CN112505755B - High-precision rock physical modeling method based on self-adaptive mixed skeleton parameters - Google Patents
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Abstract
The invention discloses a high-precision rock physical modeling method based on self-adaptive mixed skeleton parameters, which is based on GaEstablishing a rock matrix as an inversion target by using the ssmann equation and a Krief approximate expression, obtaining the rock matrix modulus K by iterative inversion calculation by using the minimum error between the actual longitudinal wave velocity and the forward longitudinal wave velocity as a target function0,μ0Further calculating to obtain the transverse wave velocity VsThrough the combined modeling inversion of the Krief formula and the Gassmann, the algorithm has loose requirements on the input known conditions and certain inclusiveness, can adapt to the research of any lithologic reservoir, and has strong interpretability and high calculation result precision in the whole calculation process according to the rock physics principle. The method aims to solve the technical problems of low rock physical modeling precision and large error in the prior art.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration, in particular to a high-precision rock physical modeling method based on self-adaptive mixed skeleton parameters.
Background
With the continuous development of oil and gas exploration and development technologies, the identification of complex lithologic reservoirs becomes one of the main targets of seismic exploration, accurate longitudinal wave, transverse wave and density logging information is the basic information of prestack forward inversion, and most wells lack transverse wave velocity information in actual production due to the high cost and high explanation difficulty of transverse wave logging, so that the transverse wave velocity plays an important role in reservoir lithology research and fluid identification.
The existing transverse wave prediction methods mainly comprise three categories, namely an estimation method based on empirical values, a prediction method based on a rock physical model and a calculation method based on machine learning, wherein the estimation method based on the empirical values is based on longitudinal and transverse wave empirical relations such as Greenberg and Gastagna; based on a rock physics modeling method, such as an Xu-White model, a Gassmann model, a Pride model, a Krief model and the like, the method can accurately calculate the transverse wave speed through rigorous experimental study and mathematical derivation, but the rock physics modeling method only depends on experience values given by geology personnel under the conditions of more required input parameters and uncertain parameters, so that accumulated errors and uncertainty can be caused to a certain extent, and the reliability of the transverse wave prediction result is reduced; the calculation method based on machine learning is characterized in that a regression mapping model is established by utilizing well data with actually measured transverse waves in a work area, and then transverse wave speeds at other well point positions are predicted by extrapolation. Therefore, how to improve the accuracy of petrophysical modeling and perform shear wave prediction is a technical problem which needs to be solved urgently.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a high-precision rock physical modeling method based on self-adaptive mixed skeleton parameters, and aims to solve the technical problems of low rock physical modeling precision and large error in the prior art.
In order to achieve the purpose, the invention provides a high-precision rock physical modeling method based on self-adaptive mixed skeleton parameters, which comprises the following steps:
the method comprises the following steps: characterization of matrix modulus K of mixed skeleton by using Krief empirical formula0,μ0Modulus of the dry rock matrix Kdry,μdryThe relational expression of (1);
step two: calculating the fluid bulk modulus K by using the relational expression obtained in the first step and the Wood formulafSubstituting the Gassmann equation and the longitudinal wave velocity calculation formula to obtain the matrix modulus K of the mixed skeleton0,μ0For an inversion model, measuring a target function with minimum error between a longitudinal wave and a predicted longitudinal wave;
step three: performing iterative inversion calculation on the target function obtained in the second step by using a Levenberg-Maquardt algorithm to obtain a matrix modulus K of the mixed skeleton0,μ0；
Step four: the modulus K of the mixed framework matrix obtained in the step three0,μ0Substituting step one to mix the matrix modulus K0,μ0Modulus of the dry rock matrix Kdry,μdryIn the relation of (A), the dry rock matrix modulus K is obtained by calculationdry,μdry；
Step five: the modulus K of the mixed framework matrix obtained in the step three0,μ0And the modulus K of the dry rock matrix obtained in the fourth stepdry,μdrySubstituting the obtained product into a Gassmann equation to calculate the modulus K of the saturated fluid matrixsat,μsatFinally using the modulus K of the saturated fluid matrixsat,μsatCalculating to obtain the transverse wave velocity Vs。
Preferably, the high-precision rock physical modeling method based on the self-adaptive mixed skeleton parameters is characterized in that the matrix modulus K of the mixed skeleton0,μ0Modulus of the dry rock matrix Kdry,μdryThe Krief empirical formula for the relationship of (1) is:
wherein: phi is the porosity, K0,μ0Is the modulus of the matrix of the mixed skeleton, Kdry,μdryIs the dry rock modulus.
Preferably, the high-precision petrophysical modeling method based on the adaptive mixed skeleton parameters includes the following steps:
μsat＝μd (2-3)
a1: substituting (1-1), (2-2) and (2-3) into (2-4) to obtain (2-5), wherein a is an intermediate variable;
a2: order to
Derivation of the deviation
Obtaining:
a3: establishing an objective function:
wherein: ksat,μsatIs the modulus of saturated fluid rock, KfIs the bulk modulus, S, of the mixed fluidw、So、Sg、Kw、Ko、KgRespectively measured water saturation, oil saturation, gas saturation, water bulk modulus, gas bulk modulus, oil bulk modulus, rho is measured density value, lambda is measured density value1Is K0And mu0The smooth constraint factor of (2) is taken as a default value of 0.001.
Preferably, the high-precision rock physical modeling method based on the self-adaptive mixed skeleton parameters is used for calculating the matrix modulus K of the mixed skeleton0,μ0The method comprises the following steps of carrying out binary iteration by using a Levenberg-Maquardt algorithm and simultaneously carrying out inversion solving to obtain an objective function:
let the inversion target m ═ K0,μ0) An initial value.
B1: calculate initial f (K)0,μ0) And actually measure
The difference value e of (a) is,
b2: building a partial derivative matrix using equations (2-6)
Using the disturbance amount e calculated in step B1 (K)0,μ0) Is (G), the disturbance amount Δ m, Δ m ═ GTG+λ1I)-1GTe；
B3: updating m to m + Δ m;
b4: calculating cumulative error
And judging whether the accumulated error is smaller than a preset error threshold value, if so, finishing the inversion calculation, and if so, repeatedly executing the steps B1-B4.
Preferably, the high-precision rock physical modeling method based on the self-adaptive mixed skeleton parameters utilizes the saturated fluid matrix modulus Ksat,μsatCalculating to obtain the transverse wave velocity VsThe method specifically comprises the following steps:
the saturated fluid matrix modulus K to be obtainedsat,μsatSubstituting into a formula:
calculating to obtain the transverse wave velocity Vs。
The invention provides a high-precision rock physical modeling method based on self-adaptive mixed skeleton parameters, which is characterized in that a rock matrix is used as an inversion target according to a Gassmann equation and a Krief approximate expression, the minimum error between an actual measured longitudinal wave velocity and a forward longitudinal wave velocity is used as a target function, and the rock matrix modulus (K) is obtained through iterative inversion calculation0,μ0) And then calculating to obtain the transverse wave velocity, and performing combined modeling inversion through a Krief formula and Gassmann to ensure that the algorithm has loose requirements on the input known conditions and certain containment, the algorithm can be suitable for the research of any lithologic reservoir, and the whole calculation process is based on the rock physical principle, so that the interpretability is strong and the calculation result precision is high. The method aims to solve the technical problems of low rock physical modeling precision and large error in the prior art.
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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 some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a schematic diagram of the modeling process and shear wave prediction in the present invention;
FIG. 2 is a schematic diagram illustrating the effect of transverse wave prediction in the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides an embodiment, and as shown in fig. 1, the invention provides a high-precision rock physical modeling method based on self-adaptive mixed skeleton parameters.
In the bookIn the example, the longitudinal wave V on the well is usedpTransverse wave VsDensity rho, porosity phi, water saturation SwGas saturation SgOil saturation SoCurve parameters are calculated according to Krief empirical formula, wood formula, Gassmann equation and longitudinal wave VpThe method for establishing the target function by the calculation formula specifically comprises the following steps:
the method comprises the following steps: characterization of matrix modulus (K) of mixed frameworks by Krief empirical formula0,μ0) Modulus of the dry rock matrix (K)dry,μdry) The relational expression of (1);
as shown in the formula (1-1), is an empirical formula of Krief, where φ is porosity, K0,μ0Is the modulus of the matrix of the mixed skeleton, Kdry,μdryIs the modulus of dry rock, and the matrix modulus K of the mixed skeleton is represented by a Krief empirical formula0,μ0Modulus of the dry rock matrix Kdry,μdryThe relational expression (c) of (c).
The rock matrix modulus calculation of the conventional rock physical modeling is obtained by calculation through a Voigt-reus-Hill method, the process needs to know the components and the percentage of the components of rock minerals, in actual production, logging data often lack the components and the percentage of the components of the rock minerals, geologists can judge according to other logging curves and geological experience, the mineral percentage is replaced by a similar argillaceous content curve, a lot of errors exist in the equivalent replacement process of dividing lithology by using a threshold value, the accumulation of subsequent calculation errors is caused, the replacement assumption is avoided by using a Krief empirical formula, the participation uncertainty data of the whole transverse wave calculation is reduced, and the accuracy of the subsequent transverse wave prediction is improved.
Step two: calculating the fluid bulk modulus K by using the relation of the step one and the Wood formulafSubstituting the Gassmann equation and the longitudinal wave velocity calculation formula to obtain the matrix modulus K of the mixed skeleton0,μ0For the inverse model, the sum of the longitudinal waves is measuredPredicting an objective function with the minimum longitudinal wave error;
μsat＝μd (2-3)
where φ is porosity, K0,μ0Is the modulus of the matrix of the mixed skeleton, Kdry,μdryIs the dry rock modulus, Ksat,μsatIs the modulus of saturated fluid rock, KfIs the bulk modulus, S, of the mixed fluidw、So、Sg、Kw、Ko、KgRespectively measuring the water saturation, the oil saturation, the gas saturation, the water bulk modulus, the gas bulk modulus and the oil bulk modulus, wherein rho is a measured density value, and substituting (1-1), (2-2) and (2-3) into a formula (2-4) to obtain a formula (2-5), wherein a is an intermediate variable;
establishing an objective function (2-7)
Wherein λ1Is K0And mu0The smooth constraint factor of (2) is taken as a default value of 0.001.
Step three: step two, iterative inversion calculation is carried out on the objective function by utilizing a Levenberg-Maquardt algorithm to obtain the matrix modulus K of the mixed skeleton0,μ0；
And (2) performing binary iteration by using a Levenberg-Maquardt algorithm and simultaneously performing inversion to solve the objective function (2-7) in the second step, wherein the method comprises the following steps:
let the inversion target m ═ K0,μ0) An initial value.
(1) Calculate initial f (K)0,μ0) And actually measure
The difference value e of (a) is,
(2) building a partial derivative matrix using equations (2-6)
Calculating (K) by using the disturbance amount e in the step (1)0,μ0) Is (G), the disturbance amount Δ m, Δ m ═ GTG+λ1I)-1GTe；
(3) Updating m to m + Δ m;
(4) calculating cumulative error
And judging whether the accumulated error is less than a preset error threshold value, if so, calculating the inverse meterAnd (4) if the calculation is finished and the value is larger than the threshold value, repeating the steps (1) to (4).
Step four: substituting the result of the third step into the matrix modulus K of the mixed framework in the first step0,μ0Modulus of the dry rock matrix Kdry,μdryIn the relation of (A), the dry rock matrix modulus K is obtained by calculationdry,μdry；
Calculating to obtain the matrix modulus K of the dry rock by using (1-1)dry,μdry。
Step five: modulus K of the mixed skeleton matrix obtained in the third step0,μ0And D, obtaining the dry rock matrix modulus Kdry,μdrySubstituting into Gassmann equation to calculate saturated fluid matrix modulus Ksat,μsatFinally using the modulus K of the saturated fluid matrixsat,μsatCalculating to obtain the transverse wave velocity Vs，
FIG. 2 is a schematic diagram of the effect of predicting shear wave in this embodiment, in which the black dotted line is the original velocity V of shear wavesThe black solid line indicates the predicted transverse wave velocity Vs。
In the embodiment, a high-precision rock physical modeling method based on self-adaptive mixed skeleton parameters is provided, a rock matrix is used as an inversion target according to a Gassmann equation and a Krief approximate expression, the minimum error between an actual measured longitudinal wave velocity and a forward longitudinal wave velocity is used as a target function, and the rock matrix modulus K is obtained through iterative inversion calculation0,μ0Further calculating to obtain the transverse wave velocity VsThrough the combined modeling inversion of the Krief formula and the Gassmann, the algorithm has loose requirements on the input known conditions and certain inclusiveness, can adapt to the research of any lithologic reservoir, and has strong interpretability and high calculation result precision in the whole calculation process according to the rock physics principle. The method aims to solve the technical problems of low rock physical modeling precision and large error in the prior art.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention, which is to be protected by the claims appended hereto.
Claims (3)
1. A high-precision rock physical modeling method based on self-adaptive mixed skeleton parameters is characterized by comprising the following steps:
s1: characterization of matrix modulus K of mixed skeleton by using Krief empirical formula0,μ0Modulus of the dry rock matrix Kdry,μdryThe relational expression of (1);
s2: calculating the fluid bulk modulus K by using the relational expression obtained in the step S1 and the Wood formulafSubstituting the Gassmann equation and the longitudinal wave velocity calculation formula to obtain the matrix modulus K of the mixed skeleton0,μ0For an inversion model, measuring a target function with minimum error between a longitudinal wave and a predicted longitudinal wave;
s3: performing iterative inversion calculation on the objective function obtained in the step S2 by using a Levenberg-Maquardt algorithm to obtain a matrix modulus K of the mixed skeleton0,μ0；
S4: the mixed skeleton matrix modulus K obtained in the step S30,μ0Substituting step S1 mixed skeleton matrix modulus K0,μ0Modulus of the dry rock matrix Kdry,μdryIn the relation of (A), the dry rock matrix modulus K is obtained by calculationdry,μdry；
S5: the mixed skeleton matrix modulus K obtained in the step S30,μ0And the dry rock matrix modulus K obtained in step S4dry,μdrySubstituting the obtained product into a Gassmann equation to calculate the modulus K of the rock saturated with the fluidsat,μsatFinally using the saturated fluid rock modulus Ksat,μsatCalculating to obtain the transverse wave velocity Vs；
The calculation steps of the objective function are as follows:
μsat＝μdry (2-3)
matrix modulus K of the mixed skeleton0,μ0Modulus of the dry rock matrix Kdry,μdryThe Krief empirical formula for the relationship of (1) is:
wherein: phi is the porosity, K0,μ0Is the modulus of the matrix of the mixed skeleton, Kdry,μdryIs the dry rock modulus;
a1: substituting (1-1), (2-2) and (2-3) into (2-4) to obtain (2-5), wherein a is an intermediate variable;
a2: order to
Derivation of the deviation
Obtaining:
a3: establishing an objective function:
wherein: ksat,μsatIs the modulus of saturated fluid rock, KfIs the bulk modulus, S, of the mixed fluidw、So、Sg、Kw、Ko、KgRespectively measured water saturation, oil saturation, gas saturation, water bulk modulus, gas bulk modulus, oil bulk modulus, rho is measured density value, lambda is measured density value1Is K0And mu0The smooth constraint factor of (2) is taken as a default value of 0.001.
2. The method for high-precision petrophysical modeling based on adaptive hybrid skeleton parameters according to claim 1, wherein the hybrid skeleton matrix modulus K is calculated0,μ0The method comprises the following steps of carrying out binary iteration by using a Levenberg-Maquardt algorithm and simultaneously carrying out inversion solving to obtain an objective function:
let the inversion target m ═ K0,μ0) An initial value;
b1: calculate initial f (K)0,μ0) And actually measure
The difference value e of (a) is,
b2: building a partial derivative matrix using equations (2-6)
K is calculated by using the disturbance e in the step B10,μ0Is (G), the disturbance amount Δ m, Δ m ═ GTG+λ1I)-1GTe；
B3: updating m to m + Δ m;
b4: calculating cumulative error
And judging whether the accumulated error is smaller than a preset error threshold value, if so, finishing the inversion calculation, and if so, repeatedly executing the steps B1-B4.
3. The method of claim 2, wherein the rock modulus K is determined by using a saturated fluidsat,μsatCalculating to obtain the transverse wave velocity VsThe method specifically comprises the following steps:
the modulus K of the obtained fluid-saturated rocksat,μsatSubstituting into a formula:
calculating to obtain the transverse wave velocity Vs。
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| US4398273A (en) * | 1979-10-02 | 1983-08-09 | Chevron Research Company | Method for interpreting seismic records to yield indications of gas/oil in an earth formation |
| US7440854B2 (en) * | 2006-04-14 | 2008-10-21 | William Christian Dickson | Density and velocity based assessment method and apparatus |
| CN102096107B (en) * | 2009-12-09 | 2012-10-17 | 中国石油天然气股份有限公司 | Method for evaluating permeability of reservoir layer according to interval transit time and density inversed pore flat degree |
| EP2856373A4 (en) * | 2012-05-24 | 2016-06-29 | Exxonmobil Upstream Res Co | System and method for predicting rock strength |
| CN103424772B (en) * | 2012-05-24 | 2016-09-14 | 中国石油化工股份有限公司 | A kind of based on petrophysical Carbonate Reservoir S-Wave Velocity Predicted Method |
| CN103792581B (en) * | 2013-12-31 | 2015-06-03 | 孙赞东 | Shale gas reservoir crustal stress logging prediction method based on rock physics model |
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| CN109782348A (en) * | 2019-01-22 | 2019-05-21 | 太原理工大学 | The recognition methods of reservoir fluid and brittleness Simultaneous Inversion based on porous media model |
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