CN113435128A - Oil and gas reservoir yield prediction method and device based on condition generation type countermeasure network - Google Patents
Oil and gas reservoir yield prediction method and device based on condition generation type countermeasure network Download PDFInfo
- Publication number
- CN113435128A CN113435128A CN202110798960.7A CN202110798960A CN113435128A CN 113435128 A CN113435128 A CN 113435128A CN 202110798960 A CN202110798960 A CN 202110798960A CN 113435128 A CN113435128 A CN 113435128A
- Authority
- CN
- China
- Prior art keywords
- data
- production
- generator
- month
- training
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 67
- 238000004519 manufacturing process Methods 0.000 claims abstract description 152
- 238000012549 training Methods 0.000 claims abstract description 134
- 230000015654 memory Effects 0.000 claims description 20
- 238000002347 injection Methods 0.000 claims description 17
- 239000007924 injection Substances 0.000 claims description 17
- 238000003860 storage Methods 0.000 claims description 15
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 12
- 230000035699 permeability Effects 0.000 claims description 11
- 230000008569 process Effects 0.000 description 15
- 238000012795 verification Methods 0.000 description 13
- 239000003129 oil well Substances 0.000 description 10
- 238000012360 testing method Methods 0.000 description 10
- 238000010801 machine learning Methods 0.000 description 8
- 239000003208 petroleum Substances 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 238000011161 development Methods 0.000 description 5
- 239000004215 Carbon black (E152) Substances 0.000 description 4
- 229930195733 hydrocarbon Natural products 0.000 description 4
- 150000002430 hydrocarbons Chemical class 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 239000002131 composite material Substances 0.000 description 2
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 239000012530 fluid Substances 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000007637 random forest analysis Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000013107 unsupervised machine learning method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Evolutionary Computation (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- Computer Hardware Design (AREA)
- Public Health (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention discloses an oil and gas reservoir yield prediction method and device based on a condition generating countermeasure network, wherein the method comprises the following steps: acquiring production parameters corresponding to each month in the life cycle of a target new well, wherein the life cycle of the target new well is T months; and inputting the production parameters corresponding to each month in the life cycle of the target new well into a pre-trained generator to obtain the yield predicted values corresponding to each month in the life cycle of the target new well, wherein the pre-trained generator is obtained by carrying out repeated iterative training on training samples, and the training samples comprise the production parameters and the yields corresponding to each month in T months in the history of putting the oil and gas well into production. The invention can realize the yield prediction of the new well in the whole life cycle.
Description
Technical Field
The invention relates to the technical field of oil and gas reservoir development, in particular to an oil and gas reservoir yield prediction method and device based on a condition generating countermeasure network.
Background
In the development process of the oil and gas reservoir, due to strong heterogeneity of a stratum, a fluid seepage rule becomes extremely complex, so that the yield is controlled by various factors, certain relation exists among the factors, the yield of the oil and gas reservoir cannot be quantitatively calculated by a formula due to various reasons, the prediction difficulty is high, the prediction precision of the traditional capacity prediction method is low, and the efficient development of the oil and gas reservoir is severely restricted.
In recent years, with the wide application of artificial intelligence in the fields of science and engineering, and the characteristics of wide data, large data volume, diversity, real reliability and the like, big data and machine learning become hot spots in the oil and gas industry. Big data and machine learning have achieved significant results in geological feature prediction, lithology judgment, oil and gas well yield master control factors, and oil and gas well yield prediction analysis. At present, the machine learning methods applied to petroleum mainly include algorithms such as Support Vector Machine (SVM), fully-connected neural network (FCNN), Convolutional Neural Network (CNN), long-short-term memory neural network (LSTM), and Random Forest (RF). Among them, neural network algorithms are most frequently applied.
Although various influence factors and nonlinear relations can be considered in the current oil and gas reservoir production prediction model based on machine learning, production prediction can be only carried out on old wells with production history, production prediction of a new well, namely wells which have never produced, in a whole life cycle can not be carried out, and the application range is limited.
Thus, the prior art lacks a method that enables full life cycle production prediction for new wells.
Disclosure of Invention
The invention provides a method and a device for predicting the oil and gas reservoir yield based on a condition generation type countermeasure network, aiming at solving the technical problems in the background technology.
In order to achieve the above object, according to one aspect of the present invention, there is provided a reservoir production prediction method based on a conditional generative countermeasure network, the method comprising:
acquiring production parameters corresponding to each month in the life cycle of a target new well, wherein the life cycle of the target new well is T months;
and inputting the production parameters corresponding to each month in the life cycle of the target new well into a pre-trained generator to obtain the yield predicted values corresponding to each month in the life cycle of the target new well, wherein the pre-trained generator is obtained by carrying out repeated iterative training on training samples, and the training samples comprise the production parameters and the yields corresponding to each month in T months in the history of putting the oil and gas well into production.
Optionally, the method for predicting the production of the hydrocarbon reservoir based on the conditional generation countermeasure network further includes:
obtaining a training sample, wherein the training sample is historical production data of a produced oil and gas well, the training sample consists of first data and second data, the first data comprises production parameters corresponding to each month in T months of the produced oil and gas well history, and the second data comprises production corresponding to each month in T months of the produced oil and gas well history;
and performing multiple iterative training according to the training sample to obtain the pre-trained generator, wherein in each iterative training, the network weight of the discriminator is trained, and then the network weight of the generator is trained in a combined model consisting of the discriminator and the generator.
Optionally, the input of the generator is the production parameters corresponding to each month in the T months, the output of the generator is the yield prediction data, and the yield prediction data is the yield prediction values corresponding to each month in the T months; the input of the discriminator is combined data formed by combining production parameters and yield prediction data which respectively correspond to each month in T months, and the output of the discriminator is the probability that the combined data is real data.
Optionally, the training the network weight of the discriminator specifically includes:
combining the first data with the yield prediction data output by the generator according to the first data to obtain combined data, and setting a label of the combined data to be 0, wherein the yield prediction data output by the generator according to the first data comprises a yield prediction value corresponding to each month in T months of the history of the produced oil-gas well;
setting the label of the training sample to 1;
and inputting the combined data after the label setting and the training sample after the label setting into a discriminator, and training the network weight of the discriminator.
Optionally, the training of the network weight of the generator in the combined model composed of the discriminator and the generator specifically includes:
inputting the first data into a generator to obtain yield prediction data output by the generator, wherein the yield prediction data output by the generator according to the first data comprise yield prediction values corresponding to each month in T months of the history of the produced oil and gas wells;
combining the first data with yield prediction data output by the generator according to the first data to obtain combined data, and setting a label of the combined data to be 1;
and inputting the combined data after the label setting into a discriminator to obtain the probability that the combined data output by the discriminator is real data.
Optionally, the production parameters include: water injection, permeability, porosity, reservoir oil saturation, and reservoir thickness.
In order to achieve the above object, according to another aspect of the present invention, there is provided a reservoir production prediction apparatus based on a condition generating countermeasure network, the apparatus including:
the new well data acquisition module is used for acquiring production parameters corresponding to each month in the life cycle of a target new well, wherein the life cycle of the target new well is T months;
and the whole-period production prediction module of the new well is used for inputting the production parameters corresponding to each month in the life cycle of the target new well into a pre-trained generator to obtain the production prediction values output by the generator and corresponding to each month in the life cycle of the target new well, wherein the pre-trained generator is obtained by carrying out repeated iterative training by adopting a training sample, and the training sample comprises the production parameters and the production corresponding to each month in T months in the history of the put-in-production oil-gas well.
Optionally, the device for predicting the production of a hydrocarbon reservoir based on the conditional generation countermeasure network further includes:
the training sample acquisition module is used for acquiring a training sample, wherein the training sample is historical production data of a produced oil and gas well, the training sample consists of first data and second data, the first data comprises production parameters corresponding to each month in T months of the produced oil and gas well history, and the second data comprises production corresponding to each month in T months of the produced oil and gas well history;
and the training module is used for carrying out a plurality of times of iterative training according to the training samples to obtain the pre-trained generator, wherein in each iterative training, the training module firstly trains the network weight of the discriminator and then trains the network weight of the generator in a combined model consisting of the discriminator and the generator.
In order 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 reservoir production prediction method based on a condition generating countermeasure network when executing the computer program.
In order to achieve the above object, according to another aspect of the present invention, 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 reservoir production prediction method based on a conditional generation countermeasure network.
The invention has the beneficial effects that: the method combines the condition generating type countermeasure network with the oil and gas reservoir yield prediction, trains a generator of the condition generating type countermeasure network as a yield prediction model, and can realize the prediction of the yield of a new well in the whole life cycle.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts. In the drawings:
FIG. 1 is a flow chart of a reservoir production prediction method based on a conditional generative countermeasure network in accordance with an embodiment of the present invention;
FIG. 2 is an overall flow chart of the training of an embodiment of the present invention;
FIG. 3 is a flow chart of the training of the arbiter according to an embodiment of the present invention;
FIG. 4 is a flow chart of the training of a generator according to an embodiment of the present invention;
FIG. 5 is a schematic view of a combined model according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a training sample according to an embodiment of the present invention;
FIG. 7 is a first block diagram of a reservoir production prediction device based on a conditional generative countermeasure network in accordance with an embodiment of the present invention;
FIG. 8 is a second block diagram of a reservoir production prediction device based on a conditional generative countermeasure network according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of a computer apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As will be appreciated by one skilled in the art, 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 should be noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of the present invention and the above-described drawings, 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, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Generative Adaptive Networks (GANs) is a Generative model proposed by Goodfellow Ian of the university of montreal in 2014, and has attracted extensive attention and research by those skilled in the art. The generative confrontation network is a deep learning model, and is one of the most promising methods for unsupervised learning in complex distribution in recent years. The model passes through (at least) two modules in the framework: the mutual game learning of the generative model (also called a generator) and the discriminant model (also called a discriminant) produces quite good output.
The generative confrontation network is an unsupervised machine learning method, so that the method is generally applied to data enhancement, namely when few training samples are provided for machine learning, the generative confrontation network can be adopted to generate a plurality of data samples for the machine to learn. Since the generation type confrontation network is late in generation, it has been used for data enhancement by a few oil workers in recent years, but the effect of the data generated by the method is pragmatically inconsistent by each researcher.
The most primitive generative countermeasure network is to input a random vector and then get a generated object, but we cannot control what object is generated. Therefore, researchers put forward a conditional generative confrontation network theory, add constraints to original GAN, introduce conditional variable y (conditional variable y) into a generative model and a discriminant model, and introduce additional information for the model to generate data in an instructive manner. In theory y can make meaningful information, such as class labels, change GAN, an unsupervised learning method, into supervised.
The occurrence of the conditional generative countermeasure network changes the generative countermeasure network from unsupervised learning to supervised learning, which means that the method can be used for parameter prediction in the aspect of petroleum and has great application prospect in the aspect of petroleum. However, according to the research and development of the current literature, the method is not combined with the petroleum industry for a while. Therefore, the invention provides that the condition generating type confrontation network machine learning method is applied to the oil and gas reservoir yield prediction to improve the yield prediction precision and expand the application field of the condition generating type confrontation network.
Fig. 1 is a flowchart of a reservoir production prediction method based on a condition generating countermeasure network according to an embodiment of the present invention, and as shown in fig. 1, the reservoir production prediction method based on the condition generating countermeasure network according to an embodiment of the present invention includes step S101 and step S102.
Step S101, obtaining production parameters corresponding to each month in the life cycle of the target new well, wherein the life cycle of the target new well is T months.
In an embodiment of the invention, the life cycle is the time from production to abandonment (non-production) of a well. In an alternative embodiment of the present invention, for convenience of description, the life cycle of the target new well is set to 24 months, i.e. T is 24 months, i.e. from the production of the well, the production is finished for 24 months, and no production is produced, the 24 months are the full life cycle of the new well, and each time point corresponds to the production, i.e. the production of the oil well at the time point. For convenience of description, the time point of the month is taken as a total of 24 time points, and the output of the oil well in each month is the prediction target. And the output per month is affected by the production parameters of the month.
In one embodiment of the invention, the production parameters include: water injection, permeability, porosity, reservoir oil saturation, and reservoir thickness. Where the water injection rate may change monthly, and the permeability, porosity, reservoir oil saturation, and reservoir thickness are geological variables that do not change over time.
In another embodiment of the present invention, the production parameters may further include: the presence or absence of fracturing, the type of fracturing fluid, the number of fracturing stages, the type of completion, and the like.
Step S102, inputting the production parameters corresponding to each month in the life cycle of the target new well into a pre-trained generator to obtain the yield prediction values corresponding to each month in the life cycle of the target new well, wherein the pre-trained generator is obtained by carrying out repeated iterative training on training samples, and the training samples comprise the production parameters and the yields corresponding to each month in T months of the history of the put-in-production oil-gas well.
In one embodiment of the present invention, the historical data of T months of the put-in-production well specifically refers to the data of the put-in-production well from the first month to the tth month of the put-in-production, that is, the data of the T months is the data of consecutive T months starting from the first month of the put-in-production. In an embodiment of the invention, the number of the put-in-production oil and gas wells is multiple, and each put-in-production oil and gas well corresponds to one training sample.
The method combines the condition generating type countermeasure network with the oil and gas reservoir yield prediction, trains a generator of the condition generating type countermeasure network as a yield prediction model, and can realize the prediction of the yield of a new well in the whole life cycle. The training process of the generator will be described in detail below.
Fig. 2 is a flowchart of an overall training process according to an embodiment of the present invention, and as shown in fig. 2, in an embodiment of the present invention, a training process of the generator trained in advance in step S102 specifically includes step S201 and step S202.
Step S201, obtain the training sample, wherein, the training sample is the historical production data of the oil and gas well of having put into production, the training sample comprises first data and second data, first data include the production parameter that each month in the historical T month of the oil and gas well of having put into production corresponds separately in each month, the second data include the production that each month in the historical T month of the oil and gas well of having put into production corresponds separately.
In the embodiment of the invention, historical production data of the full life cycle of a plurality of oil and gas wells similar to the production conditions of the target new well (such as the same well pattern conditions) are collected, the oil and gas wells with the life cycle larger than T are screened, for convenience of description, the number of the screened oil and gas wells is recorded as M, then M data samples are constructed according to the historical production data of the M wells within T months by taking the oil wells as basic units, and the M data samples are divided into three sample sets, namely a training sample set, a verification sample set and a test sample set according to a certain proportion.
And S202, carrying out a plurality of times of iterative training according to the training samples to obtain the pre-trained generator, wherein in each iterative training, the network weight of the discriminator is trained, and then the network weight of the generator is trained in a combined model consisting of the discriminator and the generator.
In the embodiment of the invention, each iterative training of the invention is divided into two steps. First, the network weights of the discriminators are trained separately. The network weights of the generator are then trained in a combined model composed of the arbiter and the generator, which may be as shown in FIG. 5. In the combined model, the weights of the network of the discriminator are not changed, and the network weights of the generator are changed along with training.
Specifically, the process of training the network weights of the discriminators can be seen in the embodiment shown in fig. 3, and the process of training the network weights of the generators in the combined model composed of the discriminators and the generators can be seen in the embodiment shown in fig. 4.
In the embodiment of the invention, in the combined model, the input of the generator is the production parameters corresponding to each month in the T months, the output of the generator is the production prediction data, and the production prediction data is the production prediction value corresponding to each month in the T months.
In the embodiment of the invention, the LSTM and the FCNN can be used for building a generator model of the composite network. Specifically, when the generator is built, firstly, LSTM is used to preprocess conditions, namely, production parameters (in a time series form) corresponding to each month in T months, then, the preprocessed data are connected with random noise data generated by the generator and connected with full connection layers, finally, a plurality of full connection layers are set, the number of neurons in the last layer of the full connection layers is the life cycle T set by the oil and gas well, and a yield predicted value (in a time series form) corresponding to each month in T months is output.
In the embodiment of the invention, in the combined model, the input of the discriminator is the combined data formed by combining the production parameters and the yield prediction data which respectively correspond to each month in T months, and the output of the discriminator is the probability that the combined data is the real data.
In one embodiment of the invention, the discriminator model of the composite network is constructed by using LSTM and FCNN, the discrimination result of the combined data is output, if the output probability is greater than 0.5, the label of the combined data is 1, and the combined data belongs to real data, otherwise, the combined data is false data, namely the data generated by the generator. When the discriminator is built, firstly, LSTM is used for preprocessing conditions, namely production parameters (in a time series form) corresponding to each month in T months, then, preprocessed data and yield prediction data (in a time series form) are connected and are connected with a full connection layer, finally, a plurality of full connection layers are set, the number of neurons of the last layer of the full connection layer is 1, the probability that current combined data is real data is output, if the output probability is greater than 0.5, the current combined data is indicated to be 1 in a label mode and belongs to the real data, and otherwise, the current combined data is false data, namely data generated by a generator.
In an embodiment of the present invention, after N times of iterative training, the present invention stops training, and selects a generator with the smallest predicted average absolute percentage error of each verification sample in the verification sample set in the N times of iterative training processes as a yield prediction model, and the generator is used as a generator trained in advance in step S102. In an alternative embodiment of the present invention, N may be 1000.
In another embodiment of the present invention, the present invention performs iterative training for multiple times according to the training samples, and stops performing the iterative training until the prediction error of the generator is smaller than the preset value, and saves the generator at this time as the yield prediction model, which is used as the pre-trained generator in step S102.
In one embodiment of the invention, historical production data of a whole life cycle of a plurality of produced oil and gas similar to the production conditions of the target new well (such as the same well pattern conditions) are collected, oil and gas wells with life cycles larger than T are screened, for convenience of description, the number of the screened oil and gas wells is recorded as M, and then the historical production data of the M wells in T months is divided into a training sample set, a verification sample set and a test sample set according to a certain proportion by taking the oil wells as basic units.
In one embodiment of the invention, because the real data of the oil field is difficult to collect and most of the data of the oil field has a confidential protocol, the embodiment constructs a training sample required by yield prediction based on numerical reservoir simulation software. In the embodiment, a four-injection (I1, I2, I3 and I4) one-production (P1) heterogeneous reservoir model injection-production system is established by adopting a five-point method well pattern in combination with the actual development condition of an oil field site. The effective thickness of the established oil reservoir model is 40m, the length and the width of each grid are 100m, the total number of 20 multiplied by 5 is 2000 grids, the whole oil reservoir is divided into 4 regions on average, the permeability and the porosity between the regions are different, the permeability and the porosity within the regions are kept consistent, and the longitudinal permeability is 0.3 time of the transverse permeability, so that the heterogeneity of the real oil reservoir is simulated.
The specific operation steps for constructing the training sample are as follows:
1: establishing a five-point method well pattern with four injection and one extraction by combining the actual water injection condition of the oilfield field through Eclipse software, setting the bottom hole flowing pressure of a production well to be 10bar, namely keeping the bottom hole flowing pressure unchanged during production, and setting the production time to be two years;
2: setting the water injection amount of 4 water injection wells, the permeability of 4 areas and the porosity of 4 areas (the oil reservoir thickness and the oil saturation are kept unchanged temporarily because the calculation operation is complicated), simulating production for 2 years to obtain oil production data of an oil well within 2 years, and forming 1 sample data according to the injection and production data of the 4 water injection wells and one production well for 2 years;
3: and (3) repeating the step (2), and under the condition that the well pattern condition is not changed, changing the water injection amount of the 4 water injection wells, the permeability of the 4 regions and the porosity of the 4 regions through a mixed orthogonal design, thereby obtaining a plurality of groups of sample data.
Wherein, each group of sample data mainly comprises monthly water injection quantity of 4 water injection wells within 2 years, permeability of 4 regions, porosity of 4 regions, oil saturation of an oil reservoir, thickness of the oil reservoir, monthly oil production of a production well and corresponding production time.
As shown in fig. 6, which is one of the 120 samples, the variable 1 is the production time, the variable 2-5 is the water injection amount per month corresponding to 4 water injection wells around the oil well under the well pattern condition, and the production variable 1-5 is changed along with the time; variables 6-15 are geological variables, set at the time of modeling, that do not change over time; the predicted variable is the production of the well for each month. There are 15 arguments (X) in total, 1 predicted target (Y).
According to the above method, the present embodiment performs 120 times value simulation, and obtains a total of 120 sets of sample data, where each set of sample has 24 data points and a total of 2880 data points. Wherein, each data point has 16 characteristics, 15 independent variable characteristics and 1 predictor variable characteristic.
The method further divides all sample data into a training sample set, a verification sample set and a test sample set according to the ratio of 6:2:2 by taking the oil wells as a basic unit, so that 72 groups of data (namely training samples) of the oil wells are collected in the training sample set, 24 groups of data (namely verification samples) of the oil wells are collected in the verification sample set, and 24 groups of data (namely test samples) of the oil wells are collected in the test sample set.
In an embodiment of the present invention, before performing multiple iterative training according to the training samples, the present invention further performs maximum and minimum normalization processing on the data of each training sample in the training sample set, and processes the data of each verification sample in the verification sample set and the data of each test sample in the test sample set by using the maximum value and the minimum value of the data of each training sample in the training sample set.
In one embodiment of the invention, the max-min normalization formula is as follows:
fig. 3 is a flowchart of training a discriminator according to an embodiment of the present invention, and as shown in fig. 3, in an embodiment of the present invention, the training of the network weight of the discriminator in step S202 specifically includes steps S301 to S303.
Step S301, combining the first data with the production prediction data output by the generator according to the first data to obtain combined data, and setting the label of the combined data to be 0, wherein the production prediction data output by the generator according to the first data comprises the production prediction value corresponding to each month in T months in the history of the produced oil and gas wells.
Step S302, setting the label of the training sample to 1.
Step S303, inputting the combined data after the label setting and the training sample after the label setting into a discriminator, and training the network weight of the discriminator.
In the embodiment of the invention, when the network weight of the discriminator is trained independently, the invention utilizes the established generator, takes the first data in the training data as the input of the generator, and obtains the output prediction data output by the generator according to the first data; then, combining the first data with yield prediction data output by the generator according to the first data, setting a label to be 0, and inputting the label into a discriminator; meanwhile, the training data, namely the combination of the first data and the second data, is set as 1 and is also sent into the discriminator, so that the discriminator learns the true and false data and trains the network weight of the discriminator. After the training is finished, the network weight of the discriminator is reserved, then the network weight of the generator is trained in the combined model, the network weight of the discriminator in the combined model is the network weight obtained by the training in the step, and the network weight of the discriminator in the combined model cannot be changed when the generator is trained, namely the invention realizes the independent training and learning of the generator without influencing the discriminator.
Fig. 4 is a flowchart of training a generator according to an embodiment of the present invention, and as shown in fig. 4, in an embodiment of the present invention, the training of the network weights of the generator in the combined model composed of the discriminator and the generator in step S202 specifically includes steps S401 to S403.
Step S401, inputting the first data into a generator to obtain yield prediction data output by the generator, wherein the yield prediction data output by the generator according to the first data comprises yield prediction values corresponding to each month in T months of the history of the produced oil and gas wells.
Step S402, the first data and the yield prediction data output by the generator according to the first data are combined to obtain combined data, and the label of the combined data is set to be 1.
Step S403, inputting the combined data after the label setting into the discriminator to obtain the probability that the combined data output by the discriminator is real data.
In the invention, when each iterative training is carried out, the network weight of the discriminator is trained firstly, then the network weight of the generator is trained in the combined model, the network weight of the discriminator in the combined model is the network weight obtained by the previous training, and the network weight of the discriminator in the combined model is not changed when the generator is trained.
When the network weight of a generator is trained in a combined model, first data in a training sample is input into the generator to obtain yield prediction data output by the generator, the first data and the yield prediction data are combined to obtain combined data, the label of the combined data is set to be 1, and the combined data is input into a discriminator to be discriminated. Therefore, the generator continuously generates yield prediction data, and the discriminator continuously discriminates the combined data, so that the generator can continuously learn until the probability that the discriminator discriminates that the combined data is real data is greater than 0.5, which indicates that the label of the combined data is 1 and belongs to the real data, the iteration is stopped, and the next iteration is started.
In an embodiment of the present invention, after 1000 times of iterative training (to avoid the performance of the generator being too low, the number of repetitions is set to be large here), the generator with the smallest predicted average absolute percentage error of each verification sample in the verification sample set in the training process is selected, the selected generator model is the model that is to be used for predicting the full life cycle yield of the new oil-gas well, and the production parameters of each month in the life cycle of the target new well designed in advance are input, so that the yield of each time point in the full life cycle of the new well can be predicted.
In one embodiment of the present invention, experiments prove that the final yield prediction model obtained after 1000 times of iterative training, i.e., the generator, has an average absolute percentage error of 6.15% in the training sample set and an average absolute percentage error of 5.21% in the verification sample set. The generalization ability test is carried out on the selected model by utilizing the test sample set which is not contacted with the model, the average absolute percentage error of the final yield prediction model, namely the generator model, selected by the invention on 24 groups of test samples is only 6.23%, and the final yield prediction model has no great difference compared with the prediction error (5.21%) on the verification sample set and is less than 10%, which shows that the generalization ability of the generator model trained by the invention is high, and the method has wide application prospect in the aspect of future petroleum yield prediction.
In an embodiment of the invention, experiments prove that the prediction error of the generator model gradually decreases with the increase of the iterative training times and is finally stabilized at a lower level, which shows that the change rule of the prediction data is gradually learned in the training process of the generator and the discriminator countervailing, and the performance is improved.
According to the embodiment, the condition generation type confrontation network in the machine learning method is combined with the oil and gas reservoir production prediction, a data-driven oil and gas reservoir single-well full life cycle production prediction model is established through the learning of a large amount of oil and gas reservoir production data, and high prediction accuracy is obtained. The productivity prediction model can consider various yield influence factors, is a new yield prediction idea and method, opens up a precedent of applying a condition generation type network in the petroleum industry, and has important significance for the yield prediction of a new well of an oil and gas reservoir and the design of an oil and gas reservoir development scheme. Moreover, the model is simple and convenient in establishing process, high in calculation efficiency, high in prediction accuracy, comprehensive and high in applicability, lays a certain foundation for large-scale application of machine learning and condition generating networks in petroleum yield prediction, and has wide application prospects.
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 different than presented herein.
Based on the same inventive concept, the embodiment of the present invention further provides a device for predicting the production of a hydrocarbon reservoir based on a conditional generation type countermeasure network, which can be used to implement the method for predicting the production of a hydrocarbon reservoir based on a conditional generation type countermeasure network described in the above embodiment, as described in the following embodiment. Because the principle of solving the problems of the oil and gas reservoir production prediction device based on the conditional generation type countermeasure network is similar to the oil and gas reservoir production prediction method based on the conditional generation type countermeasure network, the embodiment of the oil and gas reservoir production prediction device based on the conditional generation type countermeasure network can be referred to the embodiment of the oil and gas reservoir production prediction method based on the conditional generation type countermeasure network, and repeated details are omitted. 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 first block diagram of a reservoir production prediction apparatus based on a conditional generation countermeasure network according to an embodiment of the present invention, and as shown in fig. 7, the reservoir production prediction apparatus based on a conditional generation countermeasure network according to an embodiment of the present invention includes:
the new well data acquisition module 1 is used for acquiring production parameters corresponding to each month in the life cycle of a target new well, wherein the life cycle of the target new well is T months;
and the new well full-period yield prediction module 2 is used for inputting the production parameters corresponding to each month in the life cycle of the target new well into a pre-trained generator to obtain the yield prediction values corresponding to each month in the life cycle of the target new well, wherein the pre-trained generator is obtained by carrying out repeated iterative training by adopting a training sample, and the training sample comprises the production parameters and the yield corresponding to each month in T months in the history of the put-in-production oil-gas well.
Fig. 8 is a second structural block diagram of the reservoir production prediction apparatus based on the conditional generation countermeasure network according to the embodiment of the present invention, as shown in fig. 8, in an embodiment of the present invention, the reservoir production prediction apparatus based on the conditional generation countermeasure network further includes:
the training sample acquisition module 3 is used for acquiring a training sample, wherein the training sample is historical production data of a produced oil and gas well, the training sample consists of first data and second data, the first data comprises production parameters corresponding to each month in T months of the produced oil and gas well history, and the second data comprises production corresponding to each month in T months of the produced oil and gas well history;
and the training module 4 is used for carrying out a plurality of times of iterative training according to the training samples to obtain the pre-trained generator, wherein in each iterative training, the training module firstly trains the network weight of the discriminator and then trains the network weight of the generator in a combined model consisting of the discriminator and the generator.
In an embodiment of the present invention, the training module 4 specifically includes:
the first combination unit is used for combining the first data with the production prediction data output by the generator according to the first data to obtain combined data, and setting a label of the combined data to be 0, wherein the production prediction data output by the generator according to the first data comprises a production prediction value corresponding to each month in T months in the history of the produced oil and gas wells;
a label setting unit for setting the label of the training sample to 1;
and the first input unit is used for inputting the combined data after the label setting and the training sample after the label setting into the discriminator and training the network weight of the discriminator.
In another embodiment of the present invention, the training module 4 specifically includes:
the second input unit is used for inputting the first data into the generator to obtain yield prediction data output by the generator, wherein the yield prediction data output by the generator according to the first data comprise yield prediction values corresponding to each month in T months of the history of the produced oil-gas well;
the second combination unit is used for combining the first data with the yield prediction data output by the generator according to the first data to obtain combined data, and setting the label of the combined data to be 1;
and the third input unit is used for inputting the combined data after the label setting into the discriminator to obtain the probability that the combined data output by the discriminator is real data.
To achieve the above object, according to another aspect of the present application, there is also provided a computer apparatus. As shown in fig. 9, the computer device comprises a memory, a processor, a communication interface and a communication bus, wherein a computer program that can be run on the processor is stored in the memory, and the steps of the method of the embodiment are realized when the processor executes the computer program.
The processor may be a Central Processing Unit (CPU). The Processor may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or a combination thereof.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and units, such as the corresponding program units in the above-described method embodiments of the present invention. The processor executes various functional applications of the processor and the processing of the work data by executing the non-transitory software programs, instructions and modules stored in the memory, that is, the method in the above method embodiment is realized.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor, and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and such remote memory may be coupled to the processor via 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 and when executed by the processor perform the method of the above embodiments.
The specific details of the computer device may be understood by referring to the corresponding related descriptions and effects in the above embodiments, and are not described herein again.
In order 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 reservoir production prediction method based on a conditional generation countermeasure network. It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and they may alternatively 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, or fabricated separately as individual integrated circuit modules, or fabricated as a single integrated circuit module from multiple modules or steps. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for predicting the production of an oil and gas reservoir based on a conditional generation countermeasure network is characterized by comprising the following steps:
acquiring production parameters corresponding to each month in the life cycle of a target new well, wherein the life cycle of the target new well is T months;
and inputting the production parameters corresponding to each month in the life cycle of the target new well into a pre-trained generator to obtain the yield predicted values corresponding to each month in the life cycle of the target new well, wherein the pre-trained generator is obtained by carrying out repeated iterative training on training samples, and the training samples comprise the production parameters and the yields corresponding to each month in T months in the history of putting the oil and gas well into production.
2. The method for reservoir production prediction based on the conditional generation countermeasure network of claim 1, further comprising:
obtaining a training sample, wherein the training sample is historical production data of a produced oil and gas well, the training sample consists of first data and second data, the first data comprises production parameters corresponding to each month in T months of the produced oil and gas well history, and the second data comprises production corresponding to each month in T months of the produced oil and gas well history;
and performing multiple iterative training according to the training sample to obtain the pre-trained generator, wherein in each iterative training, the network weight of the discriminator is trained, and then the network weight of the generator is trained in a combined model consisting of the discriminator and the generator.
3. The method as claimed in claim 2, wherein the input of the generator is the production parameters corresponding to each month in the T months, the output of the generator is the production prediction data, and the production prediction data is the production prediction value corresponding to each month in the T months; the input of the discriminator is combined data formed by combining production parameters and yield prediction data which respectively correspond to each month in T months, and the output of the discriminator is the probability that the combined data is real data.
4. The method for predicting reservoir production based on the conditional generation countermeasure network as claimed in claim 2, wherein the training of the network weights of the discriminators specifically comprises:
combining the first data with the yield prediction data output by the generator according to the first data to obtain combined data, and setting a label of the combined data to be 0, wherein the yield prediction data output by the generator according to the first data comprises a yield prediction value corresponding to each month in T months of the history of the produced oil-gas well;
setting the label of the training sample to 1;
and inputting the combined data after the label setting and the training sample after the label setting into a discriminator, and training the network weight of the discriminator.
5. The reservoir production prediction method based on the conditional generation countermeasure network of claim 2, wherein the training of the network weights of the generator in the combined model composed of the discriminator and the generator specifically comprises:
inputting the first data into a generator to obtain yield prediction data output by the generator, wherein the yield prediction data output by the generator according to the first data comprise yield prediction values corresponding to each month in T months of the history of the produced oil and gas wells;
combining the first data with yield prediction data output by the generator according to the first data to obtain combined data, and setting a label of the combined data to be 1;
and inputting the combined data after the label setting into a discriminator to obtain the probability that the combined data output by the discriminator is real data.
6. The method of predicting reservoir production based on a conditional generative countermeasure network of claim 1, wherein the production parameters comprise: water injection, permeability, porosity, reservoir oil saturation, and reservoir thickness.
7. A reservoir production prediction device based on a conditional generative countermeasure network, comprising:
the new well data acquisition module is used for acquiring production parameters corresponding to each month in the life cycle of a target new well, wherein the life cycle of the target new well is T months;
and the whole-period production prediction module of the new well is used for inputting the production parameters corresponding to each month in the life cycle of the target new well into a pre-trained generator to obtain the production prediction values output by the generator and corresponding to each month in the life cycle of the target new well, wherein the pre-trained generator is obtained by carrying out repeated iterative training by adopting a training sample, and the training sample comprises the production parameters and the production corresponding to each month in T months in the history of the put-in-production oil-gas well.
8. The reservoir production prediction device based on the conditional generation countermeasure network of claim 7, further comprising:
the training sample acquisition module is used for acquiring a training sample, wherein the training sample is historical production data of a produced oil and gas well, the training sample consists of first data and second data, the first data comprises production parameters corresponding to each month in T months of the produced oil and gas well history, and the second data comprises production corresponding to each month in T months of the produced oil and gas well history;
and the training module is used for carrying out a plurality of times of iterative training according to the training samples to obtain the pre-trained generator, wherein in each iterative training, the training module firstly trains the network weight of the discriminator and then trains the network weight of the generator in a combined model consisting of the discriminator and the generator.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when executed in a computer processor, implements the method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110798960.7A CN113435128B (en) | 2021-07-15 | 2021-07-15 | Method and device for predicting yield of oil and gas reservoirs based on condition generation type countermeasure network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110798960.7A CN113435128B (en) | 2021-07-15 | 2021-07-15 | Method and device for predicting yield of oil and gas reservoirs based on condition generation type countermeasure network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113435128A true CN113435128A (en) | 2021-09-24 |
| CN113435128B CN113435128B (en) | 2024-09-13 |
Family
ID=77760464
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110798960.7A Active CN113435128B (en) | 2021-07-15 | 2021-07-15 | Method and device for predicting yield of oil and gas reservoirs based on condition generation type countermeasure network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113435128B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114169240A (en) * | 2021-12-08 | 2022-03-11 | 中国石油大学(北京) | MMP (matrix metalloproteinase) prediction method and device based on condition generation type countermeasure network |
| CN115935378A (en) * | 2023-03-10 | 2023-04-07 | 中国人民解放军国防科技大学 | Image fusion model security detection method based on condition generating network |
| CN117408396A (en) * | 2023-12-15 | 2024-01-16 | 北京天安智慧信息技术有限公司 | Oil and gas well yield prediction method and prediction system based on deep learning |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111144542A (en) * | 2019-12-26 | 2020-05-12 | 中国石油大学(北京) | Oil well productivity prediction method, device and equipment |
| CN112507618A (en) * | 2020-12-03 | 2021-03-16 | 中国石油大学(华东) | Automatic oil reservoir history fitting method based on generation of countermeasure network |
| US20210116598A1 (en) * | 2019-10-21 | 2021-04-22 | Chevron U.S.A. Inc. | Systems and methods for predicting hydrocarbon production and assessing prediction uncertainty |
-
2021
- 2021-07-15 CN CN202110798960.7A patent/CN113435128B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210116598A1 (en) * | 2019-10-21 | 2021-04-22 | Chevron U.S.A. Inc. | Systems and methods for predicting hydrocarbon production and assessing prediction uncertainty |
| CN111144542A (en) * | 2019-12-26 | 2020-05-12 | 中国石油大学(北京) | Oil well productivity prediction method, device and equipment |
| CN112507618A (en) * | 2020-12-03 | 2021-03-16 | 中国石油大学(华东) | Automatic oil reservoir history fitting method based on generation of countermeasure network |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114169240A (en) * | 2021-12-08 | 2022-03-11 | 中国石油大学(北京) | MMP (matrix metalloproteinase) prediction method and device based on condition generation type countermeasure network |
| CN114169240B (en) * | 2021-12-08 | 2024-09-17 | 中国石油大学(北京) | MMP prediction method and device based on condition generation type countermeasure network |
| CN115935378A (en) * | 2023-03-10 | 2023-04-07 | 中国人民解放军国防科技大学 | Image fusion model security detection method based on condition generating network |
| CN115935378B (en) * | 2023-03-10 | 2023-10-10 | 中国人民解放军国防科技大学 | Image fusion model security detection method based on conditional generation type network |
| CN117408396A (en) * | 2023-12-15 | 2024-01-16 | 北京天安智慧信息技术有限公司 | Oil and gas well yield prediction method and prediction system based on deep learning |
| CN117408396B (en) * | 2023-12-15 | 2024-02-23 | 北京天安智慧信息技术有限公司 | Oil and gas well yield prediction method and prediction system based on deep learning |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113435128B (en) | 2024-09-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Global and local surrogate-model-assisted differential evolution for waterflooding production optimization | |
| CN113435128B (en) | Method and device for predicting yield of oil and gas reservoirs based on condition generation type countermeasure network | |
| Wang et al. | Optimal well placement under uncertainty using a retrospective optimization framework | |
| Shirangi et al. | Closed-loop field development under uncertainty by use of optimization with sample validation | |
| CN102622418B (en) | Prediction device and equipment based on BP (Back Propagation) nerve network | |
| CN113537592B (en) | Oil and gas reservoir yield prediction method and device based on long-short-term memory network | |
| CN106096727A (en) | A kind of network model based on machine learning building method and device | |
| CN113052371A (en) | Residual oil distribution prediction method and device based on deep convolutional neural network | |
| CN107239845B (en) | Construction method of oil reservoir development effect prediction model | |
| CN111105097B (en) | Dam deformation prediction system and method based on convolutional neural network | |
| CN108133286B (en) | Underground water multi-target calculation method based on ground settlement substitution model | |
| CN118036477B (en) | Well position and well control parameter optimization method based on space-time diagram neural network | |
| Wang et al. | A novel surrogate-assisted multi-objective optimization method for well control parameters based on tri-training | |
| CN104732067A (en) | Industrial process modeling forecasting method oriented at flow object | |
| Zhuang et al. | Multi-objective optimization of reservoir development strategy with hybrid artificial intelligence method | |
| CN111461284A (en) | Data discretization method, device, equipment and medium | |
| Huang et al. | A deep-learning-based graph neural network-long-short-term memory model for reservoir simulation and optimization with varying well controls | |
| Shokouhifar et al. | A hybrid approach for effective feature selection using neural networks and artificial bee colony optimization | |
| Dai et al. | Multimodal deep learning water level forecasting model for multiscale drought alert in Feiyun River basin | |
| CN112906760B (en) | Horizontal well fracturing segment segmentation method, system, equipment and storage medium | |
| CN117172113A (en) | Method, system, equipment and medium for predicting rotary steerable drilling well track | |
| CN112270123A (en) | Basin reservoir group runoff random generation method based on convolution generation countermeasure network | |
| CN114169240B (en) | MMP prediction method and device based on condition generation type countermeasure network | |
| Wang et al. | Adaptive Basis Function Selection Enhanced Multisurrogate-Assisted Evolutionary Algorithm for Production Optimization | |
| CN116398114A (en) | Intelligent reservoir physical parameter prediction method and system under small sample condition |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |