Sandstone gas reservoir reserve producing rate prediction method, device, equipment and storage medium

Abstract

The application discloses a sandstone gas reservoir reserve producing rate prediction method, device, equipment and storage medium, comprising the following steps: determining the first cumulative reserve producing rate of each developed block based on the proven geological reserve data of a plurality of developed blocks obtained in advance; determining the producing mode and curve form based on the first cumulative reserve producing rate curve; constructing a producing rate prediction model according to the original model and the first cumulative reserve producing rate curve; obtaining the geological parameters of a newly developed block, selecting the producing rate prediction model corresponding to the newly developed block according to the geological parameters, and predicting the second cumulative reserve producing rate of the newly developed block according to the producing rate prediction model. The sandstone gas reservoir reserve producing rate prediction method, device, equipment and storage medium disclosed by the application solve the problem that the existing reserve producing rate calculation method is relatively complex, make the operation workload and required time of predicting the cumulative reserve producing rate of the newly developed block effectively reduced, and effectively reduce the operation difficulty.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of oil and gas exploration and development, and particularly to a method, apparatus, equipment and storage medium for predicting the utilization rate of sandstone gas reservoir reserves. Background Technology

[0002] At present, the exploration and development of low-permeability tight sandstone gas reservoirs is booming. The prediction of the utilization rate of low-permeability tight gas reservoir reserves is not only the basis for the evaluation of untapped reserves, but also an important basis for medium- and long-term exploration and development planning.

[0003] In some technologies, determining the utilization level of oil and gas reservoir reserves and calculating the reserve utilization rate requires combining seismic data, production dynamic data, oil and gas reservoir property data, well network control level, and water drive control level. However, when making medium- and long-term plans at the branch company or group company level, the above methods are quite complex, require a large amount of work, are time-consuming, and are difficult to operate. Summary of the Invention

[0004] The purpose of this invention is to provide at least one method, apparatus, equipment, and storage medium for predicting the utilization rate of sandstone gas reservoirs. This invention can at least solve the technical problems that existing methods for calculating the utilization rate of reserves are complex, require a large amount of work, are time-consuming, and are difficult to operate when making medium- and long-term plans at the branch company or group company level. This invention can at least achieve the technical effect of predicting the cumulative utilization rate of reserves in newly developed blocks, effectively reducing the workload and time required, and effectively reducing the difficulty of operation.

[0005] To address the aforementioned technical problems, at least one embodiment of this application provides a method for predicting the utilization rate of sandstone gas reservoir reserves, comprising:

[0006] Based on the proven geological reserve data of multiple developed blocks obtained in advance, the first cumulative reserve utilization rate of each developed block is determined, wherein the first cumulative reserve utilization rate of each developed block over the years forms a first cumulative reserve utilization rate curve.

[0007] Based on the first cumulative reserve utilization rate curve, determine the utilization mode and curve shape corresponding to the first cumulative reserve utilization rate curve;

[0008] A utilization rate prediction model is constructed based on the original model and the first cumulative reserve utilization rate curve, wherein the original model is related to the utilization mode and the curve shape;

[0009] Obtain the geological parameters of the newly developed block, select the utilization rate prediction model corresponding to the newly developed block based on the geological parameters of the newly developed block, and predict the second cumulative reserve utilization rate of the newly developed block based on the utilization rate prediction model.

[0010] At least one embodiment of this application also provides a sandstone gas reservoir utilization rate prediction device, comprising:

[0011] The first utilization rate determination module is used to determine the first cumulative reserve utilization rate of each developed block based on the proven geological reserve data of multiple developed blocks obtained in advance. The first cumulative reserve utilization rate of each developed block over the years forms a first cumulative reserve utilization rate curve.

[0012] The curve analysis module is used to determine the utilization mode and curve shape corresponding to the first cumulative reserve utilization rate curve based on the first cumulative reserve utilization rate curve.

[0013] A construction module is used to construct a utilization rate prediction model based on the original model and the first cumulative reserve utilization rate curve, wherein the original model is related to the utilization mode and the curve shape;

[0014] The prediction module is used to obtain the geological parameters of the newly developed block, select the utilization rate prediction model corresponding to the newly developed block based on the geological parameters of the newly developed block, and predict the second cumulative reserve utilization rate of the newly developed block based on the utilization rate prediction model.

[0015] At least one embodiment of this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described sandstone gas reservoir utilization rate prediction method.

[0016] At least one embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for predicting the utilization rate of sandstone gas reservoir reserves.

[0017] The sandstone gas reservoir reserve utilization rate prediction method, apparatus, equipment, and storage medium provided in the embodiments of this application determine the first cumulative reserve utilization rate of each developed block based on proven geological reserve data from multiple developed blocks. Based on the first cumulative reserve utilization rate curve, the utilization mode and curve shape corresponding to the curve are determined. Then, a utilization rate prediction model is constructed based on the original model and the first cumulative reserve utilization rate curve. After constructing multiple utilization rate prediction models, geological parameters of newly developed blocks are obtained. A utilization rate prediction model corresponding to the newly developed block is selected based on these parameters. The second cumulative reserve utilization rate of the newly developed block is predicted based on the utilization rate prediction model. Thus, by considering the utilization model of proven geological reserves and constructing a utilization rate prediction model to predict the cumulative reserve utilization rate of newly developed blocks, the workload and time required are effectively reduced, and the operational difficulty is significantly lowered.

[0018] In some optional embodiments, the step of constructing a utilization rate prediction model based on the original model and the first cumulative reserve utilization rate curve includes:

[0019] Based on the activation mode and the curve shape, determine the original model corresponding to the activation mode and the curve shape;

[0020] Based on the original model, the average value of the first cumulative reserve utilization rate corresponding to each developed block belonging to the same utilization mode is calculated to obtain the average cumulative reserve utilization rate. The average cumulative reserve utilization rate over the years forms the second cumulative reserve utilization rate curve.

[0021] The second cumulative reserve utilization rate curve is piecewise fitted to obtain the utilization rate prediction model corresponding to each utilization mode.

[0022] Thus, by considering the utilization model of proven geological reserves, a utilization rate prediction model corresponding to the utilization mode can be constructed. When predicting the utilization rate of cumulative reserves in newly developed or developed blocks, the corresponding utilization rate prediction model can be selected based on the geological parameters of the block and / or the corresponding curve shape and the magnitude of the cumulative utilization rate at different stages, so as to facilitate the prediction of the cumulative reserve utilization rate.

[0023] In some optional embodiments, after calculating the average of the first cumulative reserve utilization rate corresponding to each developed block belonging to the same utilization mode, the method further includes:

[0024] Based on the average cumulative reserve utilization rate, determine the utilization rate range corresponding to each utilization mode;

[0025] The step of selecting a utilization rate prediction model corresponding to the newly developed block based on the geological parameters of the newly developed block includes:

[0026] Based on the preset correlation, the estimated cumulative reserve utilization rate of the newly developed block is determined according to the geological parameters;

[0027] Based on the utilization rate range of the estimated cumulative reserves, a utilization rate prediction model corresponding to the newly developed block is determined.

[0028] For newly developed blocks, a corresponding utilization rate prediction model is selected based on the geological parameters of the newly developed blocks. The cumulative reserve utilization rate of the newly developed blocks is predicted using the utilization rate prediction model, which provides a reference for the prediction of exploration and development indicators in medium and long-term planning.

[0029] In some optional embodiments, determining the estimated cumulative reserve utilization rate of the newly developed block based on the geological parameters according to a preset correlation includes:

[0030] Multiple linear regression was performed using data analysis software to obtain a multiple linear regression equation, wherein the multiple linear regression equation is related to the geological parameters and the first cumulative reserve utilization rate;

[0031] Substituting the geological parameters of the newly developed block into the multiple linear regression equation, the estimated cumulative reserve utilization rate of the newly developed block is obtained.

[0032] By calculating the estimated cumulative reserve utilization rate, and determining the utilization rate prediction model based on the utilization rate range of the estimated cumulative reserve utilization rate, it is easier to predict the second cumulative reserve utilization rate of newly developed blocks.

[0033] In some optional embodiments, the proven geological reserves data includes: newly utilized geological reserves, cumulative utilized geological reserves, and proven geological reserves;

[0034] The determination of the first cumulative reserve utilization rate for each of the developed blocks, based on pre-acquired proven geological reserve data from multiple developed blocks, includes:

[0035] The newly utilized geological reserves, cumulative utilized geological reserves, and proven geological reserves of multiple developed blocks are normalized according to the utilization time, and the first cumulative reserve utilization rate of the developed blocks corresponding to the normalized utilization time is calculated.

[0036] By normalizing the proven geological reserves data according to the utilization time, it is easier to analyze the corresponding data in the subsequent construction of the utilization rate prediction model, thus reducing the workload of data analysis.

[0037] In some optional embodiments, the step of segmenting and fitting the second cumulative reserve utilization rate curve to obtain the utilization rate prediction model corresponding to each of the utilization modes includes:

[0038] The normalized mobilization time is divided into several time periods;

[0039] The second cumulative reserve utilization rate curve for each time period is fitted using the least squares method to obtain the utilization rate prediction model corresponding to each utilization mode.

[0040] Thus, piecewise fitting can better capture the nonlinear characteristics of the second cumulative reserve utilization rate curve, improve the accuracy of the utilization rate prediction model, and provide a scientific basis for the development of new development blocks.

[0041] In some optional embodiments, it also includes:

[0042] The third cumulative reserve utilization rate of the developed block is predicted using the utilization rate prediction model.

[0043] The goodness of fit of the utilization rate prediction model is calculated based on the proven geological reserves data of the developed blocks and the third cumulative reserve utilization rate of the developed blocks.

[0044] The prediction accuracy of the utilization rate prediction model is determined by the goodness of fit, which provides a good reference for predicting the cumulative reserve utilization rate of newly developed blocks with incomplete or missing data. Attached Figure Description

[0045] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0046] Figure 1 This is a flowchart illustrating a method for predicting the utilization rate of sandstone gas reservoirs, provided in one embodiment of this application.

[0047] Figure 2 This is a schematic flowchart of a sandstone gas reservoir utilization rate prediction device provided in one embodiment of this application;

[0048] Figure 3 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application;

[0049] Figure 4 This is a flowchart illustrating a method for predicting the utilization rate of sandstone gas reservoirs according to an embodiment of this application;

[0050] Figure 5 This is a schematic diagram of the normalized cumulative utilization rate curve of a low-permeability tight sandstone gas reservoir in the Sichuan Basin, provided in one embodiment of this application.

[0051] Figure 6 This is a schematic diagram of three low-permeability tight gas reservoir utilization modes provided in another embodiment of this application;

[0052] Figure 7 This is a schematic diagram of a fast-mobilization cumulative mobilization rate fitting prediction curve for 2006 provided in another embodiment of this application;

[0053] Figure 8 This is a schematic diagram of a fast-mobilization cumulative utilization rate fitting prediction curve for 2003 provided in another embodiment of this application;

[0054] Figure 9 This is a schematic diagram of the 2001 cumulative utilization rate fitting prediction curve provided by another embodiment of this application;

[0055] Figure 10 This is a schematic diagram of the 2002 cumulative utilization rate fitting prediction curve provided by another embodiment of this application;

[0056] Figure 11 This is a schematic diagram of the 2007 cumulative utilization rate fitting prediction curve provided by another embodiment of this application;

[0057] Figure 12 This is a schematic diagram of the cumulative utilization rate change curve of a low-speed, slow-mobilization type over different years, provided in another embodiment of this application. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0059] To facilitate understanding of the embodiments of this application, the relevant content on the method for predicting the utilization rate of sandstone gas reservoir reserves will be introduced first.

[0060] At present, the exploration and development of low-permeability tight sandstone gas reservoirs is booming. The prediction of the utilization rate of low-permeability tight gas reservoir reserves is not only the basis for the evaluation of untapped reserves, but also an important basis for medium- and long-term exploration and development planning.

[0061] In some technologies, determining the utilization level of oil and gas reservoir reserves and calculating the reserve utilization rate requires combining seismic data, production dynamic data, oil and gas reservoir property data, well network control level, and water drive control level. However, when making medium- and long-term plans at the branch company or group company level, the above methods are quite complex, require a large workload, are time-consuming, and are difficult to operate. When determining the newly added and cumulative utilized geological reserves each year in branch company and group company level planning, it is assumed that the annual reserve utilization rate is an average value and that the cumulative reserve utilization rate changes linearly over time, without considering the utilization pattern.

[0062] To address the aforementioned technical problems of complex, time-consuming, and difficult-to-operate methods for calculating reserve utilization rates in medium- and long-term planning at the branch or group company level, this invention proposes a method for predicting the reserve utilization rate of sandstone gas reservoirs. The implementation details of this sandstone gas reservoir reserve utilization rate prediction method are described below. The following content is only for ease of understanding and is not essential for implementing this solution.

[0063] Example 1:

[0064] The sandstone gas reservoir utilization rate prediction method of this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. Its specific process can be as follows: Figure 1 As shown, it includes:

[0065] Step 101: Based on the proven geological reserve data of multiple developed blocks obtained in advance, determine the first cumulative reserve utilization rate of each developed block. The first cumulative reserve utilization rate of each developed block over the years forms the first cumulative reserve utilization rate curve.

[0066] Specifically, proven geological reserve data includes newly utilized geological reserves, cumulative utilized geological reserves, and proven geological reserves. The first cumulative reserve utilization rate is the ratio of cumulative utilized geological reserves to proven geological reserves. In some examples, the newly utilized reserve utilization rate is the ratio of newly utilized geological reserves to proven geological reserves. In this embodiment, based on the acquired newly utilized geological reserves, cumulative utilized geological reserves, and proven geological reserves, the first cumulative reserve utilization rate and the newly utilized reserve utilization rate for each developed block over the years can be calculated. After obtaining the first cumulative reserve utilization rate for each developed block over the years, the first cumulative reserve utilization rate curves for each developed block are formed.

[0067] Step 102: Based on the first cumulative reserve utilization rate curve, determine the utilization mode and curve shape corresponding to the first cumulative reserve utilization rate curve.

[0068] Specifically, the first cumulative reserve utilization rate curve is a curve normalized according to the utilization time. Based on the variation characteristics of the normalized first cumulative reserve utilization rate curve, the utilization mode and curve shape are determined. The variation characteristics of the first cumulative reserve utilization rate curve are used to characterize the changing trend of the curve, that is, to characterize the change in the first cumulative reserve utilization rate over the years. In some examples, based on the variation characteristics of the first cumulative reserve utilization rate curve, the utilization mode corresponding to the proven geological reserves can be divided into rapid utilization type, step-by-step rapid utilization type, and slow-speed utilization type. Among them, the curve shape corresponding to the first cumulative reserve utilization rate curve of the rapid utilization mode is generally only one rising segment and a plateau period. It should be noted that the plateau period must last for more than three years to be considered a plateau period. The curve shape corresponding to the first cumulative reserve utilization rate curve of the step-rapid utilization mode is generally two or three rising segments and plateau periods. The curve shape corresponding to the first cumulative reserve utilization rate curve of the slow utilization mode is basically without rising segments, only a plateau period. The cumulative utilization rate remains unchanged within a single plateau period, and the cumulative utilization rate of different plateau periods increases continuously over time.

[0069] Step 103: Construct a utilization rate prediction model based on the original model and the first cumulative reserve utilization rate curve, wherein the original model is related to the utilization pattern and curve shape.

[0070] Specifically, a corresponding original model is established based on the utilization pattern and curve shape of the first cumulative reserve utilization rate curve, wherein the original model is a mathematical model. When constructing the utilization rate prediction model, the first cumulative reserve utilization rate curve of each developed block is processed and then input into the corresponding original model to obtain the utilization rate prediction model.

[0071] Step 104: Obtain the geological parameters of the newly developed block, select the utilization rate prediction model corresponding to the newly developed block based on the geological parameters of the newly developed block, and predict the second cumulative reserve utilization rate of the newly developed block based on the utilization rate prediction model.

[0072] Specifically, newly developed blocks refer to new areas with few data points or newly submitted proven geological reserves. When it is necessary to predict the utilization rate of newly developed blocks, there is no historical proven geological reserve data to support the prediction of the cumulative reserve utilization rate. Therefore, it is necessary to select a utilization rate prediction model corresponding to the newly developed block from the existing utilization rate prediction models based on the geological parameters of the newly developed block, and then predict the cumulative reserve utilization rate of the newly developed block, i.e., the second cumulative reserve utilization rate, based on the selected utilization rate prediction model.

[0073] In some cases, for the remaining developed blocks (i.e., developed blocks), a matching utilization rate prediction model can also be selected based on the geological parameters of the developed blocks, the corresponding curve shape, and the cumulative utilization rate at different stages to predict the utilization rate.

[0074] It should be noted that, in existing planning to determine the annual newly utilized geological reserves and cumulative utilized geological reserves, the average value of the annual reserve utilization rate is usually used. However, the cumulative reserve utilization rate changes linearly over time and does not consider the utilization pattern. Therefore, the accuracy of the predicted cumulative reserve utilization rate is low. In this embodiment, by considering the utilization pattern, the utilization pattern and curve shape corresponding to the first cumulative reserve utilization rate curve are determined, and then a utilization rate prediction model is constructed based on the original model and the first cumulative reserve utilization rate curve. After constructing multiple utilization rate prediction models, the geological parameters of the newly developed block are obtained. Based on the geological parameters of the newly developed block, the utilization rate prediction model corresponding to the newly developed block is selected, and the second cumulative reserve utilization rate of the newly developed block is predicted based on the utilization rate prediction model.

[0075] In this embodiment, based on the proven geological reserve data of multiple developed blocks, the first cumulative reserve utilization rate of each developed block is determined. Based on the first cumulative reserve utilization rate curve, the utilization pattern and curve shape corresponding to the first cumulative reserve utilization rate curve are determined. Then, a utilization rate prediction model is constructed based on the original model and the first cumulative reserve utilization rate curve. After constructing multiple utilization rate prediction models, the geological parameters of the newly developed block are obtained. Based on the geological parameters of the newly developed block, the utilization rate prediction model corresponding to the newly developed block is selected. The second cumulative reserve utilization rate of the newly developed block is predicted based on the utilization rate prediction model. In this way, by considering the utilization model of proven geological reserves and constructing the utilization rate prediction model to predict the cumulative reserve utilization rate of the newly developed block, the workload and time required are effectively reduced, and the operational difficulty is significantly lowered.

[0076] In some embodiments, a utilization rate prediction model is constructed based on the original model and the first cumulative reserve utilization rate curve, including:

[0077] Based on the mobilization pattern and curve shape, determine the original model corresponding to the mobilization pattern and curve shape;

[0078] Specifically, based on the utilization pattern and curve shape of the first cumulative reserve utilization rate curve, a corresponding original model is established. In some cases, it should be noted that due to the influence of geological conditions, well network completeness, and technological advancements on developed blocks, the normalized first cumulative reserve utilization rate curve corresponding to each utilization pattern will exhibit an upward segment and / or a plateau period. With the improvement of the well network and technological advancements, the cumulative reserve utilization rate continuously increases until the plateau period when the cumulative reserve utilization rate no longer changes. Therefore, using the starting point of the plateau period as the dividing point, least squares fitting is performed on the upward segment and the plateau segment respectively to obtain the original model corresponding to the utilization model.

[0079] In this embodiment, the first cumulative reserve utilization rate curve for the rapid utilization mode generally has only one rising segment and a plateau period, and the plateau period needs to last for more than three years to be considered a single plateau period. When establishing the original model, a logarithmic function is used to fit the rising segment, while the cumulative reserve utilization rate is constant for the plateau period. The original model of the first cumulative reserve utilization rate curve for the rapid utilization mode is expressed by the following relationship:

[0080]

[0081] In this embodiment, the first cumulative reserve utilization rate curve of the step-rapid utilization mode generally has two or three rising segments and plateau periods, and the plateau period needs to last for more than three years to be considered a single plateau period. When establishing the original model, the rising segments can be fitted with logarithmic or linear functions, while the cumulative reserve utilization rate for the plateau period is a constant. The original model of the first cumulative reserve utilization rate curve of the step-rapid utilization mode is expressed by the following relationship:

[0082]

[0083] or

[0084]

[0085] In this embodiment, the first cumulative reserve utilization rate curve, characterized by a low-speed, slow utilization mode, has virtually no rising segment, only a plateau period. When establishing the original model, the cumulative utilization rate remains constant within a single plateau period, while the cumulative utilization rate increases continuously over time across different plateau periods. The original model for the first cumulative reserve utilization rate curve, characterized by a low-speed, slow utilization mode, is expressed by the following relationship:

[0086]

[0087] In equations (1)-(4) above, y is the first cumulative reserve utilization rate, and x is the year.

[0088] Based on the original model, the average value of the first cumulative reserve utilization rate corresponding to each developed block belonging to the same utilization mode is calculated to obtain the average cumulative reserve utilization rate. Among them, the average cumulative reserve utilization rate over the years forms the second cumulative reserve utilization rate curve.

[0089] Specifically, after establishing an original model based on the utilization patterns and curve shapes of each first cumulative reserve utilization rate curve, the average value of the first cumulative reserve utilization rate corresponding to each developed block belonging to the same utilization pattern is calculated to obtain the average cumulative reserve utilization rate. At this point, the average cumulative reserve utilization rate represents the average value of the first cumulative reserve utilization rate of developed blocks belonging to the same utilization pattern. In other words, by aligning the utilization times of each developed block belonging to the same utilization pattern, and for each utilization time (e.g., each year from 2000 to 2020), the average value of the first cumulative reserve utilization rate of each developed block belonging to the same utilization pattern is calculated (i.e., the average cumulative reserve utilization rate). The average cumulative reserve utilization rate over the years forms the second cumulative reserve utilization rate curve, thus obtaining the cumulative reserve utilization rate curve (i.e., the second cumulative reserve utilization rate curve) for each developed block belonging to the same utilization pattern. In some cases, assuming there are three developed blocks that are of the tiered rapid utilization type, the average of the first cumulative reserve utilization rate for the three developed blocks between 2000 and 2020 is calculated, and the average of the cumulative reserve utilization rate for each year forms the second cumulative reserve utilization rate curve.

[0090] The utilization rate curve of the second cumulative reserves is piecewise fitted to obtain the utilization rate prediction model corresponding to each utilization mode.

[0091] Specifically, after obtaining the second cumulative reserve utilization rate curve, the second cumulative reserve utilization rate curve is segmented and fitted according to the utilization time. The fitted models after segmented fitting are combined to obtain the utilization rate prediction model corresponding to each utilization mode.

[0092] In some cases, the mobilization rate prediction model for the rapid mobilization pattern is expressed by the following relationship:

[0093]

[0094] Based on the above relationship (5), it can be calculated that for the rapid utilization type, the cumulative reserve utilization rate reaches about 60% in 3 years, about 80% in 5 years, and about 90% in 10 years, with the final cumulative reserve utilization rate being between 90% and 100%.

[0095] In some cases, the mobilization rate prediction model for a tiered rapid mobilization pattern is expressed by the following relationship:

[0096]

[0097] Based on the above relationship (6), it can be calculated that for the step-by-step rapid utilization type, the cumulative reserve utilization rate reaches about 30% in 3 years, about 55% in 5 years, about 75% in 10 years, and finally the cumulative reserve utilization rate is 80-90%.

[0098] In some cases, the utilization rate prediction model for low-speed, slow-mobilization patterns is expressed by the following relationship:

[0099]

[0100] Based on the above relationship (7), it can be calculated that for the low-speed, slow-utilization type, the cumulative reserve utilization rate remains unchanged at 5%, 10%, 15% or 20% for 3 to 5 years, and suddenly increases to 10%, 15%, 20% or 25% and remains unchanged for 3 to 5 years.

[0101] It should be noted that, regarding the relationships (5)-(7), the different proven geological reserves data of different developed blocks will lead to different values ​​of the constants (a, b, c) in the relationships (5)-(7).

[0102] In this embodiment, based on the utilization pattern and curve shape, an original model corresponding to the utilization pattern and curve shape is determined. Based on the original model, the average value of the first cumulative reserve utilization rate corresponding to each developed block belonging to the same utilization pattern is calculated to obtain the average cumulative reserve utilization rate. The average cumulative reserve utilization rate over the years forms the second cumulative reserve utilization rate curve. The second cumulative reserve utilization rate curve is piecewise fitted to obtain the utilization rate prediction model corresponding to each utilization pattern. In this way, by considering the utilization model of proven geological reserves, a utilization rate prediction model corresponding to the utilization pattern is constructed. When predicting the cumulative reserve utilization rate of newly developed blocks or developed blocks in the future, the corresponding utilization rate prediction model is selected according to the geological parameters of the block and / or the corresponding curve shape and the magnitude of the cumulative utilization rate at different stages, so as to facilitate the prediction of the cumulative reserve utilization rate.

[0103] In some embodiments, after calculating the average of the first cumulative reserve utilization rate corresponding to each developed block belonging to the same utilization mode, the method further includes:

[0104] Based on the average cumulative reserve utilization rate, determine the utilization rate range corresponding to each utilization mode;

[0105] Based on the geological parameters of the newly developed area, a utilization rate prediction model corresponding to the newly developed area is selected, including:

[0106] Based on the pre-defined correlation, the estimated cumulative reserve utilization rate of the newly developed block is determined according to geological parameters;

[0107] Based on the utilization rate range of the estimated cumulative reserves, a utilization rate prediction model corresponding to the newly developed blocks is determined.

[0108] Specifically, after obtaining the average cumulative reserve utilization rate, the utilization rate range corresponding to each utilization mode is determined based on the average cumulative reserve utilization rate. Different utilization modes correspond to different utilization rate ranges. In this embodiment, geological parameters include proven geological reserves, single-reservoir coefficient, effective porosity, original gas saturation, central burial depth of the gas reservoir, and formation coefficient. When selecting the utilization rate prediction model, the influencing factors of developed blocks, utilization modes, and cumulative reserve utilization rates are analyzed, and pre-defined correlations are established. These correlations refer to the relationship between the cumulative reserve utilization rate and parameters such as proven geological reserves, single-reservoir coefficient, effective porosity, original gas saturation, central burial depth of the gas reservoir, and formation coefficient. Based on these correlations, the estimated cumulative reserve utilization rate of the newly developed block can be determined according to the geological parameters of the newly developed block. Here, the estimated cumulative reserve utilization rate is an estimate. After obtaining the estimated cumulative reserve utilization rate, the range of utilization rates within which the estimated cumulative reserve utilization rate falls can be determined to establish the utilization rate prediction model corresponding to the newly developed block. For the newly developed block, the corresponding utilization rate prediction model is selected based on the geological parameters of the block. The cumulative reserve utilization rate of the newly developed block is then predicted using the utilization rate prediction model, providing a reference for the prediction of exploration and development indicators in medium- and long-term planning.

[0109] In some embodiments, based on preset correlations, the estimated cumulative reserve utilization rate of a newly developed block is determined according to geological parameters, including:

[0110] Multiple linear regression was performed using data analysis software to obtain the multiple linear regression equation, which is related to geological parameters and the first cumulative reserve utilization rate.

[0111] Substituting the geological parameters of the newly developed block into the multiple linear regression equation yields the estimated cumulative reserve utilization rate of the newly developed block.

[0112] Specifically, by analyzing the influencing factors of developed blocks, utilization patterns, and cumulative reserve utilization rates, pre-defined correlations are established. These correlations refer to the relationship between cumulative reserve utilization rates and parameters such as proven geological reserves, single-reservoir coefficient, effective porosity, initial gas saturation, reservoir depth, and formation coefficient. Data analysis software (such as SPSS statistical software) is used to perform multiple linear regression on the pre-defined correlations, yielding multiple linear regression equations. In some examples, the multiple linear regression equations are as follows:

[0113] Yn=-0.021N+1102.236Ω-14.34Φ+1.249Sg-0.042H-0.158KH+77.949, R 2 =0.89;

[0114] In the formula, Yn is the estimated cumulative reserve utilization rate over n years (N>1), N is the proven geological reserves, Ω is the single reservoir coefficient, Φ is the effective porosity, Sg is the original gas saturation, H is the burial depth in the middle of the gas reservoir, and KH is the formation coefficient.

[0115] In this embodiment, after obtaining the geological parameters of the newly developed block, the geological parameters are substituted into a multiple linear regression equation to obtain the estimated cumulative reserve utilization rate of the newly developed block. By calculating the estimated cumulative reserve utilization rate, a utilization rate prediction model is determined based on the utilization rate range in which the estimated cumulative reserve utilization rate falls, thereby facilitating the prediction of the second cumulative reserve utilization rate of the newly developed block.

[0116] In some embodiments, proven geological reserve data include: newly utilized geological reserves, cumulative utilized geological reserves, and proven geological reserves;

[0117] Based on pre-acquired proven geological reserve data from multiple developed blocks, the first cumulative reserve utilization rate for each developed block is determined, including:

[0118] The newly utilized geological reserves, cumulative utilized geological reserves, and proven geological reserves of multiple developed blocks are normalized according to the utilization time, and the first cumulative reserve utilization rate of the developed blocks corresponding to the normalized utilization time is calculated.

[0119] Specifically, the newly utilized geological reserves, cumulative utilized geological reserves, and proven geological reserves of each developed block are normalized according to the utilization time. That is, the utilization time of the initially submitted newly utilized geological reserves is recorded as 1, and the subsequent utilization times are recorded as 2, 3, ... N. After normalization, the first cumulative reserve utilization rate of the developed block corresponding to each utilization time is calculated. In some examples, the newly utilized reserve utilization rate of the developed block corresponding to each utilization time can also be calculated.

[0120] In some cases, such as when the development of blocks was initiated between 2000 and 2010, the initiation time of the initial submission of new geological reserves to be developed is recorded as 1, and the initiation times from 2001 to 2010 are recorded as 2, 3...11.

[0121] In this embodiment, by normalizing the proven geological reserve data according to the utilization time, it is easier to analyze the corresponding data in the subsequent construction of the utilization rate prediction model, thus reducing the workload of data analysis.

[0122] In some embodiments, the second cumulative reserve utilization rate curve is piecewise fitted to obtain a utilization rate prediction model corresponding to each utilization mode, including:

[0123] The normalized mobilization time is divided into several time periods;

[0124] The utilization rate curve of the second cumulative reserves for each time period is fitted using the least squares method to obtain the utilization rate prediction model corresponding to each utilization mode.

[0125] Specifically, after normalizing the utilization time, the utilization time is divided into several time periods. For each time period, the least squares method is used to fit the model parameters of the second cumulative reserve utilization rate curve, obtaining the fitting model for each time period. Then, the fitting models for each time period are combined to obtain the utilization rate prediction model corresponding to each utilization mode. In this way, piecewise fitting can better capture the nonlinear characteristics of the second cumulative reserve utilization rate curve, improve the accuracy of the utilization rate prediction model, and provide a scientific basis for the development of new development blocks.

[0126] In some embodiments, the method further includes:

[0127] Predict the third cumulative reserve utilization rate of developed blocks using a utilization rate prediction model;

[0128] The goodness of fit of the utilization rate prediction model is calculated based on the proven geological reserves data of the developed blocks and the third cumulative reserve utilization rate of the developed blocks.

[0129] Specifically, after obtaining the utilization rate prediction model, in addition to predicting the second cumulative reserve utilization rate of newly developed blocks, it can also be used to predict the third cumulative reserve utilization rate of developed blocks. The goodness of fit of the utilization rate prediction model is calculated by comparing the first cumulative reserve utilization rate calculated based on the proven geological reserve data of developed blocks with the third cumulative reserve utilization rate of developed blocks predicted by the utilization rate prediction model. The goodness of fit R0 is then used to determine the goodness of fit. 2 The Pearson coefficient, expressed as the square of the coefficient, is calculated using the following formula:

[0130] R 2 =[Cov(Y,Y') / (σY*σY')] 2 (8)

[0131] In the formula, Y and Y' represent the first cumulative reserve utilization rate dataset and the third cumulative reserve utilization rate dataset, respectively. Cov(Y,Y') represents the covariance of the first cumulative reserve utilization rate dataset Y and the third cumulative reserve utilization rate dataset Y'. σY and σY' represent the standard deviations of the first cumulative reserve utilization rate dataset Y and the third cumulative reserve utilization rate dataset, respectively.

[0132] In this embodiment, the goodness of fit between the first and third cumulative reserve utilization rates can be obtained through covariance and standard deviation, which is the goodness of fit of the utilization rate prediction model. Determining the prediction accuracy of the utilization rate prediction model through the goodness of fit provides a good reference for predicting the cumulative reserve utilization rate of newly developed blocks with incomplete or missing data.

[0133] Example 2:

[0134] like Figure 4 As shown, this embodiment provides an exemplary content of Embodiment 1, namely, an exemplary process for predicting the utilization rate of sandstone gas reservoir reserves, specifically including:

[0135] 1. Calculate the cumulative utilization rate based on the proven geological reserves of the developed blocks, the newly utilized geological reserves over the years, and the cumulative utilized geological reserves, and normalize the rate according to the utilization time.

[0136] This document compiles and summarizes the newly utilized geological reserves and cumulative utilized geological reserves over the years since the submission of proven geological reserves. It then calculates the annual utilization rate of newly utilized reserves and the cumulative utilization rate of reserves. The utilization rate of newly utilized reserves is the ratio of newly utilized geological reserves to proven geological reserves. The cumulative utilization rate of reserves is the ratio of cumulative utilized geological reserves to proven geological reserves.

[0137] The proven geological reserves submitted for all years are normalized according to the utilization time. The time when new geological reserves are first submitted is recorded as 1, and the subsequent times are recorded as 2, 3...N. The utilization rate of new reserves and the cumulative utilization rate of reserves corresponding to each time are calculated.

[0138] 2. Based on the variation characteristics of the normalized cumulative reserve utilization rate curve, the proven geological reserve utilization modes are divided into three categories: rapid utilization, step-by-step rapid utilization, and slow utilization.

[0139] 3. Establish a mathematical model based on the mobilization pattern and curve shape.

[0140] Influenced by geological conditions, the completeness of the well network, and technological advancements, the normalized curve of each model will exhibit an ascending phase and a plateau phase. As the well network improves and technology advances, the cumulative reserve utilization rate continuously increases until it plateaus and remains unchanged. Using the starting point of the plateau phase as the dividing point, least squares fitting is performed on the ascending phase and the plateau phase respectively to obtain the corresponding mathematical models (i.e., the original models).

[0141] (1) Rapid deployment type:

[0142] Generally, there is only one upward phase and one plateau phase, and the plateau phase must last for more than 3 years to be considered a plateau phase. The upward phase is fitted with a logarithmic function, and the cumulative reserve utilization rate during the plateau phase is constant.

[0143]

[0144] (2) Stepped rapid deployment type:

[0145] Generally, there are two or three rising phases and plateau periods. A plateau period lasts for more than three years to be considered a single plateau period. The rising phase can be fitted using a logarithmic function or a linear function, while the cumulative reserve utilization rate during the plateau period is constant.

[0146]

[0147] (3) Low-speed, slow-moving type

[0148] There are basically no upward phases, only plateau periods. The cumulative utilization rate remains unchanged within a single plateau period, while the cumulative utilization rate of different plateau periods increases over time.

[0149]

[0150] 4. Establishment of typical prediction models

[0151] According to different utilization modes, the average cumulative reserve utilization rate of the normalized time under the same mode is calculated, and the least squares method is used to construct a typical prediction model of the cumulative reserve utilization rate (i.e., utilization rate prediction model) in segments.

[0152] Rapid deployment

[0153]

[0154] For rapid utilization, the cumulative reserve utilization rate reaches about 60% in 3 years, about 80% in 5 years, and about 90% in 10 years, with the final cumulative reserve utilization rate being 90-100%.

[0155] Stepped rapid deployment type

[0156]

[0157] The cumulative reserve utilization rate reaches about 30% in 3 years, about 55% in 5 years, about 75% in 10 years, and finally 80-90%.

[0158] Low-speed, slow-moving type

[0159]

[0160] The cumulative reserve utilization rate remains unchanged at 5%, 10%, 15% or 20% for 3 to 5 years, then suddenly increases to 10%, 15%, 20% or 25% and remains unchanged for 3 to 5 years.

[0161] 5. Model goodness of fit

[0162] The goodness of fit R of the entire model 2 It is expressed as the square of the Pearson coefficient.

[0163] R 2 =[Cov(Y,Y') / (σY*σY')] 2 ;

[0164] Y and Y' represent the actual cumulative reserve utilization rate dataset and the model-predicted cumulative reserve utilization rate dataset, respectively. Cov(Y,Y') represents the covariance of the actual cumulative reserve utilization rate dataset Y and the model-predicted cumulative reserve utilization rate dataset Y', and σY and σY' represent the standard deviations of the cumulative reserve utilization rate Y and the model-predicted cumulative reserve utilization rate dataset, respectively. The goodness of fit between the actual cumulative reserve utilization rate and the model cumulative reserve utilization rate can be obtained through the covariance and standard deviation.

[0165] 6. Model Optimization

[0166] For already developed blocks, different models can be selected for fitting based on actual data, curve shapes, and the cumulative utilization rate at different stages. For blocks with limited data points, or newly developed areas with recently submitted proven reserves, how can the cumulative reserve utilization rate be predicted? First, a regression analysis is performed on the linear relationship between proven geological reserves (N), single-reservoir coefficient (Ω), effective porosity (Φ), initial gas saturation (Sg), reservoir mid-depth (H), formation coefficient (KH), and the five-year cumulative reserve utilization rate (Y5) for already developed low-permeability tight gas reservoirs. For undeveloped blocks, a multivariate linear relationship is used to predict the five-year cumulative reserve utilization rate, and a typical model is selected for prediction based on the range of the five-year cumulative reserve utilization rate.

[0167] In this embodiment, based on the variation characteristics of the cumulative utilization rate curve of low-permeability tight sandstone gas reservoirs over the years, it is divided into three modes: rapid utilization, stepped rapid utilization, and slow utilization. A mathematical model is established, and the least squares method is used to fit the data piecewise to obtain a prediction model for the cumulative reserve utilization rate of developed areas. The prediction accuracy of the model is above 90%. The five-year cumulative reserve utilization rate of the rapid utilization type reaches about 80%, the five-year cumulative reserve utilization rate of the stepped rapid utilization type reaches about 55%, and the five-year cumulative reserve utilization rate of the slow utilization type is between 5% and 20%. For areas not yet developed, the five-year cumulative reserve utilization rate is calculated according to a multivariate linear relationship. Based on the range of the five-year cumulative reserve utilization rate, the utilization mode and the typical prediction model of the cumulative reserve utilization rate are selected to calculate the cumulative reserve utilization rate over the years, thereby obtaining the utilization rate of newly utilized geological reserves over the years, and back-calculating the newly utilized geological reserves over the years. This effectively guides the prediction of medium- and long-term planning indicators for oil and gas exploration and development.

[0168] In some cases, based on the proven geological reserves, newly utilized reserves, and cumulative utilized geological reserves in developed areas of low-permeability tight sandstone gas reservoirs, the utilization rates of newly utilized reserves and cumulative reserves are calculated and normalized according to the utilization time. Based on the curve variation characteristics of the normalized data, a reserve utilization model and mathematical model are established, and a prediction model for the cumulative reserve utilization rate in developed areas is obtained by fitting the model piecewise using the least squares method. A typical prediction model for the cumulative utilization rate is established using the average value of the cumulative utilization rate under different development models. Multiple linear regression is used to obtain the relationship between parameters such as proven geological reserves, single-reservoir coefficient, effective porosity, original gas saturation, central burial depth of the gas reservoir, and formation coefficient, and the five-year cumulative reserve utilization rate. For areas not yet developed, the five-year cumulative reserve utilization rate is calculated according to the multiple linear relationship. Based on the range of the five-year cumulative reserve utilization rate, a utilization model and a typical prediction model for the cumulative utilization rate are selected to calculate the annual cumulative reserve utilization rate, thereby predicting the annual newly utilized geological reserve utilization rate in new areas.

[0169] Based on the submission time of proven reserves, the annual utilization rates of newly added reserves and the cumulative utilization rate of proven reserves in low-permeability tight gas reservoirs are compiled and calculated year by year. The utilization rate is then normalized according to the utilization time. The change of the normalized cumulative utilization rate over time is shown below. Figure 5 As shown. Figure 5 and Figure 6 As shown, based on the characteristics of curve changes, it is divided into three types of mobilization modes: rapid mobilization, stepped rapid mobilization, and low-speed slow mobilization.

[0170] All three models include elements such as the rising phase and plateau period, and are fitted using the least squares method in stages. Based on the actual situation, a portion of the data from the rising phase is selected and fitted using either a logarithmic or linear function. Predicted values ​​greater than 100 are uniformly replaced with 100. The goodness of fit of the fitted prediction model is calculated, and the fit coefficients all exceed 90%, indicating good fitting results. For details, see [link to details]. Figures 7-11 .

[0171] Based on different utilization modes, the average cumulative reserve utilization rate over normalized time for the same mode was calculated. A typical prediction model for the cumulative reserve utilization rate was constructed piecewise using the least squares method. This typical model was used to predict the cumulative reserve utilization rate in developed areas, and the goodness of fit of the model was calculated, with a fitting accuracy greater than 85%. This provides a good reference for predicting the cumulative reserve utilization rate in undeveloped areas with incomplete or missing data.

[0172] Rapid deployment

[0173] like Figure 7 As shown, the fitting prediction model for the cumulative utilization rate of low-permeability tight sandstone gas reservoirs in 2006 is as follows:

[0174]

[0175] Goodness of fit R 2 =0.9848, the goodness of fit of the typical model is R. 2 =0.9191.

[0176] like Figure 8 As shown, the fitting prediction model for the cumulative utilization rate of low-permeability tight sandstone gas reservoirs in 2003 is as follows:

[0177]

[0178] Goodness of fit R 2 =0.9251, the goodness of fit of the typical model is R. 2 =0.9475.

[0179] It can be seen that the goodness of fit of the fitting prediction model and the typical model are all greater than 0.9, indicating a good fitting effect.

[0180] Stepped rapid deployment type

[0181] like Figure 9 As shown, the fitting prediction model for the cumulative utilization rate of low-permeability tight sandstone gas reservoirs in 2001 is as follows:

[0182]

[0183] Goodness of fit R 2 =0.9458, the goodness of fit of the typical model is R. 2 =0.9244.

[0184] like Figure 10 As shown, the fitting prediction model for the cumulative utilization rate of low-permeability tight sandstone gas reservoirs in 2002 is as follows:

[0185]

[0186] Goodness of fit R 2 =0.9885, the goodness of fit of the typical model is R. 2 =0.961.

[0187] like Figure 11 As shown, the fitting prediction model for the cumulative utilization rate of low-permeability tight sandstone gas reservoirs in 2007 is as follows:

[0188]

[0189] Goodness of fit R 2 =0.9078, the goodness of fit of the typical model is R. 2 =0.8833. The accuracy of the fitted prediction model is slightly higher than that of the typical model.

[0190] Low-speed, slow-moving type

[0191] like Figure 12 As shown, if the cumulative utilization rate remains constant at 5, 10, 15 or 20 over 3 to 5 years, and suddenly increases to 10, 15, 20 or 25 and remains unchanged for 3 to 5 years, the curves will show significant differences and cannot form a normalized curve. This type of reservoir is basically a low-permeability tight reservoir with a single reservoir permeability coefficient of less than 0.15 and a permeability of less than 0.25 mD.

[0192] This study analyzes the influencing factors of developed blocks, utilization patterns, and five-year reserve utilization rate, establishing the relationship between the five-year utilization rate and parameters such as proven geological reserves, single-reservoir coefficient, effective porosity, initial gas saturation, central burial depth of the gas reservoir, and formation coefficient. Using SPSS statistical software, multiple linear regression was performed, yielding the equation: Y5=-0.021N+1102.236Ω-14.34Φ+1.249Sg-0.042H-0.158KH+77.949, R 2 =0.89, Yn is the estimated cumulative reserve utilization rate over n years (N>1), N is the proven geological reserves, Ω is the single-reservoir coefficient, Φ is the effective porosity, Sg is the original gas saturation, H is the burial depth in the middle of the gas reservoir, and KH is the formation coefficient. The variance inflation factor (VIF) of the independent variables is between 1.4 and 3.95, and there is basically no collinearity problem among the variables. When these parameters of the undeveloped area are known, the five-year cumulative reserve utilization rate can be preliminarily estimated. Based on the range of the five-year cumulative reserve utilization rate, a typical prediction model is selected to predict the cumulative reserve utilization rate, and then the annual new reserve utilization rate and new utilized geological reserves are obtained, which provides a good reference for the prediction of exploration and development indicators in medium and long-term planning.

[0193] In this embodiment, based on the proven geological reserves, newly utilized reserves, and cumulative utilized geological reserves in the developed area of ​​low-permeability tight sandstone gas reservoirs, the utilization rates of newly utilized reserves and cumulative reserves are calculated and normalized according to the utilization time. Based on the curve variation characteristics of the normalized data, three modes and mathematical models are established: rapid utilization, stepped rapid utilization, and slow utilization. The least squares method is used to fit the models piecewise to obtain a prediction model for the cumulative reserve utilization rate in the developed area. The prediction accuracy of the model is above 90%. The rapid utilization model achieves a 5-year cumulative reserve utilization rate of approximately 80%, the stepped rapid utilization model achieves approximately 55%, and the slow utilization model achieves approximately 50%. ~20%; A typical prediction model for cumulative utilization rate is established using the average cumulative utilization rate over the years under different development models; Multiple linear regression is used to obtain the relationship between parameters such as proven geological reserves, single reservoir coefficient, effective porosity, original gas saturation, burial depth in the middle of the gas reservoir, and formation coefficient and the five-year cumulative utilization rate; For areas that have not been put into development, the five-year cumulative utilization rate is calculated according to the multiple linear relationship. Based on the range of the five-year cumulative utilization rate, the utilization model and the typical prediction model for cumulative utilization rate are selected to calculate the cumulative utilization rate over the years, and then the annual newly added geological reserves utilization rate of the new area is predicted. This can effectively guide the prediction of medium and long-term oil and gas exploration and development planning indicators at the branch company and group company levels.

[0194] Example 3:

[0195] Another embodiment of this application relates to a sandstone gas reservoir reserve utilization rate prediction device. The implementation details of this sandstone gas reservoir reserve utilization rate prediction device are described below. The following content is only for ease of understanding and is not essential for implementing this solution. A schematic diagram of the sandstone gas reservoir reserve utilization rate prediction device in this embodiment can be seen as follows: Figure 2 As shown, it includes:

[0196] The first utilization rate determination module 201 is used to determine the first cumulative reserve utilization rate of each developed block based on the proven geological reserve data of multiple developed blocks obtained in advance. The first cumulative reserve utilization rate of each developed block over the years forms the first cumulative reserve utilization rate curve.

[0197] The curve analysis module 202 is used to determine the utilization mode and curve shape corresponding to the first cumulative reserve utilization rate curve based on the first cumulative reserve utilization rate curve.

[0198] Module 203 is used to construct a utilization rate prediction model based on the original model and the first cumulative reserve utilization rate curve, wherein the original model is related to the utilization mode and the curve shape;

[0199] The prediction module 204 is used to obtain the geological parameters of the newly developed block, select the utilization rate prediction model corresponding to the newly developed block based on the geological parameters of the newly developed block, and predict the second cumulative reserve utilization rate of the newly developed block based on the utilization rate prediction model.

[0200] In some embodiments, the construction module 203 includes:

[0201] The first determining unit is used to determine the original model corresponding to the mobilization mode and curve shape based on the mobilization mode and curve shape.

[0202] The average value calculation unit is used to calculate the average value of the first cumulative reserve utilization rate corresponding to each developed block belonging to the same utilization mode based on the original model, so as to obtain the average value of the cumulative reserve utilization rate. The average value of the cumulative reserve utilization rate over the years forms the second cumulative reserve utilization rate curve.

[0203] The fitting unit is used to perform piecewise fitting on the second cumulative reserve utilization rate curve to obtain the utilization rate prediction model corresponding to each utilization mode.

[0204] In some embodiments, the sandstone gas reservoir utilization rate prediction device includes:

[0205] The range determination module is used to determine the utilization rate range corresponding to each utilization mode based on the average cumulative reserve utilization rate.

[0206] In some embodiments, the prediction module 204 includes:

[0207] The estimated utilization rate determination unit is used to determine the estimated cumulative reserve utilization rate of newly developed blocks based on preset correlations and geological parameters.

[0208] The model determination unit is used to determine the utilization rate prediction model corresponding to the newly developed block based on the utilization rate range of the estimated cumulative reserve utilization rate.

[0209] In some embodiments, the estimated utilization rate determination unit includes:

[0210] The linear regression subunit is used to perform multiple linear regression using data analysis software to obtain the multiple linear regression equation, which is related to geological parameters and the first cumulative reserve utilization rate.

[0211] The calculation sub-unit is used to substitute the geological parameters of the newly developed block into the multiple linear regression equation to obtain the estimated cumulative reserve utilization rate of the newly developed block.

[0212] In some embodiments, the first utilization rate determination module 201 is further configured to normalize the newly utilized geological reserves, cumulative utilized geological reserves, and proven geological reserves of multiple developed blocks according to the utilization time, and calculate the first cumulative reserve utilization rate of the developed blocks corresponding to the normalized utilization time.

[0213] In some embodiments, the fitting unit includes:

[0214] Sub-units are used to divide the normalized mobilization time into several time periods;

[0215] The fitting subunit is used to fit the second cumulative reserve utilization rate curve for each time period using the least squares method to obtain the utilization rate prediction model corresponding to each utilization mode.

[0216] In some embodiments, the sandstone gas reservoir utilization rate prediction device includes:

[0217] The utilization rate prediction module is used to predict the third cumulative reserve utilization rate of developed blocks through the utilization rate prediction model;

[0218] The calculation module is used to calculate the goodness of fit of the utilization rate prediction model based on the proven geological reserves data of the developed blocks and the third cumulative reserve utilization rate of the developed blocks.

[0219] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units are absent in this embodiment.

[0220] Example 4:

[0221] Another embodiment of this application relates to an electronic device, such as... Figure 3 As shown, it includes: at least one processor 901; and a memory 902 communicatively connected to the at least one processor 901; wherein the memory 902 stores instructions executable by the at least one processor 901, the instructions being executed by the at least one processor 901 to enable the at least one processor 901 to execute the sandstone gas reservoir utilization rate prediction method in the above embodiments.

[0222] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0223] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0224] Example 5:

[0225] Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0226] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned 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.

[0227] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for predicting the utilization rate of sandstone gas reservoir reserves, characterized in that, include: Based on the proven geological reserve data of multiple developed blocks obtained in advance, the first cumulative reserve utilization rate of each developed block is determined, wherein the first cumulative reserve utilization rate of each developed block over the years forms a first cumulative reserve utilization rate curve. Based on the first cumulative reserve utilization rate curve, determine the utilization mode and curve shape corresponding to the first cumulative reserve utilization rate curve; A utilization rate prediction model is constructed based on the original model and the first cumulative reserve utilization rate curve, wherein the original model is related to the utilization mode and the curve shape; Obtain the geological parameters of the newly developed block, select the utilization rate prediction model corresponding to the newly developed block based on the geological parameters of the newly developed block, and predict the second cumulative reserve utilization rate of the newly developed block based on the utilization rate prediction model.

2. The method for predicting the utilization rate of sandstone gas reservoirs according to claim 1, characterized in that, The step of constructing a utilization rate prediction model based on the original model and the first cumulative reserve utilization rate curve includes: Based on the activation mode and the curve shape, determine the original model corresponding to the activation mode and the curve shape; Based on the original model, the average value of the first cumulative reserve utilization rate corresponding to each developed block belonging to the same utilization mode is calculated to obtain the average cumulative reserve utilization rate. The average cumulative reserve utilization rate over the years forms the second cumulative reserve utilization rate curve. The second cumulative reserve utilization rate curve is piecewise fitted to obtain the utilization rate prediction model corresponding to each utilization mode.

3. The method for predicting the utilization rate of sandstone gas reservoir reserves according to claim 2, characterized in that, After calculating the average of the first cumulative reserve utilization rate corresponding to each developed block belonging to the same utilization mode, the method further includes: Based on the average cumulative reserve utilization rate, determine the utilization rate range corresponding to each utilization mode; The step of selecting a utilization rate prediction model corresponding to the newly developed block based on the geological parameters of the newly developed block includes: Based on the preset correlation, the estimated cumulative reserve utilization rate of the newly developed block is determined according to the geological parameters; Based on the utilization rate range of the estimated cumulative reserves, a utilization rate prediction model corresponding to the newly developed block is determined.

4. The method for predicting the utilization rate of sandstone gas reservoir reserves according to claim 3, characterized in that, The determination of the estimated cumulative reserve utilization rate of the newly developed block based on the preset correlation and the geological parameters includes: Multiple linear regression was performed using data analysis software to obtain a multiple linear regression equation, wherein the multiple linear regression equation is related to the geological parameters and the first cumulative reserve utilization rate; Substituting the geological parameters of the newly developed block into the multiple linear regression equation, the estimated cumulative reserve utilization rate of the newly developed block is obtained.

5. The method for predicting the utilization rate of sandstone gas reservoir reserves according to claim 2, characterized in that, The proven geological reserves data include: newly utilized geological reserves, cumulative utilized geological reserves, and proven geological reserves; The determination of the first cumulative reserve utilization rate for each of the developed blocks, based on pre-acquired proven geological reserve data from multiple developed blocks, includes: The newly utilized geological reserves, cumulative utilized geological reserves, and proven geological reserves of multiple developed blocks are normalized according to the utilization time, and the first cumulative reserve utilization rate of the developed blocks corresponding to the normalized utilization time is calculated.

6. The method for predicting the utilization rate of sandstone gas reservoir reserves according to claim 5, characterized in that, The step of segmenting and fitting the second cumulative reserve utilization rate curve to obtain the utilization rate prediction model corresponding to each utilization mode includes: The normalized mobilization time is divided into several time periods; The second cumulative reserve utilization rate curve for each time period is fitted using the least squares method to obtain the utilization rate prediction model corresponding to each utilization mode.

7. The method for predicting the utilization rate of sandstone gas reservoir reserves according to any one of claims 1 to 6, characterized in that, Also includes: The third cumulative reserve utilization rate of the developed block is predicted using the utilization rate prediction model. The goodness of fit of the utilization rate prediction model is calculated based on the proven geological reserves data of the developed blocks and the third cumulative reserve utilization rate of the developed blocks.

8. A device for predicting the utilization rate of sandstone gas reservoir reserves, characterized in that, include: The first utilization rate determination module is used to determine the first cumulative reserve utilization rate of each developed block based on the proven geological reserve data of multiple developed blocks obtained in advance. The first cumulative reserve utilization rate of each developed block over the years forms a first cumulative reserve utilization rate curve. The curve analysis module is used to determine the utilization mode and curve shape corresponding to the first cumulative reserve utilization rate curve based on the first cumulative reserve utilization rate curve. A construction module is used to construct a utilization rate prediction model based on the original model and the first cumulative reserve utilization rate curve, wherein the original model is related to the utilization mode and the curve shape; The prediction module is used to obtain the geological parameters of the newly developed block, select the utilization rate prediction model corresponding to the newly developed block based on the geological parameters of the newly developed block, and predict the second cumulative reserve utilization rate of the newly developed block based on the utilization rate prediction model.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the sandstone gas reservoir utilization rate prediction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the sandstone gas reservoir utilization rate prediction method according to any one of claims 1 to 7.

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