CN108681793B - Deep reservoir oil extraction index prediction method and device - Google Patents
Deep reservoir oil extraction index prediction method and device Download PDFInfo
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Abstract
The embodiment of the application provides a deep reservoir oil recovery index prediction method and a deep reservoir oil recovery index prediction device, wherein the method comprises the following steps: determining a relation curve of the overburden permeability and the net effective overburden of the deep oil reservoir; determining the pressure sensitivity coefficient of the deep reservoir according to the relation curve; generating an oil extraction index prediction model according to the pressure sensitivity coefficient; and carrying out oil recovery index prediction on the deep oil reservoir according to the oil recovery index prediction model. The method and the device can quantitatively predict the oil recovery index of the deep oil reservoir, so that the accuracy of predicting the oil recovery index of the deep oil reservoir is improved.
Description
Technical Field
The application relates to the technical field of oil and gas development, in particular to a deep reservoir oil recovery index prediction method and device.
Background
The research of the oil reservoir oil recovery index prediction method starts from Darcy's law provided by Darcy of French hydraulic engineers in 1856 by experiments, and considers that the flow and the pressure difference are in a linear relationship, and the seepage process is a process of consuming energy and overcoming resistance to obtain the flow and is a basic law of seepage mechanics. In fact, during the seepage process, the liquid is always in contact with the surface of the rock pores, thus generating a certain force which is a resistance to seepage, called additional resistance. When the external force is large, the additional resistance is small and can be ignored; however, when the external force is relatively small, i.e., the seepage velocity is low and the contact surface (low-permeability rock), the additional resistance becomes relatively large. In the low-speed seepage process, active substances such as naphthenic acid, colloid and aldehyde contained in petroleum can be in sufficient contact with the surface of rock to generate adsorption, meanwhile, the rock contains clay components, and the clay has the capacity of attracting polar molecules, so that the crude oil in contact with the surface of the rock is in a bound state to form a certain amount of retentate, and the lower the seepage speed is, the more the retentate is, the higher the additional resistance is, and the calculation of the flow rate can deviate from the value of Darcy's law under the low-speed seepage condition.
Permeability also changes with reservoir pressure, known as the pressure sensitive effect, and can be described by the pressure sensitivity coefficient. The reservoir pressure sensitivity coefficient is a parameter reflecting the sensitivity of reservoir permeability to pressure, changes along with the change of permeability, and mainly influences seepage. At present, Zhao Ming Yuan et al researches the pressure sensitivity of reservoir parameters through a overburden pore permeability test, the overburden permeability and the effective overburden change in an exponential descending manner, and the overburden porosity and the effective overburden change in a secondary three-item descending manner. Shexingli et al, by studying the stress sensitivity of Krah 2 gas field reservoir rock, found that the sensitivity of the petrophysical properties to stress was overall less, with the porosity being the smallest, permeability being the second, and compressibility being the largest. The influence of effective stress on the pore permeability parameters of low-pore low-permeability media is researched by high arborescence and the like, the effective stress acting on rocks is considered to have large influence on permeability and generate irreversible damage, the influence on porosity is small, and the porosity compression coefficient is only a function of the effective stress. Aiming at the reservoir characteristics of different types of oil reservoirs in Wensha et al, the Li Wenhai et al obtains the recognition that the net overburden pressure has obvious influence on the porosity, permeability and rock compression coefficient of the reservoir and has little influence on the pore structure of the reservoir rock.
However, the above studies are qualitative ones, and at present, there is no method that can quantitatively predict the change condition of the oil recovery index of the deep reservoir based on the permeability pressure sensitivity coefficient, and it is difficult to be practically applied to the development of the deep reservoir.
Disclosure of Invention
The embodiment of the application aims to provide a deep oil reservoir oil recovery index prediction method and device so as to achieve quantitative prediction of an oil recovery index of a deep oil reservoir.
In order to achieve the above object, in one aspect, an embodiment of the present application provides a deep reservoir oil recovery index prediction method, including:
determining a relation curve of the overburden permeability and the net effective overburden of the deep oil reservoir;
determining the pressure sensitivity coefficient of the deep reservoir according to the relation curve;
generating an oil extraction index prediction model according to the pressure sensitivity coefficient;
and carrying out oil recovery index prediction on the deep oil reservoir according to the oil recovery index prediction model.
The method for predicting the oil recovery index of the deep reservoir according to the embodiment of the application, which is used for determining the relationship curve between the overburden permeability and the net effective overburden pressure of the deep reservoir, comprises the following steps:
acquiring overburden permeability data and net effective overburden pressure data obtained by carrying out an overburden pressure test on a rock sample of a deep oil reservoir;
and fitting the overburden permeability data and the net effective overburden pressure data to obtain a relation curve of the overburden permeability of the deep oil reservoir and the net effective overburden pressure.
The method for predicting the deep reservoir oil recovery index according to the embodiment of the application, wherein the step of determining the pressure sensitivity coefficient of the deep reservoir according to the relation curve comprises the following steps:
acquiring a relation curve between the overburden permeability and the net effective overburden of a plurality of rock samples of the deep oil reservoir;
and making a cross plot of the pressure sensitivity coefficient and the overburden permeability in the relation curve of the overburden permeability and the net effective overburden permeability of the plurality of rock samples, and performing regression to obtain the pressure sensitivity coefficient of the deep oil reservoir.
According to the deep reservoir oil recovery index prediction method, the relationship curve of the overburden permeability and the net effective overburden of the deep reservoir comprises the following steps:
wherein k is the overburden permeability of the deep reservoir, khHorizontal permeability, k, for deep reservoirsiPermeability when the net effective overburden pressure of the deep reservoir is zero, e is a natural constant, alpha is a reservoir pressure sensitivity coefficient, and p isiFor supplying pressure, p, to deep reservoirswThe bottom flowing pressure of the production well of the deep oil reservoir.
According to the deep reservoir oil recovery index prediction method, the oil recovery index prediction model comprises the following steps:
wherein, J0Is the oil recovery index, k, of the deep reservoirhHorizontal permeability, k, for deep reservoirsiPermeability when the net effective overburden pressure of the deep reservoir is zero, alpha is reservoir pressure sensitivity coefficient, and p isiFor supplying pressure, p, to deep reservoirswIs the bottom hole flowing pressure of the deep reservoir, h is the oil layer thickness of the deep reservoir, muoCrude oil viscosity for deep reservoirs, BoIs the volume coefficient of crude oil, r, of a deep reservoireFor the radius of supply of deep reservoirs, rwThe radius of a shaft of the deep oil reservoir, S is the skin coefficient of the deep oil reservoir, e is a natural constant, and a and b are constants.
On the other hand, the embodiment of the present application further provides a deep reservoir oil recovery index prediction device, including:
the relation curve determining module is used for determining a relation curve between the overburden permeability and the net effective overburden pressure of the deep oil reservoir;
the sensitivity coefficient determining module is used for determining the pressure sensitivity coefficient of the deep oil reservoir according to the relation curve;
the prediction model generation module is used for generating an oil extraction index prediction model according to the pressure sensitivity coefficient;
and the oil recovery index prediction module is used for predicting the oil recovery index of the deep oil reservoir according to the oil recovery index prediction model.
The deep reservoir oil recovery index prediction device of the embodiment of the application, determining the relationship curve of the overburden permeability and the net effective overburden of the deep reservoir comprises:
acquiring overburden permeability data and net effective overburden pressure data obtained by carrying out an overburden pressure test on a rock sample of a deep oil reservoir;
and fitting the overburden permeability data and the net effective overburden pressure data to obtain a relation curve of the overburden permeability of the deep oil reservoir and the net effective overburden pressure.
The deep reservoir oil recovery index prediction device according to the embodiment of the application, determining the pressure sensitivity coefficient of the deep reservoir according to the relation curve, includes:
acquiring a relation curve between the overburden permeability and the net effective overburden of a plurality of rock samples of the deep oil reservoir;
and making a cross plot of the pressure sensitivity coefficient and the overburden permeability in the relation curve of the overburden permeability and the net effective overburden permeability of the plurality of rock samples, and performing regression to obtain the pressure sensitivity coefficient of the deep oil reservoir.
The deep reservoir oil recovery index prediction device of the embodiment of the application, the relationship curve of the overburden permeability and the net effective overburden of the deep reservoir comprises:
wherein k is the overburden permeability of the deep reservoir, khHorizontal permeability, k, for deep reservoirsiPermeability when the net effective overbalance of the deep reservoir is zero, e is the natural normalNumber, alpha is the reservoir pressure sensitivity coefficient, piFor supplying pressure, p, to deep reservoirswThe bottom flowing pressure of the production well of the deep oil reservoir.
The deep reservoir oil recovery index prediction device of the embodiment of the application, the oil recovery index prediction model comprises:
wherein, J0Is the oil recovery index, k, of the deep reservoirhHorizontal permeability, k, for deep reservoirsiPermeability when the net effective overburden pressure of the deep reservoir is zero, alpha is reservoir pressure sensitivity coefficient, and p isiFor supplying pressure, p, to deep reservoirswIs the bottom hole flowing pressure of the deep reservoir, h is the oil layer thickness of the deep reservoir, muoCrude oil viscosity for deep reservoirs, BoIs the volume coefficient of crude oil, r, of a deep reservoireFor the radius of supply of deep reservoirs, rwThe radius of a shaft of the deep oil reservoir, S is the skin coefficient of the deep oil reservoir, e is a natural constant, and a and b are constants.
According to the technical scheme provided by the embodiment of the application, the pressure sensitive effect of the permeability of the deep oil reservoir is considered, and the oil recovery index of the deep oil reservoir can be quantitatively predicted, so that the applicability of calculation of the production energy of the deep oil reservoir is enhanced, and the accuracy of prediction of the oil recovery index of the deep oil reservoir is improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort. In the drawings:
FIG. 1 is a flow chart of a deep reservoir index prediction method according to an embodiment of the present disclosure;
FIG. 2 is a graphical illustration of the relationship between overburden permeability and net effective overburden in one embodiment of the present application;
FIG. 3 is a graph illustrating the relationship between overburden permeability and pressure sensitivity coefficient according to an embodiment of the present disclosure;
FIG. 4 is a graphical representation of a comparison of measured oil-recovery index, oil-recovery index predicted using the prior art, and oil-recovery index predicted using an embodiment of the present application;
fig. 5 is a block diagram illustrating a deep reservoir index prediction apparatus according to an embodiment of the present disclosure;
fig. 6 is a block diagram illustrating a deep reservoir index prediction apparatus according to another embodiment of the present disclosure.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application. For example, in the following description, forming the second component over the first component may include embodiments in which the first and second components are formed in direct contact, embodiments in which the first and second components are formed in non-direct contact (i.e., additional components may be included between the first and second components), and so on.
Also, for ease of description, some embodiments of the present application may use spatially relative terms such as "above …," "below …," "top," "below," etc., to describe the relationship of one element or component to another (or other) element or component as illustrated in the various figures of the embodiments. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements or components described as "below" or "beneath" other elements or components would then be oriented "above" or "over" the other elements or components.
Referring to fig. 1, a deep reservoir oil recovery index prediction method according to an embodiment of the present application may include the following steps:
s101, determining a relation curve between the overburden permeability and the net effective overburden pressure of the deep oil reservoir.
In an embodiment of the present application, the determining the relationship between the overburden permeability and the net effective overburden pressure of the deep oil reservoir can be implemented by:
1) and acquiring overburden permeability data and net effective overburden pressure data obtained by carrying out the overburden pressure test on the rock sample of the deep oil reservoir.
2) And fitting the overburden permeability data and the net effective overburden pressure data to obtain a relation curve of the overburden permeability of the deep oil reservoir and the net effective overburden pressure.
In an embodiment of the present application, a relationship curve between the overburden permeability and the net effective overburden pressure of the deep oil reservoir can be represented by the following formula:
wherein k is the overburden permeability of the deep reservoir, khHorizontal permeability, k, for deep reservoirsiPermeability when the net effective overburden pressure of the deep reservoir is zero, e is a natural constant, alpha is a reservoir pressure sensitivity coefficient, and p isiFor supplying pressure, p, to deep reservoirswThe bottom flowing pressure of the production well of the deep oil reservoir.
And S102, determining the pressure sensitivity coefficient of the deep oil reservoir according to the relation curve.
In an embodiment of the present application, the determining the pressure sensitivity coefficient of the deep reservoir according to the relationship curve may be implemented by:
1) and acquiring a relation curve between the overburden permeability and the net effective overburden pressure of a plurality of rock samples of the deep oil reservoir.
In an exemplary embodiment of the present application, the overburden permeability versus net effective overburden for a plurality of rock samples of the deep reservoir may be as shown in fig. 2;
2) and making a cross plot of the pressure sensitivity coefficient and the overburden permeability in the relation curve of the overburden permeability and the net effective overburden permeability of the plurality of rock samples, and performing regression to obtain the pressure sensitivity coefficient of the deep oil reservoir. Specifically, the pressure sensitivity coefficient and the overburden permeability in the relation curve of the overburden permeability and the net effective overburden permeability of the plurality of rock samples are subjected to intersection to obtain the relation curve of the pressure sensitivity coefficient and the overburden permeability, and then the actually measured overburden permeability is used as input to be substituted into the relation curve, so that the corresponding pressure sensitivity coefficient can be obtained.
In an exemplary embodiment of the present application, the relationship between the pressure sensitivity coefficient and the overburden permeability of the deep reservoir may be as shown in fig. 3.
And S103, generating an oil extraction index prediction model according to the pressure sensitivity coefficient.
In an embodiment of the present application, the oil-production index prediction model generated according to the pressure sensitivity coefficient may be represented by the following formula:
wherein, J0Is the oil recovery index, k, of the deep reservoirhHorizontal permeability, k, for deep reservoirsiThe permeability of the deep reservoir when the net effective overburden pressure is zero is shown, alpha is a reservoir pressure sensitivity coefficient, the higher the alpha value is, the more sensitive the permeability is to the pressure is shown, and p isiFor supplying pressure, p, to deep reservoirswIs the bottom hole flowing pressure of the deep reservoir, h is the oil layer thickness of the deep reservoir, muoCrude oil viscosity for deep reservoirs, BoIs the volume coefficient of crude oil, r, of a deep reservoireFor the radius of supply of deep reservoirs, rwThe radius of a shaft of the deep oil reservoir, S is the skin coefficient of the deep oil reservoir, e is a natural constant, a and b are constants。
And S104, carrying out oil recovery index prediction on the deep oil reservoir according to the oil recovery index prediction model.
In one embodiment of the present application, the above formula is used
For example, after the parameter values of the input variables on the right side of the equation are determined, the parameter values are substituted into the equation, so that the corresponding oil-recovery index can be obtained. It can be seen that the obtained oil extraction index changes correspondingly with the change of the parameter value of each input variable, so that the change rule of the oil extraction index of the deep oil reservoir is reflected.
In an exemplary embodiment of the present application, fig. 4 shows the results of comparing the measured oil-recovery index of two production wells in field 4 of a tower, the oil-recovery index predicted using the prior art, and the oil-recovery index predicted using an embodiment of the present application. Compared with the prior art, the oil recovery index predicted by the embodiment of the application is closer to the actually measured oil recovery index, so that the deep reservoir oil recovery index prediction method of the embodiment of the application is verified to have higher prediction accuracy.
Referring to fig. 5, a deep reservoir oil recovery index prediction apparatus according to an embodiment of the present application may include a memory, a processor, and a computer program stored on the memory, the computer program when executed by the processor performs the following steps:
determining a relation curve of the overburden permeability and the net effective overburden of the deep oil reservoir;
determining the pressure sensitivity coefficient of the deep reservoir according to the relation curve;
generating an oil extraction index prediction model according to the pressure sensitivity coefficient;
and carrying out oil recovery index prediction on the deep oil reservoir according to the oil recovery index prediction model.
While the process flows described above include operations that occur in a particular order, it should be appreciated that the processes may include more or less operations that are performed sequentially or in parallel (e.g., using parallel processors or a multi-threaded environment).
Referring to fig. 6, another deep reservoir index prediction apparatus of the present application may include:
the relation curve determining module 61 can be used for determining a relation curve between the overburden permeability and the net effective overburden pressure of the deep oil reservoir;
a sensitivity coefficient determining module 62, configured to determine a pressure sensitivity coefficient of the deep reservoir according to the relationship curve;
a prediction model generation module 63, configured to generate an oil index prediction model according to the pressure sensitivity coefficient;
and the oil recovery index prediction module 64 can be used for carrying out oil recovery index prediction on the deep oil reservoir according to the oil recovery index prediction model.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), 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 a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (8)
1. A deep reservoir oil recovery index prediction method is characterized by comprising the following steps:
determining a relation curve of the overburden permeability and the net effective overburden of the deep oil reservoir;
determining the pressure sensitivity coefficient of the deep reservoir according to the relation curve;
generating an oil extraction index prediction model according to the pressure sensitivity coefficient;
carrying out oil extraction index prediction on the deep oil reservoir according to the oil extraction index prediction model;
wherein the oil recovery index prediction model comprises:
wherein, J0Is the oil recovery index, k, of the deep reservoirhHorizontal permeability, k, for deep reservoirsiPermeability when the net effective overburden pressure of the deep reservoir is zero, alpha is reservoir pressure sensitivity coefficient, and p isiFor supplying pressure, p, to deep reservoirswIs the bottom hole flowing pressure of the deep reservoir, h is the oil layer thickness of the deep reservoir, muoCrude oil viscosity for deep reservoirs, BoIs the volume coefficient of crude oil, r, of a deep reservoireFor the radius of supply of deep reservoirs, rwThe radius of a shaft of the deep oil reservoir, S is the skin coefficient of the deep oil reservoir, e is a natural constant, and a and b are constants.
2. The deep reservoir pay index prediction method of claim 1, wherein determining the overburden permeability versus net effective overburden curve for the deep reservoir comprises:
acquiring overburden permeability data and net effective overburden pressure data obtained by carrying out an overburden pressure test on a rock sample of a deep oil reservoir;
and fitting the overburden permeability data and the net effective overburden pressure data to obtain a relation curve of the overburden permeability of the deep oil reservoir and the net effective overburden pressure.
3. The deep reservoir index prediction method of claim 1, wherein determining the pressure sensitivity coefficient of the deep reservoir from the relationship curve comprises:
acquiring a relation curve between the overburden permeability and the net effective overburden of a plurality of rock samples of the deep oil reservoir;
and making a cross plot of the pressure sensitivity coefficient and the overburden permeability in the relation curve of the overburden permeability and the net effective overburden permeability of the plurality of rock samples, and performing regression to obtain the pressure sensitivity coefficient of the deep oil reservoir.
4. The deep reservoir pay index prediction method of claim 1, wherein the curve relating overburden permeability to net effective overburden pressure of the deep reservoir comprises:
wherein k is the overburden permeability of the deep reservoir.
5. A deep reservoir oil recovery index prediction device, comprising:
the relation curve determining module is used for determining a relation curve between the overburden permeability and the net effective overburden pressure of the deep oil reservoir;
the sensitivity coefficient determining module is used for determining the pressure sensitivity coefficient of the deep oil reservoir according to the relation curve;
the prediction model generation module is used for generating an oil extraction index prediction model according to the pressure sensitivity coefficient;
the oil recovery index prediction module is used for predicting the oil recovery index of the deep oil reservoir according to the oil recovery index prediction model;
wherein the oil recovery index prediction model comprises:
wherein, J0Is the oil recovery index, k, of the deep reservoirhHorizontal permeability, k, for deep reservoirsiPermeability when the net effective overburden pressure of the deep reservoir is zero, alpha is reservoir pressure sensitivity coefficient, and p isiFor supplying pressure, p, to deep reservoirswIs the bottom hole flowing pressure of the deep reservoir, h is the oil layer thickness of the deep reservoir, muoCrude oil viscosity for deep reservoirs, BoIs the volume coefficient of crude oil, r, of a deep reservoireFor the radius of supply of deep reservoirs, rwThe radius of a shaft of the deep oil reservoir, S is the skin coefficient of the deep oil reservoir, e is a natural constant, and a and b are constants.
6. The deep reservoir pay index prediction device of claim 5, wherein determining a plot of overburden permeability versus net effective overburden for the deep reservoir comprises:
acquiring overburden permeability data and net effective overburden pressure data obtained by carrying out an overburden pressure test on a rock sample of a deep oil reservoir;
and fitting the overburden permeability data and the net effective overburden pressure data to obtain a relation curve of the overburden permeability of the deep oil reservoir and the net effective overburden pressure.
7. The deep reservoir index prediction device of claim 5, wherein the determining the pressure sensitivity coefficient of the deep reservoir from the relationship curve comprises:
acquiring a relation curve between the overburden permeability and the net effective overburden of a plurality of rock samples of the deep oil reservoir;
and making a cross plot of the pressure sensitivity coefficient and the overburden permeability in the relation curve of the overburden permeability and the net effective overburden permeability of the plurality of rock samples, and performing regression to obtain the pressure sensitivity coefficient of the deep oil reservoir.
8. The deep reservoir pay index prediction device of claim 5, wherein the deep reservoir plot of overburden permeability versus net effective overburden comprises:
wherein k is the overburden permeability of the deep reservoir.
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