System and method for reducing coherent and random noise in seismic data

Abstract

A method for reducing coherent noise and random noise in seismic data is disclosed. The method comprises: obtaining a plurality of spatiotemporal waveforms (300); and generating a plurality of overlapping spatiotemporal windows (310) by dividing the plurality of spatiotemporal waveforms. For each overlapping spatiotemporal window, the method further comprises: determining a transformed window from the spatiotemporal window (320); determining a coherent noise window from the transformed window by performing a first soft threshold processing based on a predetermined parameter range (330); determining a random noise window from the transformed window by performing a second soft threshold process (340); determining spatiotemporal filtering windows (350) based at least in part on subtracting the coherent noise window and the random noise window from the overlapping spatiotemporal windows: and determining a plurality of spatiotemporal filtering waveforms (360) by combining the spatiotemporal filtering windows for each of the plurality of overlapping spatiotemporal windows. A non-transitory computer readable memory and a system are also included.

Description

Background Art

[0001] While-drilling seismic surface surveys can be used to identify geological features within the subsurface area of ​​interest. Seismic receivers at the surface record the seismic waves generated by the drill bit as it drills the wellbore. The seismic waves are recorded continuously and in real time. The seismic waves generated at depth by the drill bit can be weak, making interpretation of the data acquired at the surface difficult due to the low signal-to-noise ratio. Filtering schemes specifically designed to remove coherent and random noise can be used to improve the signal-to-noise ratio of the acquired data. The de-noised seismic waveforms can then be interpreted to identify geological features within the subsurface area of ​​interest and to determine the drill bit's position in real time. The identification of these geological features can then be used to modify the wellbore trajectory while the wellbore is being drilled. Summary of the Invention

[0002] This Summary is provided to introduce a series of concepts that are further described below in the Detailed Description. This Summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used to help limit the scope of the claimed subject matter.

[0003] In general, in one aspect, embodiments disclosed herein relate to a method comprising: obtaining a plurality of spatiotemporal waveforms; and generating a plurality of overlapping spatiotemporal windows by dividing the plurality of spatiotemporal waveforms. For each overlapping spatiotemporal window, the method comprises determining a transformed window from the spatiotemporal window. The method further comprises determining a coherent noise window from the transformed window by performing a first soft thresholding process based at least in part on a predetermined parameter range. The method further comprises determining a random noise window from the transformed window by performing a second soft thresholding process. The method comprises determining a spatiotemporal filtering window based at least in part on subtracting the coherent noise window and the random noise window from the overlapping spatiotemporal window. The method further comprises determining a plurality of spatiotemporal filtered waveforms by combining the spatiotemporal filtering windows for each of the plurality of overlapping spatiotemporal windows.

[0004] In general, in one aspect, embodiments disclosed herein relate to a system comprising a well that penetrates a hydrocarbon reservoir, is drilled by a drilling rig, and is equipped with a seismic sensor while drilling configured to record a plurality of spatiotemporal waveforms. The system further comprises a signal processor configured to: obtain the plurality of spatiotemporal waveforms; and generate a plurality of overlapping spatiotemporal windows by partitioning the plurality of spatiotemporal waveforms. For each overlapping spatiotemporal window, the signal processor is configured to determine a transformed window from the spatiotemporal window. The signal processor is further configured to determine a coherent noise window from the transformed window by performing a first soft thresholding process based at least in part on a predetermined parameter range. The signal processor is further configured to determine a random noise window from the transformed window by performing a second soft thresholding process. The signal processor is further configured to determine a spatiotemporal filtering window based at least in part on subtracting the coherent noise window and the random noise window from the overlapping spatiotemporal windows. The signal processor is further configured to determine a plurality of spatiotemporal filtered waveforms by combining the spatiotemporal filtering windows for each of the plurality of overlapping spatiotemporal windows.

[0005] Other aspects and advantages of the claimed subject matter will become apparent from the following description and appended claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 An illustrative seismic acquisition while drilling system is shown deployed about a wellsite in accordance with one or more embodiments.

[0007] Figure 2 Examples of noisy and denoised common shot gathers are shown in accordance with one or more embodiments.

[0008] Figure 3 A flow chart is shown according to one or more embodiments.

[0009] Figure 4 Examples of noise and denoising spatiotemporal windows are shown in accordance with one or more embodiments.

[0010] Figure 5 Examples of noise and denoising frequency wavenumber windows are shown in accordance with one or more embodiments.

[0011] Figure 6 A block diagram of a computer system is shown in accordance with one or more embodiments. DETAILED DESCRIPTION

[0012] In the following detailed description of the embodiments of the present disclosure, numerous specific details are set forth to provide a more thorough understanding of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be practiced without these specific details. In other cases, well-known features have not been described in detail to avoid unnecessarily complicating the description.

[0013] Throughout this application, ordinal numbers (e.g., first, second, third, etc.) may be used as adjectives for elements (i.e., any noun in this application). Unless explicitly disclosed, such as with the terms "before," "after," "single," and other such terms, the use of ordinal numbers does not imply or create any particular order of elements, nor does it limit any element to only a single element. Rather, the use of ordinal numbers is intended to distinguish between elements. As an example, a first element is distinct from a second element, a first element may contain more than one element, and may be ranked after (or before) a second element in the ordering of elements.

[0014] In the following Figures 1 to 6 In the description of the various embodiments disclosed in this specification, any component described with respect to a figure may be equivalent to one or more similarly named components described with respect to any other figure. For the sake of brevity, the description of these components will not be repeated for each figure. Therefore, each embodiment of the components of each figure is incorporated by reference into each other figure having one or more similarly named components, and it is assumed that each embodiment of the components of each figure is optionally present in each other figure having one or more similarly named components. In addition, according to the various embodiments disclosed in this specification, any description of a component in one figure should be interpreted as an optional embodiment that may be implemented in addition to, in conjunction with, or in place of the embodiment described with respect to the corresponding similarly named component in any other figure.

[0015] It should be understood that the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a seismic signal" includes reference to one or more such seismic signals.

[0016] Terms such as "approximately" and "substantially" mean that the described characteristic, parameter or value need not be achieved precisely, but rather deviations or variations (including, for example, tolerances, measurement errors, measurement accuracy limitations and other factors known to those skilled in the art) may occur in amounts that do not preclude the effect that the characteristic is intended to provide.

[0017] It should be understood that one or more steps shown in the flowcharts may be omitted, repeated, and / or performed in a different order than shown. Therefore, the scope of the disclosure should not be considered limited to the specific arrangement of the steps shown in the flowcharts.

[0018] In general, the disclosed embodiments include systems and methods for reducing coherent noise and random noise from seismic data based on different soft thresholding operators. In particular, some embodiments use different criteria to identify and extract coherent noise and random noise from the seismic signal of interest. For example, coherent noise can be identified by its characteristic low frequency and low apparent velocity, while random noise can be identified by its presence in large temporal and spatial bands. In addition, denoising can be performed in the frequency wavenumber domain, where the characteristics of the noise are more easily distinguished. The effects of spectral leakage and amplitude smearing associated with the filtering process can be reduced by using soft thresholding. The resulting denoised seismic data can then be used for further seismic data interpretation, such as for updating time-depth models and seismic images.

[0019] Thus, the disclosed method is integrated into established practical applications for updating time-depth models and forming seismic images, which are themselves integrated into established processes for extracting hydrocarbons from subsurface oil and gas reservoirs. The disclosed method improves upon existing methods for reasons of at least reduced cost and increased efficacy.

[0020] Seismic While Drilling (SWD) is a data acquisition technique that uses seismic receivers placed on the surface to record seismic signals generated by the cutting action of the drill bit while drilling a wellbore. The seismic signals are then interpreted to image the formations traversed by the wellbore and the formations surrounding the wellbore, and to determine the position of the drill bit in real time. Due to the widespread use of polycrystalline diamond compact (PDC) drill bits, which generate relatively weak seismic energy compared to other drill bit designs (e.g., roller cone bits), the signals reaching the SWD receivers, especially from deep drill bit locations, can exhibit very low signal-to-noise ratios. Another cause of a low signal-to-noise ratio can be strong noise, such as strong equipment-induced vibrations that propagate from the wellbore through the surface to the seismic receivers. Therefore, in order to develop accurate seismic models from data acquired using SWD techniques, data processing schemes for improving low signal-to-noise (S / N) ratios can be implemented.

[0021] Figure 1 A seismic acquisition system while drilling is shown located around a well site according to one or more embodiments. In general, the well site can be configured in a variety of ways. Thus, Figure 1 The well site in the description is not intended to limit the well to a particular configuration of drilling equipment. The well site is described as being on land. In other examples, the well site may be offshore, and drilling may be performed with or without the use of a marine riser. In an offshore location, the SWD receiver may be deployed on the seafloor.

[0022] The SWD acquisition system 100 may include a drilling system 101 configured to drill a wellbore 102 along a wellbore trajectory 103 into a subsurface comprising various formations 104 and 105 to reach an oil and gas reservoir 106. The wellbore trajectory 103 may be a curved or straight trajectory. All or part of the wellbore trajectory 103 may be vertical, and some wellbore trajectories 103 may be deviated or have horizontal sections.

[0023] To drill a new section of the wellbore 102, the drilling rig 107 may include a drill string 108 attached to the drilling rig 107 at the surface 109 of the earth's "surface." The drill string 108 may include one or more drill pipes connected to form a conduit and a bottom hole assembly ("BHA") 110 disposed at the distal end of the conduit. The BHA 110 may include a drill bit 112 to cut into the subsurface rock. The BHA 110 may include measurement tools, such as measurement while drilling (MWD) tools and logging while drilling (LWD) tools. The measurement tools may include sensors and hardware to measure downhole drilling parameters, and these measurements may be transmitted to the surface 109 using any suitable telemetry system known in the art. The BHA 110 and drill string 108 may include other drilling tools known in the art but not specifically shown.

[0024] During drilling operations, the drill string 108 rotates relative to the wellbore 102, and weight is applied to the drill bit 112 to enable the drill bit 112 to break rock as the drill string 108 rotates. In some cases, a top drive 114 may be coupled to the top of the drill string 108 and may be operable to rotate the drill string 108. In other embodiments, the drill bit 112 may be rotated using a combination of a drilling motor and a top drive 114.

[0025] While the rock is being cut by the drill bit 112, drilling fluid (commonly referred to as "mud") may flow into the drill string 108 through appropriate flow paths in the top drive 114. The mud flows down the drill string 108 and is discharged to the bottom of the wellbore 102 through nozzles in the drill bit 112. The mud in the wellbore 102 then flows upward back to the surface along with entrained drill cuttings in the annulus between the drill string 108 and the wellbore 102. Typically, the drill cuttings are removed from the mud and the mud is reconditioned as needed before it is pumped back into the drill string 108.

[0026] When drilling, the cutting action of the drill bit 112 generates seismic waves. In one or more embodiments, the SWD acquisition system 100 can be used in real time during the drilling operation to acquire the seismic waves emitted by the drill bit 112 and convert them into usable seismic signals. Figure 1As shown, the seismic waves generated by the drill bit can be acquired at multiple receiving stations by multiple surface sensors or "receivers" 120 arranged on the ground 109 surrounding the drilling system 101. The multiple receivers 120 can include traditional seismic sensors (configured to measure ground motion velocity or acceleration), high-sensitivity seismic sensors, wireless seismic sensors, or any other specialized sensors, depending on the preferred array arrangement and acquisition requirements. The number of receivers 120 can vary based on the sensor's sensitivity to signal and noise.

[0027] The seismic waves acquired by the receivers 120 in the SWD acquisition system 100 may include direct waves that propagate from the drill bit 112 to the receivers 120 without interacting with the subsurface formations. Other types of waves acquired by the receivers 120 include refracted waves 130 and reflected waves 132 generated by the seismic waves generated by the drill bit passing through discontinuities 122 in the subsurface formations 104 and 105. Those skilled in the art will appreciate that the waves emitted by the drill bit 112 may propagate along other paths. The direct waves, refracted waves 130, and reflected waves 132 emitted by the drill bit 112 may be recorded by the receivers 120 as a time series, which is generally referred to as a "waveform." The collection of waveforms recorded by the receivers 120 may be referred to as "SWD data."

[0028] In some embodiments, the drilling system 101 can be arranged at other systems in the well environment and communicate with them. The drilling system 101 can control at least a portion of the drilling operation by providing control over various components of the drilling operation. In one or more embodiments, the drilling system 101 can receive data from one or more sensors arranged to measure controllable parameters of the drilling operation. As non-limiting examples, the sensors can be arranged to measure the weight on bit, the rotational speed of the drill bit (RPM), the flow rate of the mud pump (GPM), and the mechanical penetration rate (ROP) of the drilling operation. Each sensor can be positioned or configured to measure a desired physical stimulus.

[0029] Receiver 120 can record seismic waves generated by the drill bit in the form of SWD data in real time and transmit it to a seismic processing system 136. This transmission can be performed using wired communication or wirelessly. Seismic processing system 136 can receive the SWD data and can be used to process the SWD data. This can include processing steps such as preprocessing, noise reduction, or attribute generation. Furthermore, seismic processing system 136 can be used to form seismic images based on the processed seismic signals.

[0030] In particular, SWD data continuously recorded during drilling can be processed to convert it into a standard pulse-shaped waveform. The SWD data can be converted by deconvolving the waveform with a pilot signal. In some embodiments, the pilot signal can be drill bit vibration 128 transmitted through the drill string 108 and recorded by the top drive sensor 116. Specifically, the top drive sensor 116 can be one or more three-component accelerometers mounted on the top drive 114 to record the drill bit vibration 128 induced into the drill string 108. The top drive 114 rotates the drill string 108, and the top drive sensor 116 can be configured to measure axial, torsional, and lateral vibrations of the drill string 108. In another embodiment, the pilot signal can be drill bit vibration recorded near the drill bit by a downhole sensor. The downhole sensor can be, for example, a three-component accelerometer mounted in the BHA 110. Thus, in the SWD system 100, the top drive sensor 116 and / or the downhole sensor, together with the receiver 120 at the surface 109, can constitute a "seismic while drilling" sensor.

[0031] The vibrations 128 induced by the drill bit that are transmitted along the drill string 108 may appear random, highly variable, and not easily extracted from a single waveform of the SWD data. In some embodiments, the pilot signals acquired by the top drive sensor 116 can be pre-processed by autocorrelation and deconvolution operations and then superimposed over a predetermined drilling interval (e.g., 30 feet, one drill pipe length). A pulse-like waveform can then be generated by correlating and deconvolving the pre-processed pilot signals with the data recorded by the receiver 120. The pulse-like waveforms can also be superimposed within the predetermined drilling interval to improve the quality of the signal. In addition, the resulting pulse-like waveforms can be provided in the "common shot gather" (CSG) domain, that is, they can be classified as waveforms acquired by different receivers and having a single source location. The source location can correspond exactly to the current depth of the drill bit 112. The distance between each receiver 120 and the source is called the "source-receiver offset".

[0032] In some embodiments, the seismic processing system 136 can be used to perform travel time analysis of the direct or refracted waves 130 in the SWD data to assess the position of the drill bit 112 and optimize drilling operations. Furthermore, subsurface features, such as the formations 104 and 105, can be revealed in the refracted and reflected waves from the SWD data. The direct, refracted, and reflected waves 130 and 132 can carry information about the formations 104 and 105 and discontinuities 122 through which they propagate. Certain discontinuities 122 can be further interpreted as indicating the presence of oil and gas reservoirs 106 within the subsurface region of interest.

[0033] The knowledge of the existence and location of the reservoir 106 and other subsurface features may be transferred to the wellbore planning system 138 to update the wellbore plan. The wellbore plan may include a wellbore trajectory 103 that penetrates the reservoir 106 from the surface 109. The wellbore plan may be generated based on the best available information at the time of planning, derived from geophysical models, geomechanical models that include subsurface stress conditions, the trajectory of any existing wellbores (which may need to be avoided), and the presence of other drilling hazards such as shallow gas pockets, overpressure zones, and active fault planes. The wellbore plan may be updated during the drilling of the wellbore 102. For example, the wellbore plan may be updated based on new data about the status of the drilling equipment and about the formations 104, 105 through which the wellbore 102 is drilled. The wellbore planning system 138 may pass information about the updated wellbore trajectory 103 to Figure 1 The drilling system 101 is described in detail in FIG. The drilling system 101 can drill a wellbore 102 along a wellbore trajectory 104 to access an oil and gas reservoir 106 .

[0034] like Figure 1 As shown, receiver 120, mounted on the surface to receive seismic waves from drill bit 112, can receive other types of seismic waves transmitted to the surface by drilling rig 107. The rotation of drill bit 112 induces drill bit vibration 128, which propagates along drill string 108 and through drilling rig 107 to the surface. Other vibration sources include vehicles, generators, mud pumps, shakers, and other equipment on drilling rig 107. Some of the drilling vibration is radiated to the surface 109 in the form of surface waves 134, propagating from the wellbore as a transition point. Surface waves may include Rayleigh waves (also known as "ground roll" due to their elliptical particle motion), which are low-frequency dispersive waves that propagate at a low phase velocity. Because similar ground roll phases are found in waveforms recorded at different adjacent receivers, the ground roll phases are considered "coherent waves."

[0035] As ground roll propagates along the ground, it can reach and be recorded by the receivers 120 of the SWD acquisition system 100, and the ground roll phase can be present in the SWD data. However, the ground roll phase may be seismic waves that have not yet passed through or been affected by the ground layers 104, 105 and can be considered unwanted noise in the SWD data. Furthermore, because the amplitude of ground roll can be much stronger than direct waves, refracted waves, and reflected waves, ground roll can be considered the primary type of "coherent noise" in SWD data. Other types of coherent noise, such as air waves in terrestrial data and guided waves in shallow marine data, can also be present in significant amounts in SWD data and mask the seismic waves of interest, particularly in areas with complex geological structures.

[0036] Random noise can also be recorded by the receiver 120 of the SWD acquisition system 100 and present in the SWD data. Random noise is spatially uncorrelated noise generated by environmental activities such as acquisition equipment, drilling rig operations, vessels, and wind. Unlike coherent noise, the energy of random noise can be distributed over a wide frequency range and in multiple propagation directions. Furthermore, random noise can exhibit different characteristics in different frequency bands.

[0037] Because drilling operations can be accompanied by strong vibrations, SWD data may contain high levels of noise (coherent and / or random noise). Seismic waves generated by the drill bit at depth may be significantly attenuated as they propagate through the formation to the surface, and the direct, refracted, and reflected waves that reach the receivers may be quite weak. Therefore, SWD data acquired under these circumstances may require careful and extensive processing before they can be interpreted. Many seismic processing and imaging techniques (such as deconvolution, migration, and inversion) are more effective when applied to data with a high signal-to-noise ratio. Without proper removal of random and coherent noise, these techniques may not provide accurate results. Specialized techniques and workflows may be required to correctly process and interpret SWD data. Traditional noise reduction methods exploit the difference between noise and the seismic waves of interest. For example, coherent noise typically propagates between receivers at a slower apparent velocity than the seismic waves of interest. Consequently, coherent noise (such as ground roll and air waves) can be characterized as low-velocity events with high amplitudes.

[0038] Figure 2 An example of a common shot gather (CSG) 200 with a high noise content is shown, according to one or more embodiments. CSG 200 displays multiple waveforms resulting from the reception of direct, refracted, and reflected waves emitted by drill bit 112. The vertical axis represents travel time 202 in seconds, and the horizontal axis represents source-receiver offset 204. CSG 200 shows a direct event 206 and the presence of very high-energy coherent noise 208. Furthermore, the angle ("dip") measured from the horizontal axis of the coherent noise is much steeper than the dip of the direct event 206. Because CSG 200 provides the temporal variation of waveforms in the direction of source-receiver offset, the dip indicates the "apparent velocity" of the seismic waves. Therefore, events with steeper dips in CSG 200 will be associated with seismic waves or noise with lower apparent propagation velocities. As shown, the high-energy coherent noise present in CSG 200 significantly complicates the identification and interpretation of seismic waves of interest.

[0039] Steering Figure 3 , Figure 3 1 shows a flow chart according to one or more embodiments. Specifically, Figure 3 A general method for removing noise from waveform data is described. Figure 3The various blocks in the embodiment are presented and described in order, but those skilled in the art will appreciate that some or all of these blocks may be executed in a different order, may be combined or omitted, and may be executed in parallel. Furthermore, these blocks may be executed actively or passively.

[0040] In box 300, a plurality of spatiotemporal waveforms are obtained according to one or more embodiments. The plurality of spatiotemporal waveforms may include seismic while drilling waveforms. Specifically, in the SWD acquisition system 100, seismic waves generated at depth by the drill bit and propagated through one or more strata to the surface can be recorded by a plurality of receivers using continuous real-time recording. The signal generated by the drill bit source and propagated through the drill string 108 can be passively recorded by the top drive sensor 116 on the surface. A pilot signal can be extracted from the signal recorded by the top drive sensor 116. A series of pulse-like waveforms can then be generated by continuously deconvolving the signal recorded at the receiver with the estimated pilot signal. In some embodiments, the SWD waveform is obtained by superimposing the recordings over a drilling interval (e.g., a 32-foot length or a drill pipe length). In addition, as Figure 2 For example, a common shot gather (CSG) can be generated where the source location corresponds to the middle of the drilling interval.

[0041] In block 310, according to one or more embodiments, a plurality of overlapping spatiotemporal windows may be generated by partitioning the plurality of spatiotemporal waveforms. By generating a plurality of overlapping spatiotemporal windows and processing each overlapping window independently, processing time and cost may be reduced. Each spatiotemporal window d i (t,x) can be assumed to consist of three added space-time components:

[0042] d i (t,x)=u(t,x)+n c (t,x)+n r (t,x) (1)

[0043] where u(t,x) corresponds to the waveform of interest (the “space-time filtering window”), and n c (t,x) is the spatiotemporal coherent noise window, n r (t,x) is a spatiotemporal random noise window.

[0044] Figure 4 Examples of noise and denoising spatiotemporal windows according to one or more embodiments are shown. In particular, Figure 4 Including from Figure 2Figure 4 shows an example of a spatiotemporal window 400 extracted from CSG 200. The vertical axis represents travel time 402 in seconds, and the horizontal axis represents source-receiver offset 404. Each waveform in spatiotemporal window 400 has been normalized relative to its maximum amplitude. Once again, the strong coherent noise 406 is observed to have a steeper dip than the weaker waveform of interest 408.

[0045] In block 320, a window transformed from the spatiotemporal window is determined for each overlapping spatiotemporal window according to one or more embodiments. i (t, x) can be transformed into a transformed window D(f, k) in the frequency wavenumber domain by a two-dimensional Fourier transform. Then, each element in the transformed window D(f, k) can be a complex number, that is, it has a magnitude and a phase.

[0046] Each overlapping spatiotemporal window d i Transforming the (t,x) domain to the frequency-wavenumber domain facilitates noise reduction in SWD data because the seismic waves of interest and coherent noise are characterized by different dip angles. Furthermore, coherent noise energy is concentrated in a narrow, low-frequency band, while random noise energy is distributed across all frequency bands. Therefore, separation of coherent and random noise is easier in the frequency-wavenumber domain.

[0047] Figure 5 Examples of noise and denoising frequency wavenumber windows according to one or more embodiments are shown. In particular, Figure 5 This involves decomposing the Figure 4 The space-time window d i An example of a transformed window D(f,k) 500 obtained by transforming (t,x) 400. The vertical axis 502 represents the frequency and the horizontal axis 504 represents the wave number. Figure 5 As shown, the transformed window D(f, k) 500 provides information about the energy distribution in terms of frequency and apparent velocity. For the example of the transformed window D(f, k) 500, linear events with low apparent velocity are observed within the sector-shaped region bounded by two white dashed lines 506. These linear events correspond to the strong coherent noise present in the transformed window 500. On the other hand, energy of low amplitude and distributed over a wide range of frequencies and wavenumbers corresponding to random noise 508 is also observed in the transformed window D(f, k) 500.

[0048] In one or more embodiments, in block 330, a coherent noise window is determined from the transformed window by performing a first soft thresholding process based at least in part on a predetermined parameter range. Based on the low apparent velocity of the coherent noise, the coherent noise can be identified in the transformed window. The predetermined apparent velocity range [v min ,v max ], where vmin and v max are the minimum and maximum apparent velocities associated with the coherent noise present in the transformed window. For each apparent velocity v∈[v min ,v max ] and any zero-offset time t0, the trajectory of a linear (inclined) event can be expressed as

[0049]

[0050] In the frequency-wavenumber domain, the trajectory corresponds to an angular ray passing through the origin:

[0051] f=-vk (3)

[0052] For each identified coherent noise linear event and associated velocity range [v min ,v max ], the coherent noise sector D can be determined from the transformed window D(f,k) v (f,k), as shown below:

[0053] D v (f,k)=D(-vk,k),v∈[v min ,v max ] (4)

[0054] Then, sector D v (f,k) is composed of two rays f=-v min k and f = -v max k limited.

[0055] According to one or more embodiments, for each identified coherent noise linear event, a sector D v The amplitude of (f,k) determines the first soft thresholding operator The first soft thresholding operator Based on the transformed window D v The difference between the amplitude of (f, k) and a predetermined threshold. For example, the first soft threshold processing operator It can be designed so that D above a predetermined threshold v The amplitude of (f, k) is attenuated so as to attenuate the coherent noise in the transformed window D(f, k). v The amplitude ratio of (f, k) is close to the predetermined threshold D v The amplitude of (f,k) is attenuated more. Alternatively, the first soft thresholding operator It can be designed to extract D above a predetermined threshold v The amplitude of (f,k) is used to obtain the coherent noise window N c(f, k). Using a soft thresholding operator in the frequency-wavenumber domain can reduce the amount of spectral leakage associated with strong discontinuities in the spectral amplitude that may result from noise removal. To further reduce the effects of spectral leakage, after applying the first soft thresholding operator Previously, Sector D v The amplitude spikes of (f,k) can be removed by using a window sliding median filter along each apparent velocity v. In addition, a bandpass filter can be used to filter the sector D v (f,k) in order to limit the bandwidth of the identified coherent noise.

[0056] Based on the first soft threshold processing operator The residual window can also be obtained by attenuating the coherent noise from the transformed window D(f,k) Although the first soft thresholding operator Based on D v The amplitude of (f,k), the coherent noise window N c (f,k) and residual window It is determined by attenuating both the amplitude and phase of the transformed window D(f,k).

[0057] Steering Figure 4 , Figure 4 Including spatiotemporal residual windows 410 and spatiotemporal coherent noise window n c Example of (t,x) 420. These two windows have been processed using the first soft thresholding operator From the space-time window d i (t,x)400 is determined. The space-time window d i (t,x)400 and spatiotemporal residual window 410 comparisons show that the proposed technique effectively attenuates coherent noise without modifying the seismic waves of interest. In 410, the waveform of interest 412, which is continuously generated by the cutting action of the drill bit and propagates at a higher apparent velocity (relative to the coherent noise), is more clearly visualized. c (t,x) 420 shows a phase 422 of coherent noise (consisting primarily of ground roll) that is also continuous, but of low frequency and low apparent velocity.

[0058] Back to Figure 5 , Figure 5 This involves windowing the spatiotemporal residuals 410 is transformed into the frequency wave number domain and the residual window is obtained 510. As shown, the linear events associated with the coherent noise previously identified in the transformed window D(f,k) 500 are not present in the residual window 510. However, the random noise 508 present in the transformed window D(f,k) 500 remains in the residual window with the same amplitude and distribution 512 in the frequency wavenumber domain. 510. Obviously, the criteria used in the present disclosure to attenuate coherent noise do not affect the random noise present in the transformed window D(f,k) 500. Since coherent noise and random noise have different characteristics in the frequency wavenumber domain, different criteria can be used for effective attenuation.

[0059] According to one or more embodiments, in block 340, a random noise window is determined from the transformed window by performing a second soft thresholding operation. Unlike coherent noise, random noise energy is distributed over most frequencies and wave numbers. In addition, random noise may exhibit different characteristics in different frequency bands. Therefore, in some embodiments, the second soft thresholding operator for extracting random noise is It can be a function of frequency. In a non-limiting example, a second soft thresholding operator can be defined for multiple frequency ranges (e.g., 5Hz-20Hz, 21Hz-35Hz, 36Hz-50Hz). A random noise threshold can be determined for each frequency range to generate multiple random noise thresholds. Thus, the multiple random noise thresholds can be a function of frequency. In addition, the multiple random noise thresholds can be based on the residual window For example, the random noise threshold may be based on the average amplitude of random noise within a corresponding frequency range.

[0060] Based on the second soft threshold processing operator From the residual window Extract random noise window N r (f,k). This can be achieved by applying a second soft thresholding operator Make the residual window The amplitude and phase of the random noise window N are attenuated to extract r (f,k). In addition, the spatiotemporal random noise window n r (t,x) can be obtained by r (f,k) is obtained by performing inverse Fourier transform.

[0061] Back to Figure 4 , Figure 4 An example of a spatiotemporal random noise window 440 is included according to some embodiments. r (t,x)440 has been processed by the second soft thresholding operator Applied to residual window The resulting spatiotemporal random noise window n r(t,x) 440 shows random noise 442, which has a small amplitude and is distributed across all frequencies, and does not have any characteristic apparent velocity (or inclination). Therefore, the waveform of interest, which may be more energetic than the coherent noise, may not be affected by the second soft thresholding operator applied to extract the random noise. serious impact.

[0062] In block 350, a spatiotemporal filtering window is determined based at least in part on subtracting a coherent noise window and a random noise window from overlapping spatiotemporal windows, in accordance with one or more embodiments. c (f,k) and random noise window N r (f,k) can first be transformed into the time offset domain by a two-dimensional inverse Fourier transform. Then, the time offset domain can be obtained by i (t,x) minus the spatiotemporal coherent noise window n c (t,x) and spatiotemporal random noise window n r (t, x) to obtain the spatiotemporal filtering window u(t, x). In some embodiments, the spatiotemporal filtering window u(t, x) is obtained using adaptive subtraction. The adaptive subtraction may include using a time-varying least squares Wiener filter. The Wiener filter may adjust the coherent noise window N before performing the subtraction. c (f,k) and random noise window N r The amplitude and phase of (f,k).

[0063] Figure 4 Including by from overlapping space-time windows d i The spatiotemporal filter window u(t,x) 430 is obtained by subtracting coherent noise and random noise from (t,x) 400. Comparing window 400 and window 430, it is clear that the spatiotemporal filter window u(t,x) 430 has less noise and more clearly visualizes the waveform features of interest. For example, the sloping line 432, which slopes from left to right, clearly indicates the periodic seismic waves that continuously arrive at the receiver.

[0064] Steering Figure 5 , Figure 5 The filtered transformed window U(f,k) 520 according to some embodiments is included. The filtered transformed window U(f,k) 520 is obtained by transforming the spatiotemporal filter window u(t,x) 430 using a two-dimensional Fourier transform. It is obvious to those skilled in the art that the noise of the filtered transformed window U(f,k) 520 is significantly weaker than that of the transformed window D(f,k) 500 from which it is derived. In particular, the noise of the residual window D(f,k) 500 is still present in the filtered transformed window U(f,k) 520. The amplitude and distribution of the random noise 512 in 510 have been significantly reduced.

[0065] In block 360, a plurality of space-time filtered waveforms are determined by combining the space-time filtering windows for each of the plurality of overlapping space-time windows, in accordance with one or more embodiments. The plurality of space-time filtered waveforms should then correspond to the denoised seismic energy of interest. Figure 2 Including through the use of Figure 3 An example of a de-noised CSG 210 obtained by removing coherent noise and random noise from CSG 200 using the steps described in

[15] . The removed coherent noise and random noise are shown as 220. The waves generated by the drill bit while drilling and continuously reaching the receiver via the subsurface formation are easily discernible in the de-noised CSG 210. When comparing CSG 210 and the removed noise 220, it is again clear that the coherent noise and the waveform of interest have different apparent velocities.

[0066] Figure 3 The filtering technique described in this paper for reducing coherent and random noise has been shown to be effective for denoising SWD data. This can be an effective denoising technique for data associated with highly complex geological formations with conflicting dips and / or steeply dipping coherent noise. The proposed filtering technique, combined with the application of a soft thresholding operation, reduces the effects of spectral leakage. Furthermore, the use of overlapping spatiotemporal windows results in filtered results with minimal spatial amplitude spread.

[0067] In block 370, a time-depth model is refined based, at least in part, on the plurality of space-time filtered waveforms using a seismic processing system, in accordance with one or more embodiments. The seismic processing system 136 can be configured to collect the plurality of space-time filtered waveforms to refine a time-depth curve for use in calibrating previously generated seismic images. According to some embodiments, travel times selected from the filtered SWD data can be used to calibrate the time-depth model for one or more wellbores.

[0068] Conventional surveys to generate time-depth models involve inducing seismic waves at the surface and recording them via sensors clamped to the borehole wall. Data recorded with conventional surveys, and the resulting time-depth models, can only be interpreted after drilling operations or from very few depths recorded during interruptions in drilling operations, such as when replacing a worn drill bit. Using SWD data, the time-depth model can be recalibrated in real time, and the drill bit can be more accurately positioned in seismic images. Furthermore, the real-time time-depth model can reduce pre-drill depth uncertainty in critical formations and provide more accurate casing point selection for drilling.

[0069] In block 380, according to one or more embodiments, the planned wellbore trajectory is updated based at least in part on the time-depth model using the wellbore planning system. The seismic processing system 136 can be configured to update the location of the reservoir 106 and other formations 104, 105 using the time-depth model. The knowledge of the location of the reservoir 106 and other formations 104, 105 can then be transferred to the wellbore planning system 138. The wellbore planning system 138 can be located below. Figure 6 The wellbore planning system 138 uses knowledge of the appearance of the oil and gas reservoir 106 and other formations 104, 105 to update the wellbore trajectory 103 within the subsurface region of interest. The updated wellbore trajectory 103 may be affected by shallow drilling hazards such as gas pockets, groundwater flow, and / or unstable / metastable fault zones.

[0070] In block 390, a portion of the wellbore is drilled using the drilling system and guided by the updated planned wellbore trajectory according to one or more embodiments. The updated wellbore trajectory 103 may be transferred to Figure 1 The drilling system 101 described in the accompanying drawings can be used to drill the wellbore 102 along the updated wellbore trajectory 103 to enter the oil and gas reservoir 106 and produce the oil and gas reservoir 106 to the surface 109. In addition, the drilling system 101 can continue to drill the wellbore 102 along the updated wellbore trajectory 103 to obtain additional SWD data so that the wellbore 102 can be repeated. Figure 3 Steps 300 to 308 described in order to ultimately enter and produce the oil and gas reservoir 106.

[0071] In some embodiments, the seismic processing system 136 and the wellbore planning system 138 may include computer systems. Figure 6 is a block diagram of a computer system 600 for providing computing functionality associated with the algorithms, methods, functions, processes, flows, and programs as described in the present disclosure, according to one embodiment. The computer 600 shown is intended to encompass any computing device, such as a high-performance computing (HPC) device, a server, a desktop computer, a laptop / notebook computer, a wireless data port, a smart phone, a personal data assistant (PDA), a tablet computing device, one or more processors within such devices, or any other suitable processing device, including physical or virtual instances of computing devices (or both). In addition, the computer 600 may include a computer that includes: an input device, such as a keypad, a keyboard, a touch screen, or other device that can accept user information; and an output device that transmits information associated with the operation of the computer 600, including digital data, visual or audio information (or a combination of information); or a GUI.

[0072] Computer 600 can act as a client, a network component, a server, a database or other persistent storage, or any other component (or combination of roles) in a computer system for performing the subject matter described in this disclosure. Computer 600 is shown communicatively coupled to network 602. In some embodiments, one or more components of computer 600 can be configured to operate within an environment including a cloud-based environment, a local environment, a global environment, or other environment (or a combination of environments).

[0073] At a high level, the computer 600 is an electronic computing device that is operable to receive, transmit, process, store, or manage data and information associated with the described subject matter. According to certain embodiments, the computer 600 may also include an application server, an email server, a web server, a cache server, a streaming data server, a business intelligence (BI) server, or other server (or combination of servers), or be communicatively coupled to any of the above servers.

[0074] Computer 600 may receive requests from client applications (e.g., executing on another computer 600) via network 602 and respond to the requests by processing the received requests in an appropriate software application. Additionally, requests may be sent to computer 600 from internal users (e.g., from a command console or through other suitable access methods), external or third parties, other automated applications, and any other suitable entity, individual, system, or computer.

[0075] Each component of computer 600 can communicate using system bus 603. In some embodiments, any or all components of computer 600 (hardware or software (or a combination of hardware and software)) can use application programming interface (API) 607 or service layer 608 (or a combination of API 607 ​​and service layer 608) to interact with each other or interface 604 (or a combination of both) on system bus 603. API 607 ​​can include descriptions of routines, data structures, and object classes. API 607 ​​can be independent of or dependent on the computer language and refer to a complete interface, a single function, or even a set of APIs. Service layer 608 provides software services to computer 600 or other components (whether or not shown) that are communicatively coupled to computer 600. The functions of computer 600 can be accessible to all service consumers using service layer 608. Software services (such as those provided by service layer 608) provide reusable, defined business functionality through defined interfaces. For example, the interface can be software written in JAVA, C++, or other suitable languages ​​that provide data in Extensible Markup Language (XML) format or other suitable formats. Although shown as an integrated component of computer 600, alternative embodiments may show API 607 ​​or service layer 608 as separate components relative to or communicatively coupled to other components of computer 600 (whether shown or not). In addition, any or all portions of API 607 ​​or service layer 608 may be implemented as submodules or submodules of another software module, enterprise application, or hardware module without departing from the scope of the present disclosure.

[0076] Computer 600 includes interface 604. Although Figure 6 602, two or more interfaces 604 may be used depending on the particular needs, desires, or implementation of the computer 600. The interface 604 is used by the computer 600 to communicate with other systems in a distributed environment connected to the network 602. Generally speaking, the interface 604 includes logic that is encoded in software or hardware (or a combination of software and hardware) and is operable to communicate with the network 602. More specifically, the interface 604 may include software that supports one or more communication protocols associated with the communication so that the network 602 or the hardware of the interface is operable to communicate physical signals within and external to the computer 600 shown.

[0077] Computer 600 includes at least one computer processor 605. Although Figure 6A single computer processor 605 is shown in FIG600 , but two or more processors may be used depending on the particular needs, desires, or implementation of the computer 600. In general, the computer processor 605 executes instructions and manipulates data to perform the operations of the computer 600 and any algorithms, methods, functions, procedures, flows, and programs described herein.

[0078] The computer 600 also includes a memory 609 that stores data for the computer 600 or other components that may be connected to the network 602 (or a combination of both). For example, the memory 609 may be a database that stores data consistent with the present disclosure. Figure 6 600, two or more memories may be used depending on the particular needs, desires, or particular implementation of the computer 600 and the functionality being described. While the memory 609 is shown as an integral component of the computer 600, in alternative embodiments, the memory 609 may be external to the computer 600.

[0079] Application 606 is an algorithmic software engine that provides functionality, particularly with respect to the functionality described in the present disclosure, as specifically needed, desired, or according to a particular implementation of computer 600. For example, application 606 may be implemented as one or more components, modules, applications, etc. Furthermore, although illustrated as a single application 606, application 606 may be implemented as multiple applications 606 on computer 600. Furthermore, although illustrated as being integral to computer 600, in alternative implementations, application 606 may be external to computer 600.

[0080] There may be any number of computers 600 associated with or external to the computer system containing computer 600, with each computer 600 communicating over network 602. Furthermore, the terms "client," "user," and other suitable terminology may be used interchangeably where appropriate without departing from the scope of this disclosure. Furthermore, this disclosure contemplates that many users may use one computer 600, or that one user may use multiple computers 600.

[0081] In some embodiments, the computer 600 is implemented as part of a cloud computing system. For example, a cloud computing system may include one or more remote servers and various other cloud components, such as cloud storage units and edge servers. In particular, a cloud computing system can perform one or more computing operations without requiring direct active management of a user device or local computer system. In this way, a cloud computing system can have different functions distributed across multiple locations from a central server, which can be executed using one or more Internet connections. More specifically, a cloud computing system can operate according to one or more service models, such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), Mobile "Backend" as a Service (MBaaS), serverless computing, Artificial Intelligence (AI) as a Service (AIaaS) and / or Function as a Service (FaaS).

[0082] Although only a few exemplary embodiments have been described in detail above, it will be readily apparent to those skilled in the art that many modifications may be made in the exemplary embodiments without departing substantially from the present invention. Therefore, all such modifications are intended to be included within the scope of this disclosure as defined by the appended claims.

Claims

1. A method comprising: Obtain multiple spatiotemporal waveforms; generating a plurality of overlapping spatiotemporal windows by dividing the plurality of spatiotemporal waveforms; For each overlapping space-time window: determining a transformed window from the spatiotemporal window, determining a coherent noise window from the transformed window by performing a first soft thresholding process based at least in part on a predetermined parameter range, determining a random noise window from the transformed window by performing a second soft thresholding process, and determining a spatiotemporal filtering window based at least in part on subtracting the coherent noise window and the random noise window from the overlapping spatiotemporal windows; and A plurality of spatiotemporal filtered waveforms are determined by combining the spatiotemporal filtering windows for each of the plurality of overlapping spatiotemporal windows.

2. The method according to claim 1, wherein The plurality of space-time waveforms include seismic-while-drilling waveforms.

3. The method according to claim 1, wherein The first soft thresholding includes a difference between the amplitude of the transformed window and a predetermined threshold.

4. The method according to claim 1, wherein The second soft thresholding includes determining a plurality of random noise thresholds from a residual window, wherein the residual window is determined from the transform window and the coherent noise window.

5. The method according to claim 4, wherein The plurality of random noise thresholds are based at least in part on a combination of amplitudes of the residual windows.

6. The method according to claim 5, wherein: The plurality of random noise thresholds are functions of temporal frequency.

7. The method according to claim 1, wherein Determining the filtering window includes using a Wiener filter.

8. The method according to claim 1, wherein Determining the transformed window is based on performing a Fourier transform.

9. The method according to claim 1, further comprising: using a seismic processing system, refining a time-depth model based at least in part on the plurality of spatiotemporal filtered waveforms; and Using a wellbore planning system, a planned wellbore trajectory is updated based at least in part on the time-depth model.

10. The method of claim 9, further comprising using the drilling system to drill a portion of the wellbore guided by the updated planned wellbore trajectory.

11. A non-transitory computer-readable memory comprising computer-executable instructions stored thereon, the computer-executable instructions, when executed on a processor, causing the processor to: Obtain multiple spatiotemporal waveforms; generating a plurality of overlapping spatiotemporal windows by dividing the plurality of spatiotemporal waveforms; For each overlapping space-time window: determining a transformed window from the spatiotemporal window, determining a coherent noise window from the transformed window by performing a first soft thresholding process based at least in part on a predetermined parameter range, determining a random noise window from the transformed window by performing a second soft thresholding process, and determining a spatiotemporal filtering window based at least in part on subtracting the coherent noise window and the random noise window from the overlapping spatiotemporal windows; and A plurality of spatiotemporal filtered waveforms are determined by combining the spatiotemporal filtering windows for each of the plurality of overlapping spatiotemporal windows.

12. The non-transitory computer-readable memory of claim 11, further comprising computer-executable instructions for determining a filtering window using a Wiener filter.

13. A system comprising: a well penetrating an oil and gas reservoir, drilled by a drilling rig and equipped with a while-drilling seismic sensor configured to record a plurality of spatiotemporal waveforms; as well as, A signal processor configured to: obtaining the plurality of spatiotemporal waveforms; generating a plurality of overlapping spatiotemporal windows by dividing the plurality of spatiotemporal waveforms; For each overlapping space-time window: determining a transformed window from the spatiotemporal window, determining a coherent noise window from the transformed window by performing a first soft thresholding process based at least in part on a predetermined parameter range, determining a random noise window from the transformed window by performing a second soft thresholding process, and determining a spatiotemporal filtering window based at least in part on subtracting the coherent noise window and the random noise window from the overlapping spatiotemporal windows; and A plurality of spatiotemporal filtered waveforms are determined by combining the spatiotemporal filtering windows for each of the plurality of overlapping spatiotemporal windows.

14. The system according to claim 13, wherein: The first soft thresholding includes a difference between the amplitude of the transformed window and a predetermined threshold.

15. The system according to claim 13, wherein: The second soft thresholding includes determining a plurality of random noise thresholds from a residual window, wherein the residual window is determined from the transform window and the coherent noise window.

16. The system according to claim 15, wherein: The plurality of random noise thresholds are based at least in part on a combination of amplitudes of the residual windows.

17. The system according to claim 15, wherein: The plurality of random noise thresholds are functions of temporal frequency.

18. The system of claim 13, further comprising: A seismic processing system is configured to refine a time-depth model based at least in part on the plurality of spatiotemporal filtered waveforms.

19. The system of claim 18, further comprising: A wellbore planning system is configured to update a planned wellbore trajectory based at least in part on the time-depth model.

20. The system of claim 19, further comprising: A drilling system is configured to drill a portion of the wellbore guided by the updated planned wellbore trajectory.

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