Embodiments of the subject matter disclosed herein generally relate to methods and systems for seismic data acquisition and processing and, more particularly, to acquisition devices based on a propagated wavefield within a medium with more than one source-receiver record.
For many decades multiple source-receiver devices, as described by H. T. Fries on pages 685-707 of his 1925 publication entitled “A New Directional Receiving System,” incorporated herein by reference and by R. M. Foster on pages 292-307 of his 1926 publication in Bell System Technology entitled “Directive Diagrams of Antenna Arrays,” incorporated herein by reference, have been used to improve the quality of data acquisition.
Over time, source and/or receiver array processing has evolved to focus on electromagnetic, acoustic and/or elastic wave energy originating from a single direction. These evolutions are further described for the field of seismology by E. W. Carpenter on pages 1816-1821 of his 1965 publication entitled “An Historical Review of Seismometer Array Development” in The Proceedings of IEEE, incorporated herein by reference and for the field of radio astronomy by M. A. Garrett in his 2012 publication entitled “Radio Astronomy Transformed: Aperture Arrays—Past, Present and Future” in The Proceedings of Science, incorporated herein by reference.
Looking to more recent developments, arrays have been used to enhance the detection of parameter variations within a medium, e.g., velocity variations among others. For example, oceanography applications have been demonstrated in a laboratory scale environment in which a double-beamforming algorithm has been used to track velocity variations in water as described by I. Iturbe, P. Roux, B. Nicolas, J. Virieux and J. I. Mars (hereinafter “ITURBE1) in their publication entitled “Shallow Water Acoustic Tomography Performed from a Double Beamforming Algorithm: Simulation Results,” IEEE Journal of Oceanic Engineering, Volume 34, n. 2, pages 140-149, April 2009, incorporated herein by reference and by I. Iturbe, P. Roux, J. Virieux and B. Nicolas (hereinafter “ITURBE2”) in their publication entitled “Travel-time Sensitivity Kernels versus Diffraction Patterns Obtained Through Double Beamforming in Shallow Water,” Journal Acoustic Society of America, Volume 126, n. 2, pages 713-720, August 2009, incorporated herein by reference.
Looking to
The main issue associated with this depiction of the sensitivity kernel 102 is the fact that if a velocity variation occurs on the central path, the sensitivity kernel is null and, consequently, the variation is not seen in the wave. Attempts have been made to correct this condition in the oceanographic context by adding both sources and receivers to the acquisition system and using a double-beamforming summation algorithm as described by ITURBE1 and ITURBE2 to modify the sensitivity kernel.
For example, the sensitivity kernel 102 has been modified, as shown in
Whereas oceanography seeks to better understand the oceans themselves (including the oceans' floors), seismic data acquisition seeks to image areas of the earth or ocean's subsurfaces in an attempt to locate hydrocarbon deposits. Thus seismic data acquisition typically involves differences in both the system used to acquire data, and in the manner in which the acquired data is subsequently processed. For example, in the case of seismic explorations array processing methods like the double-beamforming algorithm described above are not commonly used. Instead, seismic exploration preferably considers a source-receiver pair, a common-depth point (CDP) collection or migrated traces instead of arrays. In all of these configurations, the corresponding travel-time sensitivity kernels continue to show oscillations around the central ray path. This is highlighted, for example, in
Accordingly, it would be desirable to provide systems and methods that avoid the afore-described problems and drawbacks, and to provide systems and methods to maximize the available information associated with parameter variations with a given set of source-receiver pairs.
These and other aspects are addressed according to various embodiments which, for example, determine subsurface parameter variations associated with sensitivity kernels without using beamforming techniques.
According to an embodiment, a method, stored in a memory and executing on a processor, for seismic data processing, includes the steps of computing at least one sensitivity kernel related to at least one wavefield parameter associated with the seismic data, computing the at least one wavefield parameter associated with the seismic data; and inverting or deconvolving the at least one wavefield parameter with the at least one sensitivity kernel to generate at least one subsurface parameter variation.
According to another embodiment, a method, stored in a memory and executing on a processor, for processing seismic data includes the steps of computing sensitivity kernels associated with source-receiver pairs based on a velocity model; filtering the source-receiver pairs to a predetermined location; adapting the sensitivity kernels to the filtered source-receiver pairs; computing wavefield parameters associated with the filtered source-receiver pairs; and inverting or deconvolving the wavefield parameters with the adapted sensitivity kernels to derive at least one subsurface parameter variation.
According to another embodiment, a system for processing seismic data includes a seismic dataset; one or more processors configured to execute computer instructions and a memory configured to store the computer instructions wherein the computer instructions further comprise: a sensitivity kernel component for computing at least one sensitivity kernel based on the seismic dataset, a wavefield parameter component for computing at least one wavefield parameter based on said seismic dataset; and an inversion or deconvolution component for inverting or deconvolving the at least one wavefield parameter with the at least one sensitivity kernel to generate at least one subsurface parameter variation .
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:
The following description of the 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 the terminology and structure of maximizing the available information associated with parameter variations with a given set of source-receiver pairs by avoiding destructive summation. However, the embodiments to be discussed next are not limited to these configurations, but may be extended to other arrangements as discussed later.
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.
The presented embodiments describe, for example, methods and systems for increasing the available information associated with one or more parameter variations for a predefined set of source-receiver pairs used in seismic data acquisition. This can be accomplished by, for example, avoiding or reducing destructive contributions during processing of the data and involve, for example, computing a sensitivity kernel for a selected parameter for each source-receiver pair based on a velocity model, filtering the collection of source-receiver pairs to an area of interest, measuring the parameter variations for the filtered source-receiver pairs and inverting the parameter variations based on the sensitivity kernels adapted to the filtered source-receiver pairs.
In order to provide some context for the subsequent embodiments, consider first some generalized aspects of seismic data acquisition processes and systems as will now be described with respect to
With this configuration, sources 308 are controlled to generate seismic waves, and the plurality of receivers 302 records waves reflected by the petroleum and/or gas reservoirs and other structures. The seismic survey can be repeated at various time intervals, e.g., months or years apart, to determine changes in the reservoirs over the selected time interval. Although repeatability of source and receiver locations is generally easier to achieve onshore, the variations caused by changes in near-surface can be significantly larger than reservoir fluid displacement, making time-lapse 4D seismic acquisition and repeatability challenging. Accordingly, variations in seismic velocity in the near-surface are a factor that impacts repeatability of 4D surveys.
Looking to
The speed of the seismic waves, as discussed later in more detail, may be estimated from recording refracted seismic waves.
With this context in mind regarding seismic data acquisition systems and methods generally, the discussion now turns to embodiments which determine sensitivity kernels associated with the acquired seismic data and use those sensitivity kernels to compute parameter variations associated with the media through which the seismic waves are travelling. Initially, it is noted that the oceanographic context presented by ITURBE1 and ITURBE2 which were described in the Background section above is specific to that environment and, accordingly, the source-receiver distance in ITURBE 1 and ITURBE 2, i.e., the offset, is more than thirty times larger than the corresponding array size, e.g., 1500 meters versus 46.5 meters. By way of contrast, embodiments described here which relate to seismic data acquisition will have proportions of the acquisition design which can be different than those used in an oceanographic context. For example, now considering a seismic instead of oceanographic context, source-receiver spacing in a seismic acquisition array can be approximately 25 meters for a target depth generally between 0.5 kilometers and 3 kilometers.
Thus it is appropriate to begin the discussion of these embodiments with a brief discussion of an exemplary seismic acquisition geometry. Looking to
Returning now to
Various differences between the time-travel sensitivity kernel 102 in
This difference in environments associated with sensitivity kernels impacts the optimization associated with the double beamforming summation proposed by Iturbe. For example, performing a trace summation on traces acquired using a seismic acquisition system, e.g., a double-beamforming processing, will show that, for a given point of the propagation such as the selected point 510, the positive and negative contributions are mixed, resulting in cancellations that will degrade the performance of the summation, thereby resulting in less useful sensitivity kernels which don't accurately track parameter variations. The embodiments described I below allow for this mixed condition and reduce or eliminate the cancellations that degrade the performance of the summation.
More specifically, and according to an embodiment, both the spatial dependency recognized above with respect to offset of the source-receiver pairs and the problems associated with using double beamforming summation to improve sensitivity kernels in the seismic context can be addressed using the method illustrated in
The embodiment of
The foregoing embodiments describe, and address, spatial source-receiver/depth dependencies associated with sensitivity kernels and their associated parameter variations in seismic acquisition environments. However other embodiments also recognize that sensitivity kernels can also have frequency/depth sensitivity kernel variations. For example, and looking to
More specifically, it can be seen that wavefield parameter variations cannot easily be detected by considering the full bandwidth 910 while separated bandwidths 906, 908 provide detection with higher sensitivity, i.e., a given velocity variation causes higher measurement variations in the sub-bandwidth. Based on this recognition of frequency dependency, various embodiments propose to perform the processing of
Various alternatives and modifications to the afore-described embodiments are also contemplated. For example, in addition to travel-time variations as described above, the embodiments can also be applied to, but are not limited to, amplitude variations, slowness variations, azimuth variations and incidence angles. As will be appreciated by those skilled in the art, the term “slowness” refers to a characteristic of a medium which is the reciprocal of velocity, i.e., such that the travel time of a seismic wave is the distance that the wave travels multiplied by the slowness of the medium. The embodiments can also be applied to any type of parameter(s) variations as soon as a sensitivity kernel can be calculated and as soon as summations are performed, e.g., beamforming, Common Depth Point Summation, etc.
Moreover, it is not always necessary to calculate the sensitivity kernel under certain circumstances, e.g., in the case of a frequency dependency, the parameter(s) variations can be detected on the data by computations for different bandwidths as described previously, which characteristic is more prominent when different bandwidths are carrying different information. Additionally, various shapes can be generated for weighting predefined geographical areas within a medium, i.e., spatial variations, temporal variations, amplitude variations and slowness or angles variations, to emphasize or de-emphasize sensitivities in those areas.
Another example is provided in the flowchart of
The methods described above can, for example, be implemented as combinations of hardware and software operating on suitable computing devices having one or more processors and memory devices including Looking now to FIG. 11, a schematic diagram of a system 1100 for processing seismic datasets in accordance with one or more of the foregoing methods is depicted. The system 1100 comprises a sensitivity kernel component 1102, a wavefield parameters component 1106, an inversion or deconvolution component 1108 and a seismic dataset 1110. The sensitivity kernel component 1102 provides the capability to compute sensitivity kernels for the seismic dataset 1110. It should be noted in the system 1100 that the sensitivity kernel can be computed for source-receiver pairs, common depth-point (CDP) collections and migrated collections. It should further be noted that sensitivity kernels can be computed for each of one or more frequency bands, as described earlier.
The wavefield parameters component 1106 provides the capability for computing wavefield parameters based on the seismic data 1110. It should be noted in the embodiment that examples of the wavefield parameters comprise travel-time, amplitude and slowness. The inversion or deconvolution component 1108 provides the capability to invert or deconvolve the wavefield parameter with the related sensitivity kernels to generate the desired subsurface parameter variations, e.g., velocity
The computing device(s) or other network nodes involved in multi-component dip filtering of ground roll noise as set forth in the above described embodiments may be any type of computing device capable of processing and communicating seismic data associated with a seismic survey. An example of a representative computing system capable of carrying out operations in accordance with these embodiments is illustrated in
Data storage unit 1216 itself can comprise hard disk drive (HDD) 1218 (these can include conventional magnetic storage media, but, as is becoming increasingly more prevalent, can include flash drive-type mass storage devices 1220, among other types), ROM device(s) 1222 (these can include electrically erasable (EE) programmable ROM (EEPROM) devices, ultra-violet erasable PROM devices (UVPROMs), among other types), and random access memory (RAM) devices 1224. Usable with USB port 1210 is flash drive device 1220, and usable with CD/DVD R/W device 1212 are CD/DVD disks 1226 (which can be both read and write-able). Usable with diskette drive device 1214 are floppy diskettes 1228. Each of the memory storage devices, or the memory storage media (1218, 1220, 1222, 1224, 1226, and 1228, among other types), can contain parts or components, or in its entirety, executable software programming code (software) 1230 that can implement part or all of the portions of the method described herein. Further, processor 1208 itself can contain one or different types of memory storage devices (most probably, but not in a limiting manner, RAM memory storage media 1224) that can store all or some of the components of software 1230.
In addition to the above described components, system 1200 also comprises user console 1232, which can include keyboard 1234, display 1236, and mouse 1238. All of these components are known to those of ordinary skill in the art, and this description includes all known and future variants of these types of devices. Display 1236 can be any type of known display or presentation screen, such as liquid crystal displays (LCDs), light emitting diode displays (LEDs), plasma displays, cathode ray tubes (CRTs), among others. User console 1232 can include one or more user interface mechanisms such as a mouse, keyboard, microphone, touch pad, touch screen, voice-recognition system, among other inter-active inter-n communicative devices.
User console 1232, and its components if separately provided, interface with server 1202 via server input/output (I/O) interface 1240, which can be an RS232, Ethernet, USB or other type of communications port, or can include all or some of these, and further includes any other type of communications means, presently known or further developed. System 1200 can further include communications satellite/global navigation satellite system (GNSS)/global positioning system (GPS) transceiver device 1242, to which is electrically connected at least one antenna 1244 (according to an embodiment, there would be at least one GPS receive-only antenna, and at least one separate satellite bi-directional communications antenna). System 1200 can access internet 1246, either through a hard wired connection, via I/O interface 1240 directly, or wirelessly via antenna 1244, and transceiver 1242.
Server 1202 can be coupled to other computing devices, such as those that operate or control the equipment of truck 112 of
According to a further embodiment, system 1200, being designed for use in seismic exploration, will interface with one or more sources 1248, 1250 and one or more receivers 1252, 1254. As further previously discussed, sources 1248, 1250 and receivers 1252, 1254 can communicate with server 1202 either through an electrical cable that is part of streamer 1256, 1258, or via a wireless system that can communicate via antenna 1244 and transceiver 1242 (collectively described as communications conduit 1260).
According to further exemplary embodiments, user console 1232 provides a means for personnel to enter commands and configuration into system 1200 (e.g., via a keyboard, buttons, switches, touch screen and/or joy stick). Display device 1236 can be used to show: source/receiver 1256, 1258 position; visual representations of acquired data; source 1248, 1250 and receiver 1252, 1254 status information; survey information; and other information important to the seismic data acquisition process. Source and receiver interface unit 1204 can receive the seismic data from receiver 1252, 1254 though communication conduit 1260 (discussed above). Source and receiver interface unit 1204 can also communicate bi-directionally with sources 1248, 1250 through the communication conduit 1260. Excitation signals, control signals, output signals and status information related to source 1248, 1250 can be exchanged by communication conduit 1260 between system 1200 and source 1248, 1250.
Bus 1206 allows a data pathway for items such as: the transfer and storage of data that originate from either the source sensors or receivers through an I/O processor 1262 ; for processor 1208 to access stored data contained in data storage unit memory 1216; for processor 1208 to send information for visual display to display 1236; or for the user to send commands to system operating programs/software 1230 that might reside in either the processor 1208 or the source and receiver interface unit 1204.
System 1200 can be used to implement the methods described above associated with multi-component dip filtering of ground roll noise according to an exemplary embodiment. Hardware, firmware, software or a combination thereof may be used to perform the various steps and operations described herein. According to an exemplary embodiment, software 1230 for carrying out the above discussed steps can be stored and distributed on multi-media storage devices such as devices 1218, 1220, 1222, 1224, 1226, and/or 1228 (described above) or other form of media capable of portably storing information (e.g., universal serial bus (USB) flash drive 1220). These storage media may be inserted into, and read by, devices such as the CD-ROM drive 1212, the disk drive 1214, among other types of software storage devices.
The disclosed exemplary embodiments provide a server node, and a method for multi-component dip filtering of ground roll noise associated with seismic depth images. 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. Further, in the detailed description of the exemplary embodiments, numerous specific details are set forth in order to provide a comprehensive understanding of the 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. The methods or flow charts provided in the present application may be implemented in a computer program, software, or firmware tangibly embodied in a computer-readable storage medium for execution by a general purpose computer or a processor.
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.
The present application is related to, and claims priority from U.S. Provisional Patent Application No. 61/840,804, filed Jun. 28, 2013, entitled “VARIATIONS PARAMETERS RESOLUTION IMPROVEMENT USING SENSITIVITY KERNELS,” to Benoit CACQUERAY, the disclosure of which is incorporated herein by reference.
Filing Document | Filing Date | Country | Kind |
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PCT/EP2014/063434 | 6/25/2014 | WO | 00 |
Number | Date | Country | |
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61840804 | Jun 2013 | US |