CN115390524A - Oil gas gathering and transportation pipe network prediction method and device, electronic equipment and storage medium - Google Patents
Oil gas gathering and transportation pipe network prediction method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method and a device for predicting an oil-gas gathering and transportation pipe network, electronic equipment and a storage medium. The method comprises the following steps: acquiring data to be processed of each pipeline in an oil gas gathering and transportation pipe network; the data to be processed comprises the temperature, the pressure, the gas content, the water content, the oil content and the pressure of a pipeline terminal of a pipeline input end; inputting the data to be processed into a pipeline parameter prediction model to execute an oil-gas gathering and transportation pipe network prediction task; outputting target data corresponding to the data to be processed through the pipeline parameter prediction model; wherein the target data comprises the variation of temperature, pressure, gas content, water content and oil content along the pipeline. According to the technical scheme, the changes of the temperature, the pressure, the gas content, the oil content and the water content at different positions of the pipeline in the oil gas gathering and transportation pipeline network system can be quickly and accurately predicted under the condition that the pipeline control condition changes.
Description
Technical Field
The invention relates to the technical field of petroleum gathering and transportation, in particular to a method and a device for predicting an oil-gas gathering and transportation pipe network, electronic equipment and a storage medium.
Background
The flow in gathering and transportation pipe networks (especially marine pipes) is very complex: on one hand, the gas-liquid two-phase flow is complex and changeable, the terrain is varied, and any change of the environment can directly influence the normal operation of the gathering and transportation pipe network; on the other hand, the oil gas gathering and transportation pipeline is long, and great instability is caused for transportation. These all bring difficulties to the simulation calculation and operation management of the pipeline, so it is very important to accurately predict the flow change rule of oil and gas in the pipeline.
Conventionally, for the above problems, it is necessary to simulate the change law of the flow parameters of the pipe network, that is, the conventional parameters of the oil and gas field, such as the pipe structure and the fluid composition, and the collected production data (pressure and temperature at each position) are used to simulate the change trend of the flow parameters along the pipe, such as pressure, temperature, flow rate and liquid holdup, by using corresponding software, so as to provide a powerful technical support for the basic design, safe and efficient operation and maintenance of the pipe network, and at the same time, it can also be used to predict the generation of hydrates and wax deposition, and provide corresponding information for the flow safety guarantee. In order to accurately simulate the distribution rule of the seabed oil-gas mixed transportation pipeline along the line, a large amount of manpower and material resources are also input into various countries to carry out the research on the calculation of the oil-gas mixed transportation pipeline network, and a commercialized simulation method comprising simulation software such as PipePhase, pipeSIM, PEPITE, OLGA, ledaFlow and the like is formed.
However, due to the complexity of pipe network simulation calculation, commercial software cannot simulate the whole oil and gas field pipe network system in real time, so that real-time prediction and updating are difficult to achieve; meanwhile, the uncertainty of oil and gas reservoir development and the requirement of adjusting and optimizing an oil and gas production allocation scheme are considered, and a large number of whole pipe network simulations under different production conditions need to be carried out. The requirement of massive simulation causes that the traditional simulation-based pipe network steady-state/unsteady-state flow calculation method is difficult to meet the requirement.
Disclosure of Invention
The invention provides a method and a device for predicting an oil-gas gathering and transportation pipe network, electronic equipment and a storage medium, which can be used for rapidly and accurately predicting the changes of temperature, pressure, gas content, oil content and water content at different positions in the oil-gas gathering and transportation pipe network system under the condition of changing pipeline control conditions.
According to one aspect of the invention, a method for predicting an oil and gas gathering and transportation pipe network is provided, which comprises the following steps:
acquiring data to be processed of each pipeline in an oil gas gathering and transportation pipe network; the data to be processed comprises the temperature, the pressure, the gas content, the water content, the oil content and the pressure of a pipeline terminal of a pipeline input end;
inputting the data to be processed into a pipeline parameter prediction model to execute an oil-gas gathering and transportation pipe network prediction task;
outputting target data corresponding to the data to be processed through the pipeline parameter prediction model; wherein the target data comprises the variation of temperature, pressure, gas content, water content and oil content along the pipeline.
According to another aspect of the present invention, there is provided a device for predicting an oil and gas gathering and transportation pipe network, the device comprising:
the to-be-processed data acquisition module is used for acquiring to-be-processed data of each pipeline in the oil and gas gathering and transportation pipe network; the data to be processed comprises the temperature, the pressure, the gas content, the water content, the oil content and the pressure of a pipeline terminal of a pipeline input end;
the oil gas gathering and transportation pipe network prediction task execution module is used for inputting the data to be processed into a pipeline parameter prediction model to execute an oil gas gathering and transportation pipe network prediction task;
the target data output module is used for outputting target data corresponding to the data to be processed through the pipeline parameter prediction model; wherein the target data comprises the variation of temperature, pressure, gas content, water content and oil content along the pipeline.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform a method of hydrocarbon gathering network prediction as described in any embodiment of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement a method for predicting a hydrocarbon gathering network according to any one of the embodiments of the present invention.
According to the technical scheme of the embodiment of the invention, the to-be-processed data of each pipeline in the oil-gas gathering and transportation pipe network are obtained, then the to-be-processed data are input into a pipeline parameter prediction model to execute the oil-gas gathering and transportation pipe network prediction task, and the target data corresponding to the to-be-processed data are output through the pipeline parameter prediction model; wherein the target data comprises the variation of temperature, pressure, gas content, water content and oil content along the pipeline. According to the technical scheme, the changes of temperature, pressure, gas content, oil content and water content at different positions in the oil gas gathering and transportation pipe network system can be quickly and accurately predicted under the condition that the pipeline control conditions change.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for predicting an oil and gas gathering and transportation pipe network according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a gas reservoir cluster provided in an embodiment of the present application;
FIG. 3 is a schematic diagram of a pipeline parameter prediction model provided in an embodiment of the present application;
FIG. 4 is a flowchart of the pipeline parameter prediction model training provided by the second embodiment of the present invention;
fig. 5 is a schematic structural diagram of an oil-gas gathering pipe network prediction device provided by the third embodiment of the invention;
fig. 6 is a schematic structural diagram of an electronic device for implementing the oil-gas gathering pipe network prediction method according to the embodiment of the 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 obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
It is to be understood that the terms "target" and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, 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.
Example one
Fig. 1 is a flowchart of an oil-gas gathering and transportation pipe network prediction method according to an embodiment of the present invention, which is applicable to rapidly predicting changes of key parameters such as on-way temperature, pressure, and fluid composition of each pipeline in an oil-gas gathering and transportation pipe network system. As shown in fig. 1, the method includes:
s110, acquiring data to be processed of each pipeline in an oil-gas gathering and transportation pipe network; the data to be processed comprises the temperature, the pressure, the gas content, the water content, the oil content and the pressure of a pipeline terminal at the pipeline input end.
In the scheme, the data of each pipeline in the oil-gas gathering and transportation pipe network can be subjected to simulation through data software, so that the data to be processed of each pipeline in the oil-gas gathering and transportation pipe network can be obtained. The data to be processed of each pipeline in the oil gas gathering and transportation pipe network can also be obtained through other seismic exploration technical means.
For example, fig. 2 is a schematic diagram of a gas reservoir group provided in an embodiment of the present application, and as shown in fig. 2, the gas reservoir group P is formed by various pipelines. Wherein, the pipeline from the P2 gas field to the P platform comprises a well A2, a well A3 and a well A1H. And acquiring data to be processed of a pipeline from a P2 gas field to a P platform in the oil and gas gathering and transportation pipe network, namely acquiring the temperature, pressure, gas content, water content, oil content and pressure of the P platform at the A2 well, the A3 well and the A1H well.
And S120, inputting the data to be processed into a pipeline parameter prediction model to execute an oil-gas gathering and transportation pipe network prediction task.
The pipeline parameter prediction model adopts a FC (full connected layers) + BN (batch normalization, normalization layer) + PReLU (Parametric normalized Linear Unit, activation function) mode to construct a minimum Unit for model feature extraction. The purpose of the normalization layer is to normalize the learned features to a specified distribution, where increasing the convergence rate of learning reduces the occurrence of over-fitting phenomena.
For example, fig. 3 is a schematic diagram of a pipeline parameter prediction model provided in an embodiment of the present application, and as shown in fig. 3, the pipeline parameter prediction model is composed of 4 minimum units and 1 full-connection layer, and can calculate changes of key parameters such as temperature, pressure, fluid composition, and the like of each pipeline in an oil and gas gathering and transportation pipeline network system in real time; meanwhile, the change of key parameters such as on-way temperature, pressure, fluid composition and the like of each pipeline in the oil-gas gathering and transportation pipeline system can be rapidly predicted under any virtual single-well production condition.
Specifically, the pipeline parameter prediction model is directly loaded in the using process, the obtained temperature, pressure, gas content, water content, oil content of the pipeline input end of each pipeline in the oil-gas gathering and transportation pipe network and the obtained pressure of the pipeline terminal are used as input, and the variation of the temperature, pressure, gas content, water content and oil content of the pipeline along the way is predicted.
S130, outputting target data corresponding to the data to be processed through the pipeline parameter prediction model; wherein the target data comprises the variation of temperature, pressure, gas content, water content and oil content along the pipeline.
According to the technical scheme of the embodiment of the disclosure, the data to be processed is processed through the pipeline parameter prediction model, the target data corresponding to the data to be processed is output, and the change conditions of key parameters such as on-way temperature, pressure, fluid composition and the like of each pipeline in the oil-gas gathering and transportation pipeline network system can be calculated in real time; meanwhile, the change of key parameters such as on-way temperature, pressure, fluid composition and the like of each pipeline in the oil-gas gathering and transportation pipeline system can be rapidly predicted under any virtual single-well production condition.
Example two
Fig. 4 is a flowchart of the pipeline parameter prediction model training provided by the second embodiment of the present invention, and the relationship between this embodiment and the foregoing embodiments is a detailed description of the pipeline parameter prediction model training process. As shown in fig. 4, the method includes:
s410, determining training sample data used by the pipeline parameter prediction model.
In the scheme, the data of each pipeline in the oil-gas gathering and transportation pipe network can be simulated through data software to obtain training sample data used by the pipeline parameter prediction model.
Wherein, 80% of the training sample data can be used as a training set, and 20% can be used as a test set to train the pipeline parameter prediction model.
In this technical solution, optionally, determining training sample data used by the pipeline parameter prediction model includes:
acquiring historical production data of each pipeline in an oil-gas gathering and transportation pipe network; wherein the historical production data comprises the temperature, pressure, gas content, water content, oil content of the pipeline input end and the pressure of the pipeline terminal end;
and simulating the historical production data by using preset data software to obtain training sample data used by the pipeline parameter prediction model.
The data software may be multiphase flow transient simulation flow assurance software (LedaFlow). And simulating the temperature, the pressure, the gas content, the water content and the oil content of the pipeline input end and the pressure of the pipeline terminal by utilizing the LedaFlow to generate corresponding training sample data.
By acquiring training sample data, a pipeline parameter prediction model can be trained on the basis of the training sample data, so that the change conditions of key parameters such as on-way temperature, pressure, fluid composition and the like of each pipeline in the oil-gas gathering and transportation pipeline network system can be calculated in real time; meanwhile, the change of key parameters such as on-way temperature, pressure, fluid composition and the like of each pipeline in the oil-gas gathering and transportation pipeline system can be rapidly predicted under any virtual single-well production condition.
In this technical solution, optionally, the simulating the historical production data by using preset data software to obtain training sample data used by the pipeline parameter prediction model includes:
processing the historical production data according to a preset data upper limit and a preset data lower limit to obtain target production data;
and simulating the target production data by using multiphase flow transient simulation flow assurance software to obtain training sample data used by the pipeline parameter prediction model.
Wherein, the upper limit and the lower limit of the data can be set according to the use requirement of the pipeline parameter prediction model.
In this embodiment, based on the historical production data, determining the upper data limit and the lower data limit and randomly modifying the historical production data, a plurality of different input conditions may be generated, and then a simulation is performed using the LedaFlow to generate corresponding training sample data.
By acquiring training sample data, a pipeline parameter prediction model can be trained on the basis of the training sample data, so that the change conditions of key parameters such as on-way temperature, pressure, fluid composition and the like of each pipeline in the oil-gas gathering and transportation pipeline network system can be calculated in real time; meanwhile, the change of key parameters such as on-way temperature, pressure, fluid composition and the like of each pipeline in the oil-gas gathering and transportation pipeline system can be rapidly predicted under any virtual single-well production condition.
And S420, controlling a pipeline parameter prediction model to execute an oil-gas gathering and transportation pipe network prediction task based on the training sample data.
In the embodiment, the oil-gas gathering and transportation pipe network prediction task is used for predicting parameters of pipelines in the oil-gas gathering and transportation pipe network along the way, so that the change conditions of key parameters such as the temperature, the pressure, the fluid composition and the like of the pipelines in the oil-gas gathering and transportation pipe network system along the way can be calculated in real time.
S430, adjusting the pipeline parameter prediction model according to the oil-gas gathering and transportation pipe network prediction task to obtain a pipeline parameter prediction model after training and updating.
According to the technical scheme of the embodiment of the disclosure, when the pipeline parameter prediction model is trained, the pipeline parameter prediction model with a good prediction effect is obtained by introducing the oil-gas gathering and transportation pipeline network prediction task, and the change conditions of key parameters such as the on-way temperature, the pressure, the fluid composition and the like of each pipeline in the oil-gas gathering and transportation pipeline network system are calculated.
In this technical solution, optionally, the adjusting the pipeline parameter prediction model according to the oil gas gathering and transportation pipe network prediction task includes:
determining a loss function value corresponding to the oil gas gathering and transportation pipe network prediction task;
and adjusting the network parameters of the pipeline parameter prediction model according to the loss function value.
The loss function may be mean square error loss (mean square error loss) or l1 loss (regression loss) or the like. The specific loss function may be set according to the training requirements of the pipeline parameter prediction model.
In the scheme, in the training process, an ADAM (Adaptive motion analysis) optimizer is adopted to process the gradient, and the initial learning rate l r =0.001, minimum learning rate l r =0.00001, the learning rate automatically decays to 0.8 times when the loss function has not changed for 15 epochs. Wherein, an epoch indicates that all data are sent into the network, and the process of forward calculation and backward propagation is completed.
Specifically, training sample data can be input into the pipeline parameter prediction model to execute the oil-gas gathering and transportation pipe network prediction task, and a loss function value corresponding to the oil-gas gathering and transportation pipe network prediction task is determined.
By determining the loss function value, the training of a pipeline parameter prediction model can be optimized, and the prediction of the variation of parameters along each pipeline in the oil-gas gathering and transportation pipe network is realized.
In this technical solution, optionally, determining a loss function value corresponding to the oil-gas gathering and transportation pipe network prediction task includes:
and determining a loss function value corresponding to the oil and gas gathering and transportation pipe network prediction task according to a prediction result obtained by processing the training sample data through the oil and gas gathering and transportation pipe network prediction task in the pipeline parameter prediction model and a pre-labeling result corresponding to the sample training data.
By determining the loss function value, the training of a pipeline parameter prediction model can be optimized, and the prediction of the variation of parameters along each pipeline in the oil-gas gathering and transportation pipe network is realized.
According to the technical scheme of the embodiment of the invention, the pipeline parameter prediction model after being trained and updated is obtained by determining the training sample data used by the pipeline parameter prediction model, controlling the pipeline parameter prediction model to execute the oil-gas gathering and transportation pipe network prediction task based on the training sample data, and then adjusting the pipeline parameter prediction model according to the oil-gas gathering and transportation pipe network prediction task. By executing the technical scheme, the training of the pipeline parameter prediction model can be optimized, and the changes of temperature, pressure, gas content, oil content and water content at different positions in the oil-gas gathering and transportation pipeline network system can be rapidly and accurately predicted under the condition that the pipeline control condition changes.
EXAMPLE III
Fig. 5 is a schematic structural diagram of a prediction device for an oil-gas gathering and transportation pipe network according to a third embodiment of the present invention. As shown in fig. 5, the apparatus includes:
a to-be-processed data acquisition module 510, configured to acquire to-be-processed data of each pipeline in the oil and gas gathering and transportation pipe network; the data to be processed comprises the temperature, the pressure, the gas content, the water content, the oil content and the pressure of a pipeline terminal of a pipeline input end;
the oil gas gathering and transportation pipe network prediction task execution module 520 is used for inputting the data to be processed into a pipeline parameter prediction model to execute an oil gas gathering and transportation pipe network prediction task;
a target data output module 530, configured to output target data corresponding to the data to be processed through the pipeline parameter prediction model; wherein the target data comprises the variation of temperature, pressure, gas content, water content and oil content along the pipeline.
In this technical solution, optionally, the oil gas gathering and transportation pipe network prediction task execution module 520 includes:
the training sample data determining unit is used for determining the training sample data used by the pipeline parameter prediction model;
the oil gas gathering and transportation pipe network prediction task execution unit is used for controlling a pipeline parameter prediction model to execute an oil gas gathering and transportation pipe network prediction task based on the training sample data;
and the pipeline parameter prediction model training unit is used for adjusting the pipeline parameter prediction model according to the oil-gas gathering and transportation pipe network prediction task to obtain a trained and updated pipeline parameter prediction model.
In this technical solution, optionally, the training sample data determining unit includes:
the historical production data acquisition subunit is used for acquiring the historical production data of each pipeline in the oil-gas gathering and transportation pipe network; wherein the historical production data comprises the temperature, pressure, gas content, water content, oil content of the pipeline input end and the pressure of the pipeline terminal end;
and the training sample data obtaining subunit is used for simulating the historical production data by using preset data software to obtain training sample data used by the pipeline parameter prediction model.
In this technical solution, optionally, the training sample data obtaining subunit is specifically configured to:
processing the historical production data according to a preset data upper limit and a preset data lower limit to obtain target production data;
and simulating the target production data by using multiphase flow transient simulation flow guarantee software to obtain training sample data used by the pipeline parameter prediction model.
In this technical solution, optionally, the pipeline parameter prediction model training unit includes:
the loss function value determining subunit is used for determining a loss function value corresponding to the oil-gas gathering and transportation pipe network prediction task;
and the pipeline parameter prediction model adjusting subunit is used for adjusting the network parameters of the pipeline parameter prediction model according to the loss function value.
In this technical solution, optionally, the loss function value determining subunit is specifically configured to:
and determining a loss function value corresponding to the oil and gas gathering and transportation pipe network prediction task according to a prediction result obtained by processing the training sample data through the oil and gas gathering and transportation pipe network prediction task in the pipeline parameter prediction model and a pre-labeling result corresponding to the sample training data.
The oil gas gathering and transportation pipe network prediction device provided by the embodiment of the invention can execute the oil gas gathering and transportation pipe network prediction method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
FIG. 6 illustrates a schematic structural diagram of an electronic device 10 that may be used to implement an embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. Processor 11 performs the various methods and processes described above, such as a hydrocarbon gathering network prediction method.
In some embodiments, a method of oil and gas gathering and transportation pipe network prediction may be implemented as a computer program tangibly embodied in a computer readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When loaded into RAM 13 and executed by processor 11, the computer program may perform one or more of the steps of a method of oil and gas gathering network forecasting as described above. Alternatively, in other embodiments, the processor 11 may be configured to perform a hydrocarbon gathering network prediction method by any other suitable means (e.g., by way of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired result of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and 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 prediction method for an oil and gas gathering and transportation pipe network is characterized by comprising the following steps:
acquiring data to be processed of each pipeline in an oil gas gathering and transportation pipe network; the data to be processed comprises the temperature, the pressure, the gas content, the water content, the oil content and the pressure of a pipeline terminal of a pipeline input end;
inputting the data to be processed into a pipeline parameter prediction model to execute an oil-gas gathering and transportation pipe network prediction task;
outputting target data corresponding to the data to be processed through the pipeline parameter prediction model; wherein the target data comprises the temperature, the pressure, the gas fraction, the water content and the oil content variation of the pipeline along the way.
2. The method of claim 1, wherein the determining of the pipeline parameter prediction model comprises:
determining training sample data used by a pipeline parameter prediction model;
controlling a pipeline parameter prediction model to execute an oil-gas gathering and transportation pipe network prediction task based on the training sample data;
and adjusting the pipeline parameter prediction model according to the oil-gas gathering and transportation pipe network prediction task to obtain a pipeline parameter prediction model after training and updating.
3. The method of claim 2, wherein determining training sample data for use by the pipeline parameter prediction model comprises:
acquiring historical production data of each pipeline in an oil-gas gathering and transportation pipe network; wherein the historical production data comprises the temperature, pressure, gas fraction, water fraction, oil fraction at the input end of the pipeline, and the pressure at the terminal end of the pipeline;
and simulating the historical production data by using preset data software to obtain training sample data used by the pipeline parameter prediction model.
4. The method of claim 3, wherein simulating the historical production data by using preset data software to obtain training sample data used by a pipeline parameter prediction model comprises:
processing the historical production data according to a preset data upper limit and a preset data lower limit to obtain target production data;
and simulating the target production data by using multiphase flow transient simulation flow assurance software to obtain training sample data used by the pipeline parameter prediction model.
5. The method of claim 2, wherein adjusting the pipeline parameter prediction model based on the oil and gas gathering and transportation pipe network prediction task comprises:
determining a loss function value corresponding to the oil gas gathering and transportation pipe network prediction task;
and adjusting the network parameters of the pipeline parameter prediction model according to the loss function value.
6. The method of claim 5, wherein determining the loss function value corresponding to the oil and gas gathering and transportation pipe network prediction task comprises:
and determining a loss function value corresponding to the oil and gas gathering and transportation pipe network prediction task according to a prediction result obtained by processing the training sample data through the oil and gas gathering and transportation pipe network prediction task in the pipeline parameter prediction model and a pre-labeling result corresponding to the sample training data.
7. The utility model provides an oil gas gathering pipe network prediction unit which characterized in that includes:
the to-be-processed data acquisition module is used for acquiring to-be-processed data of each pipeline in the oil and gas gathering and transportation pipe network; the data to be processed comprises the temperature, the pressure, the gas content, the water content, the oil content and the pressure of a pipeline terminal of a pipeline input end;
the oil gas gathering and transportation pipe network prediction task execution module is used for inputting the data to be processed into a pipeline parameter prediction model to execute an oil gas gathering and transportation pipe network prediction task;
the target data output module is used for outputting target data corresponding to the data to be processed through the pipeline parameter prediction model; wherein the target data comprises the variation of temperature, pressure, gas content, water content and oil content along the pipeline.
8. The device of claim 7, wherein the oil and gas gathering and transportation pipe network prediction task execution module comprises:
the training sample data determining unit is used for determining the training sample data used by the pipeline parameter prediction model;
the oil gas gathering and transportation pipe network prediction task execution unit is used for controlling a pipeline parameter prediction model to execute an oil gas gathering and transportation pipe network prediction task based on the training sample data;
and the pipeline parameter prediction model training unit is used for adjusting the pipeline parameter prediction model according to the oil-gas gathering and transportation pipe network prediction task to obtain a trained and updated pipeline parameter prediction model.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a hydrocarbon gathering network prediction method as claimed in any one of claims 1 to 6.
10. A computer readable storage medium storing computer instructions for causing a processor to perform a method of oil and gas gathering pipeline network prediction as claimed in any one of claims 1 to 6 when executed.
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