WO2006138112A2 - Procede de filtrage par coherence d'un signal de reseau acoustique - Google Patents

Procede de filtrage par coherence d'un signal de reseau acoustique Download PDF

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Publication number
WO2006138112A2
WO2006138112A2 PCT/US2006/021922 US2006021922W WO2006138112A2 WO 2006138112 A2 WO2006138112 A2 WO 2006138112A2 US 2006021922 W US2006021922 W US 2006021922W WO 2006138112 A2 WO2006138112 A2 WO 2006138112A2
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Prior art keywords
data
signal
coherence
array
wave
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PCT/US2006/021922
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English (en)
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WO2006138112A3 (fr
Inventor
Xiao Ming Tang
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Baker Hughes Incorporated
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Priority to EA200702585A priority Critical patent/EA013384B1/ru
Priority to CA2610997A priority patent/CA2610997C/fr
Priority to GB0723967A priority patent/GB2441692B/en
Priority to BRPI0611826-7A priority patent/BRPI0611826B1/pt
Publication of WO2006138112A2 publication Critical patent/WO2006138112A2/fr
Publication of WO2006138112A3 publication Critical patent/WO2006138112A3/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
    • G01V1/364—Seismic filtering
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
    • G01V1/44—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging using generators and receivers in the same well
    • G01V1/48—Processing data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/30—Noise handling
    • G01V2210/32—Noise reduction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/30—Noise handling
    • G01V2210/32—Noise reduction
    • G01V2210/324—Filtering
    • G01V2210/3246—Coherent noise, e.g. spatially coherent or predictable

Definitions

  • the present invention relates to a method and system of filtering signal data. More specifically, the present invention relates to a method and system for analyzing signal data collected by an array of receivers, determining the coherence function of the received data, and filtering the received raw data with the coherence function.
  • Data collection arrays i.e. a collection of more than one single position point data recorders, are used in the collection of a myriad of data.
  • array collected data include radar, seismic, acoustic, sonar, radio waves, to name but a few.
  • the data received and recorded by such arrays can include unwanted signals that intermingle with the desired data and distort the final recordings thereby providing skewed results.
  • the time lag between signals of the individual recorders is especially important. While the recorded data can be processed and filtered to remove the noise and to extract information from the time lag, there still exists room for significant improvement in processing such data.
  • acoustic signals that travel along the formation are often contaminated by other acoustic waves that travel along a different path.
  • acoustic waves may travel long the tool body (drill collar) and significantly interfere with the formation signals.
  • acoustic waves transmission along the casing may become significant if the casing is poorly bonded with cement. Moreover, this wave transmission may become overwhelmingly large if the casing is detached from cement (i.e., the free-pipe situation).
  • the present method disclosed herein involves a method of waveform processing technique utilizing signal coherence of the array data for processing signals having poor signal-to-noise ratio.
  • Raw waveform data is first transformed into f-k (frequency- wavenumber) domain.
  • a coherence function is then calculated and convolved with the data in the f-k domain, which effectively suppresses non-coherent signals in the data.
  • the unwanted part is muted and the wanted part is retained and inverse-transformed to yield the coherence-filtered array waveform data.
  • small signals that are hidden in the original data are extracted with much enhanced coherence. Subsequent processing of the data yields reliable information about formation acoustic property.
  • the present invention includes a method of processing signal data comprising, obtaining signal data, obtaining a coherence function relating to the signal data, and filtering the signal data with the coherence function thereby producing coherence filtered data.
  • the signal data comprises, among other data, downhole acoustic data.
  • the filtering of the present method can be performed in the frequency-wavenumber domain.
  • the method of can further comprise suppressing unwanted signals from the coherence filtered wave data as well as optionally further comprising converting the coherence filtered wave data into the time domain.
  • the step of obtaining signal data comprises, creating a seismic signal within a wellbore casing and recording the resulting wave propagating through the casing.
  • the signal data may comprise an array of propagating wave signals.
  • the coherence filtered wave data X cfll (Jc, ⁇ ) can be
  • coh(k, ⁇ ) represents a coherence function of one or more than one wave mode and X(k, ⁇ ) represents signal data.
  • the present invention disclosed herein may also include a data analysis system comprising, a transducer array having an array of transducers, and a data processor in communication with the array.
  • the array is capable of receiving raw data that is communicated to the processor, wherein the processor calculates a coherence function relating to the raw data and filters the raw data with the coherence function to produce coherence filtered data.
  • a downhole sonde on which the array is affixed.
  • the data analysis system may further comprise a field truck in communication with the sonde.
  • the processor may be housed within the field truck and may be in communication with the field truck.
  • the array of the data analysis system can comprise a surface mounted instrument.
  • the surface mounted instrument can comprise an accelerometer.
  • the data analysis system can further comprise a drilling system comprising a drill string and a drill bit. The array may be disposed on the drill string or optionally on the drill bit.
  • Figure 1 illustrates a flowchart of an embodiment of a method of filtering raw data.
  • Figure 2a demonstrates a collection of signal data in the time domain collected by a recorder array.
  • Figure 2b depicts raw data transformed into the /7c plane.
  • Figure 2c shows filtered data in the f-k plane.
  • Figure 2d illustrates the convolved/Vc data of Figures 2b and 2c.
  • Figure 2e shows coherence-filtered data in the time domain.
  • Figure 3a demonstrates a free pipe model.
  • Figure 3b is a depiction of modeled data.
  • Figure 3 c shows a plot of the results of a direct semblance calculation as performed on the data of Figure 3b.
  • Figure 3d illustrates the coherence-filtered formation signal f-k spectrum for the P- wave slowness range of Figure 3b.
  • Figure 3e are plots of an inverse f-k transformation of the data of Figure 3d.
  • Figure 3f is a plot of the semblance calculation of the filtered data of Figure 3e.
  • Figure 4 demonstrates a comparison of raw versus filtered cased hole acoustic data.
  • Figure 5 compares raw data to filtered data in combination with a correlogram.
  • Figure 6 depicts an embodiment of a data collection system for use with the present method.
  • Figure 7 illustrates an alternative embodiment of a data collection system for use with the present method.
  • Disclosed herein is a method of processing signals that may have poor signal-to-noise ratio. This is required so that useful information can be extracted from the signals that would otherwise be deemed unusable by conventional means. While this may be present itself in multiple situations, poor signal-to-noise ratio scenarios are frequently encountered in acoustic logging practice. For example, cased-hole acoustic data logged in free-pipe are often abandoned because the formation signals are usually untraceable due to the presence of predominant casing signals.
  • the disclosed method is not limited to acoustic logging applications; it can generally be applied to any array data that comprise propagation wave signals.
  • the wave signal array data may comprise seismic waves recorded at different depth levels in a Vertical Seismic Profiling (VSP) survey, or the seismic waves recorded by a geophone array in a surface seismic survey, m earthquake seismology, the signals may be the earthquake-generated seismic waves recoded at different stations/observatories.
  • VSP Vertical Seismic Profiling
  • the wave signals may also be the electromagnetic waves recorded by a sensor array, for example, radar waves recorded by an array of antennas.
  • the coherence-filtering technique disclosed herein significantly improves the situation of a poor signal-to-noise ratio.
  • the processing technique described herein allows for the acquisition of formation properties through poorly bonded well casing that are unobtainable by conventional techniques.
  • the technique disclosed herein has produced several important applications in acoustic logging data processing.
  • the present disclosure describes a coherence-filtering technique to significantly enhance the coherence of signal events.
  • this technique is especially advantageous when the signals are masked by other overwhelming waves or noises and thus have a poor signal-to-noise ratio.
  • the filtering process is performed in the frequency-wavenumber (commonly known as f-k) domain.
  • the technique employs a coherence filter constructed from the coherence function of the array wave data.
  • non-coherent noises are suppressed and the coherence of the wanted signal(s) is enhanced and can further be separated by either muting the unwanted (coherent) signals or passing the wanted signal(s) in the f-k domain.
  • FIG. 1 illustrates an embodiment of the present invention directed to signal processing of acoustic signals received downhole.
  • the acoustic wave time series X(t) is recorded by an array
  • the wave data in essence are a two-dimensional function of z and t, denoted as X(z,t) .
  • ⁇ 2 ⁇ f is the angular frequency and k is the axial wavenumber.
  • a very useful property of the f-k transform is that a linear moveout of slowness s for a wave signal in the z-t domain corresponds to a linear trend of the wave energy that can be traced to the center of ihsf-k plane, where k and /are respectively the horizontal and vertical coordinate of the rectangular coordinate system in the f-k plane. This property allows for delineating the moveout and dispersion (i.e., change of wave slowness or velocity with frequency) characteristics of the waveform data in a receiver array.
  • the energy density of various wave modes may be closely clustered and smeared by noise, making it difficult to distinguish data trends of the wave modes.
  • the receiver array length typically 3.5 ft or 1.07 m
  • the receiver array length typically 3.5 ft or 1.07 m
  • Another problem with the f-k technique is that strong spatial aliasing effects may exist in the f-k data of an acoustic array.
  • the Nyquist wavenumber beyond which the aliasing effect occurs is given by:
  • the aliased data may overlay with the clustered/noise-smeared/-& data, aggravating the problem.
  • the spatial aliasing effects of a wave mode can be alleviated or avoided by applying time shifts in the array data of the wave mode (step 102).
  • the shifted wave signal will then have almost no moveout in the array and, by applying the f- k transform to the data (step 106), its trend (or energy density contour) in the f-k plane will have an infinite slope. In other words, the data trend will lie on, or very close to, the frequency axis, and will therefore not be aliased in the f-k plane.
  • this technique has been developed to better delineate data trends in the f-k domain than the straightforward f-k transform.
  • the mathematical basis of the coherence-filtering technique is to approximate the spectral array data, as obtained by Fourier-transforming the acoustic wave traces, by a number of propagation wave modes,
  • M (> 1) is the total of wave modes in array;
  • a p , k p ⁇ s p , and s p are respectively the
  • step 104 the question is posed if the data is comprised of a single wave mode or multiple wave mode. If the array data is comprised primarily of a single wave mode (e.g., in dipole acoustic logging, the dipole-flexural wave is the only mode that dominates the data.), then a single-mode coherence function can be constructed in the /Vc domain (step 110), as given by (Tang and Cheng, 2004):
  • Equation (5) is essentially a semblance/coherence stacking of the array data in the f-k domain.
  • a property of the coherence function defined in equation (5) is that it is mainly applicable for single-mode data. If the data consist of more than one mode, then the coherence will be biased toward the dominant wave mode that has the highest amplitude or coherence, resulting in underestimating the contribution from other wave modes. Nevertheless, this property, if properly used, can significantly enhance the coherence of a designated wave mode.
  • the wave data consist of multiple wave modes, such as the compressional, shear, and Stoneley waves in a typical monopole logging data set acquired in a fast formation
  • a multiple-mode coherence function should be used (step 108).
  • Equation (7) is called forward prediction because the receiver whose data is being predicted is ahead of the receiver(s) whose data are used to predict.
  • equation (4) is taken and then combined with equation
  • the coherence function value is low; the function approaches zero if k is far away from k p .
  • the high-value region of the coherence function effectively delineates the trajectories/trends of the coherent part of the data in the f-k plane, especially when the data contain several propagation modes. It is worthwhile to comment on the data coherence function, as computed from equation (5) (single mode) or equation (9) (multiple mode), versus the data energy density, as obtained from the direct f-k transform (equation (I)).
  • the f-k data density reflects the wave energy distribution in the f-k plane. However, a region with high energy density may not necessarily mean that the data there is coherent.
  • the coherence function is a measure of data coherence in the f-k plane.
  • the coherence function can still be quite significant as long as the data are coherent in these regions. (An example of comparing the wave energy density and coherence is given in Figure 2b and Figure 2c.) Therefore, the data coherence function, compared to the data energy density, can better delineate data trends in the f-k domain.
  • a coherence-filtering processing can be performed (step 114).
  • the function retains the data in the coherent region and reduces/mutes the data outside the region, thus suppressing the non-coherent (or noise) part of the data.
  • step 116 further processing can be done to reject/suppress unwanted signals.
  • the dominant ringing casing waves are very coherent and should be suppressed in order to pick the formation signal of much smaller amplitude.
  • the condition for separating the wanted from unwanted signals is that they should have distinctively different propagation velocity (or slowness) values. For formations with intermediate and slow velocities, this condition is satisfied. For instance, if the formation slowness is greater than 80 ⁇ s/ft, as compared to the typical casing slowness 57 ⁇ s/ft, then the casing waves can be effectively suppressed (step 118).
  • the first is a data rejection method that uses a known fan-filtering technique in the f-k plane (e.g., Yilmaz, 1987) (step 120).
  • the fan-shaped region is bounded by two (left and right) lines originating from the center of the f-k plane. This region should cover the data trend of the unwanted signal (step 124).
  • the slowness value can be set to -20 ⁇ s/ft for the left line, and 20 ⁇ s/ft for the right line. Then, rejecting the data by muting the value of X cfil (k, ⁇ )
  • the second method to suppress the unwanted wave signals is a data passing method (step 122).
  • This method needs to have a rough estimate of the propagation slowness of the wanted wave signal.
  • the slowness in equation (3) is used to shift the data and then transform the data to f-k domain.
  • the data trend of the wanted signal should now lie in the vicinity of the frequency axis. Because now only one signal is involved, the single-mode coherence function (equation (5)) can be used to filter the data in a fan-shaped region surrounding the frequency axis.
  • the slowness corresponding to the left and right lines of the fan can be set to -30 ⁇ s/ft and 30 ⁇ s/ft, respectively.
  • Example 1 A real data example to demonstrate the coherence-filtering procedure
  • Figures 2a - 2e demonstrate the coherence filtering procedure using a field dipole data example.
  • the data were recorded by a wireline dipole acoustic logging tool.
  • the tool consists of a dipole transmitter and an array of receivers that are located about 10 ft above the transmitter and aligned longitudinally along the tool.
  • Figure 2a is a graphical depiction of raw low-frequency dipole array acoustic data recorded by an array of equally spaced acoustic receivers. This data can also be referred to as received data or a received signal.
  • the ordinate represents time and the abscissa represents the distance between the dipole source transmitter and the receivers. Significant noise contamination can be seen from the wave reverberations in the raw data of the near receivers.
  • Figure 2b The corresponding/ ⁇ spectrum is shown in Figure 2b where several closely clustered events are exhibited. Since the aliasing effect does not occur for this low-frequency data, no time-shifts were applied to the waveform data before the f-k transform.
  • Figure 2c shows the resulting plot by applying the coherence function to single-mode scenario of the raw data (equation (5)). As can be seen by comparing the plots of Figure 2b and 2c, the f-k coherence plot shows a defined data trend unlike the ⁇ scwf-k data.
  • Figure 2d illustrates the convolved/ 1 k data of Figures 2b and 2c. Converting the f-k data of Figure 2d back into the time domain produces the coherence-filtered data of Figure 2e.
  • the filtered f-k data shows a dominant trend for the dipole- flexural mode in the f-k plane.
  • Inverse-transforming the data gives the filtered array data with much improved waveform coherence across the array.
  • Example 2 Extracting formation P wave from free pipe (synthetic) acoustic data
  • Figure 3 uses simulated array acoustic data to demonstrate the ability of coherence filtering to extract formation signals through an unbonded casing. This is a free pipe situation with a 0.25-cm thick fluid annulus behind the casing.
  • Figure 3a illustrates a free-pipe model used to create the array acoustic data and Figure 3b contains the corresponding modeled data.
  • the data of Figure 3b is in the time domain, with the ordinate in time units and the abscissa in distance units.
  • the data shows strong ringing casing waves with almost no discernible formation arrivals.
  • a direct semblance calculation was performed on the data of Figure 3b and plotted in Figure 3c.
  • Figure 3d illustrates the coherence-filtered formation signal f-k spectrum for the P-wave slowness range of Figure 3b.
  • Example 3 Application to cased-hole acoustic data to extract formation P-wave slowness
  • Figure 4 uses a field data example to demonstrate the ability of the coherence-filtering technique to extract formation slowness from cased hole acoustic data, even in the free-pipe situation.
  • the acoustic data, shown in the Raw Data track include several scenarios: good cement bond (middle), poor cement bond (lower), and poor bond/free pipe (upper).
  • the data in the upper free-pipe sections are dominated by casing signals, resulting in inability to pick formation slowness from the semblance correlogram, which is shown in the Correlogram (raw data) track.
  • coherence filtering the data suppresses the casing signals and enhances the formation wave coherence.
  • the enhanced coherence enables picking the formation slowness with high confidence even in the free pipe situation (Correlogram (filtered data)).
  • Example 4 LWD (APX) data processing to suppress tool- wave effects
  • Figure 5 demonstrates the advantages of coherence filtering for logging while drilling (LWD) acoustic data for suppressing tool-wave effects.
  • LWD acoustic data is often contaminated by tool waves that travel along an associated drill collar.
  • the tool waves generate a significant semblance value and interfere with the picking of formation slowness.
  • This example may seem trivial because the tool waves are small relative to the formation waves, as compared to the cased hole example in Figure 4 where formation waves are almost indistinguishable in the free-pipe section.
  • the f-k data from LWD tools are even more hampered compared to those from wireline tools, due to a fewer number of receivers (six, versus eight, the typical number of receivers of a wireline tool) and sparser sampling (0.75ft, versus 0.5 ft; the Nyquist wavenumber k Nyqulst is now even lower, which is only 4.2/ft, versus 6.28/ft of the
  • the Raw Data track of Figure 5 displays the LWD data (receiver 1) in VDL, which shows that the data are contaminated by tool waves.
  • the tool waves produce a strong semblance in the Correlogram (raw data) track and interfere with the picking of formation slowness.
  • the Filtered Data (normalized) track coherence filtering suppresses the tool wave and removes its semblance from the Correlogram (filtered data) track.
  • the enhanced formation signal coherence allows for picking the formation slowness in areas dominated by tool waves as can be seen from the agreement between the picked LWD slowness (curve) and the wireline-measured slowness (markers).
  • a data collection system 4 utilizing an embodiment of the method of the present disclosure is illustrated in Figure 6.
  • the data collection system 4 as shown comprises a sonde 10 connect by wireline 8 to a field truck 6.
  • Signal data is collected by a sonde 10 disposed within a wellbore 14, where the wellbore 14 pierces a formation 16.
  • An array of transducers 12 is disposed on the sonde 10, the transducers 12 are capable of receiving and recording downhole signals transmitted to the receivers from within the formation 16.
  • the transducers 12 can be capable of transmitting a signal in addition to receiving a signal.
  • the raw recorded data received by the transducers 12 can be stored within the sonde 10 for later retrieval or processing, or can be transmitted to the field truck 6 via the wireline 8 or telemetry.
  • the method of coherence filtering can be performed within the sonde 10, field truck 6, or the associated processor 18.
  • the processor 18 may be a computer, or microprocessor, with memory capable of running programmed instructions.
  • the processor 18 may also have permanent data storage and hard copy output capabilities.
  • the processor 18 may be a separate unit or may be located in an enclosure attached to the field truck 6 or any other suitable enclosure commonly used in the art.
  • Combining the data collection system 4 with a processor 18 or other means of processing the signal data, such as manually, comprises a data analysis system.
  • Figure 7 illustrates an alternative data collection system 4a for use in logging while drilling operations.
  • the drilling system 25 comprises a drill string 26 having multiple elements and terminating on its lower end at a drill bit 27.
  • Transducers 28 for receiving signal data are shown on the drill string 26 and on the drill bit 27.
  • the transducers 28 can be any type of device capable of receiving signal data while being disposed within the confines of a wellbore 14. Similar to the data collection system 4 of Figure 6, the signal data collected by the drilling string transducers 28 can be transferred to the processor 18 or to data recording devices (not shown) within the field truck 6.
  • processing means can also be included within the drill string 26 for storing the collected signal data and/or processing the data in accordance with the method described herein.
  • a surface mounted transducer 20 such as an accelerometer
  • an accelerometer can be found in U.S. Patent No. 6,062,081, issued to Schendel on May 16, 2000.
  • the surface transducer 20 communicates with the processor 18 wherein coherence filtering is accomplished.
  • the filtering process can also take place within the immediate confines of the surface transducer 20.

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  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Remote Sensing (AREA)
  • Engineering & Computer Science (AREA)
  • Acoustics & Sound (AREA)
  • Geology (AREA)
  • Environmental & Geological Engineering (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
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Abstract

La présente invention se rapporte à un procédé de traitement de formes d'ondes, qui fait appel à la cohérence du signal des données de réseau pour traiter des signaux présentant un rapport signal sur bruit médiocre. Le procédé selon l'invention consiste : à transformer tout d'abord des données de formes d'ondes brutes en domaine f-k (fréquence-nombre d'ondes) ; à calculer ensuite une fonction de cohérence, et à la convolutionner avec les données du domaine f-k, ce qui supprime efficacement les signaux non cohérents contenus dans les données ; pour les données cohérentes restantes, à négliger la partie non désirée, à retenir la partie désirée, et à procéder à sa transformée inverse de manière que l'on obtienne des données de formes d'ondes de réseau à filtrage par cohérence. Un tel traitement permet d'extraire de petits signaux qui sont cachés dans les données originales avec une cohérence considérablement améliorée. Ensuite, le traitement des données permet d'obtenir des informations fiables relatives aux propriétés acoustiques de formations.
PCT/US2006/021922 2005-06-15 2006-06-06 Procede de filtrage par coherence d'un signal de reseau acoustique WO2006138112A2 (fr)

Priority Applications (4)

Application Number Priority Date Filing Date Title
EA200702585A EA013384B1 (ru) 2005-06-15 2006-06-06 Способ когерентной фильтрации акустического сигнала на выходе группы
CA2610997A CA2610997C (fr) 2005-06-15 2006-06-06 Procede de filtrage par coherence d'un signal de reseau acoustique
GB0723967A GB2441692B (en) 2005-06-15 2006-06-06 Method for coherence-filtering of acoustic array signals
BRPI0611826-7A BRPI0611826B1 (pt) 2005-06-15 2006-06-06 Método para filtração de coerência de sinal de sistema acústico

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US37607005A 2005-06-15 2005-06-15
US11/376,070 2005-06-15

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CN103901474A (zh) * 2014-04-18 2014-07-02 成都新核中创信息科技有限公司 一种基于折中小波阈值分析的微地震弱信号提取方法
WO2018111256A1 (fr) * 2016-12-14 2018-06-21 Halliburton Energy Services, Inc. Traitement de données de diagraphie acoustique en utilisant l'amplitude et la phase d'une forme d'onde

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CN103605165A (zh) * 2013-11-29 2014-02-26 中国神华能源股份有限公司 地表介质物的监测方法和装置

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US5392213A (en) * 1992-10-23 1995-02-21 Exxon Production Research Company Filter for removal of coherent noise from seismic data
US6427124B1 (en) * 1997-01-24 2002-07-30 Baker Hughes Incorporated Semblance processing for an acoustic measurement-while-drilling system for imaging of formation boundaries

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US4706225A (en) * 1984-01-19 1987-11-10 Compagnie Francaise Des Petroles Process for obtaining and processing seismic data measured at an exploratory well
US5237538A (en) * 1992-02-20 1993-08-17 Mobil Oil Corporation Method for removing coherent noise from an array of seismic traces
US5392213A (en) * 1992-10-23 1995-02-21 Exxon Production Research Company Filter for removal of coherent noise from seismic data
US6427124B1 (en) * 1997-01-24 2002-07-30 Baker Hughes Incorporated Semblance processing for an acoustic measurement-while-drilling system for imaging of formation boundaries

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103901474A (zh) * 2014-04-18 2014-07-02 成都新核中创信息科技有限公司 一种基于折中小波阈值分析的微地震弱信号提取方法
WO2018111256A1 (fr) * 2016-12-14 2018-06-21 Halliburton Energy Services, Inc. Traitement de données de diagraphie acoustique en utilisant l'amplitude et la phase d'une forme d'onde

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GB0723967D0 (en) 2008-01-30
GB2441692A (en) 2008-03-12
CA2610997C (fr) 2013-08-13
WO2006138112A3 (fr) 2008-05-08
EA013384B1 (ru) 2010-04-30
GB2441692B (en) 2009-04-29
CA2610997A1 (fr) 2006-12-28

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