method of compressing sensor data from one or more sensors in a hydrocarbon operation, and sensor sy

专利摘要:
SENSOR DATA COMPRESSION METHOD FROM ONE OR MORE SENSORS IN A HYDROCARBON OPERATION, AND SENSOR SYSTEM TO PROVIDE COMPRESSED SENSOR DATA, SYSTEM FOR RECEIVING TABLED SENSOR DATA. Systems, methods and devices are provided for compressing a variety of signals, such as signals measured from a hydrocarbon operation, which can be stored and / or transmitted in a compressed form. Segmentation tools and techniques are used to compress the signals. Segmentation techniques include breaking a signal into segments and representing samples of signal data as segment boundary points that can reflect where changes in the signal occur and segment parameters that can be used to model the segmented data. The modalities can be used in real time or in batch modes. New data samples can influence previous segment boundary points and / or segment parameters in some cases. Systems can modify what has already been stored or displayed as a result of reviewing segmentation information based on analysis using the new data samples. The modalities can use different Bayesian analysis techniques, including (...).
公开号:BR112012025784B1
申请号:R112012025784-6
申请日:2011-04-08
公开日:2020-12-08
发明作者:Maurice Ringer；Walter Aldred；Jonathan Dunlop
申请人:Prad Research And Development Limited；
IPC主号:

专利说明:

Many industries, such as the hydrocarbon industry, gather and use a wide variety of data collected from different signals from different sensors. Data often needs to be stored and / or transmitted efficiently. Data broken into smaller homogeneous segments has been widely used to compress a variety of data. These techniques, however, generally compress the entire data set as a block. Those who need to be in real time usually work by accumulating a large amount of data. This information is then compressed as a block, before being transmitted or stored.
In many industries, there are more and better sensors that provide more detailed information that must be transmitted, processed, conducted and / or similar. In information technology, cloud computing, satellite transmissions and / or the like, it is often bandwidth that is the limiting factor in data transmission and / or processing. Merely as an example, in the hydrocarbon industry, there are more and better sensors to detect data related to the exploration, extraction, production and / or transport of hydrocarbons. For better handling of the storage and transmission of data collected from sensors - such as in the hydrocarbon industry, sensors related to the exploration, extraction, production and / or transport of ■ hydrocarbons - the detected data associated with the processes need to be handled effectively and efficiently. BRIEF SUMMARY
The embodiments of the present invention provide systems, methods and / or devices for compressing data received from sensors and / or data to be transmitted over a communication channel. In aspects of the present invention, the sensors may comprise sensors used in a hydrocarbon operation. The modalities can use different tools and segmentation techniques to model the sensor data. Compressed data can be transmitted and / or stored in some cases.
In some modalities, in which each new sample of data has been transferred to the system, the most likely segmentation (until now) can be calculated efficiently and the changes are transmitted and / or stored.
These changes may include real-time data and / or historical data. The most likely reconstruction (back to some point in history) may change with the knowledge of new data. A consequence of this segmentation update is that the receiving or storage device can modify data that has already been stored or displayed. This update process can differentiate this segmentation technique from the existing techniques.
The methods and systems are provided for compression and transmission of field data from a sensor inside the well of a hydrocarbon operation according to various modalities. Multiple samples of sensor data from inside wells are identified. Multiple segmentations of multiple samples of sensor data from inside the well are determined. Each segment can include one or more segments. Each segment can include a segment boundary point, which can reflect a point in the data samples where a threshold has been exceeded with respect to data samples from a previous segment or a point where the data samples start. Each segment can also include one or more segment parameters that provide a linear representation of the data samples for the respective segment. One of the determined segments is selected to represent multiple samples of data based on a maximum of 4/67 posterior analysis of a plurality of determined segments. The segment boundary point and one or more segment parameters for each of the one or more segments of the selected segmentation are stored. A subset of the multiple sensor data samples from inside the well can be accumulated. The segment boundary point and one or more segment parameters for each of the one or more segments of the selected segmentation are transmitted to a surface device of the hydrocarbon operation.
In some embodiments, the methods and / or systems may also include the identification of additional data samples from the in-pit sensor. Multiple updated segmentations of the multiple data samples and additional data samples in one or more segments can be determined. One of the determined updated segments can be selected to represent the multiple data samples and additional data samples based on a maximum of a posteriori analysis of a plurality of determined updated segments. The difference information between the determined segmentation of the multiple data samples and the updated segmentation of the multiple data samples and the additional data samples can be determined. The difference information can include information such as adding a new segment boundary point, deleting a stored segment boundary point, and / or reviewing one or more stored segment parameters of one or more segments. The stored segment boundary point and one or more segment parameters for each of the one or more segments of the determined segmentation based on the determined difference information can be updated.
The methods and systems are provided for compression of sensor data according to various modalities. Methods and systems may include the identification of multiple samples of data from a first sensor. The segmentation of the multiple data samples from the first sensor is determined. The determined segmentation can include several segments of varying sizes. Each segment of the determined segmentation can include one or more segment parameters that provide a representation of the data samples for the respective segment. Each segment can also include a segment boundary point, which indicates a point in the data samples where a threshold has been exceeded for the data samples with respect to one or more segment parameters of a previous segment. The segment boundary point and one or more segment parameters for each of the determined segment segment (s) are stored.
In some embodiments, methods and systems may also include determining multiple segmentations of data samples from the first sensor. One of the targets can be selected based on a 6/67 analysis of the most likely targeting of the multiple targets. The selected target can be used as the target target.
The threshold may depend on at least one transmission bandwidth restriction or a storage restriction. The one or more segment parameters of a respective segment can provide a linear model of the respective segment in some cases. The linear model of a respective segment can include a gradient and / or an axis intersection. The linear model of a respective segment can include a step function and / or a ramp function. The one or more segment parameters of a respective segment can provide a nonlinear model of the respective segment, in some cases.
Some modalities may also include the transmission of the segment boundary point and one or more segment parameters for each one or more segments of the determined segmentation. The transmission of the segment boundary point and one or more segment parameters of each of the one or more segments of the determined segmentation can occur at a rate of less than 3 kilobits per second, at a rate of less than 1 kilobit per second and / or at a rate of less than 100 bits per second in some cases. The transmission of the segment boundary point and one or more segment parameters of each of the one or more segments of the determined segmentation can occur dynamically based on a bandwidth restriction. In some cases, transmission from the segment boundary point and one or more segment parameters for each of the one or more segments of the given segmentation may include delaying the transmission based on bandwidth considerations.
In some modalities, the noise variance for each segment of the determined segmentation can be determined. The noise variance for each segment of the determined segmentation can be stored and transmitted.
Some modalities may even include identification! additional data samples from the first sensor. An updated segmentation of the multiple data samples and additional data samples can be determined. The difference information between the determined segmentation of the data samples and the updated segmentation of the data samples and additional data samples can be determined. The difference between the determined segmentation information of the data samples and the updated segmentation of the data samples and the additional data samples can be stored. In some cases, the difference information may be transmitted. For example, difference information can be transmitted from a downhole device and / or system to a surface device and / or system. Difference information may include adding one or more new segment boundary points, adding one or more new segment parameters, deleting one or more stored segment boundary points, and / or modifying one or more stored segment parameters of one or more segments.
Some modalities may also include the identification of data samples from a second sensor. The segmentation of data samples from the second sensor to one or more segments can be determined. Each segment of the segmentation can include the segment boundary point from the determined segmentation of the data samples from the first sensor. Each segment can also include one or more segment parameters that provide a representation of the data samples from the second sensor for the respective segment. The one or more segment parameters for each one or more segments of the determined segmentation of the data samples of the second sensor can be stored and, in some cases, the segment boundary point and one or more segment parameters for each one or more segments of the determined segmentation of the data samples of the first sensor and the second sensor can be transmitted.
The methods and systems are provided to receive the compressed sensor data representing the data from a sensor according to various modalities. Methods and systems can include receiving multiple segment boundary points. Each segment boundary point can indicate a point at which the threshold data has been exceeded for the data. Multiple segment parameters can be received. Each of the respective segment parameters can be linked to a respective segment boundary point. Each of the one or more segment parameters can provide information about a sample representation of data for the respective segment. Segment boundary points and segment parameters are stored. Segment boundary points and segment parameters are used to represent a plurality of data samples from multiple segments.
The modalities may also include receiving one or more additional segment boundary points and / or one or more additional segment parameters. Segments can be updated using one or more additional segment boundary points and / or one or more additional segment parameters. In some cases, one or more segment update instructions may be received. The segment update instructions can include instructions for deleting at least one of the segment boundary points or one of the segment parameters. The segment update instructions can include instructions for changing at least one of the segment boundary points or one of the segment parameters.
In some cases, segments can be presented on an electronic monitor. The one or more segment parameters of a respective segment can provide a linear model of the respective segment. The linear model of the respective segment can include at least one gradient or intersection of the axis. The linear model of a respective segment can include at least one step function or a ramp function. In some cases, the segment parameters of the respective segment may provide a nonlinear model of the respective segment.
In some cases, segment boundary points and segment parameters can be received at different rates, such as at a rate of less than 3 kilobits per second, at a rate of less than 1 kilobits per second, or at a rate lower than at 100 bits per second, for example. The rate can be determined dynamically based on a bandwidth constraint. Some modalities may also include the receipt of noise variances. Each respective noise variance can be associated with a respective segment. BRIEF DESCRIPTION OF THE DRAWINGS
In the attached figures, components and / or similar characteristics may have the same numerical reference mark. In addition, several components of the same type can be distinguished by following the reference mark by a letter that distinguishes between similar components and / or characteristics. If only the first numeric reference mark is used in the specification, the description is applicable to any of the components and / or similar characteristics that have the same first numerical reference mark regardless of the letter suffix. FIG. 1 provides a schematic diagram illustrating a drilling system according to various modalities. FIG. 2 provides a schematic diagram of a transmission system according to various modalities. FIG. 3 provides a schematic diagram of a receiving system according to various modalities. FIG. 4 presents a graph that illustrates a segmentation technique according to several modalities. FIGS. 5A, 5B, and 5C provide graphs that illustrate a segmentation technique according to various modalities. FIGS. 6A, 6B, 6C, and 6D provide graphs that illustrate a segmentation technique, using batch or historical data according to various modalities. FIGS. 7A, 7B, and 7C provide three examples of segmentations according to various modalities. FIGS. 8A, 8B, 8C, 8D, and 8E provide graphs that illustrate a segmentation technique using real-time data according to various modalities. FIG. 8F provides a framework for using a segmentation technique to compress, transmit and reconstruct a signal according to various modalities. FIG. 9A and 9B provide graphs that illustrate a segmentation technique for updating segment parameters according to various modalities. FIG. 10 provides a tree data structure used with a segmentation technique according to several modalities. FIG. 11 provides a flow diagram of a method for compressing and transmitting field data from a sensor inside the well of a hydrocarbon operation according to various modalities. FIG. 12 provides a flow diagram for a method for compressing the sensor data according to various modalities. FIG. 13 provides a flow diagram of a method for receiving compressed sensor data that represents data from a sensor according to various modalities. FIG. 14 provides a schematic diagram illustrating a computer system according to various modalities. DETAILED DESCRIPTION
Tools and techniques are provided to compress one or more data channels and reconstruct a representation of the data locally and / or at a different location in real time.
The techniques can use segmentation techniques that break a signal into a sequence of segments that can better model the original data sequence. Each segment can be described by a set of parameters that can be a minimum set of parameters, in some cases. As each new sample of data is passed to the system, the best segmentation (going back to some point in history) can be efficiently recalculated and changes to the segmentation can be transmitted and / or stored. A receiver may then be able to reconstruct a close approximation of the original signal to the minimum transmitted data. These techniques can be applied to the transmission and storage of a wide variety of signals, including real-time measurements made in the oil field. The tools and techniques provided can also be applied to speech and audio signals, biomedical signals, and / or financial time series, for example.
In the detailed description that follows, reference is made to the accompanying drawings that show, by way of illustration, what specific modalities can be provided. These modalities are described in sufficient detail to allow those skilled in the art to practice the invention. It must be understood that the various modalities, although different, are not mutually exclusive. For example, a certain aspect, structure, or characteristic described here in relation to a modality can be implemented within other modalities without departing from the spirit and scope of the modalities. In addition, it should be understood that the location or arrangement of individual elements within each disclosed modality can be modified without departing from the spirit and scope of the invention. The following detailed description should therefore not be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims, properly interpreted, together with the full range of 10 equivalents to which the claims are entitled.
In the drawings, equal numbers refer to the same or similar functionality across the various views.
It should also be noted that for the development of any real modality, numerous specific decisions 15 for the circumstance must be made to achieve the specific objectives of the developers, such as compliance with system-related and business-related restrictions, which will vary from one application to another. In addition, it should be noted that such a 20 'development effort can be complex and time-consuming, but that, however, it would be a routine task for people skilled in the art having the benefit of this description. FIG. 1 shows a drilling system 10 that can • use segmentation techniques, according to several 25 modalities. As shown, a drill column 58 is shown inside a well hole 46. Well hole 46 is located in the ground 40 having a surface 42. Well hole 46 is being cut by the action of a drill bit 54. The drill bit 54 is placed at the far end of the downhole assembly 56 which is itself fixed, and forms the lower portion of the drill column 58.
Background composition 56 contains a number of devices, including several subsets. According to various modalities, measurement subsets during drilling (MWD) can be included in subsets 62. Typical examples of MWD measurements include direction, slope, survey data, pressure from inside the well (inside the drill pipe, and annular or external pressure), resistivity, density and porosity. Subassemblies 62 may also include a subassembly for measuring torque and weight on the bit.
Subgroups 62 can generate signals related to measurements made by subgroups 62. The signals from subgroups 62 can be processed in processor 66. After processing, information from processor 66 can be communicated to communication set 64. The set of communication 64 may comprise a pulsator, a signal processor, an acoustic processor and / or the like. Communication set 64 converts information from processor 66 into signals that can be communicated as pressure pulses in the drilling fluid, as communication signals via an optical fiber, a wire and / or the like, or signals for communication wireless or acoustics and / or the like. The modalities can be used with any type of sensor associated with the hydrocarbon industry and with any type of telemetry system used with the sensor to communicate data from the sensor to the 66 processor.
Subassemblies of the downhole assembly 56 may also include a turbine or motor to provide power for the rotation and direction of the drill bit 54. In different modalities, other telemetry systems, such as the wired tube, fiber optic systems , acoustic systems, wireless communication systems and / or the like can be used to transmit data to the surface system.
The drilling rig 12 includes a drilling tower 68 and lifting system, a rotary system, and a mud circulation system. The lifting system that suspends drilling column 58 includes main winch 70, quick line 71, pulley holder 75, drilling line 79, catarina and hook 72, swivel 74, and blind cable 77. The rotation system includes kelly 76, rotary table 88, and motors (not shown). The rotation system transmits a rotation force on the drilling column 58, as is well known in the art. Although a system with a rotary table and kelly is shown in FIG. 1, those skilled in the art will recognize that the modalities may also apply to a top punching arrangement. Although the drilling system is shown in FIG. 1 as being on land, those familiar with the matter will recognize that the present invention is equally applicable to marine environments.
The mud circulation system pumps the drilling fluid down from the central opening in the drilling column. The drilling fluid is often called mud, and is typically a mixture of water or diesel oil, special clays, and other chemicals. The drilling mud is stored in the mud tank 78. The drilling mud is entrained in the mud pumps (not shown), which pump the mud through the support pipe 8 6 and to the kelly 7 6 through the swivel 74, which contains a rotating seal.
The sludge passes through drill column 58 and through drill bit 54. As the drill bit teeth grind and grind the formation of dirt for the chips, the mud is ejected out of the openings or nozzles in the bit with great speed and pressure. These jets of mud lift the chips from the bottom of the well and away from the drill 54 and upwards towards the surface in the annular space between the drill column 58 and the well hole wall 46.
On the surface, mud and chips come out of the pit through a side outlet on blowout preventer 99 and through the mud return line (not shown). The overflow safety system 99 comprises a pressure control device and a rotary seal. The sludge return line feeds the sludge in a separator (not shown) which separates the sludge from the chips. From the separator, the sludge is returned to the sludge tank 78 for storage and reuse.
Several sensors are placed on the drilling rig 10 to take the measurement from the drilling rig. In particular, the hook load is measured by the hook load sensor 94 mounted on the blind cable 77, the position of the block and the speed of the related block are measured by the block sensor 95 which is part of the main winch 70. The torque surface is measured by a sensor on the rotary table 88. The pressure of the vertical tube is measured by the pressure sensor 92, located in the vertical tube 86. Additional sensors can be used to detect whether the drill bit 54 is at the bottom. The signals from these measurements can communicate with a processor located inside the well, such as processor 66 and / or communicated with a central processor 96 on the surface. In addition, the pulses of mud that travel to the drill string can be detected by the pressure sensor 92.
The pressure sensor 92 comprises a transducer that converts the pressure of the mud into electronic signals. Pressure sensor 92 can be connected to a processor inside well 66 and / or surface processor 96 that converts the signal from the pressure signal to digital form, stores and demodulates the digital signal into usable MWD data. According to various modalities, surface processors 96 and / or subsurface processors 66 can be programmed to automatically carry out the segmentation processes described herein. Processors 66 and / or 96 can transmit segmentation information from user interface system 97, or other receiving devices.
In the process of drilling a well hole, multiple sensors can be used to monitor the drilling process - including, but not limited to, the functioning of the drilling components, the status of drilling fluids or the like in the well hole, the trajectory drilling and / or similar - characterize the formation of earth around, or in front of the site being drilled, monitor properties of a hydrocarbon reservoir or water reservoir proximal to the drilling site or well hole and / or similar. FIG. 2 provides an example of a system 200 for segmenting data from one or more sensors 210- a,. . . , 210-n according to various modalities. Sensors 210 may include sensors such as subset 62, pressure sensor 92, block sensor 95, hook load sensor 94, or sensors connected with drill bit 54, just by way of example. Information from sensors 210 can be transmitted to processor 225 over a bus 215. Processor 225 can include processors, such as processors 66 and / or 96 of FIG. 1, for example. Processor 225 can segment sensor data 210 according to various modalities as discussed above and below in more detail. In some embodiments, the system 200 may include one or more accumulators 235 for temporary storage of the data samples. In addition, system 200 may include one or more memories 220 for general data storage. In some cases, processor 225, memory 220, and / or accumulator 235 may be collectively referred to as a data analyzer 220. System 200 may also include a transmitter 240 that can be used to transmit segmentation information determined by processor 225 For example, system 200 can be located inside the well, as seen in FIG. 1, as part of the downhole assembly 56. In other cases, the system 200 may be a part of the surface components associated with the drilling rig 12. FIG. 3 provides an example of a system for receiving segmentation information data, according to several modalities. System 300 may include a receiver 340 for receiving segmentation information. In some cases, receiver 340 may receive segmentation information from a transmitter such as transmitter 240 of system 200 of FIG. 2. Receiver 340 can be configured to receive data segmentation information through wired and / or wireless technologies. System 300 also includes a processor 325. In some cases, processor 325 may include processor 96 of FIG. 1. Processor 325 can process received data segmentation information and store it in memory 320 and / or temporary storage in accumulator 335. In some cases, processor 325 may prepare data segmentation information for presentation in a monitor 350. In some cases, processor 325, memory 320, and / or accumulator 335 can be collected for a data analyzer 320. System 300 can be placed, in some cases, as part of surface components associated with the drilling rig 12. In some cases, the system 300 can be placed in other remote locations from the drilling rig 12.
The modalities provide tools and techniques for data compression using a variety of tools and segmentation techniques to handle a variety of situations. In some cases, modalities can work on real-time data, while others manipulate data in batches. Methods, systems and devices are provided to break a signal or a sequence of data samples into one or more segments, according to various modalities. Each segment can be described by the model, which can be a linear model. The resulting sequence of models and their associated parameters can provide a high level description of the signal or data that can use significantly less storage memory or bandwidth to transmit.
The modalities can significantly assist transmission over generally congested channels, such as from in-pit devices to surface devices or remote field locations to a central office. The modalities can also help with data storage where memory is limited, such as in devices inside the well. Compression is becoming increasingly important during operations in oil fields, as more measurements are made, stored and transmitted, and as operations and control are increasingly being carried out remotely.
The modalities provide tools and techniques for compressing signals and / or data using a sequence of models. The output sequence of the models can efficiently describe the signal or data, by using significantly less memory for storage or bandwidth for transmission. Signal compression is becoming increasingly important during operations in oil fields, as more measurements are made, stored and transmitted and, as operations and controls are increasingly being performed remotely. For example, applications for segmentation compression include maximizing the bandwidth between the part within the well and the surface, or between remote field locations and a central office, and store the measurements recorded in tools within the well where memory is limited.
In some embodiments, a sequence of segment boundary points and segment parameters is determined in some data samples, T, for a series of data samples determined from a signal from a sensor. The segment boundary points and segment parameters can be determined in such a way that they can provide a better signal model being analyzed within each segment. For example, consider a case where there are K segments. The beginning of each segment is given by sk, which can be referred to as a segment boundary point. The signal modeled during segment k can be represented as:
Equation 1 where sk <= t <sk + ie sk + i = T. f can provide the model and θk are the segment parameters of the model for the duration of segment k. Different models for each segment can be coded in f and one of the segment parameters can be used to select the model to be used during segment k, for example. Segmentation techniques can provide K, sk and θk (for all k = 1.. K) such that yt best corresponds to the original signal, Yt, by some criteria (for all t = 1 ... T).
An example of the result of a segmentation technique, according to various modalities is shown in FIG. 4. In this case, the models used for each segment are linear models and the set of segment parameters, θk, includes a gradient and / or a y-intercept for each segment. FIG. 4 shows a signal 410 that is being modeled using the segmentation technique. Segment boundary points are shown with vertical dashed lines that can reflect where the signal changes in some way. For example, the data sampled from the signal may exceed a certain threshold, at a given segment of the fleet company. This change in data may reflect the need for a new model, or new set of segment parameters, to be used to model the data that follows. FIG. 4 shows a number of specific segment fleet points 420-a and 420-b, which can be represented by respective sk values in Equation 1. A first segment 430-a starting at segment border point 420-a can be represented by a linear model, such as y = k, where k is a constant that can be part of a set of segment parameters θk 430-a; in some embodiments, the model may also include a noise variance term.
At the segment boundary point 420-b, the signal has changed enough that a new model can be used to model the signal. In this case, segment 430-b, which starts at the border point of segment 420-b, can be modeled by a linear model, such as y = mx + c, where m reflects a gradient or slope of segment 430-b and reflects an intersection, such as an intersection of the y axis; m and c can be part of a parameter set of segment θk for segment 430-b. In some embodiments, segments 430-a and 430-b, together with their associated segment boundary points 420-a and 420-b together with segment parameters 0K, can be stored and / or transmitted.
In some embodiments, multiple signal segments 410 can be determined. The segmentation that can be stored and / or transmitted may reflect a more likely segmentation and, in some modalities, the most likely segmentation may reflect a segmentation determined from the analysis of a maximum a posteriori or another Bayesian analysis of the possible segmentations of the sampled data .
Some modalities may use arbitrary linear models. FIG. 4 shows an example in which the segment parameters can also be represented by steps and ramps, where a segment 430-a represents a step and segment 430-b represents a ramp. In some cases, steps and ramps can effectively describe a wide variety of signs, in particular from the drilling process. Other models can be used, although the models may need more additional coding than the model to be used with its linear parameters. These other models can have an advantage if the different signs are better described by very different signs (for example, if one sign contains only a few steps, while another, only quadratic or cubic or other polynomials).
Segmentation techniques, according to various modalities, provide a number of likely signal segmentations. Some modalities may use or provide the most likely targeting. This segmentation can be a posteriori maximum segmentation (MAP) for compression purposes.
FIG. 5A, FIG. 5B, and FIG. 5C provide another example of signal segmentation, according to several modalities. FIG. 5A provides a graphical representation 500 of an original signal, a segmentation of the signal according to various modalities, and a segmentation of the original signal. FIG. 5B provides an enlarged section 501 of graph 500 to better illustrate the differences between the original signal, the segmentation of the signal, and the decimation of the signal. For example, the enlarged section 501 shows original signal 510.
In addition, section 501 shows examples of the signal being decimated by an amount needed to provide the compression rate equal to that of segmentation techniques. The decimation of the signal is represented, in part, by points 525-a, 525-b, 525-c and 535-d, which are equally spaced along the time axis. A portion of the original signal 510 is then modeled by segments 535- a, 535-b, and 535-c which are delimited by points 525-a, 525-b, 525-c. This decimated signal representation 510 defines the signal at fixed time intervals. In contrast, the segmentation example shown in these figures uses the times when the signals change. Portions of this segmentation example include segment boundary points 520-a, 520-b, 520-c and 520-d, with segments defined by 530-a, 530-b, and. 530-c. The segment boundary points of 520- a, 520-b, 520-c and 520-d can reflect the points where the signal 510 changes, for example, these points can reflect where the signal 510 changes more than some threshold data. Segments 530-a, 530 b, and 530-c can then be represented by segment parameters. In aspects of the present invention, the threshold can be defined based on knowledge, which can be statistical knowledge, modeling knowledge, 'probabilistic © knowledge, experimental knowledge, knowledge of previous and / or similar data manipulation, for example, knowledge of variances in the sensor outputs, etc.
As is evident from section 501, segments and segment boundary points provide a better representation of the original signal 501, then decimates the points and segments. The modalities that use segmentation techniques as shown in section 501 can provide efficient coding strategies. FIG. 5C provides a different perspective on the content of FIG. 5B, which shows the original signal and the signal segmentation, according to various modalities, but does not show the decimated version of the signal.
Some modalities can be used as a batch process to compress a data stream, for example, log data from a tool memory dump, or a block of 'lost data to be transmitted after a communication link has been made. low for a period of time. For example, FIG. 4 and FIG. 5 may reflect that the data was collected and then the data is processed as a batch to produce the compressed data using a segmentation technique, according to several modalities. These values can also reflect data in real time, which can be analyzed in real time, according to various modalities. By way of example only, in some aspects of the present invention, when the data being received falls or falls within the threshold of the model to be applied to the data, no transmission of this fact may be necessary.
Some configurations may use previous probability distributions of unknown parameters to determine how many segment boundary points to use for a given segmentation technique. These approaches can be applicable to both real-time and batch signal processing. By adjusting these distributions of previous probabilities, - it may be possible to adjust the resolution in which different modalities follow the detail of the signal. In some cases, the previous probability distributions in the noise variance and / or the expected probability of a segment boundary point can be particularly useful, although other distributions of previous probabilities can be used.
An example of the effect of different distributions of previous probability is shown in FIGS. 6A-6D. FIG. 6A shows a graph of a signal, which can store samples in some cases, which can be modeled using a segmentation technique, according to several modalities. In FIGS. 6B-6D, three different examples with different distributions of previous probabilities are used, resulting in different numbers of segment boundary points. The segment boundary points are represented by the vertical lines horizontally distributed along each of the graphs. Segment boundary points include segments that can be represented using segment parameters. In these figures, the previous probability distributions generated 22 segment boundary points (FIG. 6B), 15 segment boundary points (FIG. 60) and 7 segment boundary points (FIG. 6D). The resulting three examples provide signal compression which is 2.5%, 1.7% and 0.8%, respectively, of the original signal of FIG. 6A. As can be seen, the previous probability distributions can be used to switch between how many detail signals can be retained and how the signal becomes compressed.
Some modalities can be used to compress a stream of data in real time directly into memory. For example, when a tool inside the well has limited memory and needs to take action over a long period, it can store the compressed data in a local memory. In this case, the tool inside the well may have no problem changing past written data into its memory and the resulting compressed signal can be identical to a batch compression scenario.
Some modes can also be used in an environment where memory is limited. In these cases, the amount of memory needed just to implement a modality (as opposed to the memory needed to store the resulting compressed signal) may need to be considered. In some modalities, segmentation techniques may consider a certain number (for example, 50 or 100) of possible segmentations and the compressed flow for each of them (for some fixed length of the story) is kept in memory.
In some embodiments, a signal can be segmented in the most likely sequence, or at most, a posteriori, in the segment sequence. This process can generate N segments, each including a starting index or segment boundary point, and two linear parameters (a gradient and y-intercept, for example). For example, FIG. 7A shows such a segmentation, where Xi x2 x3 x4 x5. . . represent the raw data and Si mi ci s2 m2 c2 ... represents the compressed signal. In this example, sn represents the segment boundary point, which can be referred to as a starting index, in some cases, of segment n and (mn, cn) are the linear parameters that describe the data in n segments. If the length of the average segment reported by the algorithm is L samples, then the size of the compressed signal can be represented as 3 / L x the size of the original raw signal (where the three numbers can be used to represent L, on average).
Some modalities can also process multiple data channels simultaneously. In some cases, each data channel is divided into the same segmentation, but each is described using different parameters, such as segment parameters such as a gradient and an intersection of the axis. This can save the segment boundary point encoding for everyone except the first of the channels. FIG. 7B shows an example of encoding multiple channels simultaneously. FIG 7B shows raw data from a first channel xi x2 x3 x4 x5 ... and a second channel yi y2 y3 y4 ys .... The compressed signal is then represented as si mi, i ci, i mi, 2 Ci, 2 s2 m2, i c2, i m2,2 c2,2. . . using the boundary points of segment sn together with the parameters of mn, k θ cn; k, which can be linear parameters, in some cases, for segment n of channel k. If this technique is used to encode the K channels, the compressed signal can be represented as (2K + 1) / L, the size of the original gross signal L.
In some embodiments, data samples from a sensor or channel can be correlated with data samples from one or more other sensors or channels. For example, drill bit data can be strongly correlated with drill weight data (WOB) within a given rock formation. Torque and WOB data from measurements inside wells can be compressed by segmenting WOB data samples with steps and ramps, or other linear parameters, with respect to time and the segmentation of torque data depending on segmentation segmentation of WOB, such as the use of ramps, or another linear parameter, with respect to WOB to model the torque data.
More generally, segmentations according to various modalities of two or more sensors can be correlated with each other or become dependent on each other in some way. An example of such a correlation or dependency may be the use of the same segment boundary points for segmentations with respect to the two or more channels or sensor data. In a particular case, segmentation of the torque and WOB ratio may involve the use of segmentation boundary points for both torque and WOB segmentation, together with the determining changes in segment parameters based on a relationship between the data of torque and WOB. Transmission changes in slope and shifts to a torque / WOB ratio, for example, may be less likely to lose formation information than modeling independently of WOB and torque with steps and ramps over time. The modalities that use relations between two or more sensors or channels can be used in general.
Some modalities may include information related to the noise of each signal. Some modalities can, for example, provide as a noise variance output, vk, for the residues of each segment. This can also be encoded with the other segmentation parameters to provide more information to the receiver or storage device in relation to the original signal. For example, some modalities can determine the average of the signal noise variance in each segment. This noise variance information can be stored and / or transmitted. The noise variance values can be encoded with the parameters that represent a given segment, increasing the number of data points used to four per segment. For example, FIG. 7C shows an example with raw data xi x2 x3 x4 x5 ... is compressed signal si mi Ci Vi s2 m2 c2 v2 ..., where sn represents the segment boundary point, which can be referred to as a starting index, in some cases, from segment n, (mn, cn) are the linear parameters that describe the data in segment n, and vn is the noise variance estimate for n segments.
The noise variance information can describe how much the signal is varying, which can provide valuable information not captured by the parameters that represent a segment individually. For example, if a signal suddenly becomes louder while its average level remains constant, some modalities can generate a segment boundary point. However, without knowledge of the noise variance, the importance of this segment boundary point may be lost.
The modalities can provide signal compression, which can match the original signal significantly better than other techniques. The advantage of segmentation techniques can be particularly evident in signals that change abruptly. For example, when the flow is stable for long periods of time (while the pumps are on), then it falls and rises suddenly (during a call). In this case, some modalities can efficiently allocate the segment boundary points, during the moments when the pumps are switched off and restarted, while a decimated signal, for example, can subsample during these times and super-show while the flow is constant.
Some modalities can be designed to work in real time. As each new data point is measured, segmentation techniques can provide a new sequence of likely segmentations with sets of segment boundary points and segment parameters. These results can be compared with those generated by the previous data points to determine whether the most recent data provide any new and meaningful information.
The segmentation technique, according to several modalities, can be used to compress and transmit a signal. In some cases, both a transmission system, such as the system 200 of FIG. 2, as for a receiving system, such as the system 300 of FIG. 3, they can keep a copy of the most recent data segmentation, which can be represented as sk, and Qk data for each K segment. In some cases, only changes in this segmentation are transmitted as new data samples are determined. When no data is being transmitted, a receiving system may be able to generate a yt model of a signal using the most recent segmentation and Equation (D.
This ability to use the new data to update segment boundary points and / or segment parameters from the past is illustrated in FIGS. 8A, 8B, 8C, 8D, and 8E. The updated segmentation information can be determined at one location, such as with the system 200 of FIG. 2. A receiving device or system, such as system 300 of FIG. 3, it can receive the compressed flow and be able to change what is already stored or displayed. The updated information can also be stored locally, such as in a memory 220 of a device 220 or system 200 of FIG. 2. In this way, only the minimum of information may need to be transmitted while the receiving system is still able to reconstruct a good estimate of the original signal. FIG. 8 A shows a stream of real data 810 that can be modeled using segmentation techniques, according to various modalities. FIG. 8B shows a first segmentation, which includes boundary points of segment 820-a and 820-b, together with segments 830-a and 830-b, which can each be described by a set of segment parameters θk- In this example , segments 830 can be represented as steps or ramps. FIG. 80 shows a segmentation at a late time. This segmentation includes an update on the segmentation shown in FIG. 8B. For example, segment 830-b of FIG. 8B has been deleted and replaced by segment 830-c of FIG. 8C. FIG. 8C shows additional segment boundary points 820-c and 820-d, along with additional segments 830-d and 830- e. FIG. 8D shows a segmentation at an even later time. With this updated segmentation, some border points from the previous segment, such as the 820-d, have been deleted, while new border points from the segment 820-e, 820-f, 820-g, and 820-h have been added. Segments 830-d and 830-e, as seen in FIG. 8C, were deleted and replaced by segment 830-f. FIG. 8C also includes new segments 830-g, 830-h, 830-i, and 830 — j. FIG. 8E then shows another updated segmentation. Again, some previous segment and / or segment boundary points have been deleted and / or changed, compared to the previous segmentations, as sampled in FIG. 8D. For example, the boundary points of segment 820-g and 820-h have been deleted, while the boundary points of segment 820-i, 820-j, and 820-k have been added. In addition, the 830-h, 830-i, and 830 — j segments have been canceled or revised in some way. The segmentation shown in FIG. 8E now includes segments 830-k, 830-1, 830-m, and 830-n. FIG. 8F shows an example of a 850 process for using segmentation techniques, according to various modalities for compressing, transmitting and / or reconstructing a model of a signal. In particular, FIG. 8F shows, in part, a process for updating segmentation information that can be used to create segmentations as shown in FIGS. 8B-8E. In block 860, a segmentation of the data samples from a signal can be stored. New data samples 8 65 can be provided which are then analyzed, along with information from the previously determined segmentation to determine possible updated segmentations 870.
In block 875, with segmentation with segment boundary points and segment parameters, it can be selected which can reflect the new data samples. This selection can be based on a more probable analysis or analysis on a maximum a posteriori of the determined segmentations. In block 880, it can be determined whether the segmentation selected in block 875 is different from the previously determined segmentation that can be stored in block 860. If it is determined that a new segmentation that was selected in block 875, an updated command can be transmitted to the block 885. This updated command can include instructions that reflect the changes between the previously selected segmentation and the new updated segmentation. Information related to the updated segmentation can also be provided and stored in block 860. On the receiving system side, it can be determined whether a new update command has been received in block 890. If the new updated information is received 891, this information can be provided for block 895, where it can be used to update the segmentation stored in the 895 receiver. The updated segmentation information can be used to reconstruct a signal model 896.
The ability to use the new data to update segment boundary points of the past, when transmitting in real time, can be addressed in different ways. If the segmentation changes, for example, a number of new segmentation boundary points and / or segment parameters may need to be transmitted and used in place of previously received segment boundary points and / or segment parameters at the receiver. This is reflected in FIG. 8A-8F. In some cases, an estimate of the segment parameters that best describe the last segment (that is, from the most recent segment boundary point to the current time) may also change when a new data point is measured. FIGS. 9A and 9B show a specific example of a change in segment parameters, without changing the segment boundary points. After transmitting the most recent segment parameter estimates, the application submission can monitor these parameter estimates to determine whether they need to be transmitted again. FIG. 9A shows a signal 910 that is being analyzed using a segmentation technique, according to several modalities. A first segment 930-a is determined along a boundary point of segment 920 that reflects a change in data at the end of segment 930-a. Sample data is determined after the segment 920 boundary point can be used as part of a segmentation technique, which initially generates a set of segment parameters that are reflected in segment 930-b. As more time passes, however, new data samples can result in a different segment providing a better signal model 910. FIG. 9B shows this process. For example, FIG. 9C shows a segment 930-c with segment parameters that provide a better signal model 910. These segment parameters can be stored and / or transmitted, providing an update of the segmentation data. In some cases, several updates may be provided.
In some modalities, different possible actions can occur when a new measurement is processed. In some cases, a new targeting boundary point can be determined. This can occur when the new data begins to differ significantly from the model that describes the current (most recent) segment. The presence of a new segment boundary point and segment parameters that describe the new segment can be transmitted. In some cases, previously transmitted segment boundary points may need to be deleted. This can occur when a new measurement results in a totally different segmentation being more likely than what was previously transmitted. This is shown in several figures, including FIGS. 8B-8F. In this case, the receipt request may delete a number of previous segment boundary points. In some cases, new segment boundary points and segment parameters can be transmitted and / or stored. In some cases, the segment parameters describing a current segment may have changed. For example, if the parameters that describe the current segment deviate significantly from those that were last transmitted, then they may need to be resent. FIG. 9B shows an example.
In some modalities, there may be different possible actions that can occur when one or more new data samples are processed using a segmentation technique, according to several modalities. The following is an example where three possible actions can occur, although other actions may occur in some modalities. These three possible actions can involve three possible commands that a transmission device or system, such as the system 200 of FIG. 2, you can use to send commands to update the segmentation information that can be maintained by a receiver, such as the system 300 of FIG. 3. In some cases, the transmission system can use these commands to update the segmentation information that it stores locally in its own memory.
A first command can include a command to start a new segment; this can be equivalent to adding a new segment boundary point. The first command can include a command identifier, information about a new segment boundary point, which can include the start time of a new segment, in some cases, and one or more segment parameters. A second command can include a command to delete one or more previously transmitted or stored segments, which can include a command to delete one or more segment boundary points. In some cases, the second command may include a command identifier and information relating to a number of specific segment boundary points or segment boundary points to be deleted. In some cases, the second command may also include new segment parameters for a newer segment. A third command can include information about updating a newer segment. The third command can include a command identifier and one or more new segment parameters. These commands can be sent as packages, in some cases. Some modalities may include additional commands or instructions associated with the described commands. For example, commands can be given in relation to noise variance information or multiple channel information.
Depending on the action required, zero, one, or several commands may need to be transmitted at a given time or situation, for example, in the case of a segmentation change, the second command described above can be transmitted followed by one or more first commands. If the segment parameters deviate too far from a previous transmission, the third command may need to be sent. In some cases, no command may need to be transmitted when a new measurement is made, for example, when the new data does not require a new segment boundary point and / or the new segment parameters. In this general situation, the receiving application can use the most recent segmentation, segment boundary points, and segment parameters to generate an estimate of the measurement.
In some cases, the amount of commands transmitted (and therefore the efficiency of the segmentation technique) can be improved by slightly delaying the signal before transmitting them. By delaying transmission, segmentation changes can be captured before causing unnecessary transmission of the first command and / or the second command described. In addition, the more samples are used to estimate the current segment parameters, the more accurately they describe the entire segment, and the less likely they are to need to be updated in the future.
Increasing the delay before transmission can ultimately reduce the number of commands that are transmitted. In some modalities, the total amount of data transmitted asymptotes for the compression achievable by batch compression. In this limit, the first command described can be the only type of command transmitted.
The compression of a signal using segmentation techniques according to various modalities can generate data to be transmitted at different times. This is different from many other data in the oil field, for example, which generally occur as a constant stream and use constant bandwidth. Communication channels, including some satellite links, often provide a pay-per-kilobit policy. In these networks, customers generally do not have a fixed bandwidth (although they may have an upper limit) and instead, time division multiplexing can be used, for example, to dynamically allocate bandwidth between users of the network as needed. Modalities that use segmentation techniques to compress data for transmission over these networks can offer an immediate and significant benefit.
Some communication channels provide a constant bandwidth and may be more suitable for constant data flows. In these systems, however, segmentation techniques, according to various modalities, can still be used to compress data from multiple sources and an intelligent transmitter can multiplex between them, for example, dynamically allocating time to channels that may need it. .
In some embodiments, it is also possible to limit bandwidth by adding latency or delay. The addition of latency can be done by accumulating packets and transmitting them during the intervals (when the segmentation algorithm is not generating packets). In some embodiments, a transmitter may be limited to sending a certain fee.
As discussed above, the sample data can be divided into different segments and each segment can be associated with a model that is particularly useful for modeling the data in that segment. In some modalities, the data is modeled using ramp or step functions, for example, using the least squares algorithm. These models can be evaluated using Bayesian Model Selection. The selection of the Bayesian model is discussed in detail in Deviderjit Sivia and Skilling John, Data Analysis: A Bayesian Tutorial (OUP Oxford, 2nd ed 2006), the total content of which is incorporated by reference. Thus, for each segment of each segmentation, a model that is either a ramp or a step can be assigned and the corresponding segmentations are assigned a weight indicating how well the segmentation and associated models are in accordance with the data flow, in compared to other targets.
In some embodiments, the analysis of sample data may be provided by treating input data as being composed of segments, including segment boundary points. Segment boundary points can be identified through data analysis to provide detection of changes in signals determined by different sensors. In certain respects, multiple sensors or the like can provide multiple data channels, which can be segmented into segments and data fusion can be used to cross-correlate, compare, contrast or similar, data segment boundary points input to provide compressed data representations of the signal data.
In one embodiment, data can be analyzed in real time to provide real-time compression, rather than retrospective data compression. In one embodiment, the data from one or more sensors can be adjusted with an appropriate model and from the analysis of input data in relation to the model, the segment boundary points can be identified. The model can be theoretically derived, from experimentation, from the analysis of previous and / or similar operations. The models can process the data according to the expected / modeled variations in the data, expected / modeled noise, expected / modeled response when other data changes occur and or similar. Using such a process, the threshold for when a boundary point is determined to have occurred can be defined in the model.
As such, in one mode, the data from one or more sensors can be analyzed with a segmentation technique. The segmentation technique can divide a heterogeneous signal, the signal being given from one or more sources associated with the process related to hydrocarbons, in a sequence of segments. Discontinuities between segments can be referred to as border points Only as a segment. For example, a modality may include data modeling in each segment as a linear model, such as a ramp or step, with additive Gaussian noise. These models can be useful when the data has a linear relationship with the index. In alternative modalities, more complex models can be used, for example, exponential, polynomial and trigonometric functions. As each new sample (data set) is received, the algorithm produces an updated estimate of the parameters of the underlying signal, for example, the average height of the steps, the average gradient of ramps and the average displacement of the ramps, and additionally the parameters of additive noise (for zero-mean Gaussian noise), the parameter is the standard deviation or variance, but for the more general noise distributions of other parameters such as asymmetry or flatness can also be estimated) . ■
In some embodiments, segment boundary points can be designated, where the noise parameters are verified to have changed. In some embodiments, the tails of a distribution can be considered in the analysis, just as when analyzing the risk of an event occurring, the tails of the distribution can provide a better analytical tool than the average of the distribution. In one embodiment, a probability can be determined as to whether the height / gradient / displacement of the sample is above / below a specific threshold.
The basic output of some systems, such as system 200 of FIG. 2, can be a set of segment boundary point lists and a probability for each list. The most likely list may be the most likely segmentation of the data according to the choice of models: Gl, ..., Gj.
The signal segmentation, according to several modalities, can be described using a tree structure, as shown in FIG. 10. The segmentation technique can be considered as a search for this tree. At time 0 (before any data has arrived) the tree consists of a single root node, R. At time 1 the root node generates J leaves, one leaf for each of the J segment models - the first leaf represents the hypothesis that the first data point is Gl, the second data point is G2, etc. In later times, the tree can grow on each leaf node generating J * 1 leaves, one for each model and one extra represented by 0, which indicates that the data point at the corresponding time belongs to the same model segment as its original. For example, if Gi were a step model and G2 was a ramp, a path through the root tree to a leaf node at 9 could be:

Where this indicates that the first six samples were generated by a step and that the remaining four samples were generated by a ramp.
Over time, the tree grows and can be searched using a collection of particles, each occupying a different leaf node. The number of particles can be chosen by the user / operator and about 20-100 can be sufficient, however other amounts of particles can be used in different aspects of the present invention. Associated with a particle is a weight, which can be interpreted as the probability that the segmentation indicated by the particle's path to the root (as in the example above) is the correct segmentation. The purpose of the segmentation technique can include the concentration of the 'particles in sheets, which means the weights of the particles will be large.
Some modalities can achieve segmentation of data flows that can include segment boundary points. The segmentation process for determining the segment boundary points and associated models, which can include segment parameters, can successively build a tree data structure, an example of which is illustrated in FIG. 10, where each node in the tree represents different segments of the data. The tree can also be periodically pruned to discard segments with low probability, that is, segments that have a poor fit to the data.
In a first step, segmentations can be initialized by establishing a root node R. Then, a data point can be received from one or more input streams. In response, the segmentation process can generate smaller segmentations that may reflect different alternatives or models. In this example, three different alternatives are provided, that is, a continuation of the previous segment, a new segment with a first model, or a new segment with a second model. Although this example provides two ramp and step models, in alternative modes, additional models can be included. In one mode, alternative models are ramp and downhill functions. As the root node does not represent any model, the first generation in the tree, reflecting the first data point, 'in general, starts a new segment which is either a ramp, which is represented in the tree as 1, or a step, which is represented in the tree, as 2.
In the example given above, the R1000002 0 0 particle can produce three new smaller nodes with corresponding particles:

A. the first of which indicates a continuation of the step segment, which begins with the 7th data point, the second, a new ramp, and the third, a new step.
The models can then be created by adjusting the data in the new segments with the models designated for the segments and the models corresponding to the existing segments are readjusted. For example, if a new ramp segment is to be created for a new smaller particle, the segment data can be adjusted for the ramp. When a new segment is created, the corresponding model that is assigned can simply be a function that places the model's value through the new data point. However, for existing segments in which the segment encompasses multiple data points, the model or segment parameters, for example, the parameters that define the gradient and deviation of a ramp, can be reevaluated. Some form of linear regression technique can be used to determine the linear function to be used to model the segment data as a ramp or step.
The segmentations produced are then evaluated; this may involve different methods of Bayesian analysis as a Bayesian Selection model or similar to calculate weights indicative of how good a fit of each segmentation is for the underlying data. In aspects of the present invention, segment adjustment can be based on knowledge of the data being processed and / or knowledge of the expected behavior of the data to be processed.
After segmentation, creation of model functions, and corresponding models have been evaluated, that is, they have been assigned weights, the tree can be pruned by removing some particles of future consideration to keep the particle population size controllable . The weights of the remaining particles can then be normalized.
After evaluating the input data stream segmentations, the corresponding segmentations and models can be used to provide a compressed representation of the input data stream. Segmentation can be transmitted to a receiving system, in some cases, where segmentation can be used to reconstruct a data flow model. In some cases, segmentation can be stored locally, as a compressed form of the input data stream; the segmentation can be retrieved at a later point in time for different purposes. FIG. 11 provides a flow diagram illustrating a method 1100 of compressing and transmitting field data from a sensor inside the well of a hydrocarbon operation, according to various modalities. The method 1100 can be implemented using different systems and / or devices, including, but not limited to, the system 200 of FIG. 2. At step 1102, multiple samples of data from the sensor inside the well are identified. Multiple segmentations of the multiple data samples from the sensor inside the well are determined at step 1104. Each segmentation can include one or more segments. Each segment can include a segment boundary point, which can reflect a point in the data samples, where a threshold has been exceeded with respect to data samples from a previous segment or a point where the data samples start. Each segment can also include one or more segment parameters that provide a linear representation of the data samples for the respective segment. In step 1106, one of the determined segmentations is selected to represent the multiple data samples based on an analysis of a maximum a posteriori of the determined segmentations. The segment boundary point and one or more segment parameters for each of the one or more segments of the selected segmentation are stored in block 1108. A subset of multiple samples of sensor data from within the well can be accumulated in step 1110. The segment boundary point and one or more segment parameters for each of the one or more segments of the selected segmentation are transmitted to a surface device of the hydrocarbon operation at step 1112.
In some embodiments, the 1100 method may also include identifying additional data samples from the in-pit sensor. Multiple updated segmentations of the multiple data samples, and additional data samples in one or more segments can be determined. One of the determined updated segments can be selected to represent the multiple data samples and additional data samples based on an analysis of a maximum a posteriori of a plurality of determined updated segments. The difference information between the determined segmentation of the multiple data samples and the updated segmentation of the multiple data samples and the additional data samples can be determined / The difference information can include information such as the addition of a new segment boundary point , deleting a stored segment boundary point, and / or reviewing one or more stored segment parameters of one or more segments. The stored segment boundary point and one or more segment parameters for each of the one or more segments of the determined segmentation based on the determined difference information can be updated. FIG. 12 provides a flow diagram illustrating a method 1200 of compressing sensor data according to various modalities. Method 1200 can be implemented using different systems and / or devices, including, but not limited to, system 200 of FIG. 2. At step 1202, multiple samples of data from a first sensor are identified. The segmentation of the multiple data samples from the first sensor is determined at step 1204. The determined segmentation can include multiple segments of varying sizes. Each segment of the determined segmentation can include one or more segment parameters that provide a sample representation of data for the respective segment. Each segment can also include a segment boundary point that indicates a point in the data samples where a threshold has been exceeded for the data samples with respect to one or more segment parameters of a previous segment. At step 1206, the segment boundary point and one or more segment parameters for each of the one or more segments of the given segmentation are stored.
In some embodiments, the '1200 method may also include the determination of multiple segmentations of the data samples from the first sensor. One of the targets can be selected based on a most likely targeting analysis of multiple targets. The selected segmentation can be used as the determined segmentation of step 1204. The threshold of step 1204 can depend at least on a transmission bandwidth restriction or a storage restriction. The one or more segment parameters of a respective segment can provide a linear model of the respective segment, in some cases. The linear model of a respective segment can include a gradient and / or an intersection of the axis. The linear model of a respective segment can include a step function and / or a ramp function. The one or more segment parameters of a respective segment can provide a non-linear model of the respective segment, in some cases.
Some modalities of method 1200 may also include the transmission of the segment boundary point and one or more segment parameters for each of the one or more segments of the determined segmentation. The transmission of the segment boundary point and one or more segment parameters of each of the one or more segments of the determined segmentation can occur at a rate of less than 3 kilobits per second, at a rate of less than 1 kilobits per second, and / or at a rate of less than 100 bits per second in some cases. The transmission of the segment boundary point and one or more segment parameters of each of the one or more segments of the determined segmentation can occur dynamically based on a bandwidth restriction. In some cases, transmission from the segment boundary point and one or more segment parameters for each of the one or more segments of the given segmentation may include delaying the transmission based on bandwidth considerations.
In some modalities, the noise variance for each segment of the determined segmentation can be determined. The noise variance for each segment of the determined segmentation can be stored and transmitted.
Some modalities may also include the identification of additional data samples from the first sensor. An updated segmentation of the multiple data samples and additional data samples can be determined. The difference information between the determined segmentation of the data samples and the updated segmentation of the data samples and the additional data samples can be determined. The difference information between the determined segmentation of the data samples and the updated segmentation of the data samples and the additional data samples can be stored. In some cases, the difference information may be transmitted. For example, difference information can be transmitted from a device and / or system inside the well to a surface device and / or system. Difference information may include adding one or more new segment boundary points, adding one or more new segment parameters, deleting one or more stored segment boundary points, and / or modifying one or more stored segment parameters of one or more segments.
Method 1200 may also include the identification of data samples from a second sensor. The segmentation of data samples from the second sensor to one or more segments can be determined. Each segment of the segmentation can include the segment boundary point from the determined segmentation of the data samples from the first sensor. Each segment can also include one or more segment parameters that provide a representation of the data samples from the second sensor for the respective segment. The one or more segment parameters for each of the one or more segments of the determined segmentation of the data samples of the second sensor can be stored and, in some cases, the segment boundary point and one or more segment parameters for each one or more segments of the determined segmentation of the data samples of the first sensor and the second sensor can be transmitted. In some embodiments, one or more segment parameters that provide a representation of the data samples from the second sensor for a respective segment are determined in relation to the segment parameters that provide a representation of the data samples from the first sensor for a respective segment.
FIG. 13 presents a flow diagram illustrating a method 1300 of receiving 60/67 compressed sensor data representing data from a sensor, according to various modalities. Method 1300 can be implemented using different systems and / or devices, including, but not limited to, system 200 of FIG. 2 and / or the system 300 of FIG. 3. Method 1300 includes receiving multiple segment boundary points at step 1302. Each segment boundary point can indicate a point in the data where a threshold has been exceeded for the data. Multiple segment parameters can be received. Each of the respective segment parameters can be linked to a respective segment boundary point. Each of the one or more segment parameters can provide information about a sample representation of data for the respective segment. At step 1304, segment boundary points and segment parameters are stored. Segment boundary points and segment parameters are used to represent a plurality of data samples as multiple segments at step 1306.
Method 1300 may also include receiving one or more additional segment boundary points and / or one or more additional segment parameters. Segments can be updated using one or more 'additional segment boundary points and / or one or more additional segment parameters. In some cases, one or more segment update instructions may be received. The segment update instructions can include instructions for deleting at least one of the segment boundary points or one of the segment parameters. The segment update instructions can include instructions for changing at least one of the segment boundary points or one of the segment parameters.
In some cases, segments can be displayed on an electronic monitor. The one or more segment parameters of a respective segment can provide a linear model of the respective segment. The linear model of the respective segment can include at least one gradient or an intersection of the axis. The linear model of a respective segment can include at least one step function or a ramp function. In some cases, the segment parameters of a respective segment can provide a nonlinear model of the respective segment.
In some cases, segment boundary points and segment parameters can be received at different rates, such as, at a rate of less than 3 kilobits per second, at a rate of less than 1 kilobits per second, or at a rate less than 100 bits per second, for example. The rate can be determined dynamically based on a bandwidth constraint. Some modalities may also include the receipt of noise variances. Each respective noise variance can be associated with a respective segment.
The methods and systems described in relation to the 1100, 1200 and / or 1300 methods and the 10, 200 and / or 300 systems can be implemented, in part, by means of a computer system 1400, as shown schematically in FIG. 14, which broadly illustrates how the individual elements of the system can be implemented separately or in a more integrated manner. The system 1400 is shown composed of hardware elements that can be electrically coupled via bus 1426. The hardware elements can include one or more processors 1402, one or more input devices 1404, one or more output devices 1406, one or more more storage devices 1408, a computer-readable storage medium reader 1410a, a communications system 1414, a processing acceleration unit 1416 such as a special-purpose processor or DSP, and a memory 1418. The media reader 1410a computer-readable storage can also be connected to a 1410b computer-readable storage medium, the comprehensive combination of removable, local, fixed and / or removable storage devices, plus storage medium to contain computer-readable information temperamentally and / or more permanently. The communications system 1414 may comprise a wired, wireless, modem, and / or other type of link interface and allows data to be collected from one or 63/67 more sensors. In some cases, data collection can be performed in real time by the communications system in determining a segmentation model of data samples from the sensors.
The system 1400 may also include software elements, shown to be currently located within working memory 1420, which may include an operating system 1424 and another code 1422, such as a program designed to apply methods of different modalities. By way of example only, system 1400 can be used to implement method 1100 of FIG. 11, method 1200 of FIG. 12, and / or method 1300 of FIG. 13. In addition, the system 10 of FIG. 1, the system 200 of FIG. 2, and / or the system 300 of FIG. 3 can use aspects of the 1400 system. It will be apparent to those skilled in the art that substantial variations can be used according to specific requirements. For example, custom hardware can also be used and / or particular elements can be implemented in hardware, software (including portable software, such as applets), or both. In addition, connection to other computing devices, such as network input / output devices can be employed.
Circuits, logic modules, blocks, processors, and / or other components can be described here as being "configured" to perform various operations. Those skilled in the art will recognize that, depending on the application, such configuration can be accomplished through the design, configuration, interconnection, and / or programming of particular components and that, again depending on the application, a configured component may or may not be reconfigurable to a different operation. For example, a programmable processor can be configured by providing suitable executable code; a dedicated logic circuit can be configured to properly connect logic gates and other circuit elements, and so on.
Although the modalities described above may refer to specific hardware and software components, those skilled in the art will appreciate that different combinations of hardware and / or software components can also be used and that specific operations described as being implemented in hardware can also be used. implemented in software or vice versa.
Computer programs that incorporate various characteristics of different modalities can be encoded in various computer-readable storage media; Suitable media include magnetic disk or tape, optical storage medium, such as compact disk (CD) or digital versatile disk (DVD), flash memory, and the like. Computer-readable storage media encoded with the program code can be packaged with a compatible device or supplied separately from other devices. In addition, the program code can be encoded and transmitted via optical wired and / or wireless networks in accordance with a variety of protocols, including the Internet, thus allowing distribution, for example, via Internet download.
The preceding description provides exemplary modalities only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Instead, the previous description of exemplary modalities will provide those skilled in the art with a description that allows the implementation of one or more exemplary modalities. It is understood that several changes can be made in the function and arrangement of the elements without departing from the scope of the invention. Several modalities have been described in this document, and at the same time the various characteristics are attributed to different modalities; it should be noted that the features described in relation to one modality can be incorporated into other modalities as well. Likewise, however, no single characteristic or characteristics of any described modality should be considered essential for each embodiment of the invention, since other embodiments of the present invention may omit such features.
Specific details are given in the previous description to provide a complete understanding of the modalities. However, it will be understood by a person skilled in the art that the modalities can be practiced without these specific details. For example, circuits, systems, networks, processes, and other elements of the invention can be presented as components in the form of a block diagram, so as not to obscure the modalities in unnecessary detail. In other cases, the well-known circuits, processes, algorithms, structures and techniques can be shown without unnecessary details, in order to avoid obscuring the modalities.
In addition, it should be noted that individual modalities can be described as a process that is described as a flow chart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart can describe operations as a sequential process, many operations can be performed in parallel or simultaneously. In addition, the order of operations can be rearranged. A process can be terminated when its operations are completed, but it can also include additional measures or operations not discussed or included in a figure. In addition, not all operations in any particular process described can occur in all modalities. A process can correspond to a mode, a function, a process, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or the main function.
In addition, the modalities of the invention can be implemented, at least in part, either manually or automatically. Manual or automatic implementations can be performed, or at least assisted, through the use of machines, hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware, or microcode, program codes or code segments to perform the necessary tasks can be stored in a machine-readable medium. A processor (s) can perform the necessary tasks.
Despite the detailed descriptions of one or more modalities above, several alternatives, modifications and equivalents will be evident to those skilled in the art, without varying the spirit of the invention. In addition, unless clearly indicated or otherwise expressly indicated, it should be considered that the characteristics, devices and / or components of different modalities can be replaced and / or combined. Thus, the above description should not be taken as limiting the scope of the invention which is defined by the appended claims.

权利要求:
Claims (19)
[0001]
1. METHOD OF COMPRESSING SENSOR DATA FROM ONE MORE SENSORS IN A HYDROCARBON OPERATION, the method being characterized by understanding: identifying a plurality of data samples from a first sensor; determining a segmentation of the plurality of data samples from the first sensor, where the determined segmentation includes a plurality of segments of varying sizes, each segment of the determined segmentation including: one or more segment parameters that provide a representation that models the samples data for the respective segment; and a segment boundary point indicating a boundary between the respective segment and a previous segment in the plurality of segments, and which indicates a point in the plurality of data samples where a threshold has been exceeded for the data samples of the respective segment with respect to data samples from the previous segment, where the threshold for at least one respective segment of the plurality of segments is indicative of a change in a noise variance of the respective segment relative to the noise variance of the previous segment; and storing the segment boundary point and one or more segment parameters for each of the one or more segments of the given segmentation.
[0002]
2. Sensor data compression method according to claim 1, further comprising: determining a plurality of segments of the plurality of data samples from the first sensor; select one of the plurality of targets based on a most likely targeting analysis of the plurality of targets; and use the selected segmentation as the determined segmentation of the plurality of data samples from the first sensor.
[0003]
3. Sensor data compression method according to claim 1, characterized in that the threshold depends on at least a transmission bandwidth restriction or a storage restriction.
[0004]
4. Sensor data compression method according to claim 1, characterized in that the one or more segment parameters of the respective segment provide a linear model of the data samples in the respective segment.
[0005]
5. Sensor data compression method according to claim 4, characterized in that the linear model of a respective segment comprises at least one of a gradient, an axis intercept, a 20-step function or a ramp function.
[0006]
6. Sensor data compression method according to claim 1, characterized in that the one or more segment parameters of the respective segment provide a non-linear model of the data samples of the respective segment.
[0007]
7. Sensor data compression method according to claim 1, further comprising: transmitting the segment boundary point and one or more segment parameters for each of the plurality of segments of the determined segmentation.
[0008]
8. Sensor data compression method according to claim 7, characterized in that the transmission of the segment boundary point and one or more segment parameters for each of the plurality of segments of the determined segmentation occurs dynamically based on a bandwidth restriction.
[0009]
9. Sensor data compression method, according to claim 1, characterized by further comprising: determining the noise variance for each segment of the determined segmentation; store the noise variance for each segment of the determined segmentation; and transmit the noise variance for each segment of the determined segmentation.
[0010]
10. Sensor data compression method according to claim 1, further comprising: identifying additional data samples from the first sensor; determine an updated segmentation of the plurality of data samples and additional data samples; determining difference information between the determined segmentation of the plurality of data samples and the updated segmentation of the plurality of data samples and additional data samples; and storing the difference information between the determined segmentation of the plurality of data samples and the updated segmentation of the plurality of data samples and the additional data sample.
[0011]
11. Sensor data compression method according to claim 10, further comprising: transmitting the difference information.
[0012]
12. Sensor data compression method according to claim 10, characterized in that the difference information includes at least adding one or more new segment boundary points, adding one or more new segment parameters, deleting one or more stored segment boundary points or reviewing one or more stored segment parameters of one or more segments in the plurality of segments.
[0013]
13. Sensor data compression method according to claim 1, further comprising: identifying a plurality of data samples from a second sensor; determining a segmentation of the plurality of data samples from the second sensor into one or more segments, wherein each segment of the segmentation includes: the segment boundary point from the determined segmentation of the plurality of data samples from the first sensor; and one or more segment parameters that provide a representation of the data samples from the second sensor for the respective segment; and storing one or more segment parameters for each of the one or more segments of the determined segmentation of the data samples of the second sensor.
[0014]
14. Sensor data compression method according to claim 13, further comprising: transmitting the segment boundary point and one or more segment parameters for each of the one or more segments of the determined segmentation of the samples data from the first sensor and the second sensor.
[0015]
15. Sensor data compression method according to claim 13, characterized in that the one or more segment parameters that provide a representation of the data samples of the second sensor for a respective segment are determined in relation to the segment parameters that provide a representation of the data samples from the first sensor for a respective segment.
[0016]
16. SENSOR SYSTEM TO PROVIDE COMPRESSED SENSOR DATA, the system characterized by comprises: one or more sensors configured to measure a property of an environment in which the one or more sensors are arranged and convert the measured property into data; a processor in communication with one or more sensors; and a memory device including instructions executable by the processor which, when executed by the processor, cause the processor to: identify a plurality of data samples from one of the sensors; determining a segmentation of the plurality of data samples from one of the sensors, wherein the determined segmentation includes a plurality of segments of varying sizes, each segment of the determined segmentation including: one or more segment parameters that provide a representation of the samples of data for the respective segment; and a segment boundary point indicating a point in the plurality of data samples between the respective segment and a previous segment where a threshold has been exceeded for the data samples of the respective sample with respect to the data samples of the previous segment, where the threshold for at least one respective segment of the plurality of segments is indicative of a change in a noise variance of the respective segment relative to the noise variance of the previous segment; and storing the segment boundary point and one or more segment parameters for each of the one or more segments of the given segmentation.
[0017]
17. Sensor system for providing compressed sensor data according to claim 16, characterized in that the instructions executable per processor further comprise instructions for causing the processor to: determine a plurality of segmentations of the plurality of data samples from the first sensor; 5 select one of the plurality of targets based on a most likely targeting analysis of the plurality of targets; and use the selected segmentation as the determined segmentation of the plurality of data samples from the first sensor.
[0018]
Sensor system for providing compressed sensor data according to claim 16, characterized in that the threshold depends at least on a transmission bandwidth restriction or a storage restriction. 15
[0019]
19. Sensor system for providing compressed sensor data according to claim 16, characterized in that the one or more segment parameters of a respective segment provide a linear model of the data samples in the respective segment.

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法律状态:
2018-12-26| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|

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2020-08-04| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|

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2022-02-01| B21F| Lapse acc. art. 78, item iv - on non-payment of the annual fees in time|Free format text: REFERENTE A 11A ANUIDADE. |

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