Patent Application
20230334199
The present application claims priority of Chinese Patent Application No. 202210393249.8, filed on Apr. 15, 2022, the entire contents of which are incorporated herein by reference.
The present disclosure relates to the technical field of oil and gas development, and in particular to a calculation method for predicting proppant embedding depth based on a shale softening effect.
Shale gas has gradually become a hotspot in the exploration and development of unconventional natural gas in the world. However, shale reservoirs have poor physical properties such as porosity and permeability, and industrial production of shale gas is obtained by performing hydraulic fracturing on the shale reservoirs. At the same time, the fracturing fluid injected into the formation by hydraulic fracturing is difficult to drain out the formation after the construction is completed. A large amount of stranded fracturing fluids contacts the formation rock for a long time, and the shale fracture surfaces change in the mechanical properties under the action of high temperature and high pressure. Therefore, it is necessary to study and analyze the calculation method for proppant embedding depth under a softening effect of shale, which is of great significance for the optimization of fracture width formed by hydraulic fracturing.
Scholars at home and abroad have mainly established two types of models for proppant embedding, namely an empirical model and a theoretical model. The empirical model is to study the process of proppant embedded into fractures under formation conditions by carrying out propped fracture width tests, and then obtained based on experimental data and statistical theory; the theoretical model includes an analytical model and a numerical model. The analytical model is to describe the process of proppant embedded into the rock mass as a contact mechanics problem, establish a physical model of suitable dimensions based on the physical process of embedding, and calculate using Hooke’s law and Hertz contact theory as the basis. With the development of numerical analysis software, scholars currently use software such as finite element and discrete element and carry out secondary development on the software to perform numerical calculations, and accurately calculate the stress conditions at any time and at any node of the 3D solid model and observe the deformation law of the contact surface directly. However, the existing numerical models generally ignore the influence of formation fluid for simulation, and the current hydraulic fracturing construction of shale gas wells is to inject thousands of cubic meters of fracturing fluid into the formation. If a calculation method for predicting proppant embedding depth based on a shale softening effect can be established, the calculation method will play a role in advancing the research on the width of artificial fractures produced by hydraulic fracturing of shale gas wells.
The technical solution provided by the present disclosure to solve the above technical problems is: a calculation method for predicting proppant embedding depth based on a shale softening effect, which is executed by a processor of a calculation system for predicting proppant embedding depth based on a shale softening effect, including the following steps.
step S1, determining a spontaneous imbibition depth-soaking time curve, wherein the curve is obtained by conducting a spontaneous imbibition experiment on faces of different standard cores at different soaking times respectively and utilizing a modified Lucas-Washburn (LW) model under a spontaneous imbibition effect, and the standard cores are obtained based on a target block shale.
step S2, determining a Young’s modulus-soaking time curve of core surfaces, wherein the curve is obtained by drying the standard cores at different soaking times, and conducting a nano-indentation experiment on surfaces of the standard cores respectively.
step S3, establishing a 3D model of proppant embedded in a rock slab by a finite element method, wherein the rock slab in the 3D model is divided into an unsoftened layer and a softened layer, and Young’s modulus of the unsoftened layer is set as Young’s modulus of standard cores; a thickness of the softened layer is set according to the spontaneous imbibition depth-soaking time curve, and Young’s modulus of the softened layer is set according to the Young’s modulus-soaking time curve, and a proppant embedding model containing the softened layer is obtained.
step S4, obtaining an embedding volume-soaking time curve by performing numerical simulation on the proppant embedding model containing the softened layer with set parameters.
step S5, modifying equivalent Young’s modulus of proppant embedded in a rock mass based on the embedding volume-soaking time curve, and obtaining a calculation formula for proppant embedding volume considering the softening effect;
Where w denotes a proppant embedding volume, a unit of which is mm; a0 and a1 denote modification factors, which are 0.0646 and 18.2 respectively, and dimensionless; R denotes a particle size of proppant, a unit of which is mm; P denotes crustal stress, a unit of which is MPa; E1 denotes Young’s modulus of proppant, a unit of which is MPa; v1 denotes a Poisson’s ratio of the proppant, which is dimensionless; v2 denotes a Poisson’s ratio of the rock slab, which is dimensionless; H denotes a thickness of a rock slab, a unit of which is mm; t denotes soaking time, a unit of which is d; Et denotes an equivalent Young’s modulus, a unit of which is MPa; E0 denotes Young’s modulus of a standard core, a unit of which is MPa; and a and b denote fitting coefficients.
A further technical solution is that the step S1 is executed based on a processor, further including: step S11, determining a first soaking time set; step S12, determining first wetting angles of the standard cores, the first wetting angles including wetting angles of the standard cores corresponding to the at least one soaking time in the first soaking time set; step S13, predicting second wetting angles of the standard cores based on the first wetting angles; the second wetting angles including wetting angles of the standard cores corresponding to the least one soaking time in a second soaking time set; and step S14, obtaining the spontaneous imbibition depth-soaking time curve by utilizing the modified LW model under the spontaneous imbibition effect based on the first wetting angles and the second wetting angles.
A further technical solution is that the different soaking times in the step S2 include the soaking time in the first soaking time set and the second soaking time set.
A further technical solution is that predicting second wetting angles of the standard cores based on the first wetting angles include: determining the second wetting angles of the standard core through processing the first wetting angles by a wetting angle prediction model, wherein the wetting angle prediction model is a machine learning model.
A further technical solution is that a training of the wetting angle prediction model includes: obtaining the no less than a preset count of training samples with labels, the training samples including sample rock quality features of sample standard cores, a sample first soaking time set, first sample wetting angles corresponding to the sample first soaking time set, a sample second soaking time set, and a liquid type of sample liquid for soaking sample standard cores; the labels of the training samples being second sample wetting angles corresponding to the second sample soaking time set; and iteratively updating an initial wetting angle prediction model by utilizing the no less than a preset count of training samples with labels to obtain the wetting angle prediction model.
A further technical solution is that the determining first wetting angles of the standard cores includes determining a corresponding equivalent soaking condition based on the soaking time in the first soaking time set, conducting a soaking experiment on the standard cores with the equivalent soaking condition, and determining wetting angles obtained from an experimental result as the first wetting angles of the standard cores.
A further technical solution is that a relationship formula of the modified LW model under the spontaneous imbibition effect in step S1 is:
Where h(t) denotes a spontaneous imbibition distance, a unit of which is m; t denotes the soaking time, a unit of which is s; r denotes an equivalent capillary radius, a unit of which is m; γ denotes a fluid interfacial tension, a unit of which is N/m; δ denotes a pore-shape factor, which is dimensionless; θ denotes the wetting angle, a unit of which is °; τ denotes pore tortuosity, which is dimensionless; µ denotes a fluid viscosity, a unit of which is Pa·s.
A further technical solution is that the drying the standard cores in step S2 is based on drying parameters, the drying parameters include a drying temperature and a drying time, and a determination of the drying parameters includes: determining the drying parameters of the standard cores at the soaking time based on rock quality features and the soaking time of the standard cores.
A further technical solution is that the drying parameters are determined by optimal historical drying parameters obtained based on vector matching, the optimal historical drying parameters are determined based on a modulus similarity threshold, and the modulus similarity threshold is related to core parameter sensitivity.
A further technical solution is that a sum of the thickness of the unsoftened layer and the thickness of the softened layer in the step S3 is equal to an overall thickness of the rock slab.
A further technical solution is that a process of the numerical simulation in the step S4 includes: setting simulated parameters of the proppant embedding model containing the softened layer respectively according to the different soaking times; applying closure stress to an upper slab of the 3D model by utilizing a stress interaction effect, and completely fixing a lower slab of the 3D model to simulate a crustal fracture closure process, outputting an average embedding volume of the upper slab and lower slab after the model is stabilized, and obtaining the embedding volume-soaking time curve at the different soaking times.
A further technical solution is that the modifying equivalent Young’s modulus of proppant embedded in a rock mass based on the embedding volume-soaking time curve in the step S5 includes the following steps.
(1) Obtaining an embedding volume at a soaking time t1 according to the embedding volume-soaking time curve obtained by the numerical simulation, and introducing the embedding volume into the calculation formula for the proppant embedding volume of the proppant embedded in a rock mass, and obtaining equivalent Young’s modulus Et1 at the soaking time t1 by calculating reversely, repeating the above process, obtaining equivalent Young’s modulus Et2 at a soaking time t2, equivalent Young’s modulus Et3 at a soaking time t3, ..., equivalent Young’s modulus Etn at a soaking time tn.
(2) According to equivalent Young’s modulus corresponding to the different soaking times, obtaining equivalent Young’s modulus of a softened rock slab by regression.
A further technical solution is that the calculation formula for the proppant embedding volume of the proppant embedded in a rock mass is:
Where w denotes the proppant embedding volume, a unit of which is mm; a0 and a1 denote modification factors, which are 0.0646 and 18.2 respectively, and dimensionless; R denotes the particle size of proppant, a unit of which is mm; P denotes the crustal stress, a unit of which is MPa; E1 denotes the Young’s modulus of proppant, a unit of which is MPa; E2 denotes the Young’s modulus of the rock slab, a unit of which is GPa; v1 denotes the Poisson’s ratio of the proppant, which is dimensionless; v2 denotes the Poisson’s ratio of the rock slab, which is dimensionless; and H denotes the thickness of the rock slab, a unit of which is mm.
Some embodiments of the present disclosure propose a calculation system for predicting proppant embedding depth based on a shale softening effect. The system includes a first determining module, a second determining module, a model module, a third determining module, and a fourth determining module. The first determining module is configured to determine a spontaneous imbibition depth-soaking time curve, wherein the curve is obtained by conducting a spontaneous imbibition experiment on faces of different standard cores at different soaking times respectively and utilizing a modified Lucas-Washburn (LW) model under the spontaneous imbibition effect, and the standard cores are obtained based on a target block shale. The second determining module is configured to determine a Young’s modulus-soaking time curve of core surfaces, wherein the curve is obtained by drying the standard cores at different soaking times, conducting a nano-indentation experiment on surfaces of the standard cores respectively. The model module is configured to establish a 3D model of proppant embedded in a rock slab by a finite element method, wherein the rock slab in the 3D model is divided into an unsoftened layer and a softened layer, and Young’s modulus of the unsoftened layer is set as Young’s modulus of standard cores; a thickness of the softened layer is set according to the spontaneous imbibition depth-soaking time curve, and Young’s modulus of the softened layer is set according to the Young’s modulus-soaking time curve, and a proppant embedding model containing the softened layer is obtained. The third determining module is configured to obtain an embedding volume-soaking time curve by performing numerical simulation on the proppant embedding model containing the softened layer with set parameters. The fourth determining module is configured to modify equivalent Young’s modulus of proppant embedded in a rock mass based on the embedding volume-soaking time curve, and obtain a calculation formula for proppant embedding volume considering the softening effect;
Where w denotes the proppant embedding volume, a unit of which is mm; a0 and a1 denote modification factors, which are 0.0646 and 18.2 respectively, and dimensionless; R denotes the particle size of proppant, a unit of which is mm; P denotes the crustal stress, a unit of which is MPa; E1 denotes the Young’s modulus of proppant, a unit of which is MPa; v1 denotes the Poisson’s ratio of the proppant, which is dimensionless; v2 denotes the Poisson’s ratio of the rock slab, which is dimensionless; H denotes the thickness of the rock slab, a unit of which is mm; t denotes the soaking time, a unit of which is d; Et denotes the equivalent Young’s modulus, a unit of which is MPa; E0 denotes the Young’s modulus of the standard core, a unit of which is MPa; and a and b denote fitting coefficients.
The present disclosure will be further illustrated by way of exemplary embodiments, which will be described in detail with the accompanying drawings. These embodiments are nonlimiting, and in these embodiments, the same number indicates the same structure, wherein:
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings that need to be used in the description of the embodiments. Apparently, the accompanying drawings in the following description are only some examples or embodiments of the present disclosure, and those skilled in the art can also apply the present disclosure to other similar scenarios without creative effort. Unless obviously obtained from the context or the context illustrates otherwise, the same numeral in the drawings refers to the same structure or operation.
It should be understood that the words “system”, “device”, “unit” and/or “module” as used herein is a method for distinguishing different components, elements, parts, parts, or assemblies of different levels. However, the words may be replaced by other expressions if other words can achieve the same purpose.
As indicated in the present disclosure and claims, the terms “a”, “an”, and/or “the” are not specific to the singular and may include the plural unless the context clearly indicates an exception. Generally speaking, the terms “comprising” and “including” only suggest the inclusion of clearly identified steps and elements, and these steps and elements do not constitute an exclusive list, and the method or device may also contain other steps or elements.
Flowcharts are used in the present disclosure to illustrate the operation performed according to the system of the embodiments of the present disclosure. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps may be processed in reverse order or simultaneously. At the same time, other operations can be added to these procedures, or a certain step or steps can be removed from these procedures.
In some embodiments, the system may include at least a processor, and the processor may be configured to process information and/or data related to the calculation system 100 for predicting proppant embedding depth based on a shale softening effect, for example, determining a spontaneous imbibition depth-soaking time curve, a Young’s modulus-soaking time curve of core surfaces. In some embodiments, the processor may a single server or a group of servers. In some embodiments, a plurality of models may be included in the processor, for example, an equivalent soaking condition determination model, a wetting angle prediction model. The processor may train an initial model based on a plurality of training samples with labels to obtain a trained model. For more content about the equivalent soaking condition determination model and its training, please refer to
The first determining module 110 may be configured to determine a spontaneous imbibition depth-soaking time curve, wherein the curve is obtained by conducting a spontaneous imbibition experiment on faces of different standard cores at different soaking times respectively and utilizing a modified Lucas-Washburn (LW) model under the spontaneous imbibition effect, and the standard cores are obtained based on a target block shale.
The first determining module 110 may be further configured to determine a first soaking time set; determine first wetting angles of the standard cores, the first wetting angles including wetting angles of the standard cores corresponding to the at least one soaking time in the first soaking time set; predict second wetting angles of the standard cores based on the first wetting angles; the second wetting angles including wetting angles of the standard cores corresponding to the least one soaking time in a second soaking time set; obtain the spontaneous imbibition depth-soaking time curve by utilizing the modified LW model under the spontaneous imbibition effect based on the first wetting angles and the second wetting angles.
The first determining module 110 may be further configured to determine the second wetting angle of the standard core through processing the first wetting angles by a wetting angle prediction model, and the wetting angle prediction model is a machine learning model.
The first determining module 110 may be further configured to obtain no less than a preset count of training samples, the training samples including sample rock quality features of sample standard cores, a sample first soaking time set, first sample wetting angles corresponding to the sample first soaking time set, a sample second soaking time set, and a liquid type of sample liquid for soaking sample standard cores; and iteratively update an initial wetting angle prediction model by utilizing the plurality of training samples to obtain the wetting angle prediction model.
The first determining module 110 may be further configured to determine a corresponding equivalent soaking condition based on the soaking time in the first soaking time set, conduct a soaking experiment on the standard core with the equivalent soaking condition, and determine wetting angles obtained from an experimental result as the first wetting angles of the standard cores.
The second determining module 120 may be configured to determine a Young’s modulus-soaking time curve of core surfaces, wherein the curve is obtained by drying the standard cores at different soaking times, and conducting a nano-indentation experiment on surfaces of the standard cores respectively.
The second determining module 120 may be further configured to determine the drying parameters of the standard cores after the soaking times based on the rock quality features and soaking time of the standard cores.
The model module 130 may be configured to establish a 3D model of proppant embedded in a rock slab by a finite element method, wherein the rock slab in the 3D model is divided into an unsoftened layer and a softened layer, and Young’s modulus of the unsoftened layer is set as Young’s modulus of the standard core; a thickness of the softened layer is set according to the spontaneous imbibition depth-soaking time curve, and Young’s modulus of the softened layer is set according to the Young’s modulus-soaking time curve, and a proppant embedding model containing the softened layer is obtained.
The third determining module 140 may be configured to obtain an embedding volume-soaking time curve by performing numerical simulation on the proppant embedding model containing the softened layer with set parameters.
The third determining module 140 may be further configured to set simulated parameters of the proppant embedding model containing the softened layer respectively according to the different soaking times; apply closure stress to an upper slab of the 3D model by utilizing a stress interaction effect, completely fix a lower slab of the 3D model to simulate a crustal fracture closure process, output an average embedding volume of the upper slab and lower slab after the model is stabilized, and obtain the embedding volume-soaking time curve at the different soaking times.
The fourth determining module 150 may be configured to modify equivalent Young’s modulus of proppant embedded in a rock mass based on the embedding volume-soaking time curve, and obtain a calculation formula for a proppant embedding volume considering the softening effect:
Where w denotes a proppant embedding volume, a unit of which is mm; a0 and a1 denote modification factors, which are 0.0646 and 18.2 respectively, and dimensionless; R denotes a particle size of proppant, a unit of which is mm; P denotes crustal stress, a unit of which is MPa; E1 denotes Young’s modulus of proppant, a unit of which is MPa; v1 denotes a Poisson’s ratio of the proppant, which is dimensionless; v2 denotes a Poisson’s ratio of the rock slab, which is dimensionless; H denotes thickness of a rock slab, a unit of which is mm; t denotes a soaking time, a unit of which is d; Et denotes an equivalent Young’s modulus, a unit of which is MPa; E0 denotes Young’s modulus of a standard core, a unit of which is MPa; and a and b denote fitting coefficients.
The fourth determining module 150 may be further configured to (1) obtain an embedding volume at a soaking time t1 according to the embedding volume-soaking time curve obtained by the numerical simulation, and introduce the embedding volume into the calculation formula for the proppant embedding volume of the proppant embedded in a rock mass, and obtain equivalent Young’s modulus Et1 at the soaking time t1 by calculating reversely, repeating the above process, obtaining equivalent Young’s modulus Et2 at a soaking time t2, equivalent Young’s modulus Et3 at a soaking time t3, ..., equivalent Young’s modulus Etn at a soaking time tn; and (2) according to equivalent Young’s modulus corresponding to the different soaking times, obtain equivalent Young’s modulus of a softened rock slab by regression.
It should be noted that the above descriptions of the system and its modules are only for the convenience of description and do not limit the present disclosure to the scope of the illustrated embodiments. It should be understood that for those skilled in the art, after understanding the principles of the system, it is possible to combine various modules arbitrarily or form a subsystem to connect with other modules without departing from the principles. In some embodiments, the first determining module 110, the second determining module 120, the model module 130, the third determining module 140 and the fourth determining module 150 disclosed in
The calculation method for predicting proppant embedding depth based on a shale softening effect in the present disclosure includes the following steps.
Step S1, determining a spontaneous imbibition depth-soaking time curve, wherein the curve is obtained by conducting a spontaneous imbibition experiment on faces of different standard cores at different soaking times respectively and utilizing a modified LW model under a spontaneous imbibition effect, and the standard cores are obtained based on a target block shale; wherein a relationship formula of the modified LW model under the spontaneous imbibition effect is:
Where h(t) denotes a spontaneous imbibition distance, a unit of which is m; t denotes the soaking time, a unit of which is s; r denotes an equivalent capillary radius, a unit of which is m; γ denotes a fluid interfacial tension, a unit of which is N/m; δ denotes a pore-shape factor, which is dimensionless; θ denotes a wetting angle, a unit of which is °; τ denotes a pore tortuosity, which is dimensionless; and µ denotes a fluid viscosity, a unit of which is Pa·s.
For example, after soaking standard cores with different core quality features under 0, 3, 5, 7, 15 days respectively, equivalent capillary radius, fluid interfacial tension, pore-shape factors, wetting angles, pore tortuosity, fluid viscosity of the standard cores with different quality features are obtained at different soaking times, and then a spontaneous imbibition distance of the standard cores soaked for different soaking times are obtained through a relationship formula of the modified LW model under the spontaneous imbibition effect.
Step S2, determining a Young’s modulus-soaking time curve of core surfaces, wherein the curve is obtained by drying the standard cores at different soaking times and conducting a nano-indentation experiment on surfaces of the standard cores respectively. For example, the Young’s modulus-soak time curve of the core surfaces is obtained by pressing an indenter into a standard core under an external load and measuring the magnitude of the load and the depth of pressing into the standard core.
Step S3, establishing a 3D model of proppant embedded in a rock slab by a finite element method, and setting the following assumptions.
(1) The proppant is embedded in a fracture under closure stress, regardless of a situation of proppant fragmentation.
(2) The proppant has ideal sphericity and good sorting property, and the proppant is laid evenly in fractures and arranged regularly.
(3) An unsoftened layer of a rock slab is a homogeneous material, and a softened layer of the rock slab is a heterogeneous material, which is evenly divided according to the grid; wherein the softened layer refers to a rock layer softened after soaking; and the unsoftened layer refers to a rock layer that is not softened after soaking.
(4) A process of proppant embedding is considered elastic; and the constitutive equation is:
Where [D]e denotes a 3D elastic matrix; {σ} denotes an element stress matrix; {ε} denotes an element strain matrix; E denotes Young’s modulus of a material, a unit of which is MPa; v denotes a Poisson’s ratio of the material, which is dimensionless.
The rock slab includes an unsoftened layer and a softened layer respectively, wherein Hsoften denotes a thickness of the softened layer, which is spontaneous imbibition depth; Hunsoften denotes a thickness of the unsoftened layer, which is calculated by a formula:
Where Hunsoften denotes the thickness of the unsoftened layer, a unit of which is mm; Hsoften denotes the thickness of the softened layer, a unit of which is mm; H denotes an overall thickness of the rock slab, a unit of which is mm; Young’s modulus of the unsoftened layer is set as Young’s modulus of the standard cores; the thickness of the softened layer is set according to the spontaneous imbibition depth-soaking time curve, and Young’s modulus of the softened layer is set according to the Young’s modulus-soaking time curve.
Step S4, obtaining an embedding volume-soaking time curve by performing numerical simulation on the proppant embedding model containing the softened layer with set parameters, including setting simulated parameters of the proppant embedding model containing the softened layer respectively according to the different soaking times; applying closure stress to an upper slab of the 3D model by utilizing a stress interaction effect, completely fixing a lower slab of the 3D model to simulate a crustal fracture closure process, outputting an average embedding volume of the upper slab and lower slab after the model is stabilized, and obtaining the embedding volume-soaking time curve at the different soaking times.
Step S5, modifying equivalent Young’s modulus of proppant embedded in a rock mass based on the embedding volume-soaking time curve, and obtaining a calculation formula for a proppant embedding volume considering the softening effect.
Where w denotes a proppant embedding volume, a unit of which is mm; a0 and a1 denote modification factors, which are 0.0646 and 18.2 respectively, and dimensionless; R denotes a particle size of proppant, a unit of which is mm; P denotes crustal stress, a unit of which is MPa; E1 denotes Young’s modulus of proppant, a unit of which is MPa; v1 denotes a Poisson’s ratio of the proppant, which is dimensionless; v2 denotes a Poisson’s ratio of the rock slab, which is dimensionless; H denotes a thickness of a rock slab, a unit of which is mm; t denotes a soaking time, a unit of which is d; Et denotes an equivalent Young’s modulus, a unit of which is MPa; E0 denotes Young’s modulus of a standard core, a unit of which is MPa; and a and b denote fitting coefficients.
The modifying equivalent Young’s modulus of proppant embedded in a rock mass based on the embedding volume-soaking time curve includes the following steps.
(1) Obtaining a embedding volume at a soaking time t1 according to the embedding volume-soaking time curve obtained by the numerical simulation, and introducing the embedding volume into the calculation formula for the proppant embedding volume of the proppant embedded in a rock mass, and obtaining equivalent Young’s modulus Et1 at the soaking time t1 by calculating reversely, repeating the above process, obtaining equivalent Young’s modulus Et2 at a soaking time t2, equivalent Young’s modulus Et3 at a soaking time t3, ..., equivalent Young’s modulus Etn at a soaking time tn.
(2) According to equivalent Young’s modulus corresponding to the different soaking times, obtaining equivalent Young’s modulus of a softened rock slab by regression.
In some embodiments, application scenarios of the calculation method for predicting proppant embedding depth based on a shale softening effect may include the processor, a storage device, network, and a terminal device, etc. The storage device may be configured to store data and/or instructions, for example, the storage device may store measurement data obtained by the processor from an experiment, etc. The network may connect components of the system and/or connect the system to external resource components, allowing communication between the components, and communication with other components outside the system, facilitating the exchange of data and/or information. For example, the processor may obtain the spontaneous imbibition depth-soaking time curve from the storage device through the network. The processor may determine the spontaneous imbibition depth-soaking time curve and the Young’s modulus-soaking time curve of core surfaces based on experimental data, and transfer the spontaneous imbibition depth-soaking time curve and the Young’s modulus-soaking time curve of core surfaces to the terminal device through the network. The terminal device may be one or more terminals used by a user. For example, the terminal device may include a mobile device, a computer laptop, a tablet, etc. or any combination thereof. The user of the terminal device may be an owner of the terminal device, and the user may view the spontaneous imbibition depth-soaking time curve and the Young’s modulus-soaking time curve of core surfaces, etc. based on the terminal device.
In some embodiments of the present disclosure, automatically obtaining measurement data of an experiment, determining the spontaneous imbibition depth-soaking time curve, the Young’s modulus-soaking time curve of core surfaces, establishing a 3D model, and finally obtaining the calculation formula for a proppant embedding volume considering the softening effect by the processor may not only process a large amount of data, make parameters of obtained curves and calculation formulas more accurate, and also enhance calculation speed efficiently and shorten calculation time.
In some embodiments of the present disclosure, assumptions considered in numerical models in the present disclosure are closer to reality, simulated fracture width values under different softening conditions have a high fit with actual measured values, and the numerical models are strongly process-oriented, which can predict a fracture width value of an actual shale reservoir after hydraulic fracturing accurately and efficiently.
Step S11, determining a first soaking time set.
The first soaking time set refers to a set of a first set of soaking times. For example, the first soaking time set may be represented as (0, 3, 5, 7, 9, 11). A count of soaking times in the first soaking time set may be more, such as ten.
In some embodiments, the processor may determine the first soaking time set in a plurality of ways. For example, the first soaking time set may be a default value, a preset value, or the like.
step S12, determining first wetting angles of the standard cores.
In some embodiments, the first wetting angles include wetting angles of the standard cores corresponding to the at least one soaking time in the first soaking time set. In some embodiments, the first wetting angles may be obtained by performing an experiment on the standard cores at soaking times in the first soaking time set respectively and calculating an experimental result. For more details about the wetting angle, please refer to step S1 and related descriptions.
In some embodiments, when performing an experiment on the standard cores at soaking times in the first soaking time set respectively, a corresponding equivalent soaking condition may be determined based on the soaking times in the first soaking time set, then a soaking experiment may be conducted on the standard core with the equivalent soaking condition, and wetting angles obtained from an experimental result may be determined as the first wetting angles of the standard cores.
The equivalent soaking condition refers to a soaking condition that has an equivalent soaking effect compared to effect at the soaking times in the first soaking time set. In some embodiments, a soaking time of the equivalent soaking condition is less than a soaking time of the first soaking time set. For example, if a soaking time of the first soaking time set is 5 days, the equivalent soaking condition may include a soaking time of 1 day, heating at a°C, and pressurization at bPa.
In some embodiments, the processor may determine the equivalent soaking condition in a plurality of ways. For example, the processor may process the soaking time through an equivalent soaking condition determination model to determine the equivalent soaking condition.
The equivalent soaking condition determination model may be a machine learning model for determining the equivalent soaking condition. The equivalent soaking condition determination model may be a Neural Networks (NN) model or other models. For example, a Recurrent Neural Network (RNN) model, etc.
In some embodiments, an input of the equivalent soaking condition determination model may include soaking times and rock quality features in the first soaking time set; an output of the equivalent soaking condition determination model may include the equivalent soaking condition.
The rock quality feature refers to a feature associated with the standard core. For example, the rock quality feature may include but is not limited to, a rock quality type (e.g., conglomerate, glutenite, sandstone), a rock pore type, a rock surface porosity, a rock permeability, a rock density, etc. of the standard cores.
In some embodiments, the equivalent soaking condition determination model may be obtained by training based on a plurality of first training samples with a first label. For example, the plurality of first training samples with a first label may be input into an initial equivalent soaking condition determination model, a value of a loss function is constructed by the first label and a result of an initial equivalent soaking condition determination model, and parameters of the model are determined by iteratively updating the initial equivalent soaking condition based on the loss function. When the loss function of the initial equivalent soaking condition determination model meets a preset iteration condition, the model training is completed, and the trained equivalent soaking condition determination model is obtained. The preset iteration condition may be that the loss function converges, a count of iterations reaches a threshold, or the like. For example, the count of iterations may be not less than a preset count, such as not less than 100,000 times.
In some embodiments, the first training samples may include sample rock quality features and a sample soaking time of the sample standard cores when experiments are performed on different sample standard cores. The first label may include a sample equivalent soaking condition of the sample standard cores corresponding to the first training samples. For example, a count of the first training samples and the first label corresponding to the first training samples may be not less than a preset count of sets, such as not less than 100,000 sets. In some embodiments, the first training samples may be obtained based on historical data (for example, historical rock quality features and historical soaking times of standard cores), and the first label may be determined in the following manner.
First, a sample soaking experiment is conducted on the sample standard cores at the sample soaking time to obtain a sample experimental result x0 of the sample soaking experiment (for example, the sample experimental result may include a wetting angle, Young’s modulus); a plurality of sets of equivalent soaking conditions are generated (for example, randomly generate)based on the sample soaking time, and an experiment is conducted on the sample standard cores in a simulated environment (for example, using simulation software to simulate) based on the plurality of sets of equivalent soaking conditions to obtain experimental results under each equivalent soaking condition x1, x2, ..., xn respectively;, and an equivalent soaking condition corresponding to an experimental result with the largest similarity (for example, an experimental result xk) is obtained by calculating a similarity between each of the experimental results x1, x2, ..., xn and a sample experimental result x0 respectively.
At the same time, in a real environment, a soaking experiment is conducted on the standard cores under an equivalent soaking condition corresponding to the experimental result xk to obtain an experimental result xk′; and a similarity between the experimental result xt′and the sample experimental result x0 is calculated. If the similarity is greater than a similarity threshold, the equivalent soaking condition corresponding to the experimental result xk is determined as the first label of the first training samples. If the similarity is less than the similarity threshold, then a soaking experiment is conducted under equivalent soaking conditions corresponding to other experimental results in other simulated environments. If an equivalent soaking condition whose similarity is greater than the similarity threshold cannot be obtained in the end, the first label of the first training samples is marked as “original soaking time, heating = 0° C., pressurization = 0 Pa”. The similarity threshold may be an experience value, a default value, a preset value, or the like.
In some embodiments, the processor may construct a first target vector based on the soaking time and the rock quality feature; determine a first correlation vector through a first vector database based on the first target vector; and determine a reference equivalent soaking condition corresponding to the first correlation vector as an equivalent soaking condition corresponding to the first target vector.
The first target vector refers to a vector constructed based on the soaking time and the rock quality feature. There are many ways to construct the first target vector. For example, the processor may input the soaking time and rock quality feature into an embedding layer for processing to obtain the first target vector. In some embodiments, the embedding layer may be obtained through joint training with the wetting angle prediction model.
The first vector database includes a plurality of first reference vectors, and each of the plurality of first reference vectors has a corresponding reference equivalent soaking condition. For example, a count of the first reference vector in the first vector database may be not less than a preset count, such as not less than 100,000.
The first reference vector refers to a vector constructed based on a historical soaking time and historical rock quality feature when the experiment is performed on the standard cores during a historical time period, the reference equivalent soaking condition corresponding to the first reference vector may be a historical equivalent soaking condition when the experiment is performed on the standard core during a historical time period. For a construction method of the first reference vector, please refer to the above construction method of the first target vector.
In some embodiments, the processor may calculate a vector distance between the first target vector and the first reference vector and determine the equivalent soaking condition of the first target vector. For example, the processor may determine a first reference vector whose vector distance from the first target vector meets a preset condition as a first correlation vector, and determine a reference equivalent soaking condition corresponding to the first correlation vector as the equivalent soaking condition corresponding to the first target vector. The preset condition may be set according to situations. For example, the preset condition may be that the vector distance is the smallest or the vector distance is smaller than a distance threshold, or the like. The vector distance may include but is not limited to, cosine distances, Mahalanobis distances, Euclidean distances, or the like.
In some embodiments, the processor may also match the first target vector with the first reference vector in the first vector database in other ways. For example, the processor may perform vector matching by means of Nearest Neighbor Search (NN), Approximate Nearest Neighbor Search (ANN), or the like.
In some embodiments, the processor may determine the equivalent soaking condition based on the soaking time and the rock quality feature, and upload the equivalent soaking condition to the storage device for storage or transfer the equivalent soaking condition to the terminal device to display to the user through the network.
In some embodiments of the present disclosure, the processor may determine the equivalent soaking condition through the soaking time and the rock quality feature, perform simulation experiments under a large number of equivalent soaking conditions to obtain an equivalent soaking condition with the shortest soaking time and the closest soaking effect to a real environment, and conduct a soaking experiment on the standard core using the equivalent soaking condition instead of the soaking time, experimental time can be significantly shortened and experimental efficiency can be increased while obtaining a same experimental result.
Step S13, predicting second wetting angles of the standard cores based on the first wetting angles.
In some embodiments, the second wetting angles includes wetting angles of the standard cores corresponding to the least one soaking time in a second soaking time set.
The second soaking time set refers to a set of a second set of soaking times.
In some embodiments, the processor may determine the second soaking time set in a plurality of ways. For example, the second soaking time set may be a default value, a preset value, or the like.
In some embodiments, a time difference between the at least two soaking times in the first soaking time set may be greater than a preset threshold, and part of or all soaking times in the second soaking time set may be interpolated among the soaking times of the first soaking time set. The preset threshold may be an experience value, a default value, a preset value, or the like. In some embodiments, a count of soaking times in the first soaking time set may be greater than a count of soaking times in the second soaking time set. For example, the first soaking time set may be (0, 3, 5, 7, 9, 11), and an optional second soaking time set may be (1, 4, 6, 8).
In some embodiments of the present disclosure, by interpolating the soaking times in the second soaking time set among the first soaking time set, and the count of soaking times in the first soaking time set being greater than the count of soaking times in the second soaking time set, a majority of time point data is used to predict a minority of time point data, which is conducive to improving the accuracy of the second wetting angles in subsequent predictions.
In some embodiments, the processor may predict the second wetting angles of the standard cores based on the first wetting angles in a plurality of ways. For example, the processor may fit each soaking time in the first soaking time set and first wetting angles corresponding to each soaking time to obtain a fitting function, and determine the second wetting angles by the fitting function based on the soaking times in the second soaking time set.
In some embodiments, the processor may construct a second target vector based on the rock quality feature and the soaking time; determine a second correlation vector through a second vector database based on the second target vector; and determine a reference wetting angle corresponding to the second correlation vector as the second wetting angle corresponding to the second target vector.
The second target vector refers to a vector constructed based on the rock quality feature and soaking time. For a construction method of the second target vector, please refer to the above-mentioned construction method of the first target vector.
The second vector database includes a plurality of second reference vectors, and each second reference vector of the plurality of second reference vectors has a corresponding reference wetting angle. For example, a count of second reference vectors in the second vector database may be not less than a preset count, such as not less than 100,000.
The second reference vector refers to a vector constructed based on a historical rock quality feature and a historical soaking time when the experiment is performed on the standard core during a historical time period. The reference wetting angle corresponding to the second reference vector may be a historical wetting angle when the experiment is performed on the standard core during a historical time period. For a construction method of the second reference vector, please refer to the above-mentioned construction method of the first target vector.
In some embodiments, the processor may calculate a vector distance between the second target vector and the second reference vector respectively, and determine the second wetting angle of the second target vector. For a manner of determining the second wetting angle of the second target vector, please refer to the above-mentioned manner of determining the equivalent soaking condition of the first target vector.
In some embodiments, the processor may process the first wetting angles through the wetting angle prediction model to obtain the second wetting angle of the standard core. For more information about the wetting angle prediction model, please refer to
Step S14, obtaining the spontaneous imbibition depth-soaking time curve by utilizing the modified LW model under the spontaneous imbibition effect based on the first wetting angles and the second wetting angles.
In some embodiments, in order to enhance the accuracy of the spontaneous imbibition depth-soaking time curve, a volume of data of the spontaneous imbibition distance of the standard cores at different soaking times by utilizing the modified LW model under the spontaneous imbibition effect may be no less than a preset volume, e.g., no less than thousands of items.
In some embodiments, the soaking time in step S2 may include soaking times in the first soaking time set and the second soaking time set.
In some embodiments, the processor may obtain the spontaneous imbibition depth-soaking time curve based on the wetting angle, soaking time, and fluid viscosity, and upload the spontaneous imbibition depth-soaking time curve to the storage device for storage, or transfer the spontaneous imbibition depth-soaking time curve to the terminal device to display to the user through the network.
In some embodiments of the present disclosure, based on the processor predicting the second wetting angle through the first wetting angles, and finally obtaining the spontaneous imbibition depth-soaking time curve, this manner shortens a determination process and calculation amount of the wetting angle, and at the same time ensures the accuracy of the spontaneous imbibition depth-soaking time curve and improves higher efficiency, and this manner also processes a large amount of wetting angle data, so as to further improve the accuracy of the spontaneous imbibition depth-soaking time curve.
The wetting angle prediction model may be a machine learning model for determining a wetting angle. The wetting angle prediction model may be Neural Networks (NN) model or other models. For example, Recurrent Neural Network (RNN) model, etc.
In some embodiments, an input of the wetting angle prediction model 340 may include rock quality features 310, each soaking time in the first soaking time set and first wetting angles 320 corresponding to each soaking time, a second soaking time set 330 and a liquid type 380; an output of the wetting angle prediction model 340 may include second wetting angles 350 corresponding to each soaking time in the second soaking time set. For more information about the first soaking time set, the second soaking time set, and the rock quality feature, please refer to
The liquid type refers to a type of liquid used to soak the standard core. For example, the liquid type may include, but is not limited to, pure water, slick water, etc.
In some embodiments, the processor may obtain no less than a preset count of training samples 370-1 with labels 370-2, the training samples 370-1 include sample rock quality features of sample standard cores, a sample first soaking time set, a sample first wetting angle corresponding to the sample first soaking time set, a sample second soaking time set, and a liquid type of sample liquid for soaking sample standard core; and the labels 370-2 of the training samples 370-1 include sample second wetting angles corresponding to the sample second soaking time set. The processor may iteratively update an initial wetting angle prediction model 360 by using the no less than a preset count of training samples 370-1 with labels 370-2 to obtain the wetting angle prediction model 340.
For example, the processor may input the no less than a preset count of training samples 370-1 into the initial wetting angle prediction model 360, construct a loss function through the labels 370-2 and a result of the initial wetting angle prediction model 360, and iteratively update parameters of the initial wetting angle prediction model 360 based on the loss function. When the loss function of the initial wetting angle prediction model 360 meets a preset iteration condition, the model training is completed, and the trained wetting angle prediction model 340 is obtained. The preset iteration condition may be that the loss function converges, a count of iterations reaches a threshold or the like. For example, the count of iterations may be not less than a preset count, such as not less than 100,000 times. The first soaking time set and the second soaking time set may include a plurality of soaking times.
In some embodiments, the processor may obtain training samples and labels in a plurality of ways. For example, the processor may obtain training samples and labels through historical data. A count of training samples and labels is not less than a preset count, such as not less than 100,000 sets.
In some embodiments, the processor my obtain the training samples and labels based on historical data, and upload the obtained training samples and labels to the storage device for storage through the network.
In some embodiments of the present disclosure, through the wetting angle prediction model to process the rock quality feature, each soaking time of the first soaking time set and a first wetting angle corresponding to the each soaking time set to determine a second wetting angle corresponding to each soaking time in the second soaking time set, this manner can consider various factors simultaneously, so that the determination of the second wetting angle is efficient and accurate, and errors in manual determination can be avoided.
In some embodiments, in step S2, before performing a nano-indentation experiment on the standard cores, it is necessary to dry the standard cores based on drying parameters. As shown in
The drying parameters refer to parameters related to drying the standard cores. For example, drying parameters may include but are not limited to, a drying temperature, a drying time, or the like.
In some embodiments, the processor may determine the drying parameters of the standard cores after the soaking time based on the rock quality feature and soaking time of the standard core in a plurality of ways. For example, the processor may determine the drying parameters through a preset data comparison table based on the rock quality features and soaking time of the standard cores. The preset data comparison table records drying parameters corresponding to different rock quality features and soaking times of the standard cores. The preset data comparison table may be preset based on prior knowledge or historical data.
In some embodiments, the drying parameters may be determined based on optimal historical drying parameters obtained through vector matching, and the optimal historical drying parameters are determined based on a modulus similarity threshold. For example, the processor may construct a third target vector based on the rock quality features and soaking time; determine a third correlation vector through a third vector database based on the third target vector; and determine reference drying parameters corresponding to the third correlation vector as drying parameters corresponding to the third target vector.
The third target vector refers to a vector constructed based on the rock quality feature and soaking time. For a construction method of the third target vector, please refer to the above-mentioned construction method of the first target vector in
The third vector database includes a plurality of third reference vectors, and each third reference vector of the plurality of third reference vectors has corresponding reference drying parameters. For example, a count of the third reference vector in the third vector database may be not less than a preset count, such as not less than 100,000.
The third reference vector refers to a vector constructed based on a historical rock quality feature and a historical soaking time when the standard cores are dried during a historical time period. The reference drying parameters corresponding to a reference vector may be historical optimal drying parameters when the standard cores are dried during a historical time period. For a construction method of the third reference vector, please refer to the construction method of the first target vector in
In some embodiments, the processor may calculate a vector distance between the third target vector and the third reference vector, and determine the drying parameters of the third target vector. For a manner to determine the drying parameters of the third target vector, please refer to the manner to determine the equivalent soaking condition of the first target vector in
In some embodiments, the historical optimal drying parameters may be determined in the following manner. In a real environment, a sample core with a rock quality feature corresponding to the third reference vector is obtained, and under a soaking condition corresponding to the third reference vector, and then after being naturally dried in an internal environment of rock layer (for example, placing the sample core in a real environment of rock layer for drying), sample Young’s modulus of the sample core is obtained through performing an experiment on the sample core and calculation; a plurality of sets of candidate drying parameters are generated (e.g., randomly generating); for each set of candidate drying parameters, the sample core is simulated drying (for example, drying by a simulated drying software), and Young’s modulus of the sample core is calculated after drying based on the set of candidate drying parameters; a similarity between Young’s modulus corresponding to the candidate drying parameters and the sample Young’s modulus is calculated, and candidate drying parameters whose similarity is greater than a modulus similarity threshold are determined as the historical optimal drying parameters. The modulus similarity threshold may be a default value, a default value, etc.
In some embodiments, if there is a plurality of candidate drying parameters whose similarity is greater than the modulus similarity threshold, candidate drying parameters with the shortest drying time may be determined as the historical optimal drying parameters, which is conducive to shortening a drying time and improving efficiency.
In some embodiments, the modulus similarity threshold may be related to core parameter sensitivity. For example, the greater the core parameter sensitivity is, the smaller the modulus similarity threshold may be set.
The core parameter sensitivity refers to a change degree of parameters of a core after the core is processed under different conditions. For example, the core parameter sensitivity may be used to reflect a change degree of parameters for evaluating rock properties such as the wetting angle and Young’s modulus of the core after soaking, heating, pressurizing, and other treatments. The core parameter sensitivity may be represented by a real number between 0 and 1. The larger the number is, the greater the change degree of parameters of a core is after the core is processed under different conditions.
In some embodiments, the processor may determine the core parameter sensitivity in a plurality of ways. For example, the processor may construct a fourth target vector based on the rock quality feature; determine a fourth correlation vector through a fourth vector database based on the fourth target vector; and determine a reference core parameter sensitivity corresponding to the fourth correlation vector as a core parameter sensitivity corresponding to the fourth target vector. For more information about the rock quality feature, please refer to
The fourth target vector refers to a vector constructed based on the rock quality feature. For a construction method of the fourth target vector, please refer to the construction method of the first target vector in
The fourth vector database includes a plurality of fourth reference vectors, and each fourth reference vector in the plurality of fourth reference vectors has a corresponding reference core parameter sensitivity. For example, a count of fourth reference vectors in the fourth vector database may be not less than a preset count, such as not less than 100,000.
The fourth reference vector refers to a vector constructed based on the historical rock quality feature of the core during a historical time period. For a construction method of the fourth reference vector, please refer to the construction method of the first target vector in
The core parameter sensitivity corresponding to the fourth reference vector may be determined in the following manner. A large number of experiments are conducted on cores with different rock quality features, after being processed under different conditions (for example, soaking, heating, pressurizing), parameters for evaluating rock properties such as a wetting angle, Young’s modulus of a core with a rock quality feature change greatly (for example, greater than a change degree threshold), then the core parameter sensitivity of the core with the rock quality feature is higher. In some embodiments, a conversion ratio, conversion equation, etc. may also be preset to convert a change degree of parameters for evaluating rock properties such as a wetting angle and Young’s modulus of the core after an experiment into the core parameter sensitivity of the core with the rock quality feature.
In some embodiments of the present disclosure, by correlating the modulus similarity threshold with the core parameter sensitivity and considering a change degree of a wetting angle, Young’s modulus, etc. of cores with different rock quality features after processing, the modulus similarity threshold is set more reasonably, which is conducive to obtaining more optimal drying parameters.
In some embodiments, the processor may determine the drying parameters based on the rock quality feature and soaking time, and upload the determined drying parameters to the storage device for storage through the network.
In some embodiments of the present disclosure, drying the standard cores based on the drying parameters realizes a more accurate simulation of a drying condition in a real environment, which is conducive to improving the accuracy of the Young’s modulus-soaking time curve. Based on the determination of drying parameters by the processor, a large number of candidate drying parameters may be generated and simulated to obtain drying conditions that are closer to the real environment, further improving the accuracy of the subsequent obtained Young’s modulus-soaking time curve.
The calculation method for predicting a proppant embedding depth based on a shale softening effect in the present disclosure includes the following steps.
Step S1, determining a spontaneous imbibition depth-soaking time curve. The spontaneous imbibition depth-soaking time curve may be obtained by making a target block shale into five standard cores, soaking the five standard cores for 0, 3, 5, 7, and 15 days respectively, then conducting a spontaneous imbibition experiment on faces of the five standard cores at different soaking times, and utilizing a modified LW model under a spontaneous imbibition effect.
Step S2, determining a Young’s modulus-soaking time curve of the standard cores. The Young’s modulus-soaking time curve of the standard cores may be obtained by conducting a nano-indentation experiment on upper and lower faces of the cores after drying the five standard cores in step S1, as shown in
Step S3, establishing a 3D model of proppant embedded in a rock slab by a finite element method, wherein the rock slab in the 3D model is divided into an unsoftened layer and a softened layer (as shown in
Step S4, obtaining an embedding volume-soaking time curve (embedding depth in
Step S5, obtaining an embedding volume at a soaking time t1 according to the embedding volume-soaking time curve obtained by the numerical simulation, and introducing the embedding volume into the calculation formula for the proppant embedding volume of the proppant embedded in a rock mass, and obtaining equivalent Young’s modulus Et1 at the soaking time t1 by calculating reversely, repeating the above process, obtaining equivalent Young’s modulus Et2 at a soaking time t2, equivalent Young’s modulus Et3 at a soaking time t3, ..., equivalent Young’s modulus Etn at a soaking time tn.
Step S6, according to equivalent Young’s modulus corresponding to the different soaking times, obtaining equivalent Young’s modulus of a softened rock slab by regression, wherein Et is a function related to a soaking time, a relationship formula of which is as follows:
Then obtaining a calculation formula for a proppant embedding volume considering the softening effect:
Where w denotes the proppant embedding volume, a unit of which is mm; a0 and a1 denote modification factors, which are 0.0646 and 18.2 respectively, and dimensionless; R denotes the a particle size of proppant, a unit of which is mm; P denotes the crustal stress, a unit of which is MPa; E1 denotes the Young’s modulus of the proppant, a unit of which is MPa; v1 denotes the Poisson’s ratio of the proppant, which is dimensionless; v2 denotes the Poisson’s ratio of the rock slab, which is dimensionless; H denotes the thickness of the rock slab, a unit of which is mm; t denotes the soaking time, a unit of which is d; Et denotes the equivalent Young’s modulus, a unit of which is MPa; and E0 denotes the Young’s modulus of the standard core, a unit of which is MPa.
The basic concepts have been described above, obviously, for those skilled in the art, the above-detailed disclosure is only an example and does not constitute a limitation to the present disclosure. Although not expressly stated here, those skilled in the art may make various modifications, improvements, and corrections to the present disclosure. Such modifications, improvements, and corrections are suggested to the present disclosure, so such modifications, improvements, and corrections still belong to the spirit and scope of the exemplary embodiments of the present disclosure.
Meanwhile, the present disclosure uses specific words to describe the embodiments of the present disclosure. For example, “one embodiment”, “an embodiment”, and/or “some embodiments” refer to a certain feature, structure, or characteristic related to at least one embodiment of the present disclosure. Therefore, it should be emphasized and noted that two or more references to “one embodiment” “an embodiment” or “an alternative embodiment” in different places in the present disclosure do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the present disclosure may be properly combined.
In addition, unless explicitly stated in the claims, the order of processing elements and sequences described in the present disclosure, the use of numbers and letters, or the use of other names are not used to limit the sequence of processes and methods in the present disclosure. While the foregoing disclosure has discussed some embodiments of the invention that are presently believed to be useful by way of various examples, it should be understood that such detail is for illustrative purposes only and that the appended claims are not limited to the disclosed embodiments, but rather, the claims are intended to cover all modifications and equivalent combinations that fall within the spirit and scope of the embodiments of the present disclosure. For example, although the implementation of various components described above may be embodied in a hardware device, it may also be implemented as a software only solution, e.g., an installation on an existing server or mobile device.
In the same way, it should be noted that in order to simplify the expression disclosed in the present disclosure and help the understanding of one or more embodiments of the present disclosure, in the foregoing description of the embodiments of the present disclosure, sometimes multiple features are combined into one embodiment, drawings, or descriptions thereof. This method of disclosure does not, however, imply that the subject matter of the present disclosure requires more features than are recited in the claims. Rather, claimed subject matter may lie in less than all features of a single foregoing disclosed embodiment.
In some embodiments, numbers describing the number of components and attributes are used, and it should be understood that such numbers used in the present disclosure of the embodiments, in some examples, use the modifiers “about”, “approximately” or “substantially”. Unless otherwise stated, “about”, “approximately” or “substantially” indicates that the stated figure allows for a variation of ±20%. Accordingly, in some embodiments, the numerical parameters used in the present disclosure and claims are approximations that can vary depending on the desired characteristics of individual embodiments. In some embodiments, numerical parameters should take into account the specified significant digits and adopt the general digit reservation method. Although the numerical ranges and parameters used in some embodiments of the present disclosure to confirm the breadth of the range are approximations, in specific embodiments, such numerical values should be set as precisely as practicable.
Each patent, patent application, patent application publication, and other material, such as article, book, specification, publication, document, etc., cited in the present disclosure is hereby incorporated by reference in its entirety. Historical application documents that are inconsistent with or conflict with the content of the present disclosure are excluded, and documents (currently or later appended to the present disclosure) that limit the broadest scope of the claims of the present disclosure are excluded. It should be noted that if there is any inconsistency or conflict between the descriptions, definitions, and/or terms used in the accompanying materials of the present disclosure and the contents of the present disclosure, the descriptions, definitions, and/or terms used in the present disclosure shall prevail.
Finally, it should be understood that the embodiments described in the present disclosure are only used to illustrate the principles of the embodiments of the present disclosure. Other modifications are also possible within the scope of the present disclosure. Therefore, by way of example and not limitation, alternative configurations of the embodiments of the present disclosure may be considered consistent with the teachings of the present disclosure. Accordingly, the embodiments of the present disclosure are not limited to the embodiments explicitly introduced and described in the present disclosure.
| Number | Date | Country | Kind |
|---|---|---|---|
| 202210393249.8 | Apr 2022 | CN | national |
