Capacity forecasting methods, devices, electronic equipment, storage media and software products

Abstract

The application provides a capacity prediction method and device, electronic equipment, storage medium and program product. The method comprises the following steps: in step S1, the reservoir open flow capacity and reservoir parameters of a target area are obtained; in step S2, based on the relationship between the reservoir open flow capacity and the reservoir parameters, a reservoir capacity comprehensive index of the target area is obtained through multiple fitting; in step S3, the relationship between the reservoir capacity comprehensive index and the reservoir open flow capacity is fitted to form a capacity prediction model; in step S4, the to-be-tested reservoir parameters of a to-be-tested reservoir of the target area are obtained; and in step S5, the capacity prediction value of the to-be-tested reservoir is determined by using the capacity prediction model and combining the to-be-tested reservoir parameters. Through multiple fitting of the reservoir parameters and optimization of the capacity prediction model, the relationship between the reservoir parameters and the capacity is established, and accurate prediction of the capacity of a new reservoir is realized.

Description

Technical Field

[0001] This invention relates to the field of capacity forecasting technology, and in particular to a capacity forecasting method, apparatus, electronic device, storage medium, and program product. Background Technology

[0002] Currently, the main methods for calculating gas well productivity include formula-based calculations and empirical model methods. Formula-based calculations are primarily based on Darcy's equations for radial flow, establishing steady-state or unsteady-state productivity equations, and then directly substituting reservoir parameters for calculation. Empirical model methods are typically based on the empirical relationship between reservoir parameters and unobstructed flow rate, and are commonly used in actual production.

[0003] However, despite the theoretical basis of formula-based calculation methods, in practical applications, the accuracy of production capacity prediction is low due to the difficulty in accurately determining parameters such as the skin factor. Furthermore, most current empirical models only consider factors such as reservoir thickness and matrix porosity, neglecting the significant impact of fractures on reservoir production capacity. Specifically, the fractured reservoirs in the DY area are fractured tight clastic gas reservoirs. When predicting the production capacity of this type of reservoir, empirical models do not consider the relationship between fractures and reservoir production capacity, thus exhibiting significant limitations in production capacity prediction. These limitations result in inaccurate production capacity predictions. Summary of the Invention

[0004] This invention provides a production capacity prediction method, apparatus, electronic device, storage medium, and program product to solve the problem of inaccurate production capacity prediction for fractured tight clastic gas reservoirs.

[0005] In a first aspect, the present invention provides a capacity forecasting method, comprising:

[0006] Step S1: Obtain the reservoir unobstructed flow rate and reservoir parameters of the tested reservoirs in the target area;

[0007] Step S2: Based on the relationship between the reservoir unobstructed flow rate and the reservoir parameters, a multivariate fitting method is used to obtain the comprehensive reservoir productivity index of the target area.

[0008] Step S3: Fit the relationship between the comprehensive reservoir productivity index and the reservoir unobstructed flow rate to form a productivity prediction model;

[0009] Step S4: Obtain the reservoir parameters of the reservoir to be tested in the target area;

[0010] Step S5: Using the production capacity prediction model and combining the parameters of the reservoir to be tested, determine the predicted production capacity value of the reservoir to be tested.

[0011] Optionally, step S2 includes:

[0012] Plot the parameter-flow rate relationship between the reservoir parameters and the reservoir unobstructed flow rate:

[0013] Based on the parameter-flow relationship diagram, the relationship between the reservoir parameters and the unobstructed flow rate is determined, and the comprehensive reservoir productivity index of the target area is obtained by multivariate fitting.

[0014] Optionally, the reservoir parameters include: effective reservoir thickness, reservoir porosity, reservoir permeability, reservoir saturation, and reservoir resistivity; the parameter-flow rate relationship diagrams include: the relationship between the effective reservoir thickness and the unrestricted flow rate; the relationship between the reservoir porosity and the unrestricted flow rate; the relationship between the reservoir permeability and the unrestricted flow rate; the relationship between the reservoir saturation and the unrestricted flow rate; and the relationship between the reservoir resistivity and the unrestricted flow rate.

[0015] Optionally, the capacity prediction model is:

[0016] Q AOF =0.0036×A 2 -0.0595×A+0.496;

[0017] Among them, Q AOF A represents the predicted production capacity, and A is the comprehensive indicator of reservoir production capacity.

[0018] Optionally, after step S3, the method further includes:

[0019] Validate the stated capacity prediction model.

[0020] Optionally, validating the capacity prediction model includes:

[0021] Obtain the verification reservoir parameters and measured unobstructed flow rate of the tested reservoirs in the target area;

[0022] Using the aforementioned production capacity prediction model and the aforementioned verification reservoir parameters, the production capacity verification value is calculated.

[0023] Determine whether the difference between the capacity verification value and the measured unobstructed flow rate is within a preset difference range. If yes, proceed to step S4; otherwise, return to step S2.

[0024] Secondly, the present invention provides a capacity prediction device, comprising:

[0025] The first acquisition module is used to acquire the reservoir unobstructed flow rate and reservoir parameters of the tested reservoirs in the target area.

[0026] The fitting module is used to obtain a comprehensive reservoir productivity index for the target area by multivariate fitting based on the relationship between the reservoir unobstructed flow rate and the reservoir parameters.

[0027] The model generation module is used to fit the relationship between the comprehensive reservoir productivity index and the reservoir unobstructed flow rate to form a productivity prediction model.

[0028] The second acquisition module is used to acquire the reservoir parameters of the reservoir to be tested in the target area.

[0029] The prediction module is used to determine the predicted production capacity of the reservoir under test by using the production capacity prediction model and combining the parameters of the reservoir under test.

[0030] Optionally, the fitting module includes:

[0031] The plotting submodule is used to plot the parameter-flow relationship between the reservoir parameters and the reservoir unobstructed flow rate.

[0032] The reservoir productivity comprehensive index determination submodule is used to determine the relationship between the reservoir parameters and the unobstructed flow rate based on the parameter-flow relationship diagram, and to obtain the reservoir productivity comprehensive index of the target area through multivariate fitting.

[0033] Optionally, the reservoir parameters include: effective reservoir thickness, reservoir porosity, reservoir permeability, reservoir saturation, and reservoir resistivity; the parameter-flow rate relationship diagrams include: the relationship between the effective reservoir thickness and the unrestricted flow rate; the relationship between the reservoir porosity and the unrestricted flow rate; the relationship between the reservoir permeability and the unrestricted flow rate; the relationship between the reservoir saturation and the unrestricted flow rate; and the relationship between the reservoir resistivity and the unrestricted flow rate.

[0034] Optionally, the capacity prediction model is:

[0035] Q AOF =0.0036×A 2 -0.0595×A+0.496

[0036] Among them, Q AOF A represents the predicted production capacity, and A is the comprehensive indicator of reservoir production capacity.

[0037] Optionally, it also includes:

[0038] The verification module is used to verify the capacity prediction model.

[0039] Optionally, the verification module includes:

[0040] The acquisition submodule is used to acquire the verification reservoir parameters and measured unobstructed flow rate of the tested reservoirs in the target area.

[0041] The capacity verification value determination submodule is used to calculate the capacity verification value using the capacity prediction model and the verification reservoir parameters.

[0042] The judgment submodule is used to determine whether the difference between the capacity verification value and the measured unobstructed flow rate is within a preset difference range. If yes, the second acquisition module is executed; otherwise, the execution fitting module is returned.

[0043] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory storing computer-readable instructions that, when executed by the processor, perform the steps of the method provided in the first aspect above.

[0044] Fourthly, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the method provided in the first aspect above.

[0045] Fifthly, the present invention provides a computer program product comprising a computer program that, when executed by a processor, performs the steps of the method provided in the first aspect above.

[0046] As can be seen from the above technical solutions, the present invention has the following advantages:

[0047] This invention provides a method, apparatus, electronic device, storage medium, and program product for predicting reservoir production capacity. The method includes: step S1, obtaining the reservoir unobstructed flow rate and reservoir parameters of tested reservoirs in a target area; step S2, obtaining a comprehensive reservoir production capacity index for the target area through multivariate fitting based on the relationship between the reservoir unobstructed flow rate and the reservoir parameters; step S3, fitting the relationship between the comprehensive reservoir production capacity index and the reservoir unobstructed flow rate to form a production capacity prediction model; step S4, obtaining the reservoir parameters to be tested in the target area; and step S5, determining the predicted production capacity value of the reservoir to be tested using the production capacity prediction model and the reservoir parameters. By optimizing the multivariate fitting of reservoir parameters and the production capacity prediction model, the relationship between reservoir parameters and production capacity is established, achieving accurate prediction of new reservoir production capacity. This solves the problem of low accuracy in traditional methods for predicting reservoir production capacity, especially for fractured tight clastic gas reservoirs, providing a scientific basis for exploration and development. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. 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 effort.

[0049] Figure 1This is a flowchart illustrating the steps of a first embodiment of the capacity forecasting method of the present invention.

[0050] Figure 2 This is a flowchart illustrating the steps of a second embodiment of the capacity forecasting method of the present invention.

[0051] Figure 3 This is a schematic diagram illustrating the relationship between the effective reservoir thickness and the unobstructed flow rate of a second embodiment of the production capacity prediction method of the present invention.

[0052] Figure 4 This is a schematic diagram illustrating the relationship between reservoir porosity and reservoir unobstructed flow rate in a second embodiment of the production capacity prediction method of the present invention.

[0053] Figure 5 This is a schematic diagram illustrating the relationship between reservoir permeability and reservoir unobstructed flow rate in a second embodiment of the production capacity prediction method of the present invention.

[0054] Figure 6 This is a schematic diagram illustrating the relationship between reservoir saturation and unobstructed flow rate in a second embodiment of the production capacity prediction method of the present invention.

[0055] Figure 7 This is a schematic diagram illustrating the relationship between reservoir resistivity and unobstructed flow rate in a second embodiment of the production capacity prediction method of the present invention.

[0056] Figure 8 This is a schematic diagram illustrating the relationship between the comprehensive reservoir productivity index and the unobstructed flow rate of a second embodiment of the productivity prediction method of the present invention.

[0057] Figure 9 A schematic diagram of the logging results of well A, which is an example of a production capacity prediction method of the present invention;

[0058] Figure 10 A comparison chart of fitted capacity and measured capacity of Well A, which is an example of a capacity prediction method of the present invention;

[0059] Figure 11 A schematic diagram of the logging results of well B, which is an example of a production capacity prediction method of the present invention;

[0060] Figure 12 A comparison chart of the fitted capacity and measured capacity of Well B, which is an example of a capacity prediction method of the present invention;

[0061] Figure 13 This is a structural block diagram of an embodiment of the capacity prediction device of the present invention. Detailed Implementation

[0062] This invention provides a production capacity prediction method, apparatus, electronic device, storage medium, and program product to address the problem of inaccurate production capacity prediction for fractured tight clastic gas reservoirs.

[0063] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0064] Example 1, please refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps of a capacity forecasting method according to an embodiment of the present invention. The method includes:

[0065] Step S1: Obtain the reservoir unobstructed flow rate and reservoir parameters of the tested reservoirs in the target area;

[0066] In this embodiment of the application, the unobstructed flow rate of the tested reservoir is recorded, that is, the maximum flow rate of gas or liquid that can flow without any obstruction, and relevant parameters of the reservoir are collected.

[0067] Step S2: Based on the relationship between the reservoir unobstructed flow rate and the reservoir parameters, a multivariate fitting method is used to obtain the comprehensive reservoir productivity index of the target area.

[0068] In this embodiment of the application, a suitable multiple regression model, such as linear regression or multinomial regression, is selected. The reservoir parameters and reservoir unobstructed flow rate collected in step S1 are used as input data. Through multiple regression analysis, a relationship model between reservoir parameters and reservoir unobstructed flow rate is established, thereby obtaining a comprehensive energy storage capacity index that can reflect the reservoir's production capacity.

[0069] Step S3: Fit the relationship between the comprehensive reservoir productivity index and the reservoir unobstructed flow rate to form a productivity prediction model;

[0070] In this embodiment of the application, the comprehensive reservoir productivity index obtained in step S2 is fitted again with the reservoir unobstructed flow rate to further optimize the model, and finally form a productivity prediction model that can predict reservoir productivity based on reservoir parameters.

[0071] Step S4: Obtain the reservoir parameters of the reservoir to be tested in the target area;

[0072] In the embodiments of this application, reservoir parameters to be tested, such as effective thickness, porosity, permeability, saturation, resistivity, etc., are collected.

[0073] Step S5: Using the production capacity prediction model and combining the parameters of the reservoir to be tested, determine the predicted production capacity value of the reservoir to be tested.

[0074] In this embodiment, the reservoir parameters collected in step S4 are input into the production capacity prediction model formed in step S3. The production capacity prediction model calculates the predicted production capacity of the reservoir based on the input reservoir parameters, providing a scientific basis for exploration and development.

[0075] This invention provides a method for predicting reservoir production capacity, comprising: step S1, obtaining the unobstructed flow rate and reservoir parameters of tested reservoirs in a target area; step S2, obtaining a comprehensive reservoir production capacity index for the target area through multivariate fitting based on the relationship between the unobstructed flow rate and the reservoir parameters; step S3, fitting the relationship between the comprehensive reservoir production capacity index and the unobstructed flow rate to form a production capacity prediction model; step S4, obtaining the reservoir parameters to be tested in the target area; and step S5, determining the predicted production capacity value of the reservoir to be tested using the production capacity prediction model and the reservoir parameters. By optimizing the multivariate fitting of reservoir parameters and the production capacity prediction model, a relationship between reservoir parameters and production capacity is established, achieving accurate prediction of new reservoir production capacity. This solves the problem of low accuracy in traditional methods for predicting reservoir production capacity, especially for fractured tight clastic gas reservoirs, providing a scientific basis for exploration and development.

[0076] Example 2: The DY Xujiahe Formation gas reservoir is characterized by "two lows and one high": extremely low porosity (average effective reservoir porosity is only 3.18%), extremely low permeability (average permeability is only 0.08 mD), and high rock fracture pressure, concentrated between 100-165 MPa. The DY Xujiahe Formation reservoir has well-developed rock fractures, which act as channels between pores, greatly improving reservoir connectivity and fluid flow conditions. Fracture development is crucial for high and stable gas well production. To accurately predict the unobstructed flow rate of a single well in this fractured, tight clastic reservoir, this application provides a production prediction method. Please refer to [link to relevant documentation]. Figure 2 The steps include:

[0077] Step S201: Obtain the reservoir unobstructed flow rate and reservoir parameters of the tested reservoirs in the target area;

[0078] In this embodiment of the application, the reservoir parameters include: effective reservoir thickness, reservoir porosity, reservoir permeability, reservoir saturation, and reservoir resistivity.

[0079] Step S202: Draw a parameter-flow rate relationship diagram between the reservoir parameters and the reservoir unobstructed flow rate;

[0080] In this embodiment of the application, the parameter-flow relationship graph includes: as follows Figure 3 The graph shown represents the relationship between the effective reservoir thickness and the reservoir's unobstructed flow rate; as shown... Figure 4The graph showing the relationship between reservoir porosity and the reservoir's free flow rate is shown below; Figure 5 The graph showing the relationship between reservoir permeability and the reservoir's unobstructed flow rate is shown below; Figure 6 The graph showing the relationship between reservoir saturation and the reservoir's unobstructed flow rate is shown below; Figure 7 The graph shown depicts the relationship between reservoir resistivity and the unobstructed flow rate of the reservoir. From... Figures 3-6 It can be seen that the unobstructed flow rate is positively correlated with the effective reservoir thickness, reservoir porosity, reservoir permeability, and reservoir saturation. Figure 7 It can be seen that the free flow rate is negatively correlated with reservoir resistivity. Reservoirs with lower free flow rates are mostly porous reservoirs, while those with higher production capacity are mostly fracture-porous reservoirs. The characteristics of fractures in the bilateral logging curves are as follows: in the bilateral logging response, the resistivity value in the fractured section is significantly lower than against a dense, high-resistivity background, resulting in a sharp curve shape. As the opening angle increases, the fractures cause an increase in the difference in resistivity between deep and shallow lateral sections. Network fractures are relatively large, generally extending far both longitudinally and laterally. The reduction in resistivity is significant in both lateral sections against a high-resistivity background, and they have a certain thickness, unlike the sharp, pointed characteristics of low-angle fractures. Therefore, utilizing the characteristic that fractures cause a significant reduction in resistivity, resistivity is incorporated into the production capacity fitting to characterize fractures and predict the free flow rate of a single well.

[0081] Step S203: Based on the parameter-flow relationship diagram, determine the relationship between the reservoir parameters and the unobstructed flow rate, and obtain the comprehensive reservoir productivity index of the target area through multivariate fitting.

[0082] In this embodiment of the application, based on the relationship between unobstructed flow rate and these energy storage parameters, a multivariate fitting is performed to obtain the comprehensive reservoir productivity index, namely:

[0083] A = f(H, POR, K, S) g ,RD);

[0084] Where A is the comprehensive reservoir productivity index, H is the effective reservoir thickness, POR is the reservoir porosity, K is the reservoir permeability, and S... g RD represents the reservoir gas saturation and the reservoir resistivity.

[0085] Step S204: Fit the relationship between the comprehensive reservoir productivity index and the reservoir unobstructed flow rate to form a productivity prediction model;

[0086] Please see Figure 8 , Figure 8 The graph shows the relationship between the comprehensive reservoir productivity index and the predicted productivity value. Based on this graph, the relationship between the comprehensive reservoir productivity index and the unobstructed flow rate is fitted to form a productivity prediction model, as follows:

[0087] Q AOF=0.0036×A 2 -0.0595×A+0.496;

[0088] Among them, Q AOF A represents the predicted production capacity, and A is the comprehensive indicator of reservoir production capacity.

[0089] Step S205: Verify the capacity prediction model;

[0090] Specifically, this includes: obtaining the verification reservoir parameters and measured unobstructed flow rate of the tested reservoirs in the target area;

[0091] Using the aforementioned production capacity prediction model and the aforementioned verification reservoir parameters, the production capacity verification value is calculated.

[0092] Determine whether the difference between the capacity verification value and the measured unobstructed flow rate is within a preset difference range. If yes, proceed to step S206; otherwise, return to step S202.

[0093] In this embodiment of the application, to verify the effectiveness of the production capacity prediction model for well logging production capacity prediction of fractured tight clastic gas reservoirs, it is necessary to extract the reservoir parameters of the new well as the verification reservoir parameters, and compare and analyze the prediction results, i.e. the production capacity verification value, with the measured unobstructed flow rate to verify the accuracy of the unobstructed flow rate prediction method for well logging production capacity prediction of fractured tight clastic gas reservoirs.

[0094] In the specific implementation, the reservoir parameters of the new well are extracted. The single-well logging project includes comprehensive logging data. The effective thickness, reservoir porosity, reservoir permeability, reservoir gas saturation and reservoir resistivity of the reservoir are calculated and statistically obtained. Based on the production capacity prediction relationship, the unobstructed flow rate of the single well is predicted to obtain the production capacity verification value.

[0095] Step S206: Obtain the reservoir parameters of the reservoir to be tested in the target area;

[0096] Step S207: Using the production capacity prediction model and the parameters of the reservoir to be tested, determine the predicted production capacity value of the reservoir to be tested.

[0097] This invention discloses a method for predicting reservoir production capacity, comprising: step S1, obtaining the reservoir unobstructed flow rate and reservoir parameters of tested reservoirs in a target area; step S2, obtaining a comprehensive reservoir production capacity index for the target area through multivariate fitting based on the relationship between the reservoir unobstructed flow rate and the reservoir parameters; step S3, fitting the relationship between the comprehensive reservoir production capacity index and the reservoir unobstructed flow rate to form a production capacity prediction model; step S4, obtaining the reservoir parameters to be tested in the target area; and step S5, determining the predicted production capacity value of the reservoir to be tested using the production capacity prediction model and the reservoir parameters. By characterizing fracture changes using conventional resistivity curves, establishing the relationship between reservoir parameters and reservoir unobstructed flow rate, and establishing a production capacity prediction model, this method accurately predicts the single-well unobstructed flow rate of a Class I fractured tight clastic gas reservoir in the DY Xujiahe Formation. This solves the problem of low accuracy in traditional methods for reservoir production capacity prediction, especially for fractured tight clastic gas reservoirs, providing a scientific basis for exploration and development.

[0098] To facilitate those skilled in the art's understanding of the beneficial effects of the present invention, an example of an evaluation method for the water volume ratio of a fractured-vuggy bottom water reservoir is provided below.

[0099] In this example, both Well A and Well B are fractured tight clastic gas reservoirs. In Well A, integrated and electrical imaging logging were conducted. Based on the integrated and electrical imaging results, the reservoir and fractures were well-developed. Pre-test intervals (4879-4963m, 4970-4999m, 5002-5043m, 5060-5102m, 5107-5157m, 5163-5215m) were extracted, revealing an effective reservoir thickness of 61.2m, porosity of 4.63%, permeability of 0.45mD, saturation of 62.1%, and reservoir resistivity of 74.8Ω·m. Figure 9 A schematic diagram of the logging results for Well A, an example of a production capacity prediction method, shows a reservoir overall production capacity index of 108.8. Based on the relationship between the reservoir overall production capacity index and unobstructed flow rate, the predicted unobstructed flow rate for Well A is 330,000 cubic meters per day. The predicted result is compared with the actual tested unobstructed flow rate to verify the accuracy of the unobstructed flow rate prediction method for fractured tight clastic gas reservoirs. Please refer to [link to relevant documentation]. Figure 10 , Figure 10 The chart shows a comparison between the fitted capacity and the measured capacity of Well A, which is an example of a capacity prediction method. The predicted unobstructed flow rate of Well A is 330,000 cubic meters per day, while the measured unobstructed flow rate is 345,400 cubic meters per day. The prediction results are consistent with the actual results.

[0100] The accuracy of the unobstructed flow prediction model was analyzed based on the unobstructed flow test results of Well B. In Well B, comprehensive logging measurements were conducted. Based on the comprehensive indication of underdeveloped reservoir and fractures, the pre-test intervals (5600-5624m, 5694-5708m, 5720-5734m, 5765-5790m, 5885-5902m) were extracted. The reservoir effective thickness was 15.6m, porosity 4.27%, permeability 0.35mD, saturation 61.7%, and reservoir resistivity 102.9Ω·m. Figure 11 The logging results of Well B, an example of a production capacity prediction method, are shown in the diagram. The reservoir's comprehensive production capacity index is 14.15. Based on the relationship between the comprehensive reservoir production capacity index and the unobstructed flow rate, the predicted unobstructed flow rate of Well B is 0.3747 million cubic meters per day, while the actual measured unobstructed flow rate is 0.358 million cubic meters per day. The comparison is shown in the figure below. Figure 12 The following is a comparison chart of the fitted capacity and measured capacity of Well B, which is an example of a capacity prediction method. The prediction results are consistent with the actual results.

[0101] Example 3, please refer to Figure 13 , Figure 13 This is a structural block diagram of an embodiment of a capacity prediction device of the present invention. The device includes:

[0102] The first acquisition module 301 is used to acquire the reservoir unobstructed flow rate and reservoir parameters of the tested reservoir in the target area.

[0103] The fitting module 302 is used to obtain the comprehensive reservoir productivity index of the target area by multivariate fitting based on the relationship between the reservoir unobstructed flow rate and the reservoir parameters.

[0104] The model generation module 303 is used to fit the relationship between the reservoir's comprehensive productivity index and the reservoir's unobstructed flow rate to form a productivity prediction model.

[0105] The second acquisition module 304 is used to acquire the reservoir parameters of the reservoir to be tested in the target area.

[0106] The prediction module 305 is used to determine the predicted production capacity of the reservoir under test by using the production capacity prediction model and combining the parameters of the reservoir under test.

[0107] In an optional embodiment, the fitting module 302 includes:

[0108] The plotting submodule is used to plot the parameter-flow relationship between the reservoir parameters and the reservoir unobstructed flow rate.

[0109] The reservoir productivity comprehensive index determination submodule is used to determine the relationship between the reservoir parameters and the unobstructed flow rate based on the parameter-flow relationship diagram, and to obtain the reservoir productivity comprehensive index of the target area through multivariate fitting.

[0110] In an optional embodiment, the reservoir parameters include: effective reservoir thickness, reservoir porosity, reservoir permeability, reservoir saturation, and reservoir resistivity; the parameter-flow rate relationship diagrams include: the relationship between the effective reservoir thickness and the unrestricted flow rate; the relationship between the reservoir porosity and the unrestricted flow rate; the relationship between the reservoir permeability and the unrestricted flow rate; the relationship between the reservoir saturation and the unrestricted flow rate; and the relationship between the reservoir resistivity and the unrestricted flow rate.

[0111] In an optional embodiment, the capacity prediction model is:

[0112] Q AOF =0.0036×A 2 -0.0595×A+0.496

[0113] Among them, Q AOF A represents the predicted production capacity, and A is the comprehensive indicator of reservoir production capacity.

[0114] In an optional embodiment, it further includes:

[0115] The verification module is used to verify the capacity prediction model.

[0116] In an optional embodiment, the verification module includes:

[0117] The acquisition submodule is used to acquire the verification reservoir parameters and measured unobstructed flow rate of the tested reservoirs in the target area.

[0118] The capacity verification value determination submodule is used to calculate the capacity verification value using the capacity prediction model and the verification reservoir parameters.

[0119] The judgment submodule is used to determine whether the difference between the capacity verification value and the measured unobstructed flow rate is within a preset difference range. If yes, the second acquisition module is executed; otherwise, the execution fitting module is returned.

[0120] Example 4: This embodiment of the invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, it causes the processor to perform the steps of a capacity forecasting method according to any embodiment, including:

[0121] Step S1: Obtain the reservoir unobstructed flow rate and reservoir parameters of the tested reservoirs in the target area;

[0122] Step S2: Based on the relationship between the reservoir unobstructed flow rate and the reservoir parameters, a multivariate fitting method is used to obtain the comprehensive reservoir productivity index of the target area.

[0123] Step S3: Fit the relationship between the comprehensive reservoir productivity index and the reservoir unobstructed flow rate to form a productivity prediction model;

[0124] Step S4: Obtain the reservoir parameters of the reservoir to be tested in the target area;

[0125] Step S5: Using the production capacity prediction model and combining the parameters of the reservoir to be tested, determine the predicted production capacity value of the reservoir to be tested.

[0126] In an optional embodiment, step S2 includes:

[0127] Plot the parameter-flow rate relationship between the reservoir parameters and the reservoir unobstructed flow rate:

[0128] Based on the parameter-flow relationship diagram, the relationship between the reservoir parameters and the unobstructed flow rate is determined, and the comprehensive reservoir productivity index of the target area is obtained by multivariate fitting.

[0129] In an optional embodiment, the reservoir parameters include: effective reservoir thickness, reservoir porosity, reservoir permeability, reservoir saturation, and reservoir resistivity; the parameter-flow rate relationship diagrams include: a diagram showing the relationship between the effective reservoir thickness and the unrestricted flow rate; a diagram showing the relationship between the reservoir porosity and the unrestricted flow rate; a diagram showing the relationship between the reservoir permeability and the unrestricted flow rate; a diagram showing the relationship between the reservoir saturation and the unrestricted flow rate; and a diagram showing the relationship between the reservoir resistivity and the unrestricted flow rate.

[0130] In an optional embodiment, the capacity prediction model is:

[0131] Q AOF =0.0036×A 2 -0.0595×A+0.496;

[0132] Among them, Q AOF A represents the predicted production capacity, and A is the comprehensive indicator of reservoir production capacity.

[0133] In an optional embodiment, after step S3, the method further includes:

[0134] Validate the stated capacity prediction model.

[0135] In an optional embodiment, validating the capacity prediction model includes:

[0136] Obtain the verification reservoir parameters and measured unobstructed flow rate of the tested reservoirs in the target area;

[0137] Using the aforementioned production capacity prediction model and the aforementioned verification reservoir parameters, the production capacity verification value is calculated.

[0138] Determine whether the difference between the capacity verification value and the measured unobstructed flow rate is within a preset difference range. If yes, proceed to step S4; otherwise, return to step S2.

[0139] Example 5: This embodiment of the invention also provides a computer storage medium storing a computer program thereon. When the computer program is executed by the processor, it implements the steps of a capacity forecasting method according to any embodiment, including:

[0140] Step S1: Obtain the reservoir unobstructed flow rate and reservoir parameters of the tested reservoirs in the target area;

[0141] Step S2: Based on the relationship between the reservoir unobstructed flow rate and the reservoir parameters, a multivariate fitting method is used to obtain the comprehensive reservoir productivity index of the target area.

[0142] Step S3: Fit the relationship between the comprehensive reservoir productivity index and the reservoir unobstructed flow rate to form a productivity prediction model;

[0143] Step S4: Obtain the reservoir parameters of the reservoir to be tested in the target area;

[0144] Step S5: Using the production capacity prediction model and combining the parameters of the reservoir to be tested, determine the predicted production capacity value of the reservoir to be tested.

[0145] In an optional embodiment, step S2 includes:

[0146] Plot the parameter-flow rate relationship between the reservoir parameters and the reservoir unobstructed flow rate:

[0147] Based on the parameter-flow relationship diagram, the relationship between the reservoir parameters and the unobstructed flow rate is determined, and the comprehensive reservoir productivity index of the target area is obtained by multivariate fitting.

[0148] In an optional embodiment, the reservoir parameters include: effective reservoir thickness, reservoir porosity, reservoir permeability, reservoir saturation, and reservoir resistivity; the parameter-flow rate relationship diagrams include: a diagram showing the relationship between the effective reservoir thickness and the unrestricted flow rate; a diagram showing the relationship between the reservoir porosity and the unrestricted flow rate; a diagram showing the relationship between the reservoir permeability and the unrestricted flow rate; a diagram showing the relationship between the reservoir saturation and the unrestricted flow rate; and a diagram showing the relationship between the reservoir resistivity and the unrestricted flow rate.

[0149] In an optional embodiment, the capacity prediction model is:

[0150] Q AOF =0.0036×A 2 -0.0595×A+0.496;

[0151] Among them, Q AOF A represents the predicted production capacity, and A is the comprehensive indicator of reservoir production capacity.

[0152] In an optional embodiment, after step S3, the method further includes:

[0153] Validate the stated capacity prediction model.

[0154] In an optional embodiment, validating the capacity prediction model includes:

[0155] Obtain the verification reservoir parameters and measured unobstructed flow rate of the tested reservoirs in the target area;

[0156] Using the aforementioned production capacity prediction model and the aforementioned verification reservoir parameters, the production capacity verification value is calculated.

[0157] Determine whether the difference between the capacity verification value and the measured unobstructed flow rate is within a preset difference range. If yes, proceed to step S4; otherwise, return to step S2.

[0158] Example 6: This embodiment of the invention also provides a computer program product, on which a computer program is stored. When the computer program is executed by the processor, it implements the steps of a capacity forecasting method of any embodiment, including:

[0159] Step S1: Obtain the reservoir unobstructed flow rate and reservoir parameters of the tested reservoirs in the target area;

[0160] Step S2: Based on the relationship between the reservoir unobstructed flow rate and the reservoir parameters, a multivariate fitting method is used to obtain the comprehensive reservoir productivity index of the target area.

[0161] Step S3: Fit the relationship between the comprehensive reservoir productivity index and the reservoir unobstructed flow rate to form a productivity prediction model;

[0162] Step S4: Obtain the reservoir parameters of the reservoir to be tested in the target area;

[0163] Step S5: Using the production capacity prediction model and combining the parameters of the reservoir to be tested, determine the predicted production capacity value of the reservoir to be tested.

[0164] In an optional embodiment, step S2 includes:

[0165] Plot the parameter-flow rate relationship between the reservoir parameters and the reservoir unobstructed flow rate:

[0166] Based on the parameter-flow relationship diagram, the relationship between the reservoir parameters and the unobstructed flow rate is determined, and the comprehensive reservoir productivity index of the target area is obtained by multivariate fitting.

[0167] In an optional embodiment, the reservoir parameters include: effective reservoir thickness, reservoir porosity, reservoir permeability, reservoir saturation, and reservoir resistivity; the parameter-flow rate relationship diagrams include: a diagram showing the relationship between the effective reservoir thickness and the unrestricted flow rate; a diagram showing the relationship between the reservoir porosity and the unrestricted flow rate; a diagram showing the relationship between the reservoir permeability and the unrestricted flow rate; a diagram showing the relationship between the reservoir saturation and the unrestricted flow rate; and a diagram showing the relationship between the reservoir resistivity and the unrestricted flow rate.

[0168] In an optional embodiment, the capacity prediction model is:

[0169] Q AOF =0.0036×A 2 -0.0595×A+0.496;

[0170] Among them, Q AOF A represents the predicted production capacity, and A is the comprehensive indicator of reservoir production capacity.

[0171] In an optional embodiment, after step S3, the method further includes:

[0172] Validate the stated capacity prediction model.

[0173] In an optional embodiment, validating the capacity prediction model includes:

[0174] Obtain the verification reservoir parameters and measured unobstructed flow rate of the tested reservoirs in the target area;

[0175] Using the aforementioned production capacity prediction model and the aforementioned verification reservoir parameters, the production capacity verification value is calculated.

[0176] Determine whether the difference between the capacity verification value and the measured unobstructed flow rate is within a preset difference range. If yes, proceed to step S4; otherwise, return to step S2.

[0177] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0178] In the several embodiments provided in this application, it should be understood that the methods, apparatuses, electronic devices, and storage media disclosed in this invention can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0179] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0180] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0181] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0182] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A capacity forecasting method, characterized in that, include: Step S1: Obtain the reservoir unobstructed flow rate and reservoir parameters of the tested reservoirs in the target area; Step S2: Based on the relationship between the reservoir unobstructed flow rate and the reservoir parameters, a multivariate fitting method is used to obtain the comprehensive reservoir productivity index of the target area. Step S3: Fit the relationship between the comprehensive reservoir productivity index and the reservoir unobstructed flow rate to form a productivity prediction model; Step S4: Obtain the reservoir parameters of the reservoir to be tested in the target area; Step S5: Using the production capacity prediction model and combining the parameters of the reservoir to be tested, determine the predicted production capacity value of the reservoir to be tested.

2. The capacity forecasting method according to claim 1, characterized in that, Step S2 includes: Plot the parameter-flow rate relationship between the reservoir parameters and the reservoir unobstructed flow rate: Based on the parameter-flow relationship diagram, the relationship between the reservoir parameters and the unobstructed flow rate is determined, and the comprehensive reservoir productivity index of the target area is obtained by multivariate fitting.

3. The capacity forecasting method according to claim 2, characterized in that, The reservoir parameters include: effective reservoir thickness, reservoir porosity, reservoir permeability, reservoir saturation, and reservoir resistivity; the parameter-flow rate relationship diagrams include: the relationship between the effective reservoir thickness and the unrestricted flow rate; the relationship between the reservoir porosity and the unrestricted flow rate; the relationship between the reservoir permeability and the unrestricted flow rate; the relationship between the reservoir saturation and the unrestricted flow rate; and the relationship between the reservoir resistivity and the unrestricted flow rate.

4. The capacity forecasting method according to claim 1, characterized in that, The capacity prediction model is as follows: Q AOF =0.0036×A 2 -0.0595×A+0.496; Among them, Q AOF A represents the predicted production capacity, and A is the comprehensive indicator of reservoir production capacity.

5. The capacity forecasting method according to claim 1, characterized in that, After step S3, the method further includes: Validate the stated capacity prediction model.

6. The capacity forecasting method according to claim 1, characterized in that, Validating the capacity prediction model includes: Obtain the verification reservoir parameters and measured unobstructed flow rate of the tested reservoirs in the target area; Using the aforementioned production capacity prediction model and the aforementioned verification reservoir parameters, the production capacity verification value is calculated. Determine whether the difference between the capacity verification value and the measured unobstructed flow rate is within a preset difference range. If yes, proceed to step S4; otherwise, return to step S2.

7. A capacity prediction device, characterized in that, include: The first acquisition module is used to acquire the reservoir unobstructed flow rate and reservoir parameters of the tested reservoirs in the target area. The fitting module is used to obtain a comprehensive reservoir productivity index for the target area by multivariate fitting based on the relationship between the reservoir unobstructed flow rate and the reservoir parameters. The model generation module is used to fit the relationship between the comprehensive reservoir productivity index and the reservoir unobstructed flow rate to form a productivity prediction model. The second acquisition module is used to acquire the reservoir parameters of the reservoir to be tested in the target area. The prediction module is used to determine the predicted production capacity of the reservoir under test by using the production capacity prediction model and combining the parameters of the reservoir under test.

8. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer-readable instructions that, when executed by the processor, perform the method as described in any one of claims 1-6.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it performs the method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1-6.

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