Data-driven separation of downgoing free-surface multiples for seismic imaging

Abstract

A method includes receiving seismic data including signals collected using a receiver, separating an upgoing wavefield in the signals from a downgoing wavefield, generating a modified downgoing wavefield by removing direct arrivals from the downgoing wavefield, estimating at least in part a first-order multiple reflection signal by deconvolving the modified downgoing wavefield and the downgoing wavefield, and generating a seismic image based at least in part on the estimated first-order multiple reflection signal.

Description

[0001] Cross-referencing relevant applications

[0002] This application claims priority to U.S. Provisional Patent Application Serial No. 62 / 979,460, filed February 21, 2020. The entire contents of that provisional application are incorporated herein by reference. Background Technology

[0003] Seafloor seismometers (OBS), seafloor cables (OBC), and seafloor nodes (OBN) refer to types of marine seismic data acquisition systems. In these systems, seismic receivers (hydrophones, seismographs, etc.) are typically located on or near the seabed, with the seismic source emitting shock waves downwards from a relatively shallow depth. This technology is often used to create records in relatively quiet environments (compared to towed cables) and / or where obstacles may make towed cable operations difficult.

[0004] In this context, and in other marine and terrestrial scenarios, seismic data may include a combination of several signals. Typically, a "primary" reflection is the signal that is being attempted to be extracted from the recorded signal. A primary wave represents a seismic wave reflected from a subsurface reflector (usually an interface between two types of rock) and then detected by the receiver as it returns upwards. Another signal that may be present is a direct wave. In the case of OBS / OBC / OBN, this can typically be a signal that travels directly from the source through water to the receiver without being reflected. Seismic signals often also include multiple reflections (or simply "multiples"). Multiple reflections occur when a descending seismic wave is reflected by a reflector, but is reflected at least once more by a second reflector before finally reaching the receiver. The second reflector can be subsurface or can be a "free surface," such as the ocean surface. Multiple reflections are generally considered a form of noise and have been the subject of many different techniques to identify and remove them from seismic data, for example, without removing the desired primary wave signal.

[0005] Recently, it has been recognized that multiples also contain supplementary information about subsurface reflectivity. Therefore, separating multiples and primary waves has been used for imaging primary waves, but now multiples are also imaged separately. Imaging multiples has shown value in enhancing primary imaging, especially in areas with poor illumination from primary waves. Mirror offset, commonly used for OBS data, is an example of imaging first-order (i.e., reflections from the free surface) receiver-side down-current multiples. Recently, the value of imaging higher-order multiples has also been demonstrated. Summary of the Invention

[0006] Embodiments of this disclosure provide a method comprising receiving seismic data including signals collected using a receiver, separating a downlink wavefield from an uplink wavefield in the signals, generating a modified downlink wavefield by removing direct waves from the downlink wavefield, estimating at least partially a first-order multiple reflection signal by deconvolving the modified downlink wavefield and the downlink wavefield, and generating a seismic image at least partially based on the estimated first-order multiple reflection signal.

[0007] Embodiments of this disclosure also provide a non-transitory computer-readable medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations. These operations include receiving seismic data comprising signals collected using a receiver, separating a downlink wavefield from an uplink wavefield in the signals, generating a modified downlink wavefield by removing direct waves from the downlink wavefield, at least partially estimating a first-order multiple reflection signal by deconvolving the modified downlink wavefield and the downlink wavefield, and generating a seismic image at least partially based on the estimated first-order multiple reflection signal.

[0008] Embodiments of this disclosure also provide a computing system including one or more processors and a storage system including one or more non-transitory computer-readable media containing one or more stored instructions that, when executed by at least one processor of the computing system, cause the computing system to perform operations. These operations include receiving seismic data comprising signals collected using a receiver, separating a downlink wavefield from an uplink wavefield in the signals, generating a modified downlink wavefield by removing direct waves from the downlink wavefield, at least partially estimating a first-order multiple reflection signal by deconvolving the modified downlink wavefield and the downlink wavefield, and generating a seismic image at least partially based on the estimated first-order multiple reflection signal.

[0009] Therefore, the computational systems and methods disclosed herein offer more efficient methods for processing collected data, which may correspond, for example, to surface and underground areas. These computational systems and methods improve the effectiveness, efficiency, and accuracy of data processing. These methods and computational systems can complement or replace traditional methods used for processing collected data. This overview is provided to introduce the selection of concepts that will be further described in the detailed description below. This overview is not intended to identify key or essential features of the claimed subject matter, nor is it intended to help limit the scope of the claimed subject matter. Attached Figure Description

[0010] The accompanying drawings, which are included in and form a part of this specification, illustrate embodiments of the teachings and, together with the specification, serve to explain the principles of the teachings. In the drawings:

[0011] Figure 1A , 1BFigures 1C, 1D, 2, 3A, and 3B show simplified schematic diagrams of an oil field and its operation according to one embodiment.

[0012] Figure 4 A flowchart of a method for seismic imaging of one or more downlink multiple reflection signals according to an embodiment is shown.

[0013] Figure 5 A flowchart of a method for seismic imaging of one or more downlink multiple reflection signals according to an embodiment is shown.

[0014] Figure 6A An environment with a first-order downlink signal is shown according to one embodiment.

[0015] Figure 6B An environment according to one embodiment is shown, in which a downlink signal is decomposed into multiple components.

[0016] Figure 6C An environment comprising a first-order free surface receiver-side multiple signal is shown according to one embodiment, the multiple signal being decomposed into its components.

[0017] Figure 7 A schematic diagram of a computing system according to one embodiment is shown. Detailed Implementation

[0018] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. Numerous specific details are set forth in the following detailed description in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure various aspects of the embodiments.

[0019] It should also be understood that although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, without departing from the scope of the invention, a first object may be referred to as a second object, and similarly, a second object may be referred to as a first object. The first object and the second object are both objects, but they are not considered to be the same object.

[0020] The terminology used in this description is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in the description of this invention and the appended claims, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any possible combination of one or more of the associated listed items. It will also be understood that the terms “includes,” “including,” “comprises,” and / or “comprising,” when used in this specification, specify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof. Furthermore, as used herein, depending on the context, the term “if” can be interpreted as “when…”, “in…”, “in response to determining…”, or “in response to detecting…”.

[0021] Now, attention is focused on the processing procedures, methods, techniques, and workflows according to some embodiments. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and / or the order of some operations may be changed.

[0022] Figure 1A-1D A simplified schematic diagram of an oil field 100 having an underground stratum 102 containing a reservoir 104 is shown, according to various techniques and methods described herein. Figure 1A This illustrates a survey operation performed by a surveying tool such as the Seismic Truck 106.1 to measure the characteristics of subsurface strata. The survey operation is a seismic survey operation used to generate sound vibrations. Figure 1A In this process, a sound vibration, such as sound vibration 112 generated by source 110, is reflected from stratum 114 in stratum 116. A set of sound vibrations is received by a sensor located on the Earth's surface, such as a seismic detector receiver 118. The received data 120 is provided as input data to the computer 122.1 of the seismic truck 106.1, and in response to the input data, the computer 122.1 generates seismic data output 124. This seismic data output can be stored, transmitted, or further processed as needed, for example, by data reduction.

[0023] Figure 1BThe diagram illustrates a drilling operation performed by a drilling tool 106.2 suspended by a drilling rig 128, which is advanced into the subsurface formation 102 to form a wellbore 136. A mud pit 130 is used to circulate drilling mud through a flow line 132 into the drilling tool. The drilling mud flows downward through the drilling tool, then upward along the wellbore 136 and back to the surface. The drilling mud is typically filtered and returned to the mud pit. The circulation system can be used to store, control, or filter the flowing drilling mud. The drilling tool is advanced into the subsurface formation 102 to reach reservoir 104. Each well can target one or more reservoirs. The drilling tool is suitable for measuring downhole characteristics using logging-while-drilling (LoWWD) tools. As shown, LoWWD tools can also be used to collect core samples 133.

[0024] Computer facilities may be located at different and / or remote locations around oilfield 100 (e.g., surface unit 134). Surface unit 134 can be used to communicate with drilling tools and / or field operations, as well as with other surface or downhole sensors. Surface unit 134 is capable of communicating with drilling tools to send commands to and receive data from them. Surface unit 134 can also collect data generated during drilling operations and produce data output 135, which can then be stored or transmitted.

[0025] Sensors (S), such as instruments, may be located around oilfield 100 to collect data related to the various oilfield operations described above. As shown, sensors (S) are positioned at one or more locations within drilling tools and / or drilling rig 128 to measure drilling parameters such as drilling pressure, drilling torque, pressure, temperature, flow rate, composition, rotational speed, and / or other parameters of field operations. Sensors (S) may also be located at one or more locations within the circulation system.

[0026] Drilling tool 106.2 may include a bottom hole assembly (BHA) (not shown) located near the drill bit (e.g., within a few drill collar lengths of the drill bit). The bottom hole assembly includes the ability to measure, process, and store information, as well as to communicate with surface unit 134. The bottom hole assembly also includes drill collars for performing various other measurement functions.

[0027] The bottomhole assembly may include a communication sub-assembly for communicating with the surface unit 134. The communication sub-assembly is adapted to transmit and receive signals to and from the surface using communication channels such as mud pulse telemetry, electromagnetic telemetry, or wired drill pipe communication. The communication sub-assembly may include, for example, a transmitter that generates signals, such as acoustic or electromagnetic signals, representing measured drilling parameters. Those skilled in the art will understand that various telemetry systems can be employed, such as wired drill pipe, electromagnetic, or other known telemetry systems.

[0028] Typically, a wellbore is drilled according to a drilling plan established before drilling begins. The drilling plan usually specifies the equipment, pressure, trajectory, and / or other parameters defining the drilling process at the well site. Drilling operations can then be performed according to the drilling plan. However, as information is gathered, drilling operations may need to deviate from the drilling plan. Furthermore, subsurface conditions may change as drilling or other operations proceed. Earth models may also need to be adjusted as new information is collected.

[0029] Data collected by the sensors (S) can be collected by ground unit 134 and / or other data collection sources for analysis or other processing. Data collected by the sensors (S) can be used alone or in combination with other data. Data can be collected in one or more databases and / or transmitted in the field or remotely. Data can be historical data, real-time data, or a combination thereof. Real-time data can be used immediately or stored for later use. Data can also be combined with historical data or other inputs for further analysis. Data can be stored in separate databases or merged into a single database.

[0030] Surface unit 134 may include transceiver 137 to allow communication between surface unit 134 and various parts or other locations of oilfield 100. Surface unit 134 may also be configured or functionally connected to one or more controllers (not shown) for actuating mechanisms at oilfield 100. Surface unit 134 can then send command signals to oilfield 100 in response to received data. Surface unit 134 can receive commands via transceiver 137 or execute commands to the controller itself. A processor may be provided to analyze data (local or remote), make decisions, and / or activate the controller. In this way, oilfield 100 can be selectively adjusted based on collected data. This technique can be used to optimize (or improve) certain field operations, such as controlling drilling, drilling pressure, pump rate, or other parameters. These adjustments can be made automatically based on computer protocols and / or manually by the operator. In some cases, well plans can be adjusted to select optimal (or improved) operating conditions or to avoid problems.

[0031] Figure 1C It shows the suspension and entry of the drill rig 128. Figure 1B Cable operations are performed by cable tools 106.3 in wellbore 136. Cable tools 106.3 are adapted to be deployed into wellbore 136 to generate logging, perform downhole tests, and / or collect samples. Cable tools 106.3 can be used to provide another method and apparatus for performing seismic exploration operations. For example, cable tools 106.3 may have an explosive, radioactive, electrical, or acoustic energy source 144 that sends and / or receives electrical signals to and / or receives electrical signals from the surrounding subsurface formation 102 and fluids therein.

[0032] The cable tool 106.3 can be operably connected to, for example... Figure 1A The seismic vehicle 106.1 includes a seismograph 118 and a computer 122.1. The cable tool 106.3 can also provide data to the surface unit 134. The surface unit 134 can collect data generated during cable operation and can generate data output 135 that can be stored or transmitted. The cable tool 106.3 can be positioned at different depths in the wellbore 136 to provide survey or other information related to the subsurface strata 102.

[0033] Sensors (S), such as instruments, can be positioned around the oilfield 100 to collect data related to the various field operations described above. As shown, sensor S is positioned in cable tool 106.3 to measure downhole parameters related to, for example, porosity, permeability, fluid composition, and / or other parameters of the field operations.

[0034] Figure 1D The diagram illustrates a production operation performed by production tool 106.4, deployed from the production unit or tree 129 into a completed wellbore 136 to draw fluid from the downhole reservoir to the surface facility 142. Fluid flows from the reservoir 104 through a perforation (not shown) in the casing and into production tool 106.4 in the wellbore 136, and reaches the surface facility 142 via a collection network 146.

[0035] Sensors (S), such as instruments, may be positioned around oilfield 100 to collect data related to the various field operations described above. As shown, sensors (S) may be positioned in production tools 106.4 or related equipment, such as treehouse 129, collection network 146, surface facility 142, and / or production facility, to measure fluid parameters, such as fluid composition, flow rate, pressure, temperature, and / or other parameters of the production operation.

[0036] Production may also include injection wells for enhanced oil recovery. One or more collection facilities are operatively connected to one or more well sites for selectively collecting downhole fluids from the well sites.

[0037] Although Figure 1B-1D Tools for measuring oilfield characteristics are illustrated; however, it should be understood that these tools can be used in conjunction with non-oilfield operations, such as gas fields, wells, aquifers, storage facilities, or other underground installations. Furthermore, while some data acquisition tools are described, it should be understood that a wide range of measuring tools capable of sensing parameters such as seismic two-way propagation time, density, resistivity, and productivity of subsurface formations and / or their geological structures can be used. Various sensors (S) can be located at different locations along the wellbore and / or monitoring tools to collect and / or monitor the required data. Other data sources may also be available from off-site locations.

[0038] Figure 1A-1D The oilfield configuration described is intended to provide a brief description of field examples that can be used as a framework for oilfield applications. Part or all of the oilfield 100 may be on land, on water, and / or offshore. Furthermore, while a single field measured at a single location is described, the oilfield application can be used in any combination of one or more oilfields, one or more processing facilities, and one or more well sites.

[0039] Figure 2 A partial cross-sectional schematic diagram of oil field 200 is shown. Oil field 200 has data acquisition tools 202.1, 202.2, 202.3, and 202.4 located at different positions along oil field 200, used to collect data on subsurface formation 204 according to the various techniques and methods described herein. Data acquisition tools 202.1-202.4 can be respectively connected to... Figure 1A-1D The data acquisition tools 106.1-106.4 are the same, or the same as other tools not shown. As shown, data acquisition tools 202.1-202.4 generate data curves or measurements 208.1-208.4, respectively. These data curves are plotted along oilfield 200 to illustrate the data generated by various operations.

[0040] Data curves 208.1-208.3 are examples of static data curves that can be generated separately by data acquisition tools 202.1-202.3; however, it should be understood that data curves 208.1-208.3 can also be real-time updated data curves. These measurements can be analyzed to better define formation characteristics and / or determine the accuracy of measurements and / or to check for errors. The curves for each corresponding measurement can be aligned and scaled for comparison and verification of characteristics.

[0041] Static data curve 208.1 shows the two-way seismic response over a period of time. Static graph 208.2 shows core sample data measured from a core sample in formation 204. Core samples can be used to provide data such as graphs of density, porosity, permeability, or other physical properties of the core sample along its length. Density and viscosity tests can be performed on the fluids in the core at different pressures and temperatures. Static data curve 208.3 shows the well logging trajectory, which typically provides formation resistivity or other measurements at different depths.

[0042] The production decline curve, or chart 208.4, is a dynamic data curve of fluid flow rate over time. Production decline curves typically provide productivity as a function of time. Fluid characteristics such as flow rate, pressure, and composition are measured as fluid flows through the wellbore.

[0043] Other data can also be collected, such as historical data, user input, economic information, and / or other measurement data and other parameters of interest. As described below, static and dynamic measurements can be analyzed and used to generate models of subsurface strata to determine their characteristics. Similar measurements can also be used to measure changes in strata over time.

[0044] Subsurface structure 204 has multiple geological formations 206.1-206.4. As shown, this structure has multiple strata or layers, including shale layer 206.1, carbonate layer 206.2, shale layer 206.3, and sand layer 206.4. Fault 207 extends through shale layer 206.1 and carbonate layer 206.2. Static data acquisition tools are suitable for measuring and detecting the characteristics of the strata.

[0045] While specific subsurface formations with particular geological structures are described, it should be understood that Oilfield 200 may contain a variety of geological structures and / or formations, sometimes exhibiting extreme complexity. In some locations, typically below the waterline, fluids may occupy the pore space of the formations. Each measuring instrument can be used to measure the properties of the formations and / or their geological characteristics. Although each acquisition tool is shown as being located at a specific location within Oilfield 200, it should be understood that one or more types of measurements may be performed at one or more locations across one or more oilfields or at other locations for comparison and / or analysis.

[0046] Then it is possible to process and / or evaluate data from various sources (e.g.) Figure 2 Data is collected using data acquisition tools. Geophysicists typically use seismic data, as shown in static data curve 208.1 from data acquisition tool 202.1, to determine the characteristics of subsurface strata and features. Geologists typically use core data shown in static diagram 208.2 and / or well logging data from well logging 208.3 to determine various features of subsurface strata. Production data from diagram 208.4 is commonly used by reservoir engineers to determine fluid flow reservoir characteristics. Data analyzed by geologists, geophysicists, and reservoir engineers can be further analyzed using modeling techniques.

[0047] Figure 3A An oil field 300 is shown for performing production operations according to various techniques and methods described herein. As shown, the oil field has multiple well sites 302 operatively connected to a central processing facility 354. Figure 3A The oilfield configuration described is not intended to limit the scope of the oilfield application system. Some or all of the oilfield may be located onshore and / or offshore. Furthermore, while a single oilfield with a single processing facility and multiple well sites is described, any combination of one or more oilfields, one or more processing facilities, and one or more well sites may exist.

[0048] Each well site 302 has equipment for forming a wellbore 336 underground. The wellbore extends through underground formations 306, including reservoirs 304. These reservoirs 304 contain fluids, such as hydrocarbons. The well site extracts fluids from the reservoirs and transports them to a processing facility via a surface network 344. The surface network 344 has piping and control mechanisms for controlling the flow of fluids from the well site to the processing facility 354.

[0049] Follow us now Figure 3B This illustration shows a side view of a marine-based survey 360 of a subsurface 362 according to one or more embodiments of the various techniques described herein. The subsurface 362 includes the seabed surface 364. Seismic sources 366 may include marine sources such as vibration sources or air guns, which can propagate seismic waves 368 (e.g., energy signals) into the Earth over a prolonged period or with near-instantaneous energy provided by a pulse source. Seismic waves can be propagated by the marine source as frequency-scanning signals. For example, a vibration source-type marine source may initially emit seismic waves at a low frequency (e.g., 5 Hz) and increase the frequency (e.g., 80-90 Hz) over time.

[0050] Components of seismic wave 368 can be reflected and converted by the seabed surface 364 (i.e., reflectors), and the reflected seismic wave 370 can be received by multiple seismic receivers 372. The seismic receivers 372 can be arranged on multiple towed cables (i.e., towed cable array 374). The seismic receivers 372 can generate electrical signals representing the received reflected seismic waves 370. These electrical signals can be embedded with information about the subsurface 362 and are captured as recorded seismic data.

[0051] In one embodiment, each towline may include towline steering equipment, such as a pod, deflector, tail buoy, etc., which are not shown in this application. According to the techniques described herein, the towline steering equipment can be used to control the position of the towline.

[0052] In one implementation, the seismic wave reflection 370 can propagate upwards and reach the water / air interface at the water surface 376, and then a portion of the reflection 370 can be reflected downwards again (i.e., a surface ghost wave 378) and received by multiple seismic receivers 372. The surface ghost wave 378 can be referred to as a surface multiple wave. The point on the water surface 376 where the wave is reflected downwards is generally referred to as the downward reflection point.

[0053] Electrical signals can be transmitted to vessel 380 via transmission cables, wireless communication, etc. Vessel 380 can then transmit the electrical signals to a data processing center. Alternatively, vessel 380 may include an onboard computer capable of processing electrical signals (i.e., seismic data). Those skilled in the art who benefit from this disclosure will understand that this illustration is highly idealized. For example, the survey may be of strata deep below the surface. The strata may typically include multiple reflectors, some of which may include tilt events, and multiple reflections (including wave conversions) may be generated for reception by seismic receiver 372. In one embodiment, seismic data may be processed to generate seismic images of the subsurface 362.

[0054] The marine seismic acquisition system tows each cable in an array of 374 cables at the same depth (e.g., 5-10 m). However, the ocean-based survey 360 can tow each cable in the array of 374 cables at different depths, allowing for the acquisition and processing of seismic data in a manner that avoids destructive interference caused by surface ghost waves. For example, Figure 3B The ocean-based survey 360 shows eight towlines being towed by vessel 380 at eight different depths. The depth of each towline can be controlled and maintained using pods arranged on each towline.

[0055] Figure 4 A flowchart of a method 400 for performing seismic imaging, for example, by imaging multiple reflection signals contained in seismic data, according to one embodiment, is shown. Method 400 can therefore begin by receiving seismic data, as at 402. As described above, the seismic data can be marine seismic data acquired using one or more receivers located at or near the seabed and using one or more sources located near the free surface (e.g., the ocean surface).

[0056] Seismic data may include an ascending wavefield and a descending wavefield, and method 400 may include separating the descending wavefield from the ascending wavefield, as at 404. In some embodiments, this separation may be achieved using multi-component measurements. For example, a hydrophone (P) and a vertical seismic detector / accelerometer (Vz or Az) may be used for vertical separation; in this case, the projection from the vertical to the actual wavefield direction can be processed in the plane wave domain or the spatiotemporal domain.

[0057] Method 400 may further include estimating the downlink direct wave in the downlink wavefield, as at 406. The estimation of the downlink direct wave can be performed by calculating the downlink direct wave (including signature, ghosting, and bubble effects) using the cross-ghosting operator difference in the wavenumber domain. In another embodiment, this direct wave estimation can be achieved using near-field hydrophone measurements or modeling to calculate directional far-field features and thus calculate the downlink direct wave. In some embodiments, the direct wave may not be estimated. In some embodiments of method 400, the purpose of estimating the direct wave is to subtract it from the downlink wavefield when estimating multiple reflected signals. In some embodiments, the direct wave can be directly muteed and therefore may not be estimated separately from the subtraction process.

[0058] Method 400 can then proceed to estimate first-order and / or higher (e.g., second-order) multiples based on the direct wave and downflow wavefield, as at 408. Typically, deconvolution of the upper / lower wavefield generates a subsurface reflectivity operator. However, to determine downflow multiples using the subsurface reflectivity operator, free surface reflectivity and delay operators for bidirectional water crossing may be required, which are typically computed using a water propagation model. Therefore, this technique relies on the accuracy of the water propagation model. However, the complexity of water propagation can render its model somewhat inaccurate. Therefore, embodiments of this method can employ data-driven measurement signal operators for both the downflow wavefield and the direct wave, allowing direct computation of multiple reflections in the downflow wavefield without using a water propagation model.

[0059] Once first-order and / or higher-order multiples are estimated in the downlink wavefield, method 400 can proceed to generate images based on the multiple reflection signals, as shown at 410. Multiples can be separated sequentially, which can enhance imaging. Furthermore, as mentioned above, these signals can provide insights into subsurface reflectivity that might otherwise be unclear from reflections from only the primary wave. Therefore, these images can be used to construct subsurface velocity models, which are more accurate representations of the subsurface region of interest. Additionally, outside the context of this disclosure, access to multiples can be used to supplement amplitude-to-void (AVO) analysis, which is typically performed on the primary wave but not on the multiples. This can enhance imaging of shallow targets.

[0060] Figure 5A flowchart of a method 500 for generating an image of multiple wave reflections in seismic data according to one embodiment is shown. Method 500 may be a more detailed embodiment of a portion of method 400, and therefore the two should not be considered mutually exclusive. Like method 400, method 500 can begin by receiving seismic data, as at 502. The seismic data may include and is thus divided into an upflow wavefield 504 and a downflow wavefield 506, for example in the plane wave domain (frequency-wavenumber or intercept-slowness). Furthermore, as described above, the downflow wavefield may include a direct wave component 508 that can be estimated.

[0061] Figure 6A An environment 600 is shown where a first-order downlink signal 601 exists. The source of the event is at depth zs, and the receiver is at depth zr. The signal 601 is reflected by a reflector 604 (e.g., the seabed) and then reflected again by a free surface 606 (e.g., the sea surface), before being received at the receiver.

[0062] Figure 6B An environment 600 according to one embodiment is shown, in which a downlink signal 601 is decomposed into its components. In particular, signal 601 includes a direct wave signal 608 and a first-order free surface receiver-side multiple wave signal (RRsZ) 610.

[0063] Figure 6C An environment 600 according to one embodiment is shown, which includes a first-order, free-surface, receiver-side multiple signal 610, which is decomposed into its components. Specifically, the multiple signal 610 includes a signal (R) 612 reflected from a reflector 604, and a component (RsZ) 614 that uses reflections from the free surface 606 to simulate propagation in water.

[0064] The downlink wave field can be approximated using the plane wave domain recursive relationship between the downlink wave field, the direct wave, and the reflectivity as follows:

[0065] DN = DA + (RR) s Z)DN (1)

[0066] Where DN is the downlink wave field, DA is the direct wave, R is the reflectivity response of the stack below the recording depth zr, Rs is the reflection response at the free surface (e.g., a scalar, such as approximately -1), and Z is the delay operator for the bidirectional propagation time between the recording depth zr and the free surface 502 (z = 0). Figures 6A-6CIt is understandable that signal 612 could therefore be an example of R, and signal 614 could be an example of (RsZ), which could result in a first-order free surface receiver-side multiple (“ghosting”) (RRsZ) referenced at zr (e.g., signal 610). Assuming DN and DA are for the source at zs and the receiver at zr, this multiple could be referenced at either zs or zr.

[0067] Before proceeding further, a brief discussion of the subsurface reflectivity R and the up / down wavefield deconvolution (UDD) process may help in understanding equation (1). The subsurface reflectivity R can be expressed as taking into account the difference between the up and down wavefields (in the FK or Fp domain), for example:

[0068] U=R(DN) (2)

[0069] It can be inverted as:

[0070]

[0071] Where ε is a stabilization factor used to prevent noise caused by spectral notches. Since the up-row and down-row wave fields are measured and / or estimated, for example, by summing using PZ, the reflectivity R can be estimated. As shown in equation (3), UDD in the plane wave domain produces an estimate of the seabed reflectivity without characteristic and source ghosting effects. Embodiments of this disclosure can be built upon UDD to form a new process that can be referred to as down-row demultiplexing processing (DGD).

[0072] During downlink demultiplexing (DGD) process, typically in Figure 5 As described in box 510, equation (1) can be inverted to estimate the first-order multiples (RRsZ) in the descending wave field. As shown in box 512, this can be expressed as:

[0073]

[0074] Based on equation (4), there are two possible options for solving the first-order downflow multiple RRsZ. The first relies on upflow / downflow deconvolution (UDD), as described above, which produces an estimate of the seabed reflectivity R. A water propagation model can then be constructed to estimate RsZ, thus the convolution of the two operators R and RsZ produces the first-order downflow multiple. However, this option may be sensitive to the accuracy of the propagation model and the complexity of the water environment being modeled.

[0075] Another option is to rely on measurements of the data operators constituting the downlink wavefield DN and the direct wave DA. Since DN and DA are data-driven operators, the complexity of propagation in water can be implicitly included, thus method 500 avoids sensitivity to water propagation models that accurately account for physical complexity. For example, the method discussed here may include the assumption of a one-dimensional (1D) or “pancake” medium. The medium includes water. Therefore, inaccuracies in the model may stem from non-1D propagation in the water layer. However, as is evident from equation (4), this option requires estimating and subtracting the direct wave, or removing the direct wave directly from the downlink wavefield (i.e., DN-DA).

[0076] As described above, various techniques can be used to estimate the direct wave. When subtracted from the downlink wave field, an intermediate or “modified” downlink wave field (DN-DA) is generated, which can then be deconvolved from the initial downlink wave field (DN) to complete the calculation on the right side of equation (4), and thus generate the data-driven calculation of the first-order downlink multiple wave RRsZ, as shown in box 512.

[0077] This deconvolution can remove source ghosting, source bubbles, and directional effects, but in some embodiments, additional shaping can be applied to user-specified ghost-free features. As mentioned above, the method shown in Equation (4) may not require a water layer model to obtain the sub-event inputs for mirror migration; however, mirror migration may still require a water layer model.

[0078] When dealing with downflow wavefields, the estimated direct wave, the first water bounce (downflow multiple wave), is the target / desired wavefield or “first wave,” and higher-order events with at least two bounces from the free surface are undesirable and considered “multiple waves.” Equation (4) directly predicts this target wavefield using a data-based operator referenced in the zr. However, in some cases, a two-part approach can be adopted, involving the prediction of undesirable events followed by adaptive subtraction. This two-part approach improves the robustness of the process against defects / assumptions in multiple wave prediction (e.g., the implicit 1D Earth assumption in the process) and generally reduces residual noise. For example, the two-part approach can predict multiple waves more accurately (using a data-driven operator), and adaptive subtraction can further enhance this approach. In practice, the use of adaptivity can be minimized where possible to favor direct subtraction. This means that the multiple wave model approximates the multiple waves in the data.

[0079] Therefore, the first part of the two-part approach is to identify unwanted higher-order multiple waves. Referring again to equation (1), data-based operators can also be used to estimate higher-order bounces in the downflow wave field. Moving to the second recursive instance of equation (1) yields:

[0080] DN = DA + (RR) S Z)(DA+(RRS Z)DN)=DA+(RR S Z)DA+(RR S Z) 2 DN (5)

[0081] The last term in the second part of equation (5) represents (approximately) second-order and higher-order descending multiples. These higher-order multiples can be expressed as:

[0082] MULT_DN=(RR S Z) 2 DN (6)

[0083] Equation (6) can be interpreted as a model-based approach describing the second- and higher-order bounces of downward multiple waves. Therefore, it will use the reflectivity R estimated from the UDD and the values ​​for R... S The propagation model in the water layer Z. This model will then be used to solve equation (6) twice. The MULT_DN wavefield can therefore be referenced at the acquired data (zr, zs).

[0084] However, the combination of equations (4) and (6) produces the terms for second-order and higher-order multiple wave estimations seen in box 518:

[0085]

[0086] Therefore, as shown in box 518, data-based operators DN and DA can be used to predict second- and higher-order multiples MULT_DN, thus avoiding the drawbacks of using propagation models in the water layer, since there is no RRsZ term. This data-driven prediction of downflow multiples can have more accurate timing, phase, and amplitude information, which in turn can facilitate adaptive subtraction, reducing the risk of also attenuating primary waves or target events. Adaptive subtraction can be performed in any domain, such as the plane wave domain or the spatiotemporal domain.

[0087] When using a two-part approach (multiple prediction and subtraction), the deconvolution process (as in Equation (4)) may not provide 3D source designature removal and 3D ghosting removal. However, this can also be achieved by deconvolving with the downlink direct wave (DA) in the plane wave domain. Following the processes of UDD and DGD (Equation (4)), such deconvolution provides 3D designature removal and 3D ghosting removal. This deconvolution can be applied to the complete downlink wave field and the predicted downlink multiple wave field before multiple wave subtraction. Alternatively, it can be applied after multiple wave subtraction. Therefore, the embodiment of method 400 uses a data-based operator to predict downlink multiple waves (second order and above) globally, and thus makes the prediction process more robust relative to the 1D Earth assumption. Furthermore, this can be generalized to any order based on the recursive nature of the downlink field (Equation (1)).

[0088] Therefore, method 500 may include adaptively subtracting second-order and higher-order multiples from the downlink wave field, as at 520. As described above, first-order multiples can be calculated using the downlink wave field, but it is possible to image the first-order multiples after subtracting second-order and higher-order multiples from it, as at 522.

[0089] In some embodiments, an estimate of the first-order downlink multiple may be sufficient, but in other embodiments, the second-order downlink multiple can be calculated again based on equations (1) and (4). Specifically, the second-order multiple can be calculated by adding the next-order term of DN to the recursive equation (1), as shown in box 514:

[0090]

[0091] This relationship can also be solved using the same data-driven operator as Equation (4), and therefore can be directly calculated based on the estimates of the downflow wave field DN and the direct wave DA, without the need for a water propagation model. In fact, the first-order free-surface downflow multiples have already been estimated using Equation (4) (using one or both of the methods discussed above). The free-surface downflow multiples can be used with Equation (1) to recursively obtain higher-order multiples data separated by order, for example, by increasing the exponent on the left side of Equation (8) and updating the right side accordingly.

[0092] As described above, the first-order and / or second-order multiple waves estimated using one or more of the above embodiments can be used to create seismic images and / or to create models (e.g., velocity models) that can help to more accurately and effectively characterize subsurface areas.

[0093] In one or more embodiments, the functionality may be implemented in hardware, software, firmware, or any combination thereof. For software implementations, the techniques described herein can be implemented using modules (e.g., procedures, functions, subroutines, programs, routines, subroutines, modules, software packages, classes, etc.) that perform the functionality described herein. Modules can be coupled to other modules or hardware circuits by passing and / or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc., can be passed, forwarded, or transmitted using any suitable means, including memory sharing, messaging, token passing, network transmission, etc. Software code can be stored in memory units and executed by a processor. Memory units can be implemented inside or outside the processor, in which case they can be communicatively coupled to the processor using various means known in the art.

[0094] In some embodiments, any of the methods disclosed herein may be performed by a computing system. Figure 7An example of such a computing system 700 according to some embodiments is shown. The computing system 700 may include a computer or computer system 701A, which may be a standalone computer system 701A or an arrangement of distributed computer systems. The computer system 701A includes one or more analysis modules 702 configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these different tasks, the analysis modules 702 execute independently or in coordination with one or more processors 704 connected to one or more storage media 706. The processor 704 is also connected to a network interface 707 to allow the computer system 701A to communicate with one or more additional computer systems and / or computing systems, such as 701B, 701C and / or 701D, via a data network 709. (Note that computer systems 701B, 701C and / or 701D may or may not share the same architecture as computer system 701A and may be located in different physical locations. For example, computer systems 701A and 701B may be located in a processing facility while communicating with one or more computer systems, such as 701C and / or 701D, located in one or more data centers and / or in different countries on different continents.)

[0095] The processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array or other control or computing device.

[0096] Storage medium 706 may be implemented as one or more computer-readable or machine-readable storage media. Note that, although in Figure 7In the example embodiments, storage medium 706 is depicted as being within computer system 701A; however, in some embodiments, storage medium 706 may be distributed within and / or across multiple internal and / or external chassis of computing system 701A and / or additional computing systems. Storage medium 706 may include one or more different forms of memory, including semiconductor storage devices such as dynamic or static random access memory (DRAM or SRAM), erasable and programmable read-only memory (EPROM), electrically erasable and programmable read-only memory (EEPROM) and flash memory, magnetic disks such as fixed, floppy, and removable disks, other magnetic media including magnetic tape, compact discs (CDs) or digital video discs (DVDs), Blu-ray discs, or other types of optical storage or other types of storage devices. Note that the instructions discussed above may be provided on a single computer-readable or machine-readable storage medium or alternatively, on multiple computer-readable or machine-readable storage media distributed across a large system that may have multiple nodes. Such computer-readable or machine-readable storage media is considered part of an article (or manufactured article). An article of article or manufactured article may refer to any single or multiple manufactured components. One or more storage media may be located in a machine that runs machine-readable instructions, or at a remote site from which machine-readable instructions can be downloaded via a network for execution.

[0097] In some embodiments, computing system 700 includes one or more wave multiple estimation modules 708. In an example of computing system 700, computer system 701A includes a wave multiple estimation module 708. In some embodiments, a single wave multiple estimation module may be used to perform some or all aspects of one or more embodiments of the method. In alternative embodiments, multiple wave multiple estimation modules may be used to perform some or all aspects of the method.

[0098] It should be understood that computing system 700 is only one example of a computing system, and computing system 700 may have more or fewer components than shown, and can be combined. Figure 7 Additional components not shown in the example embodiment, and / or the computing system 700 may have Figure 7 Different configurations or arrangements of the components shown. Figure 7 The various components shown can be implemented in hardware, software, or a combination of hardware and software, including one or more signal processing and / or application-specific integrated circuits.

[0099] Furthermore, the steps in the processing method described herein can be implemented by operating one or more functional modules in an information processing device, such as a general-purpose processor or a dedicated chip, such as an ASIC, FPGA, PLD, or other suitable device. These modules, combinations of these modules, and / or their combinations with general-purpose hardware are all included within the scope of this invention.

[0100] Explanations, models, and / or other explanatory aids can be refined iteratively; this concept applies to embodiments of the method discussed herein. This can include the use of feedback loops executed on an algorithmic basis, such as in a computing device (e.g., computing system 700). Figure 7 (And / or through user manual control, the user can determine whether a given step, action, template, model, or set of curves has become accurate enough to evaluate the underground three-dimensional geological structure under consideration.)

[0101] Furthermore, it should be understood that the steps of the methods disclosed herein may be performed in the order described herein, or in a different order, without departing from the scope of this disclosure. Moreover, according to this disclosure, these steps may be combined, separated, or performed in parallel or simultaneously.

[0102] For illustrative purposes, the foregoing description has been made with reference to specific embodiments. However, the illustrative discussion above is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in light of the above teachings. Furthermore, the order of elements shown and described in the methods may be rearranged, and / or two or more elements may appear simultaneously. The embodiments were chosen and described in order to best explain the principles of the invention and its practical application, thereby enabling others skilled in the art to best utilize the invention and its various embodiments with various modifications suitable for the intended particular use.

Claims

1. A method for data-driven separation of downlink free-surface multiples for seismic imaging, comprising: Receive seismic data, which includes signals collected using the receiver; Separate the downlink wave field from the uplink wave field in the signal; A modified downlink field is generated by removing the direct wave from the downlink field, wherein removing the direct wave includes estimating the direct wave and subtracting the direct wave from the downlink field; The first-order multiple reflection signal is estimated at least in part by deconvolving the modified downwave field and the downwave field. Estimating second-order multiple reflection signals based on downlink wave field and direct wave; Second-order and higher-order multiples are estimated by deconvolving the modified down-field and the intermediate wave field to generate the intermediate wave field, and then convolving the intermediate wave field with the modified down-field. Seismic images are generated at least in part based on estimated first-order multiple reflection signals, wherein the seismic images are generated based on a combination of first-order and second-order multiple reflection signals; Before estimating the remaining first-order multiple reflection signals, subtract the second-order and higher-order multiples from the downlink wave field; After subtracting the second-order and higher-order multiples, a seismic image is generated based on the remaining first-order multiples using the downflow wave field.

2. The method of claim 1, wherein removing the direct wave comprises directly suppressing the direct wave in the downlink wave field.

3. The method of claim 1, wherein the estimation of the first-order downflow multiple reflection signal is data-driven and independent of the water propagation model, and wherein the generated seismic image does not include the first-order wave reflection.

4. The method according to claim 1 further includes generating a velocity model representing the underground region using a first-order multiple reflection signal.

5. A non-transitory computer-readable medium storing instructions, which, when executed by at least one processor of a computing system, cause the computing system to perform operations, the operations comprising: Receive seismic data, which includes signals collected using the receiver; Separate the downlink wave field from the uplink wave field in the signal; A modified downlink field is generated by removing the direct wave from the downlink field, wherein removing the direct wave includes estimating the direct wave and subtracting the direct wave from the downlink field; The first-order multiple reflection signal is estimated at least in part by deconvolving the modified downwave field and the downwave field. Estimating second-order multiple reflection signals based on downlink wave field and direct wave; Second-order and higher-order multiples are estimated by deconvolving the modified down-field and the intermediate wave field to generate the intermediate wave field, and then convolving the intermediate wave field with the modified down-field. Seismic images are generated at least in part based on estimated first-order multiple reflection signals, wherein the seismic images are generated based on a combination of first-order and second-order multiple reflection signals; Before estimating the remaining first-order multiple reflection signals, subtract the second-order and higher-order multiples from the downlink wave field; After subtracting the second-order and higher-order multiples, a seismic image is generated based on the remaining first-order multiples using the downflow wave field.

6. The medium of claim 5, wherein removing the direct wave comprises directly suppressing the direct wave in the downlink wave field.

7. The medium of claim 5, wherein the estimation of the first-order multiple reflection signal is data-driven and independent of the water propagation model, and wherein the generated seismic image does not include first-order wave reflections.

8. The medium of claim 5, wherein the operation further comprises generating a velocity model representing the underground region using a first-order multiple wave reflection signal.

9. A computing system, comprising: One or more processors; and A memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations including: Receive seismic data, which includes signals collected using the receiver; Separate the downlink wave field from the uplink wave field in the signal; A modified downlink field is generated by removing the direct wave from the downlink field, wherein removing the direct wave includes estimating the direct wave and subtracting the direct wave from the downlink field; The first-order multiple reflection signal is estimated at least in part by deconvolving the modified downwave field and the downwave field. Estimating second-order multiple reflection signals based on downlink wave field and direct wave; Second-order and higher-order multiples are estimated by deconvolving the modified down-field and the intermediate wave field to generate the intermediate wave field, and then convolving the intermediate wave field with the modified down-field. Seismic images are generated at least in part based on estimated first-order multiple reflection signals, wherein the seismic images are generated based on a combination of first-order and second-order multiple reflection signals; Before estimating the remaining first-order multiple reflection signals, subtract the second-order and higher-order multiples from the downlink wave field; After subtracting the second-order and higher-order multiples, a seismic image is generated based on the remaining first-order multiples using the downflow wave field.

Images