Seismic interpretation is a process that may examine seismic data (e.g., with respect to location and time or depth) in an effort to identify subsurface structures (e.g., horizons, faults, geobodies, etc.). Structures may be, for example, stratigraphic formations indicative of hydrocarbon traps or flow channels. In the field of resource extraction, enhancements to seismic interpretation can increase accuracy of a model of a geologic environment, which, in turn, may improve seismic volume analysis for purposes of resource extraction. Various techniques described herein pertain to processing of seismic data, for example, for analysis of such data (e.g., for identifying structures in a geologic environment).
A method can include, within a neighborhood, selecting a sample seismic trace and neighboring seismic traces, individually time shifting each of the neighboring seismic traces with respect to the sample seismic trace to determine a set of individual time shift values that correspond to individual maximum cross correlation values for each of the neighboring seismic traces with respect to the sample seismic trace, determining series of inline direction and crossline direction first derivative values for the set of time shift values, and determining an inline average dip value and a crossline average dip value based on the series of first derivative values. A system can include one or more processors; memory; and instructions stored in the memory and executable by at least one of the one or more processors to access seismic traces, determine time shift values for pairs of the seismic traces (e.g., where a time shift value for a pair of seismic traces corresponds to a maximum cross correlation value for the pair), process the time shift values to provide dip angle values, and, for example, render to a display an image based at least in part on the dip angle values. One or more computer-readable storage media can include computer-executable instructions to instruct a computing device to: access weighted average inline dip angle values and weighted average crossline dip angle values; select an algorithm from a group of algorithms that includes a directional dip algorithm and a dip magnitude algorithm; and apply the selected algorithm to the weighted average inline dip angle values and the weighted average crossline dip angle values to generate image data. Various other apparatuses, systems, methods, etc., are also disclosed.
This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.
Features and advantages of the described implementations can be more readily understood by reference to the following description taken in conjunction with the accompanying drawings.
The following description includes the best mode presently contemplated for practicing the described implementations. This description is not to be taken in a limiting sense, but rather is made merely for the purpose of describing the general principles of the implementations. The scope of the described implementations should be ascertained with reference to the issued claims.
Seismic interpretation is a process that involves examining seismic data (e.g., with respect to location and time or depth) to identify one or more types of subsurface structures (e.g., horizons, faults, geobodies, etc.). When performing seismic interpretation, seismic data may be provided in the form of traces where, for example, each trace is an amplitude versus time recording of energy emitted by a source that has interacted with various subsurface structures. An interpretation process may involve visual display of seismic data and interaction using one or more tools (e.g., executable instruction modules stored in memory and executed by one or more processors). An interpretation process may consider vertical seismic sections, inline and crossline directions, horizontal seismic sections called horizontal time slices, etc. Seismic data may optionally be interpreted with other data such as, for example, well log data.
As an example, an interpretation process may include accessing seismic data from a data store (e.g., via a network or other connection). Seismic data may be formatted according to one of the SEG-Y format standards (Society of Exploration Geophysicists), the ZGY format standard (e.g., a bricked format) or another format. As an example, seismic data may be stored with trace header information, which may assist in analysis of the seismic data. Seismic data may optionally be accessed, for example, according to a number of traces (e.g., in an inline, crossline or inline and crossline directions), which may be entire traces or portions thereof (e.g., for one or more particular times or depths). As an example, given a number of traces across a region, a process may access some of those traces in a sub-region by specifying inline and crossline indexes (e.g., or geographic or grid coordinates) as well as a time or depth window.
An interpretation process may include determining one or more seismic attributes. A seismic attribute may be considered, for example, a way to describe, quantify, etc., characteristic content of seismic data. As an example, a quantified characteristic may be computed, measured, etc., from seismic data. A seismic attribute may be a rate of change of a quantity (or quantities) with respect to time, space or both time and space. As an example, a seismic attribute may provide for examination of seismic data in an amplitude domain, in a time domain, or in another manner. As an example, a seismic attribute may be based on another seismic attribute (e.g., a second derivative seismic attribute may be based on a first derivative seismic attribute, etc.).
An interpretation framework may include modules (e.g., processor-executable instructions stored in memory) to determine one or more seismic attributes. Seismic attributes may optionally be classified, for example, as volume attributes or surface attributes. As an example, a volume attribute may be an attribute computed from a seismic cube and may result in a new seismic cube that includes information pertaining to the volume attribute. As an example, a surface attribute may be a value associated with a surface of a seismic cube that includes information pertaining to a volume attribute.
A seismic interpretation may be performed using displayable information, for example, by rendering information to a display device, a projection device, a printing device, etc. As an example, one or more color schemes (e.g., optionally including black and white or greyscale) may be referenced for displayable information to enhance visual examination of the displayable information. A color scheme may include a palette, a range, etc. A look-up-table (LUT) or other data structure, function (e.g., linear or non-linear), etc., may allow for mapping of values associated with one or more seismic attributes to intensity, colors (e.g., RGB, YCbCr, etc.), etc. Where the human eye will be used or is used for viewing displayable information, a display scheme may be selected to enhance interpretation (e.g., to increase contrast, provide for blinking, etc.).
A module for determining one or more seismic attributes may include one or more parameters. As an example, a module may include one or more parameters that may be set via a graphic user interface, a specification file, etc. In such an example, an interpreter may wish to examine a seismic attribute for seismic data using one or more values of a parameter. As an example, such a module may provide a default value and a field, graphical control, etc., that allows for input of a value other than the default value.
As an example, seismic interpretation may be performed using seismic to simulation software such as the PETREL® seismic to simulation software framework (Schlumberger Limited, Houston, Tex.), which includes various features to perform attribute analyses (e.g., with respect to a 3D seismic cube, a 2D seismic line, etc.). While the PETREL® seismic to simulation software framework is mentioned, other types of software, frameworks, etc., may be employed for purposes of attribute analyses.
In the example of
In an example embodiment, the simulation component 120 may rely on entities 122. Entities 122 may include earth entities or geological objects such as wells, surfaces, reservoirs, geobodies, etc. In the system 100, the entities 122 can include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, interpretation, etc. (e.g., the seismic data 112 and other information 114).
In an example embodiment, the simulation component 120 may rely on a software framework such as an object-based framework. In such a framework, entities may include entities based on pre-defined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the MICROSOFT®.NET™ framework (Redmond, Wash.), which provides a set of extensible object classes. In the .NET™ framework, an object class encapsulates a module of reusable code and associated data structures. Object classes can be used to instantiate object instances for use in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data, geobody classes may define objects for representing geobodies based on seismic data, etc. As an example, an interpretation process that includes generation of one or more seismic attributes may provide for definition of a geobody using one or more classes. Such a process may occur via user input (e.g., user interaction), semi-automatically or automatically (e.g., via a feature extraction process based at least in part on one or more seismic attributes).
In the example of
In an example embodiment, the management components 110 may include features of a commercially available simulation framework such as the PETREL® seismic to simulation software framework. The PETREL® framework provides components that allow for optimization of exploration and development operations. The PETREL® framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes. Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of simulating a geologic environment).
In an example embodiment, various aspects of the management components 110 may include add-ons or plug-ins that operate according to specifications of a framework environment. For example, a commercially available framework environment marketed as the OCEAN® framework environment (Schlumberger Limited, Houston, Tex.) allows for seamless integration of add-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN® framework environment leverages .NET® tools (Microsoft Corporation, Redmond, Wash.) and offers stable, user-friendly interfaces for efficient development. In an example embodiment, various components (e.g., or modules) may be implemented as add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).
The model simulation layer 180 may provide domain objects 182, act as a data source 184, provide for rendering 186 and provide for various user interfaces 188. Rendering 186 may provide a graphical environment in which applications can display their data while the user interfaces 188 may provide a common look and feel for application user interface components.
In the example of
In the example of
In the example of
The framework 170 may provide for modeling the geologic environment 150 including the wells 154-1, 154-2, 154-3 and 154-4 as well as stratigraphic layers, lithologies, faults, etc. The framework 170 may create a model with one or more grids, for example, defined by nodes, where a numerical technique can be applied to relevant equations discretized according to at least one of the one or more grids. As an example, the framework 170 may provide for performing a simulation of phenomena associated with the geologic environment 150 using at least a portion of a grid. As to performing a simulation, such a simulation may include interpolating geological rock types, interpolating petrophysical properties, simulating fluid flow, or other calculating (e.g., or a combination of any of the foregoing).
Seismic interpretation may aim to identify and classify one or more subsurface boundaries based at least in part on azimuth and dip angle. As shown in the diagram 212 of
As shown in the diagram 214 of
As mentioned, seismic data may be acquired for a region in the form of traces. In the example of
In the example of
In the example of
In the example of
As mentioned, indicia of reflection may be found in a seismic trace. Thus, given a sample seismic trace and a neighboring seismic trace that were both collected over a dipping boundary, indicia of reflection in the two seismic traces may be offset in time. By successively shifting the neighboring seismic trace in time and performing a cross correlation between the shifted neighboring seismic trace and the fixed sample seismic trace for each successive shift, a maximum may be reached in a cross correlation value when the total shift of the neighboring seismic trace “aligns” the indicia of reflection of the dipping boundary with that of the sample seismic trace. In such an example, a downward shift in time corresponds to an upward dip angle between the neighboring seismic trace and the selected sample seismic trace and an upward shift in time corresponds to a downward dip angle between the neighboring seismic trace and the selected sample seismic trace.
While the foregoing example refers to shifting a neighboring seismic trace, as an alternative, a sample seismic trace may be shifted or both a sample seismic trace and a neighboring seismic trace may be shifted. In any of the foregoing examples, a value may be determined that represents how much shift occurred between the sample seismic trace and the neighboring seismic trace to achieve a maximum cross correlation value between the two. Such a “shift” may be given in distance, time, increments or steps, etc.
In the method 300, given the set of individual time shift values, a determination block 324 provides for determining a series of inline first derivative values and for determining a series of crossline first derivative values. As shown in the example of
In the example of
In the example of
The method 300 is shown in
As an example, a method can include: providing a neighborhood of seismic traces along inline and crossline directions; within the neighborhood, selecting a sample seismic trace and neighboring seismic traces; individually time shifting each of the neighboring seismic traces with respect to the sample seismic trace to determine a set of individual time shift values that correspond to individual maximum cross correlation values for each of the neighboring seismic traces with respect to the sample seismic trace; determining a series of inline direction first derivative values for the set of time shift values; determining a series of crossline direction first derivative values for the set of time shift values; determining an inline average dip value based on the series of inline first derivative values; determining a crossline average dip value based on the series of crossline first derivative values; and storing to a memory storage device, coordinates for the sample seismic trace, the inline average dip value, and the crossline average dip value. As an example, such a method may also include accessing previously stored inline average dip values and crossline average dip values for other sample seismic traces, processing the average dip values for the sample seismic trace and the other seismic sample traces and rendering to a display an image based at least in part on the processing.
As an example, a method may include identifying a fault, a channel, a salt body (e.g., a salt dome) or another feature in an image rendered to a display. As an example, an image may be based on directional processing of average dip values along an angle between an inline direction and a crossline direction. In such an example, the processing may include use of an Euler equation. Directional processing may act to mute information in one or more directions; thus, such processing may provide for direction muting.
As an example, a method may commence responsive to receipt of an instruction input via a graphical user interface. For example, such a graphical user interface may include an attribute menu that includes a dip attribute as a menu entry.
As an example, a method may include individually time shifting each seismic trace within a neighborhood of a sample seismic trace to determine a set of individual time shift values that correspond to individual maximum cross correlation values for each of the neighboring seismic traces with respect to the sample seismic trace. Such a time shifting process may include, for each of the neighboring seismic traces, determining a cross correlation value for a positive time shift value, a cross correlation value for a negative time shift value and a cross correlation value for a zero time shift value; comparing the cross correlation values; based on the comparing, determining additional cross correlation values for either greater positive time shift values or greater negative time shift values until a decrease occurs in one of the cross correlation values; and responsive to the decrease, assigning a time shift value that corresponds to a maximum cross correlation value.
As an example, a system can include one or more processors; memory; and instructions stored in the memory and executable by at least one of the one or more processors to access seismic traces, determine time shift values for pairs of the seismic traces where a time shift value for a pair of seismic traces corresponds to a maximum cross correlation value for the pair, process the time shift values to provide dip angle values, and render to a display an image based at least in part on the dip angle values. In such a system, the instructions to determine time shift values for pairs of the seismic traces may include instructions to shift one seismic trace of the pair with respect to the other seismic trace of the pair. As an example, instructions to process time shift values may include instructions to determine slopes based on the time shift values, instructions to provide directional dip angle values, instructions to provide dip angle magnitude values or other instructions. As an example, a system may include a display (e.g., with an appropriate display interface or interfaces) and instructions to render a graphical user interface to the display for input to adjust one or more parameters associated with instructions to process the time shift values. For example, for directional processing, such a graphical user interface may allow a user to input an angle (e.g., optionally using a pointing device, a touch display, a keyboard, a voice command, etc.).
As an example, one or more computer-readable storage media can include computer-executable instructions to instruct a computing device to: access weighted average inline dip angle values and weighted average crossline dip angle values; select an algorithm from a group of algorithms that includes a directional dip algorithm and a dip magnitude algorithm; and apply the selected algorithm to the weighted average inline dip angle values and the weighted average crossline dip angle values to generate image data. In such an example, instruction may be included to render an image to a display based on the generated image data.
As an example, one or more computer-readable storage media may include computer-executable instructions to instruct a computing device to implement a directional dip algorithm that includes the following equation:
DirectionDip=InlineDip*sin Θ+XlineDip*cos Θ
where InlineDip represents a weighted average inline dip angle value, XlineDip represents a weighted average crossline dip angle value and Θ is a directional angle (see also, e.g.,
As an example, one or more computer-readable storage media may include computer-executable instructions to instruct a computing device to implement a dip magnitude algorithm that includes the following equation:
where InlineDip represents a weighted average inline dip angle value and XlineDip represents a weighted average crossline dip angle value.
As an example, one or more computer-readable storage media may include computer-executable instructions to instruct a computing device to implement a dip magnitude algorithm that includes the following equation:
DipMag=√{square root over (InlineDip2+XlineDip2)}
where InlineDip represents a weighted average inline dip angle value and XlineDip represents a weighted average crossline dip angle value.
As mentioned, seismic data for a region may include one million traces where each trace includes one thousand samples for a total of one billion samples. Resources involved in processing such seismic data in a timely manner may be considerable.
The approach shown in
Referring to the diagram 410 of
As described in various example embodiments, a dip estimation approach may involve determinations of “apparent dip” and “local amplitude correlation”. As to local amplitude correlation, to arrive at a maximum local amplitude correlation value for two traces, a statistical technique such as gradient descent (e.g., “hill climb”) may be implemented. Where a set of time shift values that correspond to maximum local amplitude correlation values are determined, first derivatives of those values may be taken, for example, in inline and crossline directions (e.g., using a five point estimate). As an example, consider a 5×5 matrix of time shift values where 5 inline slopes and 5 crossline slopes may be determined (e.g., noting that time can equate to depth). Given a series of inline slopes and a series of crossline slopes, each of these series may be averaged, optionally using weighted averaging, to arrive at an inline slope or dip estimate and a crossline slope or dip estimate (e.g., for a center point of the 5×5 matrix). Such averaging results in a smoothing effect, which can enhance an image and optionally be adjusted by a user during visualization (e.g., different weighting, different matrix size, etc.).
As to correlation between two seismic traces, as an example, an explicit dip scan approach may be implemented to find the most coherent reflector. For example, an explicit dip scan may be performed by scanning for the dip where the trace to trace cross correlation is maximized. This procedure may be repeated for every sample in a seismic volume, and comparing against neighbor traces. Cross correlation may be calculated within a window of +/−NS samples from a center sample, for example, where the sample weight gradually decreases away from the center sample (e.g., using weighted Pearson cross correlation). The sample amplitude values from one trace may be held fixed, while the amplitudes from another trace may be interpolated to be able to extract amplitudes at any position from that trace. A search for a maximum may include a discrete search using, for example, a greedy hill climb algorithm. Such an algorithm may search with a fixed step size until it finds a correlation maximum, possibly a local one. After finding the approximate maximum position (e.g., posm), a “true” maximum position may be estimated using, for example, Newton's method (e.g., posmax=posm−[f′(posm)/f″(posm)]). Given traces to neighbor trace dips for a selection of samples, an example embodiment may estimate the dip at each sample in a volume by using a weighted average of a neighborhood around that sample. An output may include, for example, apparent dip in a specified direction (e.g., specified by a user), the dip, or other metric. As to an apparent dip, both positive and negative dip visualization may be possible. As to dip, as an example, an output may provide the steepest dip at a point, which may optionally be defined as being in either a downward direction or an upward direction for all points (e.g., dip without a plus or minus sign).
In
As to a search for a maximum cross correlation value, time shifting may include one or more limits. A limit may be set according to a distance between a sample trace and a neighboring trace, which, in turn, may allow for determination of a corresponding angle for the limit (e.g., using the Pythagorean theorem). As an example, time shifting may be limited to three samples upward and three samples downward where the three sample limits corresponds to angles of about −70 degrees and +70 degrees. Effort spent time shifting more than about +/−70 degrees may result in diminishing returns depending on the nature of a feature or features in a geologic environment.
As to the method 530, as an example, a cubic spline or other interpolation technique may be applied to one or more traces to increase a number of points for performing a cross correlation. For example, a cubic spline may be fit to amplitude values for a series of times of a trace. In such an example, the spline may allow for increasing a number of points by a factor of two or more. In turn, a time step Δt may be defined based on the increased number of points (e.g., rather than the sampling rate for the trace). Such a technique can improve cross correlation (e.g., more points) and improve time shift value determination (e.g., finer Δt).
As to the scenario 532, a sample of a neighbor trace (e.g., data point in time) may substantially align with a sample of a sample trace. As to the scenario 534, a sample of a neighbor trace may be misaligned with a sample of a sample trace. Where traces are misaligned, as an example, a spline method may be applied and one or more samples or points selected to diminish such misalignment. Such an approach may improve accuracy of geometrical metrics derived from a time shift method.
As mentioned with respect to
As to the cross correlation process 650, the process may commence by first determining whether cross correlation values are increasing in the direction of a positive time shift or a negative time shift. For example, given a time step, cross correlation values for a neighboring trace with respect to a sample trace may be initially determined for an upward shift in time, no shift in time and a downward shift in time. The process 650 may then determine a direction in which to proceed based on the direction in which cross correlation values are increasing. For the example of
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In the example of
The method 900 is shown in
As an example, a visualization process may consider dip values to be a height-field upon which a light source may cast shadows. In the image 1010, a light source is positioned perpendicular to the plane viewed (or the dip magnitude). For the time-slice associated with the image 1010, with perpendicular lighting, white is steep and black is flat, which highlights salt structures (circular shapes in the image) and fault structures that are the discontinuities between the salt structures. In the image 1030, a light source is positioned along the azimuth of the plane, which can be rotated around the view plane (e.g., calculated using the Euler equation). For the time-slice associated with the image 1030, with directional lighting along the crossline direction, salt structures and fault structures are also highlighted.
As an example, dip magnitude may be determined as the absolute value of dip, which can act to highlight steep dips, bumps and curves to provide a user with a more geologically correct image. Such an approach can also highlight edges, especially around salt data.
Referring again to the image 1010, several discontinuities exist (e.g., in this case faults), which indicates that the approach picks up on various types of structural geology with a high level of detail reflecting even minor changes in dip in the seismic data. In the image 1010, slopes and anticline features are visible as well, giving an overall better picture and understanding of the subsurface. However, such an extensive amount of detail could possibly be considered as geological noise, which could create issues when interpreting the seismic data. To assist in discrimination of noise from features, one may select another algorithm, for example, to steer lighting directionally along the azimuth of the viewing plane. In the image 1030, the same seismic data has been processed but with the light source along the crossline direction.
The approach taken in forming the image 1030 may be considered to involve muting certain information, for example, muting information that is not relevant in the perpendicular direction, which can help discriminate or reduce noise. As an example, directional muting may be achieved via use of the Euler equation on the inline and cross line dip estimates (see, e.g., the algorithm 934 of
A directional algorithm applied to dip values can, for example, enhance detection of salt, fault, anti-cline and sync-line structural features as well as channels that may be considered stratigraphic (e.g., noting that an indication of their stacking may also be seen).
As to the image 1210, it corresponds to seismic data for a time-slice showing dip with perpendicular lighting, where black is steep and white is flat. In the image 1210, highlighted are channels and meandering systems (e.g., see middle of the image) and a salt structure (e.g., see top right corner), which is covered by a black edge due to the steepness of the stratigraphy around it; also, one may note stacking information within the channel borders. As to the image 1230, it corresponds to the seismic data for the time-slice of the image 1210 but showing dip with directional lighting along the crossline direction. In the image 1230, channels and meandering systems and the salt structure are highlighted.
In the example of
As an example, the determination block 1314 of the method 1310 may include determining an instantaneous dip value by calculating dip locally by looking at a neighborhood of N samples in each dimension (e.g., where N is about 3). In each dimension, the determination block 1314 may calculate a point estimate of a derivative of a seismic signal (e.g., amplitude) and use these values to calculate an inline dip value and a crossline dip value. To provide a robust approximation of dip, the determination block 1314 may take the median of a number of closest point estimates along the trace for each sample (e.g., about 11). In such an example, the decision block 1316 decides that the approximate dip value is indicative of a steep dip, the method 1310 may proceed to a dip scan method to re-estimate the dip (e.g., via the time shifting approach).
In an example embodiment, a method can include double checking and correcting steepest dips, for example, to reduce a number of outlier dip estimates. Such a method can include a hybrid dip estimation technique that uses a combination of a spatially smoothed gradient method estimates and a dip scan that maximizes cross correlation between neighboring traces. Such a method may provide detailed dip fields with less computational demands when compared to a method that includes eigenvector analysis of a gradient structure tensor (e.g., GST approach). As an example, after having calculated Inline and crossline dip values, a method may include calculating dip magnitude values using the equation 1322 of
In comparison to a GST approach, a dip scan approach that includes time shifting individual traces can provide for more detailed images and be more conservative when it comes to steeper dipping regions. For example, such a dip scan approach can provide for sharper steep dipping boarders around salt structures when compared to an original seismic image. As to fault structures, a dip scan approach can provide for enhanced consistency as to the direction of the dipping features. Such an approach can provide overall better image for interpretation and a more accurate estimation and representation of a dip.
As mentioned, the method 1310 of
The method 1310 is shown in
Table 1, below, shows some examples of trial results from running various approaches on a 3 GB 32-bit seismic cube with the same parameters. The execution times where measured for 5 computations of the whole cube. The mean computational time is presented in Table 1. Given these results, a hybrid dip approach (see, e.g., the method 1310 of
The example trial results of Table 1 indicate that a hybrid approach can provide dip values in a reasonable amount of time, suitable for an interpretation session where various parameters may be adjusted and the approach repeated (e.g., optionally as a background process).
The images of
In an example embodiment, components may be distributed, such as in the network system 1410. The network system 1410 includes components 1422-1, 1422-2, 1422-3, . . . 1422-N. For example, the components 1422-1 may include the processor(s) 1402 while the component(s) 1422-3 may include memory accessible by the processor(s) 1402. Further, the component(s) 1402-2 may include an I/O device for display and optionally interaction with a method. The network may be or include the Internet, an intranet, a cellular network, a satellite network, etc.
Although a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments without materially departing from the embodiments of the present disclosure. Accordingly, such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not just structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures. It is the express intention of the applicant not to invoke 35 U.S.C. §112, paragraph 6 for any limitations of any of the claims herein, except for those in which the claim expressly uses the words “means for” together with an associated function.
This application claims the benefit of a U.S. provisional application having Ser. No. 61/498,773, entitled “Direction Dip Seismic Attribute”, filed Jun. 20, 2011, which is incorporated by reference herein in its entirety.
Number | Date | Country | |
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61498773 | Jun 2011 | US |