CN112228054A - Method, device and equipment for determining shale gas yield based on convolutional neural network - Google Patents
Method, device and equipment for determining shale gas yield based on convolutional neural network Download PDFInfo
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Abstract
The embodiment of the specification provides a method, a device and equipment for determining shale gas yield based on a convolutional neural network, wherein the method comprises the following steps: acquiring a characteristic parameter set of a target shale gas horizontal well; the characteristic parameter set comprises a plurality of characteristic parameter values, and the characteristic parameters are geological parameters and/or engineering parameters which affect the productivity of the target shale gas horizontal well; and determining the yield data of the target shale gas horizontal well in a preset time period according to the characteristic parameter set of the target shale gas horizontal well and a productivity prediction model obtained by utilizing convolutional neural network training, wherein the target prediction model is used for predicting the yield data of the target shale gas horizontal well in the preset time period according to a plurality of characteristic parameters of the target shale gas horizontal well. In the embodiment of the description, under the condition that the physical seepage mechanism of the shale gas horizontal well is not clear, the yield data of the target shale gas horizontal well in the preset time period can be conveniently and accurately determined by utilizing a plurality of characteristic parameters.
Description
Technical Field
The embodiment of the specification relates to the technical field of shale gas exploration and development, in particular to a shale gas yield determination method, device and equipment based on a convolutional neural network.
Background
In recent years, shale gas development is gradually becoming a new hot spot of world energy development, and shale gas mainly exists in shale rich in organic matters and interlayers and exists in adsorbed gas and free gas. The shale gas exploration and development has important social and economic significance, the shale gas productivity prediction can be accurately carried out, and the shale gas exploration and development guiding method has important guiding significance for guiding shale gas exploration and development.
In the prior art, the shale gas productivity is generally predicted by adopting an analytical formula method, wherein the analytical formula method is to deduce an analytical solution of a shale gas productivity formula by establishing a mathematical model. Before the establishment of the mathematical model, however, the ideal physical model assumption must be relied on, so that the limitation to the shale gas well is more, and the established mathematical model is only a theoretical model and has larger deviation from the actual situation. Therefore, the analytic formula method in the prior art is low in applicability and cannot accurately predict the shale gas productivity.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the specification provides a method, a device and equipment for determining shale gas yield based on a convolutional neural network, and aims to solve the problem that the shale gas yield cannot be accurately predicted in the prior art.
The embodiment of the specification provides a shale gas yield determination method based on a convolutional neural network, which comprises the following steps: acquiring a characteristic parameter set of a target shale gas horizontal well; the characteristic parameter set comprises values of a plurality of characteristic parameters, and the characteristic parameters are geological parameters and/or engineering parameters which affect the productivity of the target shale gas horizontal well; and determining the yield data of the target shale gas horizontal well in a preset time period according to the characteristic parameter set of the target shale gas horizontal well and a productivity prediction model obtained by utilizing convolutional neural network training, wherein the target prediction model is used for predicting the yield data of the target shale gas horizontal well in the preset time period according to the plurality of characteristic parameters of the target shale gas horizontal well.
An embodiment of the present specification further provides a device for determining shale gas yield based on a convolutional neural network, including: the acquisition module is used for acquiring a characteristic parameter set of the target shale gas horizontal well; the characteristic parameter set comprises values of a plurality of characteristic parameters, and the characteristic parameters are geological parameters and/or engineering parameters which affect the productivity of the target shale gas horizontal well; the determining module is used for determining the yield data of the target shale gas horizontal well in a preset time period according to the characteristic parameter set of the target shale gas horizontal well and a productivity predicting model obtained by utilizing convolutional neural network training, wherein the target predicting model is used for predicting the yield data of the target shale gas horizontal well in the preset time period according to the plurality of characteristic parameters of the target shale gas horizontal well.
The embodiment of the specification further provides a shale gas production determination device based on the convolutional neural network, which comprises a processor and a memory for storing processor executable instructions, wherein the processor executes the instructions to realize the steps of the shale gas production determination device based on the convolutional neural network.
The present specification also provides a computer readable storage medium, on which computer instructions are stored, which when executed, implement the steps of the shale gas production determination method based on convolutional neural network.
The embodiment of the specification provides a shale gas yield determination method based on a convolutional neural network, which can be used for determining the yield of a target shale gas horizontal well by obtaining a characteristic parameter set of the target shale gas horizontal well, wherein the characteristic parameter set comprises values of a plurality of characteristic parameters, and the characteristic parameters are geological parameters and/or engineering parameters influencing the yield of the target shale gas horizontal well. Further, the yield data of the target shale gas horizontal well in the preset time period can be determined according to the characteristic parameter set of the target shale gas horizontal well and a productivity prediction model obtained by utilizing convolutional neural network training, wherein the target prediction model is used for predicting the yield data of the target shale gas horizontal well in the preset time period according to the plurality of characteristic parameters of the target shale gas horizontal well. Therefore, under the condition that the physical seepage mechanism of the target shale gas horizontal well is not clear, the yield data of the target shale gas horizontal well in the preset time period can be conveniently and accurately determined by utilizing a plurality of characteristic parameters influencing the productivity of the target shale gas horizontal well.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the disclosure, are incorporated in and constitute a part of this specification, and are not intended to limit the embodiments of the disclosure. In the drawings:
fig. 1 is a schematic diagram illustrating steps of a method for determining shale gas production based on a convolutional neural network according to an embodiment of the present disclosure;
FIG. 2 is a parameter adjustment diagram of convolution kernel size provided in accordance with an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of tuning parameters of the number of convolution kernels provided in accordance with an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of parameter adjustment of the number of neurons in a full connectivity layer according to an embodiment of the present disclosure;
fig. 5 is a schematic parameter adjustment diagram of a network layer number provided in an embodiment of the present disclosure;
FIG. 6 is a parameter tuning schematic of a regularization ratio provided in accordance with an embodiment of the present description;
fig. 7 is a schematic structural diagram of a shale gas production determination apparatus based on a convolutional neural network according to an embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of a shale gas production determination apparatus based on a convolutional neural network according to an embodiment of the present disclosure.
Detailed Description
The principles and spirit of the embodiments of the present specification will be described with reference to a number of exemplary embodiments. It should be understood that these embodiments are presented merely to enable those skilled in the art to better understand and to implement the embodiments of the present description, and are not intended to limit the scope of the embodiments of the present description in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As will be appreciated by one skilled in the art, implementations of the embodiments of the present description may be embodied as a system, an apparatus, a method, or a computer program product. Therefore, the disclosure of the embodiments of the present specification can be embodied in the following forms: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
Although the flow described below includes operations that occur in a particular order, it should be appreciated that the processes may include more or less operations that are performed sequentially or in parallel (e.g., using parallel processors or a multi-threaded environment).
Referring to fig. 1, the present embodiment may provide a shale gas yield determination method based on a convolutional neural network. The shale gas production determination method based on the convolutional neural network can be used for predicting the production data of the target shale gas horizontal well within a preset time period according to a plurality of characteristic parameters of the target shale gas horizontal well. The shale gas production determination method based on the convolutional neural network can comprise the following steps.
S101: acquiring a characteristic parameter set of a target shale gas horizontal well; the characteristic parameter set comprises a plurality of values of characteristic parameters, and the characteristic parameters are geological parameters and/or engineering parameters influencing the productivity of the target shale gas horizontal well.
In this embodiment, a set of characteristic parameters for a target shale gas horizontal well may be obtained. The characteristic parameter set may include values of a plurality of characteristic parameters, and the characteristic parameters are geological parameters and/or engineering parameters that affect the productivity of the target shale gas horizontal well.
In this embodiment, since natural gas in shale exists in three forms: free gas in rock pores, free gas in natural fractures, and adsorbed gas on the surface of organic minerals, these different reservoir mechanisms directly affect the manner, speed, and efficiency of shale gas development. The reservoir mechanism, the seepage mechanism and the development mode of the shale gas are the basis of the analysis of the productivity influence factors, so that a plurality of characteristic parameters influencing the productivity of the target shale gas horizontal well can be analyzed and determined from two aspects of geology and engineering.
In this embodiment, the geological parameter may be geological exploration data, which may be obtained by exploring or detecting a geological through various means and methods. In some embodiments, the geological parameters may include, but are not limited to, at least one of: shale reservoir thickness, formation pressure, reservoir physical properties.
In this embodiment, the engineering parameter may be engineering design data, and may be a development plan or design data that is made for the shale gas horizontal well before the development of the shale gas horizontal well. In some embodiments, the above engineering parameters may include, but are not limited to, at least one of: horizontal well length, fracturing stage number, fracture half-length, fracture height, conductivity, and size of fracture modification Volume (SRV) area. Of course, it can also be understood that the above engineering parameters may also include: the volume zone permeability is reformed through fracturing, the volume zone porosity is reformed through fracturing, the crack permeability, the number of cracks and the like are determined according to actual conditions, and the method is not limited.
In one embodiment, to improve the accuracy of the prediction, the characteristic parameters may be parameters that influence the productivity of the target shale gas horizontal well, which are analyzed and determined in advance from geological parameters and/or engineering parameters. In some embodiments, the plurality of characteristic parameters may include: reservoir thickness, initial formation pressure, matrix permeability, matrix porosity, fracture modification volumetric region permeability, fracture modification volumetric region porosity, fracture permeability, fracture half-length, and fracture number. Of course, the characteristic parameters are not limited to the above examples, and other modifications are possible for those skilled in the art in light of the technical spirit of the embodiments of the present disclosure, and all such modifications are intended to be included within the scope of the embodiments of the present disclosure as long as they achieve the same or similar functions and effects as the embodiments of the present disclosure.
In this embodiment, the reservoir thickness, the initial formation pressure, the matrix permeability, and the matrix porosity may be geological parameters, wherein the matrix permeability and the matrix porosity are reservoir physical properties. The permeability of the fracturing modification volume region, the porosity of the fracturing modification volume region, the permeability of cracks, the half length of the cracks and the number of the cracks can belong to engineering parameters.
S102: and determining the yield data of the target shale gas horizontal well in the preset time period according to the characteristic parameter set of the target shale gas horizontal well and a productivity prediction model obtained by utilizing convolutional neural network training, wherein the target prediction model is used for predicting the yield data of the target shale gas horizontal well in the preset time period according to a plurality of characteristic parameters of the target shale gas horizontal well.
In this embodiment, the yield data of the target shale gas horizontal well in the preset time period may be determined according to the characteristic parameter set of the target shale gas horizontal well and the productivity prediction model obtained by using convolutional neural network training. The target prediction model can be used for predicting the production data of the target shale gas horizontal well in a preset time period according to a plurality of characteristic parameters of the target shale gas horizontal well.
In the present embodiment, the Convolutional Neural Networks (CNNs) are feed-forward Neural Networks (feed-forward Neural Networks) including convolution calculation and having a deep structure, and are one of typical algorithms for deep learning (deep learning), and the Convolutional Neural Networks have a characteristic learning capability and can perform translation invariant classification on input information according to a hierarchical structure thereof.
In this embodiment, the yield prediction model may perform yield prediction according to a plurality of input characteristic parameters, and output yield data of the target shale gas horizontal well in a preset time period. The preset time period may be a time period since the development of the target shale gas horizontal well, for example: the specific determination can be made according to actual conditions within 10 years from the time point of development and production of the target shale gas horizontal well, or within 50 years from the time point of development and production of the target shale gas horizontal well, and the application does not limit the specific determination.
In one embodiment, a one-dimensional convolutional neural network may be used, since the characteristic data of each well is one-dimensional data. The input data is the characteristic data of each well, and the output data is the corresponding production data of the well in a preset time period. The one-dimensional convolutional neural network (1D-CNN) is composed of an input layer, a convolutional layer, a pooling layer, a full-link layer and the like. Wherein convolution kernels act on the convolution layers for implementing feature extraction, one feature being extracted per convolution kernel. The pooling layer, also known as a sampling layer, samples the feature map output by the convolutional layer. The pooling layer can reduce the dimension of the feature map, so as to improve the operation speed, and a maximum pooling function is generally adopted. After features are extracted by the convolutional layer and the pooling layer, the features learned by the network are mapped into the label space of the sample through the fully-connected layer with SoftMax activation. SoftMax can be understood as normalization, for example, there are hundreds of picture classifications, and the output through the SoftMax layer is a one-hundred-dimensional vector.
In this embodiment, the objective of CNN training is to minimize the loss function of the network, and commonly used loss functions include mean square error, cross entropy function, and the like. Wherein the mean square error expression is:
wherein E is a mean square error; y iskOutput data for the network model, tkFor the exemplar label data, k represents the dimension of the data.
In one embodiment, before determining the production data of the target shale gas horizontal well within the preset time period according to the characteristic parameter set of the target shale gas horizontal well and the productivity prediction model obtained by training with the convolutional neural network, the method may further include: determining a plurality of characteristic parameters influencing the shale gas horizontal well productivity, and determining a sample data set according to the characteristic parameters, wherein the sample data set can comprise a plurality of groups of sample data, and each group of sample data can comprise values of the characteristic parameters and corresponding yield data in a preset time period. Furthermore, a convolutional neural network can be trained according to the sample data set to obtain a productivity prediction model.
In this embodiment, since predicting yield using different input data may have an effect on the accuracy of the prediction, the input data used for training may be determined first. Specifically, a plurality of characteristic parameters that affect the shale gas horizontal well productivity may be determined and may be used as input data for the model.
In this embodiment, since the plurality of characteristic parameters are used as input data of the model, the sample data set may be determined from the plurality of characteristic parameters, so that the determined sample data set may include the plurality of characteristic parameters.
In this embodiment, the sample data set may include a plurality of sets of sample data, each set of sample data may include values of a plurality of characteristic parameters and corresponding yield data in a preset time period, because a plurality of characteristic parameters are used as input data of the model, yield data in a preset time period is used as output data, and there is a correlation between the input data and the output data. And the output data in the sample data set can be used as sample label data to test the accuracy of the model training result.
In this embodiment, before training the convolutional neural network according to the sample data set to obtain the productivity prediction model, multiple groups of sample data in the sample data set may be randomly split into a training data set, a test data set, and a verification data set according to a certain ratio, where the training data set: testing the data set: the proportion of the validation data set may be: 4:1:1, or 5:1:1, etc., which can be determined according to the actual situation and is not limited in the present application.
In one embodiment, determining a plurality of characteristic parameters that affect shale gas horizontal well productivity may comprise: the method comprises the steps of obtaining a plurality of geological parameters and a plurality of engineering parameters, determining a plurality of parameters influencing the productivity of the shale gas horizontal well by using a sensitivity analysis method according to the plurality of geological parameters and the plurality of engineering parameters, and taking the parameters influencing the productivity of the shale gas horizontal well as characteristic parameters.
In the present embodiment, the sensitivity analysis is one of methods for analyzing uncertainty, and may be configured to find out the sensitivity factors having important influence on the target index one by one from a plurality of uncertainty factors, and analyze and measure the influence degree and sensitivity degree of the sensitivity factors on the target index. If a small change in a parameter can result in a large change in the target index, the parameter is called a sensitive factor, otherwise, the parameter is called a non-sensitive factor. The sensitivity assay may include: the specific adopted method can be determined according to actual conditions, and the application does not limit the method.
In this embodiment, the plurality of geological parameters may include: shale reservoir thickness, formation pressure, reservoir physical properties; the plurality of engineering parameters may include: the length of the horizontal well, the fracturing stages, the half-length of the fracture, the height of the fracture, the flow conductivity and the size of a fracture transformation volume area. In some embodiments, a plurality of influencing factors influencing shale gas productivity can be determined from geological parameters and engineering descriptions from three dimensions of matrixes, fracture transformation zones and hydraulic fracture, and the determined influencing factors are used as the plurality of characteristic parameters.
In one embodiment, determining the sample data set from the plurality of feature parameters may include: and obtaining values of a plurality of characteristic parameters of a plurality of shale gas horizontal wells which are put into production and developed, and obtaining the production data of the plurality of shale gas horizontal wells which are put into production and developed within a preset time period. Further, a sample data set can be generated according to values of a plurality of characteristic parameters of a plurality of shale gas horizontal wells which are put into production and developed and yield data within a preset time period.
In this embodiment, in order to ensure the reliability of the sample data for training, values of a plurality of characteristic parameters of a plurality of shale gas horizontal wells put into production and developed and yield data within a preset time period may be acquired, and the values of the plurality of characteristic parameters of each shale gas horizontal well put into production and developed and the yield data within the preset time period are used as a set of sample data, so as to generate a plurality of sets of sample data to form a sample data set.
In one embodiment, in case there is not enough historical data of shale gas horizontal wells in production for training, in order to ensure that the number of sample data used for training meets the training requirement and is representational, determining the sample data set according to a plurality of characteristic parameters may include: and acquiring the value range of each characteristic parameter, and generating a plurality of groups of characteristic data by utilizing a Latin hypercube sampling method according to the value range of each characteristic parameter. Furthermore, each group of characteristic data in the multiple groups of characteristic data can be respectively input into shale gas reservoir numerical simulation software, and yield data corresponding to each group of characteristic data in a preset time period can be obtained. Therefore, the sample data set can be generated according to the multiple groups of characteristic data and the yield data corresponding to the characteristic data in each group in the preset time period.
In the embodiment, the characteristic parameter values of different shale gas horizontal wells may have differences, but each characteristic parameter of the shale gas horizontal well has a reasonable value range limited by actual geological conditions, and therefore, the value range of each characteristic parameter can be obtained.
In this embodiment, the manner of obtaining the value range of each characteristic parameter may include: and pulling the characteristic parameters from a preset database, or receiving the value range of each characteristic parameter input by a user. It is understood that, the value ranges of the characteristic parameters may also be obtained in other possible manners, for example, the value ranges of the characteristic parameters are searched in a web page according to a certain search condition, which may be determined according to an actual situation, and this is not limited in this embodiment of the present specification.
In the present embodiment, a Latin Hypercube Sampling (LHS) is a method of approximate random Sampling from multivariate parameter distribution, and belongs to a hierarchical Sampling technique. In statistical sampling, a latin square refers to a square containing only one sample per row and column. The Latin hypercube is the popularization of a Latin square matrix in multiple dimensions, and each hyperplane vertical to an axis contains at most one sample. Assuming that there are N variables (dimensions), each variable can be divided into M intervals with the same probability. At this time, M sample points satisfying the latin hypercube condition may be selected. It should be noted that Latin hypercube sampling requires the same number of partitions M per variable. Suppose we want to extract m samples in an n-dimensional vector space. The Latin hypercube sampling step is as follows: (1) dividing each dimension into m intervals that do not overlap each other, so that each interval has the same probability (usually considering a uniform distribution, so that the lengths of the intervals are the same); (2) randomly extracting a point in each interval in each dimension; (3) randomly extracting points selected in the step (2) from each dimension, and forming vectors by the points to form sample data; wherein M, N, M and N are positive integers.
In an embodiment, the production data in the preset time period may be production data at a plurality of time points in the preset time period, for example, within 10 years from the time point of the development and commissioning of the shale gas horizontal well in the preset time period, and correspondingly, the production data in the preset time period may be production data of each day within 10 years from the time point of the development and commissioning of the shale gas horizontal well, or production data of each hour within 10 years from the time point of the development and commissioning of the shale gas horizontal well, which may be determined according to practical situations, and is not limited by the present application.
In an embodiment, the yield data of the target shale gas horizontal well output by the productivity prediction model in the preset time period may be output in a form of a table, or may be an image or a file, which may be determined according to actual conditions, and is not limited in this application.
In this embodiment, when the output data of the target shale gas horizontal well output by the productivity prediction model in the preset time period is output in the form of a table, the table may include a plurality of time points and output data corresponding to each time point.
In this embodiment, in order to more intuitively represent the change of the yield of the target shale gas horizontal well in the preset time period, a dynamic yield graph can be drawn by taking time as an abscissa and taking yield as an ordinate according to the yield data of the target shale gas horizontal well in the preset time period. The dynamic yield map may be a scatter plot or a line plot, or may be a smooth curve map obtained by fitting, and may be determined according to actual conditions, which is not limited in the present application.
From the above description, it can be seen that the embodiments of the present specification achieve the following technical effects: the characteristic parameter set of the target shale gas horizontal well can be obtained, wherein the characteristic parameter set comprises values of a plurality of characteristic parameters, and the characteristic parameters are geological parameters and/or engineering parameters influencing the productivity of the target shale gas horizontal well. Further, the yield data of the target shale gas horizontal well in the preset time period can be determined according to the characteristic parameter set of the target shale gas horizontal well and a productivity prediction model obtained by utilizing convolutional neural network training, wherein the target prediction model is used for predicting the yield data of the target shale gas horizontal well in the preset time period according to the plurality of characteristic parameters of the target shale gas horizontal well. Therefore, under the condition that the physical seepage mechanism of the target shale gas horizontal well is not clear, the yield data of the target shale gas horizontal well in the preset time period can be conveniently and accurately determined by utilizing a plurality of characteristic parameters influencing the productivity of the target shale gas horizontal well.
In a scene example, taking a shale gas reservoir based on an embedded discrete fracture model as an example, setting the value range of each characteristic parameter: reservoir thickness [15m, 100m ], initial formation pressure [350bar, 550bar ], matrix permeability [0.000001md, 0.001md ], matrix porosity [0.054, 0.08], SRV zone permeability [0.1md, 10md ], SRV zone porosity [0.08, 0.15], fracture half-length [60m, 280m ], fracture permeability [1000md, 50000md ], number of fractures [10, 30 ]. Where md is millidarcy in geology, and is a permeability unit. bar is a unit of pressure, 1 millibar (mbar) 0.001 bar (bar) 100 Pa.
In one scenario example, a latin hypercube sampling method may be utilized to generate 5000 sets of feature parameters as an input sample data set for a convolutional neural network model. 5000 groups of characteristic data are imported into shale gas reservoir numerical simulation software to generate corresponding yield data, an output sample data set of a convolutional neural network model is formed, and therefore a training set, a test set and a verification set can be generated according to the input sample data set and the output sample data set. Further, the convolutional neural network can be trained by utilizing a training set to obtain a shale gas productivity prediction model.
In one scenario example, the evaluation value may be a ratio of the number of test samples with relative errors smaller than a certain threshold to the number of test lumped samples. Wherein the relative error can be calculated according to the following formula:
wherein the content of the first and second substances,
actual production data; y is predicted yield data; r is the relative error.
The evaluation scheme may be:
wherein N is the total number of samples in the test set; l (y) is the number of test sets with relative errors less than a certain threshold; r0To evaluate a threshold; p (R < R)0) Is a relative error smaller than R0Is proportional to the total number of samples in the test set. If the evaluation threshold is set to 0.1, the proportion of the number of samples with a relative error of less than 10% to the total number of samples in the test set can be counted by using the above evaluation scheme.
In one example scenario, the convolutional neural network model may be optimized, where the parameters to be adjusted in the one-dimensional CNN model may include: convolution kernel size, convolution kernel number, number of neurons in full connection layer, number of network layers and regularization proportion. Fig. 2 to fig. 6 are schematic diagrams of parameter adjustment of the size of a convolution kernel, the number of convolution kernels, the number of neurons in a full connection layer, the number of network layers, and the regularization ratio, respectively, and a value with the highest relative error ratio corresponding to each parameter may be respectively preferred as a value obtained by final parameter adjustment.
Based on the same inventive concept, the embodiment of the present specification further provides a shale gas yield determination apparatus based on a convolutional neural network, as in the following embodiments. Because the principle of the shale gas yield determination device based on the convolutional neural network for solving the problems is similar to that of the shale gas yield determination method based on the convolutional neural network, the implementation of the shale gas yield determination device based on the convolutional neural network can refer to the implementation of the shale gas yield determination method based on the convolutional neural network, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated. Fig. 7 is a block diagram of a structure of a shale gas production determination apparatus based on a convolutional neural network according to an embodiment of the present disclosure, and as shown in fig. 7, the shale gas production determination apparatus may include: an obtaining module 701 and a determining module 702, the structure of which is described below.
The obtaining module 701 may be configured to obtain a characteristic parameter set of a target shale gas horizontal well; the characteristic parameter set comprises a plurality of characteristic parameter values, and the characteristic parameters are geological parameters and/or engineering parameters which affect the productivity of the target shale gas horizontal well;
the determining module 702 may be configured to determine, according to the feature parameter set of the target shale gas horizontal well and a productivity prediction model obtained by utilizing convolutional neural network training, yield data of the target shale gas horizontal well within a preset time period, where the target prediction model is configured to predict the yield data of the target shale gas horizontal well within the preset time period according to a plurality of feature parameters of the target shale gas horizontal well.
An embodiment of the present specification further provides an electronic device, which may specifically refer to a schematic structural diagram of a convolutional neural network-based shale gas yield determination device based on the convolutional neural network-based shale gas yield determination method provided by the embodiment of the present specification, shown in fig. 8, and the electronic device may specifically include an input device 81, a processor 82, and a memory 83. The input device 81 may be specifically configured to input a characteristic parameter set of the target shale gas horizontal well. The processor 82 may be specifically configured to obtain a set of characteristic parameters of the target shale gas horizontal well; the characteristic parameter set comprises a plurality of characteristic parameter values, and the characteristic parameters are geological parameters and/or engineering parameters which affect the productivity of the target shale gas horizontal well; and determining the yield data of the target shale gas horizontal well in the preset time period according to the characteristic parameter set of the target shale gas horizontal well and a productivity prediction model obtained by utilizing convolutional neural network training, wherein the target prediction model is used for predicting the yield data of the target shale gas horizontal well in the preset time period according to a plurality of characteristic parameters of the target shale gas horizontal well. The memory 83 may be specifically configured to store parameters such as production data of the target shale gas horizontal well within a preset time period.
In this embodiment, the input device may be one of the main apparatuses for information exchange between a user and a computer system. The input devices may include a keyboard, mouse, camera, scanner, light pen, handwriting input panel, voice input device, etc.; the input device is used to input raw data and a program for processing the data into the computer. The input device can also acquire and receive data transmitted by other modules, units and devices. The processor may be implemented in any suitable way. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth. The memory may in particular be a memory device used in modern information technology for storing information. The memory may include multiple levels, and in a digital system, memory may be used as long as binary data can be stored; in an integrated circuit, a circuit without a physical form and with a storage function is also called a memory, such as a RAM, a FIFO and the like; in the system, the storage device in physical form is also called a memory, such as a memory bank, a TF card and the like.
In this embodiment, the functions and effects specifically realized by the electronic device can be explained by comparing with other embodiments, and are not described herein again.
Embodiments of the present specification further provide a computer storage medium of a method for determining shale gas production based on a convolutional neural network, where the computer storage medium stores computer program instructions, and when the computer program instructions are executed, the computer storage medium may implement: acquiring a characteristic parameter set of a target shale gas horizontal well; the characteristic parameter set comprises a plurality of characteristic parameter values, and the characteristic parameters are geological parameters and/or engineering parameters which affect the productivity of the target shale gas horizontal well; and determining the yield data of the target shale gas horizontal well in the preset time period according to the characteristic parameter set of the target shale gas horizontal well and a productivity prediction model obtained by utilizing convolutional neural network training, wherein the target prediction model is used for predicting the yield data of the target shale gas horizontal well in the preset time period according to a plurality of characteristic parameters of the target shale gas horizontal well.
In this embodiment, the storage medium includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk Drive (HDD), or a Memory Card (Memory Card). The memory may be used to store computer program instructions. The network communication unit may be an interface for performing network connection communication, which is set in accordance with a standard prescribed by a communication protocol.
In this embodiment, the functions and effects specifically realized by the program instructions stored in the computer storage medium can be explained by comparing with other embodiments, and are not described herein again.
It will be apparent to those skilled in the art that the modules or steps of the embodiments of the present specification described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed over a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different from that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, embodiments of the present description are not limited to any specific combination of hardware and software.
Although the embodiments herein provide the method steps as described in the above embodiments or flowcharts, more or fewer steps may be included in the method based on conventional or non-inventive efforts. In the case of steps where no causal relationship is logically necessary, the order of execution of the steps is not limited to that provided by the embodiments of the present description. When the method is executed in an actual device or end product, the method can be executed sequentially or in parallel according to the embodiment or the method shown in the figure (for example, in the environment of a parallel processor or a multi-thread processing).
It is to be understood that the above description is intended to be illustrative, and not restrictive. Many embodiments and many applications other than the examples provided will be apparent to those of skill in the art upon reading the above description. The scope of embodiments of the present specification should, therefore, be determined not with reference to the above description, but should instead be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
The above description is only a preferred embodiment of the embodiments of the present disclosure, and is not intended to limit the embodiments of the present disclosure, and it will be apparent to those skilled in the art that various modifications and variations can be made in the embodiments of the present disclosure. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the embodiments of the present disclosure should be included in the protection scope of the embodiments of the present disclosure.
Claims (10)
1. A shale gas yield determination method based on a convolutional neural network is characterized by comprising the following steps:
acquiring a characteristic parameter set of a target shale gas horizontal well; the characteristic parameter set comprises values of a plurality of characteristic parameters, and the characteristic parameters are geological parameters and/or engineering parameters which affect the productivity of the target shale gas horizontal well;
and determining the yield data of the target shale gas horizontal well in a preset time period according to the characteristic parameter set of the target shale gas horizontal well and a productivity prediction model obtained by utilizing convolutional neural network training, wherein the target prediction model is used for predicting the yield data of the target shale gas horizontal well in the preset time period according to the plurality of characteristic parameters of the target shale gas horizontal well.
2. The method of claim 1, wherein the plurality of feature parameters comprises: reservoir thickness, initial formation pressure, matrix permeability, matrix porosity, fracture modification volumetric region permeability, fracture modification volumetric region porosity, fracture permeability, fracture half-length, and fracture number.
3. The method of claim 1, wherein before determining the production data of the target shale gas horizontal well within a preset time period according to the characteristic parameter set of the target shale gas horizontal well and a productivity prediction model trained by using a convolutional neural network, further comprising:
determining a plurality of characteristic parameters influencing the shale gas horizontal well productivity;
determining a sample data set according to the characteristic parameters; the sample data set comprises a plurality of groups of sample data, and each group of sample data comprises a plurality of values of characteristic parameters and corresponding yield data in a preset time period;
and training the convolutional neural network according to the sample data set to obtain the productivity prediction model.
4. The method of claim 3, wherein determining a plurality of characteristic parameters that affect shale gas horizontal well productivity comprises:
acquiring a plurality of geological parameters and a plurality of engineering parameters;
determining a plurality of parameters influencing the shale gas horizontal well productivity by using a sensitivity analysis method according to a plurality of geological parameters and a plurality of engineering parameters;
and taking the parameters influencing the shale gas horizontal well productivity as characteristic parameters.
5. The method of claim 4, wherein the plurality of geological parameters comprises: shale reservoir thickness, formation pressure, reservoir physical properties;
the plurality of engineering parameters includes: the length of the horizontal well, the fracturing stages, the half-length of the fracture, the height of the fracture, the flow conductivity and the size of a fracture transformation volume area.
6. The method of claim 3, wherein determining a sample data set from the plurality of feature parameters comprises:
obtaining the value range of each characteristic parameter;
generating a plurality of groups of characteristic data by utilizing a Latin hypercube sampling method according to the value range of each characteristic parameter;
respectively inputting each group of characteristic data in the plurality of groups of characteristic data into shale gas reservoir numerical simulation software to obtain yield data corresponding to each group of characteristic data within a preset time period;
and generating the sample data set according to the multiple groups of characteristic data and the yield data corresponding to the characteristic data in each group in a preset time period.
7. The method of claim 3, wherein determining a sample data set from the plurality of feature parameters comprises:
obtaining values of a plurality of characteristic parameters of a plurality of shale gas horizontal wells put into production and developed;
obtaining the production data of the plurality of shale gas horizontal wells put into production and developed within a preset time period;
and generating the sample data set according to values of a plurality of characteristic parameters of the plurality of shale gas horizontal wells put into production and developed and yield data within a preset time period.
8. A shale gas production determination apparatus based on a convolutional neural network, comprising:
the acquisition module is used for acquiring a characteristic parameter set of the target shale gas horizontal well; the characteristic parameter set comprises values of a plurality of characteristic parameters, and the characteristic parameters are geological parameters and/or engineering parameters which affect the productivity of the target shale gas horizontal well;
the determining module is used for determining the yield data of the target shale gas horizontal well in a preset time period according to the characteristic parameter set of the target shale gas horizontal well and a productivity predicting model obtained by utilizing convolutional neural network training, wherein the target predicting model is used for predicting the yield data of the target shale gas horizontal well in the preset time period according to the plurality of characteristic parameters of the target shale gas horizontal well.
9. A convolutional neural network based shale gas production determination apparatus comprising a processor and a memory for storing processor executable instructions which when executed by the processor implement the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium having stored thereon computer instructions which, when executed, implement the steps of the method of any one of claims 1 to 7.
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