CN114399120B - MMP prediction method and device based on convolutional neural network - Google Patents
MMP prediction method and device based on convolutional neural networkInfo
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Abstract
The embodiment of the invention discloses a convolutional neural network-based MMP prediction method and device, wherein the method comprises the steps of obtaining MMP influence factor data of a target oil reservoir, inputting the MMP influence factor data into a preset prediction model to obtain an MMP predicted value of the target oil reservoir output by the prediction model, wherein the prediction model is obtained by training the convolutional neural network according to a training sample set, super-parameters of the convolutional neural network are optimized through a Bayesian optimization algorithm in the training process, and each training sample in the training sample set comprises an MMP value of the oil reservoir and the MMP influence factor data of the oil reservoir. The method and the device have the beneficial effects of accurately and efficiently predicting the MMP of the oil reservoir.
Description
Technical Field
The invention relates to the technical field of oil reservoir development, in particular to an MMP prediction method and device based on a convolutional neural network.
Background
CO 2 miscible flooding is the most widely applied and highest recovery ratio oil displacement mode in low-permeability oil reservoirs CO 2 -EOR. In the process of injecting CO 2 into an oil reservoir for displacement of oil, interaction of three phases of gas, oil and water can occur in the rock stratum. Creating phase-to-phase component transfer, phase transformation, and other complex phase behavior. The fundamental mechanism of miscible flooding is that the displacement agent (CO 2 injection gas) and the driven agent (crude oil) form a stable miscible band front under reservoir conditions, which is a single phase whose movement is effective to push the crude oil forward and eventually to the production well. Because of the miscibility, the oil-gas interface disappears, so that the interfacial tension in the porous medium is reduced to zero, and the microscopic displacement efficiency can reach 100% theoretically.
The Minimum Miscible Pressure (MMP) between CO 2 and reservoir crude oil is one of the key parameters in the CO 2 displacement process, and is the limit to distinguish between CO 2 miscible and immiscible flooding. The accurate determination of the minimum miscible pressure of CO 2 and crude oil is important to improve CO 2 miscible displacement efficiency, reduce operating costs, and create socioeconomic benefits.
The prior art determines MMPs typically using experimentally measured methods that, while assuring accuracy, are complex, time consuming and costly to operate. The prior art therefore lacks a more efficient solution for determining the Minimum Miscible Pressure (MMP) between CO 2 and the reservoir crude.
Disclosure of Invention
The invention provides an MMP prediction method and device based on a convolutional neural network in order to solve at least one technical problem in the background art.
To achieve the above object, according to one aspect of the present invention, there is provided an MMP prediction method based on a convolutional neural network, the method comprising:
MMP influence factor data of a target oil reservoir are obtained;
the MMP influence factor data are input into a preset prediction model, and the MMP predicted value of the target oil deposit output by the prediction model is obtained, wherein the prediction model is obtained by training a convolutional neural network according to a training sample set, the super-parameters of the convolutional neural network are optimized through a Bayesian optimization algorithm in the training process, and each training sample in the training sample set comprises the MMP value of the oil deposit and the MMP influence factor data of the oil deposit.
Optionally, the MMP prediction method based on the convolutional neural network further includes:
Acquiring the training sample set;
training the convolutional neural network by using the training sample set, and optimizing the hyper-parameters of the convolutional neural network by taking the prediction error of the verification sample in the verification sample set as an optimization target by combining a Bayesian optimization algorithm;
and training the convolutional neural network again by using the training sample set according to the super-parameters obtained through the optimization of the Bayesian optimization algorithm to obtain the prediction model.
Optionally, the inputting the MMP influencing factor data into a preset prediction model specifically includes:
The MMP influence factor data is converted into a two-dimensional matrix, and then the two-dimensional matrix is input into the prediction model.
Optionally, the MMP prediction method based on the convolutional neural network further includes:
and when training the convolutional neural network by using the training sample set, converting MMP influence factor data in each training sample into a two-dimensional matrix.
Optionally, the super parameters comprise the number of layers of the convolutional network layer, the number of convolutional kernels of each layer of the convolutional network, the size of the convolutional kernels, the learning rate of the Adam optimizer, the number of samples in the training set substituted in each training and the total training period number.
To achieve the above object, according to another aspect of the present invention, there is provided an MMP prediction apparatus based on a convolutional neural network, the apparatus comprising:
The data acquisition unit is used for acquiring MMP influence factor data of the target oil reservoir;
The prediction unit is used for inputting the MMP influence factor data into a preset prediction model to obtain an MMP predicted value of the target oil reservoir output by the prediction model, wherein the prediction model is obtained by training a convolutional neural network according to a training sample set, the super-parameters of the convolutional neural network are optimized through a Bayesian optimization algorithm in the training process, and each training sample in the training sample set comprises an MMP value of the oil reservoir and MMP influence factor data of the oil reservoir.
Optionally, the MMP prediction device based on the convolutional neural network further includes:
The training sample set acquisition unit is used for acquiring the training sample set;
The super-parameter optimizing unit is used for training the convolutional neural network by utilizing the training sample set, and optimizing the super-parameters of the convolutional neural network by taking the prediction error of the verification sample in the verification sample set as an optimization target by combining a Bayesian optimizing algorithm;
And the model training unit is used for training the convolutional neural network again by utilizing the training sample set according to the super-parameters obtained through the optimization of the Bayesian optimization algorithm to obtain the prediction model.
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, the processor implementing the steps of the above-mentioned MMP prediction method based on a convolutional neural network when executing the computer program.
To achieve the above object, according to another aspect of the present invention, there is also provided a computer-readable storage medium having stored thereon a computer program/instruction which, when executed by a processor, implements the steps of the above-described MMP prediction method based on a convolutional neural network.
To achieve the above object, according to another aspect of the present invention, there is also provided a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the above-described convolutional neural network-based MMP prediction method.
The beneficial effects of the invention are as follows:
According to the embodiment of the invention, the prediction model is trained through the convolutional neural network and the Bayesian optimization algorithm, and then the MMP of the target oil reservoir is predicted according to the trained prediction model, so that the beneficial effects of accurately and efficiently predicting the MMP of the oil reservoir are realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a first flow chart of an MMP prediction method based on convolutional neural network in accordance with an embodiment of the present invention;
FIG. 2 is a second flowchart of an MMP prediction method based on convolutional neural network in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of training data according to an embodiment of the present invention;
FIG. 4 is a comparison of the predicted results of an embodiment of the present invention;
FIG. 5 is a first block diagram of the architecture of an MMP prediction device based on a convolutional neural network in accordance with an embodiment of the invention;
FIG. 6 is a second block diagram of the architecture of an MMP prediction device based on a convolutional neural network in accordance with an embodiment of the invention;
FIG. 7 is a schematic diagram of a computer device according to an embodiment of the invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
It is noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of the present invention and in the foregoing figures, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
It should be noted that, in the technical scheme of the application, the acquisition, storage, use, processing and the like of the data all conform to the relevant regulations of national laws and regulations.
MMP in the present application refers to the minimum miscible pressure between CO 2 and the reservoir crude.
Fig. 1 is a first flowchart of an MMP prediction method based on a convolutional neural network according to an embodiment of the present invention, as shown in fig. 1, and in one 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.
Step S101, MMP influence factor data of a target oil reservoir are obtained.
In one embodiment of the invention, MMP influence factor data specifically comprises 10 influence factors such as oil layer temperature (T R), volatile component mole fraction (X vol) in crude oil, C 2-C4 component mole fraction (X C2-4) in crude oil, C 5-C6 component mole fraction (X C5-6) in crude oil, C 7+ component molecular weight (MW C7+) in crude oil, CO 2 in injection gas and four impurity mole fractions (namely y CO2、yC1、yN2、yH2S and y HC).
Step S102, the MMP influence factor data are input into a preset prediction model, and MMP predicted values of the target oil reservoirs output by the prediction model are obtained, wherein the prediction model is obtained by training a convolutional neural network according to a training sample set, the super parameters of the convolutional neural network are optimized through a Bayesian optimization algorithm in the training process, and each training sample in the training sample set comprises an MMP value of an oil reservoir and MMP influence factor data of the oil reservoir.
In one embodiment of the present invention, the step of inputting the MMP influencing factor data into a preset prediction model specifically includes:
the MMP influence factor data is converted into a two-dimensional matrix, and then the two-dimensional matrix is input into the prediction model. The invention transforms one-dimensional MMP influence factor data into a two-dimensional matrix which can be received by a convolutional neural network layer.
Fig. 2 is a second flowchart of an MMP prediction method based on a convolutional neural network according to an embodiment of the present invention, as shown in fig. 2, in one embodiment of the present invention, the prediction model in step S102 is specifically trained by the following steps S201 to S203.
Step S201, acquiring the training sample set.
FIG. 3 shows MMP values for 105 reservoirs and corresponding MMP contributor data collected according to one embodiment of the present invention. Specifically, according to the invention, all data are divided into a training sample set, a verification sample set and a test sample set according to the ratio of 6:2:2, wherein the training sample set has 63 groups of MMP data, the verification sample set has 21 groups of MMP data, and the test sample set has 21 groups of MMP data.
In one embodiment of the invention, after a training sample set, a verification sample set and a test sample set are obtained, the training sample set is subjected to maximum and minimum normalization processing, and then the data in the verification sample set and the test sample set are subjected to the same processing by utilizing the maximum value and the minimum value of the data in the training sample set.
In one embodiment of the present invention, the maximum and minimum normalization formulas may be as follows:
Step S202, training the convolutional neural network by using the training sample set, and optimizing the hyper-parameters of the convolutional neural network by taking the prediction error of the verification sample in the verification sample set as an optimization target by combining a Bayesian optimization algorithm.
In the invention, MMP influence factor data in each training sample is converted into a two-dimensional matrix when the training sample set is used for training the convolutional neural network. Specifically, the invention can transform one-dimensional data formed by the above 10 MMP influence factor data of each training sample into a 10×1 two-dimensional matrix which can be received by a convolutional neural network layer.
In the invention, MMP influence factor data in a two-dimensional matrix form is taken as an input variable, corresponding MMP is taken as an output variable, a convolutional neural network is built, and super parameters needing to be optimized in the convolutional neural network are set as variable values.
In one embodiment of the invention, the super-parameters include the number of layers of the convolutional network, the number of convolutional kernels per layer of the convolutional network, the size of the convolutional kernels used, the learning rate of the Adam optimizer, the number of samples in the training set substituted for each training, and the total number of training cycles.
In a specific embodiment of the invention, the prediction error of the verification sample can be mean square error, and the optimal super-parameter combination obtained by Bayesian optimization can be as follows, wherein the number of layers of the convolution network layers is 4, the number of convolution kernels of each layer of convolution network is 75, the size of the convolution kernels used is 4×4, the learning rate of an Adam optimizer is 0.0005510, the number of samples in a training set substituted in each training is 40, and the total training period number is 99.
And step S203, training the convolutional neural network again by using the training sample set according to the super-parameters obtained through the optimization of the Bayesian optimization algorithm to obtain the prediction model.
In the invention, the super parameters obtained by Bayesian optimization are utilized in the step, the convolutional neural network is trained again by utilizing the training sample set, and the model after the training is saved, namely the MMP prediction model.
FIG. 4 is a graph of predicted data versus raw data for a final MMP prediction model over 21 test sample sets using Bayesian optimization. As can be seen from fig. 4, the MMP predicted by the convolutional neural network model is highly coincident with the real MMP, and the average absolute percentage error of the model on the test sample set is 9.16% obtained through calculation, so that the prediction accuracy is high, and the reliability and applicability of the convolutional neural network model on MMP prediction are illustrated.
In one embodiment of the invention, the same data set is utilized, a commonly used Fully Connected Neural Network (FCNN) is utilized to establish a MMP prediction model, and the calculation is carried out to obtain the MMP prediction model based on FCNN, wherein the prediction precision of the MMP prediction model in a 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 percent smaller than that of FCNN, which proves that the convolutional neural network has better learning ability than that of FCNN network to a certain extent, can improve the precision of the MMP prediction model, and again verifies the superiority of the built model.
From the above embodiments, it can be seen that the MMP prediction method based on convolutional neural network of the present invention achieves at least the following advantages:
1. The invention combines the convolutional neural network in the machine learning method with the oil deposit MMP prediction for the first time, learns a large amount of oil deposit MMP data, establishes a data-driven oil deposit MMP prediction model, is a new MMP prediction thought and method, opens up the precedent of the convolutional neural network in MMP prediction, and has important significance for the oil deposit MMP prediction and the oil deposit development scheme design;
2. The MMP machine learning model based on the convolutional neural network structure greatly reduces the number of parameters in the traditional full-connection layer by using the convolutional layer, so that the learning problem is easier, and the model has the advantages of strong characteristic automatic extraction and generalization capability, better robustness, and can further improve the prediction precision of MMP and realize the rapid and accurate prediction of MMP;
3. in the modeling process, the super parameters in the convolutional neural network are optimized by using a Bayesian optimization method, so that the model prediction precision is comprehensively improved, and the time consumption of manual parameter adjustment is saved.
In general, the model establishment process is simple and convenient, the calculation efficiency is high, the prediction accuracy is high, the comprehensiveness and the applicability are strong, a certain foundation is laid for machine learning and large-scale application of the convolutional neural network in oil reservoir MMP prediction, the model establishment method has a wide application prospect, and a guiding effect is provided for the design of the CO 2 oil displacement scheme.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
Based on the same inventive concept, the embodiment of the present invention also provides an MMP prediction device based on a convolutional neural network, which can be used to implement the MMP prediction method based on the convolutional neural network described in the above embodiment, as described in the following embodiments. Since the principle of the MMP prediction device based on the convolutional neural network to solve the problem is similar to that of the MMP prediction method based on the convolutional neural network, the embodiment of the MMP prediction device based on the convolutional neural network can be referred to the embodiment of the MMP prediction method based on the convolutional neural network, and the repetition is omitted. As used below, the term "unit" or "module" may be a combination of software and/or hardware that implements the intended function. While the means 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.
FIG. 5 is a first block diagram of an MMP prediction device based on a convolutional neural network according to an embodiment of the invention, as shown in FIG. 5, in one embodiment of the invention, the MMP prediction device based on a convolutional neural network of the invention includes:
the data acquisition unit 1 is used for acquiring MMP influence factor data of a target oil reservoir;
The prediction unit 2 is configured to input the MMP influence factor data into a preset prediction model, and obtain an MMP predicted value of the target oil 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 in the training process, the hyper-parameters of the convolutional neural network are optimized by a bayesian optimization algorithm, and each training sample in the training sample set includes an MMP value of the oil reservoir and the MMP influence factor data of the oil reservoir.
FIG. 6 is a second block diagram of an MMP prediction device based on a convolutional neural network according to an embodiment of the invention, as shown in FIG. 6, in one embodiment of the invention, the MMP prediction device based on a convolutional neural network further includes:
A training sample set obtaining unit 3, configured to obtain the training sample set;
the super-parameter optimizing unit 4 is used for training the convolutional neural network by utilizing the training sample set, and optimizing the super-parameters of the convolutional neural network by taking the prediction error of the verification sample in the verification sample set as an optimization target by combining a Bayesian optimizing algorithm;
and the model training unit 5 is used for training the convolutional neural network again by utilizing the training sample set according to the super-parameters obtained through the optimization of the Bayesian optimization algorithm to obtain the prediction model.
To achieve the above object, according to another aspect of the present application, there is also provided a computer apparatus. As shown in fig. 7, the computer device includes a memory, a processor, a communication interface, and a communication bus, on which a computer program executable on the processor is stored, which processor implements the steps of the method of the embodiments described above when executing the computer program.
The processor may be a central processing unit (Central Processing Unit, CPU). The Processor may also be other general purpose processors, digital Signal Processors (DSP), application SPECIFIC INTEGRATED Circuits (ASIC), field-Programmable gate arrays (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above.
The memory is used as a non-transitory computer readable storage medium for storing non-transitory software programs, non-transitory computer executable programs, and units, such as corresponding program units in the above-described method embodiments of the invention. The processor executes the various functional applications of the processor and the processing of the composition data by running non-transitory software programs, instructions and modules stored in the memory, i.e., implementing the methods of the method embodiments described above.
The memory may include a storage program area that may store an operating system, application programs required for at least one function, and a storage data area that may 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, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory may optionally include memory located remotely from the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more units are stored in the memory, which when executed by the processor, performs the method in the above embodiments.
The details of the computer device may be correspondingly understood by referring to the corresponding relevant descriptions and effects in the above embodiments, and will not be repeated here.
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-described convolutional neural network-based MMP prediction method. It will be appreciated by those skilled in the art that implementing all or part of the above-described embodiment method may be implemented by a computer program to instruct related hardware, where the program may be stored in a computer readable storage medium, and the program may include the above-described embodiment method when executed. The storage medium may be a magnetic disk, an optical disc, a Read-Only Memory (ROM), a random access Memory (RandomAccessMemory, RAM), a Flash Memory (Flash Memory), a hard disk (HARD DISK DRIVE, abbreviated as HDD), a Solid state disk (Solid-state disk STATE DRIVE, SSD), or the like, and the storage medium may further include a combination of the above types of memories.
To achieve the above object, according to another aspect of the present application, there is also provided a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the above-described convolutional neural network-based MMP prediction method.
It will be apparent to those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, or they may alternatively be implemented in program code executable by computing devices, such that they may be stored in a memory device for execution by the computing devices, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps within them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. 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.
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