CN114280670A - Multi-physical-model well logging shear wave velocity curve reconstruction method and system and electronic equipment - Google Patents
Multi-physical-model well logging shear wave velocity curve reconstruction method and system and electronic equipment Download PDFInfo
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Abstract
The invention provides a method, a system and electronic equipment for reconstructing a transverse wave velocity curve of a multi-physical model logging, wherein the method comprises the following steps: acquiring logging data and lithologic physical property parameters of a target stratum; calculating the shale content, the stratum density and the stratum porosity of the target stratum according to the logging data; calculating the rock stratum matrix modulus of the target stratum by utilizing a preset rock physical model according to the argillaceous content, the stratum density and the lithology physical property parameters; substituting the formation porosity and the rock stratum matrix modulus into a Chapman model to obtain the rock stratum equivalent modulus of the target formation; according to the preset relational expression of the rock equivalent modulus, the rock equivalent modulus and the stratum longitudinal and transverse wave velocity, the transverse wave velocity curve of the target stratum is constructed.
Description
Technical Field
The invention relates to the technical field of geophysical interpretation, in particular to a method and a system for reconstructing a transverse wave velocity curve of logging of multiple physical models and electronic equipment.
Background
The transverse wave velocity curve is important in geophysical interpretation work, but the detection cost is low, and transverse wave logging curve detection is rarely carried out in the coal field drilling process. The shear wave velocity reconstruction means that the shear wave velocity data is obtained through numerical calculation based on the existing geological and well logging data. The commonly used shear wave velocity reconstruction method mainly comprises three types: a high-low frequency separation method, an empirical formula estimation method and a rock physical model calculation method.
The high-low frequency separation method is mainly used for pseudoacoustic waves, and the pseudoacoustic waves are composed of a low-frequency part and a high-frequency part. Generally, low-pass filtering is performed on an original acoustic curve or a curve (such as stratum velocity) capable of reflecting the compaction tendency of a reservoir is used as a low-frequency part, natural gamma or natural potential sensitive to lithological reaction and the like are used as a high-frequency part, and finally, the low-frequency and high-frequency curves are combined to obtain an acoustic wave simulation curve.
The empirical formula estimation method is to estimate a speed curve and a density curve related to rock characteristics by a common empirical formula according to parameters such as explained formation porosity, lithology and pore fluid properties and a logging response equation, wherein the reconstructed curve has definite geological significance and has prominent reservoir characteristics.
The rock physical model calculation method is derived according to a strict rock physical theory to obtain a clear relation between rock elastic physical properties and lithology parameters and transverse waves thereof, and a transverse wave logging curve is obtained based on the calculation.
In the field of coal, the actual detection of transverse wave logging curves at home and abroad is rarely carried out, basic lithology and physical property parameters are generally obtained through the previously obtained empirical relational expression and experimental method, and the transverse wave velocity is calculated and obtained based on the parameters and the theoretical relation.
Disclosure of Invention
The invention solves the problem that the existing shear wave velocity reconstruction method has larger error of the shear wave velocity curve result.
In order to solve the above problems, the present invention provides a method for reconstructing a velocity curve of a transverse wave of a multi-physical model logging well, the method comprising: acquiring logging data and lithologic physical property parameters of a target stratum; the well log data includes at least one of: natural gamma-ray logging data, natural potential logging data, resistivity logging data and density logging data; calculating the shale content, the formation density and the formation porosity of the target formation according to the logging data; calculating the rock stratum matrix modulus of the target stratum by utilizing a preset rock physical model according to the argillaceous content, the stratum density and the lithology physical property parameters; the preset petrophysical model comprises any one of the following: a self-consistent model, a differential equivalent medium model and an xu-white model; substituting the formation porosity and the rock stratum matrix modulus into a Chapman model to obtain a rock stratum equivalent modulus of the target formation; and constructing a transverse wave velocity curve of the target stratum according to the preset relational expression of the rock equivalent modulus, the rock equivalent modulus and the stratum longitudinal and transverse wave velocity.
Optionally, if the logging data includes local shear velocity logging data of the target formation, calculating a formation porosity of the target formation according to the logging data includes: based on the local shear wave velocity logging data, performing inversion calculation on the shear wave velocity of the target stratum by using the preset rock physical model to obtain an optimal convergence result; and determining the formation porosity of the target formation according to the optimal convergence result.
Optionally, the formula for performing an inverse calculation on the shear wave velocity of the target formation is as follows:
wherein the gap porosity
Denotes the sum of fracture porosity and fracture porosity, alpha denotes the pore aspect ratio, epsilonfDenotes the crack density, vp—realRepresenting true longitudinal wave velocity, vS—realRepresenting true transverse wave velocity, vp-model、vS-modelRespectively representing the primary longitudinal wave velocity and transverse wave velocity reconstruction results obtained based on the preset rock physical model, a, b and c are the weight values of various parameter smoothing items,
respectively representing the inverted values of the gap porosity, the gap aspect ratio and the crack density at a certain depth point.
Optionally, the formation equivalent modulus comprises at least one of: rock stratum equivalent volume modulus and rock stratum equivalent shear modulus; the preset relational expression is as follows:
Vpis the velocity of longitudinal wave, VsIs the transverse wave velocity, KdryIs the equivalent bulk modulus, mu, of the formationdryIs the formation equivalent shear modulus, ρ is the electron density.
Optionally, the calculating the shale content of the target formation according to the logging data comprises: calculating the shale content of the target stratum according to the natural gamma logging data; wherein the content of the first and second substances,
Vshfor the shale content, GR is the natural gamma log value, GRminIs the natural gamma minimum log value, GR, of purely lithologic formationsmaxThe method is a natural gamma maximum logging value of a pure lithologic stratum, SH is a natural gamma relative value, and GCUR is an empirical coefficient related to the age of the stratum.
Optionally, the calculating the shale content of the target formation according to the logging data comprises: calculating the shale content of the target stratum according to the natural potential logging data; wherein the content of the first and second substances,
Vshfor the shale content, SP is a natural potential log value, SPminIs the minimum logging value of the natural potential of the pure lithologic stratum, SPmaxThe method is the maximum logging value of the natural potential of the pure lithologic stratum.
Optionally, the calculating the shale content of the target formation according to the logging data comprises: calculating the shale content of the target stratum according to the resistivity logging data; wherein the content of the first and second substances,
Vshin the order of the argillaceous content, RimIs the maximum resistivity value, R, of the target formationshIs mudstone resistivity, RτB is a preset coefficient, and the resistivity of the target stratum is obtained.
Optionally, the calculating the formation density of the target formation from the logging data comprises: and calculating the stratum density of the target stratum according to the density logging data and the Compton effect.
The invention provides a multi-physical model logging shear wave velocity curve reconstruction system, which comprises: the acquisition module is used for acquiring logging data and lithology physical property parameters of a target stratum; the well log data includes at least one of: natural gamma-ray logging data, natural potential logging data, resistivity logging data and density logging data; the first calculation module is used for calculating the shale content, the formation density and the formation porosity of the target formation according to the logging data; the second calculation module is used for calculating the rock stratum matrix modulus of the target stratum by utilizing a preset rock physical model according to the argillaceous content, the stratum density and the lithology physical property parameters; the preset petrophysical model comprises any one of the following: a self-consistent model, a differential equivalent medium model and an xu-white model; a substituting module for substituting the formation porosity and the formation matrix modulus into a Chapman model to obtain a formation equivalent modulus of the target formation; and the construction module is used for constructing a transverse wave velocity curve of the target stratum according to the preset relational expression of the rock equivalent modulus, the rock equivalent modulus and the stratum longitudinal and transverse wave velocity.
The invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method when executing the computer program.
The present invention provides a computer-readable storage medium storing a computer program which, when read and executed by a processor, implements the above-described method.
The embodiment of the invention provides a method, a system and electronic equipment for reconstructing a transverse wave velocity curve of a multi-physical model logging, which are used for calculating a rock matrix modulus of a stratum based on a preset rock physical model by collecting logging data of a target logging and lithological physical property parameters of the stratum, then obtaining a rock equivalent modulus based on the rock matrix modulus and a Chapman model, and further constructing a transverse wave velocity curve of the stratum.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for reconstructing a velocity curve of a multi-physical model logging shear wave according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a velocity simulation curve of transverse waves according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a system for reconstructing a velocity curve of a multi-physical model logging shear wave according to an embodiment of the present invention.
Description of reference numerals:
301-an obtaining module; 302-a first computing module; 303-a second calculation module; 304-substitution module; 305 — building blocks.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Fig. 1 is a schematic flow chart of a method for reconstructing a velocity curve of a multi-physical-model logging shear wave, which is applied to geophysical logging of a coal field and includes the following steps:
s102, obtaining logging data and lithology physical property parameters of the target stratum.
Wherein the well log data comprises: natural gamma logging data, natural potential logging data, resistivity logging data, and density logging data.
Optionally, logging data is collected through a logging method in an industry standard SY/T5132-2003, and then preprocessing of an original logging curve is carried out according to requirements in a logging curve processing and explaining industry standard SY/T6451-2000, so that logging data of a target logging is obtained.
Optionally, the lithology and physical property parameters of the rock formation are obtained through the existing experimental data and test data. The lithology parameters of the target formation include: the volume modulus, shear modulus and the aspect ratio of clay particles of the clay component; pore space bulk modulus, shear modulus, and pore aspect ratio; bulk modulus, shear modulus, and overall grain aspect ratio of other components of the formation, and for other components, the average equivalent parameters can be calculated by using rock physical averages.
And S104, calculating the shale content, the formation density and the formation porosity of the target formation according to the logging data.
Specifically, the method can comprise the following steps: calculating the shale content of the target stratum based on the logging data; calculating the stratum density of the target stratum by utilizing the Compton effect based on the density logging data; based on the formation density, the formation porosity of the target formation is calculated.
And S106, calculating the rock stratum matrix modulus of the target stratum by utilizing a preset rock physical model according to the argillaceous content, the stratum density and the lithology physical property parameters.
Wherein the preset petrophysical model comprises any one of: the model comprises a self-consistent model, a differential equivalent medium model and an xu-white model, wherein the matrix modulus of the rock stratum comprises: rock matrix equivalent bulk modulus, rock matrix equivalent shear modulus.
Optionally, the mathematical form of the preset petrophysical model includes the following three mathematical forms:
self-consistent model:
wherein v isiTo correspond to the volume content of a substance, K* SCRock matrix equivalent bulk modulus, K, obtained for self-consistent model calculationsiIs the bulk modulus, P, of a substance or component in the corresponding rock*iAnd Q*iIs the geometric factor of the current substance or component, muiIs the shear modulus, mu, of a substance or component in the corresponding rock* SCThe resulting rock matrix equivalent shear modulus is calculated for a self-consistent model, here the rock matrix equivalent shear modulus without taking into account pore conditions.
Differential equivalent medium model:
y is the ratio of a certain component content in the rock, KiIs the bulk modulus of the individual rock component, K is the rock matrix equivalent bulk modulus, μ is the rock matrix equivalent shear modulus, μiShear modulus for individual rock compositions.
xu-white model:
Kdryfor formation equivalent bulk modulus, K, without taking into account the effect of the fluid on the rock velocitymIs the bulk modulus of rock matrix, mudryFor formation equivalent shear modulus, μ, without taking into account the effect of the fluid on the rock velocitymIs the shear modulus of the rock matrix,
is the rock porosity. The above expression is based on the consideration that the influence of the porosity on the velocity needs to be taken into account during the analysis
The matrix modulus Km obtained by model calculation is used as a basic parameter for the next calculation. The existing lithologic property parameter values and logging data values can be substituted into the expression to calculate the equivalent rock modulus and directly reconstruct the speed.
And S108, substituting the formation porosity and the rock stratum matrix modulus into the Chapman model to obtain the rock stratum equivalent modulus of the target formation.
The formation equivalent modulus includes: formation equivalent bulk modulus, formation equivalent shear modulus.
Alternatively, Chapman model is as follows:
Cijkl(omega) is an element of a rock rigidity matrix, lambda is a Lame constant when the whole rock does not contain holes in an ideal state, and M is a shear modulus when the whole rock does not contain holes in an ideal state, phipIs the pore size of the round hole, lambda0Lame constant, μ for corresponding pore0In order to correspond to the shear modulus of the pores,. epsiloncIs the size of the fracture porosity, epsilonfIs the size of the porosity of the fracture, omega is the relaxation frequency of the pore space, and tau is the characteristic frequency of the pore space.
And S110, constructing a transverse wave velocity curve of the target stratum according to the preset relational expression of the rock equivalent modulus, the rock equivalent modulus and the stratum longitudinal and transverse wave velocity.
And calculating the longitudinal and transverse wave speeds of the target stratum by using a preset relational expression between the equivalent modulus of the rock stratum and the longitudinal and transverse wave speeds of the stratum in the basic rock physics theory, and then constructing a transverse wave speed curve of the target stratum based on the longitudinal and transverse wave speeds.
Wherein, the preset relational expression of the equivalent modulus of the rock stratum and the longitudinal and transverse wave speeds of the stratum is as follows:
Vpis the velocity of longitudinal wave, VsIs the transverse wave velocity, KdryIs the equivalent bulk modulus, mu, of the formationdryIs the formation equivalent shear modulus, ρ is the electron density.
The embodiment of the invention provides a multi-physical model logging transverse wave velocity curve reconstruction method, which comprises the steps of collecting logging data of target logging and lithological physical property parameters of a stratum, calculating a rock matrix modulus of the stratum based on a preset rock physical model, then obtaining a rock equivalent modulus based on the rock matrix modulus and a Chapman model, and further constructing a transverse wave velocity curve of the stratum.
For example, the shale content of the formation may be calculated using any one of natural gamma log data, natural potential log data, and resistivity log data in three ways.
(1) Calculating the shale content of the target stratum according to the natural gamma logging data; wherein the content of the first and second substances,
Vshis the shale content, GR is the natural gamma log value, GRminIs the natural gamma minimum log value, GR, of purely lithologic formationsmaxThe method is a natural gamma maximum logging value of a pure lithologic stratum, SH is a natural gamma relative value, and GCUR is an empirical coefficient related to the age of the stratum (the value of a new stratum is 3.7, and the value of an old stratum is 2.0).
(2) Calculating the shale content of the target stratum according to the natural potential logging data; wherein the content of the first and second substances,
Vshis the shale content, SP is the natural potential logging value, SPminIs the minimum logging value of the natural potential of the pure lithologic stratum, SPmaxThe method is the maximum logging value of the natural potential of the pure lithologic stratum.
(3) Calculating the shale content of the target stratum according to the resistivity logging data; wherein the content of the first and second substances,
Vshis a mud content, RimIs the maximum resistivity value, R, of the target formationshIs mudstone resistivity, RτB is a preset coefficient (the value is from 1.0 to 2.0) which is the resistivity of the target stratum.
The above three methods may be preferably used, for example, in the embodiment of the present invention, natural potential logging data is selected to calculate the shale content of the formation. Alternatively, the calculated value of the argillaceous content in the embodiment of the present invention should be above 0.0001%, and for the data below 0.0001%, the calculated value should be corrected to 0.0001%.
For example, the formation density of the target formation may be calculated from the density log data and the Compton effect. In the embodiment of the invention, the formation density of the target formation can be calculated by utilizing the Compton effect: when an isoenergetic gamma photon interacts with an outer electron of an atom, a portion of the energy is transferred to the electron, causing the electron to emit a compton electron in one direction, and the ray that lost a portion of the energy scatters out in the other direction (scattering gamma rays).
The energy lost by a gamma ray in the compton effect is related to the atomic number and the number of electrons per unit volume. The compton absorption coefficient is:
wherein σeThe Compton scattering cross section for each electron can be considered constant for gamma photons with energies between 0.25MeV and 2.5 MeV. N is a radical ofAρbZ/a represents the number of electrons per unit volume of the absorbing medium and is called the electron density, whereas Z/a is close to 0.5, as is the average value of Z/a of the usual sandstones, limestones, dolomites equal to 0.5. The compton absorption coefficient is therefore proportional to the bulk density of the rock, from which density logs have been developed for measuring formation density using the compton effect.
Specifically, based on the density log data, calculating the formation density of the target formation using the compton effect includes the following processes:
compton scattering cross section of atoms: sigmac＝Z×σc,e；
Compton scattering linear attenuation coefficient:
electron density:
electron density index:
a mineral consisting of a compound having an electron density of:
the electron density index is:
bulk density:
wherein, the electron density rho is obtained by calculation according to the volume density logging data, sigmac,eCompton scattering cross section for electrons, NAIs the Avogastron constant, Z is the atomic number, A is the atomic weight, M is the compound atomic weight, ρmaIs the rock skeleton density, ρfAs formation fluid density value, pmeIs the density, rho, of all electrons in the rock matrix under ideal conditionsfeIs the density of all electrons in the rock fluid.
Alternatively, the calculated value of the formation density in the embodiment of the invention should be 0.1g/cm3Above, for less than 0.1g/cm3The value of (A) should be corrected to 0.1g/cm3。
Specifically, calculating formation porosity of the target formation based on the formation density includes:
φDis the formation porosity, ρshAnd the density value of the mudstone is obtained. The value of the formation porosity should be non-negative, and for data with negative formation porosity, the value should be corrected to 0.
Optionally, in an embodiment of the present invention, the logging data may further include local shear velocity logging data of the target formation, and the step of calculating the formation porosity of the target formation according to the logging data may include: based on local shear wave velocity logging data, performing inversion calculation on the shear wave velocity of the target stratum by using a preset rock physical model to obtain an optimal convergence result; and determining the formation porosity of the target formation according to the optimal convergence result. The formation porosity may include: fracture porosity, pore aspect ratio, and fracture density.
For the mine area with the existing logging speed, in order to pursue the accuracy of the result, the reconstruction result obtained by the self-consistent model, the differential equivalent medium model or the xu-white model can be used, the pore structure is subjected to inversion calculation by adopting a simulated annealing optimization particle swarm algorithm, and after the relevant parameters of the pore structure are obtained, the Chapman model is further used for reconstructing the speed.
The existing actual velocity data (namely local transverse wave velocity logging data) is used, and the reconstructed velocity obtained based on a self-consistent model, a differential equivalent medium model or an xu-white model is substituted into the following inversion algorithm for calculation:
wherein the gap porosity
Denotes the sum of fracture porosity and fracture porosity, alpha denotes the pore aspect ratio, epsilonfDenotes the crack density, vp—realRepresenting true longitudinal wave velocity, vS—realRepresenting true transverse wave velocity, vp-model、vS-modelRespectively representing the primary longitudinal wave velocity and transverse wave velocity reconstruction results obtained based on a preset rock physical model, a, b and c are the weight values of various parameter smoothing items,
respectively representing the inverted values of the gap porosity, the gap aspect ratio and the crack density at a certain depth point.
The intelligent algorithm combines the particle swarm optimization algorithm and the simulated annealing algorithm, and has the characteristics of rapid convergence and capability of effectively avoiding local optimal solution. The method has good applicability to the nonlinear complex rock physical model problem.
Then based on the following theory and porosity logging results, calculating fracture porosity, fracture porosity and circular hole porosity, substituting the fracture porosity, the fracture porosity and the circular hole porosity into a chapman model as input parameters, and reconstructing and calculating to obtain a speed result:
wherein the content of the first and second substances,
for total porosity (i.e., the formation porosity) from the four formulas, one can obtain the porosity of the pores in the porosity
Porosity of crack
Porosity of crack
And substituting the result of the aperture aspect ratio alpha into further reconstruction calculation to obtain a more accurate longitudinal and transverse wave velocity result.
The essence of the method is that well logging reconstruction is carried out, and under the thought, when original well logging data does not have speed well logging, a self-consistent model, a differential equivalent medium model or an xu-white model is adopted to reconstruct and calculate to obtain a speed result.
For wells with original well logging curves and partial actually measured speed curves, the reconstruction of the well logging speed can be carried out by utilizing a self-consistent model, a differential equivalent medium model or an xu-white model, the actually measured speed curves are used as constraints, and the inversion of a pore structure is carried out by adopting a simulated annealing optimization particle swarm optimization algorithm. Specifically, the gap porosity is carried out by utilizing a self-consistent model, a differential equivalent medium model or an xu-white model
Pore aspect ratio alpha and fracture density epsilonfAnd (4) carrying out inversion calculation, combining the obtained pore aspect ratio and the corresponding stratum layering result of the non-actual-measurement-speed logging, and setting the pore structure and aspect ratio parameters.
And taking the obtained result as a speed reconstruction parameter of the well without the actually measured speed logging result, and finishing the final reconstruction of the speed logging curve by using the input parameter of the Chapman model. And obtaining other logging speed reconstruction results under the condition of the existing measured speed curve.
Fig. 2 is a schematic diagram of a shear wave velocity simulation curve according to an embodiment of the present invention. As shown in FIG. 2, the horizontal axis is vSThe vertical axis is the depth, and the curve is a transverse wave velocity simulation curve of the K4115-2 well to the original hole based on a self-consistent model.
The embodiment of the invention provides a multi-physical model well logging shear wave velocity curve reconstruction method, which comprises the steps of collecting original well logging information of a well of which the shear wave velocity is to be calculated, preprocessing the well logging information, calculating required lithology and physical property parameters by data collection and combining a common theoretical formula, calculating a rock stratum equivalent modulus based on a rock physical model on the basis, and further calculating a rock stratum shear wave velocity curve.
Fig. 3 is a schematic structural diagram of a system for reconstructing a velocity curve of a multi-physical model logging shear wave, according to an embodiment of the present invention, where the system includes:
the acquisition module 301 is configured to acquire logging data and lithology physical property parameters of a target stratum; the well log data includes at least one of: natural gamma-ray logging data, natural potential logging data, resistivity logging data and density logging data;
a first calculation module 302, configured to calculate a shale content, a formation density, and a formation porosity of the target formation according to the logging data;
the second calculation module 303 is configured to calculate a rock matrix modulus of the target stratum by using a preset rock physical model according to the shale content, the stratum density, and the lithology physical property parameter; the preset petrophysical model comprises any one of the following: a self-consistent model, a differential equivalent medium model and an xu-white model;
a substituting module 304, configured to substitute the formation porosity and the formation matrix modulus into a Chapman model to obtain a formation equivalent modulus of the target formation;
the building module 305 is configured to build a shear wave velocity curve of the target formation according to the preset relational expression of the rock equivalent modulus, and the formation shear wave velocity.
The embodiment of the invention provides a multi-physical model logging transverse wave velocity curve reconstruction system, which is characterized in that logging data of target logging and lithological physical property parameters of a stratum are collected, the matrix modulus of the stratum is calculated based on a preset rock physical model, then the equivalent modulus of the stratum is obtained based on the matrix modulus of the stratum and a Chapman model, and a transverse wave velocity curve of the stratum is further constructed.
Optionally, as an embodiment, if the logging data includes local shear velocity logging data of the target formation, the first calculating module 302 is specifically configured to:
based on the local shear wave velocity logging data, performing inversion calculation on the shear wave velocity of the target stratum by using the preset rock physical model to obtain an optimal convergence result; and determining the formation porosity of the target formation according to the optimal convergence result.
Optionally, as an embodiment, a formula for performing an inverse calculation on the shear wave velocity of the target formation is as follows:
wherein the gap porosity
Denotes the sum of fracture porosity and fracture porosity, alpha denotes the pore aspect ratio, epsilonfDenotes the crack density, vp—realRepresenting true longitudinal wave velocity, vS—realRepresenting true transverse wave velocity, vp-model、vS-modelRespectively representing the primary longitudinal wave velocity and transverse wave velocity reconstruction results obtained based on the preset rock physical model, a, b and c are the weight values of various parameter smoothing items,
respectively representing the inverted values of the gap porosity, the gap aspect ratio and the crack density at a certain depth point.
Optionally, as an embodiment, the formation equivalent modulus includes at least one of: rock stratum equivalent volume modulus and rock stratum equivalent shear modulus; the preset relational expression is as follows:
Vpis the velocity of longitudinal wave, VsIs the transverse wave velocity, KdryIs the equivalent bulk modulus, mu, of the formationdryIs the formation equivalent shear modulus, ρ is the electron density.
Optionally, as an embodiment, the first calculating module 302 is specifically configured to: calculating the shale content of the target stratum according to the natural gamma logging data; wherein the content of the first and second substances,
Vshfor the shale content, GR is the natural gamma log value, GRminIs the natural gamma minimum log value, GR, of purely lithologic formationsmaxThe method is a natural gamma maximum logging value of a pure lithologic stratum, SH is a natural gamma relative value, and GCUR is an empirical coefficient related to the age of the stratum.
The first calculating module 302 is specifically configured to: calculating the shale content of the target stratum according to the natural potential logging data; wherein the content of the first and second substances,
Vshfor the shale content, SP is a natural potential log value, SPminIs a pure lithologic stratumMinimum log of natural potential, SPmaxThe method is the maximum logging value of the natural potential of the pure lithologic stratum.
The first calculating module 302 is specifically configured to: calculating the shale content of the target stratum according to the resistivity logging data; wherein the content of the first and second substances,
Vshin the order of the argillaceous content, RimIs the maximum resistivity value, R, of the target formationshIs mudstone resistivity, RτB is a preset coefficient, and the resistivity of the target stratum is obtained.
The first calculating module 302 is specifically configured to: and calculating the stratum density of the target stratum according to the density logging data and the Compton effect.
The invention provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the computer program to realize the steps of the multi-physical model well logging shear wave velocity curve reconstruction method.
The invention provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is read and executed by a processor, the method for reconstructing the transverse wave velocity curve of the multi-physical model logging is realized.
Of course, those skilled in the art will understand that all or part of the processes in the methods of the above embodiments may be implemented by instructing the control device to perform operations through a computer, and the programs may be stored in a computer-readable storage medium, and when executed, the programs may include the processes of the above method embodiments, where the storage medium may be a memory, a magnetic disk, an optical disk, and the like.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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 identical elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A multi-physical model well logging shear wave velocity curve reconstruction method is characterized by comprising the following steps:
acquiring logging data and lithologic physical property parameters of a target stratum; the well log data includes at least one of: natural gamma-ray logging data, natural potential logging data, resistivity logging data and density logging data;
calculating the shale content, the formation density and the formation porosity of the target formation according to the logging data;
calculating the rock stratum matrix modulus of the target stratum by utilizing a preset rock physical model according to the argillaceous content, the stratum density and the lithology physical property parameters; the preset petrophysical model comprises any one of the following: a self-consistent model, a differential equivalent medium model and an xu-white model;
substituting the formation porosity and the rock stratum matrix modulus into a Chapman model to obtain a rock stratum equivalent modulus of the target formation;
and constructing a transverse wave velocity curve of the target stratum according to the preset relational expression of the rock equivalent modulus, the rock equivalent modulus and the stratum longitudinal and transverse wave velocity.
2. The method of claim 1, wherein if the well log data comprises local shear velocity well log data of the target formation, the calculating the formation porosity of the target formation from the well log data comprises:
based on the local shear wave velocity logging data, performing inversion calculation on the shear wave velocity of the target stratum by using the preset rock physical model to obtain an optimal convergence result;
and determining the formation porosity of the target formation according to the optimal convergence result.
3. The method of claim 2, wherein the formula for the inverse calculation of the shear velocity of the target formation is as follows:
wherein the gap porosity
Denotes the sum of fracture porosity and fracture porosity, alpha denotes the pore aspect ratio, epsilonfDenotes the crack density, vp—realRepresenting true longitudinal wave velocity, vS—realRepresenting true transverse wave velocity, vp-model、vS-modelRespectively representing the acquisition based on the preset petrophysical modelThe initial reconstruction results of the longitudinal wave velocity and the transverse wave velocity are that a, b and c are the weight values of each parameter smoothing item,
respectively representing the inverted values of the gap porosity, the gap aspect ratio and the crack density at a certain depth point.
4. The method of claim 1, wherein the formation-equivalent modulus comprises at least one of: rock stratum equivalent volume modulus and rock stratum equivalent shear modulus; the preset relational expression is as follows:
Vpis the velocity of longitudinal wave, VsIs the transverse wave velocity, KdryIs the equivalent bulk modulus, mu, of the formationdryIs the formation equivalent shear modulus, ρ is the electron density.
5. The method of claim 1, wherein the calculating the shale content of the target formation from the well log data comprises:
calculating the shale content of the target stratum according to the natural gamma logging data; wherein the content of the first and second substances,
Vshis that it isShale content, GR is the natural gamma log value, GRminIs the natural gamma minimum log value, GR, of purely lithologic formationsmaxThe method is a natural gamma maximum logging value of a pure lithologic stratum, SH is a natural gamma relative value, and GCUR is an empirical coefficient related to the age of the stratum.
6. The method of claim 1, wherein the calculating the shale content of the target formation from the well log data comprises:
calculating the shale content of the target stratum according to the natural potential logging data; wherein the content of the first and second substances,
Vshfor the shale content, SP is a natural potential log value, SPminIs the minimum logging value of the natural potential of the pure lithologic stratum, SPmaxThe method is the maximum logging value of the natural potential of the pure lithologic stratum.
7. The method of claim 1, wherein the calculating the shale content of the target formation from the well log data comprises:
calculating the shale content of the target stratum according to the resistivity logging data; wherein the content of the first and second substances,
Vshin the order of the argillaceous content, RimIs the maximum resistivity value, R, of the target formationshIs mudstone resistivity, RτB is a preset coefficient, and the resistivity of the target stratum is obtained.
8. The method of claim 1, wherein said calculating a formation density of the target formation from the well log data comprises:
and calculating the stratum density of the target stratum according to the density logging data and the Compton effect.
9. A multi-physics model logging shear velocity curve reconstruction system, the system comprising:
the acquisition module is used for acquiring logging data and lithology physical property parameters of a target stratum; the well log data includes at least one of: natural gamma-ray logging data, natural potential logging data, resistivity logging data and density logging data;
the first calculation module is used for calculating the shale content, the formation density and the formation porosity of the target formation according to the logging data;
the second calculation module is used for calculating the rock stratum matrix modulus of the target stratum by utilizing a preset rock physical model according to the argillaceous content, the stratum density and the lithology physical property parameters; the preset petrophysical model comprises any one of the following: a self-consistent model, a differential equivalent medium model and an xu-white model;
a substituting module for substituting the formation porosity and the formation matrix modulus into a Chapman model to obtain a formation equivalent modulus of the target formation;
and the construction module is used for constructing a transverse wave velocity curve of the target stratum according to the preset relational expression of the rock equivalent modulus, the rock equivalent modulus and the stratum longitudinal and transverse wave velocity.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any of the preceding claims 1-8 are implemented when the computer program is executed by the processor.
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