Method and device for splitting yield of multi-layer commingled recovery gas well

Abstract

The invention provides a multi-layer commingled recovery gas well yield splitting method and device, and relates to the technical field of oil and gas reservoir productivity evaluation.The method comprises the steps that static parameters and initial dynamic parameters of each layer of gas reservoir are obtained; the static parameters and the initial dynamic parameters of each layer of gas reservoir are input into a daily gas production rate prediction model, and the initial daily gas production rate of each layer of gas recovery well is determined; according to the initial daily gas production rate of each layer of gas recovery well and a gas productivity equation, the productivity index of each layer of gas recovery well is determined; according to the productivity index of each layer of gas recovery well, a gas productivity equation corrected by a yield correction coefficient, dynamic reserves of each layer of gas reservoir and a material balance equation, the total gas production rate of all layers of gas recovery wells in the production cycle is determined, and the minimum deviation of the total gas production rate and the historical actually-measured gas production rate is set as an optimization target; optimizing the yield correction coefficient; and calculating the daily gas production rate of each layer of gas recovery well in the production cycle according to the optimized gas productivity equation corrected by the yield correction coefficient.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas reservoir productivity evaluation, and in particular to a multi-layer combined production gas well production splitting method and device. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention recited in the claims. No admission is made that the description herein is prior art by inclusion in this section.

[0003] Due to the influence of diagenesis, sedimentary environment, etc., the reservoir structure of multi-layer gas reservoirs is complex, and the reservoir porosity, reservoir permeability, reservoir thickness and other physical properties of each layer of gas reservoirs are different. At the same time, the exploitation methods of each layer of gas reservoirs are also different, resulting in different production of gas wells in each layer during the exploitation process. In order to formulate development and adjustment strategies for multi-layer gas reservoirs, it is necessary to accurately split the production of multi-layer combined gas wells and determine the production rules of each layer of gas wells. In the existing technology, the geological parameter method is usually used to calculate the gas production of each layer of gas wells, but the calculation error of this method is large, so it cannot guide the formulation of development strategies for multi-layer gas reservoirs. Summary of the invention

[0004] In the embodiment of the present invention, a multi-layer commingled production gas well production splitting method is proposed to improve the calculation accuracy of the gas production of each layer of gas production wells, ensure the reliability of the multi-layer commingled production gas well production splitting, and guide the formulation of a multi-layer gas reservoir development strategy, including:

[0005] Obtain static parameters and initial dynamic parameters of each gas reservoir;

[0006] The static parameters and initial dynamic parameters of each gas reservoir are input into the daily gas production prediction model to determine the initial daily gas production of each gas production well; wherein the daily gas production prediction model is obtained by training the machine learning model based on the static parameters and initial dynamic parameters of the historical gas reservoir and the corresponding historical daily gas production;

[0007] Determine the productivity index of each gas production well according to the initial daily gas production of each gas production well and the gas productivity equation;

[0008] According to the productivity index of each layer of gas production wells, the gas productivity equation corrected by the production correction coefficient, the dynamic reserves of each layer of gas reservoirs, and the material balance equation, the total gas production of all layers of gas production wells during the production cycle is determined, and the minimum deviation between the total gas production and the historical measured gas production is set as the optimization target, and the production correction coefficient is optimized according to the optimization target; among them, the production correction coefficient is used to improve the calculation accuracy of the gas productivity equation;

[0009] The daily gas production of each gas well during the production cycle is calculated based on the gas production capacity equation corrected by the optimized production correction coefficient, the dynamic reserves of each gas reservoir, and the material balance equation.

[0010] In the embodiment of the present invention, a multi-layer commingled production gas well production splitting device is proposed to improve the calculation accuracy of the gas production of each layer of gas production wells, ensure the reliability of the multi-layer commingled production gas well production splitting, and guide the formulation of a multi-layer gas reservoir development strategy, including:

[0011] Parameter acquisition module, used to obtain static parameters and initial dynamic parameters of each gas reservoir;

[0012] The daily gas production prediction module is used to input the static parameters and initial dynamic parameters of each gas reservoir into the daily gas production prediction model to determine the initial daily gas production of each gas production well; wherein the daily gas production prediction model is obtained by training the machine learning model based on the static parameters and initial dynamic parameters of the historical gas reservoir and the corresponding historical daily gas production;

[0013] The productivity index determination module is used to determine the productivity index of each gas production well according to the initial daily gas production of each gas production well and the gas productivity equation;

[0014] The optimization and correction module is used to determine the total gas production of all gas wells in the production cycle according to the production capacity index of each layer of gas wells, the gas production capacity equation corrected by the production correction coefficient, the dynamic reserves of each layer of gas reservoirs, and the material balance equation, set the minimum deviation between the total gas production and the historical measured gas production as the optimization target, and optimize the production correction coefficient according to the optimization target; among which, the production correction coefficient is used to improve the calculation accuracy of the gas production capacity equation;

[0015] The daily gas production calculation module is used to calculate the daily gas production of each gas well during the production cycle based on the gas production capacity equation corrected by the optimized production correction coefficient, the dynamic reserves of each gas reservoir, and the material balance equation.

[0016] In an embodiment of the present invention, a computer device is proposed, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for splitting the production of a multi-layer combined production gas well is implemented.

[0017] In an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, a method for splitting the production of a multi-layer combined production gas well is implemented.

[0018] A computer program product is provided in an embodiment of the present invention. The computer program product includes a computer program. When the computer program is executed by a processor, a method for splitting the production of a multi-layer combined production gas well is implemented.

[0019] The horizontal well collaborative cold production method and device proposed in the embodiment of the present invention can solve the problem that the prior art has large errors in calculating the gas production of each layer of gas production wells and cannot guide the formulation of development strategies for multi-layer gas reservoirs; the embodiment of the present invention obtains the static parameters and initial dynamic parameters of each layer of gas reservoirs; inputs the static parameters and initial dynamic parameters of each layer of gas reservoirs into the daily gas production prediction model to determine the initial daily gas production of each layer of gas production wells; wherein the daily gas production prediction model is obtained by training a machine learning model based on the static parameters and initial dynamic parameters of historical gas reservoirs, and the corresponding historical daily gas production; based on the initial daily gas production of each layer of gas production wells, and the gas production capacity The production capacity index of each layer of gas production wells is determined; according to the production capacity index of each layer of gas production wells, the gas production capacity equation corrected by the production correction coefficient, the dynamic reserves of each layer of gas reservoirs, and the material balance equation, the total gas production of all layers of gas production wells in the production cycle is determined, and the minimum deviation between the total gas production and the historical measured gas production is set as the optimization target, and the production correction coefficient is optimized according to the optimization target; wherein, the production correction coefficient is used to improve the calculation accuracy of the gas production capacity equation; according to the gas production capacity equation corrected by the optimized production correction coefficient, the dynamic reserves of each layer of gas reservoirs, and the material balance equation, the daily gas production of each layer of gas production wells in the production cycle is calculated. The embodiment of the present invention can optimize the production correction coefficient, improve the calculation accuracy of the gas production of each layer of gas production wells, ensure the reliability of the production splitting of multi-layer combined production gas wells, and guide the formulation of the development strategy of multi-layer gas reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 1 is a schematic flow chart of a method for splitting the production of a multi-layer combined production gas well in an embodiment of the present invention;

[0022] Figure 2 This is a specific example diagram of the method for splitting the production of a multi-layer combined production gas well in an embodiment of the present invention;

[0023] Figure 3 This is a specific example diagram of the method for splitting the production of a multi-layer combined production gas well in an embodiment of the present invention;

[0024] Figure 4This is a specific example diagram of the method for splitting the production of a multi-layer combined production gas well in an embodiment of the present invention;

[0025] Figure 5 This is a specific example diagram of the method for splitting the production of a multi-layer combined production gas well in an embodiment of the present invention;

[0026] Figure 6 This is a specific example diagram of the method for splitting the production of a multi-layer combined production gas well in an embodiment of the present invention;

[0027] Figure 7 Schematic diagram of a multi-layer combined production gas well production splitting device according to an embodiment of the present invention;

[0028] Figure 8 Schematic diagram of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0030] The term "and / or" herein only describes an association relationship, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set consisting of A, B, and C.

[0031] In the description of this specification, the terms "include", "including", "have", "contain", etc. are all open terms, which mean including but not limited to. The descriptions with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", etc. mean that the specific features, structures or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. The order of steps involved in each embodiment is used to schematically illustrate the implementation of the present application, and the order of steps is not limited and can be appropriately adjusted as needed.

[0032] The principle and spirit of the present invention are explained in detail below with reference to several representative embodiments of the present invention.

[0033] Figure 1FIG. 1 is a flow chart of a multi-layer combined production gas well production splitting method according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0034] Step 101, obtaining static parameters and initial dynamic parameters of each gas reservoir;

[0035] Step 102, inputting the static parameters and initial dynamic parameters of each gas reservoir into a daily gas production prediction model to determine the initial daily gas production of each gas production well; wherein the daily gas production prediction model is obtained by training a machine learning model based on the static parameters and initial dynamic parameters of the historical gas reservoir and the corresponding historical daily gas production;

[0036] Step 103, determining the productivity index of each gas production well according to the initial daily gas production of each gas production well and the gas productivity equation;

[0037] Step 104, according to the productivity index of each gas production well, the gas productivity equation corrected by the production correction coefficient, the dynamic reserves of each gas reservoir, and the material balance equation, the total gas production of all gas production wells in the production cycle is determined, the minimum deviation between the total gas production and the historical measured gas production is set as the optimization target, and the production correction coefficient is optimized according to the optimization target; wherein the production correction coefficient is used to improve the calculation accuracy of the gas productivity equation;

[0038] Step 105, calculating the daily gas production of each gas well during the production cycle according to the gas production capacity equation corrected by the optimized production correction coefficient, the dynamic reserves of each gas reservoir, and the material balance equation.

[0039] Depend on Figure 1It can be seen from the process shown that the embodiment of the present invention obtains the static parameters and initial dynamic parameters of each gas reservoir layer; inputs the static parameters and initial dynamic parameters of each gas reservoir layer into the daily gas production prediction model to determine the initial daily gas production of each gas well layer; wherein the daily gas production prediction model is obtained by training the machine learning model based on the static parameters and initial dynamic parameters of the historical gas reservoir and the corresponding historical daily gas production; determines the production capacity index of each gas well layer according to the initial daily gas production of each gas well layer and the gas production capacity equation; and determines the production capacity index of each gas well layer according to the production capacity index of each gas well layer. , using the gas production capacity equation corrected by the production correction coefficient, the dynamic reserves of each gas reservoir layer, and the material balance equation, determine the total gas production of all gas wells in the production cycle, set the minimum deviation between the total gas production and the historical measured gas production as the optimization target, and optimize the production correction coefficient according to the optimization target; wherein, the production correction coefficient is used to improve the calculation accuracy of the gas production capacity equation; according to the gas production capacity equation corrected by the optimized production correction coefficient, the dynamic reserves of each gas reservoir layer, and the material balance equation, calculate the daily gas production of each gas well in the production cycle. The embodiment of the present invention can optimize the production correction coefficient, improve the calculation accuracy of the gas production of each gas well layer, ensure the reliability of the production splitting of multi-layer combined production gas wells, and guide the formulation of the development strategy of multi-layer gas reservoirs.

[0040] In order to explain the above-mentioned multi-layer combined production gas well production splitting method more clearly, each step is described in detail below.

[0041] In one embodiment of the present invention, the static parameters of the gas reservoir include any one or more of the following: reservoir burial depth, effective reservoir thickness, mud content, reservoir porosity, reservoir permeability, and gas saturation; the initial dynamic parameters include: initial formation pressure and initial bottom hole flowing pressure.

[0042] In one embodiment of the present invention, with respect to step 102, the static parameters and initial dynamic parameters of each layer of the gas reservoir are input into a daily gas production prediction model to determine the initial daily gas production of each layer of the gas well; wherein the daily gas production prediction model is obtained by training a machine learning model based on the static parameters and initial dynamic parameters of the historical gas reservoir and the corresponding historical daily gas production.

[0043] In the specific implementation, the correlation analysis method is used to obtain parameters related to the daily production of a single-layer gas well from the reservoir burial depth, effective reservoir thickness, mud content, reservoir porosity, reservoir permeability, gas saturation, initial formation pressure and initial bottom hole flowing pressure of the A gas field as characteristic parameter data. For example, the characteristic parameter data of the A gas field are effective reservoir thickness, reservoir porosity, reservoir permeability, gas saturation, initial formation pressure and initial bottom hole flowing pressure; five machine learning models, namely support vector machine, random forest, progressive gradient regression tree, extreme gradient regression tree and BP neural network, are established respectively, and the static parameters and initial dynamic parameters of multiple groups of gas reservoirs and the corresponding daily gas production of a single-layer gas well are divided into training set and test set according to a ratio of 7:3, and the training set is trained according to 5-fold cross validation to obtain 5 trained machine learning models; the test set accuracy of the 5 trained machine learning models is compared, and the machine learning model with the highest test set accuracy is selected as the daily gas production prediction model, and the machine learning model with the highest test set accuracy is the progressive gradient regression tree model.

[0044] In one embodiment of the present invention, the machine learning model is a progressive gradient regression tree model.

[0045] In one embodiment of the present invention, for step 103, the productivity index of each layer of gas production wells is determined based on the initial daily gas production of each layer of gas production wells and the gas productivity equation; wherein the productivity index is used to measure the daily gas production of each layer of gas production wells.

[0046] In specific implementation, the daily gas production prediction model is used to predict the daily gas production of each layer of gas production wells in gas field A when they are produced independently, and the production capacity index of each layer of gas production wells is calculated in combination with the gas production capacity equation, wherein the gas production capacity equation is as follows:

[0047]

[0048] Among them, j j represents the productivity index of the j-th gas production well; P i Indicates the initial formation pressure of the gas reservoir; P wf0 represents the initial bottom hole flowing pressure; q j represents the daily gas production of the j-th gas well; n represents the number of gas well layers.

[0049] Figure 2 It is a specific example diagram of the production splitting method of a multi-layer combined production gas well in an embodiment of the present invention.

[0050] In one embodiment of the present invention, reference Figure 2 , according to the productivity index of each layer of gas production wells, the gas productivity equation corrected by the production correction coefficient, the dynamic reserves of each layer of gas reservoir, and the material balance equation, the total gas production of all layers of gas production wells during the production cycle is determined, including:

[0051] The following steps are cyclically performed for each layer of gas production wells until the production cycle ends. According to the daily gas production of each layer of gas production wells during the production cycle, the total gas production of all layers of gas production wells during the production cycle is determined:

[0052] Step 201, determining the daily gas production of each gas production well on that day according to the production capacity index of each gas production well, the gas production capacity equation corrected by the production correction coefficient, the formation pressure of each gas reservoir on that day, and the bottom hole flowing pressure on that day;

[0053] Step 202, determining the cumulative value of the daily gas production of each gas production well according to the daily gas production of each gas production well on that day and the daily gas production before that day;

[0054] Step 203, determining the formation pressure of each gas reservoir after gas production according to the accumulated value of daily gas production of each gas production well, the initial formation pressure, the dynamic reserves of each gas reservoir and the material balance equation, and determining the formation pressure of each gas reservoir after gas production as the formation pressure of the next day;

[0055] Step 204, adjusting the dynamic reserves of each gas reservoir layer according to the accumulated value of the daily gas production of each gas production well;

[0056] Step 205, obtaining the bottom hole flow pressure after gas production, and determining the bottom hole flow pressure after gas production as the bottom hole flow pressure for the next day.

[0057] In one embodiment of the present invention, the minimum deviation between the total gas production and the historical measured gas production is set as the optimization target, and the production correction coefficient is optimized according to the optimization target, including:

[0058] The following steps are executed repeatedly until the deviation between the total gas production and the historical measured gas production is minimized:

[0059] Adjust the production correction coefficient, and determine the total gas production of all gas wells in the production cycle based on the production capacity index of each gas well, the gas production capacity equation corrected by the adjusted production correction coefficient, the dynamic reserves of each gas reservoir, and the material balance equation.

[0060] In specific implementation, the gas production capacity equation and material balance equation corrected by the adjusted production correction coefficient are continuously iterated to obtain the formation pressure of each gas reservoir layer and the daily gas production of each gas well on each day of the production cycle. The automatic fitting algorithm is used to adjust the dynamic reserves and production correction coefficient of each gas reservoir layer to fit the daily gas production of each gas well layer, and seek the best fit between the calculated total gas production of all gas wells in the production cycle and the historical measured gas production, so as to minimize the deviation and achieve the best fitting effect.

[0061] In one embodiment of the present invention, the gas production capacity equation corrected by the production correction coefficient is as follows:

[0062]

[0063] Among them, P rj represents the formation pressure of the j-th gas reservoir; P wf represents the bottom hole flowing pressure; m represents the production correction factor; n represents the number of layers of the gas well; j represents the j represents the productivity index of the j-th gas well; Q scj It represents the daily gas production of the j-th gas production well during the production cycle. The initial value of the given production correction coefficient is 1.

[0064] In one embodiment of the present invention, the material balance equation is as follows:

[0065]

[0066] Among them, P rj represents the formation pressure of the j-th gas reservoir; P i Indicates the initial formation pressure of the gas reservoir; Z j represents the deviation coefficient of the j-th gas reservoir; Z i Indicates the deviation coefficient of the initial state; G j represents the dynamic reserves of the j-th gas reservoir; G pj represents the daily gas production accumulation of the j-th gas well; n represents the number of gas wells. j The initial value of is the ratio of the dynamic reserves of the gas reservoir to the number of commingled production layers.

[0067] In specific implementation, the total gas production of all layers of gas wells during the production cycle is calculated as the sum of the daily gas production of all layers of gas wells Q sc , and the daily gas production cumulative value G of the j-th gas production well pj ; Among them, the sum of the daily gas production of all layers of gas wells is Q sc is the sum of the daily gas production of the gas wells from the 1st layer to the nth layer during the production cycle, and the daily gas production of the gas wells in the jth layer is the cumulative value G pj is the daily gas production Q of the j-th gas well during the production cycle scj The accumulation of.

[0068] Using the method provided by the present invention, an example test on 18 multi-layer combined production gas wells shows that the output results of the multi-layer combined production gas well production splitting method provided by the present invention are much more accurate than the results calculated by the existing methods and are closer to the gas production profile test results.

[0069] Figure 3 , Figure 4It is a specific example diagram of the production splitting method of a multi-layer combined production gas well in an embodiment of the present invention.

[0070] Different from the multi-layer combined production gas well production splitting method provided by the present invention, the gas production profile test method uses logging instruments to perform gas production profile tests, which is expensive and can only measure the instantaneous production contribution rate at a certain point in time. It cannot reflect the changing pattern of the production of the gas well throughout the entire production cycle and interferes with the normal production of the gas well.

[0071] In one embodiment of the present invention, the daily gas production of the Shuangxx-1 well in the A gas field is calculated as follows: Figure 3 The production contribution rate is used to represent the production splitting result of Shuangxx-1 well. The production contribution rate is the percentage of the daily gas production of the j-th layer gas production well to the daily gas production of the gas production wells in all layers. Figure 4 As shown, the evaluation results of the production splitting method of four multi-layer combined gas wells in Shuangxx-1 well on October 27, 2017, November 19, 2018, June 12, 2020 and July 22, 2021 were compared with the results of the gas production profile testing method. The average errors of Box 7, Box 8, Mountain 1, Mountain 2 and B gas fields were 8.91%, 5.58%, 3.53%, 7.16% and 6.95%, respectively.

[0072] Figure 5 , Figure 6 It is a specific example diagram of the production splitting method of a multi-layer combined production gas well in an embodiment of the present invention.

[0073] In another embodiment of the present invention, the daily gas production of Shuangxx-2 well is calculated as follows: Figure 5 As shown in Figure 2, the production splitting method evaluation results of three multi-layer combined production wells in Shuangxx-2 well on August 3, 2020, February 7, 2021, and August 1, 2021 were compared with the gas production profile test results. Figure 6 As shown, the average errors of Box 8, Mountain 1, Mountain 2 and B gas field are 4.18%, 9.56%, 5.98% and 4.73% respectively. The present invention realizes the production splitting of multi-layer combined production gas wells throughout the production cycle, reduces the investment in related dynamic monitoring projects, saves costs, and does not need to interfere with gas well production, thereby reducing the impact on gas well production. The multi-layer combined production gas well production splitting method provided by the present invention is conducive to promotion.

[0074] It should be noted that, although the operations of the method of the present invention are described in a specific order in the above embodiments and the accompanying drawings, this does not require or imply that the operations must be performed in the specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0075] The implementation of the multi-layer combined production gas well production splitting device can refer to the implementation of the above method, and the repeated parts will not be repeated. The term "module" or "unit" used below can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0076] Based on the same inventive concept, the present invention also proposes a multi-layer combined production gas well production splitting device, such as Figure 7 As shown, the device comprises:

[0077] The parameter acquisition module 701 is used to obtain the static parameters and initial dynamic parameters of each gas reservoir;

[0078] The daily gas production prediction module 702 is used to input the static parameters and initial dynamic parameters of each gas reservoir into the daily gas production prediction model to determine the initial daily gas production of each gas production well; wherein the daily gas production prediction model is obtained by training the machine learning model based on the static parameters and initial dynamic parameters of the historical gas reservoir and the corresponding historical daily gas production;

[0079] The productivity index determination module 703 is used to determine the productivity index of each gas production well according to the initial daily gas production of each gas production well and the gas productivity equation;

[0080] The optimization and correction module 704 is used to determine the total gas production of all gas wells in the production cycle according to the production capacity index of each gas well, the gas production capacity equation corrected by the production correction coefficient, the dynamic reserves of each gas reservoir, and the material balance equation, set the minimum deviation between the total gas production and the historical measured gas production as the optimization target, and optimize the production correction coefficient according to the optimization target; wherein the production correction coefficient is used to improve the calculation accuracy of the gas production capacity equation;

[0081] The daily gas production calculation module 705 is used to calculate the daily gas production of each gas well during the production cycle according to the gas production capacity equation corrected by the optimized production correction coefficient, the dynamic reserves of each gas reservoir, and the material balance equation.

[0082] In one embodiment of the present invention, the static parameters of the gas reservoir include any one or more of the following: reservoir burial depth, reservoir effective thickness, mud content, reservoir porosity, reservoir permeability, and gas saturation;

[0083] The initial dynamic parameters include: initial formation pressure and initial bottom hole flowing pressure.

[0084] In one embodiment of the present invention, the machine learning model is a progressive gradient regression tree model.

[0085] In one embodiment of the present invention, the optimization and correction module 704 is specifically used for:

[0086] The following steps are cyclically performed for each layer of gas production wells until the production cycle ends. According to the daily gas production of each layer of gas production wells during the production cycle, the total gas production of all layers of gas production wells during the production cycle is determined:

[0087] The daily gas production of each gas well is determined based on the productivity index of each gas well, the gas productivity equation corrected by the production correction coefficient, the formation pressure of each gas reservoir on that day, and the bottom hole flow pressure on that day;

[0088] Determine the cumulative value of the daily gas production of each gas production well according to the daily gas production of each gas production well on that day and the daily gas production before that day;

[0089] According to the accumulated value of daily gas production of each gas well, the initial formation pressure, the dynamic reserves of each gas reservoir and the material balance equation, the formation pressure of each gas reservoir after gas production is determined, and the formation pressure of each gas reservoir after gas production is determined as the formation pressure of the next day;

[0090] According to the accumulated daily gas production of each gas well, the dynamic reserves of each gas reservoir are adjusted;

[0091] The bottom hole flow pressure after gas production is obtained and determined as the bottom hole flow pressure for the next day.

[0092] In one embodiment of the present invention, the optimization and correction module 704 is specifically used for:

[0093] The following steps are executed repeatedly until the deviation between the total gas production and the historical measured gas production is minimized:

[0094] Adjust the production correction coefficient, and determine the total gas production of all gas wells in the production cycle based on the production capacity index of each gas well, the gas production capacity equation corrected by the adjusted production correction coefficient, the dynamic reserves of each gas reservoir, and the material balance equation.

[0095] In one embodiment of the present invention, the gas production capacity equation corrected by the production correction coefficient is as follows:

[0096]

[0097] Among them, P rj represents the formation pressure of the j-th gas reservoir; P wf represents the bottom hole flowing pressure; m represents the production correction factor; n represents the number of layers of the gas well; j represents the j represents the productivity index of the j-th gas well; Q scj It represents the daily gas production of the j-th gas well during the production cycle.

[0098] In one embodiment of the present invention, the material balance equation is as follows:

[0099]

[0100] Among them, P rj represents the formation pressure of the j-th gas reservoir; P i Indicates the initial formation pressure of the gas reservoir; Z j represents the deviation coefficient of the j-th gas reservoir; Z i Indicates the deviation coefficient of the initial state; G j represents the dynamic reserves of the j-th gas reservoir; G pj It represents the cumulative daily gas production of the j-th gas well; n represents the number of gas wells.

[0101] It should be noted that although several modules of the multi-layer combined production gas well production splitting device are mentioned in the above detailed description, this division is only exemplary and not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules described above can be embodied in one module. Conversely, the features and functions of one module described above can be further divided into multiple modules for embodiment.

[0102] Based on the above invention concept, Figure 8 As shown, the present invention also proposes a computer device 800, including a memory 801, a processor 802, and a computer program 803 stored in the memory 801 and executable on the processor 802, wherein the processor 802 implements the aforementioned multi-layer combined production splitting method for gas wells when executing the computer program 803.

[0103] Based on the aforementioned inventive concept, the present invention proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the aforementioned multi-layer combined production gas well production splitting method is implemented.

[0104] Based on the aforementioned inventive concept, the present invention proposes a computer program product, which includes a computer program. When the computer program is executed by a processor, a method for splitting the production of a multi-layer combined production gas well is implemented.

[0105] The horizontal well collaborative cold production method and device proposed in the embodiment of the present invention can solve the problem that the prior art has large errors in calculating the gas production of each layer of gas production wells and cannot guide the formulation of development strategies for multi-layer gas reservoirs; the embodiment of the present invention obtains the static parameters and initial dynamic parameters of each layer of gas reservoirs; inputs the static parameters and initial dynamic parameters of each layer of gas reservoirs into the daily gas production prediction model to determine the initial daily gas production of each layer of gas production wells; wherein the daily gas production prediction model is obtained by training a machine learning model based on the static parameters and initial dynamic parameters of historical gas reservoirs, and the corresponding historical daily gas production; based on the initial daily gas production of each layer of gas production wells, and the gas production capacity The production capacity index of each gas production well is determined; according to the production capacity index of each gas production well, the gas production capacity equation corrected by the production correction coefficient, the dynamic reserves of each gas reservoir, and the material balance equation, the total gas production of all gas production wells in the production cycle is determined, and the minimum deviation between the total gas production and the historical measured gas production is set as the optimization target, and the production correction coefficient is optimized according to the optimization target; wherein, the production correction coefficient is used to improve the calculation accuracy of the gas production capacity equation; according to the gas production capacity equation corrected by the optimized production correction coefficient, the dynamic reserves of each gas reservoir, and the material balance equation, the daily gas production of each gas production well in the production cycle is calculated. The embodiment of the present invention can optimize the production correction coefficient, improve the calculation accuracy of the gas production of each gas production well, ensure the reliability of the production splitting of multi-layer combined production gas wells, and can guide the formulation of the development strategy of multi-layer gas reservoirs. The multi-layer combined production gas well production splitting method provided by the present invention saves costs, does not need to interfere with gas well production, reduces the impact on gas well production, and is conducive to promotion and use.

[0106] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0108] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0110] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for splitting the production of a multi-layer combined production gas well, characterized in that: include: Obtain static parameters and initial dynamic parameters of each gas reservoir; The static parameters and initial dynamic parameters of each gas reservoir are input into the daily gas production prediction model to determine the initial daily gas production of each gas production well; wherein the daily gas production prediction model is obtained by training the machine learning model based on the static parameters and initial dynamic parameters of the historical gas reservoir and the corresponding historical daily gas production; Determine the productivity index of each gas production well according to the initial daily gas production of each gas production well and the gas productivity equation; According to the productivity index of each layer of gas production wells, the gas productivity equation corrected by the production correction coefficient, the dynamic reserves of each layer of gas reservoirs, and the material balance equation, the total gas production of all layers of gas production wells during the production cycle is determined, and the minimum deviation between the total gas production and the historical measured gas production is set as the optimization target, and the production correction coefficient is optimized according to the optimization target; among them, the production correction coefficient is used to improve the calculation accuracy of the gas productivity equation; The daily gas production of each gas well during the production cycle is calculated based on the gas production capacity equation corrected by the optimized production correction coefficient, the dynamic reserves of each gas reservoir, and the material balance equation.

2. The method according to claim 1, characterized in that The static parameters of the gas reservoir include any one or more of the following: reservoir burial depth, reservoir effective thickness, shale content, reservoir porosity, reservoir permeability, and gas saturation; The initial dynamic parameters include: initial formation pressure and initial bottom hole flowing pressure.

3. The method according to claim 1, characterized in that The machine learning model is a progressive gradient regression tree model.

4. The method according to claim 1, characterized in that: According to the productivity index of each layer of gas production wells, the gas productivity equation corrected by the production correction coefficient, the dynamic reserves of each layer of gas reservoirs, and the material balance equation, the total gas production of all layers of gas production wells during the production cycle is determined, including: The following steps are cyclically performed for each layer of gas production wells until the production cycle ends. According to the daily gas production of each layer of gas production wells during the production cycle, the total gas production of all layers of gas production wells during the production cycle is determined: The daily gas production of each gas well is determined based on the productivity index of each gas well, the gas productivity equation corrected by the production correction coefficient, the formation pressure of each gas reservoir on that day, and the bottom hole flow pressure on that day; Determine the cumulative value of the daily gas production of each gas well according to the daily gas production of each gas well on that day and the daily gas production before that day; According to the accumulated value of daily gas production of each gas well, the initial formation pressure, the dynamic reserves of each gas reservoir and the material balance equation, the formation pressure of each gas reservoir after gas production is determined, and the formation pressure of each gas reservoir after gas production is determined as the formation pressure of the next day; According to the accumulated daily gas production of each gas well, the dynamic reserves of each gas reservoir are adjusted; The bottom hole flow pressure after gas production is obtained and determined as the bottom hole flow pressure for the next day.

5. The method according to claim 4, characterized in that The minimum deviation between the total gas production and the historical measured gas production is set as the optimization target, and the production correction coefficient is optimized according to the optimization target, including: The following steps are executed repeatedly until the deviation between the total gas production and the historical measured gas production is minimized: Adjust the production correction coefficient, and determine the total gas production of all gas wells in the production cycle based on the production capacity index of each gas well, the gas production capacity equation corrected by the adjusted production correction coefficient, the dynamic reserves of each gas reservoir, and the material balance equation.

6. The method according to claim 4, characterized in that The gas production capacity equation corrected by the production correction coefficient is as follows: Among them, P rj represents the formation pressure of the j-th gas reservoir; P wf represents the bottom hole flowing pressure; m represents the production correction factor; n represents the number of layers of the gas well; j represents the j represents the productivity index of the j-th gas well; Q scj It represents the daily gas production of the j-th gas well during the production cycle.

7. The method according to claim 4, characterized in that The material balance equation is as follows: Among them, P rj represents the formation pressure of the j-th gas reservoir; P i Indicates the initial formation pressure of the gas reservoir; Z j represents the deviation coefficient of the j-th gas reservoir; Z i Indicates the deviation coefficient of the initial state; G j represents the dynamic reserves of the j-th gas reservoir; G pj It represents the cumulative daily gas production of the j-th gas well; n represents the number of gas wells.

8. A multi-layer combined production gas well production splitting device, characterized in that: include: Parameter acquisition module, used to obtain static parameters and initial dynamic parameters of each gas reservoir; The daily gas production prediction module is used to input the static parameters and initial dynamic parameters of each gas reservoir into the daily gas production prediction model to determine the initial daily gas production of each gas production well; wherein the daily gas production prediction model is obtained by training the machine learning model based on the static parameters and initial dynamic parameters of the historical gas reservoir and the corresponding historical daily gas production; The productivity index determination module is used to determine the productivity index of each gas production well according to the initial daily gas production of each gas production well and the gas productivity equation; The optimization and correction module is used to determine the total gas production of all gas wells in the production cycle according to the production capacity index of each layer of gas wells, the gas production capacity equation corrected by the production correction coefficient, the dynamic reserves of each layer of gas reservoirs, and the material balance equation, set the minimum deviation between the total gas production and the historical measured gas production as the optimization target, and optimize the production correction coefficient according to the optimization target; among which, the production correction coefficient is used to improve the calculation accuracy of the gas production capacity equation; The daily gas production calculation module is used to calculate the daily gas production of each gas well during the production cycle based on the gas production capacity equation corrected by the optimized production correction coefficient, the dynamic reserves of each gas reservoir, and the material balance equation.

9. The device according to claim 8, characterized in that The static parameters of the gas reservoir include any one or more of the following: reservoir burial depth, reservoir effective thickness, shale content, reservoir porosity, reservoir permeability, and gas saturation; The initial dynamic parameters include: initial formation pressure and initial bottom hole flowing pressure.

10. The device according to claim 8, characterized in that The machine learning model is a progressive gradient regression tree model.

11. The device according to claim 8, characterized in that The optimization and correction module is specifically used for: The following steps are cyclically performed for each layer of gas production wells until the production cycle ends. According to the daily gas production of each layer of gas production wells during the production cycle, the total gas production of all layers of gas production wells during the production cycle is determined: The daily gas production of each gas well is determined based on the productivity index of each gas well, the gas productivity equation corrected by the production correction coefficient, the formation pressure of each gas reservoir on that day, and the bottom hole flow pressure on that day; Determine the cumulative value of the daily gas production of each gas well according to the daily gas production of each gas well on that day and the daily gas production before that day; According to the accumulated value of daily gas production of each gas well, the initial formation pressure, the dynamic reserves of each gas reservoir and the material balance equation, the formation pressure of each gas reservoir after gas production is determined, and the formation pressure of each gas reservoir after gas production is determined as the formation pressure of the next day; According to the accumulated daily gas production of each gas well, the dynamic reserves of each gas reservoir are adjusted; The bottom hole flow pressure after gas production is obtained and determined as the bottom hole flow pressure for the next day.

12. The device according to claim 11, characterized in that The optimization and correction module is specifically used for: The following steps are executed repeatedly until the deviation between the total gas production and the historical measured gas production is minimized: Adjust the production correction coefficient, and determine the total gas production of all gas wells in the production cycle based on the production capacity index of each gas well, the gas production capacity equation corrected by the adjusted production correction coefficient, the dynamic reserves of each gas reservoir, and the material balance equation.

13. The device according to claim 11, characterized in that The gas production capacity equation corrected by the production correction coefficient is as follows: Among them, P rj represents the formation pressure of the j-th gas reservoir; P wf represents the bottom hole flowing pressure; m represents the production correction factor; n represents the number of layers of the gas well; j represents the j represents the productivity index of the j-th gas well; Q scj It represents the daily gas production of the j-th gas well during the production cycle.

14. The device according to claim 11, characterized in that The material balance equation is as follows: Among them, P rj represents the formation pressure of the j-th gas reservoir; P i Indicates the initial formation pressure of the gas reservoir; Z j represents the deviation coefficient of the j-th gas reservoir; Z i Indicates the deviation coefficient of the initial state; G j represents the dynamic reserves of the j-th gas reservoir; G pj It represents the cumulative daily gas production of the j-th gas well; n represents the number of gas wells.

15. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

17. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images