Not applicable.
The present disclosure relates generally to seismic data processing and/or subsurface modeling.
This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present disclosure, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
A seismic survey 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.
In accordance with utilization of the seismic data, various techniques may be employed. Full Waveform Inversion (FWI) is one such technique whereby earth models are refined by reducing differences between recorded seismic data and modeled data. Thus, FWI attempts to estimate the properties of the subsurface (e.g., the model) by minimizing the misfit between the observed data and the modeled data. The seismic data are modeled using the physics of wave-propagation in conjunction with a current model. The misfits are fed back in to the inversion and the model is updated. This process is iterative and it continues until a satisfactory match between the modeled data and the observed data is reached.
A key component of the FWI process is the definition of the misfit between the modeled data and the observed data, which is also known as the objective function. The most commonly used objective function is the least-squares direct data misfit. For the least-squares direct data misfit, the inversion is driven to minimize the squared difference between the two data sets (i.e., the sum of the square of the sample-by sample subtraction of modeled and observed data). While this objective function is very intuitive and straightforward it is very sensitive to the choice of the starting model. For example, if the modeled data and the observed data are shifted by more than half-a-wavelength with respect to each other, the process of FWI driven by direct data misfit does not converge to the global minimum value. It is said to be trapped in a local minimum, where the FWI has inadvertently converged to a local minimum of the squared difference.
Various aspects of this disclosure may be better understood upon reading the following detailed description and upon reference to the drawings in which:
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.
In an effort to reduce the incidences of Full Waveform Inversion (FWI) having direct data misfit that does not converge (i.e., being trapped in a local minima), different individual objective functions for FWI can be utilized to reduce any limitation of having a good starting model. Each of these objective functions pose the problem of inversion differently in order to avoid being trapped in a local minima. Although use of individual objective functions make the inversion somewhat less sensitive to the choice of starting model, the problem of local minima has not been eliminated.
Instead, different local minima that correspond to each of the different objective functions are present. The objective functions are each utilized in searching for the same answer, which is the global minima, and which does not change with the choice of an objective function. Accordingly, present techniques herein utilize not just selection of a particular objective functions in conjunction with a FWI, but rather utilize a combination of objective functions. Through the use of multiple different types of objective functions (or through the use of multiple variables in a particular type of objective function) the problem of realizing a local minima is greatly reduced and/or eliminated.
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
Referring now to
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.
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.
With the foregoing in mind,
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 other equipment 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 streamer 34 having a 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
In some embodiments, the land-based 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 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
Regardless of how the seismic data is acquired, a computing system (e.g., for use in conjunction with block 12 of
Referring now to
The processor 64 may be any type of computer processor or microprocessor capable of executing computer-executable code (e.g. instructions to cause the processor 64 to perform one or more operations). 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.
With one or more embodiments, processor 64 can instantiate or operate in conjunction with one or more classifiers. With another embodiment, the classifier 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.
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.
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.
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 the subsurface 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.
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.
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.
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 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
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 can extend 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.
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.
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.
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
FWI can be described as the process of finding a parameter that when wave propagation is simulated in that parameter model, a prediction of (i.e., a match of) the recorded data is generated. The objective function of the FWI can be a function of the observed data (received recorded seismic data) and simulated data (created from the model of the parameters selected).
As previously noted, one commonly used objective function utilized in conjunction with FWI is the least-squares direct data misfit. While this objective function is very intuitive and straightforward it is very sensitive to the choice of the starting model and if, for example, the modeled data and the observed data are shifted by more than half-a-wavelength with respect to each other, the process of FWI driven by direct data misfit does not converge. This can be referred to as a cycle skip (or cycle skipping) and causes erroneous model updates, which leads to incorrectly imaged seismic data. It can be represented by the FWI being trapped in a local minima.
An alternative is to maximize the correlation between the modeled and the observed data at zero-lag or in a narrow window around the zero lag. This is often referred to as the time-lag objective function. The time-lag objective function is less sensitive to the amplitude mismatch between the two waveforms, it instead measures the phase misfit. One possible formulation of the time-lag objective function is described below.
For time-lag FWI, it is desirable to maximize the zero-lag cross-correlation between the observed and the modeled data. Minimization of the negative of the correlation can be achieved at zero lag or in a narrow window around the zero lag. The aforesaid minimization problem can be stated as follows, where r(t)2 is the value of a cross-correlation between the modeled data and the observed data at time t:
E=−½∫n
In the above Equation 1, r(t)2 is defined as:
The gradient term for the time-lag FWI function that is used to update the model can be expressed as shown below:
The time-lag objective function is somewhat more robust, but for complex models it also has local minima. The local minima observed for the time-lag objective function as described above is a function of the number of lags used and also a function of the correlation window. Thus, even when utilizing the time-lag objective function, local minima can be realized, trapping the FWI operation resulting in production of a non-meaningful result.
Utilization of alternate objective functions can be undertaken. However, these alternate objective functions may also result in local minima, causing similar results to those discussed above (i.e., unreliable results). It should be noted that while alternate objective functions may also result in local minima, these local minima may differ amongst one another and may differ from the local minima from the time-lag objective function or may not experience a local minima at all. However, this introduces an issue of proper selection of the objective function to attempt to choose an objective function that will not result in local minima.
An alternative embodiment to choosing a particular objective function is described herein in which a plurality of objective functions may be selected and solved. Their outputs may thereafter be stacked to reduce the incidence of local minima and/or to allow the FWI to proceed to generation of an accurate result (i.e., a satisfactory match between the modeled data and the observed data is reached). This occurs because at the true solution, all of the plurality of objective functions are minimized. However, at false solutions, some of the plurality of objective functions are nearly minimized while others are not minimized (i.e., the plurality of objective functions do not share the same results). Only at the true solution are all of the plurality of objective functions minimized.
Thus, in contrast to a FWI having one objective function selected amongst the classes of possible objective functions (e.g., modeled data minus observed data squared, the time difference between the modeled data and the observed data, the cross correlation of the modeled data and the observed data, etc.) and being optimized, the present embodiment includes optimization of multiple objective functions simultaneously (e.g., computed in parallel with one another). By allowing for each objective function to generate a proposed update to the parameter model (e.g., an update to the model or a search vector in the space of possible models), there is a greater likelihood that the updates will lead the FWI to minimize a difference to the desired global-minimum value. For example, if near a local minimum of one objective function which is not the global minimum, then we will update in the wrong “direction” and the update will lead away from the desired solution. If, however, multiple different objective functions are combined (e.g., summed), they likely will not all point at the same suboptimal local minimum and the result we will more likely head towards the global minimum, thus reducing the chances of being trapped at a local minimum.
Thus, in some embodiments, leverage of the diversity of the local minima across different objective functions may be found by combining them. For example, in one embodiment, the combination may be the stack of the normalized gradients for each of a set of objective functions independently. The gradient represents the direction of movement of the FWI operation (i.e., towards a minima), which may also represent the slope of the objective function. When this is computed using a plurality of objective functions, and the gradients are summed (e.g., stacked), there is a reduced chance for trapping of the FWI operation in local false solutions (i.e., local minima).
The result is effective, as the failures that result in the independent objective functions settling in local minima are removed when the objective functions are combined. In other embodiments, other combinations are also possible, such as random selection of parameters and the objective function type.
In step 82, a misfit is computed for the modeled trace (e.g., the simulated data). In some embodiments, this computation may include, for example, using a direct data difference objective function. In other embodiments, for example, the modeled trace is shifted and the misfit is computed in conjunction with step 82. In step 84, a determination is made if a predetermined number of objective functions have been assigned. This predetermined number may be two or more objective functions. For example, the predetermined number of objection functions may be approximately 5, 6, 7, 8, 9, 10, or another number if, for example, the objective functions represent a single objective function with varied variables. Likewise, for example, the predetermined number of objection functions may be approximately 2, 3, 4, 5, or another number if, for example, the objective functions represent a different objective function, i.e., different types or classes of objective functions.
If more objective functions are to be assigned, the process returns to step 80. If instead the predetermined number of objective functions have been assigned, the process continues to step 86 in which normalization and stacking of the gradients of the objective functions is undertaken. The normalization can operate to remove any information related to a magnitude (e.g., length) of the computed misfits for the objective functions while maintaining values of their direction, which is useful when applying a gradient decent technique during the FWI iteration process. This results in, as illustrated in step 88, production of a search vector using the normalized combination of the search directions. In this manner, the process illustrated by flow diagram 78 clarifies that the computed misfit leads to computation of a gradient (or search direction), which then leads to model and/or parameter updates. It should also be noted that in some embodiments, step 80 may be undertaken, then step 84 may be undertaken and this process may be repeated until the predetermined number of objective functions has been met. Thereafter, step 82 can be undertaken, followed by steps 86 and 88 thereafter.
Likewise, while it is described in step 86 that normalization and stacking of the gradients of the objective functions is undertaken, it should be understood that this is one technique of computing search directions and combining them and that other techniques using a predetermined combination technique (i.e., a preselected combination of the search directions, such as utilizing a geometric mean, an arithmetic mean, etc.) could be employed in place of the normalization and stacking of the gradients of the objective functions. That is, the present techniques allow for combination of the objective functions and their gradients generally, and one specific technique is illustrated in conjunction with step 86. More generally, the process described in conjunction with the flow diagram 78 can include computation of objective functions and gradients and performing combination of the objective functions and gradients (e.g., whereby the combination includes using a predetermined combinatorial technique, such as a geometric mean, an arithmetic mean, etc.).
The concept behind the operation to carry out FWI with cycle skip avoidance as discussed above with respect to
The modeled trace 90 can be shifted around (as part of step 82 of
In one example, an assigned objective function may be a data difference objective function.
In another example, an assigned objective function may be a time-lag objective function.
Each of the objective functions (e.g., the time-lag objective function having different parameters applied therein) can be normalized independently and then stacked (e.g., step 86 of
Therefore, taken together,
The specific embodiments described above have been shown by way of example, and it should be understood that these embodiments may be susceptible to various modifications and alternative forms. It should be further understood that the claims are not intended to be limited to the particular forms disclosed, but rather to cover all modifications, equivalents, and alternatives falling within the spirit and scope of this disclosure.
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 [perform]ing [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).
This application is a non-provisional application claiming priority to U.S. provisional patent application No. 63/317,641 filed Mar. 8, 2022 and entitled “Method and Apparatus for Cycle Skip Avoidance,” which is hereby incorporated herein by reference in its entirety for all purposes.
Number | Date | Country | |
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63317641 | Mar 2022 | US |