Steam distribution method and device based on machine learning

Abstract

The invention discloses a steam distribution method and device based on machine learning, and the method comprises the steps: building an underground comprehensive reservoir model through a sequential Gaussian simulation method according to an underground actual stratum point of a selected target well; according to the simulation parameter values of the simulation stratum points in the underground comprehensive reservoir model, a thickened oil thermal recovery data prediction model is established through a linear model; and determining the total amount of steam distributed to the target well in real time according to the heavy oil thermal recovery data prediction model and a net present value formula, and determining the steam utilization rate according to the total steam injection amount. The thickened oil thermal recovery data prediction model is established through machine learning, and the technical effects that steam distribution can be optimized in real time, the gas injection parameters of all the oil production wells are reasonably distributed, resource waste is avoided, and the thickened oil reservoir recovery efficiency is effectively improved can be achieved.

Description

Technical Field

[0001] The present application relates to the technical field of heavy oil recovery, and in particular to a steam distribution method and device based on machine learning. Background Art

[0002] Heavy oil is difficult to extract because of its high viscosity. Compared with ordinary crude oil, heavy oil has the characteristics of high viscosity, high density and low fluidity. In the process of heavy oil extraction, it is necessary to inject wet saturated steam into the heavy oil underground to increase the temperature of the heavy oil, reduce the viscosity, increase the fluidity, and ultimately achieve the effect of improving the extraction rate of crude oil.

[0003] The steam injection distributor in heavy oil production often adopts a one-branch-multiple-branch structure, that is, a main line inputs wet saturated steam into the steam distributor, the wet steam flows in the distributor and flows out through multiple branch pipelines after the gas and liquid are remixed, and is injected into each wellhead for thermal oil production.

[0004] The wet steam distributors commonly used at present are multi-branch vertical distributors and multi-branch spherical distributors. However, due to the lack of accurate steam metering and reasonable distribution, when injecting steam into the well, the steam injection volume of a single well is unclear, resulting in insufficient steam injection in some wells and excessive steam injection in others; the oil layer heating effect of the under-injected steam wells is poor, and the over-injected steam wells experience steam crossflow, heat energy loss, and low steam thermal energy utilization. The main reason for the difficulty in metering wet saturated steam is the presence of both steam and liquid phase media in the fluid, and the presence of the liquid phase causes a large change in the differential pressure flowing through the throttling device. Since the droplets are not evenly distributed during the steam injection process, the pressure difference changes irregularly, resulting in large measurement errors, and traditional flow measurement devices cannot be used. V-cone flowmeters are not wear-resistant and have limited applications. Precise wet steam metering at home and abroad usually adopts technologies such as phase metering and equal dryness distribution to separate steam and liquid and measure them separately. The equipment is complex and costly. The steam injection time of thin-layer heavy oil is short and the frequency is high. The combination of wells for each round of steam injection is not fixed. The equipment installation and commissioning takes a long time and is costly, which is not suitable for the development of thin-layer heavy oil.

[0005] This section is intended to provide a background or context to the embodiments of the present application as recited in the claims. No admission is made that the description herein is prior art by inclusion in this section. Summary of the invention

[0006] The present application provides a steam distribution method and device based on machine learning to at least solve the problem of poor control of steam injection volume during heavy oil production in the prior art.

[0007] According to one aspect of the present application, a steam distribution method based on machine learning is provided, comprising:

[0008] According to the actual formation points of the selected target well, a downhole comprehensive reservoir model is established by sequential Gaussian simulation method;

[0009] According to the simulated parameter values ​​of the simulated formation points in the downhole comprehensive reservoir model, a heavy oil thermal recovery data prediction model is established through a linear model;

[0010] The total amount of steam allocated to the target well in real time is determined based on the heavy oil thermal recovery data prediction model and the net present value formula, and the steam utilization rate is determined based on the total steam injection volume.

[0011] In one embodiment, the number of selected target wells is at least 3 pairs.

[0012] In one embodiment, a downhole comprehensive reservoir model is established by a sequential Gaussian simulation method, including:

[0013] Establish the multivariate normal distribution function of actual stratigraphic points;

[0014] Calculate the mathematical expectation of the conditional distribution of actual stratigraphic points according to the conditional theorem of multivariate normal distribution;

[0015] The simulation parameter value at the simulated formation point is obtained according to the mathematical expectation value.

[0016] In one embodiment, a heavy oil thermal recovery data prediction model is established by a linear model, including:

[0017] Get the actual oil production and disturbance value at time t;

[0018] The simulation parameter values, actual oil production at time t and disturbance values ​​are input into the linear model to generate a heavy oil thermal recovery data prediction model.

[0019] In one embodiment, the linear model is an OE model.

[0020] In one embodiment, the total amount of steam allocated to the target well in real time is determined according to the heavy oil thermal recovery data prediction model and the net present value formula, including:

[0021] The predicted oil production of the target well at time t is obtained according to the heavy oil thermal recovery data prediction model;

[0022] The predicted oil production at time t is input into the net present value formula to obtain the total steam injection volume.

[0023] According to another aspect of the present application, there is also provided a steam distribution device based on machine learning, comprising:

[0024] A comprehensive reservoir model building unit is used to build a downhole comprehensive reservoir model through a sequential Gaussian simulation method according to the actual downhole formation points of the selected target well;

[0025] A heavy oil thermal recovery data prediction model establishment unit is used to establish a heavy oil thermal recovery data prediction model through a linear model according to the simulation parameter values ​​of the simulation formation points in the downhole comprehensive reservoir model;

[0026] The steam distribution unit is used to determine the total amount of steam allocated to the target well in real time according to the heavy oil thermal recovery data prediction model and the net present value formula, and to determine the steam utilization rate according to the total steam injection volume.

[0027] In one embodiment, the number of selected target wells is at least 3 pairs.

[0028] In one embodiment, the comprehensive reservoir model building unit includes:

[0029] Multivariate normal distribution function building module, used to build multivariate normal distribution function of actual formation points;

[0030] A mathematical expectation value calculation module is used to calculate the mathematical expectation value of the conditional distribution of the actual formation point according to the multivariate normal distribution condition theorem;

[0031] The simulation parameter module is used to obtain the simulation parameter value at the simulation formation point position according to the mathematical expectation value.

[0032] In one embodiment, the heavy oil thermal recovery data prediction model building unit includes:

[0033] Real-time data acquisition module, used to obtain the actual oil production and disturbance value at time t;

[0034] The model building module is used to input the simulation parameter values, the actual oil production at time t and the disturbance value into the linear model to generate a heavy oil thermal recovery data prediction model.

[0035] In one embodiment, the linear model is an OE model.

[0036] In one embodiment, the steam distribution unit comprises:

[0037] The oil production prediction module is used to obtain the predicted oil production of the target well at time t based on the heavy oil thermal recovery data prediction model;

[0038] The total steam injection volume calculation module is used to input the predicted oil production at time t into the net present value formula to obtain the total steam injection volume.

[0039] In one embodiment of the present application, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, a steam distribution method based on machine learning is implemented.

[0040] In one embodiment of the present application, a computer-readable storage medium is further provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a steam distribution method based on machine learning is implemented.

[0041] In one embodiment of the present application, a computer program product is also provided. The computer program product includes a computer program. When the computer program is executed by a processor, a steam distribution method based on machine learning is implemented.

[0042] Compared with the heavy oil steam injection technology scheme in the existing technology, a heavy oil thermal recovery data prediction model is established through machine learning, the steam distribution is optimized in real time, the gas injection parameters of each oil well are reasonably allocated, the waste of resources is avoided, and the efficiency of heavy oil reservoir recovery is effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0044] Figure 1 A steam distribution method based on machine learning is provided for this application;

[0045] Figure 2 This is a flow chart of establishing a downhole comprehensive reservoir model by sequential Gaussian simulation method in an embodiment of the present application;

[0046] Figure 3 This is a flow chart of establishing a heavy oil thermal recovery data prediction model through a linear model in an embodiment of the present application;

[0047] Figure 4 This is a flow chart for determining the total amount of steam allocated to a target well in real time according to the heavy oil thermal recovery data prediction model and the net present value formula in an embodiment of the present application.

[0048] Figure 5 A structural block diagram of a steam distribution device based on machine learning provided in this application;

[0049] Figure 6 This is a structural block diagram of a comprehensive reservoir model building unit in an embodiment of the present application;

[0050] Figure 7 This is a structural block diagram of a unit for establishing a heavy oil thermal recovery data prediction model in an embodiment of the present application;

[0051] Figure 8 This is a structural block diagram of a steam distribution unit in an embodiment of the present application;

[0052] Fig. 9 This is a specific implementation of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present application more clear, the embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. Here, the illustrative embodiments of the present application and their descriptions are used to explain the present application, but are not intended to limit the present application.

[0054] The steam injection distributor in heavy oil production often adopts a one-branch-multiple-branch structure, that is, a main pipeline inputs wet saturated steam into the steam distributor, the wet steam flows in the distributor and flows out through multiple branch pipelines after the gas and liquid are remixed, and is injected into each wellhead for thermal oil production. The wet steam distributors commonly used at present are one-branch-multiple-branch vertical distributors and one-branch-multiple-branch spherical distributors. However, due to the lack of accurate steam metering and reasonable distribution, when injecting steam into the well, the steam injection volume of a single well is unclear, resulting in insufficient steam injection in some wells and excessive steam injection in some wells; the oil layer heating effect of the steam-under-injected wells is poor, and the steam-over-injected wells have steam crossflow, heat energy loss, and low steam thermal energy utilization. The main reason for the difficulty of wet saturated steam metering is that there are two phases of steam and liquid in the fluid at the same time, and the presence of the liquid phase causes a large change in the differential pressure flowing through the throttling device. Since the droplets are not evenly distributed during the steam injection process, the pressure difference changes irregularly, so the measurement error is large, and traditional flow measurement devices cannot be used. V-cone flowmeters are not wear-resistant and have limited applications. Precise wet steam metering at home and abroad usually adopts technologies such as phase metering and equal dryness distribution to separate steam and liquid and measure them separately. The equipment is complex and costly. The steam injection time of thin-layer heavy oil is short and the frequency is high. The combination of wells for each round of steam injection is not fixed. The equipment installation and commissioning takes a long time and is costly, which is not suitable for the development of thin-layer heavy oil.

[0055] In order to solve the above problems, the present application provides a steam distribution method based on machine learning, such as Figure 1 As shown, including:

[0056] S101: According to the actual downhole formation points of the selected target well, a downhole comprehensive reservoir model is established by sequential Gaussian simulation method.

[0057] S102: According to the simulation parameter values ​​of the simulation formation points in the downhole comprehensive reservoir model, a heavy oil thermal recovery data prediction model is established through a linear model.

[0058] S103: Determine the total amount of steam allocated to the target well in real time according to the heavy oil thermal recovery data prediction model and the net present value formula, and determine the steam utilization rate according to the total steam injection amount.

[0059] In a specific embodiment, the steam distribution method based on machine learning includes three parts. First, the actual formation points (several) of the target well are selected, and the set vector of the actual formation points is established. The comprehensive reservoir model of the well is established according to the sequential Gaussian simulation method. After the comprehensive reservoir model is obtained, the simulated formation points and their parameters can be obtained. Then, the existing linear model is selected, and the SAGD model (heavy oil thermal recovery data prediction model) is established according to the parameters of the simulated formation points and the linear model. The heavy oil thermal recovery data prediction model can predict the oil production at a certain moment. The optimal steam distribution plan can be obtained according to the predicted oil production combined with the NPV formula (net present value formula).

[0060] Figure 1 The executors of the provided machine learning-based steam distribution method can be computers, servers, etc., and a heavy oil thermal recovery data prediction model is established through machine learning, which realizes real-time optimization of steam distribution, rationally allocates gas injection parameters of each oil well, avoids waste of resources, and effectively improves the efficiency of heavy oil reservoir recovery.

[0061] In one embodiment, the number of selected target wells is at least 3 pairs.

[0062] In a specific embodiment, the number of horizontal wells selected is 3 pairs, and the description of the subsequent embodiments is based on the selection of 3 pairs of horizontal wells as a specific implementation method. In practice, the number of horizontal wells selected can be more than 3 pairs, and the present application is not limited thereto.

[0063] In one embodiment, a downhole comprehensive reservoir model is established by a sequential Gaussian simulation method, such as Figure 2 As shown, including:

[0064] S201: Establishing the multivariate normal distribution function of the actual stratigraphic points;

[0065] S202: Calculate the mathematical expectation value of the conditional distribution of the actual formation point according to the multivariate normal distribution condition theorem;

[0066] S203: Obtain simulation parameter values ​​at the locations of simulation formation points according to the mathematical expectation values.

[0067] In a specific embodiment, a comprehensive reservoir model of three pairs of horizontal wells is established according to the sequential Gaussian simulation method, where X is the actual formation point:

[0068] Assume that the random variable X=(X1,X2,...,X I )′ is an I×1 random vector that obeys a multivariate normal distribution, and its distribution function is X~N I (μ,∑), where μ=E(X), ∑=Cov(X i ,X J ),i,j=1,2,..,I.

[0069] in, C is the covariance and ∑ is the covariance matrix.

[0070] Divide X into two parts, X=(X1,X2,...,X m ,X m+1 ,X m+2 ,...,X m+n＝I ), the corresponding μ=(μ m ,μ n ),∑=(Σ mm ,∑ mn ,∑ nm ,∑ nm ).

[0071] in,

[0072] According to the multivariate normal distribution condition calculation theorem, when X is known n (X m+1 ,X m+2 ,...,X m+n ) condition, X m The conditional distribution of

[0073]

[0074] in, For X m The variance of the conditional distribution of X m The mathematical expectation of the conditional distribution of is

[0075] Let X m The simulation parameter matrix Z′(x m ) is equal to the expected value of the conditional probability distribution function at that location plus the random number l m The product of the standard deviation of the conditional probability distribution function. m ~N(0,1) is a standard normal distribution with an expected value of 0 and a variance of 1.

[0076]

[0077] For regionalized variables, μ m Equal to x m The expected value m(x m ),∑ mn is the point x to be simulated m and the known point x n The autocovariance matrix of m ,x j) where m is the position to be simulated and can only take one value, and the value of n increases as the simulated values ​​are continuously added to the known condition matrix.

[0078] And nm is the autocovariance matrix between known points, written as C(x i ,x j ), so we have

[0079]

[0080] Among them, i,j=1,2,...,n,n+1,n+2,...,n+m-1.

[0081] As more and more simulated formation points generate simulated parameter values, the capacity of known points will increase from n to n+1, n+2, ..., n+m-1. Based on these simulated formation points, a comprehensive reservoir model of the well can be obtained.

[0082] In one embodiment, a heavy oil thermal recovery data prediction model is established by a linear model, such as Figure 3 As shown, including:

[0083] S301: Obtaining the actual oil production and disturbance value at time t;

[0084] S302: Inputting simulation parameter values, actual oil production at time t and disturbance values ​​into a linear model to generate a heavy oil thermal recovery data prediction model.

[0085] In one embodiment, the linear model is an OE model.

[0086] In a specific embodiment, the OE model expression is:

[0087]

[0088] in,

[0089] B(z -1 )=b1z -1 +...+b nb z -nb

[0090] F(z -1 )=1+f1z -1 +...+f nf z -nf

[0091] In the formula, u t is the input sequence of the system at time t, that is, the actual oil production at time t; y t is the output value of the system at time t, that is, the predicted oil production at time t; ε tis the disturbance value of the system at time t, i.e., the system error; B(z -1 )、F(z -1 ) is the unit shift operator z -1 The purpose is to better fit the predicted value with the actual value, where b and f are constants, z -1 represents the unit backshift operator. In order to simplify the expression of the difference equation and facilitate the solution, an operator that performs a "shift" operation on the time variable is introduced. The commonly used backshift operator is z -1 Used to define an operation that moves back one step in time.

[0092] The constants b and f are compared with the predicted values ​​and the actual values, and the specific values ​​are determined by the degree of fit.

[0093] In one embodiment, the total amount of steam allocated to the target well in real time is determined based on the heavy oil thermal recovery data prediction model and the net present value formula, such as Figure 4 As shown, including:

[0094] S401: Obtaining the predicted oil production of the target well at time t according to the heavy oil thermal recovery data prediction model;

[0095] S402: Input the predicted oil production at time t into the net present value formula to obtain the total steam injection volume.

[0096] In a specific embodiment, the prediction data analysis model for three pairs of target wells (horizontal wells) established according to S301-S302 is as follows:

[0097]

[0098]

[0099]

[0100] Real-time nonlinear optimization of steam distribution based on NPV formula:

[0101]

[0102] in,

[0103]

[0104]

[0105]

[0106] Where N w is the total number of oil wells, dimensionless; p o is the oil price, in $ / bbl, To predict oil production rate, dimensionless; p wis the steam treatment cost, in $ / bbl; u is the allocated steam injection rate, dimensionless; D is the discount factor, dimensionless; is the total steam injection volume, in m 3 .

[0107] Since produced water is recycled, equation (14) is used to account for produced water as part of the allocated injected steam treatment cost.

[0108]

[0109] in,

[0110]

[0111]

[0112] The steam utilization can be set during optimization as follows:

[0113] Full use

[0114]

[0115] Partial use

[0116]

[0117] Utilization rate greater than 80%

[0118]

[0119] Finding the best utilization

[0120]

[0121] The steam distribution method based on machine learning provided in this application is to establish a comprehensive reservoir model of three pairs of horizontal wells based on the sequential Gaussian simulation method, then establish a SAGD prediction data analysis model based on the OE model, and finally apply the NPV formula to achieve real-time nonlinear optimization of steam distribution.

[0122] In order to solve the problem of accurate measurement and reasonable distribution of steam in heavy oil thermal recovery technology, this application proposes to establish a comprehensive reservoir model of three pairs of horizontal wells based on the sequential Gaussian simulation method, and then establish a SAGD prediction data analysis model based on the OE model, and then combine the NPV formula steam distribution to achieve real-time nonlinear optimization of steam injection volume, so as to optimize steam distribution. This application is of great significance to the research of heavy oil thermal recovery technology.

[0123] The present application also provides a steam distribution device based on machine learning in the embodiments, as described in the following embodiments. Since the principle of the device to solve the problem is similar to that of the steam distribution method based on machine learning, the implementation of the device can refer to the implementation of the steam distribution method based on machine learning, and the repeated parts will not be repeated.

[0124] The present application provides a steam distribution device based on machine learning, such as Figure 5 As shown, including:

[0125] The comprehensive reservoir model building unit 501 is used to build a downhole comprehensive reservoir model by sequential Gaussian simulation method according to the downhole actual formation points of the selected target well;

[0126] The heavy oil thermal recovery data prediction model establishment unit 502 is used to establish the heavy oil thermal recovery data prediction model through a linear model according to the simulation parameter values ​​of the simulation formation points in the downhole comprehensive reservoir model;

[0127] The steam distribution unit 503 is used to determine the total amount of steam distributed to the target well in real time according to the heavy oil thermal recovery data prediction model and the net present value formula, and to determine the steam utilization rate according to the total steam injection amount.

[0128] In one embodiment, the number of selected target wells is at least 3 pairs.

[0129] In one embodiment, if Figure 6 As shown, the comprehensive reservoir model building unit 501 includes:

[0130] A multivariate normal distribution function establishment module 601 is used to establish a multivariate normal distribution function of an actual formation point;

[0131] The mathematical expectation value calculation module 602 is used to calculate the mathematical expectation value of the conditional distribution of the actual formation point according to the multivariate normal distribution condition theorem;

[0132] The simulation parameter module 603 is used to obtain the simulation parameter value at the simulation formation point position according to the mathematical expectation value.

[0133] In one embodiment, if Figure 7 As shown, the heavy oil thermal recovery data prediction model building unit 502 includes:

[0134] Real-time data acquisition module 701, used to obtain the actual oil production and disturbance value at time t;

[0135] The model building module 702 is used to input the simulation parameter values, the actual oil production at time t and the disturbance value into the linear model to generate a heavy oil thermal recovery data prediction model.

[0136] In one embodiment, the linear model is an OE model.

[0137] In one embodiment, if Figure 8 As shown, the steam distribution unit 503 includes:

[0138] The oil production prediction module 801 is used to obtain the predicted oil production of the target well at time t according to the heavy oil thermal recovery data prediction model;

[0139] The total steam injection amount calculation module 802 is used to input the predicted oil production at time t into the net present value formula to obtain the total steam injection amount.

[0140] An embodiment of the present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned steam distribution method based on machine learning when executing the computer program.

[0141] An embodiment of the present application also provides 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 above-mentioned steam distribution method based on machine learning is implemented.

[0142] An embodiment of the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the above-mentioned steam distribution method based on machine learning is implemented.

[0143] In the embodiment of the present application, compared with the technical solutions in the prior art, a heavy oil thermal recovery data prediction model is established through machine learning, steam distribution is optimized in real time, and gas injection parameters of each oil well are reasonably allocated to avoid waste of resources and effectively improve the efficiency of heavy oil reservoir recovery.

[0144] Fig. 9 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present application, such as Fig. 9 As shown, the electronic device includes: a processor (processor) 901, a memory (memory) 902 and a bus 903.

[0145] The processor 901 and the memory 902 communicate with each other via a bus 903 .

[0146] The processor 901 is used to call the program instructions in the memory 902 to execute the methods provided by the above-mentioned method embodiments.

[0147] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0148] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate 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 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] 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.

[0150] 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 in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0151] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A steam distribution method based on machine learning, characterized in that: include: According to the actual formation points of the selected target well, a downhole comprehensive reservoir model is established by sequential Gaussian simulation method; According to the simulated parameter values ​​of the simulated formation points in the downhole comprehensive reservoir model, a heavy oil thermal recovery data prediction model is established through a linear model; The total amount of steam allocated to the target well in real time is determined according to the heavy oil thermal recovery data prediction model and the net present value formula, and the steam utilization rate is determined according to the total steam injection amount.

2. The steam distribution method based on machine learning according to claim 1, characterized in that: The number of target wells selected is at least 3 pairs.

3. The steam distribution method based on machine learning according to claim 1, characterized in that: The method of establishing a downhole comprehensive reservoir model by sequential Gaussian simulation method comprises: Establishing a multivariate normal distribution function of the actual stratigraphic points; Calculate the mathematical expectation value of the conditional distribution of the actual formation point according to the multivariate normal distribution condition theorem; The simulation parameter value at the simulation formation point position is obtained according to the mathematical expectation value.

4. The steam distribution method based on machine learning according to claim 1, characterized in that: The method of establishing a heavy oil thermal recovery data prediction model by a linear model includes: Get the actual oil production and disturbance value at time t; The simulation parameter value, the actual oil production at time t and the disturbance value are input into the linear model to generate a heavy oil thermal recovery data prediction model.

5. The steam distribution method based on machine learning according to claim 1, characterized in that: The linear model is an OE model.

6. The method for steam distribution based on machine learning according to claim 4, characterized in that: The method of determining the total amount of steam allocated to the target well in real time according to the heavy oil thermal recovery data prediction model and the net present value formula includes: The predicted oil production of the target well at time t is obtained according to the heavy oil thermal recovery data prediction model; The predicted oil production at time t is input into the net present value formula to obtain the total steam injection amount.

7. A steam distribution device based on machine learning, characterized in that: include: A comprehensive reservoir model building unit is used to build a downhole comprehensive reservoir model through a sequential Gaussian simulation method according to the actual downhole formation points of the selected target well; A heavy oil thermal recovery data prediction model establishment unit is used to establish a heavy oil thermal recovery data prediction model through a linear model according to the simulation parameter values ​​of the simulation formation points in the downhole comprehensive reservoir model; The steam distribution unit is used to determine the total amount of steam distributed to the target well in real time according to the heavy oil thermal recovery data prediction model and the net present value formula, and to determine the steam utilization rate according to the total steam injection amount.

8. The machine learning based steam distribution device according to claim 7, characterized in that: The number of target wells selected is at least 3 pairs.

9. The machine learning based steam distribution device according to claim 7, characterized in that: The comprehensive reservoir model building unit comprises: A multivariate normal distribution function establishment module, used to establish the multivariate normal distribution function of the actual formation point; A mathematical expectation value calculation module, used for calculating the mathematical expectation value of the conditional distribution of the actual formation point according to the multivariate normal distribution condition theorem; A simulation parameter module is used to obtain the simulation parameter value at the simulated formation point position according to the mathematical expectation value.

10. The machine learning based steam distribution device according to claim 7, characterized in that: The heavy oil thermal recovery data prediction model establishment unit comprises: Real-time data acquisition module, used to obtain the actual oil production and disturbance value at time t; The model building module is used to input the simulation parameter value, the actual oil production at time t and the disturbance value into the linear model to generate a heavy oil thermal recovery data prediction model.

11. The machine learning based steam distribution device according to claim 7, characterized in that: The linear model is an OE model.

12. The machine learning based steam distribution device according to claim 10, characterized in that: The steam distribution unit comprises: The oil production prediction module is used to obtain the predicted oil production of the target well at time t based on the heavy oil thermal recovery data prediction model; The total steam injection amount calculation module is used to input the predicted oil production at time t into the net present value formula to obtain the total steam injection amount.

13. 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 6 is implemented.

14. 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 6 is implemented.

15. 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 6 is implemented.

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