Patent Application
20200271826
This disclosure relates to seismic data and image processing.
Structural information is often the most important content of seismic images, and it can be extracted for structure-oriented data processing, such as smoothing, interpolation, and picking. Smoothing along structures can enhance structural features while preserving important discontinuities such as faults or channels. Such smoothing has also been used as a model constraint in inversion-based seismic imaging. Interpolation along structures can reconstruct seismic image/model with a meaningful geologic sense, and it has been used in well-log interpolation. Picking along structures can pick horizons for structural interpretation, or the residual moveout in common-image gathers (CIGs) for prestack imaging.
Structural information can be characterized by local dip attribute, which can be estimated by several methods including semblance scanning methods and local structure tensors. Dip estimation methods can estimate an accurate dip from an image that does not include strong conflicting and steep structures.
The present disclosure discusses incorporating pattern information of the structural orientation to guide dip estimation and mitigating aliasing issues. When images have very steep structures, such dip estimation methods may suffer from aliasing problem, which can make the dip estimation challenging. The estimated dominant dip from aliased parts are undesirable, as it follows the false aliased structure instead of a true structure. In some implementations, the pattern information is incorporated to provide an initial dip for the plane-wave destruction (PWD) filter. The PWD filter solves a nonlinear inverse problem, and the solution based on the initial dip provided for the inversion. If the initial dip is pattern-related, then there is a large possibility of the estimation being closer to the dip of a true structure than that of an aliased structure. Thus, PWD can lead to a new dip that contains the pattern information and is referred to as pattern-guided dip. In some examples, when the synthetic data is aliased, the dip can be estimated to be of either true or aliased events by providing different initial dips.
Innovative aspects of the subject matter described in this specification may be embodied in methods that include obtaining a seismic data image. A first plane-wave destruction filter dip estimation is applied to the seismic data image to generate an initial dip model. A second plane-wave destruction filter dip estimation is applied to the seismic data image using the initial dip model to generate a pattern-guided dip estimation. The pattern-guided dip estimation is stored in a data store.
Other implementations of these aspects include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.
These and other implementations may each optionally include one or more of the following features. For instance, the method can include calculating a coherence map based on the seismic data image, clipping the coherence map with a predetermined threshold value, and generating a mask operator in response to the clipping. Applying the first plane-wave destruction filter dip estimation can include applying a first weighting factor to aliasing-affected areas of the seismic data image, and applying a second weighting factor to aliasing-free areas of the seismic data image. The first weighting factor has a value of zero, and the second weighting factor has a value of one. The initial dip model only includes aliasing-free areas of the seismic data image. The pattern-guided dip estimation is a nonlinear inverse estimation. Estimating a structure-oriented interpolated target image using the patterned guided dip estimation.
Particular implementations of the subject matter described in this specification can be implemented so as to realize one or more of the following advantages. For example, implementation of the subject matter mitigate aliasing issues that may exist in the structure-oriented data processing.
The details of one or more implementations of the subject matter described in this disclosure are set forth in the accompanying drawings and the description. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims.
The present disclosure describes pattern-guided dip estimation for mitigating aliasing issues present in structure-orientated data processing. Specifically, the patterned-guided dip estimation can include three plane-wave destruction (PWD) filters. The first dip estimation can be for generating a mask operator to distinguish aliasing-free data from aliasing-affected data. The second dip estimation is conducted with only the aliasing-free data, and outputs an initial model for an inversion of the third (ultimate) dip estimation. In some examples, such as identifying residual moveouts on surface-offset common image gathers (CIGs), generation of the mask operator (the first dip estimation) can be optional.
The present disclosure describes a computing system 100 for pattern-guided dip estimation, shown in
Dip estimation can be formulated as a regularized nonlinear inverse problem and defined as a least-squares approach in Equation 1:
MD(σ)≈0 [1]
In equation [1], with the smoothing regularization goal on dip σ, D(ν) is the PWD filter, D indicates the known data, M is the mask operator, and the approximately equality indicates minimization of the power of MD(σ). If the estimated dip is accurate, the data after applying the PWD filter should have no (or minimal) energy. To that end, the dip σ in the PWD filter D can be solved using analytical linearization.
Referring back to
Back to
The computing device 102 stores the pattern guided dip estimation 138 in the data store 106.
In some examples, the computing device 102 can generate the mask operator 130. Specifically, generating the mask operator 130 can include the computing device 102 calculating a coherence map based on the seismic data image 122.
The computing device 102 can apply the mask operator 130 in the first PWD filter dip estimation 132. Specifically, applying the mask operator 130 in the first PWD filter dip estimation can include applying a first weighting factor to aliasing-affected areas of the seismic data image 122 and applying a second weighting factor to aliasing-free areas of the seismic data image 122. For example, a coherence (or similarity) between a copy of the image and the original image (for example, image 200) evaluates the reliability of an estimated dip. The aliased-affected areas of the seismic data image 122 have a reduced reliability which corresponds to a smaller value in the coherence map (for example, the coherence map 400). In some examples, the first weighting factor is less than the threshold value. For example, for a threshold value of 0.4, the first weighting factor can be 0. In some examples, the second weight factor is greater than the threshold value. For example, for a threshold value of 0.4, the second weighting factor can be 1.
In some examples, the computing device 102 can estimate a structure-orientated interpolated target image using the pattern guided dip estimation 138. Referring to
In some implementations, such as selecting residual moveout on surface-offset common image gathers (CIGs), performing an additional dip estimation to obtain the mask operator can be optional. That is, the near-offset portion of CIGs can be regarded as aliasing-free areas as events near the offsets have smaller dips that can be unlikely to suffer from aliasing-related issues. In contrast, events at large offsets are more likely to suffer from aliasing as these area are migrated with a bigger accumulated velocity error, leading to larger local dips. Thus, the mask operator 130 can take the value of 1 for near-offset data and 0 for far-offset data.
To that end, predictive paintings can include two steps: dip estimation and spreading information from a seed trace to neighbors recursively by following the dip. The spreading or “painting” can be implemented using plane-wave destruction filters. That is, with a given dip σ, a local operator can be determined to propagate trace si to trace sj, with such predictions as Ai,j. Specifically, if sr is a reference trace, spreading it's information to a distance neighbor sk (for example, k>r), can be accomplished using a simple recursion as described in Equation [2]:
s
k
=A
k−1,k
. . . A
r+1,r+2
A
r,r+1
s
r [2]
The reference trace for predictive painting can be different depending the application. When selecting residual moveouts in surface-offset CIGs, the reference trace is selected at the zero offset, and its values are set to the depth axis of the CIGs. Painting results are referred to as geologic time, and each contour can represent an event.
For example, for an original image m0 that includes missing traces or missing data and for a target image m after structure-orientated interpolation, m can be reconstructed using a linear problem. Specifically, the linear model can include the PWD filter D(σ) as a linear operator and m0 as the initial model. The linear problem can be expressed as Equation [3]:
D(σ)m≈0 subject to Km=m0 [3]
where K is a diagonal matrix that maintains the know data unchanged. Equation [3] is similar to Equation [1], and the target model of Equation [3] can be m (as opposed to σ in D(σ)) in Equation [1]. Also, the approximate equality can be represent with a known dip σ, and the estimated image can includes zero (or very little) energy after destructing plane waves. The linear problem of Equation [3] can be solved using conjugate gradients. In some examples, interpolation along structures can also be carried though such methods plane-wave shaping regularization.
The computing device 102 can obtain the seismic data image 122 (702). The computing device 102 can apply a first plane-wave destruction (PWD) filter dip estimation 132 to the seismic data image 122 to generate an initial dip model 134 (704). In some examples, the mask operator 130 applies, in the first PWD filter dip estimation, a value of 1 to aliasing-free areas of the seismic data image 122, and applies a value of 0 to aliasing-affected areas of the seismic data image 122. The computing device 102 applies a second PWD filter dip estimation 136 to the seismic data image 122 using the initial dip model 134 to generate a pattern-guided dip estimation 138 (706). The computing device 102 stores the pattern guided dip estimation 138 in the data store 106 (708).
Computing device 800 includes a processor 802, memory 804, a storage device 806, a high-speed interface 808 connecting to memory 804 and high-speed expansion ports 810, and a low speed interface 812 connecting to low speed bus 814 and storage device 806. Each of the components 802, 804, 806, 808, 810, and 812, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 802 may process instructions for execution within the computing device 800, including instructions stored in the memory 804 or on the storage device 806 to display graphical information for a graphical user interface (GUI) on an external input/output device, such as display 816 coupled to high speed interface 808. In other implementations, multiple processors, and multiple buses, or both, may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices 800 may be connected. Each computing device can provide portions of the necessary operations (for example, as a server bank, a group of blade servers, or a multi-processor system).
The memory 804 stores information within the computing device 800. In one implementation, the memory 804 is a volatile memory unit or units. In another implementation, the memory 804 is a non-volatile memory unit or units. The memory 804 may also be another form of computer-readable medium, such as a magnetic or optical disk.
The storage device 806 is capable of providing mass storage for the computing device 800. In one implementation, the storage device 806 may be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. A computer program product may be tangibly embodied in an information carrier. The computer program product may also contain instructions that, when executed, perform one or more methods. The information carrier is a computer- or machine-readable medium, such as the memory 804, the storage device 806, or a memory on processor 802.
The high speed controller 808 manages bandwidth-intensive operations for the computing device 800 The low speed controller 812 manages lower bandwidth-intensive operations. Such allocation of functions is exemplary only. In one implementation, the high-speed controller 808 is coupled to memory 804, display 816 (for example, through a graphics processor or accelerator), and to high-speed expansion ports 810, which may accept various expansion cards (not shown). In the implementation, low-speed controller 812 is coupled to storage device 806 and low-speed expansion port 814. The low-speed expansion port, which may include various communication ports (for example, USB (Universal Serial Bus), Bluetooth, Ethernet, wireless Ethernet) may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, for example, through a network adapter.
The computing device 800 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 820, or multiple times in a group of such servers. It may also be implemented as part of a rack server system 824. In addition, it may be implemented in a personal computer such as a laptop computer 822. Alternatively, components from computing device 800 may be combined with other components in a mobile device (not shown), such as device 850. Each of such devices may contain one or more of computing device 800, 850, and an entire system may be made up of multiple computing devices 800, 850 communicating with each other.
Computing device 850 includes a processor 852, memory 864, an input/output device such as a display 854, a communication interface 860, and a transceiver 868, among other components. The device 850 may also be provided with a storage device, such as a microdrive or other device, to provide additional storage. Each of the components 850, 852, 864, 854, 860, and 868, are interconnected using various buses, and several of the components may be mounted on a common motherboard or in other manners as appropriate.
The processor 852 may execute instructions within the computing device 850, including instructions stored in the memory 864. The processor may be implemented as a chipset of chips that include separate and multiple analog and digital processors. The processor may provide, for example, for coordination of the other components of the device 850, such as control of user interfaces, applications run by device 850, and wireless communication by device 850. Processor 852 may communicate with a user through control interface 858 and display interface 856 coupled to a display 854. The display 854 may be, for example, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display) or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface 856 may comprise appropriate circuitry for driving the display 854 to present graphical and other information to a user. The control interface 858 may receive commands from a user and convert them for submission to the processor.
