Patent Publication Number: US-10330809-B2

Title: Device and method for optimization of 4D and 3D seismic data

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a 371 of International Application No. PCT/EP2014/058860, filed on Apr. 30, 2014, for “Device and Method for Optimization of 4D and 3D Seismic Data,” and claims priority and benefit from U.S. Provisional Patent Application No. 61/818,004, filed May 1, 2013, for “Simultaneous Optimization for 4D and 3D Seismic Data,” the entire contents of which is incorporated in its entirety herein by reference. 
    
    
     BACKGROUND 
     Technical Field 
     Embodiments of the subject matter disclosed herein generally relate to methods and systems for eliminating noise from seismic data acquired at different times at the same location (4D seismic surveys), thereby achieving a clearer image of real changes occurring in the subsurface over time. 
     Discussion of the Background 
     A widely used technique for searching for oil or gas is the seismic exploration of subsurface (i.e., geophysical structure). Seismic exploration consists of generating seismic waves directed toward the subsurface, gathering data on reflections of the generated seismic waves from interfaces between layers of the subsurface, and analyzing the data to generate a profile (image) of the geophysical structure, i.e., layers of the investigated subsurface. Seismic exploration is used for exploring both land and subsurface under the sea floor. 
     However, collected seismic data includes noise, which may be of different types, e.g., coherent and incoherent. One example of such noise is produced by multiples. Multiples are known in the art to include waves that reflect more than once before being recorded by seismic receivers. For example,  FIG. 1  shows a marine acquisition system  100  that includes a vessel  102  towing a seismic source  104  and a streamer  106 . Streamer  106  has one or more seismic receivers  108 . A wave  120   a  emitted by seismic source  104  may reflect from an interface  122  of interest and then be recorded by seismic receiver  108 . However, it is also possible to emit a wave  120   b  that reflects a first time from the interface of interest  122  and a second time from the water surface  124  prior to be being recorded by the seismic receiver. In another example, a wave  120   c  is reflected from an interface not of interest  124 , and then it reflects at least two more times, from the interface of interest  122  and from another interface, e.g., interface  124 , before being recorded by the seismic receiver. These different waves have acquired dedicated names, for example, wave  120   a  is called primary, wave  120   b  is called ghost, and wave  120   c  is called multiples. While primary  120   a  is desired for processing, the ghost and multiples are also recorded by the seismic receivers, and these waves are considered noise, i.e., they hide the interfaces of interest. 
     There are traditional methods for removing multiples. Such methods may include, as illustrated in  FIG. 2 , a step  200  of receiving various vintages for a given subsurface, a step  202  of determining the multiples for each vintage, a step  204  of subtracting from each vintage a corresponding multiple, and a step  206  of generating 4D data based on the corrected vintages. 
     However, this method affects each vintage (i.e., 3D seismic data) individually and may introduce spurious events into the 4D image of the subsurface, which is undesirable. Thus, there is a need in the industry to develop new methods that reduce the number of spurious events and also remove noise (e.g., multiples) from recorded seismic data. 
     SUMMARY 
     Various embodiments discussed next yield an enhanced image of actual changes of the surveyed subsurface by minimizing 4D differences and optimizing 3D seismic vintages while maintaining the minimized 4D differences. 
     According to an embodiment, there is a method for noise attenuation. The method includes receiving seismic data associated with at least two vintages (d i , d j ) collected for a same subsurface, wherein the first and second vintages (d i , d j ) are taken at different times; calculating a set of filters (f i , fj) that minimizes an energy function E, wherein the energy function E includes a term representing a 4D difference between the first and second vintages (d i , d j ); calculating primaries (p i , p j ) corresponding to the first and second vintages (d i , d j ) based on the set of (f i , fj); and calculating a 4D difference (Δ ij ) based on the primaries (p i , p j ). The 4D difference (Δ ij ) is minimized. 
     According to another embodiment, there is a method for denoising seismic time-lapse vintages. The method includes processing the seismic time-lapse vintages (d i , d j ) such that the vintages subtract optimally in a time-lapse 4-dimensional (4D) sense; and simultaneously processing each seismic time-lapse vintage (d i , d j ) to obtain minimal noise in a 3-dimensional (3D) sense. 
     According to still another embodiment, there is a computing device for noise attenuation and the computing device includes an interface for receiving seismic data associated with at least two vintages (d i , d j ) collected for a same subsurface, wherein the first and second vintages (d i , d j ) are taken at different times and a processor connected to the interface. The processor is configured to calculate a set of filters (f i , fj) that minimizes an energy function E, wherein the energy function E includes a term representing a 4D difference between the first and second vintages (d i , d j ); calculate primaries (p i , p j ) corresponding to the first and second vintages (d i , d j ) based on the set of filters (f i , fj); and calculate a 4D difference (Δ ij ) based on the primaries (p i , p j ). The 4D difference (Δ ij ) is minimized. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate one or more embodiments and, together with the description, explain these embodiments. In the drawings: 
         FIG. 1  is a schematic diagram of a seismic data acquisition setup; 
         FIG. 2  is a flowchart of a method for removing multiples from the acquired seismic data; 
         FIG. 3  is a flowchart of a method for generating primaries; 
         FIG. 4  is a flowchart of a method that generates primaries while minimizing 4D differences between various vintages of a same subsurface; 
         FIG. 5  is a flowchart of a method that simultaneously minimizes 4D differences between various vintages of a same subsurface while also optimizing 3D seismic data; 
         FIG. 6  is a schematic diagram of a land acquisition seismic system; 
         FIG. 7  is a flowchart of a method that minimizes 4D differences between various vintages of a same subsurface while also optimizing 3D seismic data; 
         FIG. 8  is a flowchart of another method that minimizes 4D differences between various vintages of a same subsurface while also optimizing 3D seismic data; and 
         FIG. 9  is a block diagram of a seismic data analysis apparatus according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The following description of the exemplary embodiments refers to the accompanying drawings. The same reference numbers in different drawings identify the same or similar elements. The following detailed description does not limit the invention. Instead, the scope of the invention is defined by the appended claims. Some of the following embodiments are discussed, for simplicity, with regard to two seismic data vintages, a base vintage and a monitor vintage. However, the embodiments to be discussed next are not limited to only two vintages or to seismic data, but may be applied to plural seismic data vintages and to other similar data. 
     Reference throughout the specification to “one embodiment” or “an embodiment” means that a particular feature, structure or characteristic described in connection with an embodiment is included in at least one embodiment of the subject matter disclosed. Thus, the appearance of the phrases “in one embodiment” or “in an embodiment” in various places throughout the specification is not necessarily referring to the same embodiment. Further, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments. 
     According to an embodiment, there is an algorithm for removing multiples from two or more vintages of a same subsurface so that the 4D difference between vintages is optimal, and it also improves the individual 3D vintages by finding common noise components which can be subtracted without adversely affecting the already-optimal 4D differences. 
     The algorithm includes a first step of optimally processing seismic time-lapse vintages so that the vintages subtract optimally in a (time-lapse) 4D sense, while simultaneously being optimal (having minimal noise) in a 3D sense. In processing 4D data, it often happens that operators are designed (i.e., demultiple or denoise) individually for each vintage. Thus, each vintage is individually processed to remove the multiples. Then, a 4D difference between vintages is tested to check whether it improved (e.g., by 4D subtraction or a quality control (QC) measure such as normalized root mean square (NRMS) amplitude). According to the embodiment, new operators are created that are optimal in a 4D sense, i.e., the operators are determined based on pairs of vintages, giving the best 4D difference. Realizing that the new operators that are optimal in a 4D sense may leave residual noise on the individual vintages, but that this noise by design subtracts in a 4D sense, a second processing step is applied, in which any residual remaining noise common to the vintages is subtracted from all vintages. By subtracting the residual noise (coherent or incoherent) common to the vintages, the 4D differences, already optimal, remain untouched. These concepts are now discussed in more detail. 
     In one possible implementation, optimal demultiple operators are generated, for example, by estimating individually on all vintages a possible multiple model (e.g., via deconvolution or surface-related multiple elimination (SRME) or any other known demultiple technique, in any possible domain). Then, operators are generated to adaptively subtract from the 4D differences of the input data, the 4D differences of the multiple models (using any possible adaptation technique in any possible domain). This results in optimal 4D differences. The operator thus created may be applied to individual vintages to modify them. In a second step, the common noise part is generated, which is subsequently subtracted from the modified base and monitor (all vintages), leaving the 4D difference unchanged, but removing residual noise/multiple energy from the individual vintages. In the simplest case, the process is achieved by creating the common part as the stack (average) of the residual multiples. Any other common part technique can be used. In one application, the same energy is subtracted from the 4D optimized vintages so that the 4D difference remains optimal throughout the process. 
     For illustrating the above-discussed process, a simple example is now discussed. Those skilled in the art would recognize that this example has been simplified for the sake of clarity, and the process is not limited by these simplifications. In this regard, another approach is presented later to show that other mathematical algorithms may be implemented to arrive at the same results. Also, those skilled in the art would experiment and try variations of the examples shown herein based on the same general ideas of implementing 4D adaptive subtraction and 3D vintage improvements as discussed herein. 
     According to an embodiment, consider a base survey b and a monitor survey m. Base survey b is considered a first survey in a series of seismic surveys of a same subsurface, and a monitor survey is considered any other survey later in time in the series. Note that the algorithm shown herein also works for two different monitors other than base and monitor surveys. The 4D adaptive subtraction process includes a step of finding an operator O 4D  that minimizes a function E, which may be defined as follows:
 
 E =( b−m )− O   4D ( b   m   −m   m )  (1)
 
where b m  is the multiples model for the base, and m m  is the multiples model for the monitor m. As noted above, the multiples model for the base and monitor may be determined with any known process. Knowing the base b and monitor m seismic data (measured with seismic sensors) and knowing the multiples models (calculated, for example, using the SRME method), it is now possible to determine the O 4D  operator based on minimizing the energy E of equation (1). Note that other functions may be designed to determine operator O 4D  and also, the model may be extended to more than two vintages. In one application, there is a single operator O 4D  for base b and monitor m. If two monitors m 1  and m 2  are selected instead of base b and monitor m, another operator O may be determined for the m 1  and m 2  pair. In other words, in one application, any pair of vintages has its own operator O.
 
     Having found the O 4D  operator for base b and monitor m, it can be used to modify original base b and monitor m to generate new base b′ (that includes the primaries and not the multiples) and new monitor m′ data (that includes the primaries and not the multiples) which are optimized with respect to 4D subtractions. For example, new base b′ and new monitor m′ may be given by: 
     
       
         
           
             
               
                 
                   
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     In this way, the 4D difference between new base b′ and new monitor m′ is optimized and also the multiples from each vintage are reduced. However, it is also possible to subtract additional energy from the two vintages b′ and m′ (the same term or terms from both vintages) to further remove noise from each vintage and also to maintain the optimized 4D difference. For example, in one embodiment, it is possible to select common part c to be the stack of the residual multiple model (e.g., average) as described in: 
     
       
         
           
             
               
                 
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     Note that common part c may have other forms, for example, a form that does not rely on the O 4D  operator. In matrix notation, equation (3) can be rewritten as: 
     
       
         
           
             
               
                 
                   
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     Equation (4) can now be applied to new base b′ and new monitor m′ to obtain new optimized base b″ and new optimized monitor m″ as given by equation: 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           
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     Those skilled in the art would recognize that new optimized base b″ and monitor m″ are simultaneously optimal for the 4D seismic data and also for each 3D vintage. In other words, the above-described process is a two-step procedure to optimally process seismic time-lapse vintages so that the vintages subtract optimally in a (time-lapse) 4D sense, while simultaneously being optimal (having minimal noise) in a 3D sense. 
     Another embodiment that illustrates a similar concept is now discussed. Different notations are used herein because the method is illustrated for two vintages, d 1  and d j . Note that these two vintages may be part of a set of many vintages and the methods discussed herein apply to any two vintages. In one application, the methods may be applied to more than two vintages, as described below, for example, in equation (7), where n vintages are considered. Conventionally, for each seismic data vintage d i , subtracting the predicted multiples M i  (note that capital “M” is used to indicate a matrix and lowercase “m” is used for a vector) from data d i  is achieved by minimizing a cost function E i  as follows:
 
 E   i   =∥d   i   −M   i   f   i ∥ 2   +δ∥f   i ∥ 2 ,  (6)
 
where f i  is a filter.
 
     Adaptive subtraction of multiples M i  from seismic data d i  is achieved for each seismic data d i  individually as illustrated by equation (6). However, according to a novel 4D adaptive multiple subtraction, it is possible to improve the 4D difference between various vintages as described by equation (7), i.e., to reduce multiple model leakage by introducing an extra term that minimizes the 4D difference between vintages d i  and d j : 
                     E   =         ∑     i   =   1     n     ⁢              d   i     -       M   i     ⁢     f   i              2       +     λ   ⁢       ∑     i   =   1     n     ⁢       ∑     j   =     i   +   1       n     ⁢              (       d   i     -       M   i     ⁢     f   i         )     -     (       d   j     -       M   j     ⁢     f   j         )            2           +     δ   ⁢            f   i          2           ,           (   7   )               
where n is the number of vintages and λ is a constant. With the notation used in equation (7), d i  may be either base b or monitor m, and M i  is the model of multiples in matrix form. The extra term (second term) added to equation (7) optimizes the 4D difference between the vintages. Based on equation (7), filters f i  may be calculated and applied to the various vintages to generate new optimized vintages or primaries. This process is next discussed in more detail.
 
     To better illustrate some differences between the novel algorithm and the traditional one, the traditional one is briefly illustrated in  FIG. 3 . The method includes a pre-processing step  300  during which seismic data vintages are received and prepared for processing, e.g., designature, destripping, etc. In step  302 , a SRME procedure is applied to each vintage to determine corresponding multiples models M i . In step  304  the multiple model is adapted to the data and subtracted. For example, equation (6) may be used to remove the multiples. A radon demultiple (or any other demultiple algorithm) may be applied in step  306  to the seismic. In step  308  the data may be further processed, e.g., random noise removal, amplitude calibration, etc. In step  310  the data is regularized, e.g., interpolated to desired positions on a grid. In step  312  the data is migrated, and the primaries p 1  and p 2  corresponding to vintages d 1  and d 2  are generated. Note that multiples may be removed from vintages not only in step  302  but also in step  306 . Primaries p 1  and p 2  are supposed to be free of multiples. 
     A novel method is discussed now with regard to  FIG. 4 . Steps  400  to  410  may be similar to steps  300  to  310 , respectively, and for this reason their description is omitted herein. The results of step  410  are primaries p 1  and p 2 , similar to those of the method illustrated in  FIG. 3 . However, this method has some extra steps. A step  420  may include some processing of original vintages d 1  and d 2  and a step of regularization  422 , which may be similar to steps  408  and  410 . A difference relative to the method illustrated in  FIG. 3  is the fact that steps  420  and  422  are applied to the full seismic data d 1  and d 2  (i.e., including both primaries and multiples) so that the results of step  422  include full data d 1    424   a  and d 2    424   b . A new step  430  applies 4D adaptation based on equations (1)-(5) or (7) or other equations to be discussed later. The 4D adaptation step includes determining multiples m 1    426   a  and m 2    426   b  by calculating differences between full data d 1    424   a  and d 2    424   b  and primaries p 1  and p 2  and then calculating new optimized primaries p 1 ′ and p 2 ′. The new optimized primaries may then be migrated in step  412 , and a final image of the surveyed subsurface may be generated in step  414 . This algorithm may be applied not only to seismic data, but to other kinds of data, for example, QC data. Note that the new method may be easily implemented with existing algorithms. 
     Step  430  of the above method is now discussed in more detail with regard to  FIG. 5 . According to an embodiment, energy E of equation (7) may be used to calculate filters f i  for each vintage i in step  500 . In step  502 , new noise models a i  are calculated for each vintage i. For example, it is possible to apply the matching filters f i  to the initial multiple models m i  to obtain new noise models as described in equation (8):
 
 a   i   =f   i   *m   i ,  (8)
 
where operation “*” means convolution, e.g., filter application. Primary model p i  for each vintage may be calculated in step  504  by subtracting new noise models a i  from total data d i  as follows:
 
 p   i   =d   i   −a   i .  (9)
 
     Based on equation (9), the 4D differences Δ ij  may be calculated as:
 
Δ ij   =p   i   −p   j .  (10)
 
     At this point, the 4D differences are optimized because of the calculated filters f that minimize energy E described by equation (7). To further improve each 3D vintage p i  while maintaining optimized 4D differences Δ ij , a common part c may be calculated in step  506  and then subtracted in step  508  from each 3D vintage p i . For example, common part c may be given by an average change in the noise models: 
     
       
         
           
             
               
                 
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     The average change of the noise models is subtracted in step  508  from all new primary models p i  to obtain new optimized primary models:
 
 p   i   *   =p   i   −c.   (12)
 
     The 3D primary models p i * are now improved, as well as the 4D differences. 
     One or more steps discussed herein may be modified as will be recognized by those skilled in the art, and still the advantages associated with the 4D demultiple method may be achieved. For example, instead of using equation (7) to calculate the filters f i , it is possible to use another equation for energy E as follows: 
                   E   =       ∑     i   =   1     n     ⁢       ∑     j   =     i   +   1       n     ⁢              (       d   i     -     d   j       )     -       f   ij     *     (       m   i     -     m   j       )              2                 (   13   )               
which is similar to equation (1). Elements f ij  may then be averaged (in any possible way) to obtain filters f i , e.g., f i =&lt;f ij &gt;.
 
     In another application, step  506  may use another common part c to further improve the 3D vintages by computing c as an average of a difference between the sum of all the new noise models a i  and a quantity h: 
                     c   =       1   n     ⁢     (         ∑     i   =   1     n     ⁢     a   i       -   h     )         ,           (   14   )               
where h is calculated by minimizing the 3D energy E 3D  given by:
 
                     E     3   ⁢   D       =                ∑     i   =   1     n     ⁢     d   i       -     g   *       ∑     i   =   1     n     ⁢     m   i                2             (   15   )               
and where h is given by:
 
                     h   =     g   *       ∑     i   =   1     n     ⁢     m   i           ,           (   16   )               
where g in equation (16) is the matching filter that minimizes the total 3D energy E 3D .
 
     For adaptive multiple subtraction, matching filters f i  are estimated using an L2-norm method. Using the L2-norm sometimes has shortcomings, as discussed in “Adaptive subtraction of multiples using L1-norm” by Guitton, A. and Verschuur, D. J., published in 2004 in  Geophysical Prospecting,  52, 27-38. For example, if the multiples and the primaries are not orthogonal, primaries do not have a minimum energy in the L2-norm sense, and there may be a strong primary in the original data that can be missed. To alleviate these problems, the L1-norm or hybrid norms may be used. However, these alternative types of norms are exemplary and not intended to be limiting. Those skilled in the art would understand that other norms may be used. 
     The above methods have been discussed for time-lapse seismic surveys. However, the method is also applicable to reservoir monitoring or to any other process that involves the comparison of 3D seismic surveys at two or more points in time. While the methods above have been discussed relative to a marine survey, those skilled in the art would understand that these methods may be also applied to a land survey. Such a land acquisition system for achieving 4D seismic monitoring is illustrated in  FIG. 6 , which shows a system  600  for the acquisition of land seismic data that includes plural receivers  612  (e.g., hydrophones, accelerometers, etc.) positioned over an area  612   a  of a subsurface to be explored and in contact with the surface  614  of the ground. A number of seismic sources  616  (e.g., vibratory elements) are also placed on surface  614  in an area  616   a  in a vicinity of receivers  612 . A recording device  618  is connected to the plurality of receivers  612  and placed, for example, in a station-truck  620 . Each source  616  may be composed of a variable number of vibrators, typically between 1 and 5, and may include a local controller  622 . A central controller  624  may be present to coordinate the shooting times of the sources  616 . A GPS system  626  may be used to time-correlate the shooting of sources  616  and data acquisition by receivers  612 . 
     With this configuration, sources  616  are controlled to generate seismic waves, and the plurality of receivers  612  record waves reflected by oil and/or gas reservoirs and other structures. The seismic survey may be repeated at various time intervals, e.g., months or years apart, to determine changes in the reservoirs. Although repeatability of source and receiver locations is generally easier to achieve onshore, variations caused by changes in near-surface can be significantly larger than reservoir fluid displacement, making time-lapse 4D seismic acquisition and repeatability challenging. 
     According to an embodiment illustrated in  FIG. 7 , there is a method for noise attenuation. The method includes a step  700  of receiving seismic data associated with at least two vintages (d i , d j ) collected for a same subsurface, wherein the first and second vintages (d i , d j ) are taken at different times, a step  702  of calculating a set of filters (f i , fj) that minimizes an energy function E, wherein energy function E includes a term representing a 4D difference between the first and second vintages (d i , d j ), a step  704  of calculating primaries (p i , p j ) corresponding to the first and second vintages (d i , d j ) based on the set of filters (f i , fj), and a step  706  of calculating a 4D difference (Δ ij ) based on the primaries (p i , p j ). The 4D difference (Δ ij ) is minimized. 
     According to another embodiment illustrated in  FIG. 8 , there is a method for denoising seismic time-lapse vintages. The method includes a step  800  of processing the seismic time-lapse vintages (d i , d j ) so that the vintages subtract optimally in a time-lapse 4-dimensional (4D) sense, and a step  802  of simultaneously processing each seismic time-lapse vintage (d i , d j ) to obtain minimal noise in a 3-dimensional (3D) sense. 
     The methods discussed above may be executed by a seismic data analysis apparatus  900  whose schematic diagram is illustrated in  FIG. 9 . The seismic data analysis apparatus  900  includes a data input interface  910  configured to receive seismic data vintages, corresponding to at least two different times, for a same surveyed area. The seismic data analysis apparatus  900  further includes a data processing unit  920  configured to execute instructions for implementing the methods discussed above, e.g., minimize an energy function and calculate filters. 
     The data analysis apparatus  900  may also include a memory  930 , which is a computer-readable medium non-transitorily storing executable codes. When the stored executable codes are executed on the data processing unit  920  or another computer, the effect is to make the data processing unit  920  or the other computer perform a method for generating an image of a subsurface based on seismic data, such as (but not limited to) the methods discussed above. The image may be displayed on a screen  940 . All these elements may be connected to each other by a bus  950 , which may take many forms as are known in the art. Accordingly, the embodiments may take the form of an entirely hardware embodiment or an embodiment combining hardware and software aspects. Thus, exemplary embodiments may take the form of a computer program product (i.e., executable codes) stored on a computer-readable storage medium having computer-readable instructions embodied in the medium. Any suitable computer-readable medium may be utilized, including hard disks, CD-ROMs, digital versatile disc (DVD), optical storage devices, or magnetic storage devices such a floppy disk or magnetic tape. Other non-limiting examples of computer-readable media include flash-type memories or other known memories. 
     The disclosed exemplary embodiments provide a method, an apparatus and a computer-readable medium for removing noise from various vintages so that a 4D difference is improved and each 3D vintage is also denoised. It should be understood that this description is not intended to limit the invention. On the contrary, the exemplary embodiments are intended to cover alternatives, modifications and equivalents, which are included in the spirit and scope of the invention as defined by the appended claims. Further, in the detailed description of the exemplary embodiments, numerous specific details are set forth in order to provide a comprehensive understanding of the claimed invention. However, one skilled in the art would understand that various embodiments may be practiced without such specific details. 
     Although the features and elements of the present exemplary embodiments are described in the embodiments in particular combinations, each feature or element can be used alone without the other features and elements of the embodiments or in various combinations with or without other features and elements disclosed herein. 
     This written description uses examples of the subject matter disclosed to enable any person skilled in the art to practice the same, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the subject matter is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims.