Method and device for determining bottom hole flowing pressure, electronic equipment and medium

Abstract

A well bottom flow pressure determination method, device, electronic equipment and medium are disclosed. The method comprises: determining spatial characteristics between each production well in a target area, and a relationship between sample production data and sample well bottom flow pressure; obtaining a space-time model based on a space-time dynamic prediction algorithm according to the spatial characteristics and the relationship between the sample production data and the sample well bottom flow pressure; obtaining a static model and a time series model based on a static prediction algorithm and a time series prediction algorithm according to the relationship between the sample production data and the sample well bottom flow pressure; and determining the well bottom flow pressure based on production data of a to-be-predicted production well in the target area based on the space-time model, the static model and the time series model. The technical solution introduces spatial characteristics and time characteristics, so that the predicted well bottom flow pressure is more accurate.

Description

Technical Field

[0001] This invention relates to the field of oilfield development technology, and in particular to a method, apparatus, electronic device, and medium for determining bottom hole flowing pressure. Background Technology

[0002] After a gas well is drilled, engineers deploy pressure sensors at the bottom of the well to monitor and measure the bottom-hole flowing pressure over the long term. During the years of production, some sensors may malfunction due to geological conditions or engineering quality issues, rendering them unable to continue measuring. Since the sensors at the bottom of the well cannot be replaced or repositioned after the well is put into production, virtual measurement of the bottom-hole flowing pressure using the remaining production and monitoring data plays a crucial analytical and auxiliary role in assessing the well's productivity and reservoir condition.

[0003] With the rise of machine learning, machine learning models have been developed to establish the correspondence between the three-phase flow rate, pressure, and temperature of oil, gas, and water at the wellhead and the bottom hole pressure. The disadvantage of this method is that it is purely based on data, and the machine learning samples are only actual production data. The number of learning samples is relatively small, and the generalization performance is generally poor. Therefore, it is very important to be able to accurately determine the bottom hole flowing pressure. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and medium for determining bottom hole flowing pressure, in order to solve the problems of large errors between actual and predicted bottom hole flowing pressure and the lack of effective data in simulation, thereby achieving accurate prediction of bottom hole flowing pressure.

[0005] According to one aspect of the present invention, a method for determining bottom hole flowing pressure is provided, the method comprising:

[0006] Determine the spatial characteristics among production wells in the target area, as well as the relationship between sample production data and sample well bottom flow pressure;

[0007] Based on the spatiotemporal dynamic prediction algorithm, a spatiotemporal model is obtained according to the spatial characteristics and the relationship between the sample production data and the sample bottom-hole flowing pressure;

[0008] Based on static prediction algorithms and time-series prediction algorithms, static models and time-series models are obtained according to the relationship between the sample production data and the sample bottom-hole flowing pressure.

[0009] Based on the aforementioned spatiotemporal model, static model, and time series model, the bottom hole flowing pressure is determined according to the production data of the production wells to be predicted in the target area.

[0010] According to another aspect of the present invention, a device for determining bottom hole flowing pressure is provided, the device comprising:

[0011] The information determination module is used to determine the spatial characteristics between production wells in the target area, as well as the relationship between sample production data and sample well bottom flow pressure;

[0012] The first model building module is used to obtain a spatiotemporal model based on the spatiotemporal dynamic prediction algorithm, according to the spatial characteristics and the relationship between the sample production data and the sample well bottom flow pressure.

[0013] The second model building module is used to obtain a static model and a time series model based on the static prediction algorithm and the time series prediction algorithm, according to the relationship between the sample production data and the sample bottom-hole flowing pressure.

[0014] The bottom hole flowing pressure determination module is used to determine the bottom hole flowing pressure based on the spatiotemporal model, static model, and time series model, according to the production data of the production wells to be predicted in the target area.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: at least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for determining bottom hole flowing pressure according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method for determining bottom hole flowing pressure according to any embodiment of the present invention.

[0019] The technical solution of this invention determines the spatial characteristics between production wells in a target area, as well as the relationship between sample production data and sample well bottom-hole flowing pressure. Based on a spatiotemporal dynamic prediction algorithm, a spatiotemporal model is obtained according to the spatial characteristics and the relationship between sample production data and sample well bottom-hole flowing pressure. Based on a static prediction algorithm and a time-series prediction algorithm, a static model and a time-series model are obtained according to the relationship between sample production data and sample well bottom-hole flowing pressure. Based on the spatiotemporal model, the static model, and the time-series model, the well bottom-hole flowing pressure is determined according to the production data of the production wells to be predicted in the target area. This solves the problems of large errors between actual and predicted well bottom-hole flowing pressure, and the lack of effective data in simulations, thus achieving accurate prediction of well bottom-hole flowing pressure.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for determining bottom hole flowing pressure according to Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a method for determining bottom hole flowing pressure according to Embodiment 2 of the present invention;

[0024] Figure 3 This is a schematic diagram of a device for determining bottom hole flowing pressure according to Embodiment 3 of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the method for determining bottom hole flowing pressure according to an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," and "target," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Example 1

[0029] Figure 1 This is a flowchart illustrating a method for determining bottom hole flowing pressure according to Embodiment 1 of the present invention. This embodiment is applicable to situations where bottom hole flowing pressure is accurately predicted based on the physical characteristics and bottom hole flowing pressure performance of different wells in different regions. This method can be executed by a bottom hole flowing pressure determining device, which can be implemented in hardware and / or software and can be configured in the electronic equipment of the bottom hole flowing pressure determining method. Figure 1 As shown, the method includes:

[0030] S110. Determine the spatial characteristics between production wells in the target area, as well as the relationship between sample production data and sample well bottom flowing pressure.

[0031] Spatial features are used to describe the spatial location between production wells. The relationship between sample production data and sample well bottom-hole flowing pressure is based on various state data of each production well during production, obtained in advance through various means, along with the corresponding well bottom-hole flowing pressure. Production data refers to various state data generated by production wells during oil or gas development; for example, production data may include the production rates of different oils, gas, or water, or wellhead temperatures, etc.

[0032] Optionally, the spatial characteristics between the production wells include at least one of the following: the coordinates, measurement depth, distance, connectivity, and stratigraphic characteristics of the production wells.

[0033] Connectivity can refer to the connection between different wells caused by factors such as distance. Generally speaking, the closer two wells are, the stronger the connectivity, and vice versa. Stratigraphic characteristics refer to the properties of geological strata at different elevations within a well.

[0034] Optionally, the relationship between the sample production data and the sample bottom hole flowing pressure includes the relationship between simulated production data and simulated bottom hole flowing pressure, as well as the relationship between historical production data and historical bottom hole flowing pressure; wherein, the simulated bottom hole flowing pressure is obtained by simulating and calculating the simulated production data based on a predetermined mechanism model.

[0035] The mechanistic model can be a mathematical description of the equilibrium relationship between key factors based on certain assumptions and the interaction mechanism of these factors. For example, it could be a mathematical model establishing the relationship between different operating conditions (oil, gas, and water production) of different production wells and bottom hole flowing pressure, or a mathematical model that can influence the bottom hole flowing pressure result. Finally, the simulated bottom hole flowing pressure is obtained based on these models. Simulated production data can be data strongly correlated with the actual production environment. For example, it could be data on oil production, water production, gas production, production time, dynamic engineering data, and reservoir description data of different production wells, based on actual environmental production data. Dynamic engineering data could include wellhead temperature, wellhead flowing pressure, and inlet temperature. Historical production data could be actual historical data such as oil production, water production, gas production, production time, historical dynamic engineering data, and reservoir description data of different production wells. Historical bottom hole flowing pressure could be historical actual bottom hole flowing pressure data and historical simulated bottom hole flowing pressure data.

[0036] Specifically, after obtaining all the aforementioned data, the data is cleaned and organized, and relational database modeling is performed. The relational database construction is primarily based on the third normal form of relational database design. The original data is processed, duplicate columns are eliminated, tables depend only on primary keys, and transitive dependencies between tables are eliminated. This ensures that the algorithm can more clearly understand the original data during operation and guarantees accurate subsequent queries and generation of the data and corresponding structures required by the algorithm. Furthermore, data containing outliers is filtered, removed, and interpolated for supplementation. Data standardization is performed to eliminate differences in magnitude and units between different data points. This ensures a large amount of effective data is obtained, reducing the problem of insufficient sample data and limited generalization, and improving robustness and transferability in actual production applications.

[0037] S120. Based on the spatiotemporal dynamic prediction algorithm, a spatiotemporal model is obtained according to the spatial characteristics and the relationship between the sample production data and the sample well bottom flow pressure.

[0038] Among them, the spatiotemporal dynamic prediction algorithm refers to the prediction algorithm that takes into account the influence of spatiotemporal characteristics on the result.

[0039] Specifically, spatial features related to the establishment of the spatiotemporal model are acquired, such as the coordinates, measurement depth, distance, connectivity, and stratigraphic characteristics of the production well. Simultaneously, the relationship between sample production data and sample wellbore bottom-hole flowing pressure is obtained. These spatial features and the relationship between sample production data and sample wellbore bottom-hole flowing pressure are input into a spatiotemporal dynamic prediction algorithm for training, resulting in a spatiotemporal model capable of predicting wellbore flowing pressure. The spatiotemporal model establishes a feature mapping relationship between spatial features, sample production data, and sample wellbore bottom-hole flowing pressure. Optionally, the spatiotemporal dynamic prediction algorithm includes a spatiotemporal graph convolutional neural network.

[0040] S130. Based on the static prediction algorithm and the time-series prediction algorithm, the static model and the time-series model are obtained according to the relationship between the sample production data and the sample bottom-hole flowing pressure.

[0041] Among them, static prediction algorithm refers to the traditional algorithm that predicts the result based on static parameters, while time series prediction algorithm refers to the prediction algorithm that considers the impact of time changes on the result.

[0042] Optionally, the static prediction algorithm includes at least one of the following: linear regression, random forest, support vector machine, Bayesian ridge regression, and gradient ensemble regression; the temporal prediction algorithm includes at least one of the following: long short-term memory network and convolutional neural network.

[0043] In one feasible embodiment, the production data includes at least output data, dynamic engineering data, and production time data;

[0044] Accordingly, based on the static prediction algorithm and the time-series prediction algorithm, a static model and a time-series model are obtained according to the relationship between the sample production data and the sample bottom-hole flowing pressure, including:

[0045] Based on the static prediction algorithm, a static model is obtained from the production data and dynamic engineering data;

[0046] Based on the time-series prediction algorithm, a time-series model is obtained from the production data, dynamic engineering data, and production time data.

[0047] Production data can include daily oil, water, and gas production from different production wells. Dynamic engineering data refers to engineering parameters that change dynamically over time.

[0048] Specifically, production data and dynamic engineering data relevant to the formation of the static model are acquired and trained using a static prediction algorithm to obtain a static model capable of predicting bottom hole flowing pressure. Then, production data, dynamic engineering data, and production time data relevant to the formation of the time-series model are acquired and trained using a time-series prediction algorithm to obtain a time-series model capable of predicting bottom hole flowing pressure. The static model establishes a feature mapping relationship between sample production data and sample bottom hole flowing pressure, while the time-series model establishes a feature mapping relationship between time data, sample production data, and sample bottom hole flowing pressure.

[0049] This technical solution obtains a static model and a time-series model for bottom hole pressure prediction by acquiring the relationship between sample production data and sample bottom hole pressure, based on static prediction algorithm and time-series prediction algorithm, thus achieving accuracy in predicting bottom hole pressure for different wells.

[0050] S140. Based on the spatiotemporal model, static model, and time series model, determine the bottom hole flowing pressure according to the production data of the production wells to be predicted in the target area.

[0051] Specifically, pre-trained spatiotemporal models, static models, and time-series models are used to predict the bottom hole flowing pressure of different wells. The prediction results are analyzed and processed to obtain the final bottom hole flowing pressure prediction value, thereby reducing the error in bottom hole flowing pressure prediction and improving the accuracy of bottom hole flowing pressure prediction.

[0052] The technical solution of this invention determines the spatial characteristics between production wells in a target area, as well as the relationship between sample production data and sample well bottom-hole flowing pressure. Based on a spatiotemporal dynamic prediction algorithm, a spatiotemporal model is obtained according to the spatial characteristics and the relationship between sample production data and sample well bottom-hole flowing pressure. Based on a static prediction algorithm and a time-series prediction algorithm, a static model and a time-series model are obtained according to the relationship between sample production data and sample well bottom-hole flowing pressure. Based on the spatiotemporal model, the static model, and the time-series model, the well bottom-hole flowing pressure is determined according to the production data of the production wells to be predicted in the target area. This solves the problems of large errors between actual and predicted well bottom-hole flowing pressure, and the lack of effective data in simulations, thus achieving accurate prediction of well bottom-hole flowing pressure.

[0053] Example 2

[0054] Figure 2 This is a flowchart illustrating a method for determining bottom hole flowing pressure according to Embodiment 2 of the present invention. This embodiment will describe in detail the steps following S140 in the above embodiment. For example... Figure 2 As shown, the method includes:

[0055] S210. Determine the weight information of each model based on the bottom-hole flowing pressure determined by each model and the actual bottom-hole flowing pressure; wherein the actual bottom-hole flowing pressure is determined in advance based on the target production data.

[0056] The weight information refers to the importance of the bottom hole flowing pressure determined by each model relative to the actual bottom hole flowing pressure. It is different from the general proportion. It reflects not only the percentage of the bottom hole flowing pressure determined by each model, but also the relative importance of each model, tending to the degree of contribution or importance.

[0057] For example, if the bottom hole flowing pressure predicted by the spatiotemporal model, static model, and time series model are compared with the actual bottom hole flowing pressure, the difference between the predicted bottom hole flowing pressure and the actual bottom hole flowing pressure can be determined. The larger the difference, the greater the gap between the predicted bottom hole flowing pressure and the actual bottom hole flowing pressure. If the models are arranged in ascending order according to the difference, namely static model, spatiotemporal model, and time series model, it can be determined that the static model has the largest weight information, followed by the weight information of the spatiotemporal model and the time series model. In this way, the accurate weight information corresponding to each model can be obtained by calculation, and the higher the weight information, the more accurate the bottom hole flowing pressure predicted by the model.

[0058] S220. Based on the weight information, a set model is obtained according to the spatiotemporal model, static model, and time series model.

[0059] Specifically, based on the weight information of the spatiotemporal model, static model, and time series model, the spatiotemporal model, static model, and time series model are combined to form an ensemble model, which avoids the inaccuracy of bottom hole flow pressure prediction due to the error of a single model, and achieves effective fusion of the spatiotemporal model, static model, and time series model.

[0060] S230. Determine the bottom hole flowing pressure of the production well to be predicted based on the set model.

[0061] Specifically, the ensemble model is used to predict the bottom hole flowing pressure of the production well to make the prediction results more accurate. In addition, if the production well generates new data, the ensemble model needs to be updated in real time.

[0062] In one feasible embodiment, after determining the bottomhole flowing pressure of the production well to be predicted based on the ensemble model, the method further includes:

[0063] The weighting information is updated based on the determined bottom hole flowing pressure and the relationship between new production data and new bottom hole flowing pressure.

[0064] Specifically, when new production data appears in production wells within the target area, the weight information of the spatiotemporal model, static model, and time series model is updated based on the relationship between the new production data and the new bottom hole flowing pressure. Then, the updated spatiotemporal model, static model, and time series model are combined to generate a new ensemble model, which determines the bottom hole flowing pressure of the production well to be predicted, further ensuring the reliability of the model.

[0065] This technical solution updates the weight information by determining the bottom hole flowing pressure and the relationship between new production data and new bottom hole flowing pressure, thereby obtaining a more accurate ensemble model, ensuring the output of an effective model, and realizing accurate prediction of the production well to be predicted in different time periods or under different conditions.

[0066] Example 3

[0067] Figure 3 This is a schematic diagram of a device for determining bottom hole flowing pressure according to Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0068] The information determination module 310 is used to determine the spatial characteristics between production wells in the target area, as well as the relationship between sample production data and sample well bottom flowing pressure.

[0069] The first model building module 320 is used to obtain a spatiotemporal model based on the spatiotemporal dynamic prediction algorithm, according to the spatial characteristics and the relationship between the sample production data and the sample well bottom flow pressure.

[0070] The second model building module 330 is used to obtain a static model and a time series model based on the relationship between the sample production data and the sample bottom-hole flowing pressure, using a static prediction algorithm and a time series prediction algorithm.

[0071] The bottom hole flowing pressure determination module 340 is used to determine the bottom hole flowing pressure based on the spatiotemporal model, static model and time series model, according to the production data of the production well to be predicted in the target area.

[0072] Optional, bottom hole flowing pressure determination module, specifically used for:

[0073] The weight information of each model is determined based on the bottom-hole flowing pressure determined by each model and the actual bottom-hole flowing pressure; wherein, the actual bottom-hole flowing pressure is determined in advance based on the target production data;

[0074] Based on the weight information, a set model is obtained according to the spatiotemporal model, static model, and time series model.

[0075] The bottom hole flowing pressure of the production well to be predicted is determined based on the ensemble model.

[0076] Optionally, the bottom hole flowing pressure determination module also includes an update unit, specifically used for:

[0077] The weighting information is updated based on the determined bottom hole flowing pressure and the relationship between new production data and new bottom hole flowing pressure.

[0078] Optionally, the relationship between the sample production data and the sample bottom hole flowing pressure includes the relationship between simulated production data and simulated bottom hole flowing pressure, as well as the relationship between historical production data and historical bottom hole flowing pressure; wherein, the simulated bottom hole flowing pressure is obtained by simulating and calculating the simulated production data based on a predetermined mechanism model.

[0079] Among them, production data includes at least output data, dynamic engineering data, and production time data;

[0080] Correspondingly, the second model building module is specifically used for:

[0081] Based on the static prediction algorithm, a static model is obtained from the production data and dynamic engineering data;

[0082] Based on the time-series prediction algorithm, a time-series model is obtained from the production data, dynamic engineering data, and production time data.

[0083] Optionally, the static prediction algorithm includes at least one of the following: linear regression, random forest, support vector machine, Bayesian ridge regression, and gradient ensemble regression; the temporal prediction algorithm includes at least one of the following: long short-term memory network and convolutional neural network; the spatiotemporal dynamic prediction algorithm includes spatiotemporal graph convolutional neural network.

[0084] The spatial characteristics between the production wells include at least one of the following: the coordinates, measurement depth, distance, connectivity, and stratigraphic characteristics of the production wells.

[0085] The device for determining bottom hole flowing pressure provided in this embodiment of the invention can execute the method for determining bottom hole flowing pressure provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0086] The acquisition, storage, use, and processing of data in this application comply with relevant national laws and regulations and do not violate public order and good morals.

[0087] Example 4

[0088] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0089] Figure 4A schematic diagram of an electronic device is shown that can be used to implement the method for determining bottom hole flowing pressure according to embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, 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 merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0090] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0091] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0092] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with 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 special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for determining bottom hole flowing pressure.

[0093] In some embodiments, the method for determining bottomhole flowing pressure may be implemented as a computer program tangibly contained 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 on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for determining bottomhole flowing pressure described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the method for determining bottomhole flowing pressure by any other suitable means (e.g., by means of firmware).

[0094] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0095] Computer programs used to implement 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, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0096] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0097] To provide interaction with a user, the systems and techniques described herein 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 the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the 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 sound input, voice input, or tactile input).

[0098] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0099] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0100] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0101] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining bottom hole flowing pressure, characterized in that, include: Determine the spatial characteristics among production wells in the target area, as well as the relationship between sample production data and sample well bottom flow pressure; Based on the spatiotemporal dynamic prediction algorithm, the relationship between the spatial features and the sample production data and the sample bottom flow pressure is trained to obtain a spatiotemporal model; the spatiotemporal dynamic prediction algorithm includes a spatiotemporal graph convolutional neural network. Based on static prediction algorithms and time-series prediction algorithms, static models and time-series models are obtained according to the relationship between the sample production data and the sample bottom-hole flowing pressure; wherein, the static prediction algorithm includes at least one of the following: linear regression, random forest, support vector machine, Bayesian ridge regression and gradient ensemble regression; the time-series prediction algorithm includes at least one of the following: long short-term memory network and convolutional neural network; Based on the spatiotemporal model, static model, and time series model, the bottom hole flowing pressure is determined according to the production data of the production wells to be predicted in the target area. The production data includes at least output data, dynamic engineering data, and production time data; correspondingly, based on static prediction algorithms and time-series prediction algorithms, static models and time-series models are obtained according to the relationship between the sample production data and the sample bottom-hole flowing pressure, including: A static model is obtained by training the production data and dynamic engineering data based on a static prediction algorithm. Based on the time series prediction algorithm, the production data, dynamic engineering data, and production time data are trained to obtain a time series model; Among them, based on the spatiotemporal model, static model, and time series model, the bottom hole flowing pressure is determined according to the production data of the production wells to be predicted in the target area, including: The spatiotemporal model, the static model, and the time-series model are used to predict the bottom hole flowing pressure of different wells, respectively. The weight information of each model is determined based on the bottom-hole flowing pressure determined by each model and the actual bottom-hole flowing pressure; wherein, the actual bottom-hole flowing pressure is determined in advance based on the target production data; Based on the weight information, a set model is obtained according to the spatiotemporal model, static model, and time series model. The bottom hole flowing pressure of the production well to be predicted is determined based on the ensemble model.

2. The method according to claim 1, characterized in that, After determining the bottomhole flowing pressure of the production well to be predicted based on the ensemble model, the method further includes: The weighting information is updated based on the determined bottom hole flowing pressure and the relationship between new production data and new bottom hole flowing pressure.

3. The method according to claim 1, characterized in that, The relationship between the sample production data and the sample bottom hole flowing pressure includes the relationship between simulated production data and simulated bottom hole flowing pressure, as well as the relationship between historical production data and historical bottom hole flowing pressure; wherein, the simulated bottom hole flowing pressure is obtained by simulating and calculating the simulated production data based on a predetermined mechanism model.

4. The method according to claim 1, characterized in that, The spatial characteristics between the production wells include at least one of the following: the coordinates, measurement depth, distance, connectivity, and stratigraphic characteristics of the production wells.

5. A device for determining bottom hole flowing pressure, characterized in that, include: The information determination module is used to determine the spatial characteristics between production wells in the target area, as well as the relationship between sample production data and sample well bottom flow pressure; The first model building module is used to train the spatiotemporal dynamic prediction algorithm based on the spatial features and the relationship between the sample production data and the sample well bottom flow pressure to obtain a spatiotemporal model; the spatiotemporal dynamic prediction algorithm includes a spatiotemporal graph convolutional neural network. The second model building module is used to obtain a static model and a time series model based on the relationship between the sample production data and the sample bottom-hole flowing pressure, using a static prediction algorithm and a time series prediction algorithm. The static prediction algorithm includes at least one of the following: linear regression, random forest, support vector machine, Bayesian ridge regression, and gradient ensemble regression. The time series prediction algorithm includes at least one of the following: long short-term memory network and convolutional neural network. The bottom-hole flowing pressure determination module is used to determine the bottom-hole flowing pressure based on the spatiotemporal model, static model, and time series model, according to the production data of the production wells to be predicted in the target area. The production data includes at least output data, dynamic engineering data, and production time data. Correspondingly, the second model building module is used to: train the output data and dynamic engineering data based on a static prediction algorithm to obtain a static model; and train the output data, dynamic engineering data, and production time data based on a time-series prediction algorithm to obtain a time-series model. The bottom hole flowing pressure determination module is used to: predict the bottom hole flowing pressure of different wells using the spatiotemporal model, the static model, and the time series model respectively; determine the weight information of each model based on the bottom hole flowing pressure determined by each model and the actual bottom hole flowing pressure; wherein the actual bottom hole flowing pressure is determined in advance based on target production data; obtain an ensemble model based on the weight information, according to the spatiotemporal model, the static model, and the time series model; and determine the bottom hole flowing pressure of the production well to be predicted based on the ensemble model.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for determining the bottom hole flowing pressure according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for determining the bottom hole flowing pressure according to any one of claims 1-4.

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