Patent Grant
7849934
The present invention relates generally to drill bits for drilling subterranean formations and more particularly to methods and apparatuses for monitoring operating parameters of drill bits during drilling operations.
The oil and gas industry expends sizable sums to design cutting tools, such as downhole drill bits including roller cone rock bits and fixed cutter bits, which have relatively long service lives, with relatively infrequent failure. In particular, considerable sums are expended to design and manufacture roller cone rock bits and fixed cutter bits in a manner that minimizes the opportunity for catastrophic drill bit failure during drilling operations. The loss of a roller cone or a polycrystalline diamond compact (PDC) from a fixed cutter bit during drilling operations can impede the drilling operations and, at worst, necessitate rather expensive fishing operations. If the fishing operations fail, sidetrack-drilling operations must be performed in order to drill around the portion of the wellbore that includes the lost roller cones or PDC cutters. Typically, during drilling operations, bits are pulled and replaced with new bits even though significant service could be obtained from the replaced bit. These premature replacements of downhole drill bits are expensive, since each trip out of the well prolongs the overall drilling activity, and consumes considerable manpower, but are nevertheless done in order to avoid the far more disruptive and expensive process of, at best, pulling the drillstring and replacing the bit or fishing and sidetrack drilling operations necessary if one or more cones or compacts are lost due to bit failure.
With the ever-increasing need for downhole drilling system dynamic data, a number of “subs” (i.e., a sub-assembly incorporated into the drillstring above the drill bit and used to collect data relating to drilling parameters) have been designed and installed in drillstrings. Unfortunately, these subs cannot provide actual data for what is happening operationally at the bit due to their physical placement above the bit itself.
Data acquisition is conventionally accomplished by mounting a sub in the Bottom Hole Assembly (BHA), which may be several feet to tens of feet away from the bit. Data gathered from a sub this far away from the bit may not accurately reflect what is happening directly at the bit while drilling occurs. Often, this lack of data leads to conjecture as to what may have caused a bit to fail or why a bit performed so well, with no directly relevant facts or data to correlate to the performance of the bit.
Recently, data acquisition systems have been proposed to install in the drill bit itself. However, data gathering, storing, and reporting from these systems has been limited. In addition, conventional data gathering in drill bits has not had the capability to adapt to drilling events that may be of interest in a manner allowing more detailed data gathering and analysis when these events occur.
There is a need for a drill bit equipped to gather and store long-term data that is related to performance and condition of the drill bit. Such a drill bit may extend useful bit life enabling re-use of a bit in multiple drilling operations and developing drill bit performance data on existing drill bits, which also may be used for developing future improvements to drill bits.
The present invention includes a drill bit and a data analysis system disposed within the drill bit for analysis of data sampled from physical parameters related to drill bit performance using a variety of adaptive data sampling modes.
In one embodiment of the invention, a drill bit for drilling a subterranean formation comprises a bit body, a shank, a data analysis module, and an end-cap. The bit body carries at least one cutting element (also referred to as a blade or a cutter). The shank is secured to the bit body, is adapted for coupling to a drillstring, and includes a central bore formed therethrough. The data analysis module may be configured in an annular ring such that it may be disposed in the central bore while permitting passage of drilling fluid therethrough. Finally, the end-cap is configured for disposition in the central bore such that the end-cap has the annular ring of the data analysis module disposed therearound and provides a chamber for the data analysis module by providing a sealing structure between the end-cap and the wall of the central bore.
Another embodiment of the invention comprises an apparatus for drilling a subterranean formation including a drill bit and a data analysis module disposed in the drill bit. The drill bit carries at least one blade or cutter and is adapted for coupling to a drillstring. The data analysis module comprises at least one sensor, a memory, and a processor. The at least one sensor is configured for sensing at least one physical parameter. The memory is configured for storing information comprising computer instructions and sensor data. The processor is configured for executing the computer instructions to collect the sensor data by sampling the at least one sensor. The computer instructions are further configured to analyze the sensor data to develop a severity index, compare the severity index to at least one adaptive threshold, and modify a data sampling mode responsive to the comparison.
Another embodiment of the invention includes a method comprising collecting sensor data at a sampling frequency by sampling at least one sensor disposed in a drill bit. In this method, the at least one sensor is responsive to at least one physical parameter associated with a drill bit state. The method further comprises analyzing the sensor data to develop a severity index, wherein the analysis is performed by a processor disposed in the drill bit. The method further comprises comparing the severity index to at least one adaptive threshold and modifying a data sampling mode responsive to the comparison.
Another embodiment of the invention includes a method comprising collecting background data by sampling at least one physical parameter associated with a drill bit state at a background sampling frequency while in a background mode. The method further includes transitioning from the background mode to a logging mode after a predetermined number of background samples. The method may also include transitioning from the background mode to a burst mode after a predetermined number of background samples. The method may also include transitioning from the logging mode to the background mode or the burst mode after a predetermined number of logging samples. The method may also include transitioning from the burst mode to the background mode or the logging mode after a predetermined number of burst samples.
Another embodiment of the invention includes a method comprising collecting background data by sampling at least one physical parameter associated with a drill bit state while in a background mode. The method further includes analyzing the background data to develop a background severity index and transitioning from the background mode to a logging mode if the background severity index is greater than a first background threshold. The method may also include transitioning from the background mode to a burst mode if the background severity index is greater than a second background threshold.
The present invention includes a drill bit and an electronics module disposed within the drill bit for analysis of data sampled from physical parameters related to drill bit performance using a variety of adaptive data sampling modes.
During drilling operations, drilling fluid is circulated from a mud pit 160 through a mud pump 162, through a desurger 164, and through a mud supply line 166 into the swivel 120. The drilling mud (also referred to as drilling fluid) flows through the Kelly joint 122 and into an axial central bore in the drillstring 140. Eventually, it exits through apertures or nozzles, which are located in a drill bit 200, which is connected to the lowermost portion of the drillstring 140 below drill collar section 144. The drilling mud flows back up through an annular space between the outer surface of the drillstring 140 and the inner surface of the borehole 100, to be circulated to the surface where it is returned to the mud pit 160 through a mud return line 168.
A shaker screen (not shown) may be used to separate formation cuttings from the drilling mud before it returns to the mud pit 160. The MWD communication system 146 may utilize a mud pulse telemetry technique to communicate data from a downhole location to the surface while drilling operations take place. To receive data at the surface, a mud pulse transducer 170 is provided in communication with the mud supply line 166. This mud pulse transducer 170 generates electrical signals in response to pressure variations of the drilling mud in the mud supply line 166. These electrical signals are transmitted by a surface conductor 172 to a surface electronic processing system 180, which is conventionally a data processing system with a central processing unit for executing program instructions, and for responding to user commands entered through either a keyboard or a graphical pointing device. The mud pulse telemetry system is provided for communicating data to the surface concerning numerous downhole conditions sensed by well logging and measurement systems that are conventionally located within the MWD communication system 146. Mud pulses that define the data propagated to the surface are produced by equipment conventionally located within the MWD communication system 146. Such equipment typically comprises a pressure pulse generator operating under control of electronics contained in an instrument housing to allow drilling mud to vent through an orifice extending through the drill collar wall. Each time the pressure pulse generator causes such venting, a negative pressure pulse is transmitted to be received by the mud pulse transducer 170. An alternative conventional arrangement generates and transmits positive pressure pulses. As is conventional, the circulating drilling mud also may provide a source of energy for a turbine-driven generator subassembly (not shown) which may be located near a bottom hole assembly (BHA). The turbine-driven generator may generate electrical power for the pressure pulse generator and for various circuits including those circuits that form the operational components of the measurement-while-drilling tools. As an alternative or supplemental source of electrical power, batteries may be provided, particularly as a back up for the turbine-driven generator.
A plurality of gage inserts 235 are provided on the gage pad surfaces 230 of the drill bit 200. Shear cutting gage inserts 235 on the gage pad surfaces 230 of the drill bit 200 provide the ability to actively shear formation material at the sidewall of the borehole 100 and to provide improved gage-holding ability in earth-boring bits of the fixed cutter variety. The drill bit 200 is illustrated as a PDC (polycrystalline diamond compact) bit, but the gage inserts 235 may be equally useful in other fixed cutter or drag bits that include gage pad surfaces 230 for engagement with the sidewall of the borehole 100.
Those of ordinary skill in the art will recognize that the present invention may be embodied in a variety of drill bit types. The present invention possesses utility in the context of a tricone or roller cone rotary drill bit or other subterranean drilling tools as known in the art that may employ nozzles for delivering drilling mud to a cutting structure during use. Accordingly, as used herein, the term “drill bit” includes and encompasses any and all rotary bits, including core bits, rollercone bits, fixed cutter bits; including PDC, natural diamond, thermally stable produced (TSP) synthetic diamond, and diamond impregnated bits without limitation, eccentric bits, bicenter bits, reamers, reamer wings, as well as other earth-boring tools configured for acceptance of an electronics module 290.
The end-cap 270 includes a cap bore 276 formed therethrough, such that the drilling mud may flow through the end cap, through the central bore 280 of the shank 210 to the other side of the shank 210, and then into the body of drill bit 200. In addition, the end-cap 270 includes a first flange 271 including a first sealing ring 272, near the lower end of the end-cap 270, and a second flange 273 including a second sealing ring 274, near the upper end of the end-cap 270.
In the embodiment shown in
An electronics module 290 configured as shown in the embodiment of
An electronics module may be configured to perform a variety of functions. One embodiment of an electronics module 290 (
An embodiment of a data analysis module 300 is illustrated in
The plurality of accelerometers 340A may include three accelerometers 340A configured in a Cartesian coordinate arrangement. Similarly, the plurality of magnetometers 340M may include three magnetometers 340M configured in a Cartesian coordinate arrangement. While any coordinate system may be defined within the scope of the present invention, one example of a Cartesian coordinate system, shown in
The accelerometers 340A of the
Furthermore, if initial conditions are known or estimated, bit velocity profiles and bit trajectories may be inferred by mathematical integration of the accelerometer data using conventional numerical analysis techniques. As is explained more fully below, acceleration data may be analyzed and used to determine adaptive thresholds to trigger specific events within the data analysis module. Furthermore, if the acceleration data is integrated to obtain bit velocity profiles or bit trajectories, these additional data sets may be useful for determining additional adaptive thresholds through direct application of the data set or through additional processing, such as, for example, pattern recognition analysis. By way of example and not limitation, an adaptive threshold may be set based on how far off center a bit may traverse before triggering an event of interest within the data analysis module. For example, if the bit trajectory indicates that the bit is offset from the center of the borehole by more than one inch, a different algorithm of data collection from the sensors may be invoked, as is explained more fully below.
The magnetometers 340M of the
The temperature sensor 340T may be used to gather data relating to the temperature of the drill bit 200, and the temperature near the accelerometers 340A, magnetometers 340M, and other sensors 340. Temperature data may be useful for calibrating the accelerometers 340A and magnetometers 340M to be more accurate at a variety of temperatures.
Other optional sensors 340R may be included as part of the data analysis module 300. Some non-limiting examples of sensors that may be useful in the present invention are strain sensors at various locations of the drill bit, temperature sensors at various locations of the drill bit, mud (drilling fluid) pressure sensors to measure mud pressure internal to the drill bit, and borehole pressure sensors to measure hydrostatic pressure external to the drill bit. Sensors may also be implemented to detect mud properties, such as, for example, sensors to detect conductivity or impedance to both alternating current and direct current, sensors to detect influx of fluid from the hole when mud flow stops, sensors to detect changes in mud properties, and sensors to characterize mud properties such as synthetic based mud and water based mud.
These optional sensors 340R may include sensors that are integrated with and configured as part of the data analysis module 300. These sensors may also include optional remote sensors 340R placed in other areas of the drill bit 200, or above the drill bit 200 in the bottom hole assembly. The optional remote sensors 340R may communicate across a communication link 362 using a direct-wired connection, or through a wireless connection to an optional sensor receiver 360. The sensor receiver 360 is configured to enable wireless remote sensor communication across limited distances in a drilling environment as are known by those of ordinary skill in the art.
One or more of these optional sensors may be used as an initiation sensor 370. The initiation sensor 370 may be configured for detecting at least one initiation parameter, such as, for example, turbidity of the mud, and generating a power enable signal 372 responsive to the at least one initiation parameter. A power gating module 374 coupled between the power supply 310, and the data analysis module 300 may be used to control the application of power to the data analysis module 300 when the power enable signal 372 is asserted. The initiation sensor 370 may have its own independent power source, such as a small battery, for powering the initiation sensor 370 during times when the data analysis module 300 is not powered. As with the other optional sensors 340, some non-limiting examples of parameter sensors that may be used for enabling power to the data analysis module 300 are sensors configured to sample; strain at various locations of the drill bit, temperature at various locations of the drill bit, vibration, acceleration, centripetal acceleration, fluid pressure internal to the drill bit, fluid pressure external to the drill bit, fluid flow in the drill bit, fluid impedance, and fluid turbidity.
By way of example and not limitation, an initiation sensor 370 may be used to enable power to the data analysis module 300 in response to changes in fluid impedance for fluids such as, for example, air, water, oil, and various mixtures of drilling mud. These fluid property sensors may detect a change in DC resistance between two terminals exposed to the fluid or a change in AC impedance between two terminals exposed to the fluid. In another embodiment, a fluid property sensor may detect a change in capacitance between two terminals in close proximity to, but protected from, the fluid.
For example, water may have a relatively high dielectric constant as compared with typical hydrocarbon-based lubricants. The data analysis module 300, or other suitable electronics, may energize the sensor with alternating current and measure a phase shift therein to determine capacitance, for example, or alternatively may energize the sensor with alternating or direct current and determine a voltage drop to measure impedance.
In addition, at least some of these sensors may be configured to generate any required power for operation such that the independent power source is self-generated in the sensor. By way of example and not limitation, a vibration sensor may generate sufficient power to sense the vibration and transmit the power enable signal 372 simply from the mechanical vibration.
As another example of an initiation sensor 370 embodiment,
As can be seen from
As another example, the initiation sensor 370 may be configured as a pressure activated switch.
The deformable member 252 may be a variety of devices or materials. By way of example and not limitation, the deformable member 252 may be a piezoelectric device. The piezoelectric device may be configured between the fixed member 251 and the displacement member 256 such that movement of the displacement member 256 exerts a force on the piezoelectric device causing a change in a voltage across the piezoelectric material. Electrodes attached to the piezoelectric material may couple a signal to the data analysis module 300 (
In
In operation, the pressure activated switch 250 may be configured to activate the data analysis module 300 as the drill bit 200 traverses down hole when a given depth is achieved based on the hole pressure sensed by the pressure activated switch 250. In the configuration illustrated in
In addition, while the embodiment of the pressure activated switch 250 has been described as disposed in a recess 259 of the end-cap 270, other placements are possible. For example, the cutouts illustrated in
The pressure activated switch is one of many types of sensors that may be placed in a recess such as that described in conjunction with the pressure activated switch. Any sensor that may need to be exposed to the environment of the borehole may be disposed in the recess with a configuration similar to the pressure activated switch to form a high-pressure and watertight seal within the drill bit. By way of example and not limitation, some environmental sensors that may be used are passive gamma ray sensors, corrosion sensors, chlorine sensors, hydrogen sulfide sensors, proximity detectors for distance measurements to the borehole wall, and the like.
Another significant bit parameter to measure is stress and strain on the drill bit. However, just placing strain gauges on various areas of the drill bit or chambers within the drill bit may not produce optimal results. In an embodiment of the present invention, a load cell may be used to obtain stress and strain data at the drill bit that may be more useful.
The load cell configuration may assist in obtaining more accurate strain measurements by using a load cell material that is more uniform, homogenous, and suitable for bonding strain gauges thereto when compared to bonding strain gauges directly to the bit body or sidewalls within a cavity in the bit body. The load cell configuration also may be more suitable for detecting torsional strain on the drill bit because the load cell creates a larger and more uniform displacement over which the torsional strain may occur due to the distance between the first attachment section and the second attachment section.
Furthermore, with the placement of the load cell 281, or strain gauges, in the drill bit, it may be placed in a specific desired orientation relative to elements of interest on or within the drill bit. With conventional placement of load cells, and other sensors, above the bit in another element of the drillstring it may be difficult to obtain the desired orientation due to the connection mechanism (e.g., threaded fittings) of the drill bit to the drillstring. By way of example, embodiments of the present invention allow the load cell to be placed in a specific orientation relative to elements of interest such as a specific cutter, a specific leg of a tri-cone bit, or an index mark on the drill bit. In this way, additional information about specific elements of the bit may be obtained due to the specific and repeatable orientation of the load cell 281 relative to features of the drill bit.
By way of example and not limitation, the load cell 281 may be rotated within the tube 289 to a specific orientation aligning with a specific cutter on the drill bit 200. As a result of this orientation, additional stress and strain information about the area of the drill bit near a specific cutter may be available. Furthermore, placement of the tube 289 at an angle relative to the central axis of the drill bit 200, or at different distances relative to the central axis of the drill bit 200, may enable more information about bending stresses relative to axial stresses placed on the drill bit, or specific areas of the drill bit.
This ability to place a sensor with a desired orientation relative to an arbitrary but repeatable feature of the drill bit is useful for other types of sensors, such as, for example, accelerometers, magnetometers, temperature sensors, and other environmental sensors.
The strain gauges may be connected in any suitable configuration, as are known by those of ordinary skill in the art, for detecting strain along different axis of the load cell. Such suitable configurations may include for example, Chevron bridge circuits, or Wheatstone bridge circuits. Analysis of the strain gauge measurements can be used to develop bit parameters, such as, for example, stress on the bit, weight on bit, longitudinal stress, longitudinal strain, torsional stress, and torsional strain.
Returning to
A communication port 350 may be included in the data analysis module 300 for communication to external devices such as the MWD communication system 146 and a remote processing system 390. The communication port 350 may be configured for a direct communication link 352 to the remote processing system 390 using a direct wire connection or a wireless communication protocol, such as, by way of example only, infrared, Bluetooth, and 802.11a/b/g protocols. Using the direct communication, the data analysis module 300 may be configured to communicate with a remote processing system 390 such as, for example, a computer, a portable computer, and a personal digital assistant (PDA) when the drill bit 200 is not downhole. Thus, the direct communication link 352 may be used for a variety of functions, such as, for example, to download software and software upgrades, to enable setup of the data analysis module 300 by downloading configuration data, and to upload sample data and analysis data. The communication port 350 may also be used to query the data analysis module 300 for information related to the drill bit, such as, for example, bit serial number, data analysis module serial number, software version, total elapsed time of bit operation, and other long term drill bit data which may be stored in the NVRAM.
The communication port 350 may also be configured for communication with the MWD communication system 146 in a bottom hole assembly via a wired or wireless communication link 354 and protocol configured to enable remote communication across limited distances in a drilling environment as are known by those of ordinary skill in the art. One available technique for communicating data signals to an adjoining subassembly in the drillstring 140 (
The MWD communication system 146 may, in turn, communicate data from the data analysis module 300 to a remote processing system 390 using mud pulse telemetry 356 or other suitable communication means suitable for communication across the relatively large distances encountered in a drilling operation.
The processor 320 in the embodiment of
The embodiment of
The embodiment of
In addition, software running on the processor 320 may be used to manage battery life intelligence and adaptive usage of power consuming resources to conserve power. The battery life intelligence can track the remaining battery life (i.e., charge remaining on the battery) and use this tracking to manage other processes within the system. By way of example, the battery life estimate may be determined by sampling a voltage from the battery, sampling a current from the battery, tracking a history of sampled voltage, tracking a history of sampled current, and combinations thereof.
The battery life estimate may be used in a number of ways. For example, near the end of battery life, the software may reduce sampling frequency of sensors, or may be used to cause the power control bus to begin shutting down voltage signals to various components.
This power management can create a graceful, gradual shutdown. For example, perhaps power to the magnetometers is shut down at a certain point of remaining battery life. At another point of battery life, perhaps the accelerometers are shut down. Near the end of battery life, the battery life intelligence can ensure data integrity by making sure improper data is not gathered or stored due to inadequate voltage at the sensors, the processor, or the memory.
As is explained more fully below with reference to specific types of data gathering, software modules may be devoted to memory management with respect to data storage. The amount of data stored may be modified with adaptive sampling and data compression techniques. For example, data may be originally stored in an uncompressed form. Later, when memory space becomes limited, the data may be compressed to free up additional memory space. In addition, data may be assigned priorities such that when memory space becomes limited high priority data is preserved and low priority data may be overwritten.
Software modules may also be included to track the long term history of the drill bit. Thus, based on drilling performance data gathered over the life time of the drill bit, a life estimate of the drill bit may be formed. Failure of a drill bit can be a very expensive problem. With life estimates based on actual drilling performance data, the software module may be configured to determine when a drill bit is nearing the end of its useful life and use the communication port to signal to external devices the expected life remaining on the drill bit.
The background mode 510 may be used for sampling data at a relatively low background sampling frequency and generating background data from a subset of all the available sensors 340. The logging mode 530 may be used for sampling logging data at a relatively mid-level logging sampling frequency and with a larger subset, or all, of the available sensors. The burst mode 550 may be used for sampling burst data at a relatively high burst sampling frequency and with a large subset, or all, of the available sensors 340.
Each of the different data modes may collect, process, and analyze data from a subset of sensors, at predefined sampling frequency and for a predefined block size. By way of example, and not limitations, examples of sampling frequencies, and block collection sizes may be: 2 or 5 samples/sec, and 200 seconds worth of samples per block for background mode 510, 100 samples/sec, and ten seconds worth of samples per block for logging mode 530, and 200 samples/sec, and five seconds worth of samples per block for burst mode 550. Some embodiments of the invention may be constrained by the amount of memory available, the amount of power available or combination thereof.
More memory, more power, or combination thereof may be required for more detailed modes, therefore, the adaptive threshold triggering enables a method of optimizing memory usage, power usage, or combination thereof, relative to collecting and processing the most useful and detailed information. For example, the adaptive threshold triggering may be adapted for detection of specific types of known events, such as, for example, bit whirl, bit bounce, bit wobble, bit walking, lateral vibration, and torsional oscillation.
Generally, the data analysis module 300 (
The software, which may also be referred to as firmware, for the data analysis module 300 comprises computer instructions for execution by the processor 320. The software may reside in an external memory 330, or memory within the processor 320.
Before describing the main routine in detail, a basic function to collect and queue data, which may be performed by the processor and Analog to Digital Converter (ADC) is described. The ADC routine 780, illustrated in
In the burst mode 550, samples are collected (794 and 796) for all the accelerometers and all the magnetometers. The sampled data from each accelerometer and each magnetometer is stored in a burst data record. The ADC routine 780 then sets 798 a data ready flag indicating to the main routine that data is ready to process.
In the background mode 510 (
If in the log mode, samples are collected (786, 788, and 790) for all the accelerometers, all the magnetometers, and the temperature sensor. The ADC routine 780 collects a sampled value from each accelerometer and each magnetometer and adds the sampled value to a stored value containing a sum of previous accelerometer and magnetometer measurements to create a running sum of accelerometer measurements and a running sum of magnetometer measurements. In addition, the ADC routine 780 compares the current sample for each accelerometer and magnetometer measurement to a stored minimum value for each accelerometer and magnetometer. If the current sample is smaller than the stored minimum, the current sample is saved as the new stored minimum. Thus, the ADC routine 780 keeps the minimum value sampled for all samples collected in the current data block. Similarly, to keep the maximum value sampled for all samples collected in the current data block, the ADC routine 780 compares the current sample for each accelerometer and magnetometer measurement to a stored maximum value for each accelerometer and magnetometer. If the current sample is larger than the stored maximum, the current sample is saved as the new stored maximum. The ADC routine 780 also creates a running sum of temperature values by adding the current sample for the temperature sensor to a stored value of a sum of previous temperature measurements. The ADC routine 780 then sets 792 a data ready flag indicating to the main routine that data is ready to process.
As illustrated in
Details of logging operations 610 are illustrated in
Magnetometers may be used to determine bit revolutions because the magnetometers are rotating in the earth's magnetic field. If the bit is positioned vertically, the determination is a relatively simple operation of comparing the history of samples from the X magnetometer and the Y magnetometers. For bits positioned at an angle, perhaps due to directional drilling, the calculations may be more involved and require samples from all three magnetometers.
Details of burst operations 614 are also illustrated in
Details of background block processing 617 are also illustrated in
In performing adaptive sampling, decisions may be made by the software as to what type of data mode is currently operating and whether to switch to a different data mode based on timing event triggers or adaptive threshold triggers. The adaptive threshold triggers may generally be viewed as a test between a severity index and an adaptive threshold. At least three possible outcomes are possible from this test. As a result of this test, a transition may occur to a more detailed mode of data collection, to a less detailed mode of data collection, or no transition may occur.
These data modes are defined as the background mode 510 being the least detailed, the logging mode 530 being more detailed than the background mode 510, and the burst mode 550 being more detailed than the logging mode 530.
A different severity index may be defined for each data mode. Any given severity index may comprise a sampled value from a sensor, a mathematical combination of a variety of sensors samples, or a signal processing result including historical samples from a variety of sensors. Generally, the severity index gives a measure of particular phenomena of interest. For example, a severity index may be a combination of mean square error calculations for the values sensed by the X accelerometer and the Y accelerometer.
In its simplest form, an adaptive threshold may be defined as a specific threshold (possibly stored as a constant) for which, if the severity index is greater than or less than the adaptive threshold the data analysis module may switch (i.e., adapt sampling) to a new data mode. In more complex forms, an adaptive threshold may change its value (i.e., adapt the threshold value) to a new value based on historical data samples or signal processing analysis of historical data samples.
In general, two adaptive thresholds may be defined for each data mode: A lower adaptive threshold (also referred to as a first threshold) and an upper adaptive threshold (also referred to as a second threshold). Tests of the severity index against the adaptive thresholds may be used to decide if a data mode switch is desirable.
In the computer instructions illustrated in
If test 662 fails, adaptive threshold triggering is active, and operation block 668 calculates a background severity index (Sbk), a first background threshold (T1bk), and a second background threshold (T2bk). Then, Test 670 is performed to see if the background severity index is between the first background threshold and the second background threshold. If so, operation block 672 switches the data mode to logging mode and the software exits background mode processing 640.
If test 670 fails, test 674 is performed to see if the background severity index is greater than the second background threshold. If so, operation block 676 switches the data mode to burst mode and the software exits background mode processing. If test 674 fails, the data mode remains in background mode and the software exits background mode processing 640.
If test 702 fails, adaptive threshold triggering is active, and operation block 708 calculates a logging severity index (Sig), a first logging threshold (T1lg), and a second logging threshold (T2lg). Then, test 710 is performed to see if the logging severity index is less than the first logging threshold. If so, operation block 712 switches the data mode to background mode 510 and the software exits log block processing 700.
If test 710 fails, test 714 is performed to see if the logging severity index is greater than the second logging threshold. If so, operation block 716 switches the data mode to burst mode and the software exits log block processing. If test 714 fails, the data mode remains in logging mode and the software exits log block processing 700.
If test 782 fails, adaptive threshold triggering is active, and operation block 788 calculates a burst severity index (Sbu), a first burst threshold (T1bu), and a second burst threshold (T2bu). Then, test 790 is performed to see if the burst severity index is less than the first burst threshold. If so, operation block 792 switches the data mode to background mode 510 and the software exits burst block processing 760.
If test 790 fails, test 794 is performed to see if the burst severity index is less than the second burst threshold. If so, operation block 796 switches the data mode to logging mode and the software exits burst block processing. If test 794 fails, the data mode remains in burst mode and the software exits burst block processing 760.
In the computer instructions illustrated in
Details of another embodiment of background mode processing 640 are illustrated in
Referring to
Details of another embodiment of log block processing 700 are illustrated in
Once all parameters for storage and adaptive triggering are calculated, a test is performed 736 to determine whether the mode is currently set to adaptive triggering or time based triggering. If the test fails (i.e., time based triggering is active), the trigger flag is cleared 738. A test 740 is performed to verify that data collection is at the end of a logging data block. If not, the software exits the log block processing. If data collection is at the end of a logging data block, burst mode is set 742, and the time for completion of the burst block is set. In addition, the burst block to be captured is defined as time triggered 744.
If the test 736 for adaptive triggering passes, a test 746 is performed to verify that a trigger flag is set, indicating that, based on the adaptive trigger calculations, burst mode should be entered to collect more detailed information. If test 746 passes, burst mode is set 748, and the time for completion of the burst block is set. In addition, the burst block to be captured is defined as adaptive triggered 750. If test 746 fails or after defining the burst block as adaptive triggered, the trigger flag is cleared 752 and log block processing is complete.
Details of another embodiment of burst block processing 760 are illustrated in
After many burst blocks have been processed, the amount of memory allocated to storing burst samples may be completely consumed. If this is the case, a previously stored burst block may need to be set to be overwritten by samples from the next burst block. The software checks 764 to see if any unused NVRAM is available for burst block data. If not all burst blocks are used, the software exits the burst block processing. If all burst blocks are used 766, the software uses an algorithm to find 768 a good candidate for overwriting.
It will be recognized and appreciated by those of ordinary skill in the art, that the main routine 600, illustrated in
More memory, more power, or combination thereof, may be required for more detailed modes, therefore, the adaptive threshold triggering enables a method of optimizing memory usage, power usage, or combination thereof, relative to collecting and processing the most useful and detailed information. For example, the adaptive threshold triggering may be adapted for detection of specific types of known events, such as, for example, bit whirl, bit bounce, bit wobble, bit walking, lateral vibration, and torsional oscillation.
As stated earlier, time varying data such as that illustrated above with respect to
Trigger event analysis may be as straightforward as the threshold analysis described above. However, other more detailed analysis may be performed to develop triggers based on bit behavior such as bit dynamics analysis, formation analysis, and the like.
Many algorithms are available for data compression and pattern recognition. However, most of these algorithms are frequency based and require complex, powerful digital signal processing techniques. In a downhole drill bit environment battery power, and the resulting processing power may be limited. Therefore, lower power data compression and pattern recognition analysis may be useful. Other encoding algorithms may be utilized on time varying data that are time based, rather than frequency based. These encoding algorithms may be used for data compression, wherein only the resultant codes representing the time varying waveform are stored, rather than the original samples. In addition, pattern recognition may be utilized on the resultant codes to recognize specific events. These specific events may be used, for example, for adaptive threshold triggering. Adaptive threshold triggering may be adapted for detection of specific types of known behaviors, such as, for example, bit whirl, bit bounce, bit wobble, bit walking, lateral vibration, and torsional oscillation. Adaptive threshold triggering may also be adapted for various levels of severity for these bit behaviors.
As an example, one such analysis technique includes time encoded signal processing and recognition (TESPAR), which has been conventionally used in speech recognition algorithms. Embodiments of the present invention have extended TESPAR analysis to recognize bit behaviors that may be of interest to record compressed data or to use as triggering events.
TESPAR analysis may be considered to be performed in three general processes. First, TESPAR parameters are extracted from a time varying waveform. Next, the TESPAR parameters are encoded into alphabet symbols. Finally, the resultant encodings may be classified, or “recognized.”
TESPAR analysis is based on the location of real and complex zeros in a time varying waveform. Real zeros are represented by zero crossings of the waveform, whereas complex zeros may be approximated by the shape of the waveform between zero crossings.
Another parameter defined for TESPAR analysis is the shape of the waveform in the epoch. The shape is defined as the number of positive minimas or the number of negative maximas in an epoch. Thus, the shape for the third epoch is defined as one because it has one minima for a waveform in the positive region. Similarly, the shape for the fourth epoch is defined as two because it has two maximas for the waveform in the negative region. A final parameter that may be defined for TESPAR analysis is the amplitude, which is defined as the amplitude of the largest peak within the epoch. For example, the seventh epoch has an amplitude of 13.
With the waveform now extracted into TESPAR parameters, rather than storing samples of the waveform at every point, the waveform may be stored as sequential epochs and the parameters for each epoch. This represents a type of lossy data compression wherein significantly less data needs to be stored to adequately represent the waveform, but the waveform cannot be recreated with as much accuracy as when it was originally sampled.
The waveform may be further analyzed, and further compressed, by converting the TESPAR parameters to a symbol alphabet.
While the alphabet illustrated in
Coding the epochs into alphabet symbols creates additional lossy compression as each epoch may be represented by its alphabet symbol and its amplitude. In some applications, the amplitude may not be needed and simply the alphabet symbol may be stored. Encoding the waveform of
For any given waveform, the waveform may be represented as a histogram indicating the number of occurrences of each TESPAR symbol across the duration of the TESPAR symbol stream. An example histogram is illustrated in
One of the strengths of TESPAR encoding is that it is easily adaptable to pattern recognition and has been conventionally applied to speech recognition to recognize speakers and specific words that are spoken by a variety of speakers. Embodiments of the present invention use pattern recognition to recognize specific behaviors of drill bit dynamics that may then be used as an adaptive threshold trigger. Some behaviors that may be recognized are whirl and stick/slip behaviors, as well as variations on these based on the severity of the behavior. Other example behaviors are the change in behavior of a drill bit based on how dull the cutters are or the type of formation that is being drilled, as well as specific energy determination defined as the energy exerted in drilling versus the volume of formation removed, or efficiency defined as the actual amount of work performed versus the minimum possible work performed.
Artificial neural networks may be trained to recognize specific patterns of S-matrices derived from TESPAR symbol streams. The neural networks are trained by processing existing waveforms that exhibit the pattern to be recognized. In other words, to recognize whirl, existing accelerometer data from a number of different bits or a number of different occurrences of whirl are encoded into a TESPAR symbol stream and used to train the neural network.
A single neural network configuration is shown in
At this trained stage, the trained network may be used for pattern recognition.
At process block 808, the desired time varying waveform data is converted to TESPAR parameters as described above. If this level of data compression is desired, the TESPAR parameters may be stored for each epoch, creating a TESPAR parameter stream.
At process block 810, the TESPAR parameters are converted to TESPAR symbols using the appropriate alphabet as described above. If this level of data compression is desired, the TESPAR symbols may be stored for each epoch creating a TESPAR symbol stream.
At process block 812, the TESPAR symbol stream is converted to an S-matrix by determining the number of occurrences of each symbol within the stream, as is explained above. If this level of data compression is desired, the S-matrix may be stored.
Decision block 814 determines whether pattern recognition is desired. If not, the TESPAR analysis was used for data compression only, and the process exits. If pattern recognition is desired, the S-matrix is applied to the trained neural network to determine if any trained bit behavior is a match to the S-matrix, as is shown in process block 816.
At process block 818, if there is a match to a trained bit behavior, and that matched behavior is to be used as a triggering event, the triggering event may be used to modify behavior of the data analysis module.
While the present invention has been described herein with respect to certain preferred embodiments, those of ordinary skill in the art will recognize and appreciate that it is not so limited. Rather, many additions, deletions, and modifications to the preferred embodiments may be made without departing from the scope of the invention as hereinafter claimed. In addition, features from one embodiment may be combined with features of another embodiment while still being encompassed within the scope of the invention as contemplated by the inventors.
This application is a continuation-in-part of U.S. patent application Ser. No. 11/146,934 entitled METHOD AND APPARATUS FOR COLLECTING DRILL BIT PERFORMANCE DATA filed Jun. 7, 2005 now U.S. Pat. No. 7,604,072, issued Oct. 20, 2009, the disclosure of which is hereby incorporated by reference.
| Number | Name | Date | Kind |
|---|---|---|---|
| 2507351 | Scherbatskoy | May 1950 | A |
| 4884071 | Howard | Nov 1989 | A |
| 4958517 | Maron | Sep 1990 | A |
| 5012412 | Helm | Apr 1991 | A |
| 5129471 | Maurstad et al. | Jul 1992 | A |
| 5160925 | Dailey et al. | Nov 1992 | A |
| 5415030 | Jogi et al. | May 1995 | A |
| 5493288 | Henneuse | Feb 1996 | A |
| 5553678 | Barr et al. | Sep 1996 | A |
| 5720355 | Lamine et al. | Feb 1998 | A |
| 5813480 | Zaleski, Jr. et al. | Sep 1998 | A |
| 5842149 | Harrell et al. | Nov 1998 | A |
| 5864058 | Chen | Jan 1999 | A |
| 6021377 | Dubinsky et al. | Feb 2000 | A |
| 6057784 | Schaaf et al. | May 2000 | A |
| 6101462 | King | Aug 2000 | A |
| 6175818 | King | Jan 2001 | B1 |
| 6176323 | Weirich et al. | Jan 2001 | B1 |
| 6206108 | MacDonald et al. | Mar 2001 | B1 |
| 6230822 | Sullivan et al. | May 2001 | B1 |
| 6233524 | Harrell et al. | May 2001 | B1 |
| 6419032 | Sullivan et al. | Jul 2002 | B1 |
| 6540033 | Sullivan et al. | Apr 2003 | B1 |
| 6543312 | Sullivan et al. | Apr 2003 | B2 |
| 6571886 | Sullivan et al. | Jun 2003 | B1 |
| 6626251 | Sullivan et al. | Sep 2003 | B1 |
| 6648082 | Schultz et al. | Nov 2003 | B2 |
| 6651496 | Van Steenwyk et al. | Nov 2003 | B2 |
| 6668465 | Noureldin et al. | Dec 2003 | B2 |
| 6672409 | Dock et al. | Jan 2004 | B1 |
| 6681633 | Schultz et al. | Jan 2004 | B2 |
| 6691802 | Schultz et al. | Feb 2004 | B2 |
| 6691804 | Harrison | Feb 2004 | B2 |
| 6698536 | Moran et al. | Mar 2004 | B2 |
| 6712160 | Schultz et al. | Mar 2004 | B1 |
| 6722450 | Schultz et al. | Apr 2004 | B2 |
| 6742604 | Brazil et al. | Jun 2004 | B2 |
| 6748354 | King | Jun 2004 | B1 |
| 6769497 | Dubinsky et al. | Aug 2004 | B2 |
| 6816788 | Van Steenwyk et al. | Nov 2004 | B2 |
| 6817425 | Schultz et al. | Nov 2004 | B2 |
| 6820702 | Niedermayr et al. | Nov 2004 | B2 |
| 6837314 | Krueger et al. | Jan 2005 | B2 |
| 6892812 | Niedermayr et al. | May 2005 | B2 |
| 6896055 | Koithan | May 2005 | B2 |
| 7017662 | Schultz et al. | Mar 2006 | B2 |
| 7168506 | Boucher et al. | Jan 2007 | B2 |
| 20010042643 | Krueger et al. | Nov 2001 | A1 |
| 20030130846 | King | Jul 2003 | A1 |
| 20040050590 | Pirovolou et al. | Mar 2004 | A1 |
| 20040069539 | Sullivan et al. | Apr 2004 | A1 |
| 20040112640 | Hay et al. | Jun 2004 | A1 |
| 20040124012 | Dunlop et al. | Jul 2004 | A1 |
| 20040168827 | Jeffryes | Sep 2004 | A1 |
| 20050268476 | Illfelder | Dec 2005 | A1 |
| 20050283315 | Haugland | Dec 2005 | A1 |
| 20060006000 | Weston et al. | Jan 2006 | A1 |
| 20060248735 | Haugland | Nov 2006 | A1 |
| 20060260843 | Cobern | Nov 2006 | A1 |
| 20070017705 | Lasater | Jan 2007 | A1 |
| Number | Date | Country |
|---|---|---|
| 2 395 971 | Jun 2004 | GB |
| Number | Date | Country | |
|---|---|---|---|
| 20070272442 A1 | Nov 2007 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 11146934 | Jun 2005 | US |
| Child | 11708147 | US |
