CN113435059A - Model establishing method, crack initiation event diagnosis method and device - Google Patents
Model establishing method, crack initiation event diagnosis method and device Download PDFInfo
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Abstract
The embodiment of the specification discloses a model establishing method, a crack initiation event diagnosis method and a crack initiation event diagnosis device. The model establishing method comprises the following steps: analyzing the part of the pumping pressure curve in the fracturing construction period to obtain first fracture data; analyzing the part of the pumping pressure curve during the fracturing shutdown period to obtain second fracture data; comparing pressure wave signals reflected by the fracture during the fracturing construction period and the fracturing shutdown period to obtain third fracture data; analyzing a pressure wave signal reflected by the fracture during the fracturing construction period to obtain fourth fracture data; and establishing a fracture initiation event diagnosis model according to the first fracture data, the second fracture data, the third fracture data and the fourth fracture data. The model establishing method, the fracture initiation event diagnosis method and the fracture initiation event diagnosis device disclosed by the embodiment of the specification can realize diagnosis of fracture initiation events through the pumping pressure curve and pressure wave signals reflected by the fracture.
Description
Technical Field
The embodiment of the specification relates to the technical field of computers, in particular to a model establishing method, a crack initiation event diagnosis method and a crack initiation event diagnosis device.
Background
At present, unconventional oil and gas resources such as low permeability, deep layer, deep sea and the like become main battlefields for oil and gas exploration and development in China. Large-scale hydraulic fracturing is a key technology for efficiently extracting unconventional oil and gas resources. High-pressure fluid is injected into an oil and gas reservoir with the well depth of thousands of meters through a fracturing pump, and cracks are manufactured. The fractures may be used as pathways for unconventional hydrocarbon flow production. Therefore, to meet the demand for unconventional stimulation of oil and gas resources, it is necessary to diagnose the fracture initiation events of the fractures.
Disclosure of Invention
The embodiment of the specification provides a model establishing method, a crack initiation event diagnosis method and a crack initiation event diagnosis device, so as to diagnose the crack initiation event of a crack. The technical scheme of the embodiment of the specification is as follows.
In a first aspect of embodiments of the present specification, a model building method is provided, including:
analyzing the part of the pumping pressure curve in the fracturing construction period to obtain first fracture data;
analyzing the part of the pumping pressure curve during the fracturing shutdown period to obtain second fracture data;
comparing pressure wave signals reflected by the fracture during the fracturing construction period and the fracturing shutdown period to obtain third fracture data;
analyzing a pressure wave signal reflected by the fracture during the fracturing construction period to obtain fourth fracture data;
and establishing a fracture initiation event diagnosis model according to the first fracture data, the second fracture data, the third fracture data and the fourth fracture data.
In a second aspect of the embodiments of the present specification, there is provided a fracture initiation event diagnosis method, including:
analyzing the part of the pumping pressure curve in the fracturing construction period to obtain first fracture data;
analyzing the part of the pumping pressure curve during the fracturing shutdown period to obtain second fracture data;
comparing pressure wave signals reflected by the fracture during the fracturing construction period and the fracturing shutdown period to obtain third fracture data;
analyzing a pressure wave signal reflected by the fracture during the fracturing construction period to obtain fourth fracture data;
and diagnosing the fracture initiation event of the fracture by using a pre-trained fracture initiation event diagnosis model according to the first fracture data, the second fracture data, the third fracture data and the fourth fracture data.
In a third aspect of embodiments of the present specification, there is provided a model building apparatus including:
the first analysis unit is used for analyzing the part of the pumping pressure curve in the fracturing construction period to obtain first fracture data;
the second analysis unit is used for analyzing the part of the pumping pressure curve in the fracturing shutdown period to obtain second fracture data;
the comparison unit is used for comparing pressure wave signals reflected by the fracture during the fracturing construction period and the fracturing shutdown period to obtain third fracture data;
the third analysis unit is used for analyzing pressure wave signals reflected by the fractures during the fracturing construction period to obtain fourth fracture data;
and the establishing unit is used for establishing a fracture initiation event diagnosis model based on deep learning according to the first fracture data, the second fracture data, the third fracture data and the fourth fracture data.
In a fourth aspect of the embodiments of the present specification, there is provided a fracture initiation event diagnosis apparatus including:
the first analysis unit is used for analyzing the part of the pumping pressure curve in the fracturing construction period to obtain first fracture data;
the second analysis unit is used for analyzing the part of the pumping pressure curve in the fracturing shutdown period to obtain second fracture data;
the comparison unit is used for comparing pressure wave signals reflected by the fracture during the fracturing construction period and the fracturing shutdown period to obtain third fracture data;
the third analysis unit is used for analyzing pressure wave signals reflected by the fractures during the fracturing construction period to obtain fourth fracture data;
and the diagnosis unit is used for diagnosing the crack initiation event of the crack by using a pre-trained crack initiation event diagnosis model according to the first crack data, the second crack data, the third crack data and the fourth crack data.
According to the technical scheme provided by the embodiment of the specification, a fracture initiation event diagnosis model can be established through a pumping pressure curve and a pressure wave signal reflected by a fracture. In addition, the technical scheme provided by the embodiment of the specification can also diagnose the fracture initiation event of the fracture by utilizing a fracture initiation event diagnosis model through a pumping pressure curve and a pressure wave signal reflected by the fracture.
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In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a model building method in an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of a fracture initiation event diagnosis method in an embodiment of the present disclosure;
FIG. 3 is a schematic structural diagram of a model building apparatus in an embodiment of the present disclosure;
FIG. 4 is a schematic structural diagram of a fracture initiation event diagnosis device in an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device in an embodiment of this specification.
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step should fall within the scope of protection of the present specification.
The crack monitoring in the complex environment with the well depth of several kilometers often faces a plurality of challenges such as difficult signal transmission and data interpretation, and is recognized as a world-level problem by the oil industry. Therefore, based on a new idea of monitoring a hydraulic fracturing complex fracture network by using a transient variable pressure wave in deep learning, the embodiment of the specification provides a model establishing method and a fracture initiation event diagnosis method so as to diagnose the fracture initiation event of the fracture and meet the yield increase requirement of unconventional oil and gas resources. Wherein the fractures include, but are not limited to, fractures of horizontal wells, fractures of vertical wells, and the like. By diagnosing the fracture initiation event, the fracture initiation condition (such as the fracture initiation time) of the fracture in the fracturing process can be obtained, and/or the fracture initiation condition and the complexity degree of the near-wellbore fracture can be interpreted and judged.
Please refer to fig. 1. The embodiment of the specification provides a model building method. The model building method can be applied to a server. The server may be a single server, a server cluster composed of a plurality of servers, or a server deployed in the cloud. The model building method may include the following steps.
Step S11: and analyzing the part of the pumping pressure curve during the fracturing construction period to obtain first fracture data.
In some embodiments, the pump pressure profile may be a pump pressure profile of a fracturing apparatus, which may include a fracturing pump. The pumping pressure curve can reflect the pressure of the fracturing equipment changing along with time. The pumping pressure profile may include a portion of the profile of the fracturing equipment during a fracturing job and a portion of the profile of the fracturing equipment during a fracturing shutdown. In practice, the server may derive the pump pressure profile by monitoring the pressure of the fracturing equipment in real time during the fracturing job and during the fracturing shutdown.
In some embodiments, the server may perform a discrete wavelet transform and/or multi-resolution analysis on a portion of the pumping pressure curve during a fracture construction, resulting in first fracture data. The first fracture data may be used to characterize a number of fracture initiation times of the fracture. For example, the first fracture data may include discrete wavelet transform results and/or multi-resolution analysis results. Specifically, for example, the discrete wavelet transform result may include a decomposition curve for each band after wavelet transform. The multi-resolution analysis results may include a time series energy density map of pump pressure versus displacement.
Step S12: the portion of the pump pressure curve during the fracture shutdown was parsed to obtain second fracture data.
In some embodiments, the server may perform a G-function analysis on a portion of the pump pressure curve during fracture shutdown, resulting in second fracture data. The G function analysis may be a method of analyzing a pressure drop after pressure. Through the G function analysis, the fracturing process after fracturing construction can be evaluated, and the complexity of the fracture is judged, so that the fracturing scheme is improved, the fracturing parameters of the oil and gas field are optimized, and the fracturing construction effect is improved. The second fracture data is used to characterize the complexity of the fracture. For example, the second fracture data may include a fluid loss of the fracture. In practice, the fluid loss area of the fracture can be judged through the fluid loss amount, and the complexity of the fracture can be evaluated by utilizing the fluid loss area.
Step S13: and comparing the pressure wave signals reflected by the fracture during the fracturing construction period with the pressure wave signals reflected by the fracture during the fracturing shutdown period to obtain third fracture data.
In some embodiments, the pressure wave signal may be excited by the fracturing apparatus. Pressure waves generated by changing the displacement of the fracturing equipment before and after the fracturing construction can propagate in the wellbore and are reflected back by the fractures, and reflected pressure wave signals can be received by the hydrophones. The server may obtain pressure wave signals received by the hydrophones reflected by fractures during fracture construction and during fracture shutdown; the pressure wave signals reflected by the fracture during the fracture construction and during the fracture shutdown may be compared to obtain third fracture data. The third fracture data may be used to characterize the fracture initiation of the fracture. For example, the third fracture data may include a number of initiated clusters of fractures. In practice, the server may obtain third fracture data by comparing spectrograms of pressure wave signals and/or wavelet transformed timing charts during fracture construction and during fracture shutdown.
Step S14: and analyzing the pressure wave signals reflected by the fracture during the fracturing construction period to obtain fourth fracture data.
In some embodiments, the pressure wave signal may be excited by the fracturing apparatus. Pressure waves generated by fracturing equipment after fracturing construction can propagate in a wellbore and be reflected by fractures, and reflected pressure wave signals can be received by hydrophones. The server may obtain pressure wave signals received by the hydrophones reflected by fractures during a fracture construction; the obtained pressure wave signal may be analyzed to obtain fourth fracture data. The fourth fracture data may be used to characterize the propagation of the fracture. For example, the fourth fracture data may include complexity data of the fracture, fracture initiation event data of the fracture, and geometry parameters of the fracture. In particular, for example, the fourth fracture data may include a length of the fracture, a volume of the fracture, a height of the fracture, and so forth. In practice, the server may perform discrete wavelet transform and/or multi-resolution analysis on the pressure wave signals reflected by the fracture during the fracture construction to obtain fourth fracture data.
Step S15: and establishing a fracture initiation event diagnosis model according to the first fracture data, the second fracture data, the third fracture data and the fourth fracture data.
In some embodiments, the server may train a pre-constructed fracture initiation event diagnostic model based on the first fracture data, the second fracture data, the third fracture data, and the fourth fracture data. The fracture initiation event diagnosis model may include a neural network model, a support vector machine model, and the like.
In some embodiments, the server may directly train a fracture initiation event diagnostic model based on the first fracture data, the second fracture data, the third fracture data, and the fourth fracture data. For example, the server may obtain labels for the first fracture data, the second fracture data, the third fracture data, and the fourth fracture data, respectively; a fracture initiation event diagnostic model may be trained based on the first fracture data, the label for the first fracture data, the second fracture data, the label for the second fracture data, the third fracture data, the label for the third fracture data, the fourth fracture data, and the label for the fourth fracture data.
Alternatively, the server may extract feature data from the first fracture data, the second fracture data, the third fracture data, and the fourth fracture data, respectively; and training a crack initiation event diagnosis model according to the extracted characteristic data. For example, the server may obtain a tag of the characteristic data; and training the crack initiation event diagnosis model according to the characteristic data and the label of the characteristic data.
Or, considering that the extracted feature data includes features strongly related to fracture initiation events and features unrelated to fracture initiation events, the server may further extract feature data from the first fracture data, the second fracture data, the third fracture data, and the fourth fracture data, respectively; the extracted characteristic data can be subjected to cluster analysis to obtain target characteristic data which is strongly related to crack initiation and/or expansion of the crack; the pre-constructed fracture initiation event diagnostic model may be trained using the target feature data. For example, the server may obtain a tag for the target feature data; the pre-constructed crack initiation event diagnosis model can be trained according to the target characteristic data and the label of the target characteristic data.
According to the model establishing method in the embodiment of the specification, the part of the pumping pressure curve in the fracturing construction period can be analyzed to obtain first fracture data; analyzing the part of the pumping pressure curve during the fracturing shutdown period to obtain second fracture data; the pressure wave signals reflected by the fracture during the fracturing construction period and the fracturing shutdown period can be compared to obtain third fracture data; pressure wave signals reflected by the fractures during the fracturing construction period can be analyzed to obtain fourth fracture data; a fracture initiation event diagnostic model may be established based on the first fracture data, the second fracture data, the third fracture data, and the fourth fracture data. According to the model establishing method disclosed by the embodiment of the specification, the fracture initiation event diagnosis model can be established through the pumping pressure curve and the pressure wave signals reflected by the fracture, and convenience is provided for diagnosis of the fracture initiation event.
Please refer to fig. 2. The embodiment of the specification further provides a fracture initiation event diagnosis method. The fracture initiation event diagnosis method can be applied to a server. The server may be a single server, a server cluster composed of a plurality of servers, or a server deployed in the cloud. The fracture initiation event diagnostic method may include the following steps.
Step S21: and analyzing the part of the pumping pressure curve during the fracturing construction period to obtain first fracture data.
Step S22: the portion of the pump pressure curve during the fracture shutdown was parsed to obtain second fracture data.
Step S23: and comparing the pressure wave signals reflected by the fracture during the fracturing construction period with the pressure wave signals reflected by the fracture during the fracturing shutdown period to obtain third fracture data.
Step S24: and analyzing the pressure wave signals reflected by the fracture during the fracturing construction period to obtain fourth fracture data.
Step S25: and diagnosing the fracture initiation event of the fracture by using a pre-trained fracture initiation event diagnosis model according to the first fracture data, the second fracture data, the third fracture data and the fourth fracture data.
Regarding the step S21, the step S22, the step S23 and the step S24, reference may be made to the step S11, the step S12, the step S13 and the step S14, respectively, which will not be described in detail herein. In addition, the fracture initiation event diagnosis model can be obtained by training based on the model establishment method of the embodiment corresponding to fig. 1, and is not described in detail here.
The server may diagnose a fracture initiation event of the fracture directly according to the first fracture data, the second fracture data, the third fracture data, and the fourth fracture data using a pre-trained fracture initiation event diagnostic model. For example, the server may input the first fracture data, the second fracture data, the third fracture data, and the fourth fracture data into a pre-trained fracture initiation event diagnosis model to obtain a fracture initiation event of a fracture.
Alternatively, the server may extract feature data from the first fracture data, the second fracture data, the third fracture data, and the fourth fracture data, respectively; the fracture initiation event of the fracture can be diagnosed by using a pre-trained fracture initiation event diagnosis model according to the extracted feature data. For example, the server may input the extracted feature data to a pre-trained fracture initiation event diagnosis model to obtain a fracture initiation event of the fracture.
Or, the server may further extract feature data from the first fracture data, the second fracture data, the third fracture data, and the fourth fracture data, respectively; the extracted characteristic data can be subjected to cluster analysis to obtain target characteristic data which is strongly related to crack initiation and/or expansion of the crack; the fracture initiation event of the fracture can be diagnosed by using a pre-trained fracture initiation event diagnosis model according to the target characteristic data. For example, the server may input the target feature data into a pre-trained fracture initiation event diagnosis model to obtain a fracture initiation event of the fracture.
According to the fracture initiation event diagnosis method disclosed by the embodiment of the specification, the part of a pumping pressure curve in a fracturing construction period can be analyzed to obtain first fracture data; analyzing the part of the pumping pressure curve during the fracturing shutdown period to obtain second fracture data; the pressure wave signals reflected by the fracture during the fracturing construction period and the fracturing shutdown period can be compared to obtain third fracture data; pressure wave signals reflected by the fractures during the fracturing construction period can be analyzed to obtain fourth fracture data; a fracture initiation event of the fracture may be diagnosed using a pre-trained fracture initiation event diagnostic model based on the first fracture data, the second fracture data, the third fracture data, and the fourth fracture data. The fracture initiation event diagnosis method disclosed by the embodiment of the specification can be used for diagnosing the fracture initiation event of the fracture by utilizing the fracture initiation event diagnosis model through the pumping pressure curve and the pressure wave signal reflected by the fracture, so that the yield increase requirement of unconventional oil and gas resources is met.
The model establishing method and the fracture initiation event diagnosis method provided by the embodiment of the specification can actively excite transient variable pressure waves in the fracture through the instantaneously variable pump injection displacement, and learn the mapping relation between the pressure waves and the fracture initiation event through big data based on a deep learning theory, so that the real-time and intelligent fracture monitoring of the deep well complex environment is realized. The method has the following characteristics: (1) the fracture network is used as an important geometric boundary of the transient current pressure fluctuation, the reflection and decay characteristics of the transient current pressure fluctuation are influenced, and different fracture forms generate specific pressure echo signals through reflection; (2) the existing transient flow pressure wave inversion fracture initiation event model has multiple solutions and uncertainty, pressure fluctuation characteristics are extracted from big data through deep learning, and a complex mapping relation between pressure fluctuation and a fracture initiation event is established; (3) pressure fluctuation data is fused with fracturing design and pumping pressure curve analysis, a data label is provided through a physical model, and the fracture form is accurately grasped by adopting a semi-supervised learning mode.
Please refer to fig. 3. The embodiment of the specification also provides a model building device. The model building device can be arranged on a server. The model building apparatus may include the following elements.
The first analysis unit 31 is used for analyzing the part of the pumping pressure curve during the fracturing construction period to obtain first fracture data;
the second analysis unit 32 is used for analyzing the part of the pumping pressure curve during the fracturing shutdown period to obtain second fracture data;
a comparison unit 33, configured to compare pressure wave signals reflected by the fracture during the fracturing construction period and the fracturing shutdown period to obtain third fracture data;
a third analyzing unit 34, configured to analyze a pressure wave signal reflected by the fracture during the fracturing construction to obtain fourth fracture data;
the establishing unit 35 is configured to establish a fracture initiation event diagnosis model according to the first fracture data, the second fracture data, the third fracture data, and the fourth fracture data.
Please refer to fig. 4. The embodiment of the specification also provides a fracture initiation event diagnosis device. The fracture initiation event diagnosis device may be provided in a server. The fracture initiation event diagnosis device may include the following units.
The first analysis unit 41 is used for analyzing the part of the pumping pressure curve during the fracturing construction period to obtain first fracture data;
the second analysis unit 42 is used for analyzing the part of the pumping pressure curve during the fracturing shutdown period to obtain second fracture data;
a comparison unit 43, configured to compare pressure wave signals reflected by the fracture during the fracturing construction period and the fracturing shutdown period to obtain third fracture data;
the third analyzing unit 44 is configured to analyze a pressure wave signal reflected by the fracture during the fracturing construction to obtain fourth fracture data;
and the diagnosis unit 45 is configured to diagnose a fracture initiation event of the fracture by using a pre-trained fracture initiation event diagnosis model according to the first fracture data, the second fracture data, the third fracture data and the fourth fracture data.
An embodiment of an electronic device of the present description is described below. Fig. 5 is a schematic diagram of a hardware configuration of the electronic apparatus in this embodiment. As shown in fig. 5, the electronic device may include one or more processors (only one of which is shown), memory, and a transmission module. Of course, it is understood by those skilled in the art that the hardware structure shown in fig. 5 is only an illustration, and does not limit the hardware structure of the electronic device. In practice the electronic device may also comprise more or fewer component elements than those shown in fig. 5; or have a different configuration than that shown in figure 5.
The memory may comprise high speed random access memory; alternatively, non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory may also be included. Of course, the memory may also comprise a remotely located network memory. The remotely located network storage may be connected to the blockchain client through a network such as the internet, an intranet, a local area network, a mobile communications network, or the like. The memory may be used to store program instructions or modules of application software, such as program instructions or modules used to implement the embodiments corresponding to fig. 1 or fig. 2 of the present specification.
The processor may be implemented in any suitable way. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth. The processor may read and execute the program instructions or modules in the memory.
The transmission module may be used for data transmission via a network, for example via a network such as the internet, an intranet, a local area network, a mobile communication network, etc.
This specification also provides one embodiment of a computer storage medium. The computer storage medium includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk (HDD), a Memory Card (Memory Card), and the like. The computer storage medium stores computer program instructions. The computer program instructions when executed implement: the present specification refers to the embodiment shown in fig. 1 or fig. 2.
It should be noted that, in the present specification, each embodiment is described in a progressive manner, and the same or similar parts in each embodiment may be referred to each other, and each embodiment focuses on differences from other embodiments. In addition, it is understood that one skilled in the art, after reading this specification document, may conceive of any combination of some or all of the embodiments listed in this specification without the need for inventive faculty, which combinations are also within the scope of the disclosure and protection of this specification.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
From the above description of the embodiments, it is clear to those skilled in the art that the present specification can be implemented by software plus a necessary general hardware platform. Based on such understanding, the technical solutions of the present specification may be essentially or partially implemented in the form of software products, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and include instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments of the present specification.
The description is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
While the specification has been described with examples, those skilled in the art will appreciate that there are numerous variations and permutations of the specification that do not depart from the spirit of the specification, and it is intended that the appended claims include such variations and modifications that do not depart from the spirit of the specification.
Claims (10)
1. A method of model building comprising:
analyzing the part of the pumping pressure curve in the fracturing construction period to obtain first fracture data;
analyzing the part of the pumping pressure curve during the fracturing shutdown period to obtain second fracture data;
comparing pressure wave signals reflected by the fracture during the fracturing construction period and the fracturing shutdown period to obtain third fracture data;
analyzing a pressure wave signal reflected by the fracture during the fracturing construction period to obtain fourth fracture data;
and establishing a fracture initiation event diagnosis model according to the first fracture data, the second fracture data, the third fracture data and the fourth fracture data.
2. The method of claim 1, the resolving the portion of the pumping pressure curve during the fracturing construction, comprising:
and performing discrete wavelet transform and multi-resolution analysis on the part of the pumping pressure curve in the fracturing construction period to obtain first fracture data, wherein the first fracture data is used for representing the fracture initiation times of the fracture.
3. The method of claim 1, the resolving the portion of the pumping pressure profile during fracture shutdown comprising:
and analyzing the part of the pumping pressure curve during the fracture shutdown period by using an analytic G function to obtain second fracture data, wherein the second fracture data is used for representing the complexity of the fracture.
4. The method of claim 1, comparing pressure wave signals reflected by fractures during fracture construction and during fracture shutdown, comprising:
and comparing pressure wave signals reflected by the fracture during the fracturing construction period with the fracturing shutdown period to obtain third fracture data, wherein the third fracture data is used for representing the fracture initiation condition of the fracture.
5. The method of claim 1, the analyzing pressure wave signals reflected by fractures during a fracture construction, comprising:
and analyzing pressure wave signals reflected by the fracture during the fracturing construction period to obtain fourth fracture data, wherein the fourth fracture data is used for representing the expansion condition of the fracture.
6. The method of claim 1, the establishing a fracture initiation event diagnostic model, comprising:
extracting feature data from the first fracture data, the second fracture data, the third fracture data, and the fourth fracture data, respectively; performing cluster analysis on the extracted characteristic data to obtain target characteristic data which is strongly related to crack initiation and/or expansion of the crack; and training a pre-constructed fracture initiation event diagnosis model by using the target characteristic data.
7. The method of any one of claims 1 to 6, the fracture initiation event diagnostic model comprising a neural network model.
8. A fracture initiation event diagnostic method comprising:
analyzing the part of the pumping pressure curve in the fracturing construction period to obtain first fracture data;
analyzing the part of the pumping pressure curve during the fracturing shutdown period to obtain second fracture data;
comparing pressure wave signals reflected by the fracture during the fracturing construction period and the fracturing shutdown period to obtain third fracture data;
analyzing a pressure wave signal reflected by the fracture during the fracturing construction period to obtain fourth fracture data;
and diagnosing the fracture initiation event of the fracture by using a pre-trained fracture initiation event diagnosis model according to the first fracture data, the second fracture data, the third fracture data and the fourth fracture data.
9. A model building apparatus comprising:
the first analysis unit is used for analyzing the part of the pumping pressure curve in the fracturing construction period to obtain first fracture data;
the second analysis unit is used for analyzing the part of the pumping pressure curve in the fracturing shutdown period to obtain second fracture data;
the comparison unit is used for comparing pressure wave signals reflected by the fracture during the fracturing construction period and the fracturing shutdown period to obtain third fracture data;
the third analysis unit is used for analyzing pressure wave signals reflected by the fractures during the fracturing construction period to obtain fourth fracture data;
and the establishing unit is used for establishing a fracture initiation event diagnosis model according to the first fracture data, the second fracture data, the third fracture data and the fourth fracture data.
10. A fracture initiation event diagnostic device comprising:
the first analysis unit is used for analyzing the part of the pumping pressure curve in the fracturing construction period to obtain first fracture data;
the second analysis unit is used for analyzing the part of the pumping pressure curve in the fracturing shutdown period to obtain second fracture data;
the comparison unit is used for comparing pressure wave signals reflected by the fracture during the fracturing construction period and the fracturing shutdown period to obtain third fracture data;
the third analysis unit is used for analyzing pressure wave signals reflected by the fractures during the fracturing construction period to obtain fourth fracture data;
and the diagnosis unit is used for diagnosing the crack initiation event of the crack by using a pre-trained crack initiation event diagnosis model according to the first crack data, the second crack data, the third crack data and the fourth crack data.
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