CN112230278A - Seepage field characteristic parameter determination method and device - Google Patents
Seepage field characteristic parameter determination method and device Download PDFInfo
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- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/30—Analysis
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
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- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/38—Processing data, e.g. for analysis, for interpretation, for correction
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Abstract
The invention provides a method and a device for determining characteristic parameters of a seepage field, wherein the method comprises the following steps: acquiring change data of a seepage field characteristic parameter response factor under mesoscale and/or macroscale according to geophysical data and/or geological data; the mesoscopic scale is a logging scale; the macroscopic scale is a ground seismic scale, a borehole seismic scale or a gravity electromagnetic scale; and carrying out normalization processing on the change data to obtain characteristic parameters of the seepage field. The method comprises the steps of obtaining change data of a seepage field characteristic parameter response factor under mesoscale and/or macroscale; compared with the technical scheme of researching the seepage field by adopting geological and engineering methods in the prior art, the method has the advantages of simple and efficient determination of the characteristic parameters of the seepage field, great improvement of the simulation accuracy of the seepage field, superior practicability, targeted deployment and development of the scheme, adjustment of injection-flooding relations and guidance for improving the recovery ratio of the oil field.
Description
Technical Field
The invention relates to the technical field of oil field exploration and development, in particular to a method and a device for determining characteristic parameters of a seepage field.
Background
The flow field formed by the flowing of reservoir fluid in the porous medium is not invariable, and changes along with the development process, such as the porosity, water saturation, shale content and the like of the reservoir. Changes in reservoir parameters cause changes in geophysical field response. From the microscopic expression form of the seepage field to the macroscopic effect thereof, the method for researching the seepage field is only limited to geological and engineering methods at present, and the precision for describing the oil reservoir seepage field is low.
Disclosure of Invention
The embodiment of the invention provides a method for determining characteristic parameters of a seepage field, which is used for greatly improving the simulation accuracy of the seepage field and comprises the following steps:
acquiring change data of a seepage field characteristic parameter response factor under mesoscale and/or macroscale according to geophysical data and/or geological data; the mesoscopic scale is a logging scale; the macroscopic scale is a ground seismic scale, a borehole seismic scale or a gravity electromagnetic scale;
and carrying out normalization processing on the change data to obtain characteristic parameters of the seepage field.
The embodiment of the invention also provides a device for determining the characteristic parameters of the seepage field, which is used for greatly improving the simulation accuracy of the seepage field and comprises the following components:
the parameter acquisition module is used for acquiring change data of the seepage field characteristic parameter response factors under the mesoscale and/or the macroscale according to the geophysical data and/or the geological data; the mesoscopic scale is a logging scale; the macroscopic scale is a ground seismic scale, a borehole seismic scale or a gravity electromagnetic scale;
and the calculation determination module is used for determining the characteristic parameters of the seepage field by normalization according to the change data.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the determination method of the characteristic parameters of the seepage field when executing the computer program.
An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program for executing the above-mentioned method for determining characteristic parameters of a seepage field.
In the embodiment of the invention, the change data of the characteristic parameter response factor of the seepage field under the mesoscale and/or macroscale is obtained according to the geophysical data and/or geological data; carrying out normalization processing on the change data to obtain characteristic parameters of a seepage field; compared with the technical scheme of researching the seepage field by adopting geology and engineering methods in the prior art, the method not only can simply and efficiently determine the characteristic parameters of the seepage field, but also greatly improves the simulation accuracy of the seepage field, has superior practicability, is beneficial to more pertinently deploying the development scheme and adjusting the injection-flooding relation during later development, and provides guidance for improving the recovery ratio of the oil field.
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 a seepage field characteristic parameter determination method in an embodiment of the present invention.
FIG. 2 is a schematic diagram of a reservoir geological model constructed according to an embodiment of the invention.
Fig. 3 is a schematic diagram of a specific embodiment of a seepage field characteristic parameter determination method in an embodiment of the present invention.
Fig. 4 is a schematic diagram of an implementation of a specific application of the method for determining a characteristic parameter of a seepage field in an embodiment of the present invention.
FIG. 5 is a schematic illustration of early seismic data preprocessing in an embodiment of the invention.
Fig. 6 is a schematic diagram of a seepage field characteristic parameter determination device in an embodiment of the present invention.
Fig. 7 is a schematic diagram of an embodiment of a seepage field characteristic parameter determining apparatus in an embodiment of the present 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 problem that the existing oil reservoir seepage field characterization lacks an effective high-precision seepage field characterization scheme, the embodiment of the invention provides a simple and efficient method for determining characteristic parameters of a seepage field, which is used for more accurately characterizing the oil reservoir seepage field, and as shown in fig. 1, the method comprises the following steps:
step 101: acquiring change data of a seepage field characteristic parameter response factor under mesoscale and/or macroscale according to geophysical data and/or geological data; the mesoscopic scale is a logging scale; the macroscopic scale is a ground seismic scale, a borehole seismic scale or a gravity electromagnetic scale;
step 102: and carrying out normalization processing on the change data to obtain characteristic parameters of the seepage field.
As can be seen from the flow shown in fig. 1, in the embodiment of the present invention, by applying the variation data of the seepage field characteristic parameter response factor under mesoscale and/or macroscale in the geophysical data and/or geological data, the data dimension and precision in the seepage field characteristic parameter determination method are increased, and the data error between the simulation process and the actual application is reduced, so that the simulation accuracy of the seepage field is improved, the process is simple and efficient, and the practical effect is greatly increased.
In specific implementation, firstly, the change data of the seepage field characteristic parameter response factor under the mesoscale and/or macroscale is obtained according to the geophysical data and/or geological data. In this embodiment, the mesoscopic scale is the logging scale, and the resolution range is [0.1 m, 5 m ]; the macro scale is the ground seismic scale, the well seismic scale or the gravity electromagnetic scale, and the resolution range is [5 m, 50 m ].
In this embodiment, the manner of acquiring the change data includes: existing geological data is solved and/or recalled by analysis of the geophysical data through the geophysical data. Geophysical data include: one or any combination of logging data, surface seismic data, borehole seismic data and gravity magnetic and electric data. The geological data includes: and one or any combination of the earth stress data, the structural characteristic data, the reservoir characteristic data and the reservoir characteristic data.
The change data may be, for example, effective data change of the parameter response factor of the seepage field characteristic in two stages of the development period, such as data subtraction, data division, or more complex functional relationship, such as:
G＝f(ΔX) (1)
g represents the change data of the seepage field characteristic parameter response factors under the mesoscale and/or the macro scale;
Δ X represents the variation of the relevant parameters of the secondary response factors for different development phases, such as:
ΔX＝f(Δpor,Δperm,Δsw,Δsh,ΔA,ΔI,ΔVp,ΔVs) (2)
wherein por represents porosity, perm represents permeability, sw represents water saturation, sh represents argillaceous content, A represents time-lapse seismic amplitude, and I represents time-lapse wave resistanceAnti, VpRepresenting the velocity, V, of longitudinal wavessRepresenting the shear wave velocity. And after the change data of the seepage field characteristic parameter response factor is acquired under the mesoscopic scale and/or the macroscopic scale, the change data is normalized to acquire the seepage field characteristic parameter. The characteristic parameters of the seepage field can be obtained, for example, according to the following normalization formula:
wherein, FmCharacteristic parameters of the seepage field, gminRepresents the minimum value of the variation data G; gmaxIndicates the maximum value of the variation data G. It is understood that the above formula for determining the characteristic parameter of the seepage field is only an example, and the above formula may be modified during implementation, or other formulas or methods may be used to normalize the changed data to obtain the characteristic parameter of the seepage field, for example
For example, those skilled in the art can understand that the above formulas are only examples, and all of the formulas or methods for performing normalization processing fall within the protection scope of the present invention, and are not described in detail in the embodiments.
In the embodiment of the present invention, the response factors of the seepage field characteristic parameters under the mesoscale and/or macroscale specifically may include: time-lapse seismic amplitude A, time-lapse seismic wave impedance I, seepage field characteristic parameter factor D, or reservoir related parameters simulated by a reservoir geological model.
In an embodiment, if the seepage field characteristic parameter response factor is a time-lapse seismic amplitude, the calculation may be performed according to a corresponding normalization formula as follows:
when the response factor of the characteristic parameter of the seepage field is the time-lapse seismic amplitude, namely G ═ f (delta A),
wherein Δ a represents the rate of change of amplitude differences for different development sessions;
A1representing the seismic response amplitude of a target layer at the initial stage of reservoir development;
ΔA2representing the difference of the seismic amplitude of the high water-cut period and the seismic amplitude of the medium water-cut period;
ΔA1representing the difference in seismic amplitude between the mid water phase and the initial reservoir development phase.
In an embodiment, if the response factor of the characteristic parameter of the seepage field is the time-lapse seismic wave impedance, i.e. G ═ f (Δ I),
wherein, the delta I represents the difference change rate of the seismic inversion wave impedance of different development periods;
I1representing the target layer wave impedance at the initial stage of oil reservoir development;
ΔI2the wave impedance difference between the high water-cut period and the medium water-cut period is represented;
ΔI1representing the difference in wave impedance between the mid water phase and the initial reservoir development phase.
In the embodiment, if the seepage field characteristic parameter response factor is the seepage field characteristic parameter factor, i.e. G ═ f (Δ D),
wherein, the delta D represents the difference change rate of the characteristic parameter factors of the secondary seepage field in different development periods;
D1representing a characteristic parameter factor of a seepage field of a target layer at the initial stage of oil reservoir development;
ΔD2representing the difference of characteristic parameter factors of the seepage field between the high water cut period and the medium water cut period;
ΔD1and (4) representing the difference of characteristic parameter factors of the seepage field between the water-containing period and the initial oil reservoir development period.
In an embodiment, the seepage field characteristic parameter factor D may be fit from each reservoir related parameter, and the construction process may include: and obtaining related parameters of the oil reservoir by utilizing logging interpretation, searching the relation between the related parameters of the oil reservoir and the change of the seepage field by analyzing the change rate of the related parameters of the oil reservoir, and fitting to obtain characteristic parameter factors of the seepage field. The related parameters of the oil reservoir can include one or any combination of porosity por, permeability perm, water saturation sw, shale content sh and the like, and the characteristic parameter factor D of the seepage field can be obtained according to the following formula:
D＝f(ω1Δpor、ω2Δperm、ω3Δsw、ω4Δsh) (10)
wherein, ω isnAnd n is 1,2,3,4, which represents a weight. In actual oil reservoir development application, particularly when high-porosity and high-permeability oil is hidden in water injection development, oil and water continuously permeate and flow in pore spaces, the oil and the water in the reservoir spaces can be mutually replaced and replaced, and original oil reservoir characteristics are improved. That means the weight omega of each reservoir-related parameter characterizing the characteristic parameter factors of the seepage fieldnAnd n is 1,2,3 and 4, which are constantly changed and need to be specifically analyzed according to the change rule of the logging parameters caused by specific development periods. For example, a field development undergoes four production stages, namely an initial development stage, a medium water stage, a high water stage, and an extra high water stage. For drilling at four periods, respectively using W1/W2/W3/W4To indicate. Logging curves and logs of each periodComparing well interpretation results, many results may occur: for example in W1And W2During the period, sh changes significantly and por does not, then the weight can be chosen as:
D＝f(0Δpor、0.1Δperm、0.2Δsw、0.7Δsh) (11)
and W3And W2This may be another expression, requiring analysis from wells drilled at different times. It is also to be noted that the expression form of the characteristic parameter factor of the seepage field is various and can be a complex function, for example, constructed as
It will be understood by those skilled in the art that the foregoing is merely exemplary, and that the formula may vary with the particular study and will not be described in detail herein.
In an embodiment, if the seepage field characteristic parameter response factor is an oil reservoir related parameter simulated by using an oil reservoir geological model, the specific implementation of the seepage field characteristic parameter determination method may further include: firstly, a reservoir geological model is established, and as an example, concrete implementation steps are shown in fig. 2, and the method comprises the following steps:
step 201: according to the drilled well data, restraining the establishment of a velocity field and a Q field in seismic data processing; for example, the method can be started from the drilled well data, including micro-logging data, and restricts the establishment of a velocity field and a Q field in the seismic data processing;
step 202: performing deterministic inversion on seismic data obtained by viscoelastic wave equation migration;
step 203: and performing oil reservoir parameter inversion on the basis of rock physical analysis, and constraining the establishment of an oil reservoir geological model to obtain the oil reservoir geological model.
After the reservoir geological model is built, the difference of reservoir related parameters simulated in the reservoir geological model under different development periods is obtained, and the difference specifically comprises one or any combination of porosity, permeability, water saturation, shale content and the like.
And finally, substituting the difference of the related parameters of each oil reservoir into the formula (3) to calculate the characteristic parameters of the seepage field.
In order to determine the characteristic parameter of the seepage field more accurately and further obtain a more accurate representation of the oil reservoir seepage field, as shown in fig. 3, in a specific embodiment, the method for determining the characteristic parameter of the seepage field may further include:
step 301: and taking the seepage field characteristic parameters obtained by normalization processing according to the change data as input, taking the seepage field characteristic parameter change data of core analysis or experimental analysis as known sample input, and optimizing the seepage field characteristic parameters through neural network operation.
In the embodiment, the input seepage field characteristic parameter may be, for example, a single input of 4 determination results obtained according to the time-lapse seismic amplitude, the time-lapse seismic wave impedance, the seepage field characteristic parameter factor, or 4 seepage field characteristic parameter response factors simulated by using the reservoir geological model, or may be any combination of the 4 results.
A specific example is given below to illustrate how embodiments of the present invention determine the characteristic parameters of the seepage field. The three-dimensional earthquake model is applied to a certain onshore oil field in China, three-dimensional earthquakes are historically arranged in the research area, and the three-dimensional earthquakes are named as S1, S2 and S3 respectively and represent an initial oil reservoir development stage, a medium water-cut stage and a high water-cut stage respectively. The corresponding drilling wells are classified according to the development period, and all the drilling well sets in the initial development period are W1In the medium water content period of W2And a high water cut of W3. The specific implementation process of the method for determining the characteristic parameters of the seepage field in this example is described with reference to fig. 4:
step 401: preprocessing the early-stage seismic data;
step 402: respectively extracting the root-mean-square amplitude attribute of each period of seismic data along the horizon;
step 403: and (5) calculating by applying formulas (4) and (5) to obtain the characteristic parameters of the seepage field.
The early seismic data preprocessing process is shown in fig. 5, and specifically includes:
step 501: aiming at the development layer system, well-to-well earthquake is combined with subdivided single sand bodies to carry out well-to-well comparison to obtain single sand body division;
step 502: giving depth concept and geological meaning to the time domain seismic data to realize horizon calibration;
step 503: performing rock physical analysis, and on the basis, performing fine calibration on the single sand body to determine the response characteristic of the single sand body;
step 504: and respectively calibrating the seismic data of the three phases S1, S2 and S3, and then performing seismic interpretation on single sand bodies.
The implementation of this specific application only provides an implementation method for calculating the characteristic parameters of the seepage field by using the time-lapse seismic amplitude root mean square, and those skilled in the art can understand that the embodiments of the present invention include many specific application implementations, for example, by using the time-lapse wave impedance root mean square, and the implementation of the above specific application is only an example, and the rest of the implementation modes are not described in detail.
Based on the same inventive concept, embodiments of the present invention further provide a device for determining a characteristic parameter of a seepage field, where the principle of the problem solved by the device for determining a characteristic parameter of a seepage field is similar to that of the method for determining a characteristic parameter of a seepage field, so that the implementation of the device for determining a characteristic parameter of a seepage field can refer to the implementation of the method for determining a characteristic parameter of a seepage field, repeated details are not repeated, and a specific structure is shown in fig. 6:
the parameter obtaining module 601 is configured to obtain change data of a seepage field characteristic parameter response factor under a mesoscale and/or a macroscale according to geophysical data and/or geological data; the mesoscopic scale is a logging scale; the macroscopic scale is a ground seismic scale, a borehole seismic scale or a gravity electromagnetic scale;
and a calculation and determination module 602, configured to perform normalization processing on the change data to obtain a seepage field characteristic parameter.
As shown in fig. 7, in a specific embodiment, the seepage field characteristic parameter determining apparatus shown in fig. 6 may further include:
and a result optimization module 701, configured to take the seepage field characteristic parameter as an input, take the seepage field characteristic parameter change data of core analysis or experimental analysis as a known sample input, and optimize the seepage field characteristic parameter through neural network operation.
In a specific embodiment, the result optimization module 701 is specifically configured to take the seepage field characteristic parameter obtained by performing normalization processing on the change data as input, take the seepage field characteristic parameter change data of core analysis or experimental analysis as known sample input, and optimize the seepage field characteristic parameter through neural network operation.
In a specific embodiment, the input seepage field characteristic parameter may be, for example, a single input of 4 determination results obtained according to the time-lapse seismic amplitude, the time-lapse seismic wave impedance, the seepage field characteristic parameter factor, or 4 seepage field characteristic parameter response factors simulated by using the reservoir geological model, or may be any combination of the 4 results.
In an embodiment, the parameter obtaining module 601 is specifically configured to obtain a time-lapse seismic amplitude, a time-lapse seismic wave impedance, a seepage field characteristic parameter factor, or change data of an oil reservoir related parameter simulated by using an oil reservoir geological model on a mesoscale and/or a macroscale.
In an embodiment, the calculation determining module 602 is specifically configured to substitute the change data into the following normalization formula to solve:
wherein, FmRepresenting characteristic parameters of the seepage field, G representing variation data, GminDenotes the minimum value of the variation data, gmaxRepresenting the maximum value of the variation data.
In one embodiment, the calculation determination module 602 may be specifically configured to:
if the seepage field characteristic parameter response factor is the time-lapse seismic amplitude, determining the seepage field characteristic parameter according to the following normalization formula:
wherein Δ a represents the rate of change of amplitude differences for different development sessions;
A1representing the seismic response amplitude of a target layer at the initial stage of reservoir development;
ΔA2representing the difference of the seismic amplitude of the high water-cut period and the seismic amplitude of the medium water-cut period;
ΔA1representing the difference in seismic amplitude between the mid water phase and the initial reservoir development phase.
In one embodiment, the calculation determination module 602 may be specifically configured to:
if the seepage field characteristic parameter response factor is time-lapse seismic wave impedance, determining the seepage field characteristic parameter according to the following normalization formula:
wherein, the delta I represents the difference change rate of the seismic inversion wave impedance of different development periods;
I1representing the target layer wave impedance at the initial stage of oil reservoir development;
ΔI2the wave impedance difference between the high water-cut period and the medium water-cut period is represented;
ΔI1representing the difference in wave impedance between the mid water phase and the initial reservoir development phase.
In one embodiment, the calculation determination module 602 may be specifically configured to:
if the seepage field characteristic parameter response factor is a seepage field characteristic parameter factor, determining a seepage field characteristic parameter according to the following normalization formula:
wherein, the delta D represents the difference change rate of the characteristic parameter factors of the secondary seepage field in different development periods;
D1representing a characteristic parameter factor of a seepage field of a target layer at the initial stage of oil reservoir development;
ΔD2representing the difference of characteristic parameter factors of the seepage field between the high water cut period and the medium water cut period;
ΔD1and (4) representing the difference of characteristic parameter factors of the seepage field between the water-containing period and the initial oil reservoir development period.
In one embodiment, the calculation determination module 602 may be further configured to:
and obtaining related parameters of the oil reservoir by utilizing logging interpretation, searching the relation between the related parameters of the oil reservoir and the change of the seepage field by analyzing the change rate of the related parameters of the oil reservoir, and fitting to obtain characteristic parameter factors of the seepage field.
In one embodiment, the calculation determination module 602 may be further configured to:
if the seepage field characteristic parameter response factor is an oil reservoir related parameter simulated by an oil reservoir geological model, then:
according to the drilled well data, restraining the establishment of a velocity field and a Q field in seismic data processing;
performing deterministic inversion on seismic data obtained by viscoelastic wave equation migration;
and performing oil reservoir parameter inversion on the basis of rock physical analysis, and constraining the establishment of an oil reservoir geological model to obtain the oil reservoir geological model.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the determination method of the characteristic parameters of the seepage field when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program for executing the method for determining characteristic parameters of a seepage field.
In summary, in the embodiments of the present invention, change data of the seepage field characteristic parameter response factor under the mesoscale and/or macroscale is obtained according to geophysical data and/or geological data; carrying out normalization processing on the change data to obtain characteristic parameters of a seepage field; compared with the technical scheme of researching the seepage field by adopting geology and engineering methods in the prior art, the method not only can simply and efficiently determine the characteristic parameters of the seepage field, but also greatly improves the simulation accuracy of the seepage field, has superior practicability, is beneficial to more pertinently deploying the development scheme and adjusting the injection-flooding relation during later development, and provides guidance for improving the recovery ratio of the oil field.
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.
In summary, the embodiment of the present invention increases the data dimension and precision in the seepage field characteristic parameter determination method through the application to the seismic data, and reduces the data error between the simulation process and the actual application, thereby improving the simulation accuracy of the seepage field. And the process is simple and efficient, and the practical effect is greatly increased. In later development, the development scheme can be better and pertinently deployed, the injection-flooding relation is adjusted, and guidance is provided for improving the recovery ratio of the oil field.
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.
Claims (17)
1. A method for determining characteristic parameters of a seepage field is characterized by comprising the following steps:
acquiring change data of a seepage field characteristic parameter response factor under mesoscale and/or macroscale according to geophysical data and/or geological data; the mesoscopic scale is a logging scale; the macroscopic scale is a ground seismic scale, a borehole seismic scale or a gravity electromagnetic scale;
and carrying out normalization processing on the change data to obtain characteristic parameters of the seepage field.
2. The method of claim 1, wherein the geophysical data comprises: one or any combination of logging data, surface seismic data, borehole seismic data and gravity magnetic and electric data.
3. The method of claim 1, wherein the geological data comprises: and one or any combination of the earth stress data, the structural characteristic data, the reservoir characteristic data and the reservoir characteristic data.
4. The method of claim 1, wherein the resolution of the mesoscopic scale ranges from [0.1 meters, 5 meters ].
5. The method of claim 1, wherein the resolution at the macro scale is in the range of [5 meters, 50 meters ].
6. The method of claim 1, further comprising:
and taking the characteristic parameters of the seepage field as input, taking the variation data of the characteristic parameters of the seepage field of core analysis or experimental analysis as known samples for input, and optimizing the characteristic parameters of the seepage field through neural network operation.
7. The method of claim 1, wherein the variation data is normalized to obtain a seepage field characteristic parameter according to the following formula:
wherein, FmRepresenting characteristic parameters of the seepage field, G representing the variation data, GminRepresents the minimum value of the variation data, gmaxRepresents the maximum value of the variation data.
8. The method of claim 7, wherein the seepage field characteristic parameter response factor comprises: time-lapse seismic amplitude, time-lapse seismic wave impedance, seepage field characteristic parameter factors, or reservoir-related parameters simulated by a reservoir geological model.
9. The method of claim 8, wherein if the seepage field characteristic parameter response factor is a time lapse seismic amplitude, obtaining a seepage field characteristic parameter according to the following formula:
wherein Δ a represents the rate of change of amplitude differences for different development sessions;
A1representing the seismic response amplitude of a target layer at the initial stage of reservoir development;
ΔA2representing the difference of the seismic amplitude of the high water-cut period and the seismic amplitude of the medium water-cut period;
ΔA1representing the difference in seismic amplitude between the mid water phase and the initial reservoir development phase.
10. The method of claim 8, wherein if the seepage field characteristic parameter response factor is time-lapse seismic wave impedance, obtaining a seepage field characteristic parameter according to the following formula:
wherein, the delta I represents the difference change rate of the seismic inversion wave impedance of different development periods;
I1representing the target layer wave impedance at the initial stage of oil reservoir development;
ΔI2the wave impedance difference between the high water-cut period and the medium water-cut period is represented;
ΔI1representing the difference in wave impedance between the mid water phase and the initial reservoir development phase.
11. The method according to claim 8, wherein if the seepage field characteristic parameter response factor is a seepage field characteristic parameter factor, the seepage field characteristic parameter is obtained according to the following formula:
wherein, the delta D represents the difference change rate of the characteristic parameter factors of the secondary seepage field in different development periods;
D1representing a characteristic parameter factor of a seepage field of a target layer at the initial stage of oil reservoir development;
ΔD2representing the difference of characteristic parameter factors of the seepage field between the high water cut period and the medium water cut period;
ΔD1and (4) representing the difference of characteristic parameter factors of the seepage field between the water-containing period and the initial oil reservoir development period.
12. The method of claim 11, further comprising:
and obtaining related parameters of the oil reservoir by utilizing logging interpretation, searching the relation between the related parameters of the oil reservoir and the change of the seepage field by analyzing the change rate of the related parameters of the oil reservoir, and fitting to obtain characteristic parameter factors of the seepage field.
13. The method of claim 8, wherein if the seepage field characteristic parameter response factor is a reservoir-related parameter simulated using a reservoir geological model, the method further comprises:
according to the drilled well data, restraining the establishment of a velocity field and a Q field in seismic data processing;
performing deterministic inversion on seismic data obtained by viscoelastic wave equation migration;
and performing oil reservoir parameter inversion on the basis of rock physical analysis, and constraining the establishment of an oil reservoir geological model to obtain the oil reservoir geological model.
14. A seepage field characteristic parameter determination apparatus, comprising:
the parameter acquisition module is used for acquiring change data of the seepage field characteristic parameter response factors under the mesoscale and/or the macroscale according to the geophysical data and/or the geological data; the mesoscopic scale is a logging scale; the macroscopic scale is a ground seismic scale, a borehole seismic scale or a gravity electromagnetic scale;
and the calculation determination module is used for carrying out normalization processing on the change data to obtain the characteristic parameters of the seepage field.
15. The apparatus of claim 14, further comprising:
and the result optimization module is used for taking the seepage field characteristic parameters as input, taking the seepage field characteristic parameter change data of core analysis or experimental analysis as known sample input, and optimizing the seepage field characteristic parameters through neural network operation.
16. 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 13 when executing the computer program.
17. 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 13.
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