US 20100245347 A1
A process that assists with the identification of potential hydrocarbon deposits that includes performing a structural interpretation of a three-dimensional seismic volume, transforming the three-dimensional seismic volume into a stratal-slice volume, performing a stratigraphic interpretation of the stratal-slice volume which includes the extracting of bounding surfaces and faults and transforming the stratal-slice volume into the spatial domain. As illustrated in FIGS. 24 a, b and c, an exemplary seismic volume before Domain Transformation is presented in FIG. 24 a, interpreted horizons and faults used in the transformation are presented in FIG. 24 b, and the Domain Transformed stratal-slice volume is presented in FIG. 24 c. The input seismic volume in FIG. 24 a has deformations associated with syn- and post-depositional faulting. The output Domain Transformed volume (FIG. 24 c) is substantially free of deformations.
113. A method for extracting a bounding surface in volumetric data comprising:
creating an initial bounding surface mesh having a number of vertices which either surround or are within in an element of interest;
adjusting the mesh to meet a boundary of the element of interest;
determining a projected vertex location for one or more of the vertices in the bounding surface mesh;
determining a voxel value at each of the projected vertex locations; and
moving the projected vertex to the projected vertex location if the voxel value is not within a range, and
identifying the vertex location as fixed if the voxel value is within a range.
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This application claims the benefit of and priority under 35 U.S.C. §119(e) to U.S. Patent Application Nos. 60/815,630, filed 21 Jun. 2006, entitled “Algorithm and Process to Create Geobody Bounding Surfaces,” 60/815,625, filed 21 Jun. 2006, entitled “Computed Aided and Automatic Extraction of Depositional Systems,” and 60/815,961, filed 21 Jun. 2006, entitled “Stratal-Slice Domain Transformation of a Seismic Volume,” all of which are incorporated herein by reference in their entirety.
An exemplary embodiment of this invention is in the field of 3-D interpretation, and more particularly to 3-D seismic interpretation. More specifically, an exemplary embodiment includes a workflow, including two new processes, implemented as software that is designed to enable automatic or semi-automatic interpretation of paleo-depositional features in three-dimensional seismic data for exploration, development and, for example, production of hydrocarbons.
The need for computer-aided, semi-automatic and automatic interpretation of depositional systems derives from a combination of factors. Energy resources are becoming steadily more difficult to find and develop. It has been recognized for many years that the majority of new oil and gas reserves are a function of a complex combination of geological, structural and stratigraphic elements. While the problems of exploration and the efficient development of hydrocarbon reserves have become more difficult, the volume of data to be interpreted for each project has become orders of magnitude greater over the past 20 years. Simultaneously, both the number of interpreters and the time allowed for interpretation have been substantially reduced. This drives the need for more advanced computer-aided processes that can support the interpreter by enabling more efficient, precise and effective interpretation of 3-D seismic data volumes.
Computer-aided structural interpretation of 3-D seismic data volumes has been embodied in tools in interactive seismic interpretation for a number of years. Since the early 1980s, horizon autotracking tools have been available to help increase the speed and consistency of horizon interpretation in 3-D seismic surveys (Dorn, 1998). More recently, techniques have been developed to provide computer-aided interpretation of faults and automatic fault interpretation (e.g., Crawford and Medwedeff, 1999, U.S. Pat. No. 5,987,388; Pederson, S. I., 2002, U.S. Pat. No. 7,203,342), as well as techniques beyond event autotracking to automatically interpret horizons (Dorn, 1999, U.S. Pat. No. 5,894,417; Stark, 1997, U.S. Pat. No. 5,671,344).
Computer tools to aid in stratigraphic interpretation of seismic volumes have developed much more slowly. Elements of depositional systems can most readily be identified by an interpreter when the morphology of the paleo-depositional system can be viewed. Similarly, it is most likely that a computer algorithm can be written to recognize, image, and extract elements of depositional systems if the computer algorithm is able to operate on the data in a domain where the paleo-depositional system's morphology is most readily imaged. In both of these cases, the optimal environment is the stratal-slice domain, where the slices through the volume of seismic data are close approximations of paleo-depositional surfaces.
In an undeformed data volume, horizontal slices (planar slices parallel to the (x,y) plane in the volume) may accurately represent depositional surfaces. However, in volumes with structural deformation, horizontal slices do not represent depositional surfaces for more than a small portion of the total volume. Faulting, folding, and velocity anomalies prevent the complete representation of such a surface by a simple horizontal slice.
Horizon-slicing is defined as creating a slice through a 3-D seismic volume in the shape of an interpreted seismic reflection in that volume. Horizon slicing (as opposed to horizontal slicing) has provided better images of depositional systems since the mid-to-late 1980s.
A continuous interval is a package of sediments that represent the same span of geologic age, but were deposited at different sedimentation rates in different parts of the volume. The result is an interval that represents that same amount of geologic time, but does not exhibit the same thickness. In such an interval, growth is caused by spatially variable rates of sedimentation. If we assume that sedimentation rates between a pair of bounding horizons are variable only in space (i.e., not vertically variable in a given location), stratal slices may be extracted by interpolating trace values vertically, where the interpolated sample interval at each (x,y) location is controlled by the upper and lower bounding surfaces and the number of samples desired in the interval on the output trace. This type of stratal slice has been referred to as a proportional slice.
Proportional slicing or stratal slicing developed in the mid 1990s (Posamentier, et. al., 1996; Zeng, et. al. 1998a,b,c) provides even better imaging of depositional systems, and better discrimination between stacked channel systems in the seismic data because these surfaces are typically a better approximation of paleo-depositional surfaces than either horizon slices or horizontal slices.
Zeng, et. al. (1998 a, b, c) describes the first instance of extracting slices based on geologic age. They reasoned that seismic reflectors do not always follow depositional surfaces. Thus, they interpolated seismic slices between surfaces judged to be time-equivalent. They referred to these interpolated slices as ‘stratal’ slices. Stark (2004) describes a similarly motivated effort. He used unwrapped phase as a proxy for user-interpreted age horizons. Slices were extracted from the data volume by drawing data from points of equal unwrapped phase. Stark's approach assumes that unwrapped phase closely approximates geologic age, but this is an assumption that is often in error.
Both horizon slicing and proportional slicing generally suffer from substantial limitations in that they do not accommodate and compensate for generalized 3-D structural deformation subsequent to deposition, nor do they properly account for the wide variety of depositional environments. Horizon slicing is only appropriate for a conformable sequence of horizons in the seismic volume (i.e., a spatially uniform depositional environment over time). Proportional slicing is only appropriate for an interval that exhibits growth (i.e., a spatially gradational change in depositional thickness over an area, often due to spatially differential subsidence). Horizon and proportional slicing do not properly reconstruct paleo-depositional surfaces in other depositional environments, nor do they account for post-depositional structural changes (particularly faulting) or post-depositional erosion.
Among the situations that the proportional or stratal slice volume (as defined by Zeng, et. al, 1998 a,b,c) does not handle properly are:
For example, both proportional slicing and stratal slicing (as defined by Zeng, et. al., 1998 a, b, c) produce volumes that have gaps or undefined zones where the volume is cut by a dipping fault surface.
The situation for more than one pair of horizons is shown in
In most previous attempts to solve this problem, where this simple form of proportional slicing is implemented, the indeterminate zones are filled with input seismic data rather than nulls, which can be quite misleading. Lomask et. al. (2006) have developed an approach that attempts to create a stratal volume without requiring interpreted horizons, faults or other surfaces to define and constrain the transformation. The lack of interpreted structural control in their approach produces poor results for seismic volumes that contain any significant structural deformation.
One exemplary embodiment of the Domain Transform method of this invention explicitly requires interpreted horizons, faults, and other geologic surfaces as input, and, as a result, does not suffer the limitations of the method proposed by Lomask.
Seismic-Wheeler Volumes (e.g., Stark, 2006) represent interpreted depositional systems tracts as well as hiatuses in deposition based on horizon interpretations of system boundaries in 3-D. This approach requires recognition of the system tract by the interpreter as a starting point, and does not take into account the effects of post-depositional structural deformation and faulting. The implementations of Seismic-Wheeler Volumes described by Stark (2006) also depend on association of each seismic sample in the volume with a relative geologic time (Stark, 2004; Stark 2005a, U.S. Pat. No. 6,850,845; Stark 2005b, U.S. Pat. No. 6,853,922). This constraint is not present in the process described here.
By transforming seismic data from the (x,y,time/depth) domain to the (x,y,stratal-slice) domain, data in a deformed volume can be interpreted in stratal-slice view. One exemplary goal is to reconstruct the data volume along stratal surfaces in an undeformed state using user-interpreted surfaces and user-supplied information on geologic relationships in the volume as a guide. Seismic data in this undeformed state is more easily and accurately interpreted for stratigraphy, depositional systems, and depositional environments.
Finally, a lightweight representation of volumetric data is often necessary for real-time rendering, for the segmentation of interpreted data, and for reducing visual clutter. A new Surface Wrapping technique has also been developed in accordance with an exemplary embodiment of this invention, and is described herein. For example, it allows, for example, the user to create a 3-D polygonal mesh that conforms to the exterior boundary of geobodies (such as stream channels) that offers significant improvements over existing techniques.
An inspiration for this Surface Wrapping approach was the Surface Draping algorithm (Dorn, 1999, U.S. Pat. No. 5,894,417), which allows a polygonal mesh to be defined that reflects the geometry of an interpreted horizon. The surface draping algorithm is based on the metaphor of laying an elastic sheet over a contoured surface: gravity pulls the sheet down, causing it to conform to the surface beneath it, and the tension of the elastic material allows the sheet to smoothly cover small gaps in the surface while preserving the important features.
Dorn's Surface Draping allows the user to view seismic data and define a series of points slightly above the desired horizon. These points define the initial shape of the 3-D mesh, which corresponds to the elastic sheet described above. When the user has completed this stage, the actual mesh is computed, generally using one vertex per voxel. These vertices are then iteratively “dropped” onto the horizon. At each step, the value of the voxel at each vertex's position is compared to a range that corresponds to the values found in an interpreted horizon. If the value falls within that range, the vertex is fixed in place.
The Surface Draping concept would have benefits if adapted to work on geobodies and other 3-D volumes. Other approaches have been used to define a mesh that surrounds and conforms to the shape of a volume. Acosta et. al. (2006a and b; U.S. Pat. Nos. 7,006,085 and 7,098,908) propose a technique where the bounding surface is defined slice-by-slice by a user as a set of spline curves or general polylines that are then connected in 3-D. Kobbelt et. al. (1999) describes a technique based on successive subdivision of an initially simple mesh that completely surrounds the volume. The technique described by Koo et. al. (2005) improves on the same idea by allowing the user to define an arbitrarily shaped grid around a point cloud, allowing holes in the volume to be interpreted properly. Both of the above algorithms work by moving each vertex to the nearest point in the volume.
It is an aspect of the present invention to provide a workflow and automated or semi-automated method and system for identifying and interpreting depositional environments, depositional systems and elements of depositional systems from 3-D seismic volumes.
It is a further aspect of this invention to provide such a method and system in which noise in the seismic volume after acquisition and seismic processing is removed or minimized at each step in the workflow.
It is a further aspect of this invention to provide a technique whereby the original 3-D seismic volume is transformed to a volume where every horizontal slice through the volume represents a paleo-depositional (stratal) surface, such that the effects of structural deformation are effectively removed from the volume.
It is a further aspect of this invention to provide a means of imaging, recognizing and obtaining the bounding surfaces of depositional systems or elements of depositional systems in the transformed seismic volume.
It is a further aspect of this invention to provide a technique whereby the imaging or attribute volumes created from the transformed seismic volume may be inverse-transformed to the coordinate space of the original seismic volume.
It is a further aspect of this invention to provide a technique whereby the bounding surfaces obtained for depositional systems or elements of depositional systems obtained in the transformed seismic volume may be inverse-transformed to the coordinate space of the original seismic volume.
In accordance with an exemplary embodiment of this invention, an approach including a unique new workflow that includes a combination of existing and new novel processes is presented for computer-aided interpretation of depositional systems in 3-D seismic volumes. In this discussion, channels are used as the example of a depositional system, but the approach will work for the full range of depositional systems and environments recorded in 3-D seismic data volumes.
This unique workflow includes the following general steps, illustrated in
Individual steps and series of steps of this workflow may be applied recursively to the data volume to improve the results of the overall process.
Structural Interpretation refers to the interpretation of horizons and faults imaged in the 3-D seismic volume. The original seismic volume and its structural interpretation is typically described in an orthogonal cartesian coordinate system indicated by (x,y,z) or (x,y,t), where x and y represent horizontal distance, z represents vertical distance, and t typically represents vertical composite (also called two-way) reflection time. The proposed workflow can be applied to volumes that have been processed into either (x,y,z) or (x,y,t) volumes.
Domain Transformation refers to a novel process of changing the coordinate space of the seismic volume from (x,y,z or t) to (x,y,s) where s represents “stratal-slice.” A stratal-slice is defined as a slice along an approximate paleo-depositional surface, that is, a surface upon which at some time in the past, geologic deposition (e.g., sedimentation or erosion) was occurring. The Domain Transformation creates a stratal-slice volume—a volume where every horizontal slice in the volume represents a stratal-slice or paleo depositional surface. This stratal-slice volume, created by the Domain Transformation process, is a volume that is substantially free of deformation. This Domain Transformation process is unique in that it removes the effects of deformation that has occurred both during and subsequent to the deposition, and will properly construct a stratal-slice volume for all types of geologic surfaces and intervals.
The Domain Transformation not only produces an ideal volume for the interpretation or extraction of elements of depositional systems, it also provides a valuable tool to highlight errors or omissions in the structural interpretation. Such errors or omissions are highlighted in the domain-transformed volume. Using the transformed volume to image problems in the structural interpretation, Optional Structural Refinement uniquely enables the interpreter to correct these errors and omissions in either the (x,y,s) volume or the (x,y,z or t) volume and improve both the structural interpretation and the results of the Domain Transformation.
Stratigraphic Interpretation, as used here, encompasses both the processing of the Domain Transformed volume to improve the imaging of elements of depositional systems (herein referred to as attribute calculation), and the process of extracting the bounding surfaces of those elements of depositional systems. The bounding surface extraction process (herein referred to as Surface Wrapping) is a unique process that provides numerous advantages over processes currently practiced by individuals with ordinary skill in the art to obtain the bounding surfaces of elements of depositional systems. Surface Wrapping's applicability extends to the extraction of the bounding surfaces of bodies or aspects imaged in any type of volumetric data from any discipline.
Inverse Domain Transformation refers to a process of changing the coordinate space of the seismic volume, any attribute volumes, the refined structural interpretation, and bounding surfaces from (x,y,s) to (x,y,z or t).
As mentioned, individual steps and series of steps may be applied recursively to the data volume to improve the results of the overall process. For example, initial Structural Interpretation of key horizons and major faults followed by Stratal-slice Domain Transformation of the Seismic Volume may highlight secondary horizons or smaller faults (additional structural interpretation) that must be interpreted and honored in the Domain Transformation process to achieve higher quality results.
Numerous exemplary benefits derive from the workflow and processes contained therein.
This Summary of the Invention is neither intended nor should it be construed as being representative of the full extent and scope of the present invention. While various embodiments of the present invention have been described in detail, it is apparent that modifications and alterations of those embodiments will occur to those skilled in the art. However, it is to be expressly understood that such modifications and alterations are within the scope and spirit of the present invention.
These and other features and advantages of this invention are described in, or are apparent from, the following detailed description of the exemplary embodiments.
The exemplary embodiments of the invention will be described in detail, with reference to the following figures. It should be understood that the drawings are not necessarily shown to scale. In certain instances, details which are not necessary for an understanding of the invention or which render other details difficult to perceive may have been omitted. It should be understood, of course, that the invention is not necessarily limited to the particular embodiments illustrated herein.
The exemplary embodiments of this invention will be described in relation to interpretation of data. However, it should be appreciated, that in general, the systems and methods of this invention will work equally well for any type of 3-D data (such as seismic data) from any environment.
The exemplary systems and methods of this invention will also be described in relation to seismic interpretation. However, to avoid unnecessarily obscuring the present invention, the following description omits well-known structures and devices that may be shown in block diagram form or otherwise summarized.
For purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the present invention. However, it should be appreciated that the present invention may be practiced in a variety of ways beyond the specific details set forth herein.
Furthermore, while the exemplary embodiments illustrated herein show the various components of the system collocated, it is to be appreciated that the various components of the system can be located at distant portions of a distributed network, such as a telecommunications network and/or the Internet, or within a dedicated secure, unsecured and/or encrypted system. Thus, it should be appreciated that the components of the system can be combined into one or more devices or collocated on a particular node of a distributed network, such as a telecommunications network. As will be appreciated from the following description, and for reasons of computational efficiency, the components of the system can be arranged at any location within a distributed network without affecting the operation of the system.
Furthermore, it should be appreciated that various links can be used to connect the elements and can be wired or wireless links, or any combination thereof, or any other known or later developed element(s) that is capable of supplying and/or communicating data to and from the connected elements. The term module as used herein can refer to any known or later developed hardware, software, firmware, or combination thereof that is capable of performing the functionality associated with that element. The terms determine, calculate and compute, and variations thereof, as used herein are used interchangeably and include any type of methodology, process, mathematical operation or technique, including those performed by a system, such as an expert system or neural network.
Additionally, all references identified herein are incorporated herein by reference in their entirely.
In this exemplary embodiment illustrated in
Philosophically, the approach is based on presenting the data to an interpreter and the computer processes in a manner that optimizes the imaging of depositional systems. For example, an interpreter can best recognize the existence of elements of depositional systems by looking at slices through the data that closely approximate paleo-depositional surfaces. The depositional elements are recognized in these slices from their characteristic morphology or shape, and can be readily recognized even if their presence is difficult or impossible to interpret from vertical sections of seismic data.
Examples of such processes would include, but are not limited to, noise filtering of the data along the z or t axis (1-D filtering), spatial filtering along the (x,y) planes (2-D filtering), 3-D filter operators, and any combinations of these processes. Temporal (z or t axis 1-D filtering) includes, but is not limited to, high, low and band pass filtering, spectral shaping filters, and other trace filters commonly known to individuals schooled in the art of seismic processing and interpretation. Spatial (2-D) filters include, but are not limited to, mean and median filters, spatial wavelet filtering (e.g., using a Daubechies wavelet filter), and edge preserving filtering (Al-Dossary, et. al., 2002; Jervis, 2006), and non-linear diffusion filtering (Imhof, 2003). 2-D spatial filters may operate on the volume along horizontal slices, or may be guided by local estimates of structural dip in the volume. In certain instances, the 2-D spatial filter operators may be extended into 3-D operators, depending on the type of data volume being filtered.
The above filters are all designed to reduce the level of random noise in the seismic volume. Coherent noise in the volume (e.g., “acquisition footprint”—remnant features associated with the geometry used to acquire and process the seismic data) may also need to be reduced by using a variety of coherent noise filtering techniques commonly know in the industry.
Once the input seismic volume (30) has been conditioned (46), or if no conditioning was necessary (i.e., the answer to decision 45 was “No”), then the interpreter proceeds to the interpretation of horizons and faults in the volume of data. The interpretation of horizons and faults may be conducted manually, accomplished using automatic processes, or by any combination of manual and automatic techniques. The interpretation of horizons and faults may be conducted by interpreting horizons first, faults first, or by intermingling the interpretation of horizons and faults. Thus, the process of interpreting horizons and faults are shown in parallel in
Decision 47 represents the decision by the interpreter to interpret faults (“Yes”) or not interpret faults (“No”). Decision 49 represents the decision by the interpreter to interpret horizons (“Yes”) or not interpret horizons (“No”). If both decisions 47 and 49 are “No”, then decision 51 is “Yes” and the entire process and workflow is stopped. The workflow described here requires that either faults, or horizons, or both faults and horizons, be interpreted in the input seismic volume (30) or in the conditioned seismic volume (46), or some combination of the two volumes.
If decision 47 is “Yes”, then faults are interpreted from the seismic volumes (30 and/or 46) using any fault interpretation technique of the interpreter's choosing—either manual, automatic, or a combination of manual and automatic. If decision 49 is “Yes”, then horizons are interpreted from the seismic volumes (30 and/or 46) using any horizon interpretation technique of the interpreter's choosing—either manual, automatic, or a combination of manual and automatic.
Once the interpreter has completed the interpretation of the horizons and faults of interest in process 32 shown in
Domain Transformation is a trace-by-trace approach to undoing the deforming effects of syn-depositional and post-depositional geologic processes. Syn-depositional processes take place at the same time, or as a result of, sediment deposition. Some examples of deformation resulting from these processes are differential deposition and differential compaction. The term “differential” implies variation in the horizontal direction. Post-depositional processes deform the rocks present after deposition of sediments has finished. Examples of these processes are faulting and folding of sediment layers, or the rock layers that eventually are formed by the aforementioned sediments.
All types of geologic intervals and surfaces can be accommodated including, but not limited to:
The process may also account for post-depositional structural geologic deformation including, but not limited to:
The domain-transformation algorithm requires several types of data to be input. These include the seismic data volume, interpreted horizons and faults, and user input regarding horizon types and interval types. All transform changes to be performed are stored for each trace segment in the volume. These stored parameters consist of the starting time and sampling rate in the original volume, as well as the storage location in the stratal-volume and the number of sample to be interpolated during the forward transform process (62).
The data volume is broken into several pieces for the purpose of Domain Transformation. There are two subdivisions used. The first is that each pair of user-supplied horizons defines an Interval. Each Interval may then contain one or more trace segments per trace location (an inline and crossline intersection). The trace segments are bounded by a user-supplied horizon and either a fault or another horizon (if no fault is present in that interval), or by two faults.
The Domain Transformation is performed interval-by-interval through the volume. The calculation could proceed through the interpreted intervals in any order. In its preferred implementation, the calculations proceed from the shallowest interval to the deepest interval. Within each interval, the Domain Transformation interpolates the input seismic data following a set of geometric rules. The geometric rules are a function of the type of geologic interval on which the Domain Transformation is operating.
The set of intervals included below is intended as a set of examples and is not inclusive of all the possible intervals that can be handled using the Domain Transformation approach. This subset is chosen for illustrative purposes. All types of geologic intervals can be handled using the approach presented for Domain Transformation.
Proportional intervals include conformable intervals and growth intervals, with or without post-depositional folding and differential compaction. For continuous unfaulted proportional intervals, such as in
In the case of
For relatively shallow dips, and vertical re-sampling of the volume, the desired sample rate for every other trace in the interval is equal to the local thickness (ZL) divided by the maximum number of samples (N).
This resampling of the input seismic volume may be accomplished by interpolation between the existing samples. The simple equations for determining the local desired sample rate SL guarantee that if a volume is forward transformed and then inverse transformed, none of the frequency content of the original volume will have been lost (i.e., the output sample rate is everywhere at least as frequent as the input sample rate, thereby preserving the frequency content, and avoiding aliasing problems).
This resampling will result in thinner sections of the interval having a higher sample rate in the input (x,y,z) domain than in the thicker sections. The “tick” marks on the right and left side of
All other intervals involve a generalization of this process described here for the proportional intervals.
Carbonate Platform Interval:
Carbonate reefs and the intervals that are immediately overly them require special handling. Intervals that contain carbonate reefs represent a two-fold problem. The first problem is that they represent a velocity anomaly that results in a velocity ‘pull-up’ of the underlying strata. The second problem is that they interrupt the horizontal continuity of adjacent intervals (
The first problem of a velocity pull-up is corrected by handling the strata immediately below the reef as if they were continuous flat surfaces. The second problem is corrected by assuming that the top reef structure should remain constant in the transformation (i.e., the shape of the top reef structure should be the same in the output stratal sliced volume as it is in the original input volume). The net result of these two corrections (shown in
The manner of data extraction is demonstrated in
The result of reef correction is that continuous stratal slices can be output even when they are ‘cut’ by a reef Data within the reef are stretched vertically in order to correct for the anomalous velocities within the reef (
Like carbonate reefs, intervals that include canyons require special handling. Although there typically is no velocity anomaly associated with the canyon, the sediment fill in the canyon is significantly younger, and belongs to different stratal slices than the “country rock” around the canyon.
The manner of data extraction begins by retaining the shape of the canyon unchanged in the transform. The overlying interval (B) has trace segments extracted from the top down, including the canyon fill. The country rock through which the canyon was cut by erosion (A) has trace segments extracted from bottom up. For both intervals, the maximum number of samples is calculated from the global maximum thickness (interval A+B). The maximum number of samples for the overlying interval A is equal to the maximum thickness of A divided by the sample rate of the input volume. The maximum number of samples for the canyon fill B is calculated in a similar manner. The local number of samples for each trace segment (above or below the top reef surface) is calculated by multiplying that interval's maximum number of samples by the ratio of the local time thickness divided by the maximum time thickness of the interval. The resulting output section is shown
A faulted interval can be treated as a continuous interval where both the upper and lower bounding surfaces are present. However, difficulties arise in the vicinity of the fault where only one bounding surface is defined on either side of the fault (
Conceptually, projection of the missing horizon is achieved by assuming that the time thickness in the fault zone is equal to the time thickness derived from the closest fully bound trace. This procedure takes place in two steps. First, an increasing radius search is performed in the (x,y) plane until the nearest trace is located that is bound by both horizons. Next, the time thickness is then calculated for this full trace, and assumed to be the same for the fault zone trace. This results in a projection where the missing horizon is assumed to be equidistant from the existing horizon in the fault zone. Honoring the horizontal component of dip-slip requires that data traces be shifted laterally in the (x,y) plane (
The manner of data extraction is illustrated in
These samples are interpolated from the top down for the hanging wall of the fault, and from the bottom up for the foot wall. The output interval in
The estimation of the local vertical thickness that would be present in the zones α and β were the fault not present described above assumes a constant thickness of the interval AB in the region of the fault. A refinement of this approach is to determine both the vertical thickness of the proximal complete trace segment, and the gradient (rate of change or first derivative) of this thickness as the interval approaches the fault. Then, instead of projecting a constant thickness from the proximal trace toward the fault, the estimated thickness in zones α and β would be calculated from the thickness of the proximal trace segment plus a constant gradient of that thickness.
Real faulting is commonly more complex than 2-D diagrams would imply.
The algorithm described for faults above, compensates for the dip-slip component of fault motion. The strike-slip component of motion is handled by a horizontal adjustment of the voxels in the transformed volume on one side of the fault relative to the other. The amount of the adjustment may be calculated based on a number of criteria. In its simplest form, the strike slip adjustment, if required, is the lateral displacement along the fault required to minimize the difference in amplitude across the fault on any given output stratal slice. This type of operation is demonstrated in
Interpolation for Steeply Dipping Intervals
The interpolation that has been described above (interpolation of traces vertically) works well for all cases except those that involve steeply dipping intervals. In steeply dipping intervals, extracting data vertically from the input volume is not a sufficient approximation of the geology present in the volume. Correct handling of steeply dipping intervals requires the use of non-vertical data traces, as can be seen in
The path through the volume for any point in the interval A in
Whichever method is used to calculate and project the surface normals, the projected normals define the path of interpolation in 3-D in the interval.
In the presence of the unconformity, the unconformity interval is handled in a manner similar to the foot wall of a fault. A search is performed to find the closest complete trace segment (both vertical and bed normal thickness are indicated in
Many seismic volumes contain complex 3-D salt bodies. Intervals that are partially bounded by salt are handled in a manner similar to faults and to reef top boundaries. As with faults, trace segments that are fully bounded by non-salt horizons are handled in the normal proportional manner. Where a trace segment is bounded by the salt boundary and a non-salt horizon, a search must be made for the closest full non-salt bounded trace segment. As with fault handling, the thickness from this proximal fully-bounded trace segment is used to determine the number of samples to be output in the local trace segment. This number of samples is equal to the maximum number of samples multiplied by the ratio of the thickness between the salt and non-salt horizons divided by the thickness of the fully-bounded trace segment. The resulting output of this interval is demonstrated in
Domain Transformation Specification and Determination
The Domain Transformation process described above is implemented as process 34 (
In Domain Transformation, the structural surfaces and geologic information provided by the interpreter regarding the types of geologic surfaces and intervals represented by the data are used to transform the seismic volume of data into a stratal-sliced volume. The Domain Transformation ideally removes all of the effects of structural deformation of the portion of the earth represented by the seismic volume. This results in a new seismic volume where each horizontal slice represents a paleo-depositional surface—a surface upon which deposition occurred at some time in the geologic past.
The inputs to the Domain Transformation process (34) are the interpreted structure and seismic volume(s) (conditioned or not conditioned) from process 32. There may or may not be additional input to process 34 from process 36, Refine Structural Interpretation. Process 36 is shown in detail in
Upon input of data to process 34, decision 57 (Condition Data) is made regarding conditioning of the input data (horizons, faults, and volumes) prior to process 60 (Transform Parameter Calculation). If decision 57 is “Yes”, then the input data volume(s) and surfaces may be filtered to remove or minimize a variety of types of noise, thus improving the results of the Transform Parameter Calculation (process 60) and the results of the Forward Domain Transform (62). This may include processes to remove random noise, coherent noise, or any artifacts from the volume that were introduced or resulted from the seismic acquisition and processing steps preceding interpretation. Examples of such processes would include, but are not limited to, mean, median or wavelet filtering to the volume, and acquisition footprint removal. It is important to note that the actual techniques used to Condition Data in process 58 may differ from those used in process 46 to condition data for Structural Interpretation.
Once the input volume(s) and surfaces (32) have been conditioned (58), or if no conditioning is necessary (i.e., the answer to decision 57 was “No”), then these data (represented in
Once all of the requisite input has been supplied, the Interval Index is initialized (64), the data for the first interval is obtained from the computer's memory (66, Get Interval Data), and the Maximum Interval Thickness is calculated (68). A Trace Segment is the portion of a seismic trace between the bounding surfaces that define an interval. The Trace Segment Index is initialized (70), and the Trace Segment is obtained from the volume.
All Domain Transformation operations are performed once per trace segment present in the volume. For example, in a 3-D seismic volume with two interpreted horizons bounding one interval with no faults present, the number of trace segments will be equal to the number of inlines present in the volume multiplied by the number of cross-lines present in the volume. If there are three horizons present that define two unique intervals, the number of trace segments will be twice the single-interval case. Furthermore, if the same volume had faults present in the intervals to be Domain Transformed, the number of trace segments would increase by one for each fault at each inline and cross-line intersection that has a fault present inside a Domain Transformation interval.
Any seismic Trace Segment in an interval between may be cut into one or more Sub-Segments by faults. Thus, once the Trace Segment has been obtained, decision 73 is made to determine if the trace is faulted in the interval. If the result of decision 73 is “Yes”, then process 74 determines the Number of Sub-Segments into which the trace segment is cut by faults. The Trace Sub-Segment Index is initialized (76), and the Initial Trace Segment is obtained (78). Trace Sub-Segment Transform Parameters are calculated for each Sub-Segment in process 80. Decision 81 is evaluated to determine if there are More Sub-Segments on that Trace Segment in the interval. If the result of decision 81 is “Yes”, the Sub-Segment Index is incremented in process 82, the next Trace Sub-Segment is obtained (78), and its Trace Sub-Segment Transform Parameters are calculated (80). This continues until all sub-segments of the trace have been processed.
If the result of decision 73 is “No”, then the Trace Segment is input to process 83, and the Trace Segment Parameters are calculated. The Trace Segment and Trace Sub-Segment Transform Parameters are collected in process 84. These Transform Parameters define how each Trace Segment and Trace Sub-Segment must be processed in the Domain Transformation process to properly transform that segment or sub-segment given the definitions of the bounding geologic surfaces and the geologic interval containing that sub-segment.
Decision 85 is evaluated to determine if there are more Trace Segments in the interval being processed. If the result of decision 85 is “Yes”, then the Trace Segment Index is incremented (86), the next Trace Segment is obtained (72), and decision 73 is evaluated for this new Trace Segment. If the result of decision 85 is “No”, then decision 87 is evaluated to determine if there are more intervals to process. If the answer to decision 87 is “Yes”, then the Interval Index is incremented (88), and the Interval Data for the next interval is retrieved (66).
These processes continue until Transform Parameters have been calculated for all Trace Sub-Segments and Trace Segments in all Intervals. While calculating the trace segments the Transform Displacement Volume is also created (89). This volume has the same dimensions as the output (re-sampled) Stratal Volume. Whereas the Stratal Volume stores the Domain Transformed version of the input data volume, the Transform Displacement Volume stores the x, y, and z coordinates of each data point in the Domain Transformed (x,y,s) volume. With this volume, the position of any interpretation produced from the Stratal Volume can be inverse transformed from (x,y,s) to the original (x,y,z) coordinates of the survey. Moreover, attribute volumes calculated from the stratal sliced volume can also be inverse transformed back to the original (x,y,z) coordinates as new 3-D attribute volumes.
Forward Domain Transformation
Once process 60 is complete, the data (including the seismic volume, horizons, faults, Transform Parameters, and Transform Displacement Volume) are passed from process 60 to process 62 (
In process 62 (
The type of interpolation performed by process 96 may be one of many established interpolation techniques. These techniques include, but are not limited to, linear interpolation, spline interpolation, and “sin c function” interpolation (also known as “(sin x)/x” interpolation). If interpolation is being performed in the trace (z or t) direction, the preferred implementation would use sin c interpolation. In the case of interpolation along a 3-D path in the volume (e.g., because of steep dips), some combination of techniques may be used with the horizontal and vertical parts of the interpolation operation handled separately.
The Transform Parameter Calculation process (60) outputs all of the input surfaces in Stratal-Domain coordinates. All types of surfaces are output as listed previously. Horizons are output as planar features between the intervals that they separate in the Stratal Domain. Reef Tops are output with the same shape as they were input, but with their position following the interface between the extracted reef values and the null values that exist above the valid values. Similarly, salt boundaries and erosive surfaces are output along the interface of the valid data they bound and the null regions they define. These transformed surfaces act as a cue for the interpreter, indicating how the Stratal Domain volume relates to the input volume.
When sufficient structural control in the form of interpreted horizons, faults, salt boundaries, canyon boundaries, and other possible geological surfaces is used, the Domain Transformation produces a stratal-slice volume that is substantially free of any deformation. This deformation may have been caused by post or syn-depositional folding or faulting, differential compaction and/or differential sedimentation.
The output volume is substantially free of deformation, in that there are no significant remnant effects from the faulting or from the differential sedimentation. The reflection events in the stratal-sliced volume are all flat.
Refine Structural Interpretation
One key feature of the Domain Transformed volume is that if there are any errors or omissions in the interpretation of horizons or faults in process 32 (
In decision 35 (
The details of the Refine Structural Interpretation process (36) are shown in
Data passed into process 36 from process 34 and decision 35 includes the domain transformed volume, the domain transformed surfaces (horizons, faults, etc.), and the Transform Displacement Volume. Structural refinement may involve the interpretation of additional horizons and/or faults, and may also include editing or changing the horizons or faults that were initially interpreted in process 32. This interpretation and editing may be conducted manually, accomplished using automatic processes, or by any combination of manual and automatic techniques. The interpretation of horizons and faults may be conducted by interpreting horizons first, faults first, or by intermingling the interpretation of horizons and faults. Thus, the processes of refining the interpretation of horizons and faults are shown in parallel in
Decision 103 represents the decision by the interpreter to interpret faults (“Yes”) or not interpret faults (“No”). Decision 105 represents the decision by the interpreter to interpret horizons (“Yes”) or not interpret horizons (“No”). If both decisions 103 and 105 are “No”, then decision 107 is “Yes” and the refinement process is stopped, and the transformed data and workflow passes on to process 38, Stratigraphic Interpretation. The workflow described here does not require that the interpretation be refined, even if a need for additional refinement of the structural interpretation is indicated by decision 35.
If decision 103 is “Yes”, then faults are interpreted and/or edited in the domain transformed seismic volumes (from 35) using any fault interpretation technique of the interpreter's choosing—either manual, automatic, or a combination of manual and automatic. If decision 105 is “Yes”, then horizons are interpreted and/or in the domain transformed seismic volumes (from 35) using any horizon interpretation technique of the interpreter's choosing—either manual, automatic, or a combination of manual and automatic.
Inverse Transform Refined Structural Interpretation
Once the interpreter has completed the refinement of the interpretation of the horizons and faults of interest in processes 108 and/or 110 in
The refined structural interpretation, if it is performed in the transformed volume in process 36, must be inverse transformed and merged with the original structural interpretation in process 32. The Inverse Transform of the Refined Structural Interpretation (process 112) is shown in detail in
The input to process 112 is the refined interpretation of faults and horizons (111), and the Transform Displacement Volume. Process 114 initializes the Surface Index, and the first Surface is obtained by process 116. Process 118 initializes the Point Index for points on the selected surface, and the first Point on the Surface is obtained by process 120. Process 122 is then used to convert the coordinates of the Point on the Surface from the transformed or stratal slice domain to the corresponding coordinates in the original domain of the seismic volume from 30. Decision 123 is then evaluated to determine whether there are More Points on the Surface. If decision 123 is evaluated as “Yes”, then the Point Index is incremented by process 124, and the next Point on the Surface is obtained by process 120. If decision 123 is evaluated as “No” (there are no more points on the current surface), then decision 125 is then evaluated to determine whether there are more surfaces. If decision 123 is evaluated as “Yes”, then process 126 increments the Surface Index, and the next Surface is obtained by process 116. If decision 123 is evaluated as “No”, then process 112 is completed and passes control back to process 34, to repeat the Domain Transformation of the original seismic volume using the refined structural interpretation.
Once the interpreter decides that no more Structural Refinement is required (i.e., decision 35 in
The Domain Transformed seismic volume and Domain Transformed interpreted surfaces are input to process 38 (Stratigraphic Interpretation) from process 34 and decision 35 (
Once the input domain transformed seismic volume and surfaces have been conditioned (128), or if no conditioning was necessary (i.e., the answer to decision 127 is “No”), then a number of Stratigraphic Attribute Volumes may be calculated in process 130. The goal of calculating these attribute volumes is to produce through a single volume, or through a combination of volumes, a data volume or volumes that provide improved imaging of depositional systems when compared to the domain transformed seismic volume. Individuals practiced in the art of stratigraphic interpretation from 3-D seismic data are familiar with these attribute volumes.
Attribute imaging of stratigraphy is improved by first transforming the seismic volume, and by then calculating the attribute volume in the transformed domain. This can be seen when compared to the typical practice of calculating the attributes directly on the input seismic volume without using domain transformation. Because of this, it is advantageous to provide the attribute volumes created by this workflow in the transform domain as output from process 38 to be inverse transformed in process 40, as is shown in
Once the Stratigraphic Attribute Volumes have been calculated by applying process 130 to the transformed seismic volume, decision 131 is evaluated to determine whether Multi-Attribute Imaging is going to be used to aid in the imaging of depositional systems using the attribute volumes. If decision 131 is evaluated as “Yes”, then process 132 is applied to identify optimum combinations of attributes to image the elements of depositional systems in the transformed space. There are a number of techniques in the industry that would be familiar to one who is practiced in the art of stratigraphic interpretation. These include, but are not limited to, neural network and neural network related techniques to analyze combinations of attributes for clusters that might identify elements of depositional systems (e.g., Kohonen Self-Organizing Maps and Growing Neural Gas), direct cluster analysis techniques (e.g., K-Means clustering), and techniques such as attribute cross-plot matrices and multi-dimensional attribute crossplot visualization techniques. Any of these techniques could be used in process 132 to image and analyze the multiple attribute volumes created by process 130 to image the depositional systems in the data.
As elements of depositional systems are imaged in individual attribute volumes, or combinations of attribute volumes, these elements are then “interpreted” by obtaining their bounding surfaces (i.e., the surface that completely encloses the depositional feature). Decision 135 is evaluated to determine how the bounding surfaces of the depositional systems will be interpreted. If decision 135 is evaluated as “No”, the interpreter will manually interpret the bounding surface of the depositional system or depositional system element manually (136). If decision 135 is evaluated as “Yes”, then process 142 (Surface Wrapping) will be used to semi-automatically interpret the bounding surface of the depositional system or depositional system element.
Surface Draping (Dorn, 1999) is an effective technique for creating polygonal representations of surfaces that are essentially planar, but it cannot be applied directly to the problem of finding the bounding surface of a geobody. The Surface-Wrapping algorithm creates a 3-D polygonal mesh that entirely surrounds 3-D objects.
Surface Wrapping is a semi-automatic approach for segmentation of a geobody bounding surface within volumetric data. The approach is metaphorically based upon the concept of collapsing an elastic surface onto a physical object. The desired output of the process is a polygonal mesh that may be stored as data, displayed to the end user, or used in further data processing techniques. This approach has advantages over fully automated segmentation algorithms in that it may be applied to data where the volume to be segmented is not fully imaged, or where a high level of noise is present. This approach is also significantly less time consuming for the human analyst than fully manual segmentation techniques, in that the user need only define an approximate initial bounding surface prior to application of the algorithm which determines a more detailed and accurate bounding surface.
In the preferred embodiment, an interpreter first specifies a range of voxel values that best isolates the voxels that correspond to the boundary of the intended geobody in the volume. The interpreter then defines an initial 3-D bounding surface which completely encloses the intended geobody and approximates its contours, isolating the voxels belonging to the geobody boundary from the rest of the volume.
The initial bounding surface may be constructed using manual, automatic, or semi-automatic methods, or any combination thereof. In the preferred embodiment, the method for defining the initial bounding surface is based on a technique described by Kobbelt et. al. (1999). In this method, which is similar to graphical user interfaces that are commonly found in fully manual volume segmentation software, one slice of the volume is displayed on the screen, and the user defines the region contained by the initial bounding surface using a virtual brush to “paint” the region on the screen, as shown in
Internally, the painted region is represented as a collection of cubes of equal dimensions, where each cube corresponds to a small portion of the volume that is contained within the initial bounding surface.
In order to allow the Surface Wrapping algorithm to process the bounding surface mesh as if it were an elastic material, each vertex in the mesh maintains a record of its neighboring vertices, where a neighboring vertex is defined as any vertex to which it is directly connected by an edge of a triangle. Each vertex also maintains a record of all triangles of which it is a part. Vertex locations correspond to index coordinates relative to the data volume, and there may be at most one vertex data structure in the mesh at any given spatial coordinate, thus ensuring connectivity of vertices over the entire mesh.
Once the initial bounding surface has been defined, the Surface Wrapping process iteratively moves each vertex in the mesh toward the boundary of the intended geobody, as illustrated in 2-D in
The process by which the vertices are moved is detailed below.
Each iteration of the surface wrapping algorithm begins with the calculation of the outward vertex normal vector for the first vertex in the mesh. The vertex normal is calculated as the normalized mean of the adjacent face normals, with a unit length corresponding to the grid spacing of the voxels in the data volume. A face normal, Ns, is calculated as the cross product of vectors A and B, which are vectors lying on the local “plane” of the surface.
The vertex normal N, is calculated as the ratio:
where the vector Ns, is defined as the sum of face normals Nsi divided by the total number of adjacent faces, n:
and where |Nv| is the magnitude of vector Nv.
If the initial mesh has been created everywhere outside the object whose boundary is sought, a projected location for that vertex is then calculated to be at a point one unit length from the vertex's current position in the direction opposite to the outward unit normal at that vertex. If the initial mesh has been created everywhere inside the object whose boundary is sought, a projected location for that vertex is then calculated to be at a point one unit length from the vertex's current position in the direction of the outward unit normal at that vertex.
If the voxel value at the projected location falls within the range specified by the interpreter as corresponding to the boundary of the body being wrapped, then the vertex is flagged as “fixed” (
Following the calculation of the projected location for each non-fixed vertex, a second location is computed for each non-fixed vertex, referred to here as the centralized location. For a given vertex, the centralized location is determined to be the mean of the current locations of its neighboring vertices, as illustrated in
When both the projected and centralized locations have been computed for a non-fixed vertex, its actual updated location corresponds to a point partway along the line segment between the projected location and centralized location. The proximity of the updated location to either end of the line segment is determined by a user-adjustable elasticity factor, which is defined as a percentage of the distance from the centralized location to the projected location. A higher elasticity factor causes the mesh to be treated as a more pliant material, while a lower elasticity factor simulates the effect of increased surface tension.
If at this point the mesh must be processed further, as determined either automatically or by the interpreter, the above process is then repeated until the interpreter is satisfied that the desired bounding surface has been achieved.
An additional feature that may be incorporated into the Surface Wrapping algorithm is the simulation of a semi-permeable surface, which allows outlying voxels to “push through” the mesh while maintaining the overall desired structure of the bounding surface. In the preferred implementation, this is accomplished by the use of an additional predicate immediately prior to the calculation of a vertex's projected location which determines if the vertex is creating a sharp point in the mesh. This predicate sums the surface normal vectors of the triangles that connect to the vertex and calculates the magnitude of the resulting vector. This calculation is illustrated in 2-D in
The Surface Wrapping algorithm may also be applied to a subset of the vertices in the bounding surface mesh, allowing localized editing operations. A typical mechanism for selecting the affected vertices is any picking operation in a 3-D rendered view of the mesh, but the selection of vertices can be accomplished via any combination of manual or automatic techniques.
Surface Wrapping (process 142) is shown in detail in
Once the Initial Bounding Surface Mesh has been created by the interpreter, process 148 Initializes the Vertex Index, and process 150 gets the initial Mesh Vertex. Process 152 calculates the Projected Vertex Location based on a movement of the vertex from its initial position along the direction of its unit normal toward the element of the depositional system. Process 154 then calculates an estimate of the Voxel Value at the Projected Vertex Location. Decision 155 (Voxel Value in Range) is evaluated to determine if the voxel has encountered the boundary of the depositional system or system element. If the Voxel Value is in the interpreter specified range (decision 155 evaluated as “Yes”) then the vertex is Flagged as Fixed by process 158. If the Voxel Value is outside the interpreter specified range (decision 155 evaluated as “No”) then process 156 Moves the Vertex to the Projected Location.
After either process 156 or 158 is complete, decision 159 is evaluated to determine if there are More Vertices. If decision 159 evaluates as “Yes”, then process 160 Increments the Vertex Index, and process 150 gets the next Mesh Vertex. If decision 159 evaluates as “No”, then process 162 re-initializes the Vertex Index, and process 164 Gets the Mesh Vertex. Decision 165 is then evaluated to determine if the Vertex is Fixed. If decision 165 is evaluated as “Yes” (i.e., the vertex has been flagged as fixed) then process 168 Increments the Vertex Index, and process 164 Gets the next Mesh Vertex. If decision 165 is evaluated as “No” (i.e. the vertex has not been flagged as fixed) the process 166 Centralizes the Vertex Location with respect to the neighboring vertices in the mesh.
After process 166, decision 167 is evaluated to determine whether there are More Vertices in the mesh. If decision 167 is evaluated as “Yes” (i.e., there are more vertices), then process 168 Increments the Vertex Index, and process 164 Gets the next Mesh Vertex. If decision 167 is evaluated as “No” (i.e., there are no more vertices), then decision 169 is evaluated to determine whether to Continue Shrinking the Mesh. If decision 169 is evaluated as “Yes”, then process 148 Initializes the Vertex Index for the next shrinking step, and process 150 gets the Mesh Vertex. Note that once a vertex has been flagged as fixed in process 158, its projected position in process 154 remains fixed. If decision 169 is evaluated as “No”, then the surface mesh represents the bounding surface of the depositional system or system element to the conditions set by the interpreter at the beginning of the Surface Wrapping process (142).
Referring back to
Inverse Domain Transformation
Data input into process 40 include the Domain Transformed Volume, attribute volumes calculated from the Domain Transformed Volume, the Domain Transformed Surfaces, all interpreted stratigraphic surfaces (either from manual interpretation or from Surface Wrapping), and the Transform Displacement Volume.
The Inverse Transform process (40) is shown in detail in
The Inverse Transform process (40) allows interpretation produced in the Stratal Domain to be inverted back to the coordinates of the original volume. It also allows attributes that are produced with superior quality in the Stratal Domain to be inverted to the original coordinates. In process 38, if decision 171 is evaluated as “Yes” (Inverse Transform Surfaces), each point of the surfaces in the interpretation is inverted by finding its nearest neighbors in the Transform Displacement Volume. The original positions (in spatial coordinates) of these nearest neighbors are used to invert the position of that point. In process 38, if decision 173 is evaluated as “Yes”, (Inverse Transform Volumes), each trace is re-sampled (stretched) back to the original coordinate using similar interpolation schemes to those described for the Forward Transform.
If decision 171 (Inverse Transform Surfaces) is evaluated as “Yes”, then process 174 initializes the Surface Index, and the first Surface is obtained by process 176. Process 178 initializes the Point Index for points on the selected surface, and the first Point on the Surface is obtained by process 180. Process 182 is then used to convert the coordinates of the Point on the Surface from the transformed or stratal slice domain to the corresponding coordinates in the original domain of the seismic volume from 30. Decision 181 is then evaluated to determine whether there are More Points on the Surface. If decision 181 is evaluated as “Yes”, then the Point Index is incremented by process 184, and the next Point on the Surface is obtained by process 180. If decision 181 is evaluated as “No” (there are no more points on the current surface), then decision 183 is then evaluated to determine whether there are more surfaces. If decision 183 is evaluated as “Yes”, then process 186 increments the Surface Index, and the next Surface is obtained by process 176. If decision 183 is evaluated as “No”, the workflow for inverse transformation of surfaces is completed.
If decision 173 (Inverse Transform Volumes) is evaluated as “Yes”, then process 188 initializes the Volume Index, and the first Volume is obtained by process 190. Process 192 initializes the Voxel Index for points in the selected volume, and the first Voxel in the Volume is obtained by process 194. Process 196, with additional input from process 35, Gets the Stratal Voxel Spatial Coordinates. Process 198 Gets the Stratal Voxel Value, and process 200, with additional input from process 30, Gets the Spatial Voxel Target Position in the inverse transformed volume. The results of processes 196, 198, and 200 are input into process 202, which then calculates the Voxel Value at the Target Position.
Decision 201 is then evaluated to determine whether there are More Voxels in the Volume. If decision 201 is evaluated as “Yes”, then the Voxel Index is incremented by process 204, and the next Voxel in the Volume is obtained by process 194. If decision 201 is evaluated as “No” (there are no more voxels in the current volume), then decision 203 is then evaluated to determine whether there are more volumes. If decision 203 is evaluated as “Yes”, then process 206 increments the Volume Index, and the next Volume is obtained by process 190. If decision 203 is evaluated as “No”, the workflow for inverse transformation of volumes is completed.
While the above-described flowcharts have been discussed in relation to a particular sequence of events, it should be appreciated that changes to this sequence can occur without materially effecting the operation of the invention. Additionally, the exact sequence of events need not occur as set forth in the exemplary embodiments. Additionally, the exemplary techniques illustrated herein are not limited to the specifically illustrated embodiments but can also be utilized with the other exemplary embodiments and each described feature is individually and separately claimable.
The systems, methods and techniques of this invention can be implemented on a special purpose computer, a programmed microprocessor or microcontroller and peripheral integrated circuit element(s), an ASIC or other integrated circuit, a digital signal processor, a hard-wired electronic or logic circuit such as discrete element circuit, a programmable logic device such as PLD, PLA, FPGA, PAL, any means, or the like. In general, any device capable of implementing a state machine that is in turn capable of implementing the methodology illustrated herein can be used to implement the various communication methods and techniques according to this invention.
Furthermore, the disclosed methods may be readily implemented in software using object or object-oriented software development environments that provide portable source code that can be used on a variety of computer or workstation platforms. Alternatively, the disclosed system may be implemented partially or fully in hardware using standard logic circuits or VLSI design. Whether software or hardware is used to implement the systems in accordance with this invention is dependent on the speed and/or efficiency requirements of the system, the particular function, and the particular software or hardware systems or microprocessor or microcomputer systems being utilized. The systems, methods and techniques illustrated herein can be readily implemented in hardware and/or software using any known or later developed systems or structures, devices and/or software by those of ordinary skill in the applicable art from the functional description provided herein and with a general basic knowledge of the computer and geologic arts.
Moreover, the disclosed methods may be readily implemented in software that can be stored on a storage medium, executed on programmed general-purpose computer with the cooperation of a controller and memory, a special purpose computer, a microprocessor, or the like. The systems and methods of this invention can be implemented as program embedded on personal computer such as an applet, JAVA® or CGI script, in C or C++, Fortran, or the like, as a resource residing on a server or computer workstation, as a routine embedded in a dedicated system or system component, or the like. The system can also be implemented by physically incorporating the system and/or method into a software and/or hardware system, such as the hardware and software systems of a dedicated seismic interpretation device.
It is therefore apparent that there has been provided, in accordance with the present invention, systems and methods for interpreting data. While this invention has been described in conjunction with a number of embodiments, it is evident that many alternatives, modifications and variations would be or are apparent to those of ordinary skill in the applicable arts. Accordingly, it is intended to embrace all such alternatives, modifications, equivalents and variations that are within the spirit and scope of this invention.