Fracturing sand plugging risk prediction method and device, electronic equipment and storage medium

Abstract

The invention discloses a fracturing sand plugging risk prediction method and device, electronic equipment and a storage medium. The method comprises the steps that in the fracturing construction process of an unconventional oil and gas reservoir, target fracturing construction curve data are obtained in real time; inputting the target fracturing construction curve data into a pre-trained fracturing construction pumping pressure prediction model, and determining hydraulic fracturing pumping pressure according to an output result of the fracturing construction pumping pressure prediction model; and inputting the hydraulic fracturing pump pressure into a pre-trained sand plugging risk identification model, and predicting the fracturing sand plugging risk existing in the fracturing construction process of the unconventional oil and gas reservoir according to an output result of the sand plugging risk identification model. According to the technical scheme provided by the embodiment of the invention, the fracturing construction pumping pressure prediction model is combined with the sand plugging risk identification model, so that the accuracy of fracturing sand plugging risk prediction is greatly improved, and real-time early warning of the fracturing sand plugging risk is realized.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas resource exploitation, and in particular to a method, device, electronic equipment and storage medium for predicting the risk of fracturing sand plugging. Background Art

[0002] The exploitation of unconventional oil and gas resources has changed the traditional energy structure. How to efficiently exploit deep unconventional oil and gas is recognized as an important development direction and global problem of oil and gas in the 21st century. Generally speaking, to exploit deep unconventional oil and gas, it is necessary to pump high-pressure fluids into unconventional oil and gas reservoirs at a depth of several thousand meters to create artificial seams, and to effectively support the cracks by pumping fracturing sand (proppant) so that unconventional oil and gas flows through the artificial seams for production. In other words, in order to achieve efficient exploitation of deep unconventional oil and gas, the connectivity of the artificial seams must be guaranteed. However, when pumping fracturing sand to effectively support the cracks, fracturing sand blockage will occur, affecting the exploitation efficiency. Therefore, there is an urgent need for a method to warn of fracturing sand blockage during fracturing construction to reduce fracturing sand blockage and achieve efficient exploitation of deep unconventional oil and gas. Summary of the invention

[0003] The present invention provides a method, device, electronic device and storage medium for predicting fracturing sand plugging risk. By combining a fracturing construction pump pressure prediction model with a sand plugging risk identification model, the accuracy of fracturing sand plugging risk prediction is greatly improved, and real-time early warning of fracturing sand plugging risk is achieved.

[0004] According to one aspect of the present invention, a method for predicting fracturing sand plugging risk is provided, the method comprising:

[0005] During the fracturing operation of unconventional oil and gas reservoirs, the target fracturing operation curve data can be obtained in real time;

[0006] Inputting the target fracturing construction curve data into a pre-trained fracturing construction pump pressure prediction model, and determining the hydraulic fracturing pump pressure according to the output result of the fracturing construction pump pressure prediction model;

[0007] The hydraulic fracturing pump pressure is input into a pre-trained sand plugging risk identification model, and the risk of fracturing sand plugging in the process of fracturing construction on unconventional oil and gas reservoirs is predicted based on the output result of the sand plugging risk identification model.

[0008] According to another aspect of the present invention, a device for predicting the risk of fracturing sand plugging is provided, the device comprising:

[0009] The target data acquisition module is used to obtain the target fracturing construction curve data in real time during the fracturing construction of unconventional oil and gas reservoirs;

[0010] A pump pressure determination module, used to input the target fracturing construction curve data into a pre-trained fracturing construction pump pressure prediction model, and determine the hydraulic fracturing pump pressure according to the output result of the fracturing construction pump pressure prediction model;

[0011] The risk prediction module is used to input the hydraulic fracturing pump pressure into a pre-trained sand plugging risk identification model, and predict the risk of fracturing sand plugging in the process of fracturing construction of unconventional oil and gas reservoirs based on the output results of the sand plugging risk identification model.

[0012] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for predicting the risk of fracturing sand plugging described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for predicting the risk of fracturing sand plugging described in any embodiment of the present invention when executed.

[0017] The technical solution of the embodiment of the present invention is to obtain the target fracturing construction curve data in real time during the fracturing construction of unconventional oil and gas reservoirs; input the target fracturing construction curve data into a pre-trained fracturing construction pump pressure prediction model, and determine the hydraulic fracturing pump pressure according to the output result of the fracturing construction pump pressure prediction model; input the hydraulic fracturing pump pressure into a pre-trained sand plugging risk identification model, and predict the risk of fracturing sand plugging in the process of fracturing construction of unconventional oil and gas reservoirs according to the output result of the sand plugging risk identification model. The technical solution of the embodiment of the present invention greatly improves the accuracy of fracturing sand plugging risk prediction by combining the fracturing construction pump pressure prediction model with the sand plugging risk identification model, and realizes real-time early warning of fracturing sand plugging risk.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is a flow chart of a method for predicting fracturing sand plugging risk provided according to Embodiment 1 of the present invention;

[0021] Figure 2 This is a schematic diagram of a pump pressure prediction model for fracturing construction provided according to Embodiment 1 of the present invention;

[0022] Figure 3 This is a flow chart of a method for predicting fracturing sand plugging risk provided according to Embodiment 2 of the present invention;

[0023] Figure 4 is a structural schematic diagram of a fracturing sand plugging risk prediction device provided according to Embodiment 3 of the present invention;

[0024] Figure 5 It is a structural schematic diagram of an electronic device for implementing the method for predicting the risk of fracturing sand plugging according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] Figure 1 A flowchart of a method for predicting the risk of fracturing sand plugging is provided for the first embodiment of the present invention. This embodiment is applicable to the case of predicting the risk of fracturing sand plugging in the process of fracturing construction. The method can be performed by a fracturing sand plugging risk prediction device, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0029] S110. During the fracturing operation on the unconventional oil and gas reservoir, target fracturing operation curve data is acquired in real time.

[0030] The target fracturing construction curve data include pressure construction curve data, displacement construction curve data and sand concentration construction curve data.

[0031] In the embodiment of the present invention, during the fracturing construction of unconventional oil and gas reservoirs, sensors arranged at locations such as fracturing pumps and fracturing pipelines can be used to obtain target fracturing construction curve data in real time.

[0032] Optionally, after acquiring the target fracturing construction curve data in real time, the method further includes: preprocessing the target fracturing construction curve data, wherein the preprocessing includes data filling, data discarding, and filtering and noise reduction.

[0033] For the time-discontinuous data points in the target fracturing construction curve data acquired in real time, they can be filled or discarded based on the mean filling method to ensure the continuity and integrity of the target fracturing construction curve data, and the target fracturing construction curve data after preliminary processing can be filtered and denoised to improve the data signal-to-noise ratio, restore and enhance the credibility of the data.

[0034] The embodiment of the present invention does not limit the filtering and denoising method. Preferably, the Kalman filtering method can be used to filter and denoise the target fracturing operation curve data after preliminary processing. Compared with the Savitzky-Golay filtering method, the Kalman filtering method can retain the overall morphological characteristics of the curve to the maximum extent while filtering the local minor jitter of the curve.

[0035] S120, inputting the target fracturing construction curve data into a pre-trained fracturing construction pump pressure prediction model, and determining the hydraulic fracturing pump pressure according to the output result of the fracturing construction pump pressure prediction model.

[0036] Figure 2 A schematic diagram of a fracturing construction pump pressure prediction model is shown in FIG. Figure 2As shown, the fracturing construction pump pressure prediction model includes a Seq2Seq framework, a time-series attention correlation coefficient matrix and a channel attention correlation coefficient matrix, wherein the Seq2Seq framework includes an encoder and a decoder, and the encoder and the decoder are respectively embedded with a long short-term memory LSTM model, the output end of the encoder is sequentially connected to the time-series attention correlation coefficient matrix and the channel attention correlation coefficient matrix, and the channel attention correlation coefficient matrix is ​​connected to the input end of the decoder.

[0037] The fracturing construction pump pressure prediction model adopts a time series prediction loss function. Among them, the time series prediction loss function includes methods such as soft dynamic time warping Soft-DTW, subsequence similarity measurement SBD, and dilation operation DILATE. Compared with the traditional loss function construction method based on mean square error MSE, mean absolute error MAE and its variants, the time series prediction loss function in the embodiment of the present invention can make the network fit the trend characteristics of the prediction curve more effectively.

[0038] In an embodiment of the present invention, the target fracturing construction curve data is input into a pre-trained fracturing construction pump pressure prediction model, the encoder encodes the target fracturing construction curve data, and the encoded result is multiplied one by one by the time series attention correlation coefficient matrix and the channel attention correlation coefficient matrix, and then input into the decoder for calculation to obtain the output result, and the hydraulic fracturing pump pressure is determined. Among them, the long short-term memory LSTM model embedded in the encoder can extract the long sequence time series features in the target fracturing construction curve data, and based on the time series attention correlation coefficient matrix and the channel attention correlation coefficient matrix, it can capture the key information and position feature weights that may affect the hydraulic fracturing pump pressure in the enhanced encoding results. The fracturing construction pump pressure prediction model solves the problem of the difficulty in accurately modeling and predicting the hydraulic fracturing pump pressure by effectively mining the internal correlation of the time series data fluctuation process.

[0039] Optionally, before inputting the target fracturing construction curve data into the pre-trained fracturing construction pump pressure prediction model, and determining the hydraulic fracturing pump pressure according to the output result of the fracturing construction pump pressure prediction model, it also includes: optimizing the pre-trained fracturing construction pump pressure prediction model. The optimization method includes imposing penalties on abnormal predictions such as prediction trend errors and trend occurrence time errors, adaptively adjusting the learning rate based on the adaptive moment estimation Adam algorithm to update the model weight, and conducting algorithm training based on the batch gradient descent method.

[0040] S130, inputting the hydraulic fracturing pump pressure into a pre-trained sand plugging risk identification model, and predicting the risk of fracturing sand plugging during fracturing construction of unconventional oil and gas reservoirs based on the output results of the sand plugging risk identification model.

[0041] In an embodiment of the present invention, the hydraulic fracturing pump pressure is input into a pre-trained sand plugging risk identification model. After the output result is obtained, the risk of fracturing sand plugging in the process of fracturing construction of unconventional oil and gas reservoirs can be predicted based on the output result of the sand plugging risk identification model, thereby achieving real-time early warning of fracturing sand plugging risk and reducing the cost of fracturing operations.

[0042] The technical solution of the embodiment of the present invention is to obtain the target fracturing construction curve data in real time during the fracturing construction of unconventional oil and gas reservoirs; input the target fracturing construction curve data into a pre-trained fracturing construction pump pressure prediction model, and determine the hydraulic fracturing pump pressure according to the output result of the fracturing construction pump pressure prediction model; input the hydraulic fracturing pump pressure into a pre-trained sand plugging risk identification model, and predict the risk of fracturing sand plugging in the process of fracturing construction of unconventional oil and gas reservoirs according to the output result of the sand plugging risk identification model. The technical solution of the embodiment of the present invention greatly improves the accuracy of fracturing sand plugging risk prediction by combining the fracturing construction pump pressure prediction model with the sand plugging risk identification model, and realizes real-time early warning of fracturing sand plugging risk.

[0043] Embodiment 2

[0044] Figure 3 This is a flow chart of a method for predicting the risk of fracturing sand plugging provided in the second embodiment of the present invention. The embodiment of the present invention is optimized based on the above embodiment. For solutions not described in detail in the embodiment of the present invention, please refer to the above embodiment. Figure 3 As shown, the method includes:

[0045] S210. During the fracturing operation on the unconventional oil and gas reservoir, target fracturing operation curve data is acquired in real time.

[0046] S220, inputting the target fracturing construction curve data into a pre-trained fracturing construction pump pressure prediction model, and determining the hydraulic fracturing pump pressure according to the output result of the fracturing construction pump pressure prediction model.

[0047] S230, obtaining a construction curve data sample set; wherein the construction curve data sample set includes at least two historical fracturing construction curve data.

[0048] In the embodiment of the present invention, the historical fracturing construction curve data obtained during the historical fracturing construction process can be used as a sample set to construct a training data set for the sand plugging risk identification model. It should be noted that the construction curve data sample set should include at least two historical fracturing construction curve data.

[0049] S240, traversing each historical fracturing construction curve data in the construction curve data sample set, determining corresponding pressure fluctuation characteristics according to the historical fracturing construction curve data, and determining the sand plugging risk type and sand plugging risk level according to the pressure fluctuation characteristics.

[0050] Among them, the pressure fluctuation characteristics can be divided into pump pressure fluctuation characteristics and bottom hole pressure fluctuation characteristics, including characteristic parameters such as mean, variance, extreme value difference, slope, and curve shape.

[0051] Specifically, the corresponding pressure fluctuation characteristics are determined according to the historical fracturing construction curve data, including: determining the displacement stage and the sand adding stage that appear alternately in the historical fracturing construction curve data, and taking an adjacent displacement stage and a sand adding stage as a pressure fluctuation cycle; for each pressure fluctuation cycle in the historical fracturing construction curve data, extracting the construction pressure rise rate characteristics within the pressure fluctuation cycle from the historical fracturing construction curve data; establishing a pressure rise rate statistical analysis chart based on the construction pressure rise rate characteristics; and obtaining the pressure fluctuation characteristics of the displacement stage and the pressure fluctuation characteristics of the sand adding stage respectively according to the pressure rise rate statistical analysis chart.

[0052] In the embodiment of the present invention, first, the historical fracturing construction curve data is subjected to feature analysis, and the displacement stage and sanding stage that appear alternately in the historical fracturing construction curve data are determined according to the fracturing construction pumping procedure and the on-site construction characteristics, and an adjacent displacement stage and a sanding stage are taken as a pressure fluctuation cycle, thereby the historical fracturing construction curve data can be divided into several pressure fluctuation cycles. Then, for each pressure fluctuation cycle, based on the least squares fitting method and the slope extraction method of the extreme point in the window, the construction pressure rise rate characteristics within the pressure fluctuation cycle are extracted from the historical fracturing construction curve data, and a pressure rise rate statistical analysis chart is established based on the kernel density analysis method. Finally, the pressure fluctuation characteristics of the displacement stage and the pressure fluctuation characteristics of the sanding stage are respectively obtained according to the pressure rise rate statistical analysis chart.

[0053] By extracting the pressure fluctuation characteristics and using the pressure fluctuation as the real data label, the data labeling quality errors caused by manual labeling and algorithm labeling in the supervised learning process can be effectively avoided. This can effectively retain the original characteristics of the data, reduce the difficulty of data processing, build a high-quality labeled data set, and accelerate model convergence and feature capture.

[0054] Specifically, determining the sand plugging risk type and the sand plugging risk level according to the pressure fluctuation characteristics includes: determining the sand plugging risk type and the sand plugging risk level according to the pressure fluctuation characteristics and a pre-set sand plugging risk identification strategy.

[0055] Among them, the pre-set sand plugging risk identification strategy is divided into sand plugging prompt phenomenon and preset early warning method. Sand plugging prompt phenomenon includes abnormal internal pressure drop, active reduction of displacement, active reduction of sand ratio, early termination of sand addition, too fast pressure rise and fall, close to the pressure limit, etc. The preset early warning method is to compare the pressure mean, pressure peak, and pressure valley of each cycle horizontally. If the pressure characteristic value rises continuously within three cycles, an early warning signal is output. At the same time, according to the sand plugging prompt phenomenon and the preset early warning method, the fracturing risk is qualitatively and quantitatively processed to determine the sand plugging risk type and sand plugging risk level.

[0056] Among them, the sand plugging risk types include bridge plugging type sand plugging risk and sand stripping type sand plugging risk, and the sand plugging risk levels include low risk, medium risk, and high risk, which can also be called early sand plugging risk, primary risk, and secondary risk. The embodiments of the present invention are not limited to this.

[0057] S250: annotate the corresponding historical fracturing construction curve data based on the sand plugging risk type and sand plugging risk level to generate a construction curve data training set.

[0058] In an embodiment of the present invention, after determining the sand plugging risk type and the sand plugging risk level, the corresponding historical fracturing construction curve data can be labeled based on the sand plugging risk type and the sand plugging risk level, and the historical fracturing construction curve data can be assigned corresponding labels to indicate the category of the historical fracturing construction curve data, that is, the sand plugging risk type and the sand plugging risk level of the historical fracturing construction curve data, to generate a construction curve data training set.

[0059] S260. Train a preset machine learning model based on the construction curve data training set to generate a sand plugging risk identification model.

[0060] Specifically, a preset machine learning model is trained based on a construction curve data training set to generate a sand plugging risk identification model, including: training a random forest model based on a construction curve data training set to generate a sand plugging risk identification model; wherein, in the process of training the random forest model, the target parameters of the random forest model are updated based on a particle swarm algorithm.

[0061] Among them, the random forest model is an integrated learning method composed of multiple decision trees. It improves the accuracy and stability of predictions through voting or averaging, and is suitable for various classification and regression problems.

[0062] Among them, the particle swarm algorithm is an optimization algorithm based on swarm intelligence. In an embodiment of the present invention, the target parameters of the random forest model can be updated based on the particle swarm algorithm, such as the number of trees, the maximum number of features, the maximum depth of the tree, etc., to ensure the best classification performance.

[0063] In an embodiment of the present invention, the preset machine learning model is preferably a random forest model. The random forest model is trained based on a construction curve data training set, and during the training of the random forest model, the target parameters of the random forest model are updated based on a particle swarm algorithm to generate a sand plugging risk identification model.

[0064] S270, inputting the hydraulic fracturing pump pressure into a pre-trained sand plugging risk identification model, and predicting the risk of fracturing sand plugging during fracturing construction of unconventional oil and gas reservoirs based on the output results of the sand plugging risk identification model.

[0065] The technical solution of the embodiment of the present invention is to obtain target fracturing construction curve data in real time during the process of fracturing construction on unconventional oil and gas reservoirs; input the target fracturing construction curve data into a pre-trained fracturing construction pump pressure prediction model, and determine the hydraulic fracturing pump pressure according to the output result of the fracturing construction pump pressure prediction model; obtain a construction curve data sample set; wherein the construction curve data sample set includes at least two historical fracturing construction curve data; traverse each historical fracturing construction curve data in the construction curve data sample set, determine the corresponding pressure fluctuation characteristics according to the historical fracturing construction curve data, and determine the sand plugging risk type and sand plugging risk level according to the pressure fluctuation characteristics; annotate the corresponding historical fracturing construction curve data based on the sand plugging risk type and sand plugging risk level to generate a construction curve data training set; train a preset machine learning model based on the construction curve data training set to generate a sand plugging risk identification model; input the hydraulic fracturing pump pressure into the pre-trained sand plugging risk identification model, and predict the fracturing sand plugging risk existing in the process of fracturing construction on unconventional oil and gas reservoirs according to the output result of the sand plugging risk identification model. The technical solution of the embodiment of the present invention greatly improves the accuracy of fracturing sand plugging risk prediction by combining the fracturing construction pump pressure prediction model with the sand plugging risk identification model, and realizes real-time early warning of fracturing sand plugging risk.

[0066] Embodiment 3

[0067] Figure 4 This is a schematic diagram of the structure of a device for predicting the risk of fracturing sand plugging provided in the third embodiment of the present invention. Figure 4 As shown, the device comprises:

[0068] The target data acquisition module 310 is used to acquire target fracturing construction curve data in real time during the fracturing construction of unconventional oil and gas reservoirs;

[0069] A pump pressure determination module 320 is used to input the target fracturing construction curve data into a pre-trained fracturing construction pump pressure prediction model, and determine the hydraulic fracturing pump pressure according to the output result of the fracturing construction pump pressure prediction model;

[0070] The risk prediction module 330 is used to input the hydraulic fracturing pump pressure into a pre-trained sand plugging risk identification model, and predict the risk of fracturing sand plugging in the process of fracturing construction of unconventional oil and gas reservoirs based on the output results of the sand plugging risk identification model.

[0071] Optionally, the fracturing construction pump pressure prediction model includes a Seq2Seq framework, a time-series attention correlation coefficient matrix and a channel attention correlation coefficient matrix, wherein the Seq2Seq framework includes an encoder and a decoder, and the encoder and the decoder are respectively embedded with long short-term memory (LSTM) models, the output end of the encoder is sequentially connected to the time-series attention correlation coefficient matrix and the channel attention correlation coefficient matrix, and the channel attention correlation coefficient matrix is ​​connected to the input end of the decoder.

[0072] Optionally, the fracturing construction pump pressure prediction model adopts a time series prediction loss function.

[0073] Optionally, the device further comprises:

[0074] A data sample set acquisition module is used to acquire a construction curve data sample set; wherein the construction curve data sample set includes at least two historical fracturing construction curve data;

[0075] A sand plugging risk determination module is used to traverse each historical fracturing construction curve data in the construction curve data sample set, determine the corresponding pressure fluctuation characteristics according to the historical fracturing construction curve data, and determine the sand plugging risk type and sand plugging risk level according to the pressure fluctuation characteristics;

[0076] A data training set generation module, used to annotate the corresponding historical fracturing construction curve data based on the sand plugging risk type and the sand plugging risk level, and generate a construction curve data training set;

[0077] The sand plugging risk identification model generation module is used to train a preset machine learning model based on the construction curve data training set to generate a sand plugging risk identification model.

[0078] Optionally, the sand plugging risk determination module includes:

[0079] A pressure fluctuation cycle determination unit is used to determine the displacement stage and the sand adding stage that appear alternately in the historical fracturing operation curve data, and regard an adjacent displacement stage and a sand adding stage as a pressure fluctuation cycle;

[0080] A rising rate characteristic determination unit, for extracting, for each pressure fluctuation period in the historical fracturing construction curve data, a construction pressure rising rate characteristic within the pressure fluctuation period from the historical fracturing construction curve data;

[0081] A statistical analysis chart establishing unit, used to establish a pressure rise rate statistical analysis chart based on the construction pressure rise rate characteristics;

[0082] The pressure fluctuation characteristic determination unit is used to obtain the pressure fluctuation characteristics of the displacement stage and the pressure fluctuation characteristics of the sand adding stage respectively according to the pressure rise rate statistical analysis chart.

[0083] Optionally, the sand plugging risk determination module includes:

[0084] The sand plugging risk determination unit is used to determine the sand plugging risk type and sand plugging risk level according to the pressure fluctuation characteristics and a preset sand plugging risk identification strategy.

[0085] Optionally, the sand plugging risk identification model generation module includes:

[0086] The sand plugging risk identification model generation unit is used to train the random forest model based on the construction curve data training set to generate a sand plugging risk identification model; wherein, during the training of the random forest model, the target parameters of the random forest model are updated based on the particle swarm algorithm.

[0087] The device for predicting the risk of fracturing sand plugging provided in the embodiment of the present invention can execute the method for predicting the risk of fracturing sand plugging provided in any embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method.

[0088] Embodiment 4

[0089] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, 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 processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0090] like Figure 5As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0091] 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, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0092] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for predicting the risk of fracturing sand plugging.

[0093] In some embodiments, the method for predicting the risk of fracturing sand plugging may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for predicting the risk of fracturing sand plugging described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for predicting the risk of fracturing sand plugging in any other appropriate manner (e.g., by means of firmware).

[0094] Various implementations 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0095] Computer programs 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, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0096] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0097] To provide interaction with a user, the systems and techniques described herein may 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0098] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0099] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0100] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0101] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting fracturing sand plugging risk, characterized in that: include: During the fracturing operation of unconventional oil and gas reservoirs, the target fracturing operation curve data can be obtained in real time; Inputting the target fracturing construction curve data into a pre-trained fracturing construction pump pressure prediction model, and determining the hydraulic fracturing pump pressure according to the output result of the fracturing construction pump pressure prediction model; The hydraulic fracturing pump pressure is input into a pre-trained sand plugging risk identification model, and the risk of fracturing sand plugging in the process of fracturing construction on unconventional oil and gas reservoirs is predicted based on the output result of the sand plugging risk identification model.

2. The method according to claim 1, characterized in that The fracturing construction pump pressure prediction model includes a Seq2Seq framework, a temporal attention correlation coefficient matrix and a channel attention correlation coefficient matrix, wherein the Seq2Seq framework includes an encoder and a decoder, and the encoder and the decoder are respectively embedded with a long short-term memory LSTM model, the output end of the encoder is sequentially connected to the temporal attention correlation coefficient matrix and the channel attention correlation coefficient matrix, and the channel attention correlation coefficient matrix is ​​connected to the input end of the decoder.

3. The method according to claim 2, characterized in that The fracturing construction pump pressure prediction model adopts a time series prediction loss function.

4. The method according to claim 1, characterized in that Before inputting the hydraulic fracturing pump pressure into a pre-trained sand plugging risk identification model and predicting the risk of fracturing sand plugging in the process of fracturing construction of unconventional oil and gas reservoirs according to the output result of the sand plugging risk identification model, the method further includes: Acquire a construction curve data sample set; wherein the construction curve data sample set includes at least two historical fracturing construction curve data; Traversing each piece of historical fracturing construction curve data in the construction curve data sample set, determining corresponding pressure fluctuation characteristics according to the historical fracturing construction curve data, and determining the sand plugging risk type and sand plugging risk level according to the pressure fluctuation characteristics; Based on the sand plugging risk type and the sand plugging risk level, the corresponding historical fracturing construction curve data is labeled to generate a construction curve data training set; The preset machine learning model is trained based on the construction curve data training set to generate a sand plugging risk identification model.

5. The method according to claim 4, characterized in that Determining corresponding pressure fluctuation characteristics according to the historical fracturing operation curve data includes: Determine the displacement stage and the sand adding stage that appear alternately in the historical fracturing operation curve data, and regard an adjacent displacement stage and a sand adding stage as a pressure fluctuation cycle; For each pressure fluctuation period in the historical fracturing construction curve data, extracting a construction pressure rising rate characteristic within the pressure fluctuation period from the historical fracturing construction curve data; Establishing a pressure rise rate statistical analysis chart based on the construction pressure rise rate characteristics; The pressure fluctuation characteristics of the displacement stage and the pressure fluctuation characteristics of the sand adding stage are respectively obtained according to the pressure rise rate statistical analysis chart.

6. The method according to claim 4, characterized in that Determining the sand plugging risk type and sand plugging risk level according to the pressure fluctuation characteristics includes: The sand plugging risk type and sand plugging risk level are determined according to the pressure fluctuation characteristics and a pre-set sand plugging risk identification strategy.

7. The method according to claim 5, characterized in that The preset machine learning model is trained based on the construction curve data training set to generate a sand plugging risk identification model, including: The random forest model is trained based on the construction curve data training set to generate a sand plugging risk identification model; wherein, during the training of the random forest model, the target parameters of the random forest model are updated based on the particle swarm algorithm.

8. A device for predicting the risk of fracturing sand plugging, characterized in that: include: The target data acquisition module is used to obtain the target fracturing construction curve data in real time during the fracturing construction of unconventional oil and gas reservoirs; A pump pressure determination module, used to input the target fracturing construction curve data into a pre-trained fracturing construction pump pressure prediction model, and determine the hydraulic fracturing pump pressure according to the output result of the fracturing construction pump pressure prediction model; The risk prediction module is used to input the hydraulic fracturing pump pressure into a pre-trained sand plugging risk identification model, and predict the risk of fracturing sand plugging in the process of fracturing construction of unconventional oil and gas reservoirs based on the output results of the sand plugging risk identification model.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for predicting the risk of fracturing sand plugging according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for predicting the risk of fracturing sand plugging according to any one of claims 1 to 7 when executed.

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