CN111209674B - River channel sand modeling method and device and readable storage medium - Google Patents
River channel sand modeling method and device and readable storage medium Download PDFInfo
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Abstract
The invention discloses a river sand modeling method, equipment and a readable storage medium. The river sand modeling method comprises the following steps: acquiring basic parameters of a reservoir to be modeled; generating a plurality of first river channel sand body models and training condition data corresponding to the first river channel sand body models according to the acquired basic parameters; establishing a generating network; establishing a discrimination network; training the discrimination network and the generation network to obtain a generator; and inputting the well data into a generator to obtain a sand body model of the reservoir to be modeled. The invention has the beneficial effects that: a large number of first river channel sand body models and corresponding training condition data are generated through a target-based simulation method, then a generation network and a judgment network are trained through the first river channel sand body models and the training condition data, the generation network is trained in a large number to obtain a generator capable of generating a real river channel sand body model, and well data of a research area are input into the generator to obtain a sand body model of a reservoir to be modeled in the research area.
Description
Technical Field
The invention relates to the technical field of reservoir modeling, in particular to a river sand modeling method, equipment and a readable storage medium.
Background
At present, in the modeling research of river channel sand bodies, scholars at home and abroad propose a plurality of modeling methods. The traditional two-point statistics can realize the prediction of any reservoir, plays a great role in establishing a three-dimensional geological model of a continental facies sedimentary reservoir, and powerfully pushes the development of reservoir description to the direction of fineness and quantification. However, as research progresses, more and more scholars recognize that the two-point variation function can only represent two-point correlation, and it is difficult to depict the complex morphology of the reservoir (such as a curved river channel) and to depict different reservoir space configurations. Reservoir morphology can be well described based on the target method, and reservoir cause relation is revealed. It is based primarily on simulations based on predetermined target body morphology, such as direction, amplitude, width, and thickness of the river, but uses multiple iterations in the simulation process to match well data based on the target method, and may not generally satisfy all well data. The multipoint geostatistics modeling adopts the training images as main input parameters, can better respect well data than a target-based method, but has higher requirements on the training images, if the training images are required to have stationarity, the actual situation is less, and the phenomenon that the simulated river channel is interrupted is caused.
Disclosure of Invention
In view of the above, there is a need to provide a reservoir modeling method that can overcome the defect that the two-point variation function simulation method cannot reflect the real reservoir morphology, and can also overcome the problem that it is difficult to match well data based on the target simulation method.
The invention provides a river sand modeling method, which comprises the following steps:
s100, obtaining basic parameters of a reservoir to be modeled, wherein the basic parameters comprise the average width of a river channel, the average curvature of the river channel and the trend of the river channel;
s200, generating a plurality of first river channel sand body models according to the acquired basic parameters, and generating a plurality of training condition data corresponding to the first river channel sand body models according to the first river channel sand body models;
s300, establishing a generating network according to each training condition data;
s400, establishing a discrimination network according to each training condition data and the corresponding first river channel sand body model;
s500, training the judging network and the generating network to obtain a generator;
s600, well data of the reservoir to be modeled are obtained and input into the generator to obtain a sand body model of the reservoir to be modeled.
The invention also provides a device for modeling the river channel sand, which comprises a processor and a memory; the memory has stored thereon a computer readable program executable by the processor; the processor implements the steps of the river sand modeling method provided by the invention when executing the computer readable program.
The invention also provides a computer readable storage medium storing one or more programs, which are executable by one or more processors to implement the steps in the river sand modeling method provided by the invention.
Compared with the prior art, the technical scheme provided by the invention has the beneficial effects that: a large number of first river channel sand body models and corresponding training condition data are generated through a target-based simulation method, then a generation network and a judgment network are trained through the first river channel sand body models and the training condition data, the generation network is trained in a large number to obtain a generator capable of generating a vivid river channel sand body model, and well data of a research area are input into the generator to obtain a sand body model of a reservoir to be modeled in the research area.
Drawings
Fig. 1 is a schematic flow chart of an embodiment of a river sand modeling method provided by the present invention;
FIG. 2 is a first channel sand model of several reservoir regions of interest randomly generated by Petrel software;
fig. 3 is corresponding training condition data randomly generated by Petrel software for each first channel sand body model in fig. 2;
FIG. 4 is a schematic flow chart of step S300 in FIG. 1;
FIG. 5 is a schematic flow chart of step S400 in FIG. 1;
FIG. 6 is a schematic diagram illustrating a process of inputting training condition data and a corresponding first channel sand model into a judgment network;
fig. 7 is a schematic diagram of a process of inputting the second channel sand body model generated by the generation network into the judgment network for authenticity judgment;
FIG. 8 is a schematic flow chart of step S500 in FIG. 1;
FIG. 9 is well data for a study area;
FIG. 10 is a sand model of the reservoir to be modeled for the region of interest obtained by inputting the well data of FIG. 9 into the generator.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
This example was conducted with the West lake sunken ancient system hong Kong group as the study subject. The hardware environment adopted in this experiment is Nvidia GTX 1660 graphics card, with 1408 stream processors (for machine learning calculation), the software platform is pycharm, and the configured virtual environment is tensoflow-gpu 1.11.0. The model adopts a PatchGAN structure in the discrimination process, namely, a discrimination layer of N x 1 is obtained through a plurality of convolution layers, wherein each element represents a corresponding authenticity judgment result, and the whole input authenticity judgment result is the average value of the N x N elements.
Referring to fig. 1, the present invention provides a river sand modeling method, including the following steps:
s100, obtaining basic parameters of a reservoir to be modeled, wherein the basic parameters comprise river channel average width, river channel average curvature and river channel trend; in this embodiment, according to the research results of the predecessors, the average width of the river channel of the reservoir is 500m, the average curvature of the river channel is 1.2, and the trend of the river channel is approximately in the east-west direction.
S200, generating a plurality of first river channel sand body models according to the acquired basic parameters, and generating a plurality of training condition data corresponding to the first river channel sand body models according to the first river channel sand body models; in this embodiment, 100 channel sand body models a are randomly generated by a Petrel software based target simulation method (see fig. 2, only 16 first channel sand body models are shown in fig. 2), and then a corresponding training condition data y is randomly generated for each first channel sand body model a by simulation software (see fig. 3).
S300, establishing a generating network according to each training condition data y; the method for establishing the generating network comprises the following steps (please refer to fig. 4):
s310, establishing a plurality of convolutional layers, coding each training condition data y, inputting the coded data into the convolutional layers for feature extraction, and obtaining a plurality of feature matrixes through convolution operation;
s320, establishing deconvolution layers with the same number as the convolution layers, connecting the convolution layers with the same dimensionality in the deconvolution layers, arranging the feature matrices extracted in the step S310 into one-dimensional vectors, inputting the one-dimensional vectors into the deconvolution layers, and generating corresponding river sand model matrixes through deconvolution operation;
s330, setting a correction function for generating network parameters in the training process: θ = θ g -, where θ is the parameter after network correction, θ g To generate a parameter before network correction ++ (regarding #) g And loss gradient =
Wherein n is the total training times, D is the discrimination network, G is the generation network, z is the random vector, and i is the training time variable.
S400, establishing a discrimination network D according to each training condition data y and the corresponding first river channel sand body model A; the method for establishing the discrimination network D includes the following steps (please refer to fig. 5):
s410, establishing a plurality of convolutional layers, encoding each training condition data y and the corresponding first river channel sand body model A in pairs, inputting the encoded training condition data y and the corresponding first river channel sand body model A into the convolutional layers for feature extraction, and obtaining a plurality of feature matrixes through convolution operation;
s420, comparing the training condition data y with the feature matrix of the corresponding first channel sand body model a, and respectively calculating the true probability of each first channel sand body model a (see fig. 6);
s430, inputting the sand body G (y) model generated by the generation network G into the discrimination network D, and establishing a first loss function: v (G, D) = E x,y [logD(y,x)]+E x,y [log(1-D(y,G(y,z)))]Wherein y is training condition data, x is real data, G (y, z) represents a target domain model generated by the generation network G according to the training condition data y and a random vector, D (y, x) represents the probability of judging whether the real model is real by the D network, and D (y, G (y, z)) is the probability of judging whether the model G (y, z) generated by the generation network G is real by the network D;
s440, adding a second loss function L1 between the model G (y, z) generated by the generating network G and the real data so as to accelerate the convergence of the model and improve the accuracy of the model, wherein the second loss function L 1 （G）=E x，y [||x-G(y，z)|| 1 ]Wherein y is training condition data, x is real data, and G (y, z) represents a target domain model generated by the generation network G according to the training condition data y and the random vector.
S500, training a discrimination network D and a generation network G to obtain a discriminator and a generator; referring to fig. 7 and 8, the method for training the discriminant network and the generated network includes the following steps:
s510, selecting training condition data y, and inputting the selected training condition data y into the generation network G, so that the generation network G generates a second river sand body model G (y) corresponding to the selected training condition data y;
s520, inputting the selected training condition data y and the corresponding first river channel sand body model A into a discrimination network D as judgment conditions, inputting the corresponding second river channel sand body model G (y) into the discrimination network D as a judgment object, judging the authenticity of the second river channel sand body model G (y) by the discrimination network D, and when the discrimination network D judges that the second river channel sand body model G (y) is false, adjusting the parameters of the generation network G according to feedback information, namely calculated loss values, and regenerating the second river channel sand body model G (y) corresponding to the selected training condition data y;
s530, according to the feedback information obtained in S520, adjusting the parameters of the discrimination network D, and improving the discrimination capability of the discrimination network;
s540 inputs the regenerated second channel sand body model G (y) into the discrimination network D for determination, and repeats step S520 and step S530 several times, thereby improving the accuracy of the determination network determination and improving the fidelity of the second channel sand body model generated by the generation network, so that through multiple countermeasures training, the sand body model generated by the generation network G is more and more vivid, and the capability of the discrimination network to determine the authenticity of the sand body model is more and more enhanced, until the discrimination network D does not determine the authenticity of the second channel sand body model G (y), that is, the generation countermeasures network reaches nash equilibrium, the determined probability is 0.5, finally the first loss function V (G, D) approaches a stable value, the second loss function L1 also gradually decreases, and converges to a stable value, and the established final generation function is: g = arg V (G, D) + λ L1 (G), where λ is a hyperparameter controlling the L1 (G) ratio;
s550, repeating the steps S510 to S540 on the condition data y until all the training condition data y are trained.
S600, well data of the reservoir to be modeled are obtained and input into the generator to obtain a sand body model of the reservoir to be modeled. In this embodiment, please refer to fig. 9 for the well data of the research area, which collectively includes 9 wells, where there are 6 wells drilled in the river, these data are used as the condition data for generating the reservoir model of the sand body of the river in the research area, and the sand body model satisfying the condition data can be generated through convolution and deconvolution operations (fig. 10).
The channel sand body model finally generated by the channel sand modeling method provided by the invention can meet all well data because the channel sand body model is obtained by inputting the well data of the research area into the generator, and meanwhile, the generator can reflect the real reservoir form because the generator is trained by a large number of first channel sand body models and corresponding training condition data. The technical scheme provided by the invention combines the idea of generating the confrontation network by artificial intelligence deep learning, overcomes the defects that the target-based modeling method is difficult to condition and difficult to recover the geometric morphology of a sand body and the problem that the variation function is difficult to calculate, can well reflect the reservoir deposit characteristics by the established model, reproduces the characteristics of the reservoir such as complex morphology and size, provides another direction for the research of reservoir modeling, and better serves the production of oil fields.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders.
The invention also provides a device for modeling the river channel sand, which comprises a processor and a memory; the memory has stored thereon a computer readable program executable by the processor; when the processor executes the computer readable program, the steps in the river sand modeling method are realized, and the river sand modeling method is described in detail above, so that the description is omitted here.
In summary, the invention generates a large number of first river sand body models and corresponding training condition data through a target-based simulation method, trains the generation network and the discrimination network through the first river sand body models and the training condition data, obtains a generator capable of generating a vivid river sand body model after the generation network is trained in a large number, and inputs well data of a research area into the generator to obtain a sand body model of a reservoir to be modeled in the research area.
Of course, it will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program instructing relevant hardware (such as a processor, a controller, etc.), and the program may be stored in a computer readable storage medium, and when executed, the program may include the processes of the above method embodiments. The storage medium may be a memory, a magnetic disk, an optical disk, etc.
The above-described embodiments of the present invention should not be construed as limiting the scope of the present invention. Any other corresponding changes and modifications made according to the technical idea of the present invention should be included in the protection scope of the claims of the present invention.
Claims (8)
1. A river sand modeling method is characterized by comprising the following steps:
s100, obtaining basic parameters of a reservoir to be modeled, wherein the basic parameters comprise the average width of a river channel, the average curvature of the river channel and the trend of the river channel;
s200, generating a plurality of first river channel sand body models according to the acquired basic parameters, and generating a plurality of training condition data corresponding to the first river channel sand body models according to the first river channel sand body models;
s300, establishing a generating network according to each training condition data;
s400, establishing a discrimination network according to each training condition data and the corresponding first river channel sand body model;
s500, training the judging network and the generating network to obtain a generator;
s600, well data of a reservoir to be modeled are obtained, and the well data are input into a generator to obtain a sand body model of the reservoir to be modeled;
the step S500 includes:
s510, selecting training condition data, and inputting the selected training condition data into the generation network, so that the generation network generates a second river channel sand body model corresponding to the selected training condition data;
s520, inputting the selected training condition data and the corresponding first river channel sand body model and second river channel sand body model into a discrimination network, judging the authenticity of the second river channel sand body model by the discrimination network, and when the discrimination network judges that the second river channel sand body model is false, adjusting the parameters of the generation network according to feedback information, namely the calculated loss value, and regenerating the second river channel sand body model corresponding to the selected training condition data;
s530, adjusting the parameters of the discrimination network according to the feedback information obtained in S520;
s540, inputting the second river channel sand body model regenerated in S520 into the discrimination network, and repeating the step S520 and the step S530 for a plurality of times until the discrimination network can not judge the authenticity of the second river channel sand body model;
s550 repeats steps S510 to S540 for all the condition data until all the training condition data are trained.
2. The method for modeling channel sand of claim 1, wherein the step S300 comprises:
s310, establishing a plurality of convolutional layers, coding each training condition data y, inputting the coded data into the convolutional layers for feature extraction, and obtaining a plurality of feature matrixes through convolution operation;
s320, establishing deconvolution layers with the same number as the convolution layers, connecting the convolution layers with the same dimensionality in the deconvolution layers, arranging the characteristic matrixes extracted in the step S310 into one-dimensional vectors, inputting the one-dimensional vectors into the deconvolution layers, and generating corresponding river sand model matrixes through deconvolution operation;
s330, a correction function for generating network parameters in the training process is set.
3. The method for modeling riverway sand of claim 2, wherein in step S330, the correction function for generating the network is: θ = θ g -. Wherein θ is a network corrected parameter, θ g To generate a parameter before network correction ++ (regarding #) g And loss gradient =
Wherein n is the total training times, D is the discrimination network, G is the generation network, z is the random vector, and i is the training time variable.
4. The method of modeling channel sand of claim 1, wherein the step S400 comprises:
s410, establishing a plurality of convolutional layers, encoding each training condition data and the corresponding first river channel sand body model in pairs, inputting the encoded training condition data and the corresponding first river channel sand body model into the convolutional layers for feature extraction, and obtaining a plurality of feature matrixes through convolution operation;
s420, comparing the training condition data with the corresponding characteristic matrix of the first river channel sand body model, and respectively calculating the real probability of each first river channel sand body model;
s430, inputting the sand body model generated by the generated network into a discrimination network, and establishing a first loss function;
s440 adds a second loss function between the model generated by the generating network and the real data to speed up the model convergence and improve the accuracy of the model.
5. The method for modeling channel sand of claim 4, wherein in step S430, the first loss function is: v (G, D) = E x,y [logD(y,x)]+E x,y [log(1-D(y,G(y,z)))]Wherein y is training condition data, x is real data, G (y, z) represents a target domain model generated by the generating network G according to the training condition data y and a random vector, D (y, x) represents a probability that the D network judges whether the real model is real, and D (y, G (y, z)) is a probability that the model G (y, z) generated by the judging network G by the discriminating network D is real.
6. The method for modeling channel sand of claim 4, wherein in step S440, the second loss function is: l is 1 （G）=E x,y [||x-G(y,z)|| 1 ]Wherein y is training condition data, x is real data, and G (y, z) represents a target domain model generated by the generation network G according to the training condition data y and the random vector z.
7. The device for modeling the river channel sand is characterized by comprising a processor and a memory;
the memory has stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, implements the steps in the method of river sand modeling according to any one of claims 1-3.
8. A computer readable storage medium, storing one or more programs, the one or more programs being executable by one or more processors to perform the steps of the method of modeling river sand of any one of claims 1 to 3.
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