CN112415616B - Deep buried reservoir porosity inversion method and device - Google Patents
Deep buried reservoir porosity inversion method and device Download PDFInfo
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Abstract
The invention provides a deep buried reservoir porosity inversion method and device, wherein the method comprises the following steps: based on the Taylor series expansion, carrying out linear approximation on a rock physical model of the deep buried reservoir to be measured; determining the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the argillaceous content according to the rock physical model of the deep buried reservoir to be measured after linear approximation; constructing a porosity inversion objective function according to the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the clay content; and solving the porosity inversion objective function, and determining the porosity of the deep buried reservoir to be measured. The method determines the linear relation between the longitudinal wave velocity and the transverse wave velocity and the porosity and the argillaceous content, builds the porosity inversion objective function on the basis of the linear relation, solves the problem, can improve the calculation accuracy, obtains the unique solution, is suitable for the porosity inversion of the deep buried reservoir, and provides a powerful basis for the adjustment of the deep buried reservoir development scheme, the optimization of well positions and the deployment.
Description
Technical Field
The invention relates to the technical field of petroleum and natural gas electromagnetic exploration and development, in particular to a deep buried reservoir porosity inversion method and device.
Background
With the shift of a large number of oil fields from an exploration stage to a development stage, the requirements on the accuracy of oil reservoir description are higher and higher, wherein reservoir physical property prediction plays a very important role in the oil reservoir development stage, and the method is mainly characterized in two aspects: the physical properties of the first reservoir layer and the reservoir layer directly reflect the quality of the reservoir layer, so that optimization of a development well pattern and deployment of a support development well position can be guided; secondly, reservoir physical properties reflect reservoir connectivity, and the dominant direction of water injection effectiveness is determined, which is an important basis for adjusting development schemes.
Porosity is a key parameter that indicates the quality of reservoir properties. At home and abroad, the main means of the quantitative prediction of porosity are two kinds: empirical formula fitting and porosity inversion. The empirical formula fitting method is to fit a logging speed curve and a porosity curve to obtain an empirical formula, and is applied to calculation of a 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 related to physical parameters such as the clay content, the water saturation and the like, so that the method has low calculation accuracy and relatively poor applicability. The porosity inversion method is based on a rock physical model, and utilizes elastic parameters such as longitudinal wave speed, transverse wave speed and the like to invert physical parameters such as porosity, clay content and the like. 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, and the algorithm has strong multi-solution property and huge calculated amount, so that the application of the method in actual production is limited.
Aiming at the problem, in recent years, some expert scholars at home and abroad develop related research work, and in 2016, grana propose a petrophysical model linear approximation method, so that the linear inversion of the reservoir porosity is realized, the calculated amount and inversion polynomials are greatly reduced, and a foundation is laid for popularization and application in actual production. The specific implementation process can be summarized as two steps: the first step, linear approximation is carried out on a rock physical model by utilizing 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 underdetermined equation sets, and the porosity inversion is carried out on the basis of the underdetermined equation sets, 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 view of the foregoing, there is a need to provide a porosity inversion method that is adapted to the conditions of deep reservoir burial.
Disclosure of Invention
The embodiment of the invention provides a deep-buried reservoir porosity inversion method, which is used for improving calculation accuracy and obtaining a unique solution of porosity, and is suitable for deep-buried reservoir porosity inversion, and the method comprises the following steps:
Based on the Taylor series expansion, carrying out linear approximation on a rock physical model of the deep buried reservoir to be measured;
Determining the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the argillaceous content according to the rock physical model of the deep buried reservoir to be measured after linear approximation;
constructing a porosity inversion objective function according to the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the clay content;
and solving the porosity inversion objective function, and determining the porosity of the deep buried reservoir to be measured.
The embodiment of the invention also provides a deep-buried reservoir porosity inversion device, which is used for improving calculation accuracy and obtaining a unique solution of porosity, and is suitable for deep-buried reservoir porosity inversion, and the device comprises:
the approximation module is used for carrying out linear approximation on a rock physical model of the deep buried reservoir to be measured based on Taylor series expansion;
The linear relation determining module is used for determining the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the argillaceous content according to the rock physical model of the deep buried reservoir to be detected after linear approximation;
The inversion objective function determining module is used for constructing a porosity inversion objective function according to the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the clay content;
and the solving module is used for solving the porosity inversion objective function and determining the porosity of the deep buried reservoir to be measured.
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 realizes the deep reservoir porosity inversion method when executing the computer program.
Embodiments of the present invention also provide a computer readable storage medium storing a computer program for performing the above deep buried reservoir porosity inversion method.
In the embodiment of the invention, the porosity inversion objective function is constructed and solved on the basis of the linear relation of the longitudinal wave speed and the transverse wave speed and the porosity and the clay content by determining the linear relation of the longitudinal wave speed and the transverse wave speed and the porosity and the clay content, the linear relation does not relate to density parameters and water saturation, namely, the establishment of an objective 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 can be 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 positions and the deployment.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a method for inverting the porosity of a deep buried reservoir according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of a method for iteratively solving the porosity inversion objective function in an embodiment of the present invention.
FIG. 3 is a schematic diagram of another embodiment of a deep reservoir porosity inversion method according to an embodiment of the present invention.
Fig. 4 is a comparison graph of petrophysical forward results in an embodiment of the present invention.
FIG. 5 is a flow chart of iterative inversion of porosity in an embodiment of the invention.
FIG. 6 is a schematic diagram of a deep buried reservoir porosity inversion apparatus in accordance with an embodiment of the present invention.
FIG. 7 is a schematic diagram of another embodiment of a deep reservoir porosity inversion device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to solve the problems of low calculation precision, strong multi-solution, huge calculation amount and inapplicability to a deep buried reservoir in the existing quantitative prediction technology of the porosity, the embodiment of the invention provides a deep buried reservoir porosity inversion method, which is used for improving the calculation precision to obtain a unique solution of the porosity, and is suitable for the porosity inversion of the deep buried reservoir, as shown in fig. 1, the specific steps include:
Step 101: based on the Taylor series expansion, carrying out linear approximation on a rock physical model of the deep buried reservoir to be measured;
step 102: determining the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the argillaceous content according to the rock physical model of the deep buried reservoir to be measured after linear approximation;
Step 103: constructing a porosity inversion objective function according to the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the clay content;
step 104: and solving the porosity inversion objective function, and determining the porosity of the deep buried reservoir to be measured.
As can be seen from the flow chart shown in fig. 1, in the embodiment of the invention, by determining the linear relation between the longitudinal wave velocity and the transverse wave velocity and the porosity and the argillaceous content, a porosity inversion objective function is constructed and solved on the basis of the linear relation, and the linear relation is not related to the density parameter and the water saturation because the objective function is established on the basis of the linear relation between the longitudinal wave velocity and the transverse wave velocity and the porosity and the argillaceous content, that is, the establishment of the objective function is not changed 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 provides a powerful basis for the adjustment and well position optimization and deployment of a deep-buried reservoir development scheme.
In the specific implementation, firstly, based on Taylor series expansion, a petrophysical model of a deep buried reservoir to be detected is subjected to linear approximation, as shown in a formula (1):
Wherein V p represents the deep reservoir longitudinal wave velocity; v s represents the deep reservoir shear wave velocity;
ρ represents reservoir density;
F1、F2、F3、αp、βp、γp、αs、βs、γs、αr、βr、γr Representing known coefficient items, and obtaining by expanding a rock physical model and a Taylor series of the deep buried reservoir to be measured;
phi represents porosity; represents the clay content; s w represents water saturation;
delta p、δs、δr represents an error term obtained by expanding a rock physical model of the deep buried reservoir to be measured and a Taylor series.
The rock physical model of the deep buried reservoir to be measured after linear approximation is obtained, and the reservoir density is obtained by utilizing prestack seismic synchronous inversion, but under the condition of deep burying of the reservoir, the reflection angle of the channel set data required by prestack inversion is small, and the minimum angle range required by density inversion cannot be reached, so that the density inversion result is unreliable. And deleting the reservoir density parameters in the petrophysical model of the to-be-detected deep buried reservoir after linear approximation, wherein the reservoir density parameters are shown as the formula (2):
The inventor discovers that the inversion objective function is directly constructed on the basis of the formula (2) to carry out inversion in the prior art, so that the multi-solution property is very strong. However, for oil reservoirs (which only comprise oil and water two-phase fluid media), the porosity change has the greatest influence on the longitudinal wave velocity and the transverse wave velocity of the deep-buried reservoir, and the muddy content is inferior, and the water saturation is minimum, so the inventor proposes that the linear relation between the longitudinal wave velocity and the transverse wave velocity and the porosity and the muddy content can be expressed by a model under the condition that the density parameter in the petrophysical model of the deep-buried reservoir to be measured after the linear approximation can be deleted and the water saturation is regarded as a known quantity, and the linear relation is expressed by the model, as shown in a formula (3):
Those skilled in the art will understand that the above-mentioned linear relation between the longitudinal wave velocity and the transverse wave velocity and the porosity and the clay content may be various, and may also be in the form of a formula, where the above-mentioned representation model is merely an example, and the model may be deformed during implementation, or other models, formulas or methods may be used to represent the linear relation between the longitudinal wave velocity and the transverse wave velocity and the porosity and the clay content, and these 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 and the porosity and the clay content shown in the formula (3), a porosity inversion objective function can be constructed based on the Bayesian theory, and the specific steps are as follows:
For convenience of derivation, can make Then formula (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, and is shown as a formula (5):
Wherein const represents a constant coefficient;
c e represents the covariance matrix of the error vector e; c m represents the covariance matrix of the model vector m;
m 0 represents the average value of porosity and clay content of the deep buried reservoir, is obtained by logging statistics, and is used as prior information together with a covariance matrix to restrict the solving process.
Solving for the maximum value of the probability of equation (5) is equivalent to solving for the minimum value of equation (6), L representing the porosity inversion objective function, as follows:
solving the porosity inversion objective function can enable Obtaining a least squares solution, as shown in formula (7):
After the porosity inversion result shown in the formula (7) is obtained, as for an oil reservoir, the porosity and clay content inversion result has stronger fault tolerance capability on water saturation, when the water saturation of a constant value is given, a relatively reliable inversion result can be obtained, and the closer to a real average value of the water saturation, the higher the inversion precision is, the porosity inversion objective function is solved iteratively by using a dichotomy, and the porosity of the deep buried reservoir to be measured is determined, wherein the specific steps are as follows:
step 201: inverting to obtain porosity and clay content when the water saturation is a first set value, and inverting to obtain porosity and clay content when the water saturation is a second set value;
Step 202: obtaining forward wave velocity and forward transverse wave velocity when the water saturation is the first set value according to the inversion result of the porosity and the clay content when the water saturation is the first set value and the linear relation; obtaining forward wave velocity and forward transverse wave velocity when the water saturation is the 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 the actually measured longitudinal wave speed and the forward wave speed 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 the actually measured transverse wave speed and the forward transverse wave speed under the set value when the water saturation is a first set value or a second set value respectively;
step 204: when the water saturation 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;
Step 205: performing dichotomy operation on the first set value of the water saturation and the 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: inverting to obtain porosity when the water saturation is a third set value, and taking the porosity as an inversion output result;
Step 207: determining the set values corresponding to the two larger average correlation coefficients in the average correlation coefficients with different set values, and taking 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 respectively;
Step 208: if the iteration number exceeds the preset maximum iteration number, stopping, wherein the inversion output result at the time of stopping is the porosity of the deep buried reservoir; if the preset maximum iteration number is greater than the current iteration number, and the inversion output result meets the preset convergence error, the inversion output result is cut off, and the inversion output result at the cut-off time is the porosity of the deep buried reservoir to be measured; otherwise, the next iteration is entered.
In a specific embodiment of the present invention, the first set value S 1 of the initial water saturation is taken to be 0, and the second set value S 2 is taken to be 1, for example, the effective water saturation vector S w_v＝[s1;s2 is S w_v = [0 ]; 1]. Those skilled in the art will understand that the above values are merely examples, and may be adjusted according to actual situations, and are not described herein.
In an embodiment, the longitudinal wave correlation coefficient may be determined using the following formula:
Wherein, gamma vp represents the correlation coefficient of longitudinal waves; i represents the sample point label; n represents the total number of sampling points;
Vp m represents forward longitudinal wave velocity; an average value representing forward wave velocity;
vp represents the measured longitudinal wave velocity; the average value of the measured longitudinal wave velocity is shown.
The transverse wave correlation coefficient can be determined using the following formula:
wherein, gamma vs represents the transverse wave correlation coefficient; i represents the sample point label; n represents the total number of sampling points;
vs m represents forward transverse wave velocity; an average value representing forward transverse wave velocity;
vs represents the measured longitudinal wave velocity; the average of the measured shear wave velocities is shown.
After the longitudinal wave correlation coefficient and the transverse wave correlation coefficient are obtained, the average correlation coefficient can be determined according to the following formula:
γv＝(γvp+γvs)/2 (10)
The average correlation coefficient at the initial water saturation in an embodiment of the present invention may be expressed as gamma v0,γv1.
Next, performing binary calculation to obtain S w_2＝mean(Sw_v) as 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. In the embodiment of the invention, the gamma 2 is simply written. And inverting to obtain porosity when the water saturation is a third set value, and taking the porosity as an inversion output result.
Comparing gamma 0,γ1,γ2, determining two related coefficients with larger values and corresponding water saturation set values as the first water saturation set value and the second water saturation set value of the next iteration, for example, if gamma 2＞γ0＞γ1, then S w_v＝[0;Sw_rand in the next round of inversion.
Judging whether to cut off: if the iteration times exceed the preset maximum iteration times, stopping, wherein the inversion output result at the time of stopping is the porosity of the deep buried reservoir to be detected; if the preset maximum iteration number is greater than the current iteration number, and the inversion output result meets the convergence condition, the inversion output result is cut off, and the inversion output result at the cut-off time is the porosity of the deep buried reservoir; otherwise, the next iteration is entered.
In a specific implementation, the difference between the average correlation coefficient γ 2 and the preset target correlation coefficient when the water saturation in the iteration is the third set value is smaller than the preset convergence error. The preset target correlation coefficient, the preset convergence error and the maximum iteration number can be preset to values before inversion starts, the preset target correlation coefficient can be determined according to laboratory experiments 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 will understand that the above values are merely examples, and any values may be chosen according to the requirements, and will not be described in detail herein.
And continuously performing the iterative inversion step until the step is cut off, and determining the porosity of the deep buried reservoir to be measured.
In order to determine the deep reservoir porosity more precisely, the deep reservoir porosity inversion method according to another embodiment of the present invention, as shown in fig. 3, further includes, on the basis of fig. 1:
step 301: selecting an applicable petrophysical model to carry out modeling work, and determining petrophysical parameters of the deep buried reservoir to be measured to obtain the petrophysical model of the deep buried reservoir to be measured;
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 the amplitude-preserving and fidelity prestack data set to obtain reliable longitudinal wave velocity bodies and transverse wave velocity bodies.
In specific implementation, selecting an applicable petrophysical model to carry out modeling work specifically comprises the following steps:
Based on logging statistics and experimental data, based on an applicable basic rock physical model, setting rock physical basic parameters of each component of the saturated rock;
And the rock physical forward-modeling longitudinal and transverse wave speed and the actual measurement speed reach the best fit by adjusting the aperture transverse-longitudinal ratio.
In an embodiment, fig. 4 shows a comparison of the petrophysical forward shear velocity and the measured velocity after adjusting the pore aspect ratio. The best fit may be achieved by adjusting the pore aspect ratio so that the petrophysical forward longitudinal and lateral wave velocity is as close as possible to the measured velocity, reflecting that the two curves in fig. 4 should coincide as much as possible.
In a specific embodiment, as shown in fig. 5, the iterative inversion flowchart is implemented by substituting the petrophysical parameters of the deep buried reservoir to be measured determined in step 301 and the longitudinal wave velocity data and the transverse wave velocity data in the longitudinal wave velocity body and the transverse wave velocity body obtained in step 302 into the deep buried reservoir porosity inversion method shown in fig. 1, setting the first set value of the saturation at the initial time to be 0 and the second set value to be 1, and iterating. Outputting if the inversion output result meets the convergence condition; if the result does not meet the convergence condition, the average value of the saturation set value is calculated, then the next inversion is carried out until the iterative inversion result meets the convergence condition, and then the result is output.
In actual exploitation, according to the development characteristics of the reservoir and the oil and gas production condition, the high-pore and high-permeability zone in the low-pore and low-permeability and ultra-low Kong Te low-permeability reservoir is called a high-quality reservoir, and in order to better exploit the reservoir, the high-quality reservoir needs to be analyzed in advance, and when the method is implemented, the deep buried reservoir porosity inversion method further comprises on the basis of fig. 1 or fig. 2:
determining the position of a target interval in the time dimension through well earthquake 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 a reservoir layer included in the deep buried reservoir;
And determining the high-quality reservoir stratum in the target interval according to the plane data of the porosity of the target interval, and predicting the plane spreading rule of the high-quality reservoir stratum.
The method and the system provide powerful basis for deep-buried oil reservoir development scheme adjustment, well position optimization and deployment by determining the high-quality reservoir in the target interval and predicting the plane spreading rule of the high-quality reservoir.
Based on the same inventive concept, the embodiment of the invention also provides a deep-buried reservoir porosity inversion device, and because the principle of solving the problem of the deep-buried reservoir porosity inversion device is similar to that of the deep-buried reservoir porosity inversion method, the implementation of the deep-buried reservoir porosity inversion device can be referred to the implementation of the deep-buried reservoir porosity inversion method, and the repetition is omitted, and the specific structure is shown in fig. 6:
The approximation module 601 is used for linearly approximating a petrophysical model of the deep buried reservoir to be measured based on Taylor series expansion;
The linear relation determining module 602 is configured to determine a linear relation between a longitudinal wave velocity and a transverse wave velocity and between porosity and clay content according to the rock physical model of the deep buried reservoir to be measured after linear approximation;
The inversion objective function determining module 603 is configured to construct a porosity inversion objective function according to a linear relationship between the longitudinal wave velocity and the transverse wave velocity and the porosity and the clay content;
and a solving module 604, configured to solve the porosity inversion objective function, and determine the porosity of the deep buried reservoir to be measured.
In an embodiment, the linear relation determining module 602 is specifically configured to determine a linear relation between the longitudinal wave velocity and the transverse wave velocity and the porosity and the argillaceous content when the petrophysical model of the deep buried reservoir to be measured after the linear approximation is the deletion density parameter and the water saturation is a known amount.
In an embodiment, the inversion objective function determining module 603 is specifically configured to construct a porosity inversion objective function based on bayesian theory according to a linear relationship between a longitudinal wave velocity and a transverse wave velocity, and a porosity and a clay content, as follows:
Wherein L represents a porosity inversion objective function;
d＝G·m+e；
c e represents the covariance matrix of the error vector e; c m represents the covariance matrix of the model vector m;
m 0 represents the average value of porosity and clay content of the deep buried reservoir, and is obtained by logging statistics;
V p represents the deep reservoir longitudinal wave velocity; v s represents the deep reservoir shear wave velocity;
F 1、F2、αp、βp、γp、αs、βs、γs represents a known coefficient term, which is obtained by expanding a rock physical model and a Taylor series of a deep buried reservoir to be measured;
Delta p、δs represents an error term which is obtained by expanding a rock physical model and a Taylor series of a deep buried reservoir to be measured;
phi represents porosity; Represents the clay content; s w represents the water saturation.
In a specific embodiment, the solving module 604 is specifically configured to:
the following iterative inversion steps are circularly executed to determine the porosity of the deep buried reservoir to be measured:
Inverting to obtain porosity and clay content when the water saturation is a first set value, and inverting to obtain porosity and clay content when the water saturation is a second set value;
Obtaining forward wave velocity and forward transverse wave velocity when the water saturation is the first set value according to the inversion result of the porosity and the clay content when the water saturation is the first set value and the linear relation; obtaining forward wave velocity and forward transverse wave velocity when the water saturation is the 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 the actually measured longitudinal wave speed and the forward wave speed 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 the actually measured transverse wave speed and the forward transverse wave speed under the set value when the water saturation is a first set value or a second set value respectively;
when the water saturation 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;
Performing dichotomy operation on the first set value of the water saturation and the 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;
inverting to obtain porosity when the water saturation is a third set value, and taking the porosity as an inversion output result;
Determining the set values corresponding to the two larger average correlation coefficients in the average correlation coefficients with different set values, and taking 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 respectively;
If the iteration number exceeds the preset maximum iteration number, stopping, wherein the inversion output result at the time of stopping is the porosity of the deep buried reservoir; if the preset maximum iteration number is greater than the current iteration number, and the inversion output result meets the convergence condition, the inversion output result is cut off, and if the inversion output result is the porosity of the deep buried reservoir to be detected, the next iteration is carried out.
In order to determine the deep reservoir porosity more precisely, the deep reservoir porosity inversion device according to 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 configured to select an applicable petrophysical model to perform modeling work, determine petrophysical parameters of a to-be-measured deep buried reservoir, and obtain the petrophysical model of the to-be-measured deep buried reservoir;
The speed determining module 702 is configured to perform a transverse wave estimation according to the petrophysical model of the deep buried reservoir to be measured, and perform prestack elastic inversion by using the amplitude-preserving and fidelity prestack data set to obtain a reliable longitudinal wave speed body and a reliable transverse wave speed body.
In specific implementation, the petrophysical model determining module 701 is specifically configured to:
Based on logging statistics and experimental data, based on an applicable basic rock physical model, setting rock physical basic parameters of each component of the saturated rock;
And the rock physical forward-modeling longitudinal and transverse wave speed and the actual measurement speed reach the best fit by adjusting the aperture transverse-longitudinal ratio.
In actual exploitation, according to reservoir development characteristics and oil gas production conditions, the high-pore and high-permeability zones in the low-pore and low-permeability and ultra-low Kong Te 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 when the deep buried reservoir porosity inversion device is implemented, the deep buried reservoir porosity inversion device further comprises on the basis of fig. 6 or 7:
The application module is used for determining the position of a target interval in the time dimension through well earthquake 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 a reservoir included in the deep buried reservoir; and determining the high-quality reservoir stratum in the target interval according to the plane data of the porosity of the target interval, and predicting the plane spreading rule of the high-quality reservoir stratum.
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 realizes the deep reservoir porosity inversion method when executing the computer program.
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:
By determining the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the clay content, a porosity inversion objective function is constructed and solved on the basis of the linear relation, and because the objective function is established on the basis of the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the clay content, the linear relation does not relate to density parameters and water saturation, namely, the establishment of an objective 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 can be obtained, the linear relation 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 positions and the deployment; the porosity inversion objective function is solved iteratively, and a given value of the water saturation is gradually close to the real water saturation by using a dichotomy, so that the inversion accuracy is greatly improved.
It will be apparent to those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations can be made to the embodiments of the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A method of inversion of porosity of a deep buried reservoir, comprising:
Based on the Taylor series expansion, carrying out linear approximation on a rock physical model of the deep buried reservoir to be measured;
Determining the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the argillaceous content according to the rock physical model of the deep buried reservoir to be measured after linear approximation;
constructing a porosity inversion objective function according to the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the clay content;
solving the porosity inversion objective function, and determining the porosity of the deep buried reservoir to be measured;
Solving the porosity inversion objective function to determine deep reservoir porosity, comprising:
the following iterative inversion steps are circularly executed to determine the porosity of the deep buried reservoir:
Inverting to obtain porosity and clay content when the water saturation is a first set value, and inverting to obtain porosity and clay content when the water saturation is a second set value;
Obtaining forward wave velocity and forward transverse wave velocity when the water saturation is the first set value according to the inversion result of the porosity and the clay content when the water saturation is the first set value and the linear relation; obtaining forward wave velocity and forward transverse wave velocity when the water saturation is the 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 the actually measured longitudinal wave speed and the forward wave speed 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 the actually measured transverse wave speed and the forward transverse wave speed under the set value when the water saturation is a first set value or a second set value respectively;
when the water saturation 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;
Performing dichotomy operation on the first set value of the water saturation and the 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;
inverting to obtain porosity when the water saturation is a third set value, and taking the porosity as an inversion output result;
Determining the set values corresponding to the two larger average correlation coefficients in the average correlation coefficients with different set values, and taking 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 respectively;
If the iteration number exceeds the preset maximum iteration number, stopping, wherein the inversion output result at the time of stopping is the porosity of the deep buried reservoir; if the preset maximum iteration number is greater than the current iteration number, and the inversion output result meets the convergence condition, the inversion output result is cut off, and the inversion output result at the cut-off time is the porosity of the deep buried reservoir to be measured; otherwise, the next iteration is entered.
2. The method as recited in claim 1, further comprising:
determining the position of a target interval in the time dimension through well earthquake 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 a reservoir layer included in the deep buried reservoir;
And determining the high-quality reservoir stratum in the target interval according to the plane data of the porosity of the target interval, and predicting the plane spreading rule of the high-quality reservoir stratum.
3. The method of claim 1 or 2, wherein determining the linear relationship between longitudinal wave velocity and transverse wave velocity and porosity and argillaceous content according to the petrophysical model of the deep buried reservoir to be measured after linear approximation comprises:
and under the condition that the rock physical model of the deep buried reservoir to be detected after the linear approximation is the deleted density parameter and the water saturation is the known quantity, determining the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the argillaceous 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 e represents the covariance matrix of the error vector e; c m represents the covariance matrix of the model vector m;
m 0 represents the average value of porosity and clay content of the deep buried reservoir, and is obtained by logging statistics;
d. G is an intermediate variable, wherein V p represents the deep buried reservoir longitudinal wave velocity; v s represents the deep reservoir shear wave velocity;
F 1、F2、αp、βp、γp、αs、βs、γs represents a known coefficient term, which is obtained by expanding a rock physical model and a Taylor series of a deep buried reservoir to be measured;
Delta p、δs represents an error term which is obtained by expanding a rock physical model and a Taylor series of a deep buried reservoir to be measured;
phi represents porosity; Represents the clay content; s w represents the water saturation.
5. A deep reservoir porosity inversion device, comprising:
the approximation module is used for carrying out linear approximation on a rock physical model of the deep buried reservoir to be measured based on Taylor series expansion;
The linear relation determining module is used for determining the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the argillaceous content according to the rock physical model of the deep buried reservoir to be detected after linear approximation;
The inversion objective function determining module is used for constructing a porosity inversion objective function according to the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the clay content;
the solving module is used for solving the porosity inversion objective function and determining the porosity of the deep buried reservoir to be measured;
The solving module 604 is specifically configured to:
the following iterative inversion steps are circularly executed to determine the porosity of the deep buried reservoir to be measured:
Inverting to obtain porosity and clay content when the water saturation is a first set value, and inverting to obtain porosity and clay content when the water saturation is a second set value;
Obtaining forward wave velocity and forward transverse wave velocity when the water saturation is the first set value according to the inversion result of the porosity and the clay content when the water saturation is the first set value and the linear relation; obtaining forward wave velocity and forward transverse wave velocity when the water saturation is the 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 the actually measured longitudinal wave speed and the forward wave speed 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 the actually measured transverse wave speed and the forward transverse wave speed under the set value when the water saturation is a first set value or a second set value respectively;
when the water saturation 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;
Performing dichotomy operation on the first set value of the water saturation and the 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;
inverting to obtain porosity when the water saturation is a third set value, and taking the porosity as an inversion output result;
Determining the set values corresponding to the two larger average correlation coefficients in the average correlation coefficients with different set values, and taking 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 respectively;
If the iteration number exceeds the preset maximum iteration number, stopping, wherein the inversion output result at the time of stopping is the porosity of the deep buried reservoir; if the preset maximum iteration number is greater than the current iteration number, and the inversion output result meets the convergence condition, the inversion output result is cut off, and the inversion output result at the cut-off time is the porosity of the deep buried reservoir to be measured; otherwise, the next iteration is entered.
6. The apparatus as recited in claim 5, further comprising:
The application module is used for determining the position of a target interval in the time dimension through fine well earthquake 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 a reservoir included in the deep buried reservoir;
And determining the high-quality reservoir stratum in the target interval according to the plane data of the porosity of the target interval, and predicting the plane spreading rule of the high-quality reservoir stratum.
7. The apparatus of claim 5 or 6, wherein the linear relationship determination module is specifically configured to:
and under the condition that the rock physical model of the deep buried reservoir to be detected after the linear approximation is the deleted density parameter and the water saturation is the known quantity, determining the linear relation between the longitudinal wave speed and the transverse wave speed and the porosity and the argillaceous content.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 4 when executing the computer program.
9. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the method of any of claims 1 to 4.
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