Disclosure of Invention
The present invention has been made in view of the above problems, and has as its object to provide a formation water type identification method, a pollution rate determination method and a computing device which overcome or at least partially solve the above problems.
According to one aspect of the present invention, there is provided a formation water type identification method comprising:
collecting pumping parameters and actual fluid density measured values corresponding to a plurality of moments in a preset time window;
generating a fluid density change curve according to the collected pumping parameters and the actual fluid density measured values;
based on the fluid density profile, a formation water type is identified.
In an alternative embodiment, the identifying the formation water type based on the fluid density profile further comprises:
Judging whether a slug flow section exists in the fluid density change curve;
if yes, extracting a plurality of density maxima from a slug flow section of the fluid density change curve;
Generating a density maximum change curve according to the density maxima and pumping parameters corresponding to each density maximum;
and identifying the stratum water type according to the density maximum change curve.
In an alternative embodiment, the identifying the formation water type based on the density maxima profile further comprises:
If the smoothness of the density maximum change curve is greater than a preset threshold, determining that the stratum water type is the same layer of oil and water or the same layer of gas and water;
and if the smoothness of the density maximum change curve is smaller than or equal to a preset threshold value, determining that the stratum water type is an oil layer or a gas layer.
In an alternative embodiment, if no slugging section exists in the fluid density profile, the formation water type is determined to be a formation water layer.
In an alternative embodiment, the slug flow section is a section with regular oscillation of density values.
In an alternative embodiment, the pumping parameters and the actual fluid density measurements are acquired by a preset sampling device;
the preset sampling equipment at least comprises a pumping module and a density sensor, wherein the density sensor is positioned at the downstream of the pumping module.
According to another aspect of the present invention, there is provided a formation water pollution rate determining method, including:
identifying the formation water type by adopting the formation water type identification method;
if the formation water type is identified as the oil-water layer or the gas-water layer, extracting a plurality of density maxima of a plug flow section in the middle section of the fluid density change curve, and determining pumping parameters corresponding to each density maximum;
Calculating the density of pure stratum water according to the pumping parameters corresponding to the density maxima;
and calculating the pollution rate of the formation water according to the pure formation water density, the pure water-based drilling fluid filtrate density and the currently acquired actual fluid measurement density.
In an alternative embodiment, the calculating the water density of the pure formation according to the plurality of density maxima and the pumping parameters corresponding to each density maxima further comprises:
Taking the density maxima and pumping parameters corresponding to the density maxima as sample data;
And training a preset neural network model through the sample data to obtain the association relationship between the actual fluid density measured value and the pumping parameter of the stratum water type oil-water same layer or gas-water same layer.
And determining the density value of the pure formation water according to the preset pumping parameter and the association relation, wherein the preset pumping parameter is the pumping parameter when the pure formation water is expected to be collected.
According to yet another aspect of the present invention, there is provided a computing device comprising a processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface completing communication with each other through the communication bus;
the storage is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the formation water type identification method;
and/or the executable instructions enable the processor to execute the operations corresponding to the formation water type identification method.
According to still another aspect of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, the executable instruction causing the processor to perform operations corresponding to the above-mentioned formation water type identification method;
and/or the executable instructions enable the processor to execute the operations corresponding to the formation water type identification method.
The invention discloses a stratum water type identification method, a pollution rate determination method and computing equipment. And acquiring pumping parameters and actual fluid density measured values corresponding to a plurality of moments in a preset time window, generating a fluid density change curve according to the acquired pumping parameters and the actual fluid density measured values, and identifying the formation water type based on the fluid density change curve, so that the formation water type can be monitored in real time without repeatedly sampling from underground to the ground for analysis. On one hand, the identification efficiency of the formation water type is improved, a basis is provided for the sampling time of the formation water, and on the other hand, the sampling cost is saved and the operation risk is reduced.
And after the formation water type is identified as the oil-water layer or the gas-water layer, extracting a plurality of density maxima of a plug flow section in the middle section of the fluid density change curve, determining pumping parameters corresponding to the density maxima, calculating the pure formation water density according to the density maxima and the pumping parameters corresponding to the density maxima, and calculating the formation water pollution rate according to the pure formation water density, the pure water-based drilling fluid filtrate density and the currently acquired actual fluid measurement density, thereby accurately calculating the formation water pollution rate and providing a basis for sampling and analyzing the formation water.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 shows a schematic flow chart of a formation water type identification method according to an embodiment of the present invention. Specifically, the method for identifying the type of the formation water provided by the embodiment is applied to a logging scene of water-based mud.
As shown in fig. 1, the method comprises the steps of:
step S110, collecting pumping parameters and actual fluid density measurement values corresponding to a plurality of moments within a preset time window.
The pumping parameters and actual fluid density measurements are acquired by a preset sampling device. The preset sampling device at least comprises a pumping module and a density sensor. In addition, the preset sampling device may also include a probe, a sample bottle, and the like.
Fig. 2a and 2b show schematic data collection diagrams of a preset sampling device for fluid extraction from top to bottom and fluid extraction from bottom to top, respectively. As can be seen from fig. 2a and 2b, the density sensor in the preset sampling device is located downstream of the pumping module. With this structure, the pumping module can separate oil-water, gas-water or gas-oil-water under gravity according to the density difference of the fluid, thereby forming a slug flow. The density sensor is arranged downstream of the pumping module, and the change can be accurately measured.
In an alternative embodiment, the predetermined sampling device may be a cable formation pressure measurement sampler (EFDT). The data acquisition process is that before logging, the cable stratum pressure measuring and sampling instrument is placed at the underground target depth, and the probe seat is sealed on the well wall. After the seat seal is successful, a pumping module is started, stratum fluid enters the pipeline through the suction port, the actual density value of the fluid is measured in real time through a density sensor, and the density value measured by the density sensor is the actual fluid density measured value. The pumping module will pump parameters and the density sensor will upload actual fluid density measurements to the surface system via remote transmission of the cable.
In another alternative embodiment, the predetermined sampling device may be a sampling while drilling Instrument (IFSA). The data acquisition process is that before logging, the sampling instrument while drilling is placed to the underground target depth, the ground system is communicated with the underground mud transmission device through the mud transmission device, and the underground mud transmission device sends a ground command to the sampling instrument while drilling. And then, the sampling instrument while drilling seats the probe on the well wall, after the seat sealing is successful, the pumping module is started, stratum fluid enters the pipeline through the suction port, the actual density value of the fluid is measured in real time through the density sensor, and the density value measured by the density sensor is the actual fluid density measured value. The pumping module uploads the pumping parameters and the density sensor to the surface system in real time via the mud delivery device.
Further, the data collected by the preset sampling device is divided at different moments, i.e. each moment has a corresponding pumping parameter and an actual fluid density measurement. The pumping parameters are in particular pumping time or pumping volume, etc.
It can be seen that, unlike the prior art, the present embodiment does not send samples collected at a plurality of moments to the surface platform for analysis, but collects pumping parameters and actual fluid density measurement values at a plurality of moments by the preset sampling device, and sends the collected data back to the surface system for analysis. Therefore, in the prior art, the sample is collected and transmitted, and the identification of the formation water type can be realized only by collecting and transmitting data in the pumping process, so that the overall identification efficiency is improved.
Step S120, generating a fluid density variation curve according to the collected pumping parameters and the actual fluid density measurement.
And taking the pumping parameter as an independent variable, and taking the actual fluid density measured value as the dependent variable to construct a fluid density and pumping parameter change curve, namely generating a fluid density change curve. For example, a fluid density-pump time curve or a fluid density-pump volume curve, etc. may be generated.
Step S130, identifying the formation water type based on the fluid density change curve.
The fluid density profile implies formation water type characteristics. The fluid density profile varies from formation water type to formation water type. The formation water type can be accurately identified through analysis of the fluid density change curve. The stratum water type comprises stratum water layer, oil layer or gas layer, oil-water layer or gas-water layer and the like.
In an alternative embodiment, to improve the accuracy of identifying the formation water type, the formation water type may be specifically identified through the steps shown in fig. 3, and in particular, as shown in fig. 3, the formation water type may be identified through the following steps S131 to S136 based on the fluid density change curve.
Step S131, judging whether a slug flow section exists in the fluid density change curve, if so, executing step S132, and if not, executing step S136.
Because of the different miscibility of water-based drilling fluid filtrate with formation water and oil or gas, slugging can occur in some formation water types. Specifically, the water-based drilling fluid filtrate is completely miscible with formation water, so that slugging is not formed when the formation water type is a formation water layer, while the water-based drilling fluid filtrate is completely immiscible with oil or gas, so that slugging is formed in the formation water type is an oil-water or gas-water layer and an oil or gas layer. Based on this feature, the present embodiment first determines whether a slug flow exists based on the fluid density change curve.
Specifically, if a slugging flow exists, the actual fluid density measurement will regularly fall and rise with pumping parameters. The fluid density change curve is embodied as a section with regular oscillation of a density value, and the section with regular oscillation of the density value in the fluid density change curve is a slug flow section. In order to avoid that the error fluctuation is considered as a slug flow section, the difference between two adjacent maximum values and minimum values in the slug flow section in the embodiment is greater than or equal to a preset density value.
Step S132, extracting a plurality of density maxima from the slug flow section of the fluid density variation curve, and generating a density maximum variation curve according to the plurality of density maxima and pumping parameters corresponding to each density maxima.
Because the formation water type is oil-water or gas-water and oil-or gas-layer, and the slug flow is formed in the oil-or gas-layer, after determining that the slug flow exists based on the fluid density change curve, the oil-water or gas-water and oil-or gas-layer is further identified through step S132 and step S133.
Specifically, although the oil-water layer or the gas-water layer and the oil layer or the gas layer both form a slug flow, the oil-water layer or the gas-water layer and the oil layer or the gas layer have different meanings of the maximum value of the plug flow section in the middle section of the fluid density change curve. The maximum value of the plug flow section in the middle section of the fluid density change curve in the oil-water layer or the gas-water layer corresponds to the density value of the water-based drilling fluid filtrate and the stratum water mixture, which is smoother along with the change of the pumping parameter, and the maximum value of the plug flow section in the middle section of the fluid density change curve in the oil layer or the gas layer corresponds to the density value of the water-based drilling fluid filtrate, which is more severe along with the change of the pumping parameter.
Based on the above, the step firstly extracts a plurality of density maxima from the plug flow section in the middle section of the fluid density change curve, and determines pumping parameters corresponding to each extracted density maxima. And finally, generating a density maximum change curve according to the plurality of density maxima and pumping parameters corresponding to each density maximum. The independent variable in the density maximum change curve is pumping parameter, and the dependent variable is density maximum. And then identifying the formation water type according to the density maximum change curve.
Step S133, judging whether the smoothness of the density maximum change curve is larger than a preset threshold, if so, executing step S134, and if not, executing step S135.
The smoothness of the density maximum change curve shows the speed of the density maximum along with the change of the pumping parameter, namely, the faster the density maximum along with the change of the pumping parameter, the lower the smoothness of the density maximum change curve, otherwise, the slower the density maximum along with the change of the pumping parameter, the higher the smoothness of the density maximum change curve.
Step S134, determining that the stratum water type is the same water-oil layer or the same gas-water layer.
If the slug flow exists based on the fluid density change curve, and the smoothness of the density maximum change curve is larger than a preset threshold value, determining that the stratum water type is the oil-water same layer or the gas-water same layer.
FIG. 4 shows a schematic diagram of a fluid density change curve of an oil-water layer or a gas-water layer. As shown in fig. 4, because the water-based drilling fluid filtrate is completely miscible with formation water and completely immiscible with oil or gas in the same layer of oil and water or gas and water, there may be a slug flow section in the fluid density profile of the same layer of oil and water. And the frequency of the slug flow is relatively stable. And in the fluid density change curve of the oil-water layer or the gas-water layer, the density maximum value corresponds to the density value of the water-based drilling fluid filtrate and the stratum water mixture. The density maxima drop smoothly and slightly with changes in pumping time or pumping volume. Specifically, the density maximum value is changed from a density value mainly comprising water-based drilling fluid filtrate at the early stage of pumping to a density value mainly comprising formation water relatively smoothly, and finally a relatively stable density value is achieved.
In step S135, the formation water type is determined to be an oil or gas reservoir.
And if the slug flow exists based on the fluid density change curve, and the smoothness of the density maximum change curve is smaller than or equal to a preset threshold value, determining that the stratum water type is an oil layer or a gas layer.
Fig. 5a shows a schematic representation of the fluid density profile of an oil or gas reservoir. As shown in fig. 5a, in an oil layer or a gas layer, since the water-based drilling fluid filtrate is completely insoluble with oil or gas, a shock section corresponding to a slug flow exists in a fluid density change curve of the same layer of oil and water. And the fluid density change curve can fluctuate dramatically up and down the oscillation section. Wherein, the minimum value of the fluid density change curve corresponds to the density of oil or gas, and the minimum value is stable. The maximum value of the fluid density profile corresponds to the density of the water-based drilling fluid filtrate, which has a large change over time, i.e. the smoothness of the density maximum profile is low. When the water-based drilling fluid filtrate invasion zone is small or the pumping time is long, the frequency of slugging becomes smaller and smaller as the pumping time or volume increases, the density maxima slowly thin and disappear, and finally only the density value of stable oil or gas is obtained. Furthermore, if the water-based drilling fluid filtrate invasion zone requires a long time to pump the water-based drilling fluid filtrate, the maximum density is a stable value for a long period of time in the early stage of pumping, as shown in fig. 5 b.
In step S136, the formation water type is determined to be the formation water layer.
And if the slug flow is not determined to exist based on the fluid density change curve, determining the formation water type as a formation water layer. As shown in fig. 6, no slugging sections exist in the formation water type fluid density profile due to the complete miscibility of the water-based drilling fluid filtrate with the formation water. The density value in the fluid density change curve of the formation water type changes smoothly with the pumping time or the volume, in particular, the density value of the water-based drilling fluid filtrate is changed smoothly to the density value of the formation water.
Therefore, in this embodiment, the pumping parameters and the actual fluid density measurement values corresponding to a plurality of moments within the preset time window are collected, the fluid density change curve is generated according to the collected pumping parameters and the actual fluid density measurement values, and finally the formation water type is identified based on the fluid density change curve. By adopting the scheme, the stratum water type can be monitored in real time without multiple times of underground sampling to the ground for analysis. On one hand, the identification efficiency of the formation water type is improved, a basis is provided for the sampling time of the formation water, and on the other hand, the sampling cost is saved and the operation risk is reduced.
Fig. 7 shows a schematic flow chart of a method for determining a pollution rate of formation water according to an embodiment of the present invention.
When the water-based drilling fluid filtrate used in the drilling process invades into the stratum deeply, the water-based drilling fluid filtrate and the original stratum water are mutually dissolved to cause stratum pollution, and the stratum water pollution rate is calculated through the follow-up steps in the embodiment, so that a foundation is provided for stratum water sampling time, stratum water component analysis and the like.
As shown in fig. 7, the method includes the steps of:
step S710, identify formation water type.
The specific implementation process of this step may refer to the description in the embodiment shown in fig. 1, and will not be repeated here.
Step S720, if the formation water type is identified as the oil-water layer or the gas-water layer, extracting a plurality of density maxima of the plug flow section in the middle section of the fluid density change curve, and determining pumping parameters corresponding to each density maximum.
If the formation water type is identified as the oil-water or gas-water layer, a fluid density change curve is generated according to the collected pumping parameters and the actual fluid density measurement value according to the method in the embodiment shown in fig. 1, and a plurality of density maxima of a plug flow section in the middle section of the fluid density change curve are extracted. And obtaining pumping parameters corresponding to any density maximum value. Wherein the density maxima correspond to the same time as the corresponding pumping parameters.
And step S730, calculating the pure formation water density according to the pumping parameters corresponding to the density maxima.
Specifically, a preset neural network model is firstly built in advance, a plurality of density maxima and pumping parameters corresponding to the density maxima are used as sample data, the preset neural network model is trained through the sample data, and the association relation between the actual fluid density measured value of the stratum water type of the oil-water same layer or the gas-water same layer and the pumping parameters is obtained. The association relation specifically refers to the association relation between the density maximum value and the pumping parameter in the slug flow section. It should be understood herein that other models such as a meditation function, a logarithmic function, an exponential function, and a hyperbolic function may be used to obtain the association relationship, which is not limited in this embodiment.
In an alternative training mode, the pumping parameters may be used as input data to the neural network model and the density maxima may be used as output data from the neural network model. The neural network model may be an ANN model. As shown in fig. 8, the pumping volume v= [ V1,V2,…,Vn ] or the pumping time t= [ t1,t2,…,tn ] is taken as an input variable of the neural network model input layer, the model parameters are assumed to be W and b in the hidden layer (the hidden layer may be a single layer or multiple layers), the output variable of the neural network is a=g (wp+b), where p is the input variable and g is a conversion function such as Sigmoid function or other functions. The output layer predicts the density ρ= [ ρ1,ρ2,…,ρn ] by using the neural network model. In this prediction process, a loss function is calculatedThe parameters W and b are adjusted by gradient descent or quasi-newton to minimize the loss function globally, resulting in an optimal model ρ=f (V, W, b) or ρ=f (t, W, b). After the trained neural network model is obtained by adopting the training mode, the preset pumping parameters are used as input data, and the output data corresponding to the neural network model is the pure formation water density.
Fig. 9 shows an effect diagram of the density maxima predicted by the neural network model, and it can be seen from the figure that the density maxima predicted by the neural network model have a high matching degree with the density maxima actually measured obtained by using the EFDT, so that the neural network model in the embodiment has a higher accuracy.
In yet another alternative training mode, the data of the variation of the density maximum value along with the pumping parameter can be used as the input data of the neural network model, and the relationship between the fluid density value and the pumping parameter can be used as the output data of the neural network model. After the trained neural network model is obtained by adopting the training mode, the preset pumping parameters are used as input data, and the association relation between the fluid density value output by the neural network model and the pumping parameters is obtained. And then obtaining the pure stratum water density according to the preset pumping parameters and the association relation.
Further optionally, when the pumping parameter is pumping volume, the preset pumping parameter is pumping volume greater than a preset threshold, and when the pumping parameter is pumping time, the preset pumping parameter is pumping time greater than the preset threshold. The preset threshold may be infinite or a fixed value that is preset. For example, when the pumping parameters are approaching infinity, the training model has a limit value, the preset threshold value is infinity, and when the pumping parameters are approaching infinity, the training model has no significant limit value, the preset threshold pumping volume is 500 liters or the pumping time is the time required to calculate 500 liters of fluid to be pumped based on the pumping rate. The preset threshold may be adjusted based on formation and fluid properties and probe type, for example, the preset threshold may be smaller (e.g., 50 liters) when formation drilling fluid invasion is small and the preset threshold may be larger (e.g., 1000 liters) when formation drilling fluid invasion is large.
In addition, if the formation water type is identified as a formation water layer, pumping parameters corresponding to a plurality of moments in a preset time window and actual fluid density measurement values are used as sample data, a preset neural network model is trained through the sample data, the association relation between the actual fluid density measurement values and the pumping parameters of the formation water type as the formation water layer is obtained, and the density value of the pure formation water is determined according to the preset pumping parameters and the association relation. The model training and predicting process of the formation water layer with the formation water type can refer to the description of the corresponding part in the oil-water layer or the gas-water layer with the formation water type, and the description is omitted herein.
Step S740, calculating the pollution rate of the formation water according to the density of the pure formation water, the density of the filtrate of the pure water-based drilling fluid and the density of the currently collected actual fluid measurement.
Wherein the pure water based drilling fluid filtrate density is determined by at least one of:
the first determination mode is that when pumping is started, the fluid density is measured as the filtrate density of the pure water-based drilling fluid;
Obtaining a drilling fluid filter cake from underground to the ground, extruding filtrate, measuring the density of the fluid by using an EFDT (electronic differential pressure transducer) measuring instrument, and correcting the density to the density at the temperature and the pressure corresponding to the acquired stratum to be used as the density of the filtrate of the pure water-based drilling fluid;
and in the third determination mode, during measurement, calculating the density of the water-based drilling fluid filtrate during measurement as the density of the pure water-based drilling fluid filtrate according to the preset corresponding relation between the density of various pure drilling fluids and the temperature and pressure, the type of the drilling fluid and the corresponding temperature and pressure of the stratum during measurement.
After obtaining the pure formation water density, the pure water-based drilling fluid filtrate density, and the actual fluid measurement density currently acquired, the formation water pollution rate is obtained by the following equation 7-1:
In the formula, epsilon is the pollution rate of the filtrate of the water-based drilling fluid in the formation water, rho is the current acquired actual fluid measurement density, rhomf is the filtrate density of the pure water-based drilling fluid, and rhofw is the density of the pure formation water.
FIG. 10 shows a schematic of the effect of a calculated formation water pollution rate. As can be seen from fig. 10, the formation water pollution rate calculated in this embodiment has a high matching degree with the formation water pollution rate obtained by the conventional EFDT multiple sampling method, so that the accuracy of the formation water pollution rate calculated in this embodiment is high.
Therefore, the embodiment adopts the formation water type identification method to identify the formation water type, if the formation water type is identified as the oil-water layer or the gas-water layer, a plurality of density maxima of a plug flow section in a fluid density change curve are extracted, pumping parameters corresponding to the density maxima are determined, pure formation water density is calculated according to the density maxima and the pumping parameters corresponding to the density maxima, and the formation water pollution rate is calculated according to the pure formation water density, the pure water-based drilling fluid filtrate density and the currently acquired actual fluid measurement density. By adopting the scheme, the pollution rate of the formation water can be accurately calculated, and a foundation is provided for sampling the formation water and analyzing the composition.
FIG. 11 illustrates a schematic diagram of a computing device provided by an embodiment of the present invention. The specific embodiments of the present invention are not limited to a particular implementation of a computing device.
As shown in FIG. 11, the computing device may include a processor 1102, a communication interface (Communications Interface) 1104, memory 1106, and a communication bus 1108.
Wherein the processor 1102, the communication interface 1104, and the memory 1106 communicate with each other via a communication bus 1108. A communication interface 1104 for communicating with network elements of other devices, such as clients or other servers. The processor 1102 is configured to execute the program 1110, and may specifically perform relevant steps in the foregoing embodiment of the method for identifying a formation water type and/or the embodiment of the method for determining a contamination rate of formation water.
In particular, program 1110 may include program code including computer-operating instructions.
The processor 1102 may be a central processing unit CPU, or an Application-specific integrated Circuit ASIC (Application SPECIFIC INTEGRATED Circuit), or one or more integrated circuits configured to implement embodiments of the present invention. The computing device may include one or more processors of the same type, such as one or more CPUs, or of different types, such as one or more CPUs and one or more ASICs.
Memory 1106 for storing program 1110. The memory 1106 may include high-speed RAM memory or may also include non-volatile memory (non-volatile memory), such as at least one disk memory. The specific implementation process may refer to the description in the corresponding method embodiment, and will not be described herein.
The embodiment of the invention provides a non-volatile computer storage medium, which stores at least one executable instruction, and the computer executable instruction can execute the formation water type identification method and/or the formation water pollution rate determination method in any of the method embodiments. The specific implementation process may refer to the description in the corresponding method embodiment, and will not be described herein.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the above description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components according to embodiments of the present invention may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specifically stated.