阅读说明：本技术 深埋储层孔隙度反演方法及装置 (Deep-buried reservoir porosity inversion method and device ) 是由 田军 凌东明 刘永雷 姚仙洲 白建朴 陈建功 于 2019-08-22 设计创作，主要内容包括：本发明提供了一种深埋储层孔隙度反演方法及装置,其中该方法包括：基于泰勒级数展开,对待测深埋储层的岩石物理模型进行线性近似化；根据线性近似化后的待测深埋储层的岩石物理模型,确定纵波速度、横波速度与孔隙度、泥质含量的线性关系；根据纵波速度、横波速度与孔隙度、泥质含量的线性关系,构建孔隙度反演目标函数；求解所述孔隙度反演目标函数,确定待测深埋储层的孔隙度。本发明确定纵波速度、横波速度与孔隙度、泥质含量的线性关系,在线性关系的基础上构建孔隙度反演目标函数并求解,能够提高计算精度,得到唯一解,适用于深埋储层的孔隙度反演,为深埋油藏开发方案调整及井位优选、部署提供有力依据。(The invention provides a deep-buried reservoir porosity inversion method and a deep-buried reservoir porosity inversion device, wherein the method comprises the following steps: performing linear approximation on the rock physical model of the deep reservoir to be detected based on Taylor series expansion; determining linear relations between longitudinal wave velocity and transverse wave velocity and porosity and shale content according to the linearly approximated rock physical model of the deep buried reservoir to be detected; constructing a porosity inversion target function according to linear relations between longitudinal wave velocity and transverse wave velocity and porosity and shale content; and solving the porosity inversion target function, and determining the porosity of the deep buried reservoir to be detected. The method determines the linear relation between the longitudinal wave velocity and the transverse wave velocity and the porosity and the shale content, constructs the porosity inversion objective function on the basis of the linear relation and solves the porosity inversion objective function, can improve the calculation precision, obtains a unique solution, is suitable for the porosity inversion of a deep reservoir, and provides a powerful basis for the adjustment of a deep reservoir development scheme and the optimization and deployment of well positions.)

1. A deep-buried reservoir porosity inversion method is characterized by comprising the following steps:

performing linear approximation on the rock physical model of the deep reservoir to be detected based on Taylor series expansion;

determining linear relations between longitudinal wave velocity and transverse wave velocity and porosity and shale content according to the linearly approximated rock physical model of the deep buried reservoir to be detected;

constructing a porosity inversion target function according to linear relations between longitudinal wave velocity and transverse wave velocity and porosity and shale content;

and solving the porosity inversion target function, and determining the porosity of the deep buried reservoir to be detected.

2. The method of claim 1, further comprising:

determining the position of the target interval in the time dimension through well seismic calibration, and extracting the plane data of the porosity of the target interval according to the porosity of the deeply buried reservoir, wherein the target interval is the reservoir included in the deeply buried reservoir;

and determining a high-quality reservoir in the target interval according to the plane data of the porosity of the target interval, and predicting the plane distribution rule of the high-quality reservoir.

3. The method according to claim 1 or 2, wherein the linear relation between the compressional wave velocity and the shear wave velocity and the porosity and the shale content is determined according to the petrophysical model of the deep-buried reservoir to be tested after linear approximation, and the method comprises the following steps:

and under the condition that the linearly approximated rock physical model of the deep buried reservoir to be detected is the deleted density parameter and the water saturation is a known quantity, determining the linear relation between the longitudinal wave velocity and the transverse wave velocity and the porosity and the shale content.

4. The method of claim 1 or 2, wherein the porosity inversion objective function is:

wherein L represents a porosity inversion objective function;

C_ea covariance matrix representing the error vector e; c_mA covariance matrix representing the model vector m;

m₀the average value of the porosity and the shale content of the representative deep-buried reservoir is obtained by logging statistics;

V_prepresenting the longitudinal wave velocity of a deep-buried reservoir; v_sRepresenting the transverse wave velocity of a deep-buried reservoir;

F₁、F₂、α_p、β_p、γ_p、α_s、β_s、γ_srepresenting a known coefficient term, and obtaining the known coefficient term by the rock physical model of the deep-buried reservoir to be tested and Taylor series expansion;

δ_p、δ_sthe representative error term is obtained by the rock physical model of the deep buried reservoir to be detected and Taylor series expansion;

phi represents porosity;represents the argillaceous content; s_wRepresenting the water saturation.

5. The method of claim 1 or 2, wherein solving the porosity inversion objective function to determine the deep-buried reservoir porosity comprises:

and circularly executing the following iterative inversion steps to determine the porosity of the deep-buried reservoir:

when the water saturation is a first set value, the porosity and the shale content are obtained through inversion, and when the water saturation is a second set value, the porosity and the shale content are obtained through inversion;

obtaining forward compressional velocity and forward shear velocity when the water saturation is a first set value according to the inversion result of the porosity and the shale content when the water saturation is the first set value and the linear relation; and obtaining forward compressional velocity and forward shear velocity when the water saturation is a second set value according to the inversion result of the porosity and the argillaceous content when the water saturation is the second set value and the linear relation;

determining a longitudinal wave correlation coefficient under a set value according to an actually measured longitudinal wave velocity and a forward longitudinal wave velocity under the set value when the water saturation is a first set value or a first set value respectively; determining a transverse wave correlation coefficient under a set value according to an actually measured transverse wave speed and a forward transverse wave speed under the set value when the water saturation is a first set value or a first set value respectively;

respectively aiming at the water saturation which is a first set value or a second set value, taking the average value of the longitudinal wave correlation coefficient and the transverse wave correlation coefficient under the set value to obtain the average correlation coefficient under the set value;

carrying out dichotomy operation on a first set value of the water saturation and a second set value of the water saturation to obtain a third set value of the water saturation, and calculating to obtain an average correlation coefficient when the water saturation is the third set value;

when the water saturation is a third set value, obtaining the porosity through inversion, and taking the porosity as an inversion output result;

determining two set values corresponding to the larger average correlation coefficients in the average correlation coefficients at different set values, and respectively using the two determined set values as a first set value of the water saturation and a second set value of the water saturation of the next iteration;

if the iteration times exceed the preset maximum iteration times, stopping, and outputting an inversion output result at the stopping as the porosity of the deep buried reservoir; if the preset maximum iteration times are larger than the current iteration times and the inversion output result meets the convergence condition, stopping, wherein the inversion output result at the stopping is the porosity of the deep buried reservoir to be detected; otherwise, entering the next iteration.

6. A deep-buried reservoir porosity inversion apparatus, comprising:

the approximation module is used for carrying out linear approximation on the rock physical model of the deep buried reservoir to be detected based on Taylor series expansion;

the linear relation determining module is used for determining linear relations between longitudinal wave velocity and transverse wave velocity and porosity and shale content according to the rock physical model of the deep buried reservoir to be detected after linear approximation;

the inversion target function determination module is used for constructing a porosity inversion target function according to the linear relation between the longitudinal wave velocity and the transverse wave velocity and the porosity and the shale content;

and the solving module is used for solving the porosity inversion target function and determining the porosity of the deep reservoir to be detected.

7. The apparatus of claim 6, further comprising:

the application module is used for determining the position of the target interval in the time dimension through fine well seismic calibration and extracting the plane data of the porosity of the target interval according to the porosity of the deep-buried reservoir, wherein the target interval is the reservoir included in the deep-buried reservoir;

and determining a high-quality reservoir in the target interval according to the plane data of the porosity of the target interval, and predicting the plane distribution rule of the high-quality reservoir.

8. The apparatus of claim 6 or 7, wherein the linear relationship determination module is specifically configured to:

and under the condition that the linearly approximated rock physical model of the deep buried reservoir to be detected is the deleted density parameter and the water saturation is a known quantity, determining the linear relation between the longitudinal wave velocity and the transverse wave velocity and the porosity and the shale content.

9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 5.

Technical Field

The invention relates to the technical field of electromagnetic exploration and development of petroleum and natural gas, in particular to a method and a device for inverting the porosity of a deep-buried reservoir.

Background

With the shift of a large number of oil fields from an exploration phase to a development phase, the precision requirement on the oil reservoir description is higher and higher, wherein the reservoir physical property prediction plays a very important role in the oil reservoir development phase and mainly shows two aspects: firstly, the physical property of a reservoir directly reflects the quality of the reservoir, and can guide the optimization of a development well pattern and support the deployment of a development well position; and secondly, the physical property of the reservoir reflects the connectivity of the reservoir, determines the dominant direction of water injection effect, and is an important basis for adjusting a development scheme.

Porosity is a key parameter that indicates the quality of the reservoir properties. At home and abroad, two main measures for the quantitative prediction of porosity are provided: empirical formula fitting and porosity inversion. The empirical formula fitting method is to fit the logging speed curve and the porosity curve to obtain an empirical formula, and the empirical formula is applied to calculation of the spatial porosity body. The method is simple to apply and easy to realize, but in reality, the speed and the porosity are not in a single mapping relation and are also related to physical parameters such as the shale content, the water saturation and the like, so the method has low calculation precision and relatively poor applicability. The porosity inversion method is based on a rock physical model, and utilizes elastic parameters such as longitudinal wave velocity and transverse wave velocity to invert physical parameters such as porosity and shale content. At present, the method mostly uses a nonlinear rock physical model as a basis to construct an inversion objective function, and adopts a nonlinear optimization algorithm to solve, such as Monte Carlo, simulated annealing and the like, but the algorithm has strong multi-solution property and huge calculation amount, so that the application of the method in actual production is limited.

In the past few years, some experts and scholars at home and abroad develop related research works, in 2016, Grana provides a rock physical model linear approximation method, so that the linearized inversion of the reservoir porosity is realized, the calculated amount and the inversion multi-solution are greatly reduced, and a foundation is laid for the popularization and the application of the reservoir porosity in actual production. The specific implementation process can be summarized into two steps: firstly, performing linear approximation on a rock physical model by using Taylor series expansion; and secondly, constructing an objective function based on a rock physical model linear approximate general formula, and solving the porosity by adopting a linear inversion algorithm. However, the target function formula comprises two equations and three unknowns, which are an underdetermined equation set, and the porosity inversion is carried out on the basis of the underdetermined equation set, so that a unique solution cannot be obtained, and the application of the current linear inversion method in the deep-buried oil reservoir is limited.

In summary, it is necessary to provide a porosity inversion method, which is suitable for the condition of deep reservoir.

Disclosure of Invention

The embodiment of the invention provides a deep-buried reservoir porosity inversion method, which is used for improving the calculation precision and obtaining a unique solution of porosity and is suitable for the porosity inversion of a deep-buried reservoir, and the method comprises the following steps:

performing linear approximation on the rock physical model of the deep reservoir to be detected based on Taylor series expansion;

determining linear relations between longitudinal wave velocity and transverse wave velocity and porosity and shale content according to the linearly approximated rock physical model of the deep buried reservoir to be detected;

constructing a porosity inversion target function according to linear relations between longitudinal wave velocity and transverse wave velocity and porosity and shale content;

and solving the porosity inversion target function, and determining the porosity of the deep buried reservoir to be detected.

The embodiment of the invention also provides a deep-buried reservoir porosity inversion device, which is used for improving the calculation precision and obtaining the only solution of the porosity and is suitable for the porosity inversion of the deep-buried reservoir, and the device comprises:

the approximation module is used for carrying out linear approximation on the rock physical model of the deep buried reservoir to be detected based on Taylor series expansion;

the linear relation determining module is used for determining linear relations between longitudinal wave velocity and transverse wave velocity and porosity and shale content according to the rock physical model of the deep buried reservoir to be detected after linear approximation;

the inversion target function determination module is used for constructing a porosity inversion target function according to the linear relation between the longitudinal wave velocity and the transverse wave velocity and the porosity and the shale content;

and the solving module is used for solving the porosity inversion target function and determining the porosity of the deep reservoir to be detected.

The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the deep-buried reservoir porosity inversion method.

Embodiments of the present invention also provide a computer-readable storage medium storing a computer program for executing the above-mentioned deep-buried reservoir porosity inversion method.

In the embodiment of the invention, the linear relation of the longitudinal wave velocity, the transverse wave velocity, the porosity and the shale content is determined, the porosity inversion target function is constructed and solved on the basis of the linear relation, and the linear relation does not relate to the density parameter and the water saturation because the target function is established on the basis of the linear relation of the longitudinal wave velocity, the transverse wave velocity, the porosity and the shale content, namely the establishment of the target function does not change along with the change of the density parameter and the water saturation, so that the calculation precision can be improved, the unique solution is obtained, the method is suitable for the porosity inversion of a deep-buried reservoir, and a powerful basis is provided for the adjustment of a deep-buried reservoir development scheme, the well location optimization and the deployment.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic diagram of an inversion method of the porosity of a deep-buried reservoir in the embodiment of the invention.

Fig. 2 is a schematic diagram of a method for iteratively solving the porosity inversion objective function in the embodiment of the present invention.

Fig. 3 is a schematic diagram of another embodiment of a method for inversion of porosity of a deep-buried reservoir in an embodiment of the present invention.

Fig. 4 is a diagram comparing the forward rock physics modeling results in the embodiment of the invention.

FIG. 5 is a flow chart of iterative inversion of porosity in an embodiment of the present invention.

Fig. 6 is a schematic diagram of a deep-buried reservoir porosity inversion apparatus according to an embodiment of the present invention.

Fig. 7 is a schematic diagram of another embodiment of a deep-buried reservoir porosity inversion apparatus in an embodiment of the invention.

Detailed Description

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.

In order to solve the problems of low calculation precision, strong multi-solution, large calculation amount and unsuitability for a deeply buried reservoir in the existing quantitative prediction technology of porosity, the embodiment of the invention provides a method for inverting the porosity of the deeply buried reservoir, which is used for improving the calculation precision, obtaining a unique solution of the porosity and being applicable to the inversion of the porosity of the deeply buried reservoir, and as shown in fig. 1, the method comprises the following specific steps:

step 101: performing linear approximation on the rock physical model of the deep reservoir to be detected based on Taylor series expansion;

step 102: determining linear relations between longitudinal wave velocity and transverse wave velocity and porosity and shale content according to the linearly approximated rock physical model of the deep buried reservoir to be detected;

step 103: constructing a porosity inversion target function according to linear relations between longitudinal wave velocity and transverse wave velocity and porosity and shale content;

step 104: and solving the porosity inversion target function, and determining the porosity of the deep buried reservoir to be detected.

It can be known from the process shown in fig. 1 that, in the embodiment of the present invention, a porosity inversion objective function is constructed and solved on the basis of a linear relationship by determining the linear relationship between the longitudinal wave velocity, the transverse wave velocity and the porosity, and the shale content, and the linear relationship does not relate to the density parameter and the water saturation because the objective function is established based on the linear relationship between the longitudinal wave velocity, the transverse wave velocity and the porosity, and the shale content, i.e., the establishment of the objective function does not change with the change of the density parameter and the water saturation, so that the calculation accuracy can be improved, a unique solution is obtained, the method is suitable for the porosity inversion of a deep-buried reservoir, and a powerful basis is provided for the adjustment of a deep-buried reservoir development scheme, the optimization of well location, and the deployment.

During specific implementation, firstly, based on Taylor series expansion, linear approximation is carried out on a petrophysical model of a deep-buried reservoir to be detected, as shown in formula (1):

wherein, V_pRepresenting the longitudinal wave velocity of a deep-buried reservoir; v_sRepresenting the transverse wave velocity of a deep-buried reservoir;

ρ represents the reservoir density;

F₁、F₂、F₃、α_p、β_p、γ_p、α_s、β_s、γ_s、α_r、β_r、γ_rrepresenting a known coefficient term, and obtaining the known coefficient term by the rock physical model of the deep-buried reservoir to be tested and Taylor series expansion;

phi represents porosity;represents the argillaceous content; s_wRepresents the water saturation;

δ_p、δ_s、δ_rand the representative error term is obtained by the rock physical model of the deep-buried reservoir to be detected and Taylor series expansion.

The method comprises the steps of obtaining a linear approximation rock physical model of a deep buried reservoir to be detected, wherein the reservoir density is obtained by utilizing pre-stack seismic synchronous inversion, but under the condition of deep buried reservoir, the reflection angle of trace set data required by pre-stack inversion is small, and the minimum angle range required by density inversion cannot be reached, so that the density inversion result is unreliable. Therefore, the reservoir density parameter in the rock physical model of the deep buried reservoir to be detected after linear approximation is deleted, as shown in formula (2):

the inventor of the application finds that in the prior art, an inversion target function is directly constructed on the basis of the formula (2) for inversion, so that the method has strong multi-solution. However, for an oil reservoir (only containing an oil-water two-phase fluid medium), the influence of the porosity change on the longitudinal wave velocity and the transverse wave velocity of a deeply buried reservoir is the largest, the shale content is the second order, and the water saturation is the smallest, so that the inventor of the present application proposes that the linear relationship between the longitudinal wave velocity and the transverse wave velocity and the porosity and the shale content can be obtained under the condition that the density parameter in the petrophysical model of the deeply buried reservoir to be tested after the linear approximation is deleted and the water saturation is regarded as a known quantity, and the linear relationship can be expressed by a model as shown in formula (3):

those skilled in the art can understand that there are various expression modes of the linear relationship between the longitudinal wave velocity and the transverse wave velocity and the porosity and the shale content, and the expression mode may also be a formula form, and a required expression mode is selected according to an actual situation, the expression model is only an example, and the model may be deformed during implementation, or other models, formulas or methods are adopted to express the linear relationship between the longitudinal wave velocity and the transverse wave velocity and the porosity and the shale content, and the models, formulas or methods all fall within the protection scope of the present invention, and are not described in detail in the embodiments.

According to the linear relation between the longitudinal wave velocity and the transverse wave velocity shown in the formula (3) and the porosity and the shale content, a porosity inversion target function can be constructed on the basis of a Bayes theory, and the method comprises the following specific steps:

for the convenience of derivation, can orderEquation (3) can be abbreviated as follows:

d＝G·m+e (4)

the error vector e and the model vector m are subjected to Gaussian distribution, and a posterior probability density distribution function approximation formula is constructed based on a formula (4) according to a Bayesian theory, wherein the formula (5) is as follows:

wherein const represents a constant coefficient;

C_ea covariance matrix representing the error vector e; c_mA covariance matrix representing the model vector m;

m₀the mean value of the porosity and the shale content of the representative deep-buried reservoir is obtained by logging statistics, and is used as prior information together with the covariance matrix to constrain the solving process.

Solving the maximum value of the probability of equation (5) is equivalent to solving the minimum value of equation (6), and L represents the porosity inversion objective function, as follows:

solving the porosity inversion objective function to obtain a solutionObtaining a least squares solution as shown in formula (7):

after the porosity inversion result shown in the formula (7) is obtained, as the porosity and shale content inversion result has stronger fault-tolerant capability on water saturation for an oil reservoir, when constant water saturation is given, a relatively reliable inversion result can be obtained, the closer to the real water saturation mean value, the higher the inversion precision is, so that the porosity inversion target function is iteratively solved by utilizing a dichotomy to determine the porosity of the deep buried reservoir to be detected, and the specific steps are as follows:

step 201: when the water saturation is a first set value, the porosity and the shale content are obtained through inversion, and when the water saturation is a second set value, the porosity and the shale content are obtained through inversion;

step 202: obtaining forward compressional velocity and forward shear velocity when the water saturation is a first set value according to the inversion result of the porosity and the shale content when the water saturation is the first set value and the linear relation; and obtaining forward compressional velocity and forward shear velocity when the water saturation is a second set value according to the inversion result of the porosity and the argillaceous content when the water saturation is the second set value and the linear relation;

step 203: determining a longitudinal wave correlation coefficient under a set value according to an actually measured longitudinal wave velocity and a forward longitudinal wave velocity under the set value when the water saturation is a first set value or a second set value respectively; determining a transverse wave correlation coefficient under a set value according to an actually measured transverse wave speed and a forward transverse wave speed under the set value when the water saturation is a first set value or a first set value respectively;

step 204: respectively aiming at the water saturation which is a first set value or a first set value, taking the average value of the longitudinal wave correlation coefficient and the transverse wave correlation coefficient under the set value to obtain the average correlation coefficient under the set value;

step 205: carrying out dichotomy operation on a first set value of the water saturation and a second set value of the water saturation to obtain a third set value of the water saturation, and calculating to obtain an average correlation coefficient when the water saturation is the third set value;

step 206: when the water saturation is a third set value, obtaining the porosity through inversion, and taking the porosity as an inversion output result;

step 207: determining two set values corresponding to the larger average correlation coefficients in the average correlation coefficients at different set values, and respectively using the two determined set values as a first set value of the water saturation and a second set value of the water saturation of the next iteration;

step 208: if the iteration times exceed the preset maximum iteration times, stopping, and outputting an inversion output result at the stopping as the porosity of the deep buried reservoir; if the preset maximum iteration times are larger than the current iteration times and the inversion output result meets the preset convergence error, stopping, wherein the inversion output result at the stopping is the porosity of the deep buried reservoir to be detected; otherwise, entering the next iteration.

In the embodiment of the invention, the first set value s of the water saturation is initially set₁Take 0, the second set value s₂Taking 1, e.g. effective water saturation vector S_{w_v}＝[s₁；s₂]Is S_{w_v}＝[0；1]. Those skilled in the art can appreciate that the above values are merely examples, and can be adjusted according to actual situations, and are not described in detail here.

In an embodiment, the compressional wave correlation coefficient can be determined by the following formula:

wherein, γ_vpRepresenting the correlation coefficient of longitudinal waves; i represents a sampling point index; n represents the total number of sampling points;

Vp_mrepresenting the forward compressional velocity;an average value representing the forward longitudinal wave velocity;

vp represents the measured longitudinal wave velocity;the average value of the measured longitudinal wave velocity is shown.

The shear wave correlation coefficient can be determined by the following formula:

wherein, γ_vsRepresenting the correlation coefficient of the transverse wave; i represents a sampling point index; n represents the total number of sampling points;

Vs_mrepresenting a forward transverse wave velocity;an average value representing a forward transverse wave velocity;

vs represents the measured longitudinal wave velocity;the average value of the measured shear wave velocity is shown.

After the compressional wave correlation coefficient and the shear wave correlation coefficient are obtained, the average correlation coefficient can be determined according to the following formula:

γ_v＝(γ_vp+γ_vs)/2 (10)

the average correlation coefficient at initial water saturation in an embodiment of the invention may be expressed as gamma_v0，γ_v1。

Then, a binary calculation is performed to obtain S_{w_2}＝mean(S_{w_v}) As third setting of water saturationAnd (5) fixing the value, and calculating to obtain an average correlation coefficient when the water saturation is a third set value. In the embodiments of the present invention, it is abbreviated as γ₂. And when the water saturation is a third set value, inverting to obtain the porosity as an inversion output result.

Contrast gamma₀，γ₁，γ₂Determining two correlation coefficients with larger values and corresponding set values of water saturation as a first set value of water saturation and a second set value of water saturation of the next iteration, for example if gamma is₂＞γ₀＞γ₁S in the next round of inversion_{w_v}＝[0；S_{w_rand}]。

Judging whether cutoff is performed: if the iteration times exceed the preset maximum iteration times, stopping, and outputting an inversion output result at the stopping as the porosity of the deep buried reservoir to be detected; if the preset maximum iteration times are larger than the current iteration times and the inversion output result meets the convergence condition, stopping, wherein the inversion output result at the stopping is the porosity of the deep buried reservoir; otherwise, entering the next iteration.

In a specific implementation, the convergence condition may be an average correlation coefficient γ when the water saturation in the iteration is a third set value₂The difference value with the preset target correlation coefficient is smaller than the preset convergence error. The preset target correlation coefficient, the preset convergence error and the maximum iteration number can be preset values before inversion starts, and the preset target correlation coefficient can be determined according to laboratory tests or historical data researches, for example, the preset target correlation coefficient can be 0.999, the convergence error can be 0.001, and the maximum iteration number can be 1000. Those skilled in the art can understand that the above values are only examples, and any values can be taken as required, and are not described here again.

And continuously carrying out the iterative inversion steps until the iterative inversion steps are cut off, and determining the porosity of the deep-buried reservoir to be detected.

In order to determine the porosity of the deep-buried reservoir more accurately, the method for inverting the porosity of the deep-buried reservoir in another embodiment of the present invention, as shown in fig. 3, further includes, on the basis of fig. 1:

step 301: selecting an applicable rock physical model to carry out modeling work, and determining rock physical parameters of the to-be-tested deep buried reservoir to obtain the rock physical model of the to-be-tested deep buried reservoir;

step 302: and performing transverse wave estimation according to the rock physical model of the deep buried reservoir to be detected, and performing prestack elastic inversion by using amplitude and fidelity prestack gather data to obtain a reliable longitudinal wave velocity body and a reliable transverse wave velocity body.

In specific implementation, selecting an appropriate petrophysical model to perform modeling specifically includes:

based on logging statistics and experimental data and an applicable basic rock physical model, giving rock physical basic parameters of each component of saturated rock;

by adjusting the transverse-longitudinal ratio of the pores, the rock physics forward longitudinal and transverse wave speed and the measured speed can achieve the best fit.

In the specific embodiment, fig. 4 shows a comparison between the forward transverse wave velocity and the measured velocity of the rock physics after the transverse-to-longitudinal ratio of the pore is adjusted. The best fit can be achieved by adjusting the transverse-to-longitudinal ratio of the pores, so that the forward transverse-to-longitudinal wave velocity of the petrophysical is close to the actual measurement velocity as much as possible, and the forward transverse-to-longitudinal wave velocity is reflected in fig. 4 that the two curves are overlapped as much as possible.

In a specific embodiment, as shown in fig. 5, an iterative inversion flowchart is obtained, and the petrophysical parameters of the deep buried reservoir to be tested determined in step 301 and the longitudinal wave velocity data and the shear wave velocity data in the longitudinal wave velocity body and the shear wave velocity body obtained in step 302 are substituted into the deep buried reservoir porosity inversion method shown in fig. 1, and the first set value of the initial saturation is set to be 0, and the second set value is set to be 1, so that iteration is performed. If the inversion output result meets the convergence condition, outputting; if not, calculating the average value of the saturation set values, and then carrying out next inversion until the iterative inversion result meets the convergence condition and then outputting.

In actual exploitation, according to reservoir development characteristics and oil and gas production conditions, high-porosity and high-permeability zones in low-porosity and ultra-low-permeability reservoirs are called high-quality reservoirs, in order to better exploit oil reservoirs, the high-quality reservoirs need to be analyzed in advance, and in specific implementation, the deep-buried reservoir porosity inversion method further comprises the following steps on the basis of fig. 1 or fig. 2:

determining the position of the target interval in the time dimension through well seismic calibration, and extracting the plane data of the porosity of the target interval according to the porosity of the deeply buried reservoir, wherein the target interval is the reservoir included in the deeply buried reservoir;

and determining a high-quality reservoir in the target interval according to the plane data of the porosity of the target interval, and predicting the plane distribution rule of the high-quality reservoir.

By determining the high-quality reservoir in the target interval and predicting the plane distribution rule of the high-quality reservoir, a powerful basis is provided for adjustment of a deep-buried oil reservoir development scheme and well position optimization and deployment.

Based on the same inventive concept, embodiments of the present invention further provide a deep-buried reservoir porosity inversion apparatus, and since the principle of the problem solved by the deep-buried reservoir porosity inversion apparatus is similar to that of the deep-buried reservoir porosity inversion method, the implementation of the deep-buried reservoir porosity inversion apparatus can refer to the implementation of the deep-buried reservoir porosity inversion method, and the repeated parts are not repeated, and the specific structure is shown in fig. 6:

the approximation module 601 is used for performing linear approximation on the petrophysical model of the deep buried reservoir to be tested based on Taylor series expansion;

the linear relation determining module 602 is configured to determine linear relations between longitudinal wave velocity and transverse wave velocity and porosity and shale content according to the linearly approximated rock physical model of the deep-buried reservoir to be detected;

an inversion target function determination module 603, configured to construct a porosity inversion target function according to linear relationships between the longitudinal wave velocity and the transverse wave velocity and the porosity and the shale content;

and a solving module 604, configured to solve the porosity inversion objective function, and determine the porosity of the deep reservoir to be detected.

In an embodiment, the linear relationship determining module 602 is specifically configured to determine linear relationships between longitudinal wave velocity and transverse wave velocity and porosity and shale content when the linearly approximated petrophysical model of the deep-buried reservoir to be detected is a deletion density parameter and the water saturation is a known quantity.

In an embodiment, the inversion target function determining module 603 is specifically configured to construct a porosity inversion target function based on a bayesian theory according to a linear relationship between a longitudinal wave velocity and a transverse wave velocity and porosity and shale content, as follows:

wherein L represents a porosity inversion objective function;

C_ea covariance matrix representing the error vector e; c_mA covariance matrix representing the model vector m;

m₀the average value of the porosity and the shale content of the representative deep-buried reservoir is obtained by logging statistics;

V_prepresenting the longitudinal wave velocity of a deep-buried reservoir; v_sRepresenting the transverse wave velocity of a deep-buried reservoir;

F₁、F₂、α_p、β_p、γ_p、α_s、β_s、γ_srepresenting a known coefficient term, and obtaining the known coefficient term by the rock physical model of the deep-buried reservoir to be tested and Taylor series expansion;

δ_p、δ_sthe representative error term is obtained by the rock physical model of the deep buried reservoir to be detected and Taylor series expansion;

phi represents porosity;represents the argillaceous content; s_wRepresenting the water saturation.

In a specific embodiment, the solving module 604 is specifically configured to:

circularly executing the following iterative inversion steps to determine the porosity of the deep buried reservoir to be detected:

when the water saturation is a first set value, the porosity and the shale content are obtained through inversion, and when the water saturation is a second set value, the porosity and the shale content are obtained through inversion;

obtaining forward compressional velocity and forward shear velocity when the water saturation is a first set value according to the inversion result of the porosity and the shale content when the water saturation is the first set value and the linear relation; and obtaining forward compressional velocity and forward shear velocity when the water saturation is a second set value according to the inversion result of the porosity and the argillaceous content when the water saturation is the second set value and the linear relation;

determining a longitudinal wave correlation coefficient under a set value according to an actually measured longitudinal wave velocity and a forward longitudinal wave velocity under the set value when the water saturation is a first set value or a first set value respectively; determining a transverse wave correlation coefficient under a set value according to an actually measured transverse wave speed and a forward transverse wave speed under the set value when the water saturation is a first set value or a first set value respectively;

respectively aiming at the water saturation which is a first set value or a second set value, taking the average value of the longitudinal wave correlation coefficient and the transverse wave correlation coefficient under the set value to obtain the average correlation coefficient under the set value;

carrying out dichotomy operation on a first set value of the water saturation and a second set value of the water saturation to obtain a third set value of the water saturation, and calculating to obtain an average correlation coefficient when the water saturation is the third set value;

when the water saturation is a third set value, obtaining the porosity through inversion, and taking the porosity as an inversion output result;

determining two set values corresponding to the larger average correlation coefficients in the average correlation coefficients at different set values, and respectively using the two determined set values as a first set value of the water saturation and a second set value of the water saturation of the next iteration;

if the iteration times exceed the preset maximum iteration times, stopping, and outputting an inversion output result at the stopping as the porosity of the deep buried reservoir; and if the preset maximum iteration times are larger than the current iteration times and the inversion output result meets the convergence condition, stopping, and if the inversion output result at the stopping is the porosity of the deep buried reservoir to be detected, entering the next iteration.

In order to determine the deep-buried reservoir porosity more accurately, the deep-buried reservoir porosity inversion apparatus in another embodiment of the present invention, as shown in fig. 7, further includes, on the basis of fig. 6:

the petrophysical model determining module 701 is used for selecting an applicable petrophysical model to carry out modeling work, determining petrophysical parameters of the to-be-detected deep buried reservoir and obtaining the petrophysical model of the to-be-detected deep buried reservoir;

and the velocity determination module 702 is configured to perform transverse wave estimation according to the petrophysical model of the deep-buried reservoir to be measured, and perform prestack elastic inversion by using amplitude-preserving and fidelity prestack gather data to obtain a reliable longitudinal wave velocity body and a reliable transverse wave velocity body.

In specific implementation, the petrophysical model determining module 701 is specifically configured to:

based on logging statistics and experimental data and an applicable basic rock physical model, giving rock physical basic parameters of each component of saturated rock;

by adjusting the transverse-longitudinal ratio of the pores, the rock physics forward longitudinal and transverse wave speed and the measured speed can achieve the best fit.

In actual exploitation, according to the development characteristics of a reservoir and the oil and gas production condition, a high-porosity zone and a high-permeability zone in a low-porosity and ultra-low-permeability reservoir are called as a high-quality reservoir, in order to better exploit an oil reservoir, the high-quality reservoir needs to be analyzed in advance, and in specific implementation, the deep-buried reservoir porosity inversion device further comprises, on the basis of fig. 6 or fig. 7:

the application module is used for determining the position of the target interval in the time dimension through well seismic calibration and extracting the plane data of the porosity of the target interval according to the porosity of the deeply buried reservoir, wherein the target interval is the reservoir included in the deeply buried reservoir; and determining a high-quality reservoir in the target interval according to the plane data of the porosity of the target interval, and predicting the plane distribution rule of the high-quality reservoir.

The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the deep-buried reservoir porosity inversion method.

The embodiment of the invention also provides a computer readable storage medium, which stores a computer program for executing the deep-buried reservoir porosity inversion method.

In summary, the deep-buried reservoir porosity inversion method and device provided by the invention have the following advantages:

the method comprises the steps of establishing a porosity inversion target function on the basis of a linear relation by determining the linear relation among longitudinal wave velocity, transverse wave velocity, porosity and shale content, and solving the porosity inversion target function, wherein the linear relation is established on the basis of the linear relation among the longitudinal wave velocity, the transverse wave velocity, the porosity and the shale content, and the linear relation does not relate to density parameters and water saturation, namely the establishment of the target function does not change along with the change of the density parameters and the water saturation, so that the calculation precision can be improved, a unique solution is obtained, the method is suitable for the porosity inversion of a deep-buried reservoir, and a powerful basis is provided for the adjustment of a deep-buried reservoir development scheme, well location optimization and deployment; and solving a porosity inversion target function through iteration, and enabling the given value of the water saturation to be gradually close to the real water saturation by utilizing a dichotomy so as to greatly improve the inversion accuracy.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made to the embodiment of the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.