosity which is difficult to accurately and comprehensively represent the pore characteristics of the carbonate reservoir. Therefore, there is a need for a more accurate method for determining the pore characteristics of the reservoir, so as to improve the accuracy and comprehensiveness of the pore characteristic analysis of the reservoir, and further improve the accuracy of the reservoir space formation mechanism analysis.

Disclosure of Invention

An object of the embodiments of the present disclosure is to provide a method, an apparatus, and a device for determining a pore characteristic of a reservoir, which can more accurately determine a pore characteristic of a carbonate reservoir, thereby improving accuracy of analysis of a formation mechanism of a carbonate reservoir space.

The specification provides a method, a device and equipment for determining reservoir pore characteristics, which are realized by the following modes:

a reservoir pore characteristics determination method, comprising:

and acquiring the volume content of the rock framework, the shear modulus of the rock framework, the fluid volume modulus, the equivalent volume modulus and the equivalent shear modulus of the target reservoir. And the equivalent volume modulus and the equivalent shear modulus are obtained by calculation according to the elastic parameters determined by utilizing the longitudinal and transverse wave joint inversion. Carrying out inversion processing on the volume content of the rock framework, the shear modulus of the rock framework, the volume modulus of the fluid, the equivalent volume modulus and the equivalent shear modulus by using a pore characteristic parameter inversion model to obtain pore characteristic parameter data of the target reservoir; wherein the pore characteristic parameters include porosity and pore morphology index. The pore morphology index is used for characterizing the pore structure of the reservoir; the pore characteristic parameter inversion model comprises a model for representing the functional relationship between the pore characteristic parameter and the volume content, the shear modulus, the fluid volume modulus, the equivalent volume modulus and the equivalent shear modulus of the rock framework. And analyzing the pore characteristics of the target reservoir stratum based on the pore characteristic parameter data obtained by inversion to obtain a reservoir stratum pore characteristic analysis result of the target reservoir stratum.

In other embodiments of the methods provided herein, the pore characteristic parametric inversion model comprises:

μ＝μ_S(1-φ)^γ

wherein, K_SRepresenting volume of rock skeletonAmount, μ_SExpressing shear modulus of rock skeleton, K_fDenotes the fluid bulk modulus, K denotes the equivalent bulk modulus, μ denotes the equivalent shear modulus, phi denotes the porosity, and gamma denotes the pore morphology index.

In other embodiments of the method provided herein, the equivalent bulk modulus and the equivalent shear modulus are calculated by: and performing transverse interpolation on initial longitudinal wave impedance, initial transverse wave impedance and initial density obtained by calculation based on logging data by taking longitudinal wave horizon data of the target reservoir as transverse constraint to obtain an initial elastic parameter model. And taking the longitudinal wave seismic data, the transverse wave seismic data and the longitudinal wave seismic wavelet data under the longitudinal wave domain and the transverse wave seismic wavelet data of the target reservoir stratum as input, and performing inversion processing on the initial elastic parameter model to obtain the longitudinal wave impedance, the transverse wave impedance and the density of the target reservoir stratum. And calculating to obtain the equivalent bulk modulus and the equivalent shear modulus of the target reservoir according to the longitudinal wave impedance, the transverse wave impedance and the density obtained by inversion.

In other embodiments of the method provided in this specification, the performing inversion processing on the initial elastic parameter model includes:

carrying out inversion processing on the initial elastic parameter model according to the following longitudinal and transverse wave joint inversion model:

wherein R is_ppDenotes the longitudinal wave reflection coefficient, R_psRepresents the transverse wave reflection coefficient, theta,Respectively representing the incident angle of longitudinal waves and the reflection angle of transverse waves, I_P、I_SRespectively, longitudinal wave impedance and transverse wave impedance, ρ density, Δ I_PIndicating upper and lower mediaChange in velocity of longitudinal wave, Δ I_SShowing the change of the transverse wave velocity of the upper and lower media, Deltarho showing the change of the density of the upper and lower media, V_P、V_SThe longitudinal wave velocity and the transverse wave velocity are respectively indicated.

In other embodiments of the method provided herein, the method further comprises: and analyzing the pore aspect ratio, the relation between longitudinal wave impedance and porosity, the relation between transverse wave impedance and porosity and the relation between permeability and porosity of the target reservoir based on the pore characteristic parameter data obtained by inversion to obtain a reservoir characteristic analysis result of the target reservoir.

In another aspect, embodiments of the present specification also provide a reservoir pore characteristic determination apparatus, including: the data acquisition module is used for acquiring the rock framework volume content, the rock framework shear modulus, the fluid volume modulus, the equivalent volume modulus and the equivalent shear modulus of the target reservoir; and the equivalent volume modulus and the equivalent shear modulus are obtained by calculation according to the elastic parameters determined by utilizing the longitudinal and transverse wave joint inversion. The first inversion processing module is used for performing inversion processing on the rock framework volume content, the rock framework shear modulus, the fluid volume modulus, the equivalent volume modulus and the equivalent shear modulus by using a pore characteristic parameter inversion model to obtain pore characteristic parameter data of the target reservoir; wherein the pore characteristic parameters include porosity and pore morphology index; the pore morphology index is used for characterizing the pore structure of the reservoir; the pore characteristic parameter inversion model comprises a model for representing the functional relationship between the pore characteristic parameter and the volume content, the shear modulus, the fluid volume modulus, the equivalent volume modulus and the equivalent shear modulus of the rock framework. And the pore characteristic determining module is used for analyzing the pore characteristics of the target reservoir stratum based on the pore characteristic parameter data obtained by inversion to obtain a reservoir stratum pore characteristic analysis result of the target reservoir stratum.

In other embodiments of the apparatus provided herein, the pore characteristic parameter inversion model comprises:

μ＝μ_S(1-φ)^γ

wherein, K_SRepresents the volume content of the rock skeleton, mu_SExpressing shear modulus of rock skeleton, K_fDenotes the fluid bulk modulus, K denotes the equivalent bulk modulus, μ denotes the equivalent shear modulus, phi denotes the porosity, and gamma denotes the pore morphology index.

In other embodiments of the apparatus provided herein, the apparatus further comprises: and the elastic parameter model building module is used for carrying out transverse interpolation on the initial longitudinal wave impedance, the initial transverse wave impedance and the initial density which are obtained based on the well logging data calculation by taking the longitudinal wave horizon data of the target reservoir as transverse constraint to obtain an initial elastic parameter model. And the second inversion processing module is used for performing inversion processing on the initial elastic parameter model by taking the longitudinal wave seismic data, the transverse wave seismic data and the longitudinal wave seismic wavelet data under the longitudinal wave domain and the transverse wave seismic wavelet data of the target reservoir as input so as to obtain the longitudinal wave impedance, the transverse wave impedance and the density of the target reservoir. And the calculation module is used for calculating to obtain the equivalent bulk modulus and the equivalent shear modulus of the target reservoir according to the longitudinal wave impedance, the transverse wave impedance and the density obtained by inversion.

In other embodiments of the apparatus provided herein, the apparatus further comprises: and the reservoir characteristic determining module is used for analyzing the pore aspect ratio, the relation between longitudinal wave impedance and porosity, the relation between transverse wave impedance and porosity and the relation between permeability and porosity of the target reservoir based on the pore characteristic parameter data obtained by inversion to obtain a reservoir characteristic analysis result of the target reservoir.

In another aspect, the present specification further provides a reservoir pore characteristics determining apparatus, including a processor and a memory for storing processor-executable instructions, which when executed by the processor implement the steps of the method according to any one or more of the above embodiments.

According to the method, the device and the equipment for determining the reservoir pore characteristics, provided by one or more embodiments of the specification, the reservoir pore characteristics can be comprehensively analyzed in combination with the porosity and the pore form index by further introducing the pore form index representing the reservoir pore structure as the reservoir characteristic parameter, so that the reservoir pore characteristics can be accurately and comprehensively analyzed from two aspects of reservoir pore types and reservoir pore measurement, the accuracy of reservoir pore characteristic analysis is further improved, and the accuracy of reservoir space formation mechanism analysis is further improved. Meanwhile, the influence of longitudinal waves and converted waves on elastic parameters of the reservoir can be considered when the characteristic parameters of the reservoir pores are determined, and the accuracy of quantitative determination of the characteristic parameters of the reservoir pores is further improved.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort. In the drawings:

fig. 1 is a schematic flow chart of a reservoir pore characteristic determination method provided in an embodiment of the present disclosure;

FIG. 2 is a graphical illustration of predicted shear and compressional data in some embodiments provided herein;

fig. 3(a) and 3(b) are schematic diagrams of a longitudinal wave prestack gather and a converted wave prestack gather obtained by inversion respectively;

FIGS. 4(a) and 4(b) are schematic diagrams of a longitudinal wave seismic section and a converted wave seismic section of a longitudinal wave domain, respectively;

FIGS. 5(a), 5(b) and 5(c) are schematic diagrams of inversion profiles of longitudinal wave impedance, transverse wave impedance and density of the well;

FIGS. 6(a) and 6(b) are schematic diagrams of pore size and pore morphology index inversion sections, respectively;

FIG. 7 is a schematic diagram of a comparison of a pore characteristic parameter of a well bypass with a pore characteristic parameter calculated from a log;

FIG. 8 is a schematic diagram showing the pore morphology index obtained by inversion compared to a well-log computed pore morphology index;

FIG. 9 is a schematic diagram showing the inverted porosity versus the porosity calculated from the log;

fig. 10(a), 10(b), and 10(c) are planar views of the longitudinal wave impedance, the transverse wave impedance, and the density of the work area obtained by inversion, respectively;

fig. 11(a) and 11(b) are respectively a pore form index and a porosity plan view of a research work area obtained by inversion;

FIG. 12 is a schematic diagram of the correspondence between pore morphology index and pore type for a typical well in a research work area;

FIG. 13 is a schematic diagram illustrating the effect of pore morphology index on the intersection of longitudinal wave velocity and porosity in the study region;

FIG. 14 is a schematic diagram illustrating an analysis of the effect of pore morphology index on the intersection of shear wave impedance and porosity;

FIG. 15 is a schematic illustration of an analysis of the effect of pore morphology index on the intersection of permeability and porosity in a study area;

fig. 16 is a schematic block diagram of a reservoir pore characteristic determining apparatus provided in an embodiment of the present disclosure.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in one or more embodiments of the present specification will be clearly and completely described below with reference to the drawings in one or more embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the specification, and not all embodiments. All other embodiments obtained by a person skilled in the art based on one or more embodiments of the present specification without making any creative effort shall fall within the protection scope of the embodiments of the present specification.

Fig. 1 illustrates a reservoir pore characterization method provided in some embodiments of the present description. The method may be applied to an apparatus, such as a server, that performs reservoir pore characterization. As shown in fig. 1, the method may include the following steps.

S20: obtaining the volume content of a rock framework, the shear modulus of the rock framework, the volume modulus of fluid, the equivalent volume modulus and the equivalent shear modulus of a target reservoir; and the equivalent volume modulus and the equivalent shear modulus are obtained by calculation according to the elastic parameters determined by utilizing the longitudinal and transverse wave joint inversion.

The server can obtain the predetermined rock framework volume content, rock framework shear modulus, fluid volume modulus, equivalent shear modulus and other parameter data of the target reservoir. The parametric data may be obtained for well log data and/or seismic record data estimates based on the target reservoir, or may be obtained with reference to developed reservoir estimates having similar characteristics to the target reservoir.

In some embodiments, the rock skeleton bulk modulus K of the target reservoir can be calculated by using V-R-H model formulas (1) to (3) based on a mineral content curve_SAnd shear modulus mu of rock skeleton_S。

Wherein N is the number of mineral types, f_iIs mineral content, M_iDenotes the bulk or shear modulus, M, of the i-th mineral_SIs the bulk modulus K of the rock skeleton_SOr shear modulus mu_S。

In other embodiments, the water saturation curve S can be determined from_WCalculating the equivalent bulk modulus K of the oil-or gas-containing fluid of the reservoir by using the Wood equation (formula (4) below)_f。

Wherein, K_g(o)Is the bulk modulus of gas or oil, K_WIs the bulk modulus of water.

In other embodiments, the equivalent bulk modulus and the equivalent shear modulus may be calculated from elastic parameters determined by using a compressional-compressional joint inversion.

The equivalent bulk modulus and the equivalent shear modulus are obtained by calculation in the following way: and performing transverse interpolation on the initial longitudinal wave impedance, the initial transverse wave impedance and the initial density which are obtained based on the well logging data calculation by taking the longitudinal wave domain stratum bit data of the target reservoir as transverse constraint to obtain an initial elastic parameter model. And taking the longitudinal wave seismic data, the transverse wave seismic data and the longitudinal wave seismic wavelet data under the longitudinal wave domain and the transverse wave seismic wavelet data of the target reservoir stratum as input, and performing inversion processing on the initial elastic parameter model to obtain the longitudinal wave impedance, the transverse wave impedance and the density of the target reservoir stratum. And calculating to obtain the equivalent bulk modulus and the equivalent shear modulus of the target reservoir according to the longitudinal wave impedance, the transverse wave impedance and the density obtained by inversion.

In some embodiments, acquiring multi-wave multi-component seismic data may be utilized. Multi-wave multi-component seismic data collected in the field using, for example, a three-component detector may be utilized. And then, performing wave field separation on the multi-wave multi-component data acquired in the field to respectively obtain a longitudinal wave field and a converted wave (transverse wave) field. According to the propagation characteristics of the longitudinal wave and converted wave data, deconvolution, superposition and offset imaging processing are respectively carried out on the longitudinal wave and converted wave data to obtain prestack data of the longitudinal wave and the converted wave, such as prestack angle gather data. Through the deconvolution, superposition and offset imaging processing, the obtained pre-stack angle gather data has certain fidelity and keeps stable AVO characteristics. Meanwhile, the obtained pre-stack angle gather data has higher signal-to-noise ratio and resolution ratio, and the stability of pre-stack inversion is ensured.

After obtaining the multi-wave multi-component seismic data, in some embodiments, the longitudinal wave seismic wavelets and converted wave seismic wavelet extraction and the well-seismic calibration of the longitudinal wave and converted wave data may be performed based on the following method.

The conventional well logging curves such as the sound wave time difference curve, the transverse wave time difference curve, the density curve and the like can be preprocessed by removing a wild value, standardizing and the like.

And calculating a longitudinal wave reflection coefficient by using a longitudinal wave time difference curve and a density curve, extracting longitudinal wave seismic wavelets from the seismic data after the longitudinal wave seismic folding, and performing convolution on the longitudinal wave seismic wavelets and the longitudinal wave reflection coefficient to obtain a longitudinal wave synthetic seismic record. And carrying out calibration processing according to the seismic data of the primary wave well side channel. Specifically, the compressional synthetic seismic records may be shifted up and down to align their primary waveform characteristics with compressional well side-channel data. Then, the correlation coefficient of the calibrated compressional wave synthetic seismic record and the well side channel data can be obtained.

If the correlation coefficient is lower, the wavelet is re-extracted, and the steps are repeated until the correlation coefficient of the longitudinal wave synthetic seismic record and the well side channel data meets the requirement. The wavelet at the moment is used as the final output longitudinal wave seismic wavelet, and the time-depth relation at the moment is used as the final longitudinal wave well seismic calibration result.

The converted wave reflection coefficient can then be calculated using the transverse wave moveout curve and the density curve. And extracting converted wave seismic wavelets from the converted wave post-stack seismic data, and performing convolution on the converted wave seismic wavelets and the reflection coefficient to obtain a synthetic seismic record of the converted waves. And adopting the same calibration method of the longitudinal waves to carry out calibration and correlation coefficient judgment processing until the correlation coefficient meets the requirement. And obtaining converted wave seismic wavelets and converted wave well seismic calibration results.

In some embodiments, horizon picking of compressional and converted wave seismic data may also be performed. The method can pick up the horizon data of the same underground geologic body on the corresponding longitudinal wave post-stack seismic data and the corresponding converted wave post-stack seismic data respectively to obtain the interpretation horizon of a longitudinal wave domain and the interpretation horizon of a converted wave domain, so as to ensure the rationality of the interpretation horizon, and meanwhile, the interpretation horizon is smooth and no jump point can appear, so as to improve the accuracy of subsequent data estimation.

After horizon picking, the in-phase axes of the longitudinal wave and the converted channel set can be matched. Time matching can be performed on the longitudinal wave seismic data and the converted wave stacked seismic data based on the picked longitudinal wave domain horizon data and the picked converted wave domain horizon data, and the event of the converted waves is matched into the longitudinal wave domain according to the formula (5), so that the converted wave seismic data of the longitudinal wave domain is obtained.

Wherein, t_ppWhen travelling in a longitudinal wave, t_psWhen traveling for a converted wave, h is the thickness of a single geologic body and l is the ratio of the longitudinal wave velocity to the transverse wave velocity. Generally, the ratio of compressional to shear velocity can be determined by the compressional and shear velocity fields.

In some embodiments, shear wave velocity prediction may be based on petrophysical modeling. According to the characteristics of the carbonate reservoir, a petrophysical model of the fractured reservoir can be constructed from logging curves such as a sonic curve, a density curve, a GR curve, an SP curve, porosity, saturation, mineral components, shale content and the like. Shear velocity estimates may then be made for wells without measured shear curves based on the petrophysical model.

Elastic parameters such as compressional impedance, shear impedance, and density can then be calculated using the measured log data and the estimated shear velocity. The longitudinal wave horizon data can be used as transverse constraint, and the longitudinal wave impedance, the transverse wave impedance and the density on the logging well are subjected to transverse extrapolation interpolation on corresponding horizons by using the well seismic calibration result to obtain an initial elastic parameter model of the longitudinal wave impedance, the transverse wave impedance and the density. The initial elastic parameter model is three-dimensional space distribution of longitudinal wave impedance, transverse wave impedance and density obtained after interpolation.

And taking the longitudinal wave seismic data of the target reservoir stratum, the transverse wave seismic data under the longitudinal wave domain, the longitudinal wave seismic wavelet data and the transverse wave seismic wavelet data as input, and performing inversion processing on the initial elastic parameter model. In some embodiments, for example, the initial elastic parameter model may be used to calculate a longitudinal wave reflection coefficient and a transverse wave reflection coefficient, and then convolution processing may be performed on the longitudinal wave reflection coefficient and the transverse wave reflection coefficient, and the longitudinal wave seismic wavelet data and the transverse wave seismic wavelet data distribution, so as to obtain longitudinal wave synthetic seismic data and transverse wave synthetic seismic data. And then comparing the longitudinal wave synthetic seismic data and the transverse wave synthetic seismic data with well side seismic channel data, if the correlation does not meet the preset requirement, adjusting the initial elastic parameter model based on a preset inversion algorithm, then repeating the steps based on the adjusted elastic parameters, and re-judging the correlation between the synthetic seismic data and the well side seismic data. And outputting the elastic parameters which finally meet the requirements until the correlation meets the preset requirements.

In some embodiments, the inversion process described above can be performed under a bayesian framework, for example. The elastic parameter distribution morphological characteristics can be determined by taking geological, well logging and rock physical analysis data as prior information, so that the inversion result is kept in good consistency with the information, and higher goodness of fit is kept with geological knowledge, a more reliable inversion result is obtained, and the accuracy of determining the reservoir stratum void characteristics is improved.

By adopting multi-wave multi-component seismic data and under the joint constraint of longitudinal wave and converted wave seismic data, elastic parameters are estimated based on a longitudinal and transverse wave prestack joint inversion method, richer converted wave propagation characteristics are further introduced into the inversion process, and the inversion resolution and the result stability are greatly improved.

In some embodiments, the initial elastic parameter model may be inverted according to the following compressional-compressional wave joint inversion model (equations (6) and (7) below):

wherein R is_ppDenotes the longitudinal wave reflection coefficient, R_psRepresents the transverse wave reflection coefficient, theta,Respectively representing the incident angle of longitudinal waves and the reflection angle of transverse waves, I_P、I_SRespectively, longitudinal wave impedance and transverse wave impedance, ρ density, Δ I_PShowing the change in longitudinal wave velocity of the upper and lower media,. DELTA.I_SShowing the change of the transverse wave velocity of the upper and lower media, Deltarho showing the change of the density of the upper and lower media, V_P、V_SThe longitudinal wave velocity and the transverse wave velocity are respectively indicated.

By utilizing the longitudinal and transverse wave joint inversion model to carry out inversion processing, the accuracy of the inversion processing can be further improved.

Then, the equivalent bulk modulus K and the equivalent shear modulus μ of the target reservoir can be calculated according to the longitudinal wave impedance, the transverse wave impedance and the density obtained by inversion.

In some embodiments, for example, the longitudinal wave impedance and the shear wave impedance obtained by inversion can be used to calculate the longitudinal wave velocity and the shear wave velocity, and then the equivalent bulk modulus and the equivalent shear modulus can be calculated based on the following calculation formulas using the longitudinal wave velocity and the shear wave velocity:

of course, the calculation of the equivalent bulk modulus and the equivalent shear modulus may be performed in other manners, and is not limited herein.

S22: carrying out inversion processing on the volume content of the rock framework, the shear modulus of the rock framework, the volume modulus of the fluid, the equivalent volume modulus and the equivalent shear modulus by using a pore characteristic parameter inversion model to obtain pore characteristic parameter data of the target reservoir; wherein the pore characteristic parameters include porosity and pore morphology index; the pore morphology index is used to characterize the pore structure of the reservoir.

The server can utilize the pore characteristic parameter inversion model to perform inversion processing on the volume content, the shear modulus, the fluid volume modulus, the equivalent volume modulus and the equivalent shear modulus of the rock framework, so as to obtain pore characteristic parameter data of the target reservoir. In the embodiments of the present disclosure, the pore characteristics parameters may include porosity and pore morphology index. The pore morphology index may be used to characterize the pore structure of the reservoir. The pore characteristic parameter inversion model can comprise a model for representing the functional relationship between the pore characteristic parameter and the volume content, the shear modulus, the fluid volume modulus, the equivalent volume modulus and the equivalent shear modulus of the rock framework.

The pore morphology index characterizes the pore structure characteristics of the reservoir, so that the pore morphology index and the pore type can have better consistency. For example, the pore morphology index may be set inversely proportional to the void aspect ratio. Correspondingly, the pore form index can correspond to a fracture storage space when being larger, the middle value of the pore form index can correspond to a corrosion pore, and the pore form index can correspond to an inter-granular pore when being smaller. The pore characteristics of the reservoir are described by simultaneously utilizing two parameters of porosity and pore form index, so that the pore characteristics of the reservoir can be comprehensively described from the aspects of the pore structure and the pore measurement, the accuracy and the comprehensiveness of the description of the pore characteristics of the reservoir are further improved, and the accuracy of the recognition of the reservoir characteristics is further improved.

In some embodiments, the pore characteristic parametric inversion model may include:

μ＝μ_S(1-φ)^γ(11)

wherein, K_SRepresents the volume content of the rock skeleton, mu_SExpressing shear modulus of rock skeleton, K_fDenotes the fluid bulk modulus, K denotes the equivalent bulk modulus, μ denotes the equivalent shear modulus, phi denotes the porosity, and gamma denotes the pore morphology index.

And the pore characteristic parameter inversion is carried out based on the pore characteristic parameter inversion model, so that the accuracy of the inversion result can be greatly improved.

S24: and analyzing the pore characteristics of the target reservoir stratum based on the pore characteristic parameter data obtained by inversion to obtain a reservoir stratum pore characteristic analysis result of the target reservoir stratum.

After obtaining the void characteristic parameter data of the target reservoir, the server may further analyze the void characteristic of the target reservoir by using the void characteristic parameter data to obtain a reservoir characteristic analysis result of the target reservoir. As described above, the type of the reservoir voids may be analyzed using the pore morphology index, whether the target reservoir belongs to a fracture void, a erosion void or an intergranular void may be determined, and the porosity, the void aspect ratio, and the like at different positions may be analyzed based on the determined type. Therefore, the void characteristics of the reservoir are comprehensively described on the basis of the void structure and the void measurement, the accuracy and comprehensiveness of the description of the void characteristics of the reservoir are further improved, and the accuracy of the recognition of the reservoir characteristics is further improved. Correspondingly, the reservoir void characteristic analysis result can be output according to the requirement of actual analysis.

Meanwhile, in the prediction process of the reservoir pore characteristic parameters, different petrophysical models are further utilized, and various factors such as mineral components, pore forms and fluid types in the reservoir are fully considered, so that the reservoir pore characteristic parameters are estimated more accurately, and the method has the characteristics of high calculation efficiency and simplicity and feasibility.

In other embodiments, the method further comprises: and analyzing the pore aspect ratio, the relation between longitudinal wave impedance and porosity, the relation between transverse wave impedance and porosity and the relation between permeability and porosity of the target reservoir based on the pore characteristic parameter data obtained by inversion to obtain a reservoir characteristic analysis result of the target reservoir. The method further analyzes the relationship between longitudinal wave impedance and porosity, transverse wave velocity and porosity, and permeability and porosity by combining with the pore form index, can comprehensively consider the influence of the reservoir pore structure on the reflection of reservoir characteristic parameters, and improves the accuracy and comprehensiveness of the knowledge of reservoir characteristics.

Taking a certain practical multi-wave multi-component earthquake work area in the west of China as an example, the description of the carbonate reservoir pore characteristic quantitative characterization analysis is carried out as follows. The prediction of pore characteristic parameters and the analysis of reservoir pore characteristics are carried out according to the scheme provided by the embodiment.

FIG. 2 is a schematic diagram of the comparison of measured values with transverse wave and longitudinal wave data predicted by logging data in a work area. The well also contains measured transverse waves, and the effectiveness of the transverse wave speed predicted by the rock physical modeling method can be verified by using the measured transverse wave data of the well. Fig. 2 (a) shows a schematic view in which the measured longitudinal wave velocity and the predicted longitudinal wave velocity are superimposed, and as can be seen from fig. 2 (a), the prediction and the measured result almost overlap. Fig. 2 (b) is a schematic diagram showing an error between the predicted result of the longitudinal wave velocity and the actual measurement result. Fig. 2 (c) is a schematic diagram showing the superposition of the measured shear wave velocity and the predicted shear wave velocity. Fig. 2 (d) is a schematic diagram showing an error between the predicted result of the shear wave velocity and the actual measurement result. The prediction error is less than 1% for the longitudinal wave velocity and less than 5% for the transverse wave velocity, and the transverse wave velocity predicted by the rock physical model is more reliable from the viewpoint of error analysis.

Fig. 3(a) and 3(b) are schematic diagrams of a compressional wave prestack gather and a converted wave prestack gather for the compressional-shear wave prestack joint inversion, respectively. The arrow positions in fig. 3(a), 3(b) indicate the target interval.

Fig. 4(a) and 4(b) are schematic diagrams of a longitudinal wave seismic section and a converted wave seismic section in the longitudinal wave domain, respectively, after matching of a longitudinal wave and a converted wave.

Fig. 5(a), 5(b), and 5(c) are schematic diagrams of longitudinal wave impedance, transverse wave impedance, and density inversion profiles of the cross-well W23 obtained by longitudinal-transverse wave prestack joint inversion, respectively.

Fig. 6(a) and 6(b) are schematic diagrams of pore form index inversion sections of the through-well W23, respectively.

FIG. 7 is a schematic diagram comparing a pore characterization parameter of a well bypass with a pore characterization parameter calculated from a log. From fig. 7, it can be seen that the inverted pore morphology index has good consistency with the well-logging calculated pore morphology index, and meanwhile, the inverted pore morphology index has high similarity with the well-logging calculated morphology index.

FIG. 8 is a graphical representation of an inverted Pore morphology index (Predicted Pore Structure) compared to a well log calculated Pore morphology index (Measured Pore Structure).

FIG. 9 is a graph of inverted Porosity (Predicted Porosity) versus log calculated Porosity (MeasuredPorosity).

Fig. 8 and 9 can show that the inverted pore form index, the porosity and the porosity calculated by logging have high goodness of fit, and the inversion of the elastic parameter and the inversion of the pore form index have certain reliability.

Fig. 10(a), 10(b), and 10(c) are longitudinal wave impedance, transverse wave impedance, and density plan views obtained by longitudinal-transverse wave prestack joint inversion in the study work area, respectively.

Fig. 11(a) and 11(b) are respectively a pore morphology index and a porosity prediction plan obtained by inversion in the study region. Analysis of the interpretation of the logs in the study area showed that the average porosity in the interval was 3.84% on average, while the inverted porosity was between 1.0% and 7.0% and the average porosity was about 3.0%, consistent with the interpretation of the logs. The interpretation result of the inverted pore form index is consistent with the interpretation result of the pore characteristics of the logging reservoir and is highly consistent with geological knowledge. The analysis result further proves the effectiveness and the applicability of the scheme of the embodiment, and the method is simple and easy to implement.

FIG. 12 is a graphical representation of the correspondence between pore morphology index and pore type in an exemplary well intersection curve within a study area. The scattered data points in fig. 12 represent pore morphology index sample point data. Different symbols represent that the pore form index values corresponding to the sampling points are different, wherein the pore form index values corresponding to the squares, the triangles and the solid circles are gradually increased. As can be seen from fig. 12, as the pore morphology index increases, the pore type gradually transitions from fracture pores, erosion pores, to intergranular pores, and the aspect ratio of the pores gradually decreases. Therefore, it can be said that the pore morphology gradually transits from a flat shape to a circular shape as the pore morphology index increases. Wherein, P wave velocity represents the longitudinal wave velocity, and Porosity represents the Porosity.

FIG. 13 is a graphical representation of the analysis of the effect of pore morphology index on the intersection of longitudinal wave velocity and porosity in the study area. The scattered data points in fig. 13, 14, and 15 represent pore morphology index sampling point data. Different symbols represent that the pore form index values corresponding to the sampling points are different, wherein the pore form index values corresponding to the squares, the triangles and the solid circles are gradually reduced. Fig. 13 shows that the velocity increases with decreasing pore morphology index, which is inversely proportional to the velocity. Meanwhile, the smaller the porosity, the more concentrated the porosity-velocity trend line, the larger the porosity, the more divergent the porosity-velocity trend. And the linear relationship between longitudinal wave velocity and porosity is also significantly different at different pore morphology indices. Thus, it can be shown that pore morphology has significant control over reservoir pores.

FIG. 14 is a schematic diagram illustrating the effect of pore morphology index on the intersection of shear wave impedance and porosity. As can be seen from fig. 14, the pore morphology index is beneficial for distinguishing the transverse wave impedance from the porosity trend line, thereby improving the prediction accuracy of the porosity. Different porosity-impedance trend lines can be distinguished according to different pore form indexes to calculate the porosity of the reservoir. Wherein S wave impedance represents the transverse wave impedance.

FIG. 15 is a graphical representation of the analysis of the effect of pore morphology index on the intersection of permeability and porosity in the study area. The relationship between the porosity and the permeability is changed along with the change of the pore form index, so that permeability-porosity trend lines under different pore structures can be established based on the pore form index for analysis, and the precision of permeability prediction is favorably improved. Wherein Permeability represents Permeability.

The analysis can show that the parameters describing the reservoir characteristics, such as longitudinal wave impedance, transverse wave speed, permeability and the like, are analyzed by combining the pore form index and the porosity, the reservoir pore characteristics can be more accurately explained, the accuracy of understanding the reservoir characteristics is improved, the accuracy of reservoir space formation mechanism analysis is further improved, and a large-scale favorable reservoir area is accurately predicted.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. For details, reference may be made to the description of the related embodiments of the related processing, and details are not repeated herein.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

Based on one or more embodiments, the pore morphology index representing the pore structure of the reservoir is further introduced to serve as a reservoir characteristic parameter, the reservoir pore characteristics are comprehensively analyzed in combination with the porosity and the pore morphology index, the reservoir pore characteristics are accurately and comprehensively analyzed from two aspects of reservoir pore types and reservoir pore metrics, the accuracy of reservoir pore characteristic analysis is further improved, and the accuracy of reservoir space formation mechanism analysis is further improved. Meanwhile, the influence of longitudinal waves and converted waves on elastic parameters of the reservoir can be considered when the characteristic parameters of the reservoir pores are determined, and the accuracy of quantitative determination of the characteristic parameters of the reservoir pores is further improved.

Based on the reservoir pore characteristic determination method, one or more embodiments of the present specification further provide a reservoir pore characteristic determination device. The apparatus may include systems, software (applications), modules, components, servers, etc. that utilize the methods described in the embodiments of the present specification in conjunction with hardware implementations as necessary. Based on the same innovative conception, embodiments of the present specification provide an apparatus as described in the following embodiments. Since the implementation scheme of the apparatus for solving the problem is similar to that of the method, the specific implementation of the apparatus in the embodiment of the present specification may refer to the implementation of the foregoing method, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated. Specifically, fig. 16 is a schematic diagram illustrating a module structure of an embodiment of the apparatus for determining a reservoir pore characteristic provided in the specification, and as shown in fig. 16, the apparatus may include:

the data acquisition module 102 may be configured to acquire a rock skeleton volume content, a rock skeleton shear modulus, a fluid volume modulus, an equivalent volume modulus, and an equivalent shear modulus of the target reservoir; the equivalent volume modulus and the equivalent shear modulus are obtained by calculation according to elastic parameters determined by utilizing longitudinal and transverse wave joint inversion;

the first inversion processing module 104 may be configured to perform inversion processing on the volume content of the rock framework, the shear modulus of the rock framework, the volume modulus of the fluid, the equivalent volume modulus, and the equivalent shear modulus by using a pore characteristic parameter inversion model, so as to obtain pore characteristic parameter data of the target reservoir; wherein the pore characteristic parameters include porosity and pore morphology index; the pore morphology index is used for characterizing the pore structure of the reservoir; the pore characteristic parameter inversion model comprises a model for representing the functional relationship between pore characteristic parameters and the volume content, the shear modulus, the fluid volume modulus, the equivalent volume modulus and the equivalent shear modulus of the rock framework;

the pore characteristic determining module 106 may be configured to analyze the pore characteristics of the target reservoir based on the pore characteristic parameter data obtained by inversion, so as to obtain a reservoir pore characteristic analysis result of the target reservoir.

In other embodiments, the pore characteristic parametric inversion model may include:

μ＝μ_S(1-φ)^γ

wherein, K_SRepresents the volume content of the rock skeleton, mu_SExpressing shear modulus of rock skeleton, K_fDenotes the fluid bulk modulus, K denotes the equivalent bulk modulus, μ denotes the equivalent shear modulus, phi denotes the porosity, and gamma denotes the pore morphology index.

In other embodiments, the apparatus may further comprise:

and the elastic parameter model building module can be used for carrying out transverse interpolation on initial longitudinal wave impedance, initial transverse wave impedance and initial density which are obtained based on the well logging data calculation by taking the longitudinal wave horizon data of the target reservoir as transverse constraint to obtain an initial elastic parameter model.

And the second inversion processing module is used for performing inversion processing on the initial elastic parameter model by taking the longitudinal wave seismic data, the transverse wave seismic data and the longitudinal wave seismic wavelet data under the longitudinal wave domain and the transverse wave seismic wavelet data of the target reservoir as input so as to obtain the longitudinal wave impedance, the transverse wave impedance and the density of the target reservoir.

And the calculation module is used for calculating to obtain the equivalent bulk modulus and the equivalent shear modulus of the target reservoir according to the longitudinal wave impedance, the transverse wave impedance and the density obtained by inversion.

In other embodiments, the apparatus may further comprise:

and the reservoir characteristic determining module is used for analyzing the pore aspect ratio, the relation between longitudinal wave impedance and porosity, the relation between transverse wave impedance and porosity and the relation between permeability and porosity of the target reservoir based on the pore characteristic parameter data obtained by inversion to obtain a reservoir characteristic analysis result of the target reservoir.

It should be noted that the above-described apparatus may also include other embodiments according to the description of the method embodiment. The specific implementation manner may refer to the description of the related method embodiment, and is not described in detail herein.

The reservoir pore characteristic determination device provided in one or more embodiments of the present specification may further introduce a pore morphology index representing a reservoir pore structure as a reservoir characteristic parameter, and comprehensively analyze reservoir pore characteristics in combination with porosity and a pore morphology index, so as to implement accurate and comprehensive analysis of reservoir pore characteristics from two aspects of reservoir pore type and reservoir pore measurement, further improve accuracy of reservoir pore characteristic analysis, and further improve accuracy of reservoir space formation mechanism analysis. Meanwhile, the influence of longitudinal waves and converted waves on elastic parameters of the reservoir can be considered when the characteristic parameters of the reservoir pores are determined, and the accuracy of quantitative determination of the characteristic parameters of the reservoir pores is further improved.

The method or apparatus provided by the present specification and described in the foregoing embodiments may implement service logic through a computer program and record the service logic on a storage medium, where the storage medium may be read and executed by a computer, so as to implement the effect of the solution described in the embodiments of the present specification. Accordingly, the present specification also provides a reservoir pore characteristics determination apparatus comprising a processor and a memory storing processor-executable instructions which, when executed by the processor, implement steps comprising the method of any one of the embodiments described above.

The storage medium may include a physical device for storing information, and typically, the information is digitized and then stored using an electrical, magnetic, or optical media. The storage medium may include: devices that store information using electrical energy, such as various types of memory, e.g., RAM, ROM, etc.; devices that store information using magnetic energy, such as hard disks, floppy disks, tapes, core memories, bubble memories, and usb disks; devices that store information optically, such as CDs or DVDs. Of course, there are other ways of storing media that can be read, such as quantum memory, graphene memory, and so forth.

It should be noted that the above description of the apparatus according to the method embodiment may also include other embodiments. The specific implementation manner may refer to the description of the related method embodiment, and is not described in detail herein.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description of the specification, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.