Methods and systems for enhancing selected subsurface features from seismic data

Abstract

A method for enhancing one or more subsurface features represented in a seismic attribute of a subsurface region includes receiving an initial seismic attribute of the subsurface region that is based on seismic data captured by one or more seismic receivers and associated with the subsurface region, determining one or more wavenumbers from the initial seismic attribute, and generating an enhanced seismic attribute from the initial seismic attribute using the one or more wavenumbers. The method further includes generating an output image based on the one or more wavenumbers, the output image containing the one or more subsurface features, and performing an inverse Fourier transform on the enhanced seismic attribute to generate an output image in the spatial domain. In some embodiments, the method includes generating an output image containing the one or more subsurface features by applying a directional derivative to the initial seismic attribute.

Description

METHODS AND SYSTEMS FOR ENHANCING SELECTED SUBSURFACE FEATURES FROM SEISMIC DATACROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of U.S. provisional patent application No. 63 / 679,255 filed August 5, 2024, entitled “Methods and Systems for Enhancing Selected Subsurface Features from Seismic Data”, which is incorporated herein by reference in its entirety.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] Not applicable.BACKGROUND

[0003] A seismic survey typically includes generating an image or map of a subsurface region of the Earth by sending sound energy down into the ground and recording the reflected sound energy that returns from the geological layers within the subsurface region. During a seismic survey, an energy source is placed at various locations on or above the surface region of the Earth, which may include hydrocarbon deposits. Each time the source is activated, the source generates a seismic (e.g., sound wave) signal that travels downward through the Earth, is reflected, and, upon its return, is recorded using one or more receivers disposed on or above the subsurface region of the Earth. The seismic data recorded by the receivers may then be used to create an image or profile of the corresponding subsurface region.

[0004] Often times, images generated from a seismic survey or other methods may contain surrounding clutter or may need to be enhanced in order to more precisely detect and identify important subsurface features in a complex environment (e.g., geological layers within the subsurface). Therefore, techniques that distinguish or accentuate objects or areas of interest within the seismic image of a subsurface region are useful in understanding the subsurface structure and improving geological interpretation.SUMMARY

[0005] In an embodiment, a method for enhancing one or more subsurface features represented in a seismic attribute of a subsurface region containing the one or more subsurface features comprises (a) receiving an initial seismic attribute of the subsurface region and that is based on seismic data captured by one or more seismic receivers and associated with the subsurface region; (b) determining one or more wavenumbers from the initial seismic attribute, wherein the initial seismic attribute is in a spatial domain; and (c) generating an enhanced seismic attribute from the initial seismic attribute using the one or more wavenumbers. In some embodiments, the method further comprises (d) generating an output image based on the one or more wavenumbers, the output image containing the one or more subsurface features. In certain embodiments, the method further comprises (e) performing an inverse Fourier transform on the enhanced seismic attribute to generate an output image in the spatial domain. In other embodiments, method (c) comprises determining a global derivative of the initial seismic attribute in a frequency-wavenumber domain. In some embodiments, determining the one or more wavenumbers comprises applying a directional derivative to the initial seismic attribute in a frequency-wavenumber domain. In certain embodiments, applying the directional derivative comprises computing a derivative normal to the one or more subsurface features. In other embodiments, the initial seismic attribute of the subsurface region comprises at least a two- dimensional (2-D) representation. In some embodiments, the method further comprises (f) applying a shaping filter to the enhanced seismic attribute to adjust an amplitude response of a directional directive of the enhanced seismic attribute. In certain embodiments, the shaping filter is reconfigurable between an activated state in which the shaping filter is configured to adjust the amplitude response of the directional derivative of the enhanced seismic attribute and a disabled state in which the shaping filter is not configured to adjust the amplitude response of the directional derivative. In other embodiments, the shaping filter comprises a self-adjusting shaping filter. In some embodiments, the method further comprises (g) applying a plurality of separate shaping filters having different frequency bands to adjust an amplitude response of the enhanced seismic attribute.

[0006] In an embodiment, a method for enhancing one or more subsurface features represented in a seismic attribute of a subsurface region containing the subsurface features comprises (a) receiving a spatial domain seismic attribute of the subsurfaceregion and that is based on seismic data captured by one or more seismic receivers and associated with the subsurface region; and (b) generating, from the spatial domain seismic attribute, a frequency-wavenumber domain seismic attribute in which a contrast of the one or more subsurface features has been increased. In some embodiments, the method further comprises (c) applying one or more filters to the frequency-wavenumber domain seismic attribute; and (d) generating an output image based on the frequency-wavenumber domain seismic attribute. In certain embodiments, the frequency-wavenumber domain seismic attribute comprises the one or more subsurface features which correspond to one or more predefined features present in the subsurface region. In other embodiments, (b) comprises computing a derivative normal to one or more predefined features present in the subsurface region. In some embodiments, (b) further comprises determining a global derivative of the spatial domain seismic attribute in a frequency-wavenumber domain.

[0007] In an embodiment, a system for enhancing one or more subsurface features represented in a seismic attribute of a subsurface region containing the subsurface features comprises a one or more processors; and a storage device coupled to the one or more processors, the storage device configured to store instructions that, when executed by the one or more processors, configure the one or more processors to (a) receive an initial seismic attribute of the subsurface region and that is based on seismic data captured by one or more seismic receivers and associated with the subsurface region; (b) determine one or more wavenumbers from the initial seismic attribute, wherein the initial seismic attribute is in a spatial domain; and (c) generate an enhanced seismic attribute from the initial seismic attribute using the one or more wavenumbers. In some embodiments, the system further comprises (d) generate an output image based on the wavenumbers, the output image containing the one or more subsurface features. In certain embodiments, the system further comprises (e) perform an inverse Fourier transform on the enhanced seismic attribute to generate an output image in the spatial domain. In other embodiments, (c) comprises determining a global derivative of the initial seismic attribute in a frequencywavenumber domain.

[0008] Embodiments described herein comprise a combination of features and characteristics intended to address various shortcomings associated with certain prior devices, systems, and methods. The foregoing has outlined rather broadly the features and technical characteristics of the disclosed embodiments in order that thedetailed description that follows may be better understood. The various characteristics and features described above, as well as others, will be readily apparent to those skilled in the art upon reading the following detailed description, and by referring to the accompanying drawings. It should be appreciated that the conception and the specific embodiments disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes as the disclosed embodiments. It should also be realized that such equivalent constructions do not depart from the spirit and scope of the principles disclosed herein.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Various aspects of this disclosure may be better understood upon reading the following detailed description and upon reference to the drawings in which:

[0010] FIG. 1 is a flow chart of various processes that may be performed based on analysis of seismic data acquired via a seismic survey system in accordance with principles disclosed herein;

[0011] FIG. 2 is an exemplary marine survey system in a marine environment in accordance with principles disclosed herein;

[0012] FIG. 3 is an exemplary land survey system in a land environment in accordance with principles disclosed herein;

[0013] FIG. 4 is an exemplary computing system that may perform operations described herein based on data acquired via the marine survey system of FIG. 2 and / or the land survey system of FIG. 3 in accordance with principles disclosed herein;

[0014] FIG. 5 is a flow diagram of an embodiment of an exemplary method for enhancing one or more subsurface features represented in a seismic attribute of a subsurface region containing the one or more subsurface features in accordance with principles disclosed herein;

[0015] FIG. 6 is a flow diagram of an embodiment of another exemplary method for enhancing one or more subsurface features represented in a seismic attribute of a subsurface region containing the one or more subsurface features in accordance with principles disclosed herein;

[0016] FIG. 7 is an exemplary graph illustrating amplitude responses of different exemplary frequency-wavenumber domain derivative operators across different wavenumbers in accordance with principles disclosed herein;

[0017] FIG. 8A illustrates an exemplary initial image in accordance with principles disclosed herein;

[0018] FIG. 8B illustrates an exemplary frequency-wavenumber domain image based on the initial image of FIG. 8A in accordance with principles disclosed herein;

[0019] FIG. 9A illustrates another exemplary initial image in accordance with principles disclosed herein; and

[0020] FIG. 9B illustrates another exemplary frequency-wavenumber domain image based on the initial image of FIG. 9A in accordance with principles disclosed herein;DETAILED DESCRIPTION

[0021] The following discussion is directed to various exemplary embodiments. However, one skilled in the art will understand that the examples disclosed herein have broad application, and that the discussion of any embodiment is meant only to be exemplary of that embodiment, and not intended to suggest that the scope of the disclosure, including the claims, is limited to that embodiment.

[0022] Certain terms are used throughout the following description and claims to refer to particular features or components. As one skilled in the art will appreciate, different persons may refer to the same feature or component by different names. This document does not intend to distinguish between components or features that differ in name but not function. The drawing figures are not necessarily to scale. Certain features and components herein may be shown exaggerated in scale or in somewhat schematic form and some details of conventional elements may not be shown in interest of clarity and conciseness.

[0023] Unless the context dictates the contrary, all ranges set forth herein should be interpreted as being inclusive of their endpoints, and open-ended ranges should be interpreted to include only commercially practical values. Similarly, all lists of values should be considered as inclusive of intermediate values unless the context indicates the contrary. Where numerical ranges or limitations are expressly stated, such express ranges or limitations should be understood to include iterative ranges or limitations of like magnitude falling within the expressly stated ranges or limitations (e.g., from about 1 to about 10 includes, 2, 3, 4, etc.; greater than 0.10 includes 0.11 , 0.12, 0.13, etc.).

[0024] In the following discussion and in the claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted tomean “including, but not limited to... .” The term “couple” or “couples” is intended to mean either an indirect or direct connection. Thus, if a first device couples to a second device, that connection may be through a direct engagement between the two devices, or through an indirect connection that is established via other devices, components, nodes, and connections. In addition, as used herein, the terms “axial” and “axially” generally mean along or parallel to a particular axis (e.g., central axis of a body or a port), while the terms “radial” and “radially” generally mean perpendicular to a particular axis. For instance, an axial distance refers to a distance measured along or parallel to the axis, and a radial distance means a distance measured perpendicular to the axis. As used herein, the terms “approximately,” “about,” “substantially,” and the like mean within 10% (i.e., plus or minus 10%) of the recited value. Thus, for example, a recited angle of “about 80 degrees” refers to an angle ranging from 72 degrees to 88 degrees.

[0025] One or more specific embodiments will be described below. In an effort to provide a concise description of these embodiments, not all features of an actual implementation are described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers’ specific goals, such as compliance with system-related and business- related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.

[0026] By way of introduction, seismic data may be acquired using a variety of seismic survey systems and techniques, two of which are discussed with respect to FIG. 2 and FIG. 3. Regardless of the seismic data gathering technique utilized, after the seismic data is acquired, a computing system may analyze the acquired seismic data and may use the results of the seismic data analysis (e.g., seismogram, map of geological formations, etc.) to perform various operations within the hydrocarbon exploration and production industries. For instance, FIG. 1 illustrates a flow chart of a method 10 that details various processes that may be undertaken based on the analysis of the acquired seismic data. Although the method 10 is described in a particular order, it should be noted that the method 10 may be performed in any suitable order.

[0027] Referring now to FIG. 1 , at block 12, locations and properties of hydrocarbon deposits within a subsurface region of the Earth associated with the respective seismic survey may be determined based on the analyzed seismic data. In one embodiment, the seismic data acquired may be analyzed to generate a map or profile that illustrates various geological formations within the subsurface region. Based on the identified locations and properties of the hydrocarbon deposits, at block 14, certain positions or parts of the subsurface region may be explored. That is, hydrocarbon exploration organizations may use the locations of the hydrocarbon deposits to determine locations at the surface of the subsurface region to drill into the Earth. As such, the hydrocarbon exploration organizations may use the locations and properties of the hydrocarbon deposits and the associated overburdens to determine a path along which to drill into the Earth, how to drill into the Earth, and the like.

[0028] After exploration equipment has been placed within the subsurface region, at block 16, the hydrocarbons that are stored in the hydrocarbon deposits may be produced via natural flowing wells, artificial lift wells, and the like. At block 18, the produced hydrocarbons may be transported to refineries and the like via transport vehicles, pipelines, and the like. At block 20, the produced hydrocarbons may be processed according to various refining procedures to develop different products using the hydrocarbons.

[0029] It should be noted that the processes discussed with regard to the method 10 may include other suitable processes that may be based on the locations and properties of hydrocarbon deposits as indicated in the seismic data acquired via one or more seismic survey. As such, it should be understood that the processes described above are not intended to depict an exhaustive list of processes that may be performed after determining the locations and properties of hydrocarbon deposits within the subsurface region.

[0030] With the foregoing in mind, FIG. 2 is a schematic diagram of a marine survey system 22 (e.g., for use in conjunction with block 12 of FIG. 1) that may be employed to acquire seismic data (e.g., waveforms) regarding a subsurface region of the Earth in a marine environment. Generally, a marine seismic survey using the marine survey system 22 may be conducted in an ocean 24 or other body of water over a subsurface region 26 of the Earth that lies beneath a seafloor 28.

[0031] The marine survey system 22 may include a vessel 30, one or more seismic sources 32, a (seismic) streamer 34, one or more (seismic) receivers 36, and / or otherequipment that may assist in acquiring seismic images representative of geological formations within a subsurface region 26 of the Earth. The vessel 30 may tow the seismic source(s) 32 (e.g., an air gun array) that may produce energy, such as sound waves (e.g., seismic waveforms), that is directed at a seafloor 28. The vessel 30 may also tow the seismic streamer 34 having a seismic receiver 36 (e.g., hydrophones) that may acquire seismic waveforms that represent the energy output by the seismic source(s) 32 subsequent to being reflected off of various geological formations (e.g., salt domes, faults, folds, etc.) within the subsurface region 26. Additionally, although the description of the marine survey system 22 is described with one seismic source 32 (represented in FIG. 2 as an air gun array) and one seismic receiver 36 (represented in FIG. 2 as a set of hydrophones), it should be noted that the marine survey system 22 may include multiple seismic sources 32 and multiple seismic receivers 36. In the same manner, although the above descriptions of the marine survey system 22 is described with one seismic streamer 34, it should be noted that the marine survey system 22 may include multiple streamers similar to seismic streamer 34. In addition, additional vessels 30 may include additional seismic source(s) 32, seismic streamer(s) 34, and the like to perform the operations of the marine survey system 22.

[0032] FIG. 3 is a block diagram of a land survey system 38 (e.g., for use in conjunction with block 12 of FIG. 1 ) that may be employed to obtain information regarding the subsurface region 26 of the Earth in a non-marine environment. The land survey system 38 may include a land-based seismic source 40 and land-based seismic receiver 44. In some embodiments, the land survey system 38 may include multiple land-based seismic sources 40 and one or more land-based seismic receivers 44 and 46. Indeed, for discussion purposes, the land survey system 38 includes a land-based seismic source 40 and two land-based seismic receivers 44 and 46. The land-based seismic source 40 (e.g., seismic vibrator) that may be disposed on a surface 42 of the Earth above the subsurface region 26 of interest. The land-based seismic source 40 may produce energy (e.g., sound waves, seismic waveforms) that is directed at the subsurface region 26 of the Earth. Upon reaching various geological formations (e.g., salt domes, faults, folds) within the subsurface region 26 the energy output by the land- based seismic source 40 may be reflected off of the geological formations and acquired or recorded by one or more land-based receivers (e.g., 44 and 46).

[0033] In some embodiments, the land-based seismic receivers 44 and 46 may be dispersed across the surface 42 of the Earth to form a grid-like pattern. As such, each land-based seismic receiver 44 or 46 may receive a reflected seismic waveform in response to energy being directed at the subsurface region 26 via the seismic source 40. In some cases, one seismic waveform produced by the seismic source 40 may be reflected off of different geological formations and received by different receivers. For example, as shown in FIG. 3, the seismic source 40 may output energy that may be directed at the subsurface region 26 as seismic waveform 48. A first seismic receiver 44 may receive the reflection of the seismic waveform 48 off of one geological formation and a second seismic receiver 46 may receive the reflection of the seismic waveform 48 off of a different geological formation. As such, the first seismic receiver 44 may receive a reflected seismic waveform 50 and the second seismic receiver 46 may receive a reflected seismic waveform 52.

[0034] Regardless of how the seismic data is acquired, a computing system (e.g., for use in conjunction with block 12 of FIG. 1 ) may analyze the seismic waveforms acquired by the seismic receivers 36, 44, 46 to determine seismic information regarding the geological structure, the location and property of hydrocarbon deposits, and the like within the subsurface region 26. FIG. 4 is a block diagram of an example of such a computing system 60 that may perform various data analysis operations to analyze the seismic data acquired by the seismic receivers 36, 44, 46 to determine the structure and / or predict seismic properties of the geological formations within the subsurface region 26.

[0035] Referring now to FIG. 4, the computing system 60 may include a communication component 62, a processor 64, memory 66, storage 68, input / output (I / O) ports 70, and a display 72. In some embodiments, the computing system 60 may omit one or more of the display 72, the communication component 62, and / or the input / output (I / O) ports 70. The communication component 62 may be a wireless or wired communication component that may facilitate communication between the seismic receivers 36, 44, 46, one or more databases 74, other computing devices, and / or other communication capable devices. In one embodiment, the computing system 60 may receive receiver data 76 (e.g., seismic data, seismograms, etc.) via a network component, the database 74, or the like. The processor 64 of the computing system 60 may analyze or process the receiver data 76 to ascertain various features regarding geological formations within the subsurface region 26 of the Earth.

[0036] The processor 64 may be any type of computer processor or microprocessor capable of executing computer-executable code. The processor 64 may also include multiple processors that may perform the operations described below. The memory 66 and the storage 68 may be any suitable articles of manufacture that can serve as media to store processor-executable code, data, or the like. These articles of manufacture may represent computer-readable media (e.g., any suitable form of memory or storage) that may store the processor-executable code used by the processor 64 to perform the presently disclosed techniques. Generally, the processor 64 may execute software applications that include programs that process seismic data acquired via receivers of a seismic survey according to the embodiments described herein.

[0037] With one or more embodiments, processor 64 can instantiate or operate in conjunction with one or more seismic inversion techniques. With another embodiment, the computing system 60 can be implemented by using neural networks. The one or more neural networks can be software-implemented or hardware-implemented. One or more of the neural networks can be a convolutional neural network.

[0038] The memory 66 and the storage 68 may also be used to store the data, analysis of the data, the software applications, and the like. The memory 66 and the storage 68 may represent non-transitory computer-readable media (e.g., any suitable form of memory or storage) that may store the processor-executable code used by the processor 64 to perform various techniques described herein. It should be noted that non-transitory merely indicates that the media is tangible and not a signal.

[0039] The I / O ports 70 may be interfaces that may couple to other peripheral components such as input devices (e.g., keyboard, mouse), sensors, input / output (I / O) modules, and the like. I / O ports 70 may enable the computing system 60 to communicate with the other devices in the marine survey system 22, the land survey system 38, or the like via the I / O ports 70.

[0040] The display 72 may depict visualizations associated with software or executable code being processed by the processor 64. In one embodiment, the display 72 may be a touch display capable of receiving inputs from a user of the computing system 60. The display 72 may also be used to view and analyze results of the analysis of the acquired seismic data to determine the geological formations within the subsurface region 26, the location and property of hydrocarbon deposits within the subsurface region 26, predictions of seismic properties associated with one or more wells in thesubsurface region 26, and the like. The display 72 may be any suitable type of display, such as a liquid crystal display (LCD), plasma display, or an organic light emitting diode (OLED) display, for example. In addition to depicting the visualization described herein via the display 72, it should be noted that the computing system 60 may also depict the visualization via other tangible elements, such as paper (e.g., via printing) and the like.

[0041] With the foregoing in mind, the present techniques described herein may also be performed using a supercomputer that employs multiple computing systems 60, a cloud-computing system, or the like to distribute processes to be performed across multiple computing systems 60. In this case, each computing system 60 operating as part of a super computer may not include each component listed as part of the computing system 60. For example, each computing system 60 may not include the display 72 since multiple displays 72 may not be useful to for a supercomputer designed to continuously process seismic data.

[0042] After performing various types of seismic data processing, the computing system 60 may store the results of the analysis in one or more databases 74. The databases 74 may be communicatively coupled to a network that may transmit and receive data to and from the computing system 60 via the communication component 62. In addition, the databases 74 may store information regarding the subsurface region 26, such as previous seismograms, geological sample data, seismic images, and the like regarding the subsurface region 26.

[0043] Although the components described above have been discussed with regard to the computing system 60, it should be noted that similar components may make up the computing system 60. Moreover, the computing system 60 may also be part of the marine survey system 22 or the land survey system 38, and thus may monitor and control certain operations of the seismic sources 32 or 40, the seismic receivers 36, 44, 46, and the like. Further, it should be noted that the listed components are provided as example components and the embodiments described herein are not to be limited to the components described with reference to FIG. 4.

[0044] In some embodiments, the computing system 60 may generate a two- dimensional representation or a three-dimensional representation of the subsurface region 26 based on the seismic data received via the receivers mentioned above. Additionally, seismic data associated with multiple source / receiver combinations may be combined to create a near continuous profile of the subsurface region 26 that canextend for some distance. In a two-dimensional (2-D) seismic survey, the receiver locations may be placed along a single line, whereas in a three-dimensional (3-D) survey the receiver locations may be distributed across the surface in a grid pattern. As such, a 2-D seismic survey may provide a cross sectional picture (vertical slice) of the Earth layers as they exist directly beneath the recording locations. A 3-D seismic survey, on the other hand, may create a data “cube” or volume that may correspond to a 3-D picture of the subsurface region 26.

[0045] In addition, a 4-D (or time-lapse) seismic survey may include seismic data acquired during a 3-D survey at multiple times. Using the different seismic images acquired at different times, the computing system 60 may compare the two images to identify changes in the subsurface region 26.

[0046] In any case, a seismic survey may be composed of a very large number of individual seismic recordings or traces. As such, the computing system 60 may be employed to analyze the acquired seismic data to obtain an image representative of the subsurface region 26 and to determine locations and properties of hydrocarbon deposits. To that end, a variety of seismic data processing algorithms may be used to remove noise from the acquired seismic data, migrate the pre-processed seismic data, identify shifts between multiple seismic images, align multiple seismic images, and the like.

[0047] After the computing system 60 analyzes the acquired seismic data, the results of the seismic data analysis (e.g., seismogram, seismic images, map of geological formations, etc.) may be used to perform various operations within the hydrocarbon exploration and production industries. For instance, as described above, the acquired seismic data may be used to perform the method 10 of FIG. 1 that details various processes that may be undertaken based on the analysis of the acquired seismic data.

[0048] Seismic imaging plays an important role in the exploration and characterization of subsurface structures such as hydrocarbon reservoirs, geological formations, and for monitoring potential hazards. However, seismic images derived from conventional imaging techniques may encounter various challenges that compromise the accuracy and reliability of the resulting images. For example, a seismic image derived from a velocity model may not fully capture all the features of a complex subsurface structure, especially in areas with lateral variation or lack the resolution required for accurate interpretation. Additionally, seismic images derived from seismic inversion (e.g., Fullwaveform inversion (FWI)-derived seismic image) may be susceptible to artifacts orrequire extensive computational resources and time in order to accurately represent the subsurface region and characterize the reservoir. Thus, oftentimes, it is necessary to enhance the contrast present within a seismic image or a map / grid representing the characteristics or attributes of the subsurface in order to improve or enhance visual and / or quantitative interpretation of seismic data. As used herein, the term “seismic attribute” refers to measurable properties (e.g., polarity, phase, frequency, velocity) of seismic data associated with a selected subsurface region. Seismic attributes may vary in dimensionality and thus may comprise, for example, a two-dimensional (2D) or three-dimensional (3D) seismic image. In addition, the term “attribute volume” or “volume attribute” as used herein refers to measurable properties of seismic data that can be determined or estimated for a 3D seismic volume.

[0049] More particularly, there are various seismic data inversion methods employed in geophysics including, for example, FWI techniques. These methods are particularly valuable in hydrocarbon exploration for structurally characterizing or describing subsurface regions (e.g., subsurface reservoirs) and identifying fluid content or other salient subsurface contained therein. For example, FWI is an iterative technique for modelling subsurface regions using seismic data in which seismic data collected from seismic nodes or sensors is iteratively compared with modeled or synthetic seismic waveforms (e.g., produced by a model such as a velocity model of the subsurface region) to minimize the misfit between the observed seismic data and the synthetic seismic data generated by the model. This iterative process adjusts the parameters of the model (e.g., velocity and density parameters) until the modeled waveforms closely match the observed data across different frequencies and arrival times.

[0050] Conventionally, FWI and similar iterative subsurface modeling techniques have employed a velocity model of the subsurface region which may be utilized in subsequent analysis and investigation. However, as FWI techniques have progressed and matured, the accuracy and precision of the subsurface model (e.g., velocity model) produced by FWI and related techniques are sufficiently great such that seismic images representing the subsurface region may be generated directly from the FWI-produced subsurface model. Analysis of the obtained seismic image can provide valuable information, such as the location and / or change of hydrocarbon deposits within a subsurface region of the Earth.

[0051] In some applications, seismic images, including seismic images generated by subsurface models such as velocity models, are used to enhance one or moresubsurface features of the subsurface region. As used herein, the term “subsurface features” refers to structural features of the subsurface region identifiable in images (e.g., seismic images) thereof. Conventionally, seismic imaging has comprised a two- step process: First, a velocity model of the subsurface region is constructed. Then, seismic traces obtained from a seismic survey of the subsurface region are used along with the constructed velocity model to generate a seismic image of the subsurface structure using one or more imaging or migration algorithms. Recently, as FWI processes have advanced in their detail and accuracy in modeling subsurface regions, velocity models generating using FWI processes possess sufficient accuracy and detail such that seismic images may be taken directly from the FWI-derived velocity model without the need for utilizing one or more imaging or migration algorithms in generating the seismic image in at least some applications.

[0052] In some embodiments, the contrast or resolution in a seismic attribute such as a velocity model (e.g., an FWI-derived velocity model from which seismic images may be directly obtained) may be selectably increased by using a derivative operator in order to further characterize or enhance one or more selected subsurface features captured in the velocity model. As an example, applying a derivative operator to the velocity model of a subsurface region may provide valuable insights into the subsurface geological structure (e.g., discontinuities present in the velocity model of the subsurface region (representing specific geological structures of the subsurface region) which have been enhanced or highlighted through the use of the derivative operator on the velocity model) and aid in the interpretation and analysis of seismic data by highlighting the changes and / or differences within the subsurface region.

[0053] In some applications, the derivative of the volume or velocity model that is taken, is a directional derivative which represents the instantaneous change of a selected function (e.g., the velocity model or other seismic data) in a selected direction (e.g., increasing or decreasing seismic velocity or other property of the subsurface region) at a given point (e.g., a given point in space). As an example, the rate of change of seismic properties across a velocity model (or a selected portion of the velocity model). In this example, taking a vertical directional derivative along the Y- axis of the velocity model will highlight features that are flat or horizontal but not other features such as vertical features given that such vertical features do not change rapidly along the Y-axis. In another example, a directional derivative may be taken to highlight or detect edges, faults, etc., and / or other discontinuities captured in thevelocity model. Although directional derivatives along the coordinate axes X, Y, and Z may be used, it is often desirable to compute the directional derivative normal to a selected subsurface feature also referred to herein as the “normal derivative.” For instance, it may be desired to determine the normal derivative of the dominant structure in a velocity model since the direction normal to the structure is the direction of locally maximum contrast.

[0054] For example, subsurface features in the form of “dips” or “folds” formed between adjacent rock layers of the subsurface region may be of particular interest when exploring for hydrocarbon deposits in the subsurface region, especially with respect to their directional derivatives. Thus, in some instances, it is desirable to determine the directional derivatives of such dips or other subsurface features using velocity models representing the subsurface region. Conventionally, it is necessary in order to determine the normal derivative to first estimate the orientation or input dip field of the structure and then compute the directional directive. However, estimating the dip field and then computing the normal derivative may be computationally intensive and less accurate especially when the dip is not apparent or in situations where there are conflicting or multiple dips at the same spatial location. Accordingly, the methods and systems disclosed herein, leverage properties of Fourier transformation by converting the spatial domain representation of the velocity model into a frequency-wavenumber domain representation and computing the derivative in frequency-wavenumber domain. Particularly, the methods and systems disclosed herein do not require an input dip field and thus, allows for faster, more efficient and more accurate interpretation of seismic data without relying on a supplied dip value or the need to first estimate the dip field. As used herein, the term “spatial domain” refers to the representation of a signal or function in terms of its amplitude varying with spatial coordinates (e.g., time or physical space). Additionally, as used herein, the term “frequency-wavenumber domain” refers to, on the other hand, the same signal or function in terms of its frequency components, obtained through Fourier transform, facilitating analysis in terms of frequency content rather than spatial variation.

[0055] Determining the input dip field (or other structural elements captured in an FWI- derived velocity model from which seismic images may be directly obtained without the need for migration or similar processes) is often both time intensive for relevant personnel involved and computationally intensive on the computer systems tasked with carrying out such modeling. Given that the determination of the input dip fieldgreatly increases the complexity, time, and costs associated with determining the normal derivative of the dip, such techniques (e.g., conventional techniques for determining the normal derivative of the dip) are limited in their applicability.

[0056] Embodiments disclosed herein generally relate to methods and systems that may be used to improve or enhance identification and characterization of subsurface features of interest in a subsurface region. Particularly, embodiments disclosed herein allow for more timely, efficient (both computationally and in terms of utilizing the time of relevant personnel), and accurate quantitative characterization of subsurface features without requiring directly characterizing or modelling the structure of the subsurface region (e.g., estimating the dip field for a dip present in the subsurface region). Instead, embodiments disclosed herein include applying Fourier analysis to a seismic attribute of a subsurface region whereby structural features or elements of one or more selected subsurface features captured in the seismic attribute may be automatically extracted by increasing or enhancing the contrast of discontinuous present in the seismic attribute and which may represent one or more geological subsurface features of interest of the subsurface region. In this manner, the selected subsurface features become more prominent and distinguishable, making the seismic data easier to interpret and understand. In at least some instances, the structural elements associated with the subsurface feature of interest is directly (e.g., visually) observable in the given seismic image where the subsurface feature may not be directly observable and instead may be determined or determined from the observable structural elements. Additionally, embodiments disclosed herein allow for enhancement of the contrast present within a seismic attribute of the subsurface region by implementing a directional derivative operator in the frequency-wavenumber domain which enables the identification and characterization of subsurface features of interest (e.g., faults, fractures, stratigraphic boundaries etc.) based on their frequency and spatial signatures.

[0057] By eliminating the need to pre-characterize structural elements associated with or from which one or more subsurface features of interest arise (e.g., a directional directive of a dip), the process for characterizing such subsurface features associated with those structural elements (e.g., a directional derivative of a dip that arises from the structural elements of the dip) may be streamlined whereby the time, labor, and costs involved are substantially reduced such that embodiments disclosed herein may be used in a far broader range of applications than conventional techniques which areless timely, less accurate, and potentially more costly. More particularly, by not having to estimate the geometry of a subsurface feature of interest (e.g., a dip field of a dip present in the subsurface region), the resulting seismic attribute (e.g., an FWI-derived velocity model or seismic image obtained therefrom) may be more detailed and accurate, especially when conflicting or multiple structural dips are present in the subsurface region.

[0058] As previously discussed, FWI may be used to estimate a velocity model and derive a seismic image therefrom. Such seismic images can be represented in the form of a 2D) or 3D seismic image, and used to derive seismic volumes, such as bandlimited reflectivity and elastic impedance. Additionally, seismic images can be used to estimate subsurface elastic parameters (e.g., p-wave and s-wave velocities, density, etc.) from processed seismic data. Furthermore, seismic images can be used in computing elastic moduli and inferring other petrophysical properties, such as fluid and lithology types. However, in certain instances, the seismic image may not be sufficient for direct geological interpretation and reservoir characterization as will be discussed further herein. Thus, a velocity model (e.g., an FWI-derived velocity model) of a subsurface region may be enhanced, in accordance with the principles disclosed herein, using a derivative operator to more accurately represent the structural features in a subsurface region. The specific embodiments described herein allow for improved quality and resolution of seismic images by implementing a normal derivative operator in the frequency-wavenumber domain which enables a more accurate and computationally efficient method for the identification and characterization of subsurface features of a subsurface region.

[0059] Referring now to FIG. 5, an embodiment of a method 100 for enhancing one or more subsurface features represented in a seismic attribute (e.g., a velocity model such as an FWI-derived velocity model, a seismic image) of a subsurface region containing the one or more subsurface features is shown. Initially, at block 102, method 100 comprises receiving an initial seismic attribute of the subsurface region that is based on seismic data captured by one or more seismic receivers and associated with the subsurface region. In some embodiments, the seismic attribute of method 100 comprises an attribute volume of the subsurface region.

[0060] As previously described, the seismic data may be collected by generating seismic waves at the earth’s surface (e.g., the seismic waveform shown in FIG. 2 and seismic waveform 48 shown in FIG. 3) and recording the reflections. The seismicwaves may be generated by one or more land- or offshore-based seismic sources (e.g., seismic sources 32 shown in FIG. 2 and seismic sources 40 shown in FIG. 3) and the reflections may be recorded by one or more land- or offshore-based seismic receivers (e.g., seismic receivers 36 shown in FIG. 2, seismic receivers 44 and 46 illustrated in FIG. 3). Once the seismic data is processed, it is used to generate the seismic attribute of the subsurface region. For example, a computing system (e.g., computing system 60 shown in FIG. 4) may be used to create a velocity model (e.g., an FWI-derived velocity model) of the subsurface region from seismic data collected by one or more seismic sources. The velocity model may be generated from the collected seismic data using various seismic data processing techniques such as, for example, FWI, Reverse Time Migration (RTM), or other inversion techniques depending on the requirements of the given application.

[0061] At block 104, method 100 comprises determining or extracting one or more wavenumbers from the initial seismic attribute. The wavenumbers of the seismic attribute represent the frequency content (e.g., the spacial frequencies) of the seismic attribute, which can reveal important details contained within the seismic attribute (e.g., regarding subsurface features) along different orientations.

[0062] In this exemplary embodiment, Fourier analysis is leveraged by method 100 for enhancing the characterization of one or more selected subsurface features in the seismic attribute (e.g., an attribute volume, a velocity model such as a FWI-derived velocity model) of the subsurface region In other words, at block 104 in some embodiments, the data defining the seismic attribute is placed in the frequencywavenumber domain whereby the data is decomposed into constituent frequency components where each wavenumber corresponds to the rate of change of the phase of a sinusoidal component along a given spatial dimension. This allows the seismic attribute (e.g., the velocity model) to be analyzed in terms of the spatial frequencies (wavenumbers). Generally, embodiments of Fourier analysis disclosed herein includes applying a Fourier transform to a velocity model (e.g., the spatial domain data defining the velocity model) which converts an initial or spatial velocity model from the spatial domain to a second or frequency velocity model in the frequency or frequency-wavenumber (f-k) domain, allowing the derivative of the velocity model to be performed in the frequency-wavenumber domain (e.g., to be performed on the frequency velocity model) without explicitly requiring or supplying an input dip field and the like.

[0063] At block 106, method 100 comprises generating an enhanced seismic attribute from the initial seismic attribute using the one or more wavenumbers. In this exemplary embodiment, the directional derivative is computed globally along different directions corresponding to the different wavenumbers (e.g., determined at block 104). The derivative may be taken in the frequency-wavenumber or frequency (and not in the spatial domain) by combining the different wavenumbers together through the convenient means of multiplying the wavenumbers. Through implementing the derivative operation in the frequency-wavenumber domain, a directional derivative may be taken normal to the direction of features of interest whereby edges, faults, etc., and / or other discontinuities captured in the seismic attribute may be highlighted or detected. Given that the derivative operator when conveniently implemented in the frequency-wavenumber domain automatically or inherently highlights discontinuous in a given function (e.g., within a given seismic attribute), by applying the derivative operator in the frequency-wavenumber domain, the dip or normal derivative of a subsurface discontinuity may be automatically calculated without first requiring the input dip field. Thus, the dip field is implicitly computed in the desired direction normal to a feature of interest frequency-wavenumber domain irrespective of the orientation of the features in an initial or unenhanced seismic attribute. In this approach, the operation is not constrained to a particular dip field, instead it evaluates all possible orientations in the frequency-wavenumber domain. Only the orientations that exist in the unenhanced seismic attribute (e.g., the initial or unenhanced velocity model or seismic image obtained therefrom), indicated by wavenumbers with significant energy, will contribute to the resulting final or enhanced seismic attribute (e.g., the final or enhanced velocity model or seismic image obtained therefrom) generated from the initial seismic attribute through applying the derivative operator to the seismic attribute in the frequency-wavenumber domain. The energy in these wavenumbers represents the strength of the features in the initial seismic attribute, ensuring that only relevant orientations influence the enhanced seismic attribute. In some embodiments, the enhanced seismic attribute comprises the directional derivative of the unenhanced seismic attribute.

[0064] For example, and not intending to be bound by any particular theory, a derivative of a function f(x, y, z) along an arbitrary unit vector u = (ux, uy, uz) can be expressed in accordance with Equation (1 ) as follows:

[0065] In frequency-wavenumber domain, and again not intending to be bound by any particular theory, the derivative operator can be expressed in accordance with Equation (2) as follows, where, kx, ky, and kzrepresent the corresponding wavenumbers:

[0066] If n is the unit vector representing the dip or the orientation for a given combination of wavenumbers, and again not intending to be bound by any particular theory, n can be defined in accordance with Equation (3) presented below:

[0067] Combining the equations above, and again not intending to be bound by any particular theory, the derivative in frequency-wavenumber domain can be expressed in accordance with Equation (4) as follows:

[0068] In some embodiments, method 100 comprises enhancing the one or more subsurface features using the directional derivative. In this instance, the rate of change of the seismic data in different directions may be analyzed and quantified to reveal gradients, discontinuities, or spatial variations indicative of subsurface features. For example, in areas with geological faults or folds, the directional derivative may show changes along the direction of those features, highlighting their presence in the subsurface region. In another example, the direction of the normal vector of a “dip” may be enhanced using the directional derivative. Other examples include identifying hydrocarbon reservoirs, boundaries, faults, stratigraphic layers, etc.

[0069] Additionally, in some embodiments, the method described herein includes generating an output or enhanced seismic image from the enhanced seismic attribute (in the form of a velocity model in the phase-wavenumber domain) by performing an inverse Fourier transform of the enhanced seismic attribute to convert the frequency-wavenumber seismic attribute back to spatial domain in the form of a seismic image. Further, in some embodiments, at least some of the blocks of method 100 (e.g., blocks 102, 104, and 106) are implemented or facilitated by a computing system, such as the computing system 60 shown in FIG. 4.

[0070] Referring now to FIG. 6, an embodiment of another method 150 for enhancing one or more subsurface features represented in a seismic attribute of a subsurface region containing the one or more subsurface features is shown. Similar to block 102 of Fig. 5, method 150 begins at block 152 with receiving a spatial domain seismic attribute of the subsurface region that is based on seismic data captured by one or more seismic receivers (e.g., seismic receivers 36 shown in FIG. 2, seismic receivers 44 and 46 illustrated in FIG. 3) and associated with the subsurface region. As previously described, the seismic attribute may be generated from the collected seismic data using various seismic data processing techniques such as, for example, FWI, Reverse Time Migration (RTM), or other inversion techniques depending on the requirements of the given application.

[0071] At block 154, method 150 comprises generating, from the spatial domain seismic attribute, a frequency-wavenumber domain seismic attribute in which a contrast of the one or more subsurface features have been increased. In some embodiments, generating the frequency-wavenumber domain seismic attribute comprises applying a Fourier transform to the spatial domain seismic attribute which converts the seismic attribute from the spatial domain to the frequency-wavenumber domain, and performing a global directional derivative of the frequency-wavenumber domain seismic attribute in accordance with Equations (1 )-(4) above. In some embodiments, an output seismic image in the spatial domain may be generated from the frequency-wavenumber domain seismic image by performing an inverse Fourier transform of the directional derivative. Additionally, in some embodiments, at least some of the blocks of method 150 (e.g., blocks 152, and / or 154) are implemented or facilitated by a computing system, such as the computing system 60 shown in FIG. 4.

[0072] In at least some instances, the presence of artifacts in the seismic data and / or other issues regarding the quality of the seismic data can significantly impact the accuracy and reliability of the directional derivative computation. For instance, in some embodiments, the amplitude response of the frequency-wavenumber domain derivative operator (e.g., the derivative operator shown in Equation (4) above may be shaped or filtered to avoid the resulting presence of artifacts in the frequency-wavenumber domain seismic image and thereby improve the quality of the output seismic image. For example, an adaptive filtering technique which uses a plurality or combination of different shaping filters having different frequency bands. In some embodiments, the plurality of shaping filters comprises adaptive filters having self- adjusting characteristics. For instance, each adaptive filter may comprise one or more filter coefficients which the self-adjusting filter may adjust automatically (e.g., via an adaptive module or algorithm of the adaptive filter) to adapt an input signal received by the adaptive filter. This combination of adaptive filters may in concert filter out concurrently several different types of artifacts, noise, and other interference associated with different frequency ranges. For example, the plurality of adaptive filters may include both narrowband and wideband adaptive filters) to selectively remove both narrowband interference at low frequencies and broadband noise at higher frequencies

[0073] Referring now to FIG. 7 an exemplary graph 700 illustrating the amplitude response of different exemplary frequency-wavenumber domain derivative operators across different wavenumbers in accordance with principles disclosed herein is shown. Particularly, graph 700 is a plot of amplitude spectrum on the y-axis thereof against wavenumber on the x-axis thereof. In this instance, amplitude spectrum and wavenumber are normalized and thus dimensionless. The amplitude spectrum represents the magnitude of the frequency components in the seismic data while the wavenumber represents the spatial frequencies present in the seismic data.

[0074] In this example, graph 700 plots amplitude response for a frequencywavenumber domain derivative operator without any amplitude shaping (unshaped response 701) applied. In addition, graph 700 plots amplitude response for a frequency-wavenumber domain derivative operator with some or limited amplitude shaping (limited shaping response 702) applied. Further, graph 700 plots amplitude response for a frequency-wavenumber domain derivative operator with heavy or extensive amplitude shaping (extensive shaping response 703) applied that is greater in magnitude or scope than the limited shaping.

[0075] As shown in graph 700, in this example, unshaped response 701 shows a sharp increase to the peak and a sharp decrease to the trough which may not accurately represent the actual frequency content and spatial frequencies present in the illustrated seismic data. By applying some shaping (limited shaping response 702) and / or heavy shaping (extensive shaping response 703), a more accuraterepresentation of the actual response may be captured having filtered some of the narrowband interference and broadband noise from the response.

[0076] In some embodiments, a combination of two shaping filters may be applied to the frequency-wavenumber domain derivative operator based on prior knowledge of the user. The first shaping filter may be configured to shape the low frequency end of the amplitude response of the derivative operator while the second filter may be alternatively configured to shape the amplitude response of the derivative operator around the “Nyquist” frequency (e.g., half the sampling rate).

[0077] Not intending to be bound by any particular theory, the first or low frequency shaping filter may be expressed in accordance with Equation (5) below where k represents the wavenumber, and ai, a2, bi, and £>2 each represent user defined filter parameters:

[0078] Again, not intending to be bound by any particular theory, in some embodiments, the second or Nyquist shaping filter may be configured in accordance with Equation (6) below:

[0079] In this example, the user defined parameters a? and a? dictate the shape of the filter, and bi and b2 determine if the filter will be applied or not. In other words, parameters bi and b2 determine whether the respective shaping filter is in an activated state in which the shaping filter will be applied to the derivative operator and a disabled state in which the shaping filter is not applied to the derivative operator. For example, if either bi or b2 is set to zero, the respective shaping filter response reduces to multiplication by 1 and thus has no effect. In some embodiments, the filter parameters may be selected by or via the adaptive algorithm of the adaptive filter.

[0080] Referring now to Figs. 8A and 8B, images 800 and 810 are shown illustrating an exemplary implementation of the methods 100 and / or 150 of Figs. 5 and 6, respectively, in accordance with principles disclosed herein is shown. Particularly, Figs. 8A and 8B illustrate the output from method 100 when there are multiple or conflicting dips where image 800 comprises an initial image 800 while image 810comprises a frequency-wavenumber domain image that is generated from or based on initial image 800 using method 100 and / or 150. Initial image 800 shows an image of a seismic velocity model, and frequency-wavenumber domain image 810 shows the same image after applying the directional derivative in the frequency-wavenumber domain using method 100 and / or 150. In this example, the y-axis and x-axis in Fig. 8A and Fig. 8B are in units of sample numbers. The sample numbers represent specific regions or areas in the image.

[0081] To highlight differences or contrast, a derivative may be taken along the edges (e.g., a directional derivative perpendicular or normal to the edges of the image). However, as can be seen in Fig. 8A, the orientation of the features of the seismic velocity model (e.g., velocity anomalies, velocity discontinuities) changes rapidly and appears random throughout the image. Thus, a derivative operator that is similarly capable of automatically changing in direction to follow the rapid and random changes present in the initial image 800 is needed in order to conveniently (e.g., without needing to characterize a field for the features shown in initial image 800) compute the derivative in the direction perpendicular to these features. Conventionally, the orientations (dip) of each feature is first estimated and used to design a mathematical operator for determining the derivative normal to the direction of each feature, making the process generally less accurate and substantially more computationally expensive limiting its applicability. Using the methods and systems disclosed herein, the image of the seismic velocity model in Fig. 8A was transformed to the frequency-wavenumber domain and the derivative operator applied to the transformed image without the need to explicitly determine the dip for each feature. As seen in Fig. 8B, the contrast in the image is highlighted and all the features of the seismic velocity model are enhanced.

[0082] Referring now to Figs. 9A and 9B, another exemplary implementation of the methods of Figs. 5 and 6 is shown in accordance with principles disclosed herein is shown. Particularly, Fig. 9A illustrates an initial image 900 of an exemplary office space and Fig. 9B illustrates a frequency-wavenumber domain image 910 that is generated from or based on initial image 900 shown in Fig. 9A using methods 100 and / or 150. In this example, the y-axis and x-axis in Fig. 9A and Fig. 9B are in units of sample numbers. The sample numbers represent specific regions or areas in the image. As seen in Fig. 9B, the contour or shape of the laptop computer, charger, wall, etc., in the office space are highlighted indicating the contrast within the image as enhanced by taking a global derivative of the image of Fig. 9A.

[0083] While preferred embodiments have been shown and described, modifications thereof can be made by one skilled in the art without departing from the scope or teachings herein. The embodiments described herein are exemplary only and are not limiting. Many variations and modifications of the systems, apparatus, and processes described herein are possible and are within the scope of the disclosure. For example, the relative dimensions of various parts, the materials from which the various parts are made, and other parameters can be varied. Accordingly, the scope of protection is not limited to the embodiments described herein, but is only limited by the claims that follow, the scope of which shall include all equivalents of the subject matter of the claims. Unless expressly stated otherwise, the steps in a method claim may be performed in any order. The recitation of identifiers such as (a), (b), (c) or (1 ), (2), (3) before steps in a method claim are not intended to and do not specify a particular order to the steps, but rather are used to simplify subsequent reference to such steps.

[0084] Each and every claim is incorporated into the specification as an aspect of the present disclosure. Thus, the claims are a further description and are an addition to the aspects of the present invention. The discussion of a reference herein is not an admission that it is prior art to the presently disclosed subject matter, especially any reference that may have a publication date after the priority date of this application. The disclosures of all patents, patent applications, and publications cited herein are hereby incorporated by reference, to the extent that they provide exemplary, procedural or other details supplementary to those set forth herein.

[0085] The techniques presented and claimed herein are referenced and applied to material objects and concrete examples of a practical nature that demonstrably improve the present technical field and, as such, are not abstract, intangible or purely theoretical. Further, if any claims appended to the end of this specification contain one or more elements designated as “means for [perform]ing [a function]...” or “step for [performing [a function]...”, it is intended that such elements are to be interpreted under 35 U.S.C. 112(f). However, for any claims containing elements designated in any other manner, it is intended that such elements are not to be interpreted under 35 U.S.C. 112(f).

Claims

CLAIMSWhat is claimed is:

1. A method for enhancing one or more subsurface features represented in a seismic attribute of a subsurface region containing the one or more subsurface features, the method comprising:(a) receiving an initial seismic attribute of the subsurface region and that is based on seismic data captured by one or more seismic receivers and associated with the subsurface region;(b) determining one or more wavenumbers from the initial seismic attribute, wherein the initial seismic attribute is in a spatial domain; and(c) generating an enhanced seismic attribute from the initial seismic attribute using the one or more wavenumbers.

2. The method of claim 1 , further comprising:(d) generating an output image based on the one or more wavenumbers, the output image containing the one or more subsurface features.

3. The method of claim 1 , further comprising:(e) performing an inverse Fourier transform on the enhanced seismic attribute to generate an output image in the spatial domain.

4. The method of claim 1 , wherein (c) comprises determining a global derivative of the initial seismic attribute in a frequency-wavenumber domain.

5. The method of claim 1 , wherein determining the one or more wavenumbers comprises applying a directional derivative to the initial seismic attribute in a frequency-wavenumber domain.

6. The method of claim 5, wherein applying the directional derivative comprises computing a derivative normal to the one or more subsurface features.

7. The method of claim 1 , wherein the initial seismic attribute of the subsurface region comprises at least a two- dimensional (2-D) representation.

8. The method of claim 1 , further comprising:(f) applying a shaping filter to the enhanced seismic attribute to adjust an amplitude response of a directional directive of the enhanced seismic attribute.

9. The method of claim 8, wherein: determining the one or more wavenumbers comprises applying a directional derivative to the initial seismic attribute in a frequency-wavenumber domain; and the shaping filter is reconfigurable between an activated state in which the shaping filter is configured to adjust the amplitude response of the directional derivative of the enhanced seismic attribute and a disabled state in which the shaping filter is not configured to adjust the amplitude response of the directional derivative.

10. The method of claim 8, wherein the shaping filter comprises a self-adjusting shaping filter.11 . The method of claim 1 , further comprising:(g) applying a plurality of separate shaping filters having different frequency bands to adjust an amplitude response of the enhanced seismic attribute.

12. A method for enhancing one or more subsurface features represented in a seismic attribute of a subsurface region containing the subsurface features, the method comprising:(a) receiving a spatial domain seismic attribute of the subsurface region and that is based on seismic data captured by one or more seismic receivers and associated with the subsurface region; and(b) generating, from the spatial domain seismic attribute, a frequencywavenumber domain seismic attribute in which a contrast of the one or more subsurface features has been increased.

13. The method of claim 12, further comprising:(c) applying one or more filters to the frequency-wavenumber domain seismic attribute; and(d) generating an output image based on the frequency-wavenumber domain seismic attribute.

14. The method of claim 12, wherein the frequency-wavenumber domain seismic attribute comprises the one or more subsurface features which correspond to one or more predefined features present in the subsurface region.

15. The method of claim 12, wherein (b) comprises computing a derivative normal to one or more predefined features present in the subsurface region.

16. The method of claim 12, wherein (b) comprises determining a global derivative of the spatial domain seismic attribute in a frequency-wavenumber domain.

17. A system for enhancing one or more subsurface features represented in a seismic attribute of a subsurface region containing the subsurface features, the system comprising: a one or more processors; and a storage device coupled to the one or more processors, the storage device configured to store instructions that, when executed by the one or more processors, configure the one or more processors to:(a) receive an initial seismic attribute of the subsurface region and that is based on seismic data captured by one or more seismic receivers and associated with the subsurface region;(b) determine one or more wavenumbers from the initial seismic attribute, wherein the initial seismic attribute is in a spatial domain; and(c) generate an enhanced seismic attribute from the initial seismic attribute using the one or more wavenumbers.

18. The system of claim 17, further comprising:(d) generate an output image based on the wavenumbers, the output image containing the one or more subsurface features.

19. The system of claim 17, further comprising:(e) perform an inverse Fourier transform on the enhanced seismic attribute to generate an output image in the spatial domain.

20. The system of claim 17, wherein (c) comprises determining a global derivative of the initial seismic attribute in a frequency-wavenumber domain.

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