WO2018157260A1 - Réduction adaptative du bruit en vue de la surveillance d'événements pendant des opérations de fracturation hydraulique - Google Patents

Réduction adaptative du bruit en vue de la surveillance d'événements pendant des opérations de fracturation hydraulique Download PDF

Info

Publication number
WO2018157260A1
WO2018157260A1 PCT/CA2018/050253 CA2018050253W WO2018157260A1 WO 2018157260 A1 WO2018157260 A1 WO 2018157260A1 CA 2018050253 W CA2018050253 W CA 2018050253W WO 2018157260 A1 WO2018157260 A1 WO 2018157260A1
Authority
WO
WIPO (PCT)
Prior art keywords
sensors
signals
signal
pipe
sensor
Prior art date
Application number
PCT/CA2018/050253
Other languages
English (en)
Inventor
William Dean Warner
Matthew Johan ZIELEMAN
Original Assignee
Cold Bore Technology Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from CA2977316A external-priority patent/CA2977316C/fr
Application filed by Cold Bore Technology Inc. filed Critical Cold Bore Technology Inc.
Priority to US16/489,685 priority Critical patent/US11215044B2/en
Publication of WO2018157260A1 publication Critical patent/WO2018157260A1/fr
Priority to US17/542,311 priority patent/US11585198B2/en

Links

Classifications

    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E21B47/12—Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling
    • E21B47/14—Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling using acoustic waves
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E21B43/25—Methods for stimulating production
    • E21B43/26—Methods for stimulating production by forming crevices or fractures

Definitions

  • This application relates to communications in hydraulic fracturing (fracking) operations and/or in other operations involving downhole pipes, drill strings and/or the like.
  • Particular embodiments provide event monitoring and/or detection of downhole events at a surface portion of the pipe in the face of considerable surface noise.
  • Hydraulic Fracturing (more commonly known as, and hereinafter referred to as, "fracking") is a well-stimulation technique in the oil & gas industry in which underground rock formations are fractured by pressurized liquid or gas/liquid formulation. Fracking involves high-pressure injection of "fracking fluid" through a pipe located in a wellbore to create cracks/fissures in the deep-rock formations, which are then held open with proppant (e.g., sand) which is added to the fracking fluid, through which natural gas and/or other petroleum resources may flow more freely.
  • proppant e.g., sand
  • Fracking is typically most effective when it is performed in multiple stages along the length of a wellbore.
  • One common technique for implementing this staged operation referred to as a "plug & perf” technique, involves inserting a steel tube/pipe into the wellbore spanning from the toe (deepest point of the wellbore) to the surface.
  • the plug & perf pipe typically made of steel, commonly incorporates a series of expanding rings lining the outside of the pipe. After insertion into the wellbore, these rings, often referred to as “packers”, expand and seal against the surface of the wellbore (e.g. against the deep-rock formation).
  • Explosive charges are inserted into the bore of the pipe and then detonated deep within the pipe at locations (along the pipe axis) between two consecutive packers.
  • the exploding charge penetrates through the steel pipe creating pathways from the bore of the pipe into the surrounding wellbore.
  • Fracking fluid is then pumped into the pipe at high pressure.
  • the high pressure tracking fluid travels down the pipe, from a bore of the pipe to an exterior of the pipe through the pathways created by the explosive charge, and into the region of the wellbore localized by the bounding packers.
  • the pressure of the tracking fluid causes cracks/fissures to occur in the formation around the wellbore.
  • a proppant such as sand or ceramic material for example, is typically added to the tracking fluid.
  • the proppant travels down the pipe with the tracking fluid and, under pressure, is embedded into the cracks/fissures in the formation, such that the cracks/fissures remain open when pressure is removed.
  • an expanding plug is typically inserted into the pipe to seal off the section of the well that was just tracked. Then, another explosive charge is inserted into the bore of the pipe and detonated in a new location of the pipe to create pathways through the pipe and to the wellbore at a new region of the pipe. This part of the wellbore and the corresponding formation is then tracked in a manner similar to the deeper section described above. This tracking process is repeated over a plurality of repetitions - e.g. until a desired length of the wellbore residing in the formation of interest has been tracked. After tracking the desired length of the wellbore, the expanding plugs that were inserted into the pipe are typically drilled out or otherwise removed from the bore of the pipe to create fluid flow pathways from all sections of the wellbore to the surface.
  • the plug & pert technique has a fundamental limitation of having to repeatedly insert and retract equipment from the surface to the downhole region of localized tracking, a distance that can exceed 10km. This insertion and retraction of equipment is time consuming and expensive. It can also be dangerous when the explosive charges do not fully detonate and are unknowingly brought to the surface in an undetonated state.
  • An alternative to the plug & pert technique is referred to as a "ball-activated" or “ball and sleeve” tracking technique.
  • a ball-activated technique functional sleeves are included inline in the steel tracking pipe at locations, along the pipe axis, between adjacent pairs of packers. Each of these sleeves allow tracking fluid to flow through them (down the pipe bore) until a suitably designed ball is launched into the pipe bore from the surface and lodges in a receptor within a particular sleeve, thereby sealing off the flow of tracking fluid down the pipe bore beyond the particular sleeve.
  • a ball received in a particular sleeve typically shifts within the sleeve, revealing openings in the wall of the sleeve (referred to as fracking ports), thereby providing a pathway for the pressurized fracking fluid to flow from within the bore of the pipe into a localized region of the wellbore outside of the pipe bore and between a corresponding pair of packers.
  • each sleeve has a ball seat of different dimension (e.g. different diameter) such that sleeves located relatively close to the surface (at uphole locations) have relatively large diameter and sleeves located farthest from the surface (at downhole locations) have relatively small diameter.
  • Uphole sleeves, with relatively large diameter seats allow balls with smaller diameter to pass through unimpeded. This way, downhole zones (i.e. zones relatively far from the surface along the pipe axis) are tracked first and uphole zones (i.e. zones relatively close to the surface along the pipe axis) are tracked last.
  • each ball has the same diameter and sleeves are designed to let a specific number of balls pass through before preventing a ball from passing through and thus sealing flow of fracking fluid (down the pipe bore) to downhole locations beyond the sleeve.
  • fracking ports to a localized fracking zone can commence. It is also desirable to be able to identify when formation fractures occur (fracture events). Typically, there is desire to cause a plurality of formation fractures in each localized zone. Using current pressure monitoring techniques, fracture events can frequently, but not reliably, be detected. There is a general desire for a more reliable and/or sensitive method for detecting formation fracture events. Accurate knowledge of formation fracture events allows personnel to decrease the time and resources expended to adequately track a localized formation region when that formation region is fracturing easily, and to increase time and resources expended if a formation region is not fracturing easily.
  • acoustic-wave-producing downhole events e.g. ball seat events, sleeve-shifting/port opening events, fracture formation events, launching of activation balls, plug & pert detonation events, undesired tracking pipe rupture events, fracture events in adjacent wells and/or the like.
  • Figure 1 is a schematic cross-sectional view of a sensor system disposed at (or above) the surface of a well head on a tracking pipe according to a particular embodiment.
  • Figures 2A, 2B and 2C respectively depict relative mounting positions about the pipe axis for groups of sensors comprising two, three and four sensors per group according to a particular embodiment.
  • Figure 3 is a schematic cross-sectional view of a sensor system mounted to a tracking pipe according to another embodiment.
  • Figure 4 illustrates an exemplary mounting of a sensor with its primary sensitivity axis oriented perpendicular to the pipe axis and the direction of tracking fluid flow within the pipe according to a particular embodiment.
  • Figure 5 illustrates an exemplary mounting of a sensor with its primary sensitivity axis oriented parallel to the pipe axis and the direction of tracking fluid flow within the pipe according to a particular embodiment.
  • Figure 6 is a schematic cross-sectional view in a plane perpendicular to the pipe axis showing a sensor group according to a particular embodiment.
  • FIGS 7, 7 A schematically illustrate signal processing circuits according to particular embodiments, which receive sensor signals from sensors and generate therefrom reduced-noise downhole event signals.
  • Figures 8, 8A show adaptive noise reduction signal processing circuits according to particular implementations of the more general architecture of signal processing circuits shown in Figures 7, 7A.
  • Figure 9 is a schematic block diagram providing more detail of an adaption core according to a particular embodiment.
  • Figure 10 shows an exemplary hardware implementation of the signal processing circuits of Figure 7 and 8 according to a particular embodiment.
  • aspects of the invention described and/or claimed herein provide methods and systems for detecting acoustic-wave-producing downhole events (e.g. ball seat events, sleeve-shifting/port opening events, fracture formation events, launching of activation balls, plug & perf detonation events, undesired tracking pipe rupture events, fracture events in adjacent wells and/or the like).
  • acoustic-wave-producing downhole events e.g. ball seat events, sleeve-shifting/port opening events, fracture formation events, launching of activation balls, plug & perf detonation events, undesired tracking pipe rupture events, fracture events in adjacent wells and/or the like.
  • Such methods and systems may be more sensitive and/or more reliable than prior art techniques.
  • One aspect of the invention provides a system for detecting an acoustic-wave- producing downhole event associated with a pipe extending below a surface of the earth at an uphole location located above a downhole location of the acoustic-wave- producing downhole event in the presence of acoustic-wave-producing uphole activity (which may be referred to as uphole noise and/or surface noise).
  • the system comprises: a pipe extending below the surface of the earth along a pipe axis; a first plurality of sensors located a first axial position along the pipe, the first plurality of sensors oriented symmetrically about the pipe axis at the first axial position, each of the first plurality of sensors generating a corresponding signal in response to acoustic waves in a vicinity thereof; a second plurality of sensors located a second axial position along the pipe, the second axial position spaced apart from the first axial position along the pipe axis, the second plurality of sensors oriented symmetrically about the pipe axis at the second axial position, each of the second plurality of sensors generating a corresponding signal in response to acoustic waves in a vicinity thereof; and a processor connected to receive the signals from the first and second pluralities of sensors and configured to digitally process the signals from the first and second pluralities of sensors to thereby produce an output signal.
  • the processor is configured to adjust the digital processing, based on the signals from the first and second pluralities of sensors, to minimize a contribution of the acoustic-wave- producing uphole activity to the output signal, thereby permitting a contribution of the acoustic-wave-producing downhole event to be discernable from within the output signal.
  • Another aspect of the invention provides a method for detecting an acoustic- wave-producing downhole event associated with a pipe extending below a surface of the earth along a pipe axis at an uphole location located above a downhole location of the acoustic-wave-producing downhole event in the presence of acoustic-wave- producing-uphole activity.
  • the method comprises: locating a first plurality of sensors at a first axial position along the pipe and orienting the first plurality of sensors symmetrically about the pipe axis at the first axial position, each of the first plurality of sensors generating a corresponding signal in response to acoustic waves in a vicinity thereof; locating a second plurality of sensors at a second axial position along the pipe, the second axial position spaced apart from the first axial position along the pipe axis, and orienting the second plurality of sensors symmetrically about the pipe axis at the second axial position, each of the second plurality of sensors generating a corresponding signal in response to acoustic waves in a vicinity thereof; digitally processing the signals from the first and second pluralities of sensors to produce an output signal; and adjusting the digital processing, based on the signals from the first and second pluralities of sensors, to minimize a contribution of the acoustic-wave- producing uphole activity to the output signal, thereby permitting a contribution of the acous
  • a tracking ball making contact with a ball seat in the "track sleeve" of a ball- activated tracking system is an example of a downhole event which produces an acoustic wave (i.e. a vibratory mechanical pressure and/or displacement wave).
  • a downhole event which produces an acoustic wave is when the track sleeve opens to reveal its tracking ports.
  • Yet another example of an acoustic- wave-producing downhole event is the fracturing of a formation around a track pipe due to the intense pressure exerted by the tracking fluid.
  • the acoustic energy from these and/or other downhole acoustic-wave-producing events propagates in all directions, including up the pipe axis of track pipe, which can act as an acoustic propagation conduit. As the distance travelled by the propagating acoustic wave increases, the amplitude of the sound wave gets weaker and the acoustic wave becomes correspondingly harder to detect.
  • AWGN Additive White Gaussian Noise
  • the ability to detect and/or identify relatively weak downhole acoustic-wave- producing events in the presence of relatively strong surface noise may be implemented, in some embodiments, by the combination of specific arrangements of pluralities of acoustic sensors (i.e. sensors which have output signals correlated with acoustic waves in a vicinity thereof) located at suitable positions along and about the pipe axis and adaptive digital signal processing (DSP) noise reducing algorithms (implemented by suitably one or more configured processors), which process digitally sampled signals from the pluralities of sensors to generate a corresponding output signal.
  • DSP digital signal processing
  • Such adaptive DSP noise reducing algorithms may be adapted or adjusted, based on the signals from the pluralities of sensors, to minimize a contribution of the surface noise to the output signal, thereby permitting a contribution of the acoustic- wave-producing downhole event to be more readily discernable from within the output signal (as compared to without adjusting the DSP noise reducing algorithms).
  • Figure 1 depicts a sensor system 10 of an exemplary embodiment comprising a plurality (e.g two in the illustrated embodiment) of sensor groups 12A, 12B, each sensor group 12A, 12B comprising a corresponding plurality (e.g two in the illustrated embodiment) of sensors 14AA, 14BA, 14AB, 14BB suitably located on the wellhead 16 of a tracking pipe 18 having a pipe axis 20.
  • Sensors 14AA, 14BA, 14AB, 14BB may be collectively and/or individually referred to herein as sensors 14.
  • Groups or pluralities of sensors 12A, 12B may be collectively or individually referred to herein as groups or pluralities of sensors 12.
  • Sensors 14 of the Figure 1 embodiment are mounted on the flanges of valves 18A, which are common at the wellhead of a tracking pipe 18.
  • components of a pipe e.g. pipe 18
  • Each sensor 14 within a group 12 may be located at the same axial position (i.e. effective location along pipe axis 20).
  • groups 12A, 12B of sensors 14 may be spaced apart from each other along pipe axis 20, but the corresponding sensors 14AA, 14AB and 14AB, 14BB within each sensor group 12A, 12B may be located at the same axial position along pipe axis 20.
  • Sensors 14 within a sensor group 12 may be distributed evenly or symmetrically around pipe axis 20.
  • sensors 14AA, 14BA in sensor group 12A are distributed at 180° relative to one another about pipe axis 20 and sensors 14AB, 14BB in sensor group 12B are distributed at 180° relative to one another about pipe axis 20.
  • sensors 14 are mounted on valve flanges 22 of pipe 18, although this is not necessary.
  • Figures 2A, 2B and 2C respectively depict relative mounting positions about pipe axis 20 for groups 12 of sensors 14 comprising two, three and four sensors 14 per group 12 according to a particular embodiment.
  • Figure 2A depicts a sensor group 12C comprising a pair of sensors 14A, 14B located at the same axial location along pipe axis 20 and having 180° angular separation about pipe axis 20.
  • Figure 2B depicts a sensor group 12D comprising three sensors 14C, 14D, 14E located at the same axial location along pipe axis 20 and having 120° angular separation about pipe axis 20.
  • Figure 2C depicts a sensor group 12E comprising four sensors 14F, 14G, 14H, 141 located at the same axial location along pipe axis 20 and having 90° angular separation about pipe axis 20.
  • each sensor group may comprise a plurality of sensors located at the same axial location along pipe axis 20, where the plurality of sensors is symmetrically distributed about pipe axis 20. Perfectly precise sensor placement is not necessary, but precise placement leads to improved noise reduction.
  • sensor system 10 comprises multiple (two or more) groups 12 of sensors 14, each sensor group 12 comprising a plurality of sensors 14 symmetrically distributed about pipe axis 20. As shown in Figure 1 , each group 12 of sensors 14 is placed at a different axial location along pipe axis 20 of tracking pipe 18. In some embodiments, different sensor groups 12 spaced apart from one another along pipe axis 20 may comprise the same number or different numbers of sensors 14.
  • Figure 3 shows a sensor system 1 10 according to another embodiment.
  • Sensor system 1 10 is deployed at or near the wellhead 16 of tracking pipe 18.
  • Sensor system 1 10 is similar to sensor system 10 of Figure 1 , but differs because sensor system 1 10 comprises a first group 12A of sensors 14AA, 14BA directly mounted directly onto the wellhead, but a second group 12C of sensors 14AC, 14BC mounted on a sub-pipe 18A which is feeding pressurized tracking fluid to wellhead 16 and to main tracking pipe 18.
  • Sensory system 1 10 of the Figure 3 embodiment demonstrates that sensors 14 need not be mounted directly on the main tracking pipe 18 to perform effectively.
  • the term "pipe” should be understood to include sub-pipes or the like which feed tracking fluid into a main tracking pipe or other extensions of a main pipe in a drilling assembly.
  • Figure 3 also demonstrates that pipe axis 20 need not be linear.
  • Pipe axis 20 of the Figure 3 embodiment extends into sub-pipe 18A and may have bends, curvature and/or the like.
  • pipe axis should be understood to include the axis of a pipe, whether such pipe comprises a main tracking pipe, sub-pipes or the like which feed tracking fluid into a main tracking pipe or other extensions of a main pipe in a drilling assembly.
  • Sensors 14AC, 14BC of second sensor group 12C may be located at the same axial location along pipe axis 20 and may be symmetrically located about pipe axis 20, as discussed above.
  • Sensors 14 are sensitive to acoustic waves (i.e. vibratory mechanical pressure and/or displacement waves). That is, sensors 14 generate corresponding signals in response to acoustic waves in a vicinity thereof.
  • Various embodiments of the invention may comprise various types of acoustic wave sensors 14 which may be mounted in or on pipe 18 to generate corresponding electrical signals in response to acoustic waves in a vicinity thereof.
  • sensors 14 produce electrical signals dependent upon sensed acceleration, velocity, or position of pipe 18 in the vicinity of where each sensor 14 is mounted to pipe 18.
  • Sensors 14 could additionally or alternatively be mounted within the bore of pipe 18 and produce electrical signals dependent upon the instantaneous pressure of the tracking fluid in the bore of pipe 18.
  • each sensor 14 may comprise one or more acce I ero meters.
  • acceleration and velocity sensors 14 may be magnetically mounted to pipe 18. Sensors 14 may be physically mounted to or within a housing that incorporates a magnetic surface that provides a strong magnetic bond with pipe 18 (pipe 18 usually being fabricated from ferrous material(s)). In some embodiments, sensors 14 may be physically mounted to pipe 18 using threaded fasteners and/or other types of mechanical fasteners or attachment mechanisms. The attachment of pressure sensors to tracking pipes (such as pipe 18) is well known in the tracking industry and any such attachment techniques may be used for sensors 14.
  • Sensors 14 that are sensitive to motion typically have a primary axis of sensitivity, although some are designed with multi-axis sensitivity.
  • a multi-axis sensor may effectively be considered to be multiple separate single axis sensors integrated into a single unit.
  • Single axis sensors 14 tend to be strongly sensitive to motion in the identified axis of operation and significantly less sensitive along axes orthogonal to the identified axis of operation.
  • FIG. 4 illustrates an exemplary mounting of a sensor 14-1 with its primary sensitivity axis 22- 1 oriented perpendicular to pipe axis 20 and the direction 24 of flow of tracking fluid within pipe 18 according to a particular embodiment.
  • Figure 5 illustrates an exemplary mounting of a sensor 14-2 with its primary axis 22-2 of sensitivity oriented parallel to pipe axis 20 and the direction 24 of flow of tracking fluid within pipe 18 according to a particular embodiment.
  • magnetic components 26-1 , 26-2 are used to mount the housings 28-1 , 28-2 to ferrous pipe 18.
  • perpendicular sensor mount i.e. alignment of sensors with their primary sensitivity axes 22 perpendicular to pipe bore 20, as shown, for example in Figure 4
  • parallel sensor mount i.e. alignment of sensors with their primary sensitivity axes 22 parallel to pipe bore 20, as shown, for example in Figure 5
  • sensors 14 With a parallel sensor mount, sensors 14 will be less sensitive to mechanical vibration of pipe 18 in directions orthogonal to pipe axis 20, but may also be less sensitive to vibrations of interest in pipe 18 and/or in the high-pressure tracking fluid in the bore of pipe 18.
  • a perpendicular sensor mount may be more sensitive to both undesirable vibrations of pipe 18 in directions orthogonal to pipe axis 20 and vibrations of interest in pipe 18 and/or the tracking fluid in the bore of pipe 18.
  • sensors 14 could be mounted to pipe 18 using other techniques.
  • sensors 14 could be mounted to pipe 18 with their sensitivity axes at angles that are other than perpendicular to and parallel to pipe axis 20.
  • sensors 14 within a particular sensor group 12 are oriented in a consistent manner (e.g. with their respective sensitivity axes 22 arranged with perpendicular mounts or with their respective sensitivity axes 22 arranged with parallel mounts). This is not necessary, however.
  • sensors 14 within a particular sensor group 12 are oriented in a consistent manner (e.g. with their respective sensitivity axes 22 arranged with perpendicular mounts or with their respective sensitivity axes 22 arranged with parallel mounts). This is not necessary, however.
  • two or more sensor groups 12 are provided with sensors 14 that have consistent orientation - that is, two or more sensor groups 12 are provided with sensors arranged with parallel mounts or two or more sensor groups 12 are provided with sensors arranged with perpendicular mounts. This is not necessary, however.
  • FIG. 6 shows a cross-sectional view of Figure 6 which is taken in a plane that is generally orthogonal to pipe axis 20 (shown in Figure 6 as an X-Y plane).
  • the illustrated view of Figure 6 shows a plurality of sensors 14AA, 14BA in a sensor group 12A
  • the plurality of sensors 14 in group 12A comprises a pair of sensors, but sensor group 12A could comprise a large number of sensors 14. Because of the symmetrical orientation of sensors 14, the primary sensitivity axes 22AA, 22BA of sensors 14AA, 14BA are aligned with one another and their respective directions of positive sensitivity can be opposite to one another.
  • IS barriers may be deployed to isolate 14 sensors from signal processing circuitry 100 (described in more detail below), which may be used to process signals 102 received from sensors 14 and to generate therefrom a reduced-noise downhole event signal 104.
  • the IS barriers do not unduly distort signals 102 from sensors 14.
  • FIG. 7 schematically illustrates a signal processing circuit 100 according to a particular embodiment, which receives sensor signals 102 from sensors 14 and generates therefrom a reduced-noise downhole event signal 104.
  • Reduced-noise downhole event signal 104 may be at or near zero except for a short period after startup and when a downhole event is detected.
  • a number of the components of signal processing circuit 100 may be implemented by a suitably configured processor 101 (shown in dashed lines in Figure 7).
  • Signal processing circuit 100 of the illustrated embodiment comprises N groups of sensors 12A, 12B ... 12N (collectively, and individually sensor groups 12).
  • Sensor group 12A is shown to have n individual sensors 14A1 , 14A2 ...
  • Sensors 14 within each group 12 and groups of sensors 12 may have the characteristics discussed elsewhere herein.
  • Each sensor 14A1 , 14A2 ... 14An, 14B1 ... 14Bm ... 14Np (collectively and individually, sensors 14) generates a corresponding sensor signal 102A1 , 102A2 ... 102An, 102B1 ...102Bm ... 102Np (collectively and individually, sensor signals 102).
  • signal processing circuit 100 may comprise a variety of non-illustrated signal conditioning/processing circuitry components known in the art that are not germane to the understanding of the Figure 7 embodiment.
  • circuitry components may comprise intrinsic barriers (discussed briefly above), amplifiers, filters and/or the like.
  • Sensor signals 102 are received by corresponding analog to digital converters (ADCs) 106A1 , 106A2 ... 106An, 106B1 ... 106Bm ... 106Np (collectively and individually, ADCs 106).
  • ADCs 106 share a common sampling clock (which may be provided by processor 101 or otherwise), so that analog sensor signals 102 are digitized with a common clock to generate corresponding digital signals 108A1 , 108A2 ... 108An, 108B1 ... 108Bm ... 108Np (collectively and individually, digital signals or digital data streams 108).
  • a currently preferred sampling frequency is 48kHz, but a wide range of suitable sampling frequencies may be used in various embodiments. Lower sampling frequencies tend to ease the associated
  • ADCs 106 are provided separately from processor 101 . This is not necessary.
  • processor 101 may be suitably configured to implement ADCs 106.
  • digital data streams 108 are high pass filtered by high pass filters (HPFs) 1 10A1 , 1 10A2 ... 1 10An, 1 10B1 ... 1 10Bm ... 1 10Np (collectively and individually, HPFs 1 10).
  • HPFs high pass filters
  • Such high pass filtering may remove low frequency components which may be of relatively low interest, but which may be quite strong.
  • the resulting high pass filtered digital data streams 1 12A1 , 1 12A2 ...1 12An from all of the sensors 14 in first sensor group 12A are summed together (summing junction 1 14A).
  • the aggregated first sensor group 12A output data stream 1 16A from summing junction 1 14A will tend to emphasize the signal component that is common to the sensors 14 of first sensor group 12A, while suppressing the signal component that is differential.
  • uniform radial expansion of the pipe outward from pipe axis 20 will result in components of sensor signals 102 which are common to each sensor 14, while asymmetrical mechanical vibration of pipe in the X-Y plane will result in components of sensor signals 102 that are different for each sensor 14 and which will tend to sum to zero when the first data streams 1 12A1 , 1 12A2, 1 12An are combined.
  • the summed data stream 1 16A from all sensors 14 in first sensor group 12A are then delayed (at delay block 1 18) to generate a resultant delayed aggregate signal 120A which accounts for expected filtering delay of the sensor signals 102B ...102N in sensor groups 12B to 12N.
  • the delay selected for delay block 1 18 may account for expected delays associated with Finite Impulse Response (FIR) filtering of the sensor signals 102B ...102N in sensor groups 12B to 12N, as discussed further below.
  • FIR Finite Impulse Response
  • High pass filtered digital data streams 1 12B1 ...1 12Bm ...1 12Np from sensor groups 12B to 12N are independently filtered using adaptive FIR filters 122B1 ...122Bm ...122Np (collectively and individually, FIR filters 122, described further below) and their corresponding filtered output signals 124B1 ...124Bm ...124Np (collectively and individually, FIR output signals 124, described further below) are summed to create aggregate FIR filtered signal 125. Aggregate FIR filtered signal 125 is then subtracted from the delayed aggregate signal 120A from first sensor group 12A (at summing junction 126) to output a residual signal 128.
  • Each of FIR filters 122 may be independently adapted (e.g.
  • each FIR filter 122 may be adapted independently in the sense that the adaptation of FIR filters 122 may be performed by processor 101 without knowledge/interaction as between FIR filters 122.
  • LMS least mean squares
  • Downhole events of interest e.g. ball seating events, sleeve activation events, tracking events and/or the like
  • undesired surface sounds tend to be regular and continuous (long-duration) in nature.
  • Continuous adaptation of (i.e. updating filter coefficients of) the plurality of FIR filters 122 will generate an aggregate FIR filtered signal 125 which, when subtracted from delayed aggregate signal 120A from first sensor group 12A (at summing junction 126), will successfully reduce the level of residual signal 128 to at or near zero.
  • the acoustic energy waveform follows a different propagation path to the collection of sensors 14 relative to the path followed by acoustic energy from surface equipment and is detectable as a non-zero event in residual signal 128.
  • it is possible to completely cancel (or in practice to effectively minimize) undesired acoustic energy from surface activity, without severely impacting acoustic energy waveforms originating from downhole events, which are desirable to detect.
  • residual signal 128 (which is effectively a noise-reduced downhole event signal 128) can be amplified (e.g. numerically and/or the like) by amplifier 130 to generate noise-reduced downhole event signal 104.
  • downhole event signals are extremely weak.
  • amplifier 130 With appropriate amplification by amplifier 130, a user can physically listen to the noise-reduced downhole event signal 104 by applying the amplified signal (optionally after conversion to an analog format) to an appropriate audio port (not shown).
  • suitable circuits, processes and/or methods may use noise-reduced downhole event signal 104 (and/or an analog version thereof) to automatically detect the occurrence of downhole events and/or to discriminate between different types of (e.g. to classify) downhole events.
  • Adaptation of filters 122 represents selection of suitable filter parameters (e.g. filter coefficients and/or the like, often referred to as "filter taps") of FIR filters 122 to achieve an adaption objective.
  • Such an adaptation objective may involve adjusting the filter parameters of FIR filters 122 to minimize a suitably configured objective function.
  • the adaptation of FIR filters 122 corresponding to sensor groups 12B ... 12N can be adapted using a Least Mean Squares (LMS) algorithm, with the objective being to minimize residual signal 128.
  • LMS adaptation method provides an adapted approximation to the optimal Minimum Mean Squared Error (MMSE) solution.
  • MMSE Minimum Mean Squared Error
  • LMS represents the currently preferred adaptation mechanism
  • some embodiments may additionally or alternatively use other adaptation algorithms.
  • filter adaptation algorithms known to those skilled in the art of adaptive filtering via digital signal processing.
  • Non-limiting examples of such adaptation techniques include Normalized LMS, Root Least Squares (RLS), and/or the like.
  • RLS Root Least Squares
  • Downhole events represent anomalies to the more regular surface noise.
  • An overly aggressive adaptation technique may tend to suppress the acoustic waveforms caused by downhole events (in an effort to minimize residual signal 128). Accordingly, some embodiments make use of a relatively low level of adaptation aggressiveness, so that the adaptation will suitably suppress surface noise at start-up, but will also permit the recognition of a downhole event within residual signal 128.
  • an aggressiveness parameter (e.g. ⁇ ) having a normalized range of (0,1 ) or some other appropriate range, can be set to have a normalized value of ⁇ 0.1 .
  • this aggressiveness parameter is set to ⁇ 0.05.
  • this aggressiveness parameter is set to ⁇ 0.025.
  • the cost of a relatively low level of adaptation aggressiveness is a longer initial adaptation time - i.e. more iterations to suppress surface noise at startup.
  • a typical tracking pipe 18 and, in particular, a tracking wellhead (the portion of pipe 18 above the surface) comprises a number of different components with varying shapes. These varying components and their varying shape yields a relatively complex acoustic reflection environment. There is a desire that the adaptive noise reduction signal processing circuit 100 be robust to such variation. Those
  • Figure 8 shows an adaptive noise reduction signal processing circuit 200 according to a particular implementation of the Figure 7 signal processing circuit 100.
  • the time domain architecture of signal processing circuit 100 ( Figure 7) has been converted to frequency domain.
  • the time domain adaptation of the "many-tap" FIR filters 122 of circuit 100 ( Figure 7) has been converted into the parallel adaptation of many single-tap filters in circuit 200 ( Figure 8).
  • a number of the components of signal processing circuit 200 may be implemented by a suitably configured processor 201 (shown in dashed lines in Figure 8).
  • Signal processing circuit 200 of the Figure 8 embodiment may be similar in many respects to signal processing circuit 100 of Figure 7 embodiment.
  • signal processing circuit 200 may receive sensor signals 102 from sensors 14 and generate therefrom a reduced-noise downhole event signal 104.
  • Reduced-noise downhole event signal 104 may be at or near zero except for a short period after startup and when a downhole event is detected.
  • sensor 14, sensor signals 102, ADCs 106, digital data streams 108, optional high pass filters 1 10, high pass filtered digital data streams 1 12, summing junction 1 14A, aggregated sensor group 12A, data stream 1 16A and delayed aggregated sensor group 12A data stream 120A of circuit 200 may be substantially similar to those of circuit 100 described elsewhere herein.
  • Circuit 200 differs from circuit 100 in the adaptation and filtering of the remaining sensor signals 102B1 ... 102Bn ... 102Np prior to summing with delayed aggregated sensor group 12A data stream 120A.
  • each serial data stream is segmented into contiguous blocks of K samples 204A, 204B1 ... 204Np by Serial-ln-Parallel-Out (SIPO) blocks 202A, 202B1 ... 202Np (collectively and individually SIPOs 202). Then, for each set of K samples 204A, 204B1 ... 204Np, a K-point Fast Fourier Transform (FFT) is computed at FFT blocks 206A, 206B1 ...
  • FFT Fast Fourier Transform
  • Frequency domain data 208A resulting from aggregated sensor group 12A data stream 1 16A is modified by a frequency dependent phase vector e i ⁇ l>f (at block 21 OA) that mimics the time delay introduced by block 1 18, resulting in an aggregate sensor group A spectral signal 212A.
  • the frequency domain data 208B1 ... 208Np from each individual sensor 14 of the Figure 8 embodiment then goes through an adaptive filtering process (explained in more detail below) resulting in adaptively processed frequency domain sensor streams 214B1 ... 214Np (collectively and individually, adaptively processed frequency domain sensor streams 214).
  • These adaptively processed frequency domain sensor streams 214 are then summed at summing junctions 216 to provide aggregate adaptively filtered frequency domain signal 225.
  • Adaptively filter frequency domain signal 225 is then subtracted from aggregate sensor group A spectral signal 212A at summing junction 218.
  • the output 220 of summing junction 218 represents the residual complex spectrum 220 and is provided to the adaptation processes for the frequency domain data 208B1 ... 208Np from each of the sensors 14 in sensor groups 12B to 12N.
  • the adaptation cores 222B1 ... 222Np are substantially similar for each of the frequency domain data 208B1 ... 208Np from each of the sensors 14 in sensor groups 12B to 12N.
  • Spectral information for the corresponding current K-sample blocks of data (corresponding frequency domain data 208) and current residual complex spectrum 220 are passed to adaptation cores 222.
  • Each adaptation core 222 then outputs an adapted spectral modification vector 224B1 ... 224Np (collectively and individually, adapted spectral modification vector 224) that is applied to the spectral information for the current K- sample block (frequency domain data 208) at multiplication junction 226B1 ... 226Np (collectively and individually, multiplication junction 226), resulting in the adaptively processed frequency domain sensor streams 214 discussed above. Additional detail of adaptation cores 222 according to a particular embodiment is discussed further below in connection with Figure 9.
  • Adapted spectral modification vectors 224 are converted back into time domain impulse responses 228B1 ... 228Np (collectively and individually, time domain impulse responses 228) by inverse FFT blocks 230B1 ... 230Np (collectively and individually inverse FFTs 230).
  • the resulting time domain impulse responses 228 and either data streams 108 or, optionally, high pass filtered data streams 1 12 may be passed to frequency domain FIR filters 232B1 ...232Np (collectively and individually FIR filters 232).
  • Each FIR filter 232 receives two time domain input signals: a time domain impulse response 228 of its particular filter that is being adapted; and either a corresponding data stream 108 or a corresponding high pass filtered data stream 1 12.
  • FIR filters 232 convert these time domain inputs to the frequency domain, filter the resultant signals in the frequency domain, and output time domain FIR output signals 234B1 ... 234N (collectively and individually, time domain FIR output signals 234).
  • FIR filters 232 may use information from previously filtered data as part of the filtering process, which can preserve continuity as between blocks of data.
  • the FIR output signal 234 from each frequency domain FIR filter 232 is the portion of the corresponding sensor's data that may be used to cancel delayed aggregate signal 120A from first sensor group 12A. More particularly, time domain FIR output signals 234 from each sensor are summed to produce aggregate time domain FIR signal 235, which is subtracted from delayed aggregate signal 120A for first sensor group 12A (at summing junction 126) to produce time domain residual signal 128.
  • Time domain residual signal 128, amplifier 130 and reduced-noise downhole event signal 104 may be similar to and have characteristics similar to those discussed above in connection with Figure 7.
  • adapted spectral modification vectors 224 may be converted back to the time domain.
  • FIG. 9 is a schematic block diagram providing more detail of an adaption core 222 according to a particular embodiment.
  • adaptation core 222 comprises a pair of inputs including the frequency domain data 208 for a K- sample block of data for a corresponding sensor 14 and residual complex spectrum 220.
  • Frequency domain data 208 for the current K-sample block of sensor data (from FFT 206 ( Figure 8)) is complex conjugated at block 250 and the complex conjugated signal 252 is multiplied by the residual complex spectrum 220 at multiplication block 254, resulting in signal 256.
  • This resulting signal 256 is scaled (at multiplication block 258) by a reciprocal of an average magnitude-square signal 260 that represents a reciprocal of an average (over successive K-sample FFT blocks 208) magnitude- square for each of the K frequency bins.
  • average magnitude-square signal 260 is computed over a plurality of consecutive FFT blocks (i.e. a plurality of consecutive K-sample blocks of frequency domain data) 208 using a low pass filter (LPF) 266.
  • LPF 266 is a K-parallel LPF, which functions independently on each of the K bins of frequency domain data 208.
  • Block 270 represents as reciprocal function which takes the reciprocal of the output from LPF 266 for each of the K frequency bins.
  • the output of the scaling at multiplication block 258 is a K-sample block 272 of scaled complex-conjugate data 272.
  • a particular frequency bin of scaled complex- conjugate data 272 may have unusually large values in some circumstances.
  • the power of a particular frequency bin of scaled complex-conjugate data 272 may be unusually high when a downhole event occurs or the power of a particular frequency bin of scaled complex-conjugate data 272 may be unusually high when the signal power of the corresponding bin of sensor spectral data 108 is really low (i.e. such that the block 270 inversion and block 258 scaling result in a high value for scaled complex-conjugate data 272.
  • the scaled complex-conjugate data 272 is clipped at complex clip block 274 to preserve it's phase, but to limit its magnitude to some suitable threshold (e.g. unity).
  • the output data 276 from complex clip block 274 may be further scaled (at multiplication block 278) by a configurable (e.g. user configurable) adaptation parameter ⁇ , which may be used to control the rate of adaptation.
  • a configurable adaptation parameter ⁇ which may be used to control the rate of adaptation.
  • the value of adaptation parameter ⁇ may have the ranges discussed above.
  • the output 280 of multiplication block 278 is then applied to an integrating function at block 282.
  • the output 284 of the block 282 integrating function is the adapted spectral modification vector 224 ( Figure 8) that is used to suppress unwanted surface noise.
  • signal processing circuit 200 in the illustrated embodiment of Figures 9 and 10 represents one particular embodiment of the algorithmic architecture of signal processing circuit 100 shown in Figure 7.
  • the aggregate signal of the first sensor group 12A could be converted into frequency domain, modified by some frequency response vector and then this modified aggregate signal could be used to drive adaptation.
  • the frequency domain FIR filters used on each individual sensor signal from sensor groups 12B to 12N can be further optimized.
  • Figure 10 illustrates a particular hardware implementation of signal processing circuit 100 ( Figure 7) and signal processing circuit 200 ( Figure 8) according to an example embodiment.
  • Signals 102 from multiple sensors 14 are interfaced to an interface device 300 that incorporates circuitry to support the sensors 14 of choice, and then digitizes all sensor signals 102 using a common sample clock.
  • the digital data from interface device 300 may then be provided to a suitable processor 101 via a suitable data transfer connection 302.
  • processor 101 is embodied in a laptop 101 A or other similar computational device and data transfer connection 302 comprises a network interface 302A, such as an Ethernet connection or the like.
  • network interface 302A such as an Ethernet connection or the like.
  • sensory system 10 includes an electromagnetic (EM) noise sensor 15 for detecting electromagnetic energy and/or other forms of electrical noise (referred to herein as EM noise, for brevity) present at, and/or proximate to, the wellhead of tracking pipe 18.
  • EM noise electromagnetic noise
  • the presence of EM noise may distort output signals of sensors 14 making it more difficult to detect and/or identify downhole acoustic-wave- producing events of interest.
  • detected EM noise by EM noise sensor 15 may be subtracted from signals corresponding to sensors 14 reducing, if not completely eliminating, EM noise distortion present in the output signal.
  • EM noise sensor 15 may be implemented by the same type of sensor as sensors 14 (e.g. acoustic sensors 14) described herein.
  • EM noise sensor 15 is sensitive to electromagnetic energy and/or other forms of electrical noise. That is, EM noise sensor 15 generates a corresponding electrical signal in response to electromagnetic energy and/or other forms of electrical noise in a vicinity thereof.
  • EM noise sensor 15 may, for example, comprise various types of sensors sensitive to electromagnetic energy commonly known in the art.
  • EM noise sensor 15 may be installed proximate to a section of pipe 18 on which sensors 14 are mounted as illustrated by the dashed lines in Figures 2A to 2C.
  • EM noise sensor 15 may be located in a vicinity of the other sensors 14, but may be spaced apart from pipe 18 to minimize the susceptibility of EM noise sensor to acoustic waves present on the pipe.
  • the combination of signals corresponding to pluralities of sensors 14, an EM noise signal corresponding to EM noise sensor 15 and adaptive digital signal processing (DSP) noise reducing algorithms (as described herein) may be used to generate a EM noise reduced output signal.
  • DSP digital signal processing
  • the adaptive DSP noise reducing algorithms described elsewhere herein may be adjusted to process a digitally sampled EM noise signal output from EM noise sensor 15 (e.g. based on the signals from the pluralities of sensors 14 and EM noise sensor 15), to minimize a contribution of EM noise to the output signal, thereby permitting a contribution of the acoustic- wave-producing downhole event to be more readily discernable from within the output signal (as compared to the DSP noise reducing techniques of Figures 7 and 8, which do not account for EM noise).
  • Figure 7A schematically illustrates a signal processing circuit 100' according to one EM noise reducing embodiment, which receives sensor signals 102 from sensors 14 and EM noise signal 152 from EM noise sensor 15 and generates therefrom a reduced acoustic and EM noise downhole event signal 104'.
  • circuit 100' may be similar to circuit 100 ( Figure 7) described elsewhere herein including that a number of the components of signal processing circuit 100' may be implemented by a suitably configured processor 101 ' (shown in dashed lines in Figure 7A).
  • Circuit 100' differs from circuit 100 by the inclusion of EM noise signal 152 from EM noise sensor 15 into circuit 100'.
  • EM noise sensor 15 generates a corresponding EM noise signal 152.
  • EM noise signal 152 is received by analog to digital converter (ADC) 156 and converted, by ADC 156 into EM noise data stream 158.
  • digital EM noise data stream 158 is high pass filtered by high pass filter (HPF) 160.
  • High pass filtered digital EM noise data stream 162 is filtered using adaptive FIR filter 164 and its corresponding filtered output signal 166 is summed with FIR output signals 124 to create aggregate filtered signal 125'. Aggregate FIR filtered signal 125' is then subtracted from the delayed aggregate signal 120A to output residual signal 128'.
  • Each of FIR filters 122, 164 may be independently adapted based on residual signal 128' as disclosed elsewhere herein (e.g. in the description of Figure 7).
  • residual signal 128' may be amplified by amplifier 130 to generate acoustic and EM noise- reduced downhole event signal 104'.
  • ADC 156, optional HPF 160 and FIR filter 164 may be substantially the same as and operate in a manner substantially similar to ADCs 106, optional HPFs 1 10 and FIR filters 122 described elsewhere herein (e.g. in the description of Figure 7).
  • acoustic and EM noise-reduced downhole event signal 104' may be at or near zero except for a short period after startup and when a downhole event is detected.
  • the time domain architecture of signal processing circuit 100' may be converted to the frequency domain.
  • Figure 8A illustrates an adaptive noise reduction signal processing circuit 200' according to a particular implementation of the Figure 7A signal processing circuit 100' in the frequency domain. Except as described herein, circuit 200' may be similar to circuit 200 ( Figure 8) described elsewhere herein including that a number of the components of signal processing circuit 200' may be implemented by a suitably configured processor 201 ' (shown in dashed lines in Figure 8A). Circuit 200' differs from circuit 200 in the integration of EM noise sensor 15 into circuit 200'.
  • EM noise signal 252 generated by EM noise sensor 15 is processed in a manner substantially similar to corresponding signals from sensor groups 12B1 ... 12Bn ... 12Np.
  • high pass filtered EM noise data stream 162 is segmented into a contiguous block of K samples 254 by Serial-ln-Parallel-Out (SlPO) block 252.
  • SlPO Serial-ln-Parallel-Out
  • a K-point Fast Fourier Transform is computed at FFT block 256, resulting in EM noise frequency domain data 258.
  • FFT Fast Fourier Transform
  • Adaptation core 272 then outputs an adapted spectral modification vector 274 that is applied to the spectral information for the current K-sample block at multiplication junction 276.
  • Adapted spectral modification vector 274 is converted back into EM noise time domain impulse response 278 by inverse FFT block 280.
  • EM noise time domain impulse response 278 and either data stream 158 or optionally high pass filtered data stream 162 may be passed to frequency domain FIR filter 282.
  • FIR output signal 284 is the portion of the EM noise sensor's data that may be used to cancel EM noise from delayed aggregate signal 120A.
  • ADC 156, optional HPF 160, SlPO 252, FFT 256, adaption core 272, inverse FFT 280 and FIR filter 282 may be substantially the same as and operate in a manner substantially similar to ADCs 106, optional HPFs 1 10, SIPOs 202, FFTs 206, adaption cores 222, inverse FFTs 230 and FIR filters 232 described elsewhere herein.
  • coupling either direct or indirect, between two or more elements; the coupling or connection between the elements can be physical, logical, or a combination thereof;
  • Embodiments of the invention may be implemented using specifically designed hardware, configurable hardware, programmable data processors configured by the provision of software (which may optionally comprise "firmware") capable of executing on the data processors, special purpose computers or data processors that are specifically programmed, configured, or constructed to perform one or more steps in a method as explained in detail herein and/or combinations of two or more of these.
  • software which may optionally comprise "firmware”
  • Examples of specifically designed hardware are: logic circuits, application-specific integrated circuits ("ASICs”), large scale integrated circuits (“LSIs”), very large scale integrated circuits (“VLSIs”), and the like.
  • programmable hardware examples include one or more programmable logic devices such as programmable array logic (“PALs”), programmable logic arrays (“PLAs”), and field programmable gate arrays (“FPGAs”)).
  • PALs programmable array logic
  • PLAs programmable logic arrays
  • FPGAs field programmable gate arrays
  • programmable data processors are: microprocessors, digital signal processors ("DSPs"), embedded processors, graphics processors, math coprocessors, general purpose computers, server computers, cloud computers, mainframe computers, computer workstations, and the like.
  • DSPs digital signal processors
  • embedded processors embedded processors
  • graphics processors graphics processors
  • math coprocessors general purpose computers
  • server computers cloud computers
  • mainframe computers mainframe computers
  • computer workstations and the like.
  • one or more data processors in a control circuit for a device may implement methods as described herein by executing software instructions in a program memory accessible to the processors.
  • Processing may be centralized or distributed. Where processing is distributed, information including software and/or data may be kept centrally or distributed. Such information may be exchanged between different functional units by way of a communications network, such as a Local Area Network (LAN), Wide Area Network (WAN), or the Internet, wired or wireless data links, electromagnetic signals, or other data communication channel.
  • a communications network such as a Local Area Network (LAN), Wide Area Network (WAN), or the Internet, wired or wireless data links, electromagnetic signals, or other data communication channel.
  • Software and other modules may reside on servers, workstations, personal computers, tablet computers, image data encoders, image data decoders, PDAs, color-grading tools, video projectors, audio-visual receivers, displays (such as televisions), digital cinema projectors, media players, and other devices suitable for the purposes described herein.
  • aspects of the system can be practised with other communications, data processing, or computer system configurations, including: Internet appliances, hand-held devices (including personal digital assistants (PDAs)), wearable computers, all manner of cellular or mobile phones, multi-processor systems, microprocessor-based or programmable consumer electronics (e.g., video projectors, audio-visual receivers, displays, such as televisions, and the like), set-top boxes, color-grading tools, network PCs, mini-computers, mainframe computers, and the like.
  • PDAs personal digital assistants
  • wearable computers all manner of cellular or mobile phones
  • multi-processor systems e.g., microprocessor-based or programmable consumer electronics (e.g., video projectors, audio-visual receivers, displays, such as televisions, and the like), set-top boxes, color-grading tools, network PCs, mini-computers, mainframe computers, and the like.
  • the invention may also be provided in the form of a program product.
  • the program product may comprise any non-transitory medium which carries a set of computer-readable instructions which, when executed by a data processor, cause the data processor to execute a method of the invention.
  • Program products according to the invention may be in any of a wide variety of forms.
  • the program product may comprise, for example, non-transitory media such as magnetic data storage media including floppy diskettes, hard disk drives, optical data storage media including CD ROMs, DVDs, electronic data storage media including ROMs, flash RAM, EPROMs, hardwired or preprogrammed chips (e.g., EEPROM semiconductor chips), nanotechnology memory, or the like.
  • the computer-readable signals on the program product may optionally be compressed or encrypted.
  • the invention may be implemented in software.
  • "software” includes any instructions executed on a processor, and may include (but is not limited to) firmware, resident software, microcode, and the like. Both processing hardware and software may be centralized or distributed (or a combination thereof), in whole or in part, as known to those skilled in the art. For example, software and other modules may be accessible via local memory, via a network, via a browser or other application in a distributed computing context, or via other means suitable for the purposes described above.
  • a component e.g. a software module, processor, assembly, device, circuit, etc.
  • reference to that component should be interpreted as including as equivalents of that component any component which performs the function of the described component (i.e., that is functionally equivalent), including components which are not structurally equivalent to the disclosed structure which performs the function in the illustrated exemplary embodiments of the invention.
  • the resulting processed signal may actually represent the first derivative and second derivative, respectively, of the event sound.
  • first order or second order integration it is a relatively simple matter to provide first order or second order integration if needed or desired.
  • sensor group 12A may use acceleration sensors, but sensor group 12B may use pressure sensors. This situation can easily be accommodated by applying appropriate derivative or integrating functions to one or more of the sensor group signal sets.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Mining & Mineral Resources (AREA)
  • Geology (AREA)
  • Remote Sensing (AREA)
  • Environmental & Geological Engineering (AREA)
  • Fluid Mechanics (AREA)
  • Geophysics (AREA)
  • Acoustics & Sound (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geochemistry & Mineralogy (AREA)
  • Geophysics And Detection Of Objects (AREA)

Abstract

L'invention concerne un système qui détecte un événement de fond de trou produisant des ondes acoustiques associé à un tuyau au niveau d'un emplacement de haut de trou en présence de bruit de surface. Le système comprend : une première pluralité de capteurs acoustiques situés dans une première position axiale le long du tuyau et orientés symétriquement autour de l'axe de tuyau ; et une seconde pluralité de capteurs acoustiques situés dans une seconde position axiale le long du tuyau et orientés symétriquement autour de l'axe de tuyau, la seconde position axiale étant espacée de la première position axiale. Un processeur est connecté pour recevoir les signaux provenant des première et seconde pluralités de capteurs et configuré pour traiter les signaux de capteur afin de produire un signal de sortie. Le processeur est configuré pour régler le traitement numérique, sur la base des signaux de capteur, de manière à réduire au minimum une contribution du bruit de surface au signal de sortie.
PCT/CA2018/050253 2017-03-03 2018-03-02 Réduction adaptative du bruit en vue de la surveillance d'événements pendant des opérations de fracturation hydraulique WO2018157260A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
US16/489,685 US11215044B2 (en) 2017-03-03 2018-03-02 Adaptive noise reduction for event monitoring during hydraulic fracturing operations
US17/542,311 US11585198B2 (en) 2017-03-03 2021-12-03 Adaptive noise reduction for event monitoring during hydraulic fracturing operations

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US201762466834P 2017-03-03 2017-03-03
US62/466,834 2017-03-03
CA2977316A CA2977316C (fr) 2017-03-03 2017-08-23 Reduction de bruit adaptative destinee a la surveillance d'evenement pendant les operations de fracturation hydraulique
CA2977316 2017-08-23

Related Child Applications (2)

Application Number Title Priority Date Filing Date
US16/489,685 A-371-Of-International US11215044B2 (en) 2017-03-03 2018-03-02 Adaptive noise reduction for event monitoring during hydraulic fracturing operations
US17/542,311 Continuation US11585198B2 (en) 2017-03-03 2021-12-03 Adaptive noise reduction for event monitoring during hydraulic fracturing operations

Publications (1)

Publication Number Publication Date
WO2018157260A1 true WO2018157260A1 (fr) 2018-09-07

Family

ID=63369550

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CA2018/050253 WO2018157260A1 (fr) 2017-03-03 2018-03-02 Réduction adaptative du bruit en vue de la surveillance d'événements pendant des opérations de fracturation hydraulique

Country Status (1)

Country Link
WO (1) WO2018157260A1 (fr)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2209947C (fr) * 1995-01-12 1999-06-01 Baker Hughes Incorporated Systeme de diagraphie acoustique pendant le forage utilisant des emetteurs segmentes multiples et des recepteurs
US7817062B1 (en) * 2005-08-04 2010-10-19 Intelliserv, LLC. Surface communication apparatus and method for use with drill string telemetry
US8242928B2 (en) * 2008-05-23 2012-08-14 Martin Scientific Llc Reliable downhole data transmission system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2209947C (fr) * 1995-01-12 1999-06-01 Baker Hughes Incorporated Systeme de diagraphie acoustique pendant le forage utilisant des emetteurs segmentes multiples et des recepteurs
US7817062B1 (en) * 2005-08-04 2010-10-19 Intelliserv, LLC. Surface communication apparatus and method for use with drill string telemetry
US8242928B2 (en) * 2008-05-23 2012-08-14 Martin Scientific Llc Reliable downhole data transmission system

Similar Documents

Publication Publication Date Title
US10995609B2 (en) Method for evaluating and monitoring formation fracture treatment closure rates and pressures using fluid pressure waves
US20100157737A1 (en) Microhydraulic fracturing with downhole acoustic measurement
EP3204605A1 (fr) Système intégré de détection de multiples paramètres et procédé de détection de fuites
US20150198733A1 (en) Transducer assembly
US9097097B2 (en) Method of determination of fracture extent
NO344527B1 (no) Innsamling av seismisk energi generert fra borestøy, samt prosessering av seismiske data
US11585198B2 (en) Adaptive noise reduction for event monitoring during hydraulic fracturing operations
WO2011044060A2 (fr) Combinaison de données sismiques provenant de capteurs pour atténuer le bruit
WO2006091516A1 (fr) Instruments de diagraphie acoustique en cours de forage a configuration de source hexapolaire et procedes de diagraphie associes
WO2012021393A3 (fr) Lenteur d'ondes de cisaillement à basse fréquence dans une formation à partir de données dérivant d'un bruit de forage et obtenues grâce à un réseau quadripolaire
US20150177405A1 (en) Systems and Methods for Removing Coherent Noise in Log Data
Patterson et al. Utilization of electromagnetic acoustic transducers in downhole cement evaluation
CA2977316C (fr) Reduction de bruit adaptative destinee a la surveillance d'evenement pendant les operations de fracturation hydraulique
US11946364B2 (en) Removing guided wave noise from recorded acoustic signals
US10598563B2 (en) Downhole acoustic source localization
US20150293251A1 (en) Apparatus for Monopole and Multipole Sonic Logging of A Downhole Formation
WO2018157260A1 (fr) Réduction adaptative du bruit en vue de la surveillance d'événements pendant des opérations de fracturation hydraulique
US20040125696A1 (en) Method and installation for monitoring microseismic events
US20080079605A1 (en) Sonde Attachment Means
JP2010071672A (ja) 地下水圧計測装置
SG193106A1 (en) Determination of particle displacement or velocity from particle acceleration measurements
US20180100938A1 (en) Continuous Subsurface Carbon Dioxide Injection Surveillance Method
Castellanos et al. Acoustic Characteristics of Liquid Hydrocarbon Releases From Buried Pipelines: An Experimental Evaluation
Li et al. Experimental investigations of offshore sand production monitoring based on the analysis of vibration in response to weak shocks
RU2745048C1 (ru) Способ определения положения полиэтиленового газопровода и мест возможных несанкционированных врезок

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18761816

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 18761816

Country of ref document: EP

Kind code of ref document: A1