CN114924318A - Seismic rock physical modeling method for stable prediction of mineral modulus - Google Patents
Seismic rock physical modeling method for stable prediction of mineral modulus Download PDFInfo
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- 229910052500 inorganic mineral Inorganic materials 0.000 title claims abstract description 117
- 239000011707 mineral Substances 0.000 title claims abstract description 117
- 239000011435 rock Substances 0.000 title claims abstract description 64
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000005070 sampling Methods 0.000 claims abstract description 23
- 239000011148 porous material Substances 0.000 claims description 44
- 239000004576 sand Substances 0.000 claims description 20
- 239000004927 clay Substances 0.000 claims description 18
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 7
- 238000005457 optimization Methods 0.000 abstract description 4
- 238000011160 research Methods 0.000 abstract description 2
- 239000011159 matrix material Substances 0.000 description 14
- 239000012530 fluid Substances 0.000 description 7
- 230000035699 permeability Effects 0.000 description 4
- 239000002245 particle Substances 0.000 description 3
- 229920006395 saturated elastomer Polymers 0.000 description 3
- 201000004569 Blindness Diseases 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 239000002734 clay mineral Substances 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- NLYAJNPCOHFWQQ-UHFFFAOYSA-N kaolin Chemical compound O.O.O=[Al]O[Si](=O)O[Si](=O)O[Al]=O NLYAJNPCOHFWQQ-UHFFFAOYSA-N 0.000 description 2
- 229910052622 kaolinite Inorganic materials 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 239000002023 wood Substances 0.000 description 2
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- FFBHFFJDDLITSX-UHFFFAOYSA-N benzyl N-[2-hydroxy-4-(3-oxomorpholin-4-yl)phenyl]carbamate Chemical compound OC1=C(NC(=O)OCC2=CC=CC=C2)C=CC(=C1)N1CCOCC1=O FFBHFFJDDLITSX-UHFFFAOYSA-N 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000011162 core material Substances 0.000 description 1
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- 230000014509 gene expression Effects 0.000 description 1
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- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
- G01V1/44—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging using generators and receivers in the same well
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Abstract
The invention relates to the technical field of seismic rock physics research, and particularly discloses a seismic rock physics modeling method for mineral modulus stability prediction. The method comprises the following steps: setting a function of a rock physical model; step 1: determining the value ranges of the volume modulus and the shear modulus of the minerals; and 2, step: determining the value range of the soft hole ratio; and step 3: iteration is carried out to obtain the optimal solution of the mineral shear modulus and the optimal solution of the soft hole ratio; and 4, step 4: iteratively obtaining an optimal solution of the mineral bulk modulus; and 5: iteratively solving the optimal solution of the soft hole ratio at each sampling point; step 6: and repeating the steps 3, 4 and 5, and performing iterative solution to stably predict the bulk modulus and the shear modulus of the mineral. The method of the invention takes the measured longitudinal wave velocity and transverse wave velocity as constraints, introduces the idea of optimization algorithm and step-by-step inversion, realizes the modeling of seismic rock physics, and can stably predict the elastic modulus of rock minerals, thereby improving the accuracy of predicting the transverse wave velocity.
Description
Technical Field
The invention relates to the field of seismic rock physics research, in particular to a seismic rock physics modeling method for mineral modulus stability prediction.
Background
Seismic petrophysics is a bridge that connects petrophysical microscopic parameters (porosity, permeability, water saturation, etc.) with macroelastic parameters (longitudinal wave velocity, transverse wave velocity, etc.). Petrophysical models play a key role in studying reservoir elastic properties. Through the rock physical model, the longitudinal wave speed and the transverse wave speed of the rock can be predicted, so that the condition that some areas lack transverse wave speed is compensated. Shear wave velocity is indispensable data in seismic prestack inversion and other works. Therefore, it is important to construct a suitable petrophysical model.
When the shear wave prediction is carried out, required logging data and empirical parameters need to be input into the constructed rock physical model. Wherein the well log data comprises: porosity, water saturation, density, longitudinal wave velocity, mineral content, etc.; empirical parameters include: the modulus (including bulk and shear modulus), pore aspect ratio, etc. of the matrix mineral. In actual work, the influence of the selection of the mineral modulus on the prediction of the shear wave velocity is found to be a great weight compared with other empirical parameters. Because the deposition environment and the diagenetic environment of the rock are different, the modulus of the same mineral is greatly different in different regions. Therefore, the prediction of the mineral modulus of the rock matrix becomes an urgent problem to be solved.
Based on the theory of equivalent media, the rock can be divided into four parts, namely a matrix, a framework, pores and fluid. The existing transverse wave prediction method mainly selects a proper rock physical model, respectively obtains the modulus (also called elastic modulus) of each part of the rock, and further obtains the equivalent elastic modulus of the saturated rock, thereby finally realizing the estimation of the transverse wave velocity. The disadvantage is that a suitable mineral modulus cannot be selected.
The existing rock mineral modulus acquisition method can be mainly divided into two types: the first type is a rock physical experiment measuring method, which has the defects that core materials are difficult to obtain, and experiment conditions are difficult to meet; the second type is an empirical parameter method, which mainly refers to the measurement data of predecessors, and has the defect that the empirical parameters are not applicable in actual work, because the matrix mineral modulus is comprehensively influenced by sedimentary diagenesis, diagenesis after diagenesis, formation pressure, temperature, lithology and the like, the same matrix mineral modulus has larger difference in different areas. For example, the average bulk modulus of Kaolinite Kaolinite (a clay mineral) is 1.5GPa, while the bulk modulus of clay mineral in the Gulf clay Gulf can reach 25GPa, which brings certain blindness to the selection of the mineral modulus and further influences the accuracy of transverse wave prediction.
Disclosure of Invention
In order to reduce the blindness of mineral modulus selection, the invention provides a seismic rock physical modeling method for mineral modulus stable prediction. According to the method, the shear modulus and the volume modulus of the rock matrix mineral are calculated step by taking the actually measured longitudinal wave velocity and transverse wave velocity as constraints and combining a particle swarm global optimization algorithm, and the number of inversion parameters at each time is reduced, so that the accurate prediction of the multi-component rock mineral modulus is realized.
In order to solve the technical problems, the invention adopts the technical scheme that: a seismic petrophysical modeling method for stable prediction of mineral modulus comprises the following steps:
the function for setting the petrophysical model is:
in the formulas (1) and (2), F 1 、F 2 Respectively representing functions of calculating longitudinal wave velocity and transverse wave velocity of the rock through the rock physical model; rho is density; vsh is the argillaceous content; k sand 、U sand Respectively representing the bulk modulus and the shear modulus of the sandy mineral; k clay 、U clay Respectively representing the bulk modulus and the shear modulus of the argillaceous minerals; r is stiff 、r soft Represents the pore aspect ratio of the hard and soft pores, respectively; phi is a unit of stiff 、φ soft Denotes the porosity of hard and soft pores, respectively, phi being the total porosity, phi ═ phi stift +φ soft (ii) a Sw represents the water saturation;
to predict longitudinal wave velocity;
to predict shear wave velocity; subscript i represents the ith sample point in the log data;
step 1: determining the value ranges of the volume modulus and the shear modulus of the sandy minerals and the value ranges of the volume modulus and the shear modulus of the argillaceous minerals;
step 2: determining the value range of the soft pore ratio, wherein the soft pore ratio is the ratio of the porosity of the soft pores to the total porosity;
and 3, step 3: selecting different values of the shear modulus of the sandy minerals, the shear modulus of the argillaceous minerals and the soft pore ratio determined in the step (2) according to the value ranges of the shear modulus of the sandy minerals and the shear modulus of the argillaceous minerals and the value range of the soft pore ratio determined in the step (2) and substituting the values into the formula (2);
defining an objective function psi by using the measured transverse wave velocity as a constraint 1 Comprises the following steps:
objective function psi 1 Wherein n is the total number of sampling points in the logging data, the subscript i represents the ith sampling point in the logging data,
predicted shear velocity, V, for the ith sample point in the log data s i is the measured transverse wave speed of the ith sampling point in the logging data when the target function psi 1 When the shear modulus value of the selected sandy mineral and the shear modulus value of the argillaceous mineral are the optimal solutions of the shear modulus of the sandy mineral and the shear modulus of the argillaceous mineral, and the selected soft pore ratio value is the optimal solution of the soft pore ratio;
and 4, step 4: selecting the volume modulus of the sandy minerals and different values of the volume modulus of the argillaceous minerals according to the value ranges of the volume modulus of the sandy minerals and the volume modulus of the argillaceous minerals determined in the step 1 by using the shear modulus of the sandy minerals, the optimal solution of the shear modulus of the argillaceous minerals and the optimal solution of the soft pore proportion obtained in the step 3, and substituting the values into the formula (1);
defining an objective function as psi by using the measured longitudinal wave velocity as a constraint 2 ：
Objective function psi 2 In (1),
the predicted longitudinal wave velocity of the ith sampling point in the logging data is obtained, Vpi is the measured longitudinal wave velocity of the ith sampling point in the logging data, and when the target function psi 2 When the minimum value is reached, the selected value of the bulk modulus of the sandy mineral and the value of the bulk modulus of the argillaceous mineral are the optimal solutions of the bulk modulus of the sandy mineral and the bulk modulus of the argillaceous mineral;
and 5: taking the optimal solution of the soft pore ratio obtained in the step 3 as an initial value, and substituting the optimal solution of the shear modulus of the sandy minerals and the shear modulus of the argillaceous minerals, the optimal solution of the volume modulus of the sandy minerals and the optimal solution of the volume modulus of the argillaceous minerals, which are obtained in the steps 3 and 4, into a formula (1);
defining an objective function psi by using the measured longitudinal wave velocity as a constraint 3 Comprises the following steps:
solving the optimal solution of the soft hole ratio at each sampling point in the logging data;
and 6: and repeating the steps 3, 4 and 5, carrying out iterative solution, and stably predicting the volume modulus and the shear modulus of the sandy minerals and the volume modulus and the shear modulus of the argillaceous minerals when the predicted longitudinal wave speed is the same and is consistent with the actually measured longitudinal wave speed and the predicted transverse wave speed is the same and is consistent with the actually measured transverse wave speed after a plurality of iterations.
Preferably, the values of the volume modulus and the shear modulus of the sandy mineral are as follows:
33.0GPa≤U sand ≤45.6GPa
7.0GPa≤U clay ≤9.0GPa (3)；
the value ranges of the volume modulus and the shear modulus of the argillaceous minerals are as follows:
34.0GPa≤K sand ≤39.0GPa
21.0GPa≤K clay ≤25.0GPa (4)。
preferably, the soft pore ratio has a value range of:
the method of the invention takes the measured longitudinal wave velocity and transverse wave velocity as constraints, introduces the idea of optimization algorithm and step-by-step inversion, realizes the modeling of seismic rock physics, and can stably predict the elastic modulus of rock minerals, thereby improving the accuracy of predicting the transverse wave velocity.
Drawings
FIG. 1 is a flow chart of an embodiment of a seismic rock physics modeling method for mineral modulus stability prediction provided by the invention.
FIG. 2a is a schematic diagram showing the comparison between the measured compressional velocity and the compressional velocity calculated by the method of the present invention in a well.
FIG. 2b is a schematic diagram showing the comparison between the shear velocity calculated by the method of the present invention and the measured shear velocity in a well.
FIG. 3a is a histogram of the error distribution of compressional velocity calculated for a well using the method of the present invention.
FIG. 3b is a histogram of shear wave velocity error distribution calculated for a well using the method of the present invention.
Detailed Description
For a better understanding of the method and the resulting effects of the invention, reference will now be made in detail to the following examples of the accompanying drawings, which are provided for purposes of reference and illustration only and are not intended to limit the invention.
As shown in FIG. 1, the invention constructs a seismic rock physics modeling method aiming at mineral modulus stable prediction.
Through the constructed rock physical model, the conventional logging data, the explanatory logging data and some empirical parameters are combined to predict the longitudinal wave velocity and the transverse wave velocity of the rock.
Wherein, F 1 、F 2 Respectively representing functions of longitudinal wave velocity and transverse wave velocity of the rock calculated through a rock physical model; rho is density; vsh is the argillaceous content; k is sand 、U sand Respectively representing the bulk modulus and the shear modulus of the sandy mineral; k clay 、U clay Respectively representing the bulk modulus and the shear modulus of the argillaceous minerals; r is a radical of hydrogen stiff 、r s o ft Represents the pore aspect ratio of the hard and soft pores, respectively; phi is a stiff 、φ s o ft Denotes the porosity of hard and soft pores, respectively, phi being the total porosity, phi ═ phi stift +φ 5oft (ii) a Sw represents the water saturation;
to predict longitudinal wave velocity;
to predict shear wave velocity; the index i represents the ith sample point in the log data.
According to the formula (2), the shear wave speed predicted by the rock physical model is only related to the shear modulus of the mineral and is not related to the bulk modulus; according to the formula (1), the predicted longitudinal wave velocity is related to both the shear modulus and the bulk modulus of the mineral, and a theoretical basis is provided for calculating the elastic modulus of the mineral step by step. Due to the number of sampling points of logging dataFar greater than the number of minerals contained in the rock, the formulas (1) and (2) become an overdetermined problem about the mineral modulus of the rock, namely K sand 、U sand 、K clay 、U clay For function F 1 、F 2 There is a unique solution.
As shown in the flow chart of fig. 1, the specific steps for predicting the elastic modulus of rock minerals are as follows:
step 1: and giving a certain value range to the mineral modulus. For practical purposes, the bulk and shear moduli of sandy, argillaceous minerals exist with the following upper and lower limits:
taking dense sandstone as an example. Considering the situation that the tight sandstone sand and the argillaceous minerals coexist and the hard holes and the soft holes coexist, the matrix of the tight sandstone is equivalent to a mixture of the sand and the argillaceous materials, and the total pores are divided into the hard holes and the soft holes. Over a range of depths, the elastic modulus of rock matrix minerals can be considered a constant value because the lithology and mineral composition of the formation do not vary much.
And 2, step: giving a soft pore ratio (soft pore porosity phi) s o ft The ratio of the total porosity phi) is determined. For practical purposes, the upper and lower limits for the existence of soft-hole ratios are as follows:
and step 3: according to the intervals given by the formulas (3) and (5), U is given sand 、U clay And
different values are substituted into the formula (2) to calculate the transverse wave velocity. And taking the actually measured shear wave velocity as constraint, and at the moment, solving the shear modulus of the rock mineral is converted into a least square problem. The objective function is defined as:
wherein n is the total number of sampling points in the logging data, the subscript i represents the ith sampling point in the logging data,
predicted shear velocity, V, for the ith sample point in the log data si The measured transverse wave speed of the ith sampling point in the logging data is obtained. When the objective function ψ 1 At the minimum, the selected U at that time is considered sand And U clay Is the optimal solution of the mineral shear modulus,
is the optimal solution of the soft hole ratio. Because a complex nonlinear relation exists between the elastic modulus of the shale and the sandy rock and the rock speed, an optimization method is required to be adopted for solving, and the particle swarm algorithm is adopted in the method (the particle swarm algorithm is the prior art and is not described any more).
And 4, step 4: using the U obtained in the previous step sand 、U clay And
given a range given by the formula (4), K sand And K clay And substituting different values into the formula (1) to calculate the longitudinal wave velocity. And (4) iteratively obtaining the optimal solution of the mineral bulk modulus by taking the actually measured longitudinal wave velocity as a constraint. The objective function is defined as:
wherein the content of the first and second substances,
predicted compressional velocity, V, for the ith sample point in the log data p And i is the measured longitudinal wave speed of the ith sampling point in the logging data. When the objective function ψ 2 At the minimum, the K selected at that time is considered sand And K clay Is the optimal solution of mineral bulk modulus.
And 5: since the change of the microstructure of the rock pore space in the depth is not negligible, the microstructure also needs to be optimized, and the optimal solution of the soft pore ratio at each sampling point on the well is calculated. The soft hole ratio obtained in the step 3 is compared
And (5) taking the optimal solution as an initial value, predicting the longitudinal wave velocity again by combining the optimal solution of the mineral elastic modulus obtained in the steps 3 and 4, and substituting the predicted longitudinal wave velocity into the formula (1) to obtain the longitudinal wave velocity. Solving each sampling point by taking the actually measured longitudinal wave speed as constraint
The optimal solution of (1). The objective function is defined as:
at a sampling point as the target function ψ 3 At the minimum, the selection is considered to be at that time
And the optimal solution of the soft hole ratio at the sampling point is obtained.
And 6: because the processes of obtaining the elastic modulus of the rock mineral and the pore microstructure are preconditions and influence each other, the steps ( steps 3, 4 and 5) are repeated, and the two processes (the process 1: obtaining the elastic modulus of the rock mineral and the process 2: obtaining the pore microstructure (proportion of hard holes and soft holes)) are iteratively solved until the predicted longitudinal wave speed and the predicted transverse wave speed tend to be stable, so that after a plurality of iterations, the predicted longitudinal wave speed is the same and is consistent with the actually measured longitudinal wave speed each time, and the predicted transverse wave speed is the same and is consistent with the actually measured transverse wave speed each time.
The method provided by the invention can be applied to various rock physical models. The final aim of the method is to predict the mineral elastic modulus, and the predicted longitudinal and transverse wave speeds are only used as verification. The closer the predicted speed is to the actually measured speed, the more accurate the mineral modulus selection is proved.
In the method provided by the invention, the rock physical modeling is an important part, and corresponding rock physical models need to be established according to the characteristics of different reservoirs. Taking a tight sandstone reservoir as an example, when analyzing the tight sandstone reservoir, not only the characteristics of low porosity and low permeability of the rock but also the influence of various mineral components, a micro-pore structure and permeability on the elastic parameters of the rock need to be considered. The specific modeling process is as follows:
the first step is as follows: considering the insufficient mixing of the fluids in the pores under the condition of sandstone densification, the bulk modulus K of the fluids is obtained by using the Wood formula and the Patchy formula f 。
First, the bulk modulus of the mixed fluid was calculated using the Wood's equation
Wherein Kw and Kg are respectively the volume modulus of water and gas, and Sw is the water saturation.
Secondly, calculating the bulk modulus of the mixed fluid by using a Patchy model
Finally, the volume modulus K of the mixed fluid is obtained by using Hill average f ：
Secondly, calculating the elastic modulus K of the rock matrix by using the average V-R-H m 、U m 。
First, the upper limit K of the bulk modulus and the shear modulus of the rock matrix is calculated by using the upper limit of Viogt V 、U V ：
Secondly, calculating the lower limit K of the bulk modulus and the shear modulus of the rock matrix by using the lower limit of Reuss R 、U R ：
Finally, the bulk modulus K of the rock matrix is calculated by using the Hill average m And shear modulus U m ：
Thirdly, calculating the elastic modulus K of the saturated rock under the high-frequency condition by utilizing a Chapman model h 、U h 。
Wherein, ω is 0 The angular frequency under the high-frequency condition; k h And U h Expressed as bulk modulus and shear modulus of the high frequency saturated rock, respectively; e is the rock fracture density; phi is a unit of stiff Is the hard pore porosity, which is the difference between the total porosity and the soft pore porosity; lambda [ alpha ] m Is the Lame constant in the elastic parameters of the rock matrix; τ is a time scale factor, related to rock permeability and fluid viscosity. r is the soft pore aspect ratio; the expressions for the other parameters are:
fourthly, calculating the high-frequency matrix modulus K of the rock by using an iteration method by utilizing an SCA model sc 、U sc 。
(1-φ)(K h -K sc )P *m +φ stiff (K f -K sc )P *stiff +φ soft (K f -K sc )P *soft ＝0 (28)
(1-φ)(U h -U sc )Q *m +φ stiff (U f -U sc )Q *stiff +φ soft (U f -U sc )Q *soft ＝0 (29)
Wherein, U f Shear modulus of inclusions in the pores; p and Q are geometric factors related to pore structure.
Fifthly, calculating the equivalent modulus K of the rock in the logging frequency band by using the Chapman model again eff 、U eff 。
Wherein, ω is 1 The angular frequency of the logging band.
And sixthly, calculating the longitudinal wave velocity and the transverse wave velocity of the compact sandstone.
There are many methods of modeling tight sandstone reservoirs, one of which is cited herein. Various rock physical models can be used for stably predicting the mineral elastic modulus by using the method provided by the invention.
A well in a work area is selected, petrophysical modeling is carried out by using the method disclosed by the invention, the estimation of the elastic modulus of the mineral is completed, and the result is shown in a table 1.
TABLE 1 prediction of mineral modulus for a well
| Parameter(s) | Sand texture | Argillaceous material |
| Bulk modulus/Gpa | 35.0 | 24.9 |
| Shear modulus/Gpa | 34.5 | 8.6 |
The accuracy of the prediction modulus was verified from the calculation results of the longitudinal wave velocity and the transverse wave velocity, and the results are shown in fig. 2a, fig. 2b, fig. 3a, and fig. 3b, in which the predicted longitudinal wave velocity and the predicted transverse wave velocity are indicated by broken lines and the actually measured longitudinal wave velocity and transverse wave velocity are indicated by solid lines. As can be seen from the figure, the method of the invention can predict the mineral modulus of the rock very accurately, which proves the rationality and correctness of the method of the invention.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitutions or changes made by the person skilled in the art on the basis of the present invention are all within the protection scope of the present invention.
Claims (3)
1. A seismic petrophysical modeling method for mineral modulus stability prediction is characterized by comprising the following steps:
the function for setting the petrophysical model is:
in the formulas (1) and (2), F 1 、F 2 Respectively representing functions of calculating longitudinal wave velocity and transverse wave velocity of the rock through the rock physical model; rho is density; vsh is the argillaceous content; k sand 、U sand Respectively representing the bulk modulus and the shear modulus of the sandy mineral; k is clay 、U clay Respectively representing the bulk modulus and the shear modulus of the argillaceous minerals; r is stiff 、r soft Represents the pore aspect ratio of the hard and soft pores, respectively; phi is a stiff 、φ soft Denotes the porosity of hard and soft pores, respectively, phi being the total porosity, phi ═ phi stift +φ soft (ii) a Sw represents the water saturation;
to predict longitudinal wave velocity;
to predict shear wave velocity; subscript i represents the ith sample point in the log data;
step 1: determining the value ranges of the volume modulus and the shear modulus of the sandy minerals and the volume modulus and the shear modulus of the argillaceous minerals;
step 2: determining the value range of the soft pore ratio, wherein the soft pore ratio is the ratio of the porosity of the soft pores to the total porosity;
and step 3: selecting different values of the shear modulus of the sandy minerals, the shear modulus of the argillaceous minerals and the soft pore ratio determined in the step (2) according to the value ranges of the shear modulus of the sandy minerals and the shear modulus of the argillaceous minerals and the value range of the soft pore ratio determined in the step (2) and substituting the values into the formula (2);
defining an objective function psi by using the measured transverse wave velocity as a constraint 1 Comprises the following steps:
objective function psi 1 Wherein n is the total number of sampling points in the logging data, the subscript i represents the ith sampling point in the logging data,
the predicted transverse wave speed of the ith sampling point in the logging data is obtained, Vsi is the measured transverse wave speed of the ith sampling point in the logging data, and when the target function psi 1 When the shear modulus of the selected sandy minerals and the shear modulus of the argillaceous minerals are the optimal solutions of the shear modulus of the sandy minerals and the shear modulus of the argillaceous minerals, and the selected soft pore proportion value is the optimal solution of the soft pore proportion;
and 4, step 4: selecting the volume modulus of the sandy minerals and different values of the volume modulus of the argillaceous minerals according to the value ranges of the volume modulus of the sandy minerals and the volume modulus of the argillaceous minerals determined in the step 1 by using the shear modulus of the sandy minerals, the optimal solution of the shear modulus of the argillaceous minerals and the optimal solution of the soft pore proportion obtained in the step 3, and substituting the values into the formula (1);
defining an objective function as psi by using the measured longitudinal wave velocity as a constraint 2 ：
Objective function psi 2 In (1),
the predicted longitudinal wave velocity of the ith sampling point in the logging data is obtained, Vpi is the measured longitudinal wave velocity of the ith sampling point in the logging data, and when the objective function psi 2 When the minimum value is reached, the selected value of the bulk modulus of the sandy mineral and the value of the bulk modulus of the argillaceous mineral are the optimal solutions of the bulk modulus of the sandy mineral and the bulk modulus of the argillaceous mineral;
and 5: taking the optimal solution of the soft pore ratio obtained in the step 3 as an initial value, and substituting the optimal solution of the shear modulus of the sandy minerals and the shear modulus of the argillaceous minerals, the optimal solution of the volume modulus of the sandy minerals and the optimal solution of the volume modulus of the argillaceous minerals, which are obtained in the steps 3 and 4, into a formula (1);
defining an objective function psi using the measured longitudinal wave velocity as a constraint 3 Comprises the following steps:
solving the optimal solution of the soft hole ratio at each sampling point in the logging data;
and 6: and repeating the steps 3, 4 and 5, carrying out iterative solution, and stably predicting the volume modulus and the shear modulus of the sandy minerals and the volume modulus and the shear modulus of the argillaceous minerals when the predicted longitudinal wave speed is the same and is consistent with the actually measured longitudinal wave speed and the predicted transverse wave speed is the same and is consistent with the actually measured transverse wave speed after a plurality of iterations.
2. The method of seismic petrophysical modeling with stable prediction of mineral modulus of claim 1,
the value ranges of the volume modulus and the shear modulus of the sandy minerals are as follows:
33.0GPa≤U sand ≤45.6GPa
7.0GPa≤U clay ≤9.0GPa (3)；
the value ranges of the volume modulus and the shear modulus of the argillaceous minerals are as follows:
34.0GPa≤K sand ≤39.0GPa
21.0GPa≤K clay ≤25.0GPa (4)。
3. the method for seismic petrophysical modeling for mineral modulus stationary prediction according to claim 1 or 2,
the soft hole ratio has the following value range:
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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