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    • 专利摘要:
    invention patent: "methods and systems for correcting slope of pennant depth in marine seismic inspections". computational systems and methods to be performed by computer systems to determine the slope of pennant depth during exploration seismology experiments are disclosed. an exploration seismology vessel tows a number of streamers that form a smoothly varying data acquisition surface located beneath a fluid surface. in one aspect of this disclosure, the • method determines a set of image points that represent a profile of the fluid surface above each streamer. the imaging points are determined based on pressure and velocity wave fields measured on the double streamer sensors. the method then determines an image point elevation parameter that minimizes the deviation of dc in a spectral domain of image points. the elevation parameter corresponds to the streamer slope and can be used to correct the streamer slope in subsequent calculations of a fluid surface profile.
    公开号:BR102013003210B1
    申请号:R102013003210-7
    申请日:2013-02-08
    公开日:2020-03-17
    发明作者:Okwudili Orji；Walter Söllner
    申请人:Pgs Geophysical As；
    IPC主号:
    专利说明:

    Descriptive Report of the Invention Patent for "SYSTEM OF EXPLORATION SYSTEM AND METHOD FOR CORRECTION OF STREAMER DEPTH SLOPE". BACKGROUND
    [0001] In the past decades, the oil industry has invested heavily in the development of maritime seismic inspection techniques that have brought knowledge of underground formations below a body of water in order to extract valuable mineral resources, such as oil and natural gas. High resolution seismic images of an underground formation are essential for quantitative seismic interpretation and improved reservoir monitoring. For a typical marine seismic inspection, an exploration seismology vessel tows a seismological source and one or more streamers just below the surface of the water over an underground formation to be investigated for mineral deposits. The vessel contains seismic acquisition equipment, such as navigation control, seismic source control, seismic sensor control, and logging equipment. The seismic source control causes the seismic source, which is typically an air gun, to produce an acoustic impulse at selected times. The impulse sound waves travel down through the water and into the underground formation. At each interface between different types of rock, a portion of the sound wave is reflected back to the surface and into the body of water. The streamers towed behind the ship are structures similar to elongated cables. Each streamer includes a number of sensors that detect the pressure and velocity changes in the water created by the sound waves reflected back into the water from the underground formation and by any sound waves reflected outside the water surface. The pressure and velocity changes associated with sound waves reflected off the water's surface are referred to as a "surface reflection" that creates a "ghost" signal in the seismic signals recorded by the recording equipment. The phantom signal is manifested as spectral indentations in the recorded signals that make it difficult to obtain accurate high-resolution seismic images of the underground formation. [0002] The phantom signal is influenced by the topography of the water surface and reflection coefficients. As a result, a number of techniques are developed to characterize the topography of the water's surface varying over time in an effort to record the recorded phantom signals. However, when the images of the water surface and the reflection coefficients do not properly characterize the water surface, the seismic images of the underground formations produced from the defantamized signals are generally inaccurate. In particular, images of the water surface obtained without taking into account an accurate estimate of the depth of the streamers can result in an inaccurate vertical position of the images of the water surface, which contributes to the imprecision of the images of the underground formation. As a result, those working in the oil industry continue to look for systems and methods to more precisely determine the depth of the streamers.
    BRIEF DESCRIPTION OF THE DRAWINGS
    [0003] Figure 1 shows a volume of domain of the earth's surface.
    [0004] Figure 2 shows subsurface characteristics of an underground formation in the lower portion of the domain volume shown in Figure 1.
    [0005] Figures 3A-3C show an exploration seismology method by which digitally encoded data is acquired instrumentally for subsequent processing and analysis of exploration seismology in order to characterize the structures and distributions of the characteristics and materials underlying the solid surface of the earth .
    [0006] Figures 4A-4B show waveforms generated from hydrophone and geophone outputs.
    [0007] Figure 5A shows a top view of an exemplary exploration seismology vessel towing a fountain and four streamers.
    [0008] Figure 5B shows a side view of one of the streamers shown in figure 5A.
    [0009] Figures 6A-6D show aspects of a directed processing method to correct the pitch slope of the streamers.
    [00010] Figure 7 shows an example of spectral analyzes performed on an example of a fluid surface profile.
    [00011] Figures 8A-8B show an example of correction of the pitch inclination of the streamers in the fluid surface profile and in the streamer.
    [00012] Figure 9 shows a conceptual representation of calculating the profiles of the fluid surface in different time windows of a sliding time window.
    [00013] Figure 10A shows a flow control diagram of a method for correcting slope of streamer depth.
    [00014] Figure 10B shows a flow control diagram of a method for correcting a fluid surface profile and the depth of streamers named in step 1012 of figure 10A.
    [00015] Figure 11 shows an example of a generalized computer system.
    DETAILED DESCRIPTION
    [00016] This disclosure is directed to computational methods and systems to correct streamer depth slope in data collected by streamer sensors. Accurate streamer depth estimates are in turn used to calculate an accurate estimate of a fluid surface elevation. In particular, an exploration seismology vessel tows a number of streamers that form a smoothly varying data acquisition surface located behind a fluid surface. The streamers include sensors that measure pressure and / or velocity wave fields, which are digitally encoded and stored. The method computationally determines and stores a digitally encoded set of image points that represent a profile of the fluid surface above each streamer, based on representations of digitally encoded pressure data and velocity wave fields. The image points are then transformed into a spectral domain where the method calculates and stores an elevation parameter that minimizes a deviation of CC in the spectral domain. The elevation parameter corresponds to the streamer pitch and can be used to correct the streamer pitch in subsequent fluid surface profile calculations.
    [00017] The following discussion includes two subsections: (1) an overview of exploration seismology and an example of slope of pennant depth; and (2) a method for correcting streamer slope as an example of methods and systems in a computational manner to which the present description is addressed. The reading of the first subsection can be omitted by those already familiar with maritime exploration seismology and slope of pennant depth. AN OVERVIEW OF EXPLORATION SISMOLOGY AND AN EXAMPLE OF SLOPE DEPTH DECLINE [00018] Figure 1 shows a volume of domain of the earth's surface. Volume domain 102 comprises a solid volume of sediment and rock 104 below the solid surface 106 of the earth which, in turn, underlies a volume of water 108 within an ocean, an inlet or bay, or a lake of water big sweet. The domain volume shown in figure 1 represents an exemplary experimental domain for a class of analytical exploration seismology techniques and observation systems referred to as "maritime exploration seismology".
    [00019] Figure 2 shows the subsurface characteristics of an underground formation in the lower portion of the domain volume in Figure 1. As shown in Figure 2, for exploration seismology purposes, fluid volume 108 is a volume that is generally homogeneous relatively uncharacteristic overlapping the solid volume 104 of interest. However, although the fluid volume can be explored, analyzed and characterized with relative precision using many different types of methods and probes, including remote sensing submersibles, sonar and other such devices and methods, the solid crust volume underlying the fluid volume is comparatively much more difficult to probe and characterize. Unlike the overlapping fluid volume, the solid volume is significantly heterogeneous and anisotropic and includes many different types of characteristics and materials of interest to seismologists. For example, as shown in figure 2, the solid volume 104 may include a first layer of sediment 202, a first layer of fractured and raised rock 204, and a second layer of underlying rock 206 below the first layer of rock. In certain cases, the second rock layer 206 may be porous and contain a significant concentration of liquid hydrocarbon 208 which is less dense than the second rock layer material and therefore increases upward into the second rock layer . In the case shown in figure 2, the first layer of rock is not porous and therefore forms a cap that prevents further migration upwards of liquid hydrocarbon, which therefore therefore coagulates in a layer of saturated hydrocarbon 208 below the first layer of rock 204. An objective of exploration seismology is to identify the locations of the porous extracts of saturated hydrocarbon within the volume of the earth's crust underlying the solid surface of the earth.
    [00020] Figures 3A-3C show a method of exploration seismology by which digitally encoded data is acquired instrumentally for subsequent processing and analysis of exploration seismology in order to characterize the structures and distributions of characteristics and materials of an underground formation. Figure 3A shows an example of an exploration seismology vessel 302 equipped to carry out a continuous series of exploration seismology experiments and data collections. In particular, vessel 302 tows one or more streamers 304-305 across an approximately constant flat depth usually located a number of meters below the fluid surface 306. Streamers 304-305 are long cables containing power and transmission lines data so that sensors, such as the sensor represented by the shaded disk 308 in figure 3A, are connected at regular intervals. In a type of exploration seismology, each sensor, such as sensor 308 in Figure 3A, comprises a pair of seismic sensors including a geophone, which detects vertical displacement within the fluid medium over time by detecting particle speeds or accelerations. , and a hydrophone that detects variations in pressure over time. Streamers 304-305 and vessel 302 include sophisticated sensing electronics and data processing facilities that allow sensor readings to be correlated with absolute positions on the fluid surface and absolute three-dimensional positions with respect to an arbitrary three-dimensional coordinate system. Figure 3A and subsequent figures include a Cartesian coordinate system 307 in order to specify the position and orientation within the fluid volume 108. Note, in particular, that by convention the positive z direction points downward from the surface of fluid 306. In figure 3A, the sensors along the streamers are shown to be below the fluid surface 306, with the sensor positions correlated with the overlapping surface positions, such as a surface position 310 correlated with the position of the sensor 308 Ship 302 also tows one or more acoustic sources 312 that produce pressure pulses at regular spatial and temporal intervals as ship 302 and towed streamers 304-305 move forward across the fluid surface 306.
    [00021] Figure 3B shows an expanding spherical frontal acoustic wave, represented by semicircles of magnifying radius centered on the acoustic source 312, such as semicircle 316, after an acoustic pulse emitted by the acoustic source 312. The frontal waves are, in effect, shown in the flat xz vertical cross section in figure 3B. As shown in figure 3C, the up and down expansion acoustic wave field, shown in figure 3B, eventually reaches the solid surface 106, at which point the expanding outward and downward acoustic waves partially reflect from the surface solid and partially retracts down into the solid volume, becoming elastic waves within the solid volume. In other words, in the fluid volume, the waves are compression pressure waves, or P waves, the propagation of which can be modeled by the elastic wave equation. Within the solid volume, at each interface between different types of materials or in discontinuities in density or in one or more of the boos other physical characteristics or parameters, the waves propagating downwards are partially reflected and partially retracted, as on the solid surface 106 As a result, each point on the solid surface and within the underlying solid volume 104 becomes a source of potential secondary points from which acoustic and elastic waves, respectively, can emanate upwards towards the sensors in response to the pressure pulse emitted. by the acoustic source 312 and elastic waves propagating downwards generated from pressure pulse.
    [00022] As shown in figure 3C, secondary waves of significant amplitude are generally emitted from points on or near solid surface 106, such as point 320, and from points on or very close to a discontinuity in the solid volume , such as points 322 and 324. Tertiary waves can be emitted from the fluid surface back to the solid surface in response to secondary waves emitted from the solid surface and subsurface characteristics. In many seismological exploration analyzes, the complicated wave field formed by the overlaps of all secondary, tertiary and higher order waves emitted in response to the initial acoustic wave, both in the fluid and in the solid medium, is modeled as acoustic waves as an approach that is often reasonable and more accurate, but the superposition model of elastic and acoustic waves is much more complicated mathematically and computationally.
    [00023] Figure 3C also shows the fact that secondary waves are generally emitted at different times within a time range after the initial pressure pulse. A point on the solid surface, such as point 320, receives the pressure disturbance corresponding to the initial pressure pulse more quickly than a point within the solid volume, such as points 322 and 324. Similarly, a point on the surface solid directly underlying the acoustic source receives the pressure impulse earlier than a point being further away on the solid surface. Thus, the times in which the secondary and higher-order waves are emitted from various points within the solid volume are relative to the distance, in three-dimensional space, of the points from the acoustic source.
    [00024] Acoustic and elastic waves, however, travel at different speeds within different materials as well as within the same material under different pressures. Therefore, the travel times of the initial pressure pulse and secondary waves emitted in response to the initial pressure pulse are complex functions of the distance from the acoustic source as well as the material and physical characteristics of materials through which the acoustic wave travels correspondingly. the initial pressure impulse paths. In addition, as shown in figure 3C for the secondary wave emitted from point 322, the expanding frontal waveforms can be changed as the frontal waves through the interfaces and as the speed of the sun varies in the medium traversed by the wave. The superposition of waves emitted from within the domain volume 102 in response to the initial pressure pulse is a generally very complicated wave field that includes information about the shapes, sizes and characteristics of the domain volume 102 material, including information about the shapes , sizes and locations of the various reflection characteristics within the underground formation of interest to the exploration seismologist.
    [00025] The complicated wave field that occurs in response to the initial pressure pulse is sampled, over time, by sensors positioned along the streamers towed by an exploration seismology vessel. Figures 4A-4B show processed waveforms generated from hydrophone and geophone outputs, respectively. As shown in figure 4A, the waveform recorded by the hydrophone represents the pressure in the moments after the initial pressure pulse, with the amplitude of the waveform to a point in time relative to the pressure on the hydrophone at the time point. Similarly, as shown in figure 4B, the geophone provides an indication of fluid velocity or acceleration, in a vertical direction, with respect to time.
    [00026] The pressure and velocity wave fields also include reflections from the fluid surface which, when not counted properly, can create phantom signals in the recorded waveforms. Phantom signals can lead to inaccurate seismic images of underground formation located below the volume of fluid. Many of the techniques for eliminating phantom signals and providing an accurate seismic image of an underground formation depend on having an accurate estimate of the fluid surface. This disclosure is directed at computing systems and methods that can be used to correct inaccurate estimates of fluid surface elevation. In particular, this disclosure is aimed at correcting inaccuracies in the estimated pennant depth, which, in turn, is used to calculate an accurate estimate of fluid surface elevation. A number of environmental factors contribute to inaccurate streamer depth estimates. For example, large surface waves often cause the depth to fluctuate and changes in local atmospheric pressure can lead to an inaccurate estimate of streamer depth. These and other factors can introduce a slope varying slowly in the estimated depth of a streamer that is difficult to count in calculating an accurate estimate of the elevation of the fluid surface.
    [00027] Figure 5A shows a top or xy plan view of an exemplary exploration seismology vessel 502 towing a source 504 and four separate streamers 506-509 located below a fluid surface. Each streamer is attached to one end of vessel 502 and to the opposite end of a buoy, such as buoy 510 attached to streamer 508. Streamers 506-509 ideally form a horizontal planar acquisition surface located below the fluid surface. However, in practice, the acquisition surface varies smoothly in the z direction due to active sea currents and weather conditions. In other words, towed streamers can also nodulate as a result of dynamic fluid conditions. Note that the time variation in a streamer is much less than the time variation associated with surface waves. Figure 5B shows a side or xz plane view of one of the streamers 508 located below the fluid surface 512. Figure 5B represents a quick-fit detonation in an instant of time, an undulating fluid surface 512 and similar shape the corresponding smooth wave in streamer 508. The depth, zR, of the acquisition surface can be estimated from hydrostatic pressure measurements made by depth controllers (not shown) attached to the streamers. Depth controllers are typically placed at intervals of about 300 m along each streamer and depth values for intermediate sensor locations are interpolated. The zR depth and the elevation of the fluid surface profile are estimated with respect to the geoid, which is represented in figure 5B by the dotted line 516. The geoid is the hypothetical surface of the land that coincides everywhere with average sea level and is used to define zero elevation.
    [00028] In the example in figure 5B, the dotted curve 514 represents an inaccurately estimated depth for pennant 508 because of environmental factors that introduce pennant depth slope within the estimated pennant depth 508. The incorrectly estimated pennant depth z ' can lead to an inaccurate estimate of fluid surface elevation. The following description is directed to computational methods and systems to correct the slope of streamer depth and to determine an accurate estimate of fluid surface elevation with respect to the geoid. A METHOD FOR CORRECTING THE DEPTH OF FLAMMULA DEPTH AS IN THE EXAMPLE OF COMPUTATIONAL PROCESSING METHODS AND SYSTEMS [00029] Figures 6A-6D illustrate aspects of an example of a computational processing method aimed at correcting streamer depth slope in the formation of a fluid surface profile representing the topography of the fluid surface and reflection coefficients with respect to each streamer towed by an exploration seismology vessel. In the following description, the streak depth slope is corrected as part of a calculation to determine a fluid surface profile. Note that, although an example of a method for correcting pennant depth is described with reference to a particular method of estimating the fluid surface profile, the methods disclosed to correct pennant depth are not intended to be limited to this particular method of form the profile of the fluid surface, but can instead be used with many different methods used to determine the profile of the fluid surface. In other words, the methods described below are not intended to be limited to streamer processing of a three-dimensional data set. Streamer processing of three-dimensional data can only be used for a set of three-dimensional data that is sparingly in a line direction.
    [00030] Figure 6A shows an xz side or plan view of an exploration seismology vessel 602 and a streamer 604 of a number of streamers towed by the ship forming a smoothly varying data acquisition surface located beneath the fluid surface. 606. Streamers include depth controllers that measure hydrostatic pressure. These measurements are used to estimate the depths of the zR streamers along the streamer with respect to the geoid. The estimated pennant depths are then used to calculate an interpolated pennant configuration that approximates the current pennant wave-like configuration in an instant of time. As shown in figure 6A, streamer 604 includes a number of receivers or dual sensors represented by shaded discs, such as a shaded disc 608, which are spaced at regular intervals, Δχ, along streamer 604. An enlarged view 610 of the sensor double 608 reveals that the double sensor includes a pressure sensor 612, such as a hydrophone, and a motion sensor 614, such as a geophone. Each of the pressure sensors located along streamer 408 measures a pressure wave field indicated by p (x, y, zR, t) and each of the motion sensors located along streamer 604 measures the wave field of velocity indicated by Vn (x, y, zR, t), where x = mLx, y represents the spatial coordinate y of the sensor, et represents time. The m index is an integer used to identify the receiver and is also referred to as a "channel" index. For example, in figure 6A, streamer 604 has 14 dual sensors. Double sensor 616 located closest to vessel 602 can be assigned a channel index m = 0, and double sensor 618 located furthest from vessel 602 can be assigned a channel index m = 13, with the remaining double sensors between numbered consecutively. The subscript vector n represents a normal unit vector that points in the normal direction towards the acquisition surface. The motion sensors detect the particular motion in a normal direction towards the acquisition surface as represented by the normal unit vector 620 in magnified view 610. Thus, the motion sensors are sensing the normal wave velocity va field for surface acquisition varying smoothly.
    [00031] In the following description of an example to correct streamer depth slope, a fluid surface profile must be calculated for each streamer. The spatial component y is ignored in this example for clarity and to simplify the following description. However, note that in practice, the spatial component y is included. In other words, in the following discussion, the triedpacial coordinates of the measured pressure wave field p (x, y, zR, t) are reduced by bispacial coordinates in the representation of the pressure wave field p (x, zR, t) , and the triedpacial coordinates of the measured velocity wave field Vn (x, y, zR, t) are reduced to bispacial coordinates in the representation of the velocity wave field Vn (x, zR, t). The reduction to bispace spatial coordinates gives a clear perception while preserving the main characteristics of the method.
    [00032] The pressure and motion sensors of each double sensor measure the pressure and velocity wave fields, but in order to calculate the profile of the fluid surface, the pressure and velocity wave fields are decomposed into components of pressure and speed up and down. In figure 6A, directional arrow 622 represents the direction of an upward wave field detected by a double sensor 626 and dotted line 624 represents the direction of a downward wave field reflected off the fluid surface 606 and detected by the double sensor 626. In other words, the pressure wave field p (x, zR, t) is composed of an upward pressure component and a downward pressure component, and the waveform Vn (x, zR, t) it consists of a vertical upward component and a vertical downward component. The profile of the fluid surface associated with each streamer can be determined from the up and down pressure components or from the up and down velocity components.
    [00033] The decomposition of the wave field can be performed by arbitrarily selecting an observation level at a constant depth zobs between the acquisition surface and the geoid shown in figure 6A. Figure 6B shows an exemplary graph of an interpolated streamer 628 based on the estimated depths obtained from depth controllers attached to the current streamer 604. The shape of the interpolated streamer substantially matches the shape of the current streamer 604 shown in figure 6A. Shaded discs, like the shaded disc 629, represent the locations of the double sensors located along the interpolated streamer 628. The horizontal axis 630 is an x-coordinate axis that represents the spatial dimension x, and the vertical axis 632 is an axis z coordinate that represents the depth or spatial dimension z. The spatial coordinate "0" z corresponds to the geoid. Dotted line 634 represents an example of an observation level selected at a constant depth of zobs located between the geoid and the estimated depth of the interpolated streamer 628. As shown in figure 6B, the decomposition of the wave field 630 combines the wave field pressure p (m. x, z ^, t) and velocity wave field vl, (m ^ x, zlf, t) measured in sensors in the spatial time domain, where z ^ is the interpolated depth in the mth channel , to produce a component of pressure down Pdown (kx, zobs, M) 636, a component of pressure up Puv (kx, zobs, M) 637, a component of speed down l ^ down (kx, zoès, w ) 638 and an upward velocity component Vzup (kx, zobs, ω) 639 in the wave number frequency domain, where ω = 2nf is the angular frequency for the acoustic frequencies f detected by the dual sensors, and kx is the number horizontal waveform in the x direction.
    [00034] The decomposition of the wave field can be performed as follows. First, the pressure wave field p (x, zR, t) and the velocity wave field Vn (x, zR, t) are transformed from the spatial time domain using a Fourier transformation to obtain the wave field pressure P (x, zR, ω) and the velocity wave field Vn (x, zR, ω) in the spatial frequency domain. The transformation can be performed by a Fast Fourier transformation ("FFT") for computational efficiency. Note that according to the convention, the lower case letters p and p are used to represent the quantities in the space time domain while the upper case letters P and V are used to represent the quantities in the space frequency or number frequency domain. of waves.
    [00035] Then, after the pressure and velocity wave fields have been transformed from the spatial time domain into the spatial frequency domain, the arbitrary plane observation level at constant depth zobs is selected as shown in figure 6B. Note that the fluid surface 606 shown in figure 6A is an indeterminate surface and does not necessarily have to be a horizontal plane defined by the geoid as z = 0. The upward pressure component in the frequency domain of the wave number is calculated applying the following equation: where j is the imaginary unit V — 1; kz is the number of vertical waves in the z direction given by the speed of sound in the fluid; m is the dual sensor or channel index; M is the total number of dual sensors located along the streamer; p is the density of fluid; is the interpolated depth of the streamer on the double mth sensor; nx is the x component of the normal vector n; and nz is the z component of the normal vector n.
    [00036] The pressure component above the observation level zobs 628 is calculated from [00037] The pressure component down in the frequency domain of wave number at z = 0 is calculated using the following equation: [00038] From the pressure down component the pressure down component, at the observation level zobs 628 is calculated by: [00039] As described in more detail below, the pressure up and down components can be used alone to calculate the fluid surface profile. In other embodiments, the fluid surface profile can be calculated using the up and down vertical velocity components that can be determined from the up and down pressure components as follows. The upward speed component at the zobs 628 observation level is calculated from the upward pressure component at the zobs 628 arbitrary observation level by: [00040] The upward speed component at the zobs 628 observation level is calculated from the downward pressure component) to the arbitrary zobs 628 observation level as follows: [00041] Note that the triespacial-dimensional version of the up and down pressure components and the up and down vertical speed components can be obtained by replacing the number of vertical waves kz with: where ky is the number of horizontal waves in the y spatial direction.
    [00042] After the decomposition of the wave field has been carried out, the up and down pressure components and / or the up and down vertical velocity components are iteratively extrapolated in small steps in the spatial direction za from the level of upward observation through the fluid to the fluid surface 606. Extrapolation can be performed as follows. Figure 6C shows an exemplary representation of extrapolation from the observation level zobs 634 towards the geoid to the mth channel 629. Open circles 640 represent a series of z values or small steps in the z space direction or small steps in the z space direction that extend from observation level 634 towards the geoid or x 630 axis. The upward pressure component is extrapolated by applying the following equation: for each z point in the z 640 value series. The downward pressure component is extrapolated by applying the following equation: for each point z in the series of values z 640.
    [00043] In another modality, the component of vertical velocity upwards is extrapolated from the observation level zobs towards the geoid applying the following equation: for each point z in the series of values z 640. The component of vertical velocity upwards is extrapolated by applying the following equation: for each point z in the series of values z 640.
    [00044] After the pressure components extrapolated up and down and / or vertical speed components extrapolated up and down have been determined in the frequency domain of wave number, an inverse Fourier transformation is used to obtain pressure components extrapolated up and down and / or vertical velocity components extrapolated up and down in the spatial frequency domain. The transformation can be an inverse Fast Fourier transformation ("IFFT") for computational efficiency. For example, in figure 6C, extrapolation is performed at point 642 of the z 640 value series to produce a downward pressure component Ρά0'Μη (χ, ζ, ω) 641, an upward pressure component Ρ "μ ( χ, ζ, ω) 642, a vertical downward component νζά0} νη (χ, ζ, ω) 643 and a vertical upward component ν ™ ρ (χ, ζ, ω) 644 in the spatial frequency domain .
    [00045] After each extrapolation of the pressure component or vertical velocity component up and down at a point z in the z 640 value series has been performed, an imaging condition is applied to obtain a series of image values corresponding I (x, z) associated with each z value in the 640 series. The imaging condition is a cross-correlation of the vertical speed and up and down pressure components in the spatial frequency domain. In one embodiment, the imaging condition that represents an image value of the fluid surface for a selected x-channel position and z-extrapolation depth is calculated by applying the following cross correlation: where the over-bar designates complex conjugation. Note that in one mode, Ρ (χ, ζ, ω) and υ (χ, ζ, ω) represent Ρά0 ™ η (χ, ζ, ω) and Ριψ (χ, ζ, ω), respectively. In another modality, ΰ (χ, ζ, ω) and υ (χ, ζ, ω) represent Vzdown (x, z, ω) and ν ™ ρ (χ, ζ, ω), respectively. In other embodiments, the imaging condition used to represent a fluid surface image value for a selected x-channel position and z-extrapolation depth is calculated using the following normalized cross-correlation equation: [00046] The z-value or extrapolation depth associated with the maximum image value I (x, z) for a given channel position x corresponds to the elevation of the fluid surface z in the channel position x. Figure 6D shows an exemplary graph of a fluid surface profile obtained by determining the extrapolation depth z associated with each channel x position of the interpolated streamer 628. In the example in figure 6D, the z 640 values associated with channel 629 are represented as dotted line open circles except for circle 45 which corresponds to an elevation za that a maximum transverse correction Imax (x, zo). The point (x, z0) is an image point of the fluid surface profile. Other image points represented by open circles, such as open circles 648 are obtained for each channel in the same way by extrapolating and applying the same imaging condition to each of the channels. This collection of image points forms a stationary time image of the fluid surface profile along the streamer in a selected time window. In figure 6D, the dotted curve 650 represents a fluid surface profile for the set of image points obtained by applying an imaging condition to each interpolated streamer channel 628 in the selected time window.
    [00047] After the profile of the fluid surface is determined, the spectral analysis is performed on the image points comprising the profile of the fluid surface in order to determine whether or not the streamer's depth is tilted. The fluid surface profile obtained without taking into account an accurate estimate of the streamer depth results in an inaccurate estimate of the fluid surface elevation, which, in turn, can result in inaccurate images of the underground formation located below the volume of fluid. For example, the image points, z (x), where x is a channel coordinate of a periodic fluid surface profile, are a set of smoothly varying points that can be modeled by a Fourier series as follows: where the coefficients are determined by: with k =. . -1, 0, 1,. . . and Lx is the fundamental wavelength. The coefficients. . ., a-i, ao, ai,. . . are the amplitudes of the cosine and sine wave components with the coefficients referred to as "DC deviation". A nonzero value for the CC deviation indicates that the fluid surface profile is displaced vertically, as shown above, as a result of a slope in the estimated streamer elevation. Imaging points can be analyzed spectrally to determine whether or not the streamer pitch is present, and when the streamer pitch is present, correct the streamer elevation estimate to obtain a more accurate elevation for the fluid surface profile and fluid surface profile in different time windows. In one embodiment, the spectral analysis can be performed by applying a discrete Fourier transformation to transform the image points comprising a fluid surface profile from the spatial domain x to a spectrum in the horizontal wave number domain k. The transformation can be a discrete FFT for computational effectiveness. An examination of the spectrum at k = 0 reveals whether or not the pennant depth slope is present, because the CC deviation appears in the spectrum as a non-zero value k = 0. [00048] An example of spectral analysis is now described with reference to the synthetic data shown in figure 7. Figure 7 shows a graph 702 of a fluid surface profile 704 generated from a Pierson-Moskowitz spatial roughness spectrum for a fully developed fluid surface. The horizontal axis 706 represents the horizontal distance in the x direction and vertical axis 708 represents depth with "0" corresponding to the geoid. The profile portion of the fluid surface in box 710 is transformed using a Fourier transformation to obtain a spectral graph 712. The horizontal axis 714 represents a number of horizontal k waves and the vertical axis 716 represents normalized amplitude. Solid curve 718 and dashed curve 720 represent normalized amplitude spectra associated with horizontal wave numbers. Note that the dashed curve 720 overlaps most of the curve 718. The spectrum represented by the dashed curve 720 overlaps most of the curve 718. The spectrum represented by curves 718 and 720 can be computed by applying a discrete Fourier transformation to the image points M z (x) of the spectrum shown in box 710. For example, a discrete Fourier transformation is given by is: where m = 0,. . ., M-1, and the normalized amplitude is computed as follows;
    [00049] In practice, for computational efficiency, the amplitude can currently be calculated using a discrete fast Fourier transformation. The solid curve 718 represents the spectrum of the fluid surface profile shown in box 710 where the depth of the sensors where erroneously determined to be 50.5 m during image formation. Stake 722 at k = 0 indicates a non-zero DC offset or that the fluid surface profile in housing 710 is displaced vertically in the z spatial direction. On the other hand, the dashed curve 720 represents the spectrum of the fluid surface profile where a more accurate depth of the sensors at 50 m took into account during the imaging of the fluid surface. In other modalities, instead of computing the spectrum associated with the normalized amplitude, the spectrum can be computed. In other modalities instead of computing the spectrum associated with the normalized amplitude, the spectrum can be computed using a non-J 2 amplitude [Z (k)] + Im [z (k)] normalized, the real component Re [z (k)], or the imaginary component Im [Z (k)].
    [00050] The DC offset can be minimized by incrementally displacing the z coordinates of the fluid surface profile image points by a displacement factor ± δ followed by performing spectral analysis to determine whether the displacement at + δ or a displacement -δ reduces the CC deviation. The displacement factor that reduces the DC offset is then applied incrementally until a minimum DC offset, DCmin, is obtained. The magnitude of the displacement factor can be in the size range of tens of one meter to several meters. Figure 8A shows an exemplary graph of displacement factors ± δ applied to the set of slanted image points of the fluid surface profile 650. The slope in the vertical displacement of the fluid surface profile 650 is represented by the image points above the geoid , which is represented by the x axis. The image points are offset by adding the offset factor to the z coordinates of image points. As shown in figure 8A, the displacement factor + δ displaces the image points in the positive z direction, while the displacement factor -δ displaces the image points in the negative z direction. After each incremental displacement by the appropriate displacement factor, the spectral analysis is performed on the image points to determine whether or not the displacement resulted in a minor DC deviation. If the DC offset is less, then the image points are shifted incrementally by the same shift factor until a DCmin is obtained. Figure 8B shows an exemplary graph of an adjusted fluid surface profile 802 and an adjusted interpolated streamer 804. In figure 8B, the z coordinates of the image points of the adjusted fluid surface profile 802 and the z coordinates of the interpolated streamer channels 804 have been shifted by at least the elevation parameter a. The Fourier Transformation of the image points of the adjusted fluid surface profile 802 gives a spectrum in which the CC deviation 806 is minimized, as does the exemplary spectrum represented by the dashed curve 720 shown in figure 7.
    [00051] Element parameter a can then be applied to adjust the streamer depth used to recalculate the wave field separation and calculate the fluid surface profile. The repeated spectral analysis and if necessary a second iteration of updating the sea surface is initiated. When the CC component is sufficiently small, maritime surface imaging is applied to each time window of a sliding time window. In other words, after the elevation to be determined for the initial time window, the same elevation a can be used to adjust the streamer depth slope for each time window of a sliding time window. Different fluid surface profile can be obtained as described above with reference to figures 6A-6D for each time window of the sliding time window, but with the interpolated streamer depth shifted to count the streamer slope depth. Figure 9 shows a conceptual representation of calculating the profile of the fluid surface in different time windows 901-910 of a sliding time window. In the time window 901 t = 0, the fluid surface profile 650 is calculated from the interpolated streamer 628 as described above with reference to figures 6A-6D, and spectral analysis is used to determine the elevation a used to correct the depth estimated streamer and fluid profile elevation surface, as described above with reference to figures 7 and 8. The fluid surface profile associated with the subsequent 902-910 time windows can be calculated in the same way as described above with reference figures 6A-6D, but with the streamer depth for each of the 902-910 time windows corrected by elevation a.
    [00052] Figure 10A shows a flow control diagram of a method for correcting the streamer depth slope. In block 1001, the pennant depths are estimated based on hydrostatic pressure measurements obtained from the depth controllers along the pennants, as described above with reference to figures 5B and 6A. In block 1002, pressure and velocity wave fields are measured in the dual sensors as described above with reference to figure 6A. In block 1003, the decomposition of the wave field is carried out in the pressure and velocity wave fields to obtain up and down pressure components or vertical up and down velocity components as described above with reference to figure 6B. In block 1004, the vertical velocity and / or pressure components with each channel associated with each channel are extrapolated from an arbitrary observation level through a series of steps in the vertical or z direction, as described above with reference to the figure 6C. At the beginning of the for loop on block 1005, the processes associated with blocks 10061009 are performed for each streamer. At the beginning of the for-loop loop on block 1006, the processes associated with blocks 1007-1009 are executed for each channel. In block 1007, an imaging condition is applied as described above with reference to figure 6D. In block 1008, the z values associated with the maximum imaging condition for a given channel are determined, as described above with reference to figure 6D. In block 1009, when another channel is available, the method proceeds to block 1007 and the processes associated with blocks 1007-1008 are repeated, otherwise, the method proceeds to block 1010. In block 1010, when another streamer is available, the processes associated with blocks 1006-1009 are repeated, otherwise the method proceeds to block 1011. In block 1011, the image points associated with each channel and streamer are collected to form the profile of the fluid surface . In block 1012, a method described below with reference to figure 10B is called to correct the elevation of the fluid surface profile, the streamer depth slope and returns an elevation value which can be used to correct the depth slope of streamer on the subsequently determined fluid surface profile. [00053] Figure 10B shows a flow control diagram of a method for correcting a fluid surface profile and pitch depth gradient called in step 1012 of figure 10A. In block 1021, a minimum DC deviation of DCmin is initialized to a large value, a parameter N is initialized to 0 "the vertical displacement factor δ is initialized to a value greater than zero, such as one meter, and the elevation a is initialized to "0." In block 1022, the image points of a fluid surface profile calculated as described above with reference to figure 10A are transformed to obtain a spectrum in the horizontal wave number domain. The transformation can be a Fourier transformation, such as an FFT, described above with reference to figure 7. In block 1023, the spectrum is analyzed to determine the DC deviation, indicated by DC, as described above with reference to figure 7. For example, DC can be assigned the amplitude value, normalized amplitude, the real Fourier transformation component, or the imaginary Fourier Transformation component, as described above with reference to figure 7. In block 1024, when DC is less r than DCmin, the method proceeds to block 1025, otherwise the process proceeds to block 1026. In block 1025, DCmin is assigned the value of DC. In block 1027, N is assigned the value "1." In block 1028, the z components of the fluid surface profile image points are displaced by parameter S, as described above with reference to figures 8A-8B. In block 1029, elevation a is increased by S. In block 1026, when parameter N is equal to "1," the method returns the elevation a. Otherwise, the method proceeds to block 1030 to change the displacement factor to a negative value and the processes performed on blocks 1022-1029 are repeated.
    [00054] Figure 11 shows an example of a generalized computer system that performs an effective streamer tilt correction and therefore represents a system for processing seismic data. The internal components of any small, medium and large size computer systems as well as specialized processor-based storage systems can be described with respect to this generalized architecture, although each particular system can feature many additional components, subsystems and the like, parallel systems with architectures similar to this generalized architecture. The computer system contains multiple central processing units ("CPUs") 1102-1105, one or more electronic memories 1108 interconnected with the CPUs by a CPU bus / memory subsystem 1110 or multiple buses, a first bridge 1112 that interconnects the CPU bus / memory subsystem 1110 with additional buses 1114 and 1116, or other types of high-speed interconnect media, including multiple high-speed serial interconnects. These buses or serial interconnections in turn connect CPUs and memory with specialized processors, such as a 1118 graphics processor, and with one or more 1120 bridges, which are interconnected with high-speed serial links or with multiple 1122 controllers. -1127, such as controller 1127, which provides access to several different types of mass storage devices 1128, electronic monitors, input devices, and other such components, subcomponents, and computing resources. Electronic monitors, including visual monitor screens, speakers with audio, and other input interfaces, and input devices, including mice, keyboards, touch screens and other such interfaces, together constitute input and output interfaces that allow the computer system to interact with human users. Computer-readable data storage devices include various types of electronic memories, optical and magnetic disk drives, USB drives, and other such devices. Computer-readable data storage media include optical discs, magnetic discs and other media in which instructions for optical discs, magnetic discs and other media in which computer instructions and data can be encoded, during storage operations, and of which encrypted data can be recovered during storage operations and of which encrypted data can be recovered during read operations by computer systems, data storage systems and peripheral devices.
    [00055] Although several modalities have been described, it is intended that this disclosure be limited to these modalities. For example, any number of different computational processing implementations that perform streamer depth slope correction based on pressure and velocity wave fields can be designed and developed using several different programming languages and computer platforms and by parameters of different implementation, including variable control structures, data structures, modular organization, and other such parameters. Computational representations of wave fields and other computational objects can be implemented in different ways. Although the streamer depth correction discussed above can be performed on a single one-dimensional streamer correction, the streamer depth correction can be performed on the entire acquisition surface.
    [00056] It is appreciated that the foregoing description of the disclosed modalities is provided to enable an expert in the art to make or use the present disclosure. Various modifications of these modalities will be readily apparent to those skilled in the art, and the generic principles defined in this document can be applied to other modalities without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the modalities shown in this document, but must be in accordance with the broader scope in accordance with the principles and new characteristics disclosed in this document.
    权利要求:
    Claims (20)
    [1]
    1. Exploration seismology computer system that corrects slope of streamer depth in a fluid surface profile, exploration exploration computer system comprising: one or more processors; one or more data storage devices; and a routine stored in one or more of the one or more data storage devices and to be performed by one or more processors, characterized by the fact that the routine is intended to: determine a set of image points associated with the profile of fluid surface (650) of a fluid located above a streamer based on wave fields measured in streamer sensors (628), determining the set of image points for each sensor (612, 614): decomposes the field wave in a component going up and a component going down at an arbitrary observation level (634) located between an estimated streamer depth and a geoid; extrapolates the components by going up and down from the observation level (634) towards the geoid by computing a series of extrapolation points (640) above the observation level (634); and determines an image point based on the correlation of components going up and going down; determine an elevation parameter of the image points, the elevation parameter to correspond to the streamer depth slope, the determination: apply a displacement factor that vertically displaces the image points in the spatial domain; transforms the image points from the special domain to the spectral domain to obtain a spectrum in the spectral domain; and selects the elevation parameter based on a DC deviation in the spectrum; and correct the streamer depth slope in the elevation parameter.
    [2]
    2. Computer system, according to claim 1, characterized by the fact that it still comprises: receiving seismic data from dual sensors, seismic data representative of pressure wave fields and velocity wave fields; measure hydrostatic pressures in depth controllers spaced along the streamer; estimate the streamer depth on depth controllers, based on hydrostatic pressures; and generating an interpolated streamer that approximates the wave-like configuration of the streamer based on the estimated streamer depths (304, 305).
    [3]
    3. Computer system according to claim 1, characterized by the fact that determining the set of image points associated with the fluid surface profile (306) still comprises: for each sensor in the streamer, extrapolating the components going to up and going down from the observation level towards the geoid still comprises computing a series of extrapolation points that extend from the observation level towards the geoid; apply an image formation condition at each extrapolation point; and determining the imaging point further comprises determining the imaging point which corresponds to a maximum imaging value of the imaging condition.
    [4]
    4. Computer system according to claim 3, characterized by the fact that decomposing the wave field into a component going up and a component going down further comprises decomposing a pressure wave field in a spatial time domain in a pressure component going upwards and a pressure component going downwards in a frequency domain of number of waves.
    [5]
    5. Computer system according to claim 3, characterized by the fact that decomposing the wave field into a component going up and a component going down further comprises decomposing a speed wave field in a space time domain in a vertical velocity component going upwards and a vertical velocity component going downwards in a frequency domain of number of waves.
    [6]
    6. Computer system according to claim 3, characterized by the fact that extrapolating the components going up and going down still comprises iteratively extrapolating the components going up and going down from the observation level up through of the fluid towards the geoid at each sensor location.
    [7]
    7. Computer system, according to claim 3, characterized by the fact that the condition of image formation still comprises a cross correlation of the component going up and the component going down in the spatial frequency domain.
    [8]
    8. Computer system, according to 1, characterized by the fact that selecting the elevation parameter still comprises: identifying the deviation of CC in the spectrum; and repeatedly applying the displacement factor, transforming the image points and identifying the DC offset until a minimum DC offset is obtained, where the total amount by which the image points are displaced vertically is the elevation parameter.
    [9]
    9. Computer system, according to claim 8, characterized by the fact that transforming the image points from the spatial domain to a spectral domain still comprises applying a discrete Fast Fourier transformation computational operator at each of the image in the spatial domain to obtain a point in the spectral domain.
    [10]
    10. Computer system according to claim 8, characterized by the fact that the spectral domain still comprises a horizontal wave number domain.
    [11]
    11. Method to be performed by a computer system that includes one or more processors and one or more data storage devices to correct streamer depth slope in a fluid surface profile, the method characterized by the fact that it comprises: determining a set of image points (640) associated with the fluid surface profile of a fluid located above a streamer based on wave fields measured in sensors (612, 614) of the streamer, determining the set of image points, for each sensor (612, 614): decomposes the wave field into a component going up and a component going down into an arbitrary observation level (634) located between an estimated streamer depth and a geoid; extrapolates the components by going up and down from the observation level (634) towards the geoid by computing a series of extrapolation points (640) above the observation level (634); and determines an image point based on the correlation of the components going up and going down; determine an elevation parameter of the image points, the elevation parameter to correspond to the streamer depth slope, the determination: apply a displacement factor that vertically displaces the image points in the spatial domain; transforms the image points from the special domain to a spectral domain to obtain a spectrum in the spectral domain; and selects the elevation parameter based on a CC deviation in the spectrum; and store the elevation parameter on a data storage device; and correct the streamer depth slope in the elevation parameter.
    [12]
    12. Method, according to claim 11, characterized by the fact that it still comprises: receiving seismic data from dual sensors, the seismic data representative of pressure wave fields and velocity wave fields; measure hydrostatic pressures in depth controllers spaced along the streamer; estimate the streamer depth on depth controllers, based on hydrostatic pressures; and generating an interpolated streamer that approximates the wave-like configuration of the streamer based on the estimated depths of streamers (304, 305).
    [13]
    13. Method according to claim 11, characterized by the fact that determining the set of image points associated with the fluid surface profile (306) still comprises: for each sensor in the streamer, extrapolating the components by going up and going to low from the observation level towards the geoid still comprises computing a series of extrapolation points that extend from the observation level towards the geoid; apply an image formation condition at each extrapolation point; and determining the imaging point further comprises determining the imaging point which corresponds to a maximum imaging value of the imaging condition.
    [14]
    14. Method according to claim 13, characterized by the fact that decomposing the wave field into a component going up and a component going down further comprises decomposing a pressure wave field in a spatial time domain into a component of pressure going up and a pressure component going down in a frequency domain of the number of waves.
    [15]
    15. Method according to claim 13, characterized by the fact that decomposing the wave field into a component going up and a component going down further comprises decomposing a speed wave field in a spatial time domain into a component of vertical velocity going upwards and a vertical velocity component going downwards in a frequency domain of number of waves.
    [16]
    16. Method according to claim 13, characterized by the fact that extrapolating the components going up and going down further comprises iteratively extrapolating the components going up and going down from the observation level upwards through the fluid towards to the geoid at each sensor location, each sensor being a dual sensor.
    [17]
    17. Method, according to claim 13, characterized by the fact that the imaging condition still comprises a cross correlation of the component going up and the component going down in the spatial frequency domain.
    [18]
    18. Method, according to claim 11, characterized by the fact that selecting the elevation parameter still comprises: identifying a deviation of CC in the spectrum; and repeatedly apply the displacement factor, transform the image points and identify the CC deviation until a minimum CC deviation is obtained, where the total amount by which the image points are vertically displaced is the elevation parameter.
    [19]
    19. Method, according to claim 18, characterized by the fact that transforming the image points from the spatial domain to a spectral domain still comprises applying a discrete Fourier transformation computational operator to each of the image points in the spatial domain to obtain a point in the spectral domain.
    [20]
    20. Method according to claim 18, characterized by the fact that the spectral domain still comprises a horizontal wave number domain.
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    同族专利:
    公开号 | 公开日
    EP2626726A3|2014-12-17|
    SG193075A1|2013-09-30|
    BR102013003210A2|2017-11-28|
    EP2626726A2|2013-08-14|
    SG10201506241TA|2015-09-29|
    US9279898B2|2016-03-08|
    AU2013200475B2|2016-03-24|
    AU2013200475A1|2013-08-29|
    MX2013001598A|2013-08-26|
    US20130208565A1|2013-08-15|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US7433264B2|2005-03-18|2008-10-07|Westerngeco L.L.C.|Methods and systems for determination of vertical correction of observed reflection seismic signals|
    US7505361B2|2007-04-11|2009-03-17|Pgs Geophysical As|Method for prediction of surface related multiples from marine towed dual sensor seismic streamer data|
    EP2177993A4|2007-07-10|2011-01-12|Nec Corp|Computer system, management device, and computer system management method|
    US7872942B2|2008-10-14|2011-01-18|Pgs Geophysical As|Method for imaging a sea-surface reflector from towed dual-sensor streamer data|US8554719B2|2007-10-18|2013-10-08|Palantir Technologies, Inc.|Resolving database entity information|
    US8688749B1|2011-03-31|2014-04-01|Palantir Technologies, Inc.|Cross-ontology multi-master replication|
    US10140664B2|2013-03-14|2018-11-27|Palantir Technologies Inc.|Resolving similar entities from a transaction database|
    US8924388B2|2013-03-15|2014-12-30|Palantir Technologies Inc.|Computer-implemented systems and methods for comparing and associating objects|
    US9678235B2|2013-07-01|2017-06-13|Pgs Geophysical As|Variable depth multicomponent sensor streamer|
    US10579647B1|2013-12-16|2020-03-03|Palantir Technologies Inc.|Methods and systems for analyzing entity performance|
    US9734217B2|2013-12-16|2017-08-15|Palantir Technologies Inc.|Methods and systems for analyzing entity performance|
    US10356032B2|2013-12-26|2019-07-16|Palantir Technologies Inc.|System and method for detecting confidential information emails|
    US9619557B2|2014-06-30|2017-04-11|Palantir Technologies, Inc.|Systems and methods for key phrase characterization of documents|
    US9256664B2|2014-07-03|2016-02-09|Palantir Technologies Inc.|System and method for news events detection and visualization|
    US9501851B2|2014-10-03|2016-11-22|Palantir Technologies Inc.|Time-series analysis system|
    US9984133B2|2014-10-16|2018-05-29|Palantir Technologies Inc.|Schematic and database linking system|
    US10552994B2|2014-12-22|2020-02-04|Palantir Technologies Inc.|Systems and interactive user interfaces for dynamic retrieval, analysis, and triage of data items|
    US9348920B1|2014-12-22|2016-05-24|Palantir Technologies Inc.|Concept indexing among database of documents using machine learning techniques|
    US9335911B1|2014-12-29|2016-05-10|Palantir Technologies Inc.|Interactive user interface for dynamic data analysis exploration and query processing|
    US9817563B1|2014-12-29|2017-11-14|Palantir Technologies Inc.|System and method of generating data points from one or more data stores of data items for chart creation and manipulation|
    EP3070622A1|2015-03-16|2016-09-21|Palantir Technologies, Inc.|Interactive user interfaces for location-based data analysis|
    US9348880B1|2015-04-01|2016-05-24|Palantir Technologies, Inc.|Federated search of multiple sources with conflict resolution|
    US10103953B1|2015-05-12|2018-10-16|Palantir Technologies Inc.|Methods and systems for analyzing entity performance|
    US9671776B1|2015-08-20|2017-06-06|Palantir Technologies Inc.|Quantifying, tracking, and anticipating risk at a manufacturing facility, taking deviation type and staffing conditions into account|
    US9485265B1|2015-08-28|2016-11-01|Palantir Technologies Inc.|Malicious activity detection system capable of efficiently processing data accessed from databases and generating alerts for display in interactive user interfaces|
    US10393901B2|2015-08-31|2019-08-27|Pgs Geophysical As|Wavefield interpolation and regularization in imaging of multiple reflection energy|
    US9984428B2|2015-09-04|2018-05-29|Palantir Technologies Inc.|Systems and methods for structuring data from unstructured electronic data files|
    US9424669B1|2015-10-21|2016-08-23|Palantir Technologies Inc.|Generating graphical representations of event participation flow|
    US9760556B1|2015-12-11|2017-09-12|Palantir Technologies Inc.|Systems and methods for annotating and linking electronic documents|
    US9514414B1|2015-12-11|2016-12-06|Palantir Technologies Inc.|Systems and methods for identifying and categorizing electronic documents through machine learning|
    US10089289B2|2015-12-29|2018-10-02|Palantir Technologies Inc.|Real-time document annotation|
    US9996236B1|2015-12-29|2018-06-12|Palantir Technologies Inc.|Simplified frontend processing and visualization of large datasets|
    US10248722B2|2016-02-22|2019-04-02|Palantir Technologies Inc.|Multi-language support for dynamic ontology|
    US10849264B1|2019-05-21|2020-12-01|Farmobile Llc|Determining activity swath from machine-collected worked data|
    法律状态:
    2017-11-28| B03A| Publication of a patent application or of a certificate of addition of invention [chapter 3.1 patent gazette]|
    2018-12-04| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2020-01-28| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|
    2020-03-17| B16A| Patent or certificate of addition of invention granted [chapter 16.1 patent gazette]|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 08/02/2013, OBSERVADAS AS CONDICOES LEGAIS. |
    2021-11-30| B21F| Lapse acc. art. 78, item iv - on non-payment of the annual fees in time|Free format text: REFERENTE A 9A ANUIDADE. |
    优先权:
    申请号 | 申请日 | 专利标题
    US13/369.895|2012-02-09|
    US13/369,895|US9279898B2|2012-02-09|2012-02-09|Methods and systems for correction of streamer-depth bias in marine seismic surveys|
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