WO2016012191A1 - Procédé de filtrage adaptatif de reflexions sismiques multiples - Google Patents
Procédé de filtrage adaptatif de reflexions sismiques multiples Download PDFInfo
- Publication number
- WO2016012191A1 WO2016012191A1 PCT/EP2015/064455 EP2015064455W WO2016012191A1 WO 2016012191 A1 WO2016012191 A1 WO 2016012191A1 EP 2015064455 W EP2015064455 W EP 2015064455W WO 2016012191 A1 WO2016012191 A1 WO 2016012191A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- decomposition
- seismic data
- seismic
- multiple reflections
- model
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 115
- 238000001914 filtration Methods 0.000 title claims abstract description 38
- 230000003044 adaptive effect Effects 0.000 title claims abstract description 29
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 81
- 238000005215 recombination Methods 0.000 claims abstract description 20
- 230000006798 recombination Effects 0.000 claims abstract description 20
- 230000015572 biosynthetic process Effects 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 4
- 230000009977 dual effect Effects 0.000 claims description 4
- 238000004891 communication Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 7
- 230000001427 coherent effect Effects 0.000 abstract description 3
- 230000001419 dependent effect Effects 0.000 abstract description 2
- 238000004364 calculation method Methods 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 5
- 239000002131 composite material Substances 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000012937 correction Methods 0.000 description 3
- 239000012530 fluid Substances 0.000 description 3
- 238000013508 migration Methods 0.000 description 3
- 230000005012 migration Effects 0.000 description 3
- 230000003071 parasitic effect Effects 0.000 description 3
- 238000004321 preservation Methods 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000003208 petroleum Substances 0.000 description 2
- 229910052704 radon Inorganic materials 0.000 description 2
- SYUHGPGVQRZVTB-UHFFFAOYSA-N radon atom Chemical compound [Rn] SYUHGPGVQRZVTB-UHFFFAOYSA-N 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000002238 attenuated effect Effects 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 229930195733 hydrocarbon Natural products 0.000 description 1
- 150000002430 hydrocarbons Chemical class 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 238000007620 mathematical function Methods 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 235000012431 wafers Nutrition 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
- G01V1/364—Seismic filtering
Definitions
- the present invention relates to the field of the petroleum industry, particularly the field of exploration and exploitation of petroleum reservoirs or geological gas storage sites.
- Seismic prospecting generally consists of three steps: the acquisition of seismic data, the processing of these data, and finally the interpretation of the processed data, then called seismic image.
- the acquisition step of the seismic data generally implements the principle of seismic reflection.
- Seismic reflection consists of emitting one or more waves (by explosive or vibration in earth seismic, by air or water cannon in marine seismic) and recording the signals representing the amplitude variations of the waves propagated in the earth. water or subsoil, and having at least partially reflected at the level of at least one boundary (also called interface) of geological layer characterized by a seismic impedance contrast.
- the recording of the waves thus reflected is carried out by acceleration sensors (seismometers), vibration sensors (geophones) or pressure sensors (hydrophones), or by a combination of elementary sensors of the above types (for example multi-sensor sensors).
- -composing or OBC Ocean Bottom Cable
- the signal recorded, typically for a few seconds, by a given sensor is called a seismic trace.
- the seismic data corresponds to a collection of one or more seismic traces, from sensors located at different spatial positions, forming a volume in two or three dimensions (one time, one or two of space), or even four dimensions if repetitive seismic acquisitions (acquired in the same region at different time periods) are included.
- the distance between a source and a seismic sensor is called offset (or "offset" in English).
- the seismic data recorded during a seismic reflection experiment are said to be multi-offsets or even prestack ("prestack" in English), ie the signal emitted by a given source is recorded by several sensors. located at different offsets. Recorded seismic data, called raw, are often unusable.
- seismic images are most often represented on a computer, by a mesh or grid, each mesh corresponding to a lateral and vertical position (the vertical direction corresponding to the time or to the depth depending on whether the processing has resulted in an image time or an image depth) within the formation studied, and being characterized by a seismic amplitude. If the seismic processing applied to the recorded seismic data is optimal, the seismic amplitude in a given mesh of a seismic image must reflect the amplitude of the seismic wave having undergone a single reflection (so-called primary reflection) at the position of the mesh in the studied formation.
- geological models which make it possible to determine many technical parameters relating to the research, the study or the exploitation of a reservoir, hydrocarbons for example.
- a particularly delicate step of the seismic treatment consists in filtering the noise generated by coherent parasitic waves, called multiple reflections, which undergo one or more rebounds in the layer of water or between at least two geological interfaces.
- FIG. 1 illustrates the path of a primary reflection P1 arriving at a point A and the path of two multiple reflections M1 and M2 arriving at a point B.
- the multiple reflections have known a share of path in common with the primary reflection P1, but have, in addition, undergone a rebound between two interfaces limiting at the top and bottom a geological layer.
- a multiple reflection arriving at B is recorded with a certain delay compared to the primary reflection arriving at A, corresponding to the propagation time to effect the rebound between the two interfaces.
- a multiple reflection recorded at B interferes with primary reflections associated with deeper seismic reflectors, such as with the primary reflection P2 shown in Figure 1.
- the multiple reflections can mask or still distort the useful information contained in the primary reflections, by modifying the geometry of the primary reflections or their seismic amplitude.
- a family based on the use of a decomposition method such as the Fourier transform, the Radon transform or the wavelet transform, based on the assumption that the seismic data correspond to a summation of components each component having its own characteristics (for example a particular frequency range coupled to a range of orientations in the particular space).
- a component is defined by a mathematical function, dependent on the type of decomposition chosen, and by a decomposition coefficient.
- This method consists of predicting one or more models of multiple reflections and then subtracting it from the seismic data. More precisely, from one or more multiple reflection models, this method consists in estimating one or more adaptive filters, having a limited number of coefficients. Different methods of obtaining the filter coefficients are known. Such a technique is for example described in FR2994746. A particular mode of applying adaptive filtering is to minimize the quadratic difference between the seismic data and the multiple reflection model, by making an orthogonality assumption between the primary reflections and the multiple reflections.
- the methods of the prior art do not allow optimal filtering of the seismic data, that is to say ensuring a complete elimination of the multiple reflections, while preserving the characteristics of the primary reflections, such as the amplitudes and the frequencies.
- the current seismic acquisition methods such as the BroadSeis TM technology developed by CGG (France), making it possible to acquire seismic data having a very large frequency content, it seems essential that the filtering of the multiple reflections guarantees at best the preservation of the frequency content of the recorded seismic data.
- the present invention is an alternative method of filtering multiple reflections present in seismic data, combining a decomposition method and an adaptive filtering method, followed by a weighted recombination of the filtered components.
- the present invention aims to improve the filtering of multiple reflections, to better preserve the amplitude of the primary reflections, especially when the latter are of low amplitude compared to multiple reflections and random noise.
- the present invention relates to a method for constructing a filtered seismic image of multiple reflections from a seismic data record comprising primary reflections and multiple reflections, from at least one model of said multiple reflections.
- the method comprises at least the following steps:
- a decomposition method is used to decompose, in N decomposition directions, said seismic data into a set of N components of said seismic data;
- said decomposition method is applied to decompose, according to said N decomposition directions, said multiple reflection model into a set of N components of said model;
- a weighted recombination of said components of said filtered seismic data is calculated from a weighting calculated for each direction of decomposition as a function of said relative concentrations calculated in said decomposition direction.
- said decomposition method may be a wavelet transform.
- said decomposition method can be a M-band wavelet transform into a dual tree.
- said adaptive filtering may be a unified Wiener filter in complex wavelet frame.
- said relative concentration between a signal S and a two-dimensional signal S 'can be calculated according to the following formula:
- said weighted recombination can be calculated as follows:
- n 1, N
- a is a constant
- ⁇ ⁇ is said weighting for said decomposition direction n
- D ' is an inverse of said decomposition method.
- said weighted recombination can be calculated as follows:
- n 1, N
- a is a constant
- ⁇ ⁇ is said weighting for said decomposition direction n
- D ' is an inverse of said decomposition method.
- said inverse of said decomposition method is approximated, one can add to said components of said seismic data, a residue corresponding to the difference between said seismic data and the result of said approximate inverse .
- a method of operating an underground formation can be defined by performing the following steps:
- a filtered seismic image of the multiple reflections is constructed by means of the method as described according to one of claims 1 to 13;
- a geological model representative of the studied formation is constructed from at least the seismic image thus determined;
- the said reservoir is exploited by implementing the said optimal exploitation scheme.
- the invention relates to a computer program product downloadable from a communication network and / or recorded on a computer readable medium and / or executable by a processor, comprising program code instructions for the implementation of the method as described above, when said program is run on a computer.
- Figure 1 shows a seismic data acquisition device and examples of path of primary reflections and multiple reflections generated by this device.
- Figures 2A and 2B show an example of pre-summed seismic data and the corresponding multiple reflection model.
- Figure 3 shows a set of different wavelets, each wavelet being characterized by a range of frequencies and a range of orientations in the particular space.
- 4A, 4B, 4C, 4D illustrate the decomposition coefficients of the data presented in FIG. 2A, using 4 different oriented wavelets, and making it possible to obtain preferential directions on four components SD h SD 2, SD 3, and SD 4 .
- FIGS. 4E, 4F, 4G, 4H illustrate the decomposition coefficients of the multiple model presented in FIG. 2B, using 4 different oriented wavelets, and making it possible to obtain preferential directions on four components MDj MD 2, MD 3i and MD 4 .
- Figure 5A shows the seismic data already presented in Figure 2A, before random noise filtering.
- Figure 5B shows the result obtained after application of the method described in Ventosa et al. (2012).
- Figure 5C shows the result obtained after application of the method according to the invention.
- - multiple model this is an approximate model of the multiple reflections contained in seismic data.
- One method consists of obtaining an approximate version of the primary reflection (for example by filtering), then convoluting it with itself or again with the initial seismic trace (see, for example, Pica et al.
- a multiple model can also be obtained by solving the wave equation in the medium under consideration.
- multiple models are satisfactory approximations of multiple reflections. However, they can be shifted on the vertical axis (time or depth axis), have amplitudes and / or a frequency spectrum different from the true multiple reflections. Because of these inaccuracies, the attenuation treatment of multiple reflections uses an adaptation of the multiple models to the seismic data, by adaptive filtering, also called registration. Note that a model of multiples may have limited relevance, for example being representative of a multiple reflection for a limited range of offsets, or for a given order of multiples. Multiple models can then be used to simulate, completely, multiple reflection recorded in seismic data.
- the pair ( ⁇ , ⁇ ) defines the coordinates of a vector in a given direction, the norm of this vector defining the frequency, and the terms ⁇ ⁇ and ⁇ ⁇ correspond to the envelope widths in the x and y directions.
- Each choice of parameters provides a particular directional wavelet.
- Figure 3 shows different directional wavelets, each characterized by an envelope, a main frequency and a particular orientation in space.
- - energy of a signal in the case of a two-dimensional signal S characterized by if samples in one direction and s2 samples in the other direction, the energy of a signal is defined by the following formula:
- the object of the present invention is a method for constructing a filtered seismic image of the multiple reflections present in seismic recordings, from one or more models of multiples.
- This invention uses a decomposition of both the seismic data and the model of multiples according to preferential decomposition directions, and a weighted recombination of each of the seismic components filtered according to the components of the corresponding multiple model.
- the seismic data may have been recorded by two-dimensional or three-dimensional acquisition devices and organized according to any type of collection (i.e. common shot collection, common receiver, etc.). Seismic data can be indifferent to pre-summation or summed collections, before migration or after migration.
- the present invention may be applied at any stage of the seismic treatment, and preferably before the step of interpretation of the seismic image resulting from the application of the entire seismic treatment.
- the invention requires having at least one model of multiple reflections.
- the present invention comprises at least the following steps:
- a decomposition method is used to decompose, in N decomposition directions, said seismic data into a set of N components of said seismic data; b) for at least one of said models of said multiple reflections, said decomposition method is applied to decompose, according to said N decomposition directions, said multiple reflection model into a set of N components of said model; c) for each direction of decomposition and for at least one of said models of said multiple reflections, a relative concentration is calculated between said component of said seismic data in said direction and said component of said pattern in said direction;
- a weighted recombination of said components of said filtered seismic data is calculated from a weighting calculated for each direction of decomposition as a function of said relative concentrations calculated in said decomposition direction.
- the main steps of the present invention are described hereinafter in the case of seismic data S after two-dimensional summation, but the method can equally well be applied to three-dimensional seismic data.
- the main steps of the present invention are described below in the case of a single model of multiple M.
- the case of several models of multiples is declined in a variant.
- this step it is necessary to decompose the seismic data S into N components according to a decomposition method D.
- a decomposition method D is chosen for decomposing seismic data according to different frequency bands and different orientation ranges in space.
- a decomposition direction is a pair formed by a frequency band and an orientation range in space.
- a dual-band M-band wavelet decomposition method is used, as described in Chaux et al. (2006). These are particular directional wavelets, obtained by choosing a set of filters, called primary filterbank, each filter being defined for a clean frequency band, and all the filters making it possible to cover a predefined frequency range. Then another set of filters deduced from the previous ones, called dual filter bank, is calculated. This set is obtained by shifting each half-coefficient filter by conventional interpolation techniques or by calculation in the Fourier domain. These filters are then applied separately on the horizontal and vertical directions, and combined to provide diagonal directions.
- the expert defines P frequency bands, and the dual filter bank is constructed as described above so as to obtain the 4P decompositions according to the different ranges of orientations in space and frequency bands.
- P is between four to eight.
- the envelopes are defined by the choice of frequency ranges and orientations in space.
- a decomposition method is used for which exactly at least one inverse D is known, that is to say such that 1
- D D.
- a decomposition method for which an inverse is known in an approximate manner, also called pseudo-inverse
- FIGS. 4A to 4D The result of the decomposition method described in Chaux et al (2006), applied to the seismic data presented in FIG. 2A, is given in FIGS. 4A to 4D.
- the S seismic data were decomposed using 4 different directional wavelet to obtain four components lt SD SD SD 2 3> and SD 4 oriented at 4 different space directions, specifically, a low frequency isotropic component (Figure 4A), and three directional components, approximately covering the following angular ranges: -23 ° to + 23 ° ( Figure 4B), 22 ° to 68 ° (Figure 4C), 67 ° to 13 ° ( Figure 4D).
- step a) of decomposition of the seismic data It is in this step to decompose the model of multiple M in N components, with the same method of decomposition D and according to the same N directions as those defined in step a) of decomposition of the seismic data.
- the relative concentration between two signals corresponds to the ratio of the concentration of each of the signals.
- the concentration C of a two-dimensional signal S defined by whether samples in one direction and s2 samples in the other direction is defined as follows:
- the concentration thus defined is therefore between 0 and 1. More precisely, a highly concentrated signal, for example such that a single value of this signal is non-zero, has a concentration C equal to 1. On the other hand, a signal with very low concentration, for example for which all the signal values are equal to each other, will have a concentration C equal to 0.
- concentration makes it possible in particular to qualify the quality of a treatment intended to concentrate the energy of a signal given optimally. For example, a sinusoidal signal is converted by Fourier transform into a well-located frequency peak. Its concentration index is then maximal.
- the relative concentration CR between a signal S and a signal S 'both two-dimensional and characterized by if samples in one direction and s2 samples in the other direction is then written:
- the relative concentration CR "for a given direction n is greater than 1, the seismic signal is more concentrated than the multiple model for the direction considered.
- the relative concentration is less than 1, then the multiple model is more concentrated than the seismic signal for that same direction.
- This step uses a method F adaptive filtering primary reflections and multiple reflections, from a model of multiples. More specifically, during this step, an adaptive filtering method F is applied to each of the SD components derived from the seismic data and calculated in step a). Thus, N seismic SDF components are filtered from the multiple reflections as follows:
- the adaptive filtering is applied not to the seismic data themselves but to the seismic components resulting from the decomposition of the seismic data according to frequencies and orientations in the privileged space.
- the seismic components being simpler and / or more homogeneous than the original seismic data, the parameters of the adaptive filtering are easier to estimate, and consequently, the adaptive filtering is more efficient.
- applying an adaptive filter on each component from the seismic data is intended to better eliminate multiple reflections and better preserve the frequency content, component by component.
- the adaptive filtering method F is chosen for its quality of preservation of the frequency content, in particular the low and high frequencies of the primary reflections.
- an adaptive filtering method for multiple unia Wiener filter reflections in wavelet complex mono-dimensional wafers as described in Ventosa et al. (2012) This method makes it possible to best preserve the frequency content, especially the low and high frequencies of the seismic data.
- the objective of this step is to recombine the various filtered seismic components, in taking into account the efficiency of the decomposition method applied to seismic data and the multiple model.
- an inverse D 'of the decomposition method D as defined in step a) is used.
- a pseudo-inverse D or inverse approximation of the method D as defined in step a) is used.
- the concentrated components are favored in equivalent ways, using a maximum weighting when the relative concentration in the considered direction is close to 1, and minimum in the opposite case.
- the evaluation x of the function W is necessarily between 0 and 1. Moreover, since W is chosen to be increasing, we give a greater weight to the values of x close to 1, that is to say to the equitably concentrated components.
- an estimate SR of the filtered seismic data of the multiple reflections is calculated, resulting from the best combinations of the N SDF n seismic components filtered by method F and respecting the relative concentration of the different components resulting from the decomposition method D, according to the following formula:
- a is chosen such that if null values are assigned to the multiple M model, SR is characterized by the same energy as the input seismic data S. According to a particular embodiment of the invention, a is constant and equal to 1. In this way, the filtered SDF components of the multiple reflections are annealed in accordance with an inverse of the D method, canceling all but one, and assigning the calculated weighting to the recombination of the component in question.
- an estimate SR of the filtered seismic data of the multiple reflections is calculated as follows:
- a is a constant
- a is chosen such that if null values are assigned to the multiple M model, SR is characterized by the same energy as the input seismic data S.
- a is constant and equal to 1.
- a filtered seismic image of the multiple reflections is obtained by means of adaptive filtering applied to seismic data decomposed in preferential directions, followed by recombination of the filtered components taking into account the efficiency of the decomposition method.
- the filtering method F taking into account the different models of multiples. More precisely, the N filtered seismic components are calculated in the following way:
- the invention relates to a method for operating an underground formation, in which the following steps are carried out:
- a filtered seismic image of the multiple reflections is constructed by means of the method as previously described;
- a geological model representative of the studied formation is constructed from at least the seismic image thus determined;
- the said reservoir is exploited by implementing the said optimal exploitation scheme.
- the specialists can determine several exploitation plans corresponding to different possible configurations of exploitation of the underground reservoir: location of producing wells and / or injectors, target values for flows per well and / or tank, type of tools used, fluids used, injected and / or recovered, etc. For each of these schemes, their production forecasts should be determined. These probabilistic production forecasts can be obtained using flow simulation software as well as the calibrated reservoir model.
- a reservoir simulation is a technique for simulating fluid flows within a reservoir using a software called flow simulator. For example, PumaFlow ® software (IFP Énergies Hospital, France) is a flow simulator.
- One or more possible exploitation schemes are defined, adapted to the studied geological model. For each of these schemes, responses are determined by simulation.
- the reservoir is then exploited according to the exploitation scheme defined for example by drilling new wells (producer or injector), by modifying the tools used, by modifying the flows and / or the nature of fluids injected, etc.
- the invention also relates to a computer program product downloadable from a communication network and / or recorded on a computer readable medium and / or executable by a processor.
- This program includes program code instructions for implementing the method as described above, when the program is run on a computer.
- the method according to the invention is applied to a case of two-dimensional real seismic data after summation in the case of the presence of a random noise. It is observed in FIG. 5A that the seismic data in question correspond to a superposition of primary seismic reflections and multiple reflections, the multiple reflections having a very high amplitude relative to the primary reflections. It can also be observed that primary reflections and multiple reflections are strongly disturbed by a large random noise.
- Figure 5B shows the result of the application of adaptive filtering according to the prior art (described in Ventosa et al (2012)) to the data of Figure 5A.
- FIG. 5C shows the result of the method according to the invention applied to the seismic data presented in Figure 5A.
- the process according to the invention has been implemented using the technique described in Chaux et al. (2006) for the decomposition method and the technique described in Ventosa et al. (2012) for adaptive filtering. It is very clearly observed that the method according to the invention makes it possible to extract more clearly the primary reflection of interest, in particular in the left part of FIG. 50.
- the present invention makes it possible to improve the adaptive filtering of multiple reflections contained in seismic data, operating selectively according to frequency bands and ranges of orientations. in the privileged space.
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
Description
Claims
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/328,820 US20170219734A1 (en) | 2014-07-24 | 2015-06-25 | Method of adaptive filtering of multiple seismic reflections |
EP15731592.0A EP3172596A1 (fr) | 2014-07-24 | 2015-06-25 | Procédé de filtrage adaptatif de reflexions sismiques multiples |
CA2954695A CA2954695A1 (fr) | 2014-07-24 | 2015-06-25 | Procede de filtrage adaptatif de reflexions sismiques multiples |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
FR1457140 | 2014-07-24 | ||
FR1457140A FR3024243B1 (fr) | 2014-07-24 | 2014-07-24 | Procede de filtrage adaptatif de reflexions sismiques multiples |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2016012191A1 true WO2016012191A1 (fr) | 2016-01-28 |
Family
ID=51417529
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/EP2015/064455 WO2016012191A1 (fr) | 2014-07-24 | 2015-06-25 | Procédé de filtrage adaptatif de reflexions sismiques multiples |
Country Status (5)
Country | Link |
---|---|
US (1) | US20170219734A1 (fr) |
EP (1) | EP3172596A1 (fr) |
CA (1) | CA2954695A1 (fr) |
FR (1) | FR3024243B1 (fr) |
WO (1) | WO2016012191A1 (fr) |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11662492B2 (en) | 2018-10-25 | 2023-05-30 | Saudi Arabian Oil Company | Seismic random noise attenuation |
US11409012B2 (en) * | 2019-10-21 | 2022-08-09 | Saudi Arabian Oil Company | Frequency based method for reducing the effect of multiples in seismic data |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2012087632A2 (fr) * | 2010-12-23 | 2012-06-28 | Westerngeco Llc | Retrait de bruit à partir d'une mesure sismique |
-
2014
- 2014-07-24 FR FR1457140A patent/FR3024243B1/fr active Active
-
2015
- 2015-06-25 US US15/328,820 patent/US20170219734A1/en not_active Abandoned
- 2015-06-25 EP EP15731592.0A patent/EP3172596A1/fr not_active Withdrawn
- 2015-06-25 WO PCT/EP2015/064455 patent/WO2016012191A1/fr active Application Filing
- 2015-06-25 CA CA2954695A patent/CA2954695A1/fr not_active Abandoned
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2012087632A2 (fr) * | 2010-12-23 | 2012-06-28 | Westerngeco Llc | Retrait de bruit à partir d'une mesure sismique |
Non-Patent Citations (2)
Title |
---|
DANIELA DONNO: "Improving multiple removal using least-squares dip filters and independent component analysis", GEOPHYSICS, SOCIETY OF EXPLORATION GEOPHYSICISTS, US, vol. 76, no. 5, 1 September 2011 (2011-09-01), pages V91 - V104, XP001571002, ISSN: 0016-8033, [retrieved on 20111117], DOI: 10.1190/GEO2010-0332.1 * |
SERGI VENTOSA ET AL: "Adaptive multiple subtraction with wavelet-based complex unary Wiener filters", GEOPHYSICS, SOCIETY OF EXPLORATION GEOPHYSICISTS, US, vol. 77, no. 6, 1 November 2012 (2012-11-01), pages V183 - V192, XP001579415, ISSN: 0016-8033, [retrieved on 20120918], DOI: 10.1190/GEO2011-0318.1 * |
Also Published As
Publication number | Publication date |
---|---|
FR3024243B1 (fr) | 2016-08-19 |
CA2954695A1 (fr) | 2016-01-28 |
US20170219734A1 (en) | 2017-08-03 |
EP3172596A1 (fr) | 2017-05-31 |
FR3024243A1 (fr) | 2016-01-29 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Gómez et al. | A simple method inspired by empirical mode decomposition for denoising seismic data | |
Yu et al. | Attenuation of noise and simultaneous source interference using wavelet denoising | |
FR2986335B1 (fr) | Procede et appareil de traitement de donnees sismiques | |
Bekara et al. | Random and coherent noise attenuation by empirical mode decomposition | |
Moldoveanu et al. | Over/under towed-streamer acquisition: A method to extend seismic bandwidth to both higher and lower frequencies | |
Mayhan et al. | First application of Green’s theorem-derived source and receiver deghosting on deep-water Gulf of Mexico synthetic (SEAM) and field data | |
FR2843202A1 (fr) | Methode pour former un modele representatif de la distribution d'une grandeur physique dans une zone souterraine, affranchi de l'effet de bruits correles entachant des donnees d'exploration | |
US11880011B2 (en) | Surface wave prediction and removal from seismic data | |
Liu et al. | One-step data-domain least-squares reverse time migration | |
Wang et al. | Reverse time migration of multiples: Eliminating migration artifacts in angle domain common image gathers | |
EP2755057A2 (fr) | Traitement pour la séparation de multiples d'évènements primaires par curvelet adaptatif haute fidélité de données sismiques | |
Staring et al. | Robust estimation of primaries by sparse inversion and Marchenko equation-based workflow for multiple suppression in the case of a shallow water layer and a complex overburden: A 2D case study in the Arabian Gulf | |
Métivier et al. | A review of the use of optimal transport distances for high resolution seismic imaging based on the full waveform | |
Ishiyama et al. | 3D surface‐wave estimation and separation using a closed‐loop approach | |
EP3172596A1 (fr) | Procédé de filtrage adaptatif de reflexions sismiques multiples | |
Zhao et al. | Wavelet-crosscorrelation-based interferometric redatuming in 4D seismic | |
Liu et al. | Random noise reduction using SVD in the frequency domain | |
Dondurur et al. | Swell noise suppression by Wiener prediction filter | |
Reilly et al. | The case for separate sensor processing: Meeting the imaging challenge in a producing carbonate field in the Middle East | |
EP3245542B1 (fr) | Suppression des traces fantômes de sommation | |
Staring et al. | R-EPSI and Marchenko equation-based workflow for multiple suppression in the case of a shallow water layer and a complex overburden: A 2D case study in the Arabian Gulf | |
Lu | Deep learning realm for geophysics: Seismic acquisition, processing, interpretation, and inversion | |
Lin et al. | A robust adaptive rank-reduction method for 3D diffraction separation and imaging | |
Edme et al. | Near-surface imaging using ambient-noise body waves | |
Wellington et al. | Preconditioning full-waveform inversion with efficient local correlation operators |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 15731592 Country of ref document: EP Kind code of ref document: A1 |
|
ENP | Entry into the national phase |
Ref document number: 2954695 Country of ref document: CA |
|
REEP | Request for entry into the european phase |
Ref document number: 2015731592 Country of ref document: EP |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2015731592 Country of ref document: EP |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
WWE | Wipo information: entry into national phase |
Ref document number: 15328820 Country of ref document: US |