Method and apparatus for predicting oil production amount under polymer-surfactant binary flooding

Abstract

Disclosed in the present invention are a method and apparatus for predicting an oil production amount under polymer-surfactant binary flooding. The method comprises: when oil displacement is performed on different well groups in the same oil reservoir by means of polymer-surfactant binary flooding, acquiring dynamic parameter data before the polymer-surfactant binary flooding and static parameter data of an oil reservoir stratum; inputting, into a prediction model for an oil production amount per ton of polymer, the dynamic parameter data before the polymer-surfactant binary flooding and the static parameter data of the oil reservoir stratum, and outputting a prediction result for the oil production amount per ton of polymer for the polymer-surfactant binary flooding, wherein the prediction model for an oil production amount per ton of polymer is obtained by means of performing fitting on historical dynamic parameter data and static parameter data of different well groups which have been subjected to the polymer-surfactant binary flooding, and actually collected oil production amounts per ton of polymer of the different well groups, and the prediction model for an oil production amount per ton of polymer is a multiple linear regression model. Thus, oil production amounts per ton of polymer of different well groups to be subjected to polymer-surfactant binary flooding in an oil reservoir can be predicted accurately and rapidly, thereby facilitating smooth implementation of a petroleum development project, and preventing the risk of an investment decision.

Description

Method and device for predicting oil production of polymer-surface binary flooding Technical Field

[0001] The present invention relates to the technical field of oil reservoir development engineering, and in particular to a method and device for predicting oil production of polymer-surface binary flooding. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. No statement herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] Polymer-surfactant binary flooding (abbreviated as binary flooding) refers to an oil recovery method in which two chemical agents, polymers and surfactants, are mixed with water to form aqueous solutions of different molecular weights and concentrations, which are then injected into the formation for oil displacement. Compared to pure polymer flooding, polymer-surfactant binary flooding shares the working principle of polymer viscoelastic fluids, which "holds pressure to store energy and expands the spread." KPS surfactant solution has the mechanism of "micelle solubilization and emulsification to carry oil," which improves oil displacement efficiency. When the two are mixed, "synergistic enhancement" can significantly increase the recovery rate. This technology also solves the harmful effects of the use of alkali in ternary composite flooding, achieving a green transformation of chemical composite flooding technology from "alkaline" to "alkaline-free," and is being used in tertiary oilfield development.

[0004] At present, there is no effective method to accurately and quickly predict the oil production capacity (tons of oil production) of binary flooding reservoir well groups, which affects the smooth implementation of oil development projects and easily leads to investment decision-making risks.

[0005] Summary of the Invention

[0006] The present invention provides a method for predicting the oil production of a polymer-surface binary flooding, which is used to accurately and quickly predict the oil production per ton of polymer in a binary flooding oil reservoir well group, thereby facilitating the smooth implementation of oil development projects and avoiding investment decision-making risks. The method includes:

[0007] When using polymer-surface binary flooding to flood different well groups in the reservoir, obtain dynamic parameter data before polymer-surface binary flooding and static parameter data of the reservoir;

[0008] The dynamic parameter data before the polymer-surface binary flooding and the static parameter data of the oil reservoir are input into the ton-polymer oil production prediction model, and the prediction result of the ton-polymer oil production of the polymer-surface binary flooding is output; the ton-polymer oil production prediction model is obtained by fitting the historical dynamic parameter data and static parameter data of different well groups that have completed the polymer-surface binary flooding, as well as the actual collected ton-polymer oil production of the polymer-surface binary flooding, and the ton-polymer oil production prediction model of the polymer-surface binary flooding is a multiple linear regression model.

[0009] The present invention also provides a device for predicting the oil production of a polymer-surface binary flooding, which is used to accurately and quickly predict the oil production per ton of polymer in a binary flooding oil reservoir well group, thereby facilitating the smooth implementation of oil development projects and avoiding investment decision-making risks. The device includes:

[0010] The parameter data acquisition module is used to obtain dynamic parameter data before polymer-surface binary flooding and static parameter data of the reservoir when using polymer-surface binary flooding to flood different well groups in the reservoir;

[0011] The ton-polymer oil production prediction module is used to input the dynamic parameter data before the polymer-surface binary flooding and the static parameter data of the oil reservoir into the ton-polymer oil production prediction model, and output the prediction result of the ton-polymer oil production of the polymer-surface binary flooding; the ton-polymer oil production prediction model is obtained by fitting the historical dynamic parameter data and static parameter data of different well groups that have completed the polymer-surface binary flooding, as well as the actual collected ton-polymer oil production of the polymer-surface binary flooding, and the ton-polymer oil production prediction model of the polymer-surface binary flooding is a multiple linear regression model.

[0012] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned method for predicting oil production of poly-surface binary flooding is implemented.

[0013] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for predicting oil production of the above-mentioned poly-surface binary flooding is implemented.

[0014] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for predicting the oil production of the polymer-surface binary flooding.

[0015] In an embodiment of the present invention, when different well groups in an oil reservoir are driven by a polymer-surface binary drive, dynamic parameter data before the polymer-surface binary drive and static parameter data of the oil reservoir are obtained; the dynamic parameter data before the polymer-surface binary drive and the static parameter data of the oil reservoir are input into a ton-polymer oil production prediction model, and a prediction result of the ton-polymer oil production of the polymer-surface binary drive is output; the ton-polymer oil production prediction model is obtained by fitting the historical dynamic parameter data and static parameter data of different well groups that have completed the polymer-surface binary drive and the actual collected ton-polymer oil production of the polymer-surface binary drive. The ton-polymer oil production prediction model for the polymer-surface binary drive is a multiple linear regression model, which can accurately and quickly predict the ton-polymer oil production of the binary drive oil reservoir well group, which is conducive to the smooth implementation of the oil development project and avoids investment decision-making risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0017] FIG1 is a method for predicting oil production of polymer-surface binary flooding according to an embodiment of the present invention;

[0018] FIG2 is a diagram showing the relationship between gasoline ratio and saturation pressure in an embodiment of the present invention;

[0019] FIG3 is a diagram showing the contact relationship between different single sand bodies in a typical reservoir section according to an embodiment of the present invention;

[0020] FIG4 is a diagram showing the relationship between different communication modes between single sand bodies and oil volume in an embodiment of the present invention;

[0021] FIG5 is a flow chart of a method for fitting a per-ton oil production prediction model according to an embodiment of the present invention;

[0022] Figures 6(a) to 6(h) are diagrams showing the relationship between different types of parameter data and oil production per ton of polymer according to an embodiment of the present invention;

[0023] 7 is a schematic structural diagram of a device for predicting oil production of polymer-surface binary flooding according to an embodiment of the present invention;

[0024] FIG8 is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0026] First, the technical terms in the embodiments of the present invention are introduced:

[0027] Multiple Linear Regression: In regression analysis, if there are two or more independent variables, it is called multiple regression. In fact, a phenomenon is often related to multiple factors. Predicting or estimating the dependent variable by using the optimal combination of multiple independent variables is more effective and more realistic than using only one independent variable.

[0028] The inventors discovered that when statistically plotting the relationship between reservoir static parameters, the state parameters of the polymer-surface dual drive, and ton-of-polymer oil production on a chart, and establishing a mathematical model for predicting ton-of-polymer oil production based on a single parameter, significant errors occurred when comparing the predicted data with the actual data. To address this technical problem, the inventors proposed a method for predicting ton-of-polymer oil production using polymer-surface dual drive. This method comprehensively considers reservoir static parameters and the state parameters of the polymer-surface dual drive, and combines multiple regression to establish a new ton-of-polymer oil production prediction model to improve the accuracy of polymer-surface dual drive oil production prediction.

[0029] FIG1 is a method for predicting the oil production of a polymer-surface-carbon dual flooding according to an embodiment of the present invention. As shown in FIG1 , the method for predicting the oil production of a polymer-surface-carbon dual flooding according to an embodiment of the present invention may include:

[0030] Step 101: When flooding different well groups in an oil reservoir using a polymer-surface binary flooding method, obtain dynamic parameter data before the polymer-surface binary flooding and static parameter data of the oil reservoir;

[0031] Step 102: The dynamic parameter data before the polymer-surface binary flooding and the static parameter data of the oil reservoir are input into a ton-polymer oil production prediction model, and a prediction result of the ton-polymer oil production of the polymer-surface binary flooding is output; the ton-polymer oil production prediction model is obtained by fitting the historical dynamic parameter data and static parameter data of different well groups that have completed the polymer-surface binary flooding, as well as the actual collected ton-polymer oil production of the polymer-surface binary flooding, and the ton-polymer oil production prediction model of the polymer-surface binary flooding is a multiple linear regression model.

[0032] The following describes the specific steps of the method for predicting oil production of polymer-surface binary flooding according to the embodiment of the present invention:

[0033] In step 101, before adopting the poly-surface binary flooding method to flood the oil reservoir well group, various data and information of the mine where the oil reservoir well group is located can be collected in real time, and the dynamic parameter data of the poly-surface binary flooding and the static parameter data of the oil reservoir reservoir can be screened out from the various data and information; among them, the dynamic parameter data of the poly-surface binary flooding can characterize the dynamic changes of related data during the oil flooding process, and the static parameter data of the oil reservoir reservoir can characterize various static characteristics inherent in the oil reservoir reservoir.

[0034] In one embodiment, the dynamic parameter data of the polymer-table binary flooding may include the following data: the ton polymer oil production of different well groups in the reservoir after the binary flooding is implemented, the formation pressure of different well groups in the reservoir before the binary flooding is implemented, and the secondary saturation pressure of the reservoir before the binary flooding is implemented;

[0035] The static parameter data of the oil reservoir may include the following data: effective connected thickness of the well group, permeability of the effective connected thickness section of the well group, and permeability difference of the effective connected thickness section of the well group.

[0036] It should be noted that the oil production per ton of polymer is the crude oil production of the well group under the consumption of unit polymer, the amount of chemical agent used in the polymer-surfactant binary flooding is the amount of polymer used plus the amount of surfactant converted into polymer by price ratio; the secondary saturation pressure is the saturation pressure under reservoir conditions after the end of water flooding and before the start of binary flooding.

[0037] FIG2 is a graph showing the relationship between the gasoline ratio and the saturation pressure in an embodiment of the present invention. As shown in FIG2 , the reservoir temperature is considered constant. Under the original formation conditions, the measured saturation pressure is called the original saturation pressure, and the amount of natural gas dissolved in a unit volume or weight of crude oil is called the original gas-oil ratio. Through the initial testing of the reservoir, a linear relationship between the gas-oil ratio and the saturation pressure can be obtained, that is, the relationship between the original saturation pressure and the original gas-oil ratio is obtained. After the reservoir is developed, as the formation fluid is continuously produced, the formation pressure gradually decreases, the dissolved natural gas in the formation fluid gradually precipitates, and the dissolved gas-oil ratio gradually decreases. When the reservoir is developed to a certain period, the remaining dissolved gas volume in the reservoir and the corresponding dissolved gas-oil ratio can be obtained. The corresponding saturation pressure is obtained according to the linear relationship between the gas-oil ratio and the saturation pressure. Under this pressure condition, the foam flow formed prevents the dissolved gas from overflowing, prompting the dissolved gas to be redistributed inside the reservoir, reaching a temporary secondary equilibrium. At this time, the corresponding formation pressure is called the secondary saturation pressure.

[0038] In one embodiment, the static parameter data of the oil reservoir may include the following data: effective connected thickness of the well group, permeability of the effective connected thickness section of the well group, and permeability difference of the effective connected thickness section of the well group.

[0039] Next, the meaning of the effective connectivity thickness of the oil layer will be introduced. Figure 3 shows the contact relationship between different single sand bodies in a typical reservoir section in an embodiment of the present invention. The contact relationship can specifically include splicing connectivity and direct connectivity. Figure 4 shows the relationship between different connectivity modes between single sand bodies and oil volume. As shown in Figure 4, by establishing the relationship between different connectivity modes between single sand bodies and oil volume during the peak period of polymer-surface binary flooding, it was found that it was closely related to the connectivity mode between single sand bodies (splicing connectivity or direct connectivity). The concept of "effective connectivity thickness" was introduced: Due to the different connectivity modes of single sand bodies, the actual oil production in the polymer-surface binary flooding process depends on the minimum connectivity thickness of the oil layer between oil and water wells, which is the "effective connectivity thickness". The permeability difference of the effective connectivity thickness section is inversely proportional to the permeability of the single well group in the test area. A static parameter reflecting the binary flooding reservoir, namely the binary flooding coefficient, was constructed:

[0040] After obtaining the dynamic parameter data of the polymer-surface binary drive and the static parameter data of the oil reservoir, the dynamic parameter data of the polymer-surface binary drive and the static parameter data of the oil reservoir can be input into a ton-polymer oil production prediction model to output a prediction result of the ton-polymer oil production; the ton-polymer oil production prediction model is obtained by fitting the historical dynamic parameter data, historical static parameter data, and the actual collected ton-polymer oil production of different oil reservoir well groups, wherein the ton-polymer oil production prediction model can be a multivariate linear regression model.

[0041] FIG5 is a flow chart of a method for fitting a ton-aggregate oil production prediction model according to an embodiment of the present invention. As shown in FIG5 , in one embodiment, the fitting process of the ton-aggregate oil production prediction model may include:

[0042] Step 501: construct a square term of the difference between the formation pressure and the secondary saturation pressure according to the formation pressure of different well groups in the reservoir before binary flooding and the secondary saturation pressure of the reservoir before binary flooding.

[0043] Step 502: construct a binary flooding coefficient term for the well group based on the effective connected thickness of the well group, the permeability of the effective connected thickness section of the well group, and the permeability difference of the effective connected thickness section of the well group;

[0044] Step 503: Apply multivariate regression to construct the oil production per ton prediction model based on the oil production per ton of different well groups in the reservoir after binary flooding, the square term of the difference between the formation pressure and the secondary saturation pressure, and the binary flooding coefficient term of the well group.

[0045] It should be noted that although the construction process of the ton-aggregate oil production prediction model seems simple, it actually requires pre-fitting to obtain: the fitting relationship (correlation) between each type of historical dynamic parameter data and the actual collected ton-aggregate oil production, as well as the fitting relationship between each type of static parameter data and the actual collected ton-aggregate oil production; then each fitting relationship is analyzed, the influence of multiple parameters is comprehensively considered, and multiple regression is applied to establish the ton-aggregate oil production prediction model.

[0046] The following example illustrates the detailed construction process of the per-ton oil production prediction model.

[0047] Table 1 shows the historical dynamic parameter data of different oil reservoir well groups, and Table 2 shows the historical static parameter data of different oil reservoir well groups.

[0048] Table 1

[0049] Table 2

[0050] In specific implementation, the data in Table 1 can be used as the first sample data to draw a relationship diagram between the dynamic parameter data of each type of binary flooding and the oil production per ton of aggregate; the data in Table 2 can be used as the second sample data to draw a relationship diagram between the static parameter data of each type of oil reservoir and the oil production per ton of aggregate; and then, based on the data relationship in each relationship diagram, multiple first correlation relationships and multiple second correlation relationships are fitted.

[0051] FIG6(a) is a relationship diagram between the formation pressure in the binary flooding stage and the oil production per ton of aggregate in an embodiment of the present invention, and a first correlation relationship between the formation pressure in the binary flooding stage and the oil production per ton of aggregate can be obtained by fitting the relationship diagram; FIG6(b) is a relationship diagram between the effective connected thickness and the oil production per ton of aggregate in an embodiment of the present invention, and a second correlation relationship between the effective connected thickness and the oil production per ton of aggregate can be obtained by fitting the relationship diagram; FIG6(c) is a relationship diagram between the porosity of the effective connected thickness segment and the oil production per ton of aggregate in an embodiment of the present invention, and a second correlation relationship between the porosity of the effective connected thickness segment and the oil production per ton of aggregate can be obtained by fitting the relationship diagram; FIG6(d) is a relationship diagram between the permeability of the effective connected thickness segment and the oil production per ton of aggregate in an embodiment of the present invention, and a second correlation relationship between the permeability of the effective connected thickness segment and the oil production per ton of aggregate can be obtained by fitting the relationship diagram; FIG6(e) is a relationship diagram between the effective connected thickness segment and the oil production per ton of aggregate in an embodiment of the present invention FIG6(f) is a relationship diagram between the permeability variation coefficient of the effective connected thickness section and the oil production per ton, according to which a second correlation relationship between the permeability variation coefficient of the effective connected thickness section and the oil production per ton can be obtained by fitting; FIG6(g) is a relationship diagram between the permeability breakthrough coefficient of the effective connected thickness section and the oil production per ton in an embodiment of the present invention, according to which a second correlation relationship between the permeability breakthrough coefficient of the effective connected thickness section and the oil production per ton can be obtained by fitting; FIG6(h) is a relationship diagram between the permeability difference of the effective connected thickness section and the oil production per ton in an embodiment of the present invention, according to which a second correlation relationship between the permeability difference of the effective connected thickness section and the oil production per ton can be obtained by fitting.

[0052] Table 3 shows a number of fitting formulas obtained from the first correlation relationship and the second correlation relationship, where R 2 The coefficient of determination ( ) is a measure of goodness of fit, ranging from 0 to 1, with larger values ​​representing a higher goodness of fit. Therefore, we can select the optimal fitting equation to determine the relationship between a single parameter and oil yield per ton of aggregate. Based on this selected equation, and taking into account the influence of multiple parameters, we apply multivariate regression to establish a prediction model for oil yield per ton of aggregate.

[0053] Table 3

[0054] Where, Q is the oil production per ton of polymer, t / t; h is the effective connected thickness, m; K is the permeability of the effective connected thickness, mD; J is the permeability of the effective connected thickness, mD; k ——Permeability difference of effective connected thickness; P——Formation pressure during binary flooding, MPa; P e ——Secondary saturation pressure, MPa; φ——Reservoir porosity, %; ω——Shali content, %; V k ——Effective thickness section variation coefficient; T k ——Effective thickness segment mutation coefficient.

[0055] It should be noted that the formation pressure during the peak period of effectiveness refers to the difference in formation pressure between the injection well and the production well during the peak period of effectiveness (highest annual oil production) in the process of polymer-surface binary composite flooding.

[0056] In one embodiment, the ton-polymer oil production prediction model includes the following formula:

[0057] Where Q is the oil production per ton of polymer, t / t; h is the effective connected thickness, m; k is the permeability of the effective connected thickness, mD; J k is the permeability difference of the effective connected thickness; P is the formation pressure during the binary flooding stage, MPa; P e is the secondary saturation pressure, MPa.

[0058] In specific implementation, still taking the above reservoir data as an example, we comprehensively consider the reservoir static parameters and the binary driving parameters of the aggregate and surface, and combine them with multiple regression to establish a mathematical model for predicting oil production per ton of aggregate. The following formula can be fitted:

[0059] Where Q is the oil production per ton of polymer, t / t; h is the effective connected thickness, m; k is the permeability of the effective connected thickness, mD; J k is the permeability difference of the effective connected thickness; P is the formation pressure in the binary flooding stage, MPa.

[0060] Since 11.9 MPa is the secondary saturation pressure of the reservoir, it can be seen from formula (1-1) that when the formation pressure in the binary flooding stage is equal to the secondary saturation pressure, the oil production is the highest and the development effect is the best. Therefore, the secondary saturation pressure P can be used to calculate the secondary saturation pressure. e Substituting 11.9, we get the following formula:

[0061] Where Q is the oil production per ton of polymer, t / t; h is the effective connected thickness, m; k is the permeability of the effective connected thickness, mD; J k is the permeability difference of the effective connected thickness; P is the formation pressure during the binary flooding stage, MPa; P eis the secondary saturation pressure, MPa.

[0062] Table 4 shows the error ranges for oil yield per ton of aggregate using a single-parameter relationship and for a model that considers multiple parameters. As shown in Table 4, the oil yield per ton of aggregate prediction model, built using multiple parameters and combined with multivariate regression, has the smallest error range and the smallest average error, indicating the highest prediction accuracy.

[0063] Table 4

[0064] The present invention also provides a device for predicting the oil production of a polymer-surface-carbon dual flooding, as described in the following embodiments. Because the principles of this device are similar to those of the method for predicting the oil production of a polymer-surface-carbon dual flooding, the implementation of this device can refer to the implementation of the method for predicting the oil production of a polymer-surface-carbon dual flooding, and any repetitions will not be repeated.

[0065] FIG7 is a schematic diagram of the structure of a device for predicting oil production of a polymer-surface-carbon dual flooding according to an embodiment of the present invention. As shown in FIG7 , the device for predicting oil production of a polymer-surface-carbon dual flooding according to an embodiment of the present invention may specifically include:

[0066] The parameter data acquisition module 701 is used to obtain dynamic parameter data before the polymer-surface binary flooding and static parameter data of the reservoir when the polymer-surface binary flooding is used to flood different well groups in the reservoir;

[0067] The ton-polymer oil production prediction module 702 is used to input the dynamic parameter data before the polymer-surface binary flooding and the static parameter data of the oil reservoir into the ton-polymer oil production prediction model, and output the prediction result of the ton-polymer oil production of the polymer-surface binary flooding; the ton-polymer oil production prediction model is obtained by fitting the historical dynamic parameter data and static parameter data of different well groups that have completed the polymer-surface binary flooding, as well as the actual collected ton-polymer oil production of the polymer-surface binary flooding, and the ton-polymer oil production prediction model of the polymer-surface binary flooding is a multiple linear regression model.

[0068] In one implementation, the dynamic parameter data of the polymer-surface binary flooding includes the following data: the ton polymer oil production of different well groups in the reservoir after the binary flooding is implemented, the formation pressure of different well groups in the reservoir before the binary flooding is implemented, and the secondary saturation pressure of the reservoir before the binary flooding is implemented;

[0069] The static parameter data of the oil reservoir include the following data: effective connected thickness of the well group, permeability of the effective connected thickness section of the well group, and permeability difference of the effective connected thickness section of the well group.

[0070] In one implementation, the process of fitting the per-ton oil production prediction model includes:

[0071] A square term construction module is used to construct a square term of the difference between the formation pressure and the secondary saturation pressure according to the formation pressure of different well groups in the reservoir before binary flooding and the secondary saturation pressure of the reservoir before binary flooding;

[0072] A binary flooding coefficient term construction module is used to construct a binary flooding coefficient term of a well group based on the effective connected thickness of the well group, the permeability of the effective connected thickness section of the well group, and the permeability difference of the effective connected thickness section of the well group;

[0073] The prediction model construction module is used to apply multivariate regression to construct the oil production per ton prediction model based on the oil production per ton of different well groups in the reservoir after binary flooding, the square term of the difference between the formation pressure and the secondary saturation pressure, and the binary flooding coefficient term of the well group.

[0074] In one embodiment, the oil production per ton prediction model includes the following formula:

[0075] Where Q is the oil production per ton of different well groups in the reservoir after binary flooding, t / t; h is the effective connected thickness of the well group, m; k is the permeability of the effective connected thickness of the well group, mD; J k is the permeability difference of the effective connected thickness of the well group; P is the formation pressure of different well groups in the reservoir before binary flooding, MPa; P e is the secondary saturation pressure of the reservoir before binary flooding is implemented, MPa.

[0076] Based on the aforementioned inventive concept, as shown in FIG8 , the present invention further proposes a computer device 800, comprising a memory 810, a processor 820, and a computer program 830 stored on the memory 810 and executable on the processor 820. When the processor 820 executes the computer program 830, the aforementioned method for predicting oil production by poly-nano binary flooding is implemented.

[0077] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for predicting oil production of the above-mentioned poly-surface binary flooding is implemented.

[0078] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for predicting the oil production of the polymer-surface binary flooding.

[0079] In summary, in the embodiments of the present invention,

[0080] When adopting the polymer-surface binary drive to drive oil from different well groups in the reservoir, dynamic parameter data before the polymer-surface binary drive and static parameter data of the reservoir reservoir are obtained; the dynamic parameter data before the polymer-surface binary drive and the static parameter data of the reservoir reservoir are input into a ton-polymer oil production prediction model, and a prediction result of the ton-polymer oil production of the polymer-surface binary drive is output; the ton-polymer oil production prediction model is obtained by fitting the historical dynamic parameter data and static parameter data of different well groups that have completed the polymer-surface binary drive and the actual collected ton-polymer oil production of the polymer-surface binary drive. The ton-polymer oil production prediction model for the polymer-surface binary drive is a multiple linear regression model, which can accurately and quickly predict the ton-polymer oil production of the binary drive reservoir well group, which is conducive to the smooth implementation of the oil development project and avoids investment decision-making risks.

[0081] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0083] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0085] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for predicting oil production from polymer-surface binary flooding, characterized in that: include: When using polymer-surface binary flooding to flood different well groups in the reservoir, obtain dynamic parameter data before polymer-surface binary flooding and static parameter data of the reservoir; The dynamic parameter data before the polymer-surface binary flooding and the static parameter data of the oil reservoir are input into the prediction model of the oil production per ton of polymer, and the prediction result of the oil production per ton of polymer by the polymer-surface binary flooding is output; the prediction model of the oil production per ton of polymer is obtained by fitting the historical dynamic parameter data and static parameter data of different well groups that have completed the polymer-surface binary flooding, and the actual collected oil production per ton of polymer by the polymer-surface binary flooding, and the prediction model of the oil production per ton of polymer by the polymer-surface binary flooding is a multivariate linear regression model.

2. The method according to claim 1, characterized in that The dynamic parameter data of the clustered table binary drive includes the following data: The oil production per ton of different well groups in the reservoir after binary flooding, the formation pressure of different well groups in the reservoir before binary flooding, and the secondary saturation pressure of the reservoir before binary flooding; The static parameter data of the oil reservoir include the following data: The effective connected thickness of the well group, the permeability of the effective connected thickness section of the well group, and the permeability difference of the effective connected thickness section of the well group.

3. The method according to claim 1, characterized in that The fitting process of the per-ton oil production prediction model includes: According to the formation pressure of different well groups in the reservoir before binary flooding and the secondary saturation pressure of the reservoir before binary flooding, constructing a square term of the difference between the formation pressure and the secondary saturation pressure; According to the effective connected thickness of the well group, the permeability of the effective connected thickness section of the well group and the permeability difference of the effective connected thickness section of the well group, the binary flooding coefficient term of the well group is constructed; Multivariate regression is applied to construct the prediction model for oil production per ton of aggregate according to the oil production per ton of aggregate of different well groups in the reservoir after binary flooding, the square term of the difference between the formation pressure and the secondary saturation pressure, and the binary flooding coefficient term of the well group.

4. The method according to claim 1, characterized in that The oil production per ton prediction model is expressed by the following formula: Wherein, Q is the oil production per ton of different well groups in the reservoir after binary flooding, t / t; h is the effective connected thickness of the well group, m; k is the permeability of the effective connected thickness of the well group, mD; J k is the permeability difference of the effective connected thickness of the well group; P is Formation pressure of different well groups in the reservoir before Yuanflooding, MPa; P e It is the secondary saturation pressure of the reservoir before binary flooding is implemented, MPa.

5. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following method is implemented: When using polymer-surface binary flooding to flood different well groups in the reservoir, obtain dynamic parameter data before polymer-surface binary flooding and static parameter data of the reservoir; The dynamic parameter data before the polymer-surface binary flooding and the static parameter data of the oil reservoir are input into the prediction model of the oil production per ton of polymer, and the prediction result of the oil production per ton of polymer by the polymer-surface binary flooding is output; the prediction model of the oil production per ton of polymer is obtained by fitting the historical dynamic parameter data and static parameter data of different well groups that have completed the polymer-surface binary flooding, and the actual collected oil production per ton of polymer by the polymer-surface binary flooding, and the prediction model of the oil production per ton of polymer by the polymer-surface binary flooding is a multivariate linear regression model.

6. The computer device according to claim 5, characterized in that The dynamic parameter data of the clustered table binary drive includes the following data: The oil production per ton of different well groups in the reservoir after binary flooding, the formation pressure of different well groups in the reservoir before binary flooding, and the secondary saturation pressure of the reservoir before binary flooding; The static parameter data of the oil reservoir include the following data: The effective connected thickness of the well group, the permeability of the effective connected thickness section of the well group, and the permeability difference of the effective connected thickness section of the well group.

7. The computer device according to claim 5, characterized in that The fitting process of the per-ton oil production prediction model includes: According to the formation pressure of different well groups in the reservoir before binary flooding and the secondary saturation pressure of the reservoir before binary flooding, constructing a square term of the difference between the formation pressure and the secondary saturation pressure; According to the effective connected thickness of the well group, the permeability of the effective connected thickness section of the well group and the permeability difference of the effective connected thickness section of the well group, the binary flooding coefficient term of the well group is constructed; Multivariate regression is applied to construct the prediction model for oil production per ton of aggregate according to the oil production per ton of aggregate of different well groups in the reservoir after binary flooding, the square term of the difference between the formation pressure and the secondary saturation pressure, and the binary flooding coefficient term of the well group.

8. The computer device according to claim 5, characterized in that The oil production per ton prediction model is expressed by the following formula: Wherein, Q is the oil production per ton of different well groups in the reservoir after binary flooding, t / t; h is the effective connected thickness of the well group, m; k is the permeability of the effective connected thickness of the well group, mD; J k is the permeability difference of the effective connected thickness of the well group; P is the formation pressure of different well groups in the reservoir before binary flooding, MPa; P e It is the secondary saturation pressure of the reservoir before binary flooding is implemented, MPa.

9. A computer-readable storage medium, characterized in that: The computer readable storage medium stores a computer program, and when the computer program is executed by a processor, the following method is implemented: When using polymer-surface binary flooding to flood different well groups in the reservoir, obtain dynamic parameter data before polymer-surface binary flooding and static parameter data of the reservoir; The dynamic parameter data before the polymer-surface binary flooding and the static parameter data of the oil reservoir are input into the prediction model of the oil production per ton of polymer, and the prediction result of the oil production per ton of polymer by the polymer-surface binary flooding is output; the prediction model of the oil production per ton of polymer is obtained by fitting the historical dynamic parameter data and static parameter data of different well groups that have completed the polymer-surface binary flooding, and the actual collected oil production per ton of polymer by the polymer-surface binary flooding, and the prediction model of the oil production per ton of polymer by the polymer-surface binary flooding is a multivariate linear regression model.

10. The computer-readable storage medium of claim 9, wherein: The dynamic parameter data of the clustered table binary drive includes the following data: The oil production per ton of different well groups in the reservoir after binary flooding, the formation pressure of different well groups in the reservoir before binary flooding, and the secondary saturation pressure of the reservoir before binary flooding; The static parameter data of the oil reservoir include the following data: The effective connected thickness of the well group, the permeability of the effective connected thickness section of the well group, and the permeability difference of the effective connected thickness section of the well group.

11. The computer-readable storage medium of claim 9, wherein: The fitting process of the per-ton oil production prediction model includes: According to the formation pressure of different well groups in the reservoir before binary flooding and the secondary saturation pressure of the reservoir before binary flooding, constructing a square term of the difference between the formation pressure and the secondary saturation pressure; According to the effective connected thickness of the well group, the permeability of the effective connected thickness section of the well group and the permeability difference of the effective connected thickness section of the well group, the binary flooding coefficient term of the well group is constructed; Multivariate regression is applied to construct the prediction model for oil production per ton of aggregate according to the oil production per ton of aggregate of different well groups in the reservoir after binary flooding, the square term of the difference between the formation pressure and the secondary saturation pressure, and the binary flooding coefficient term of the well group.

12. The computer-readable storage medium of claim 9, wherein: The oil production per ton prediction model is expressed by the following formula: Wherein, Q is the oil production per ton of different well groups in the reservoir after binary flooding, t / t; h is the effective connected thickness of the well group, m; k is the permeability of the effective connected thickness of the well group, mD; J k is the permeability difference of the effective connected thickness of the well group; P is the formation pressure of different well groups in the reservoir before binary flooding, MPa; P e It is the secondary saturation pressure of the reservoir before binary flooding is implemented, MPa.

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