MMP Prediction Method and Device Based on Convolutional Neural Network

Abstract

An embodiment of the present invention discloses an MMP prediction method and device based on a convolutional neural network. The method includes: obtaining MMP influencing factor data of a target reservoir; inputting the MMP influencing factor data into a preset prediction model to obtain an MMP prediction value of the target reservoir output by the prediction model, where the prediction model is obtained by training a convolutional neural network according to a training sample set, and during the training process, hyperparameters of the convolutional neural network are optimized through a Bayesian optimization algorithm, and each training sample in the training sample set includes: the MMP value of the reservoir and the MMP influencing factor data of the reservoir. The present invention achieves the beneficial effect of accurately and efficiently predicting the MMP of the reservoir.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir development, and in particular, to a method and device for predicting MMP based on a convolutional neural network. Background Art

[0002] CO2 miscible flooding is the most widely used and highest oil recovery method in CO2-EOR for low-permeability reservoirs. During the process of injecting CO2 into the reservoir for oil displacement, there will be an interaction among gas, oil, and water phases in the rock formation, resulting in interphase component transfer, phase change, and other complex phase behaviors. The basic mechanism of miscible flooding is that the displacing agent (injected CO2 gas) and the displaced agent (crude oil) form a stable miscible zone front under reservoir conditions. This front is a single phase, and its movement can effectively push the crude oil forward and finally reach the production well. Due to miscibility, the oil-gas interface disappears, reducing the interfacial tension in the porous medium to zero. Therefore, theoretically, the microscopic displacement efficiency can reach 100%.

[0003] The minimum miscibility pressure (MMP) between CO2 and reservoir crude oil is one of the key parameters in the CO2 displacement process and is the boundary between CO2 miscible flooding and immiscible flooding. Accurately determining the minimum miscibility pressure of CO2 and crude oil is very important for improving the CO2 miscible displacement efficiency, reducing operating costs, and generating social and economic benefits.

[0004] The existing technology usually determines MMP by experimental measurement. Although this method can ensure accuracy, it is complex to operate, time-consuming, and costly. Therefore, the existing technology lacks a more efficient solution for determining the minimum miscibility pressure (MMP) between CO2 and reservoir crude oil. Summary of the Invention

[0005] In order to solve at least one of the above technical problems in the background art, the present invention proposes a method and device for predicting MMP based on a convolutional neural network.

[0006] To achieve the above object, according to one aspect of the present invention, there is provided a method for predicting MMP based on a convolutional neural network, the method comprising:

[0007] Obtaining MMP influencing factor data of a target reservoir;

[0008] Inputting the MMP influencing factor data into a preset prediction model to obtain an MMP prediction value of the target reservoir output by the prediction model, wherein the prediction model is obtained by training a convolutional neural network according to a training sample set, and during the training process, the hyperparameters of the convolutional neural network are optimized by a Bayesian optimization algorithm, and each training sample in the training sample set includes: the MMP value of the reservoir and the MMP influencing factor data of the reservoir.

[0009] Optionally, the MMP prediction method based on a convolutional neural network further includes:

[0010] Obtain the training sample set;

[0011] Use the training sample set to train the convolutional neural network, and combine the Bayesian optimization algorithm to take the prediction error of the verification samples in the verification sample set as the optimization target to optimize the hyperparameters of the convolutional neural network;

[0012] According to the hyperparameters optimized by the Bayesian optimization algorithm, use the training sample set to train the convolutional neural network again to obtain the prediction model.

[0013] Optionally, inputting the MMP influencing factor data into a preset prediction model specifically includes:

[0014] First, convert the MMP influencing factor data into a two-dimensional matrix, and then input the two-dimensional matrix into the prediction model.

[0015] Optionally, the MMP prediction method based on a convolutional neural network further includes:

[0016] When using the training sample set to train the convolutional neural network, convert the MMP influencing factor data in each training sample into a two-dimensional matrix.

[0017] Optionally, the hyperparameters include: the number of convolutional network layers, the number of convolutional kernels in each convolutional network layer, the size of the convolutional kernels used, the learning rate of the Adam optimizer, the number of samples in the training set input in each training epoch, and the total number of training epochs.

[0018] To achieve the above object, according to another aspect of the present invention, there is provided an MMP prediction device based on a convolutional neural network, and the device includes:

[0019] A data acquisition unit, configured to acquire MMP influencing factor data of a target reservoir;

[0020] A prediction unit, configured to input the MMP influencing factor data into a preset prediction model to obtain the MMP prediction value of the target reservoir output by the prediction model, where the prediction model is obtained by training a convolutional neural network according to a training sample set, and the hyperparameters of the convolutional neural network are optimized by the Bayesian optimization algorithm during the training process, and each training sample in the training sample set includes: the MMP value of the reservoir and the MMP influencing factor data of the reservoir.

[0021] Optionally, the MMP prediction device based on a convolutional neural network further includes:

[0022] A training sample set acquisition unit for acquiring the training sample set;

[0023] A hyperparameter optimization unit for training the convolutional neural network using the training sample set, and optimizing the hyperparameters of the convolutional neural network by taking the prediction error of the validation samples in the validation sample set as the optimization target in combination with the Bayesian optimization algorithm;

[0024] A model training unit for training the convolutional neural network again using the training sample set according to the hyperparameters optimized by the Bayesian optimization algorithm to obtain the prediction model.

[0025] To achieve the above object, according to another aspect of the present invention, there is also provided a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the above MMP prediction method based on a convolutional neural network are implemented.

[0026] To achieve the above object, according to another aspect of the present invention, there is also provided a computer-readable storage medium, on which a computer program / instructions are stored. When the computer program / instructions are executed by a processor, the steps of the above MMP prediction method based on a convolutional neural network are implemented.

[0027] To achieve the above object, according to another aspect of the present invention, there is also provided a computer program product, including a computer program / instructions. When the computer program / instructions are executed by a processor, the steps of the above MMP prediction method based on a convolutional neural network are implemented.

[0028] The beneficial effects of the present invention are as follows:

[0029] In the embodiments of the present invention, a prediction model is trained through a convolutional neural network and a Bayesian optimization algorithm, and then the MMP of the target reservoir is predicted according to the trained prediction model, achieving the beneficial effect of accurately and efficiently predicting the MMP of the reservoir. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings:

[0031] Figure 1 is the first flowchart of the MMP prediction method based on a convolutional neural network in the embodiments of the present invention;

[0032] Figure 2 is the second flowchart of the MMP prediction method based on a convolutional neural network according to an embodiment of the present invention;

[0033] Figure 3 is a schematic diagram of training data according to an embodiment of the present invention;

[0034] Figure 4 is a schematic diagram for comparing prediction results according to an embodiment of the present invention;

[0035] Figure 5 is the first structural block diagram of the MMP prediction device based on a convolutional neural network according to an embodiment of the present invention;

[0036] Figure 6 is the second structural block diagram of the MMP prediction device based on a convolutional neural network according to an embodiment of the present invention;

[0037] Figure 7 is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed implementation manners

[0038] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0039] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can 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.

[0040] It should be noted that the terms "including" and "having" in the specification and claims of the present invention and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0041] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0042] It should be noted that in the technical solution of this application, the acquisition, storage, use, processing, etc. of data all comply with the relevant regulations of national laws and regulations.

[0043] It should be noted that the MMP in this application refers to the minimum miscibility pressure between CO2 and reservoir crude oil.

[0044] Figure 1 is the first flowchart of the MMP prediction method based on a convolutional neural network in an embodiment of the present invention. As Figure 1 shown, in an embodiment of the present invention, the MMP prediction method based on a convolutional neural network of the present invention includes step S101 and step S102.

[0045] Step S101, obtain the MMP influencing factor data of the target reservoir.

[0046] In an embodiment of the present invention, the MMP influencing factor data specifically includes: reservoir temperature (T R ), mole fraction of volatile components in crude oil (X vol ), mole fraction of C2-C4 components in crude oil (X C2-4 ), mole fraction of C5-C6 components in crude oil (X C5-6 ), molecular weight of C 7+ components in crude oil (MW C7+ ), mole fractions of CO2 and four impurities in the injection gas (i.e., y CO2 , y C1 , y N2 , y H2S and y HC ) and other 10 influencing factors.

[0047] Step S102, input the MMP influencing factor data into a preset prediction model to obtain the MMP prediction value of the target reservoir output by the prediction model. Among them, the prediction model is obtained by training a convolutional neural network according to a training sample set, and during the training process, the hyperparameters of the convolutional neural network are optimized by the Bayesian optimization algorithm. Each training sample in the training sample set includes: the MMP value of the reservoir and the MMP influencing factor data of the reservoir.

[0048] In an embodiment of the present invention, the inputting the MMP influencing factor data into a preset prediction model in this step specifically includes:

[0049] First, convert the MMP influencing factor data into a two-dimensional matrix, and then input the two-dimensional matrix into the prediction model. In the present invention, the one-dimensional MMP influencing factor data is transformed into a two-dimensional matrix that can be received by the convolutional neural network layer.

[0050] Figure 2 It is the second flowchart of the MMP prediction method based on the convolutional neural network in the embodiment of the present invention. As Figure 2 shown, in an embodiment of the present invention, the prediction model in the above step S102 of the present invention is specifically obtained through the following steps S201 to S203.

[0051] Step S201, obtain the training sample set.

[0052] Figure 3 As shown in the present invention, in an embodiment, the MMP values and corresponding MMP influencing factor data of 105 reservoirs collected in the present invention are shown. Specifically, in the present invention, all data is divided into a training sample set, a validation sample set, and a test sample set according to a ratio of 6:2:2. Then, there are 63 groups of MMP data in the training sample set, 21 groups of MMP data in the validation sample set, and 21 groups of MMP data in the test sample set.

[0053] In an embodiment of the present invention, after obtaining the training sample set, the validation sample set, and the test sample set, first perform maximum-minimum normalization processing on the training sample set, and then use the maximum and minimum values of the data in the training sample set to perform the same processing on the data in the validation sample set and the test sample set.

[0054] In an embodiment of the present invention, the maximum-minimum normalization formula can be as follows:

[0055]

[0056] Step S202, use the training sample set to train the convolutional neural network, and combine the Bayesian optimization algorithm to use the prediction error of the validation samples in the validation sample set as the optimization target to optimize the hyperparameters of the convolutional neural network.

[0057] In the present invention, when using the training sample set to train the convolutional neural network, convert the MMP influencing factor data in each training sample into a two-dimensional matrix. Specifically, in the present invention, the one-dimensional data formed by the above 10 MMP influencing factor data of each training sample can be transformed into a 10×1 two-dimensional matrix that can be received by the convolutional neural network layer.

[0058] In the present invention, in this step, the MMP influencing factor data in the form of a two-dimensional matrix is used as the input variable, and the corresponding MMP is used as the output variable. A convolutional neural network is built, and the hyperparameters to be optimized in the convolutional neural network are set as variable values.

[0059] In an embodiment of the present invention, the hyperparameters include: the number of convolutional network layers, the number of convolutional kernels in each convolutional network layer, the size of the convolutional kernels used, the learning rate of the Adam optimizer, the number of samples in the training set introduced in each training epoch, and the total number of training epochs.

[0060] In a specific embodiment of the present invention, the prediction error of the validation samples can be the mean square error. After Bayesian optimization, the optimal combination of hyperparameters obtained can be as follows: the number of convolutional network layers is 4, the number of convolutional kernels in each convolutional network layer is 75, the size of the convolutional kernels used is 4×4, the learning rate of the Adam optimizer is 0.0005510, the number of samples in the training set introduced in each training epoch is 40, and the total number of training epochs is 99.

[0061] Step S203, according to the hyperparameters optimized by the Bayesian optimization algorithm, the convolutional neural network is trained again using the training sample set to obtain the prediction model.

[0062] In the present invention, in this step, the hyperparameters obtained by Bayesian optimization are used to train the convolutional neural network again using the training sample set, and the trained model is saved. This model is the MMP prediction model.

[0063] Figure 4 It is a comparison graph of the prediction data and the original data of the final MMP prediction model obtained by Bayesian optimization on 21 groups of test sample sets. From Figure 4 it can be seen that the MMP predicted by the convolutional neural network model coincides highly with the real MMP, and through calculation, the average absolute percentage error of this model on the test sample set is 9.16%, with high prediction accuracy, indicating the reliability and applicability of the convolutional neural network model in MMP prediction.

[0064] In an embodiment of the present invention, using the same data set, a prediction model of MMP is established using a common fully connected neural network (FCNN). Through calculation, the prediction accuracy of the MMP prediction model based on FCNN in the test set is 10.71%. It can be seen that the prediction error of the convolutional neural network model in the test set is 1.5 percentage points smaller than that of FCNN, which to a certain extent shows that the convolutional neural network has better learning ability than the FCNN network, can improve the accuracy of the MMP prediction model, and verifies the superiority of the established model again.

[0065] As can be seen from the above embodiments, the MMP prediction method based on convolutional neural network of the present invention has at least achieved the following beneficial effects:

[0066] 1. The present invention combines the convolutional neural network in machine learning methods with reservoir MMP prediction for the first time, learns from a large amount of reservoir MMP data, and establishes a data-driven reservoir MMP prediction model. It is a new MMP prediction idea and method, opening up a precedent for convolutional neural network in MMP prediction, and is of great significance for reservoir MMP prediction and reservoir development plan design;

[0067] 2. The MMP machine learning model based on the convolutional neural network structure of the present invention uses convolutional layers to greatly reduce the number of parameters in the traditional fully connected layer, making the learning problem easier, and having the advantages of automatic feature extraction, strong generalization ability, and good robustness, which can further improve the prediction accuracy of MMP and achieve fast and accurate prediction of MMP;

[0068] 3. In the modeling process of the present invention, the Bayesian optimization method is used to optimize the hyperparameters in the convolutional neural network, comprehensively improving the model prediction accuracy and saving the time-consuming of manual parameter tuning.

[0069] Generally speaking, the model establishment process of the present invention is simple, has high computational efficiency, high prediction accuracy, strong comprehensiveness and applicability, laying a certain foundation for the large-scale application of machine learning and convolutional neural network in reservoir MMP prediction, having a broad application prospect, and playing a guiding role in the design of CO2 flooding schemes.

[0070] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0071] Based on the same inventive concept, the embodiments of the present invention also provide an MMP prediction device based on convolutional neural network, which can be used to implement the MMP prediction method based on convolutional neural network described in the above embodiments, as described in the following embodiments. Since the principle of the MMP prediction device based on convolutional neural network to solve problems is similar to that of the MMP prediction method based on convolutional neural network, the embodiments of the MMP prediction device based on convolutional neural network can refer to the embodiments of the MMP prediction method based on convolutional neural network, and the repeated parts will not be described again. As used below, the term "unit" or "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0072] Figure 5 FIG. 1 is the first structural block diagram of the MMP prediction device based on a convolutional neural network according to an embodiment of the present invention. As Figure 5 shown, in one embodiment of the present invention, the MMP prediction device based on a convolutional neural network of the present invention includes:

[0073] A data acquisition unit 1 for acquiring MMP influencing factor data of a target reservoir;

[0074] A prediction unit 2 for inputting the MMP influencing factor data into a preset prediction model to obtain an MMP prediction value of the target reservoir output by the prediction model, where the prediction model is obtained by training a convolutional neural network according to a training sample set, and during the training process, hyperparameters of the convolutional neural network are optimized by a Bayesian optimization algorithm, and each training sample in the training sample set includes: the MMP value of the reservoir and the MMP influencing factor data of the reservoir.

[0075] Figure 6 FIG. 2 is the second structural block diagram of the MMP prediction device based on a convolutional neural network according to an embodiment of the present invention. As Figure 6 shown, in one embodiment of the present invention, the MMP prediction device based on a convolutional neural network of the present invention further includes:

[0076] A training sample set acquisition unit 3 for acquiring the training sample set;

[0077] A hyperparameter optimization unit 4 for training the convolutional neural network by using the training sample set, and taking the prediction error of the verification samples in the verification sample set as an optimization target in combination with the Bayesian optimization algorithm to optimize the hyperparameters of the convolutional neural network;

[0078] A model training unit 5 for training the convolutional neural network again by using the training sample set according to the hyperparameters optimized by the Bayesian optimization algorithm to obtain the prediction model.

[0079] To achieve the above object, according to another aspect of the present application, a computer device is further provided. As Figure 7 shown, the computer device includes a memory, a processor, a communication interface, and a communication bus, and a computer program that can run on the processor is stored on the memory. When the processor executes the computer program, the steps in the method of the above embodiment are implemented.

[0080] The processor may be a Central Processing Unit (CPU). The processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., in the form of chips, or combinations of the above types of chips.

[0081] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs, non-transitory computer-executable programs, and units, such as the program units corresponding to the above method embodiments of the present invention. By running the non-transitory software programs, instructions, and modules stored in the memory, the processor executes various functional applications and work data processing of the processor, that is, implements the methods in the above method embodiments.

[0082] The memory may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created by the processor, etc. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely provided relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0083] The one or more units are stored in the memory and, when executed by the processor, execute the methods in the above embodiments.

[0084] Specific details of the above computer device can be understood by referring to the corresponding relevant descriptions and effects in the above embodiments, and will not be elaborated here.

[0085] To achieve the above object, according to another aspect of the present application, there is also provided a computer-readable storage medium storing a computer program, which when executed in a computer processor implements the steps in the above-mentioned MMP prediction method based on a convolutional neural network. Those skilled in the art can understand that all or part of the processes in the above-described embodiment methods can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-described method embodiments. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memories.

[0086] To achieve the above object, according to another aspect of the present application, there is also provided a computer program product including a computer program / instructions, which when executed by a processor implements the steps of the above-mentioned MMP prediction method based on a convolutional neural network.

[0087] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by program code executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple of them can be fabricated into a single integrated circuit module. In this way, the present invention is not limited to any specific combination of hardware and software.

[0088] The above is only the preferred embodiment of the present invention and is not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting MMP based on a convolutional neural network, characterized in that, Including: Obtain the MMP influencing factor data of the target reservoir, where the MMP influencing factor data includes: reservoir temperature T R , mole fraction X of volatile components in crude oil vol , mole fraction X of C2-C4 components in crude oil C2-4 , mole fraction X of C5-C6 components in crude oil C5-6 , C in crude oil 7+ , molecular weight MW of C components C7+ , mole fraction y of CO2 in the injection gas CO2 , and mole fractions y of four impurities in the injection gas C1 , y N2 , y H2S , and y HC ; Input the MMP influencing factor data into a preset prediction model to obtain the MMP prediction value of the target reservoir output by the prediction model. The prediction model is obtained by training a convolutional neural network according to a training sample set. When training the convolutional neural network using the training sample set, the MMP influencing factor data in each training sample is converted into a two-dimensional matrix, and during the training process, the hyperparameters of the convolutional neural network are optimized through the Bayesian optimization algorithm. Each training sample in the training sample set includes the MMP value of the reservoir and the MMP influencing factor data of the reservoir. The hyperparameters include the number of convolutional network layers, the number of convolutional kernels in each convolutional network layer, the size of the convolutional kernels used, the learning rate of the Adam optimizer, the number of samples in the training set input each time during training, and the total number of training epochs.

2. The MMP prediction method based on a convolutional neural network according to claim 1, wherein Also including: Obtain the training sample set; Use the training sample set to train the convolutional neural network, and combine the Bayesian optimization algorithm to optimize the hyperparameters of the convolutional neural network with the prediction error of the verification samples in the verification sample set as the optimization target; According to the hyperparameters optimized by the Bayesian optimization algorithm, use the training sample set to train the convolutional neural network again to obtain the prediction model.

3. The MMP prediction method based on a convolutional neural network according to claim 1, characterized in that The step of inputting the MMP influencing factor data into a preset prediction model specifically includes: First convert the MMP influencing factor data into a two-dimensional matrix, and then input the two-dimensional matrix into the prediction model.

4. A MMP prediction device based on a convolutional neural network, characterized in that, Including: A data acquisition unit for acquiring MMP influencing factor data of a target reservoir, where the MMP influencing factor data includes: reservoir temperature T R , mole fraction X of volatile components in crude oil vol , mole fraction X of C2-C4 components in crude oil C2-4 , mole fraction X of C5-C6 components in crude oil C5-6 , in crude oil C 7+ component molecular weight MW C7+ , mole fraction y of CO2 in the injection gas CO2 and mole fractions y of four impurities in the injection gas C1 , y N2 , y H2S and y HC ; A prediction unit for inputting the MMP influencing factor data into a preset prediction model to obtain the MMP prediction value of the target reservoir output by the prediction model. The prediction model is obtained by training a convolutional neural network according to a training sample set. When training the convolutional neural network using the training sample set, the MMP influencing factor data in each training sample is converted into a two-dimensional matrix, and during the training process, the hyperparameters of the convolutional neural network are optimized through the Bayesian optimization algorithm. Each training sample in the training sample set includes the MMP value of the reservoir and the MMP influencing factor data of the reservoir. The hyperparameters include the number of convolutional network layers, the number of convolutional kernels in each convolutional network layer, the size of the convolutional kernels used, the learning rate of the Adam optimizer, the number of samples in the training set input each time during training, and the total number of training epochs.

5. The MMP prediction device based on a convolutional neural network according to claim 4, characterized in that Also including: A training sample set acquisition unit for obtaining the training sample set; A hyperparameter optimization unit for using the training sample set to train the convolutional neural network, and combining the Bayesian optimization algorithm to optimize the hyperparameters of the convolutional neural network with the prediction error of the verification samples in the verification sample set as the optimization target; A model training unit for using the training sample set to train the convolutional neural network again according to the hyperparameters optimized by the Bayesian optimization algorithm to obtain the prediction model.

6. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

7. A computer-readable storage medium having computer programs / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, the steps of the method according to any one of claims 1 to 3 are implemented.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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